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Among the challenges of sustainable management of meat production, the key issue is to improve the energy efficiency of production processes, which will consequently affect the reduction of greenhouse gas emissions. Such effects are achieved by combining various chilling systems with electrical stimulation that determines the quality of meat at the slaughter stage. The novelties of the research undertaken included determining the impact of various variants of meat production (chilling method: slow, fast, accelerated + HVES/NES) on changes in the basic (industrial) quality indicators (pH and temperature) of beef produced from Polish Holstein-Friesian breed cattle, and then indicating the optimal variant for energy-efficient (sustainable) beef production. The HVES and the fast chilling method yielded positive economic (meat weight loss), technological (high quality, hot-boning), energetic (lower electricity consumption), and organizational effects (reduced chilling and storage surfaces and expenditures for staff wages) compared to the slow and accelerated methods. Reaching the desired final temperature with an increased amount of chilled meat enables obtaining a few-fold decrease in the specific energy consumption and a higher energy efficiency of the process. This allows recommending the above actions to be undertaken by entrepreneurs in the pursuit of sustainable meat production.

Fundamental ecological principles of ecosystem-level respiration are extensively applied in greenhouse gas and elemental cycle studies. A laboratory system termed CEMS (Carbon Dioxide Evolution Measurement System), developed to explore microbial biofilm growth and metabolic responses, was evaluated as an early-warning system for microbial disturbances in industrial settings: in (a) potable water system contamination, and (b) bioreactor inhibition. Respiration was detected as CO2 production, rather than O2 consumption, including aerobic and anaerobic metabolism. Design, thresholds, and benefits of the remote CO2 monitoring technology were described. Headspace CO2 correlated with contamination levels, as well as chemical (R2 > 0.83–0.96) and microbiological water quality indicators (R2 > 0.78–0.88). Detection thresholds were limiting factors in monitoring drinking water to national and international standards (0 CFU/100 mL fecal coliforms) in both open- (>1500 CFU/mL) and closed-loop CO2 measuring regimes (>100 CFU/100 mL). However, closed-loop detection thresholds allow for the detection of significant contamination events, and monitoring less stringent systems such as irrigation water (2 fluctuations from chemical CO2 dynamics, to optimize this real-time, sustainable, low-waste technology, facilitating timeous responses to biological disturbances in bioreactors.

Endo-s-1,4-glucanase AkEG21 belonging to glycosyl hydrolase family 45 (GHF45) is the most abundant cellulase in the digestive fluid of sea hare (Aplysia kurodai). The specific activity of this 21-kDa enzyme is considerably lower than those of other endo s-1,4-glucanases in the digestive fluid of A. kurodai, therefore its role in whole cellulose hydrolysis by sea hare is still uncertain. Although AkEG21 has a catalytic domain without a cellulose binding domain, it demonstrated stable binding to cellulose fibers, similar to that of fungal cellobiohydrolase (CBH) 1 and CBH 2, which is strongly inhibited by cellohexaose, suggesting the involvement of the catalytic site in cellulose binding. Cellulose-bound AkEG21 hydrolyzed cellulose to cellobiose, cellotriose and cellotetraose, but could not digest an external substrate, azo-carboxymethyl cellulose. Cellulose hydrolysis was considerably stimulated by the synergistic action of cellulose-bound AkEG21 and AkEG45, another s-1,4-endoglucanase present in the digestive fluid of sea hare; however no synergy in carboxymethylcellulose hydrolysis was observed. When AkEG21 was removed from the digestive fluid by immunoprecipitation, the cellulose hydrolyzing activity of the fluid was significantly reduced, indicating a critical role of AkEG21 in cellulose hydrolysis by A. kurodai. These findings suggest that AkEG21 is a processive endoglucanase functionally equivalent to the CBH, which provides a CBH-independent mechanism for the mollusk to digest seaweed cellulose to glucose.

The sustainable recovery of resources from wastewater streams can provide many social and environmental benefits. A common strategy to recover valuable resources from wastewater is to harness the products of fermentation by complex microbial communities. In these fermentation bioreactors high microbial community diversity within the inoculum source is commonly assumed as sufficient for the selection of a functional microbial community. However, variability of the product profile obtained from these bioreactors is a persistent challenge in this field. In an attempt to address this variability, the impact of inoculum on the microbial community structure and function within the bioreactor was evaluated using controlled laboratory experiments. In the course of this work, sequential batch reactors were inoculated with three complex microbial inocula and the chemical and microbial compositions were monitored by HPLC and 16S rRNA amplicon analysis, respectively. Microbial community dynamics and chemical profiles were found to be distinct to initial inoculate and highly reproducible. Additionally we found that the generation of a complex volatile fatty acid profile was not specific to the diversity of the initial microbial inoculum. Our results suggest that the composition of the original inoculum predictably contributes to bioreactor community structure and function., United States. National Institutes of Health (P30-ES002109)

This study aimed to develop and test an unbiased and rapid methodology to estimate the length of external arbuscular mycorrhizal fungal (AMF) hyphae in soil. The traditional visual gridline intersection (VGI) method, which consists in a direct visual examination of the intersections of hyphae with gridlines on a microscope eyepiece after aqueous extraction, membrane-filtration, and staining (e.g., with trypan blue), was refined. For this, (i) images of the stained hyphae were taken by using a digital photomicrography technique to avoid the use of the microscope and the method was referred to as "digital gridline intersection" (DGI) method; and (ii), the images taken in (i) were processed and the hyphal length was measured by using ImageJ software, referred to as the "photomicrography-ImageJ processing" (PIP) method. The DGI and PIP methods were tested using known grade lengths of possum fur. Then they were applied to measure the hyphal lengths in soils with contrasting phosphorus (P) fertility status. Linear regressions were obtained between the known lengths (Lknown) of possum fur and the values determined by using either the DGI (LDGI) (LDGI = 0.37 + 0.97 × Lknown, r2 = 0.86) or PIP (LPIP) methods (LPIP = 0.33 + 1.01 × Lknown, r2 = 0.98). There were no significant (P > 0.05) differences between the LDGI and LPIP values. While both methods provided accurate estimation (slope of regression being 1.0), the PIP method was more precise, as reflected by a higher value of r2 and lower coefficients of variation. The average hyphal lengths (6.5-19.4 m g-1) obtained by the use of these methods were in the range of those typically reported in the literature (3-30 m g-1). Roots growing in P-deficient soil developed 2.5 times as many hyphae as roots growing in P-rich soil (17.4 vs 7.2 m g-1). These tests confirmed that the use of digital photomicrography in conjunction with either the grid-line intersection principle or image processing is a suitable method for the measurement of AMF hyphal lengths in soils for comparative investigations.

Five full-scale wastewater treatment plants (WWTPs) in China using typical biodegradation processes (SBR, oxidation ditch, A2/O) were selected to assess the removal of four popular artificial sweeteners (ASs). All four ASs (acesulfame (ACE), sucralose (SUC), cyclamate (CYC) and saccharin (SAC)) were detected, ranging from 0.43 to 27.34μg/L in the influent. Higher concentrations of ASs were measured in winter. ACE could be partly removed by 7.11–50.76% through biodegradation and especially through the denitrifying process. The A2/O process was the most efficient at biodegrading ASs. Adsorption (by granular activated carbon (GAC) and magnetic resin) and ultraviolet radiation-based advanced oxidation processes (UV/AOPs) were evaluated to remove ASs in laboratory-scale tests. The amounts of resin adsorbed were 3.33–18.51 times more than those of GAC except for SUC. The adsorption ability of resin decreased in the order of SAC > ACE > CYC > SUC in accordance with the pKa. Degradation of ASs followed pseudo-first-order kinetics in UV/H2O2 and UV/PDS. When applied to the secondary effluent, ASs could be degraded from 30.87 to 99.93% using UV/PDS in 30 minutes and UV/PDS was more efficient and economic.

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

Eggs contain the essential cholesterol and protein for the human body, which plays an irreplaceable role in human survival, production and life. There are significant differences in the development of laying hens feeding in different regions. It is of great significance to improve egg production and reduce pollution emission for China’s laying hens industry. Based on the SBM model, this paper constructs MML index, considering unexpected output under common frontier, to comprehensively evaluate the green total factor productivity on Chinese laying hens (GTCL). The results show that: (1) GTCL shows a large spatial and temporal differentiation under both the common frontier and the regional frontier. Compared with the eastern region and central region, the western region has obvious advantages in GTCL. (2) GTCL overall shows a downward trend, however, it emerges an upward trend in recent years. (3) Compared with small-scale and large-scale, middle-scale GTCL has advantages. According to the above empirical results, combined with the China’s actual national situation, this paper finally puts forward some policy recommendations to improve GTCL.

There is increasing scrutiny around how science is communicated to the public. For instance, a Twitter account @justsaysinmice (with 70.4K followers in January 2021) was created to call attention to news headlines that omit that mice, not humans, are the ones for whom the study findings apply. This is the case of many headlines reporting on Alzheimer disease (AD) research. AD is characterized by a degeneration of the human brain, loss of cognition, and behavioral changes, for which no treatment is available. Around 200 rodent models have been developed to study AD, even though AD is an exclusively human condition that does not occur naturally in other species and appears impervious to reproduction in artificial animal models, an information not always disclosed. It is not known what prompts writers of news stories to either omit or acknowledge, in the story’s headlines, that the study was done in mice and not in humans. Here, we raised the hypothesis that how science is reported by scientists plays a role on the news reporting. To test this hypothesis, we investigated whether an association exists between articles’ titles and news’ headlines regarding the omission, or not, of mice. To this end, we analyzed a sample of 623 open-access scientific papers indexed in PubMed in 2018 and 2019 that used mice either as models or as the biological source for experimental studies in AD research. We found a significant association (p < 0.01) between articles’ titles and news stories’ headlines, revealing that when authors omit the species in the paper’s title, writers of news stories tend to follow suit. We also found that papers not mentioning mice in their titles are more newsworthy and significantly more tweeted than papers that do. Our study shows that science reporting may affect media reporting and asks for changes in the way we report about findings obtained with animal models used to study human diseases., This study reveals that when scientists omit from the papers’ title that research findings were obtained using mice, as opposed to humans, the media tend to replicate the trend by omitting the use of mice from the news stories headlines that report on these papers, thereby perpetuating the misleading omission.

Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.

This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit model consisting of three interconnected components: LGN and two input layers (Layer 4Ca and Layer 6) of the primary visual cortex V1, covering several hundred hypercolumns. Anatomical structures are incorporated and physiological parameters from realistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also discussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parameters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the introduction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems., Author summary The visual cortex is a part of the cortex that processes visual signal. The visual signal from the retina is relayed through the lateral geniculate nucleus (LGN), and enters the primary visual cortex directly. The primary visual cortex consists of many layers. A phenomenon called surround suppression, which means a reduction of neuronal activities in response to stimuli of increasing size, is well-observed in all layers of the primate primary visual cortex. In this paper, we built a large scale mathematical model consisting of the LGN input and tens of thousands of neuron groups in Layer 4 and Layer 6 of the primary visual cortex to study the mechanism of surround suppression. This model is constrained by both anatomical structures and realistic physiological parameters. Remaining unknown parameters are reverse-engineered from experimental data. We showed how the character of the surround suppression depends on the long-range connections in the feedback layer, as well as the feedback current from Layer 6 to Layer 4.

Domestic dogs are responsible for 99% of all cases of human rabies and thus, mass dog vaccination has been demonstrated to be the most effective approach towards the elimination of dog-mediated human rabies. Namibia demonstrated the feasibility of this approach by applying government-led strategic rabies vaccination campaigns to reduce both human and dog rabies incidences in the Northern Communal Areas of Namibia since 2016. The lessons learnt using paper-based form for data capturing and management of mass dog vaccination campaign during the pilot and roll out phase of the project (2016–2018) led to the implementation of a simple and accurate data collection tool in the second phase (2019–2022) of the rabies elimination program. In this paper, we describe the implementation of such custom-developed vaccination tracking device, i.e. the Global Alliance for Rabies Control (GARC) Data Logger (GDL), and the integration of the collected data into a website-based rabies surveillance system (Rabies Epidemiological Bulletin—REB) during 2019 and 2020 campaigns. A total of 10,037 dogs and 520 cats were vaccinated during the 2019 campaign and 13,219 dogs and 1,044 cats during the 2020 campaign. The vaccination data were recorded with the GDL and visualized via REB. Subsequent GIS-analysis using gridded population data revealed a suboptimal vaccination coverage in the great majority of grid cells (82%) with a vaccination coverage below 50%. Spatial regression analysis identified the number of schools, estimated human density, and adult dog population were associated with the vaccination performance. However, there was an inverse correlation to human densities. Nonetheless, the use of the GDL improved data capturing and monitoring capacity of the campaign, enabling the Namibian government to improve strategies for the vaccination of at-risk areas towards achieving adequate vaccination coverage which would effectively break the transmission of rabies., Author summary We used a custom-developed vaccination tracking device—the Global Alliance for Rabies Control (GARC) Data Logger—to capture dog rabies vaccination data during the 2019 and 2020 mass vaccination campaign in the Northern Communal Areas of Namibia, and then integrated the collected data into the web-based Rabies Epidemiological Bulletin, a rabies-specific disease surveillance platform for rabies-endemic countries. This approach allowed automatic collation, analysis and, visualization of data and drastically improved the data capturing and monitoring capacity of the Namibian government led campaign. Additionally, subsequent GIS analysis enabled a better estimation of vaccination coverage at a much higher spatial resolution, thus identifying areas where improvements in the vaccination strategy are needed to ensure long-term success of the project.

Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.

Analyzing the rhythm of animals’ acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data., Author summary In the analysis of animal communication more and more interest is shown in rhythm of animal communication and what information this might convey. In this paper, we establish a workflow to analyze the temporal structure–namely the rhythm–of any particular animals’acoustic signal with methods that are applicable for a wide range of signals and results that are easily comparable and interpretable. This workflow will enhance the understanding of rhythmicality in animals’ acoustic signals as well as facilitate comparison between species. Methods we conducted ranged from simple distributional and visual analysis to higher mathematics such as Fourier analysis. All analyses rely on Inter-Onset-Intervals, the duration between the beginning of one element and the next. We used different datasets from two neotropical bat species as well as from the sperm whale. With this selection, we cover very short sequences with only few elements up to sequences of around 200 elements, multisyllabic and monosyllabic sequences and social communication as well as sounds used for orientation and foraging.

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot’s obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence., Author summary The sonar system of bats provides only limited information about the location of obstacles. In particular, it is unlikely that bats can localize multiple, complex obstacles that return a multitude of interfering echoes. Nevertheless, these animals can fly swiftly through densely cluttered habits. To explain how bats do this, we proposed they only need to compare the loudness of the echoes at the left and the right ear. If the echoes at the left ear are louder than at the right ear, obstacles are probably located to the left. Therefore, the bat should bank right (and vice versa). In this paper, we test whether such a simple strategy would allow bats to avoid obstacles. We equipped a robot with a sonar system resembling that of a bat, and we implemented the obstacle avoidance strategy above. We tested this robotic bat in environments mimicking those used in experimental studies of their sonar behavior. The robot was able to avoid most of the obstacles in both environments. Therefore, we conclude that bats could rely on a simple strategy when avoiding obstacles in complex environments.

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structurewildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than fieldbased variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities, CJ was partially funded by the Universidad Autónoma de Madrid (Spain) for performing the fieldwork, thanks to an aid program for postgraduates students. This paper is for postgraduates students. This paper is a contribution to the REMEDINAL 3 (S2013/MAE-2719) network of the CAM 2719) network of the CAM

© 2019 Cadenas, Megson. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This paper presents a method to reduce a set of n 2D points to a smaller set of s 2D points with the property that the convex hull on the smaller set is the same as the convex hull of the original bigger set. The paper shows, experimentally, that such reduction accelerates computations; the time it takes to reduce from n down to s points plus the time of computing the convex hull on the s points is less than the time to compute the convex hull on the original set of n points. The method accepts 2D points expressed as real numbers and thus extends our previous method that required points as integers. The method achieves a percentage of reduction of points of over 90% in a collections of four datasets. This amount of reduction provides speedup factors of at least two for various common convex hull algorithms. Theoretically, the reduction method executes in time within O(n) and thus is suitable for preprocessing 2D data before computing the convex hull by any known algorithm.

This paper presents vehicle-to-vehicle telescreen (VVT) and a multicast scheme to disseminate digital signage multimedia services to vehicular ad hoc networks (VANETs). Multimedia dissemination in VANETs is challenging because of the high packet losses (PLs), delays and longer disconnection times, which degrade the network quality of service (QoS) and user quality of experience (QoE). To reduce the PLs and delays, most existing multimedia multicast schemes in VANETs primarily select routes based on longer route expiration times (RET) or lowest path delays. The RET-based schemes suffer less from PLs when there are fewer active multicastings in the network. When the number of active multicastings increases, delay-based schemes suffer less from PLs comparatively. This tradeoff implies to design an adaptive mechanism by mutually complementing the RET-based and delay-based schemes to reduce PLs and delays. In this paper, we propose a multicriteria adaptive opportunistic treecast routing protocol (MAOTRP), which adapts the route selection mechanism according to active multicastings for efficient multimedia dissemination in VVT. The MAOTRP adjusts the weights of route selection parameters, including RET and delays, by considering their contribution in improving packet delivery ratio. MAOTRP extends a tree-based multicast protocol to provide robustness through alternate routes for link failures to reduce PLs. Through several experimental evaluations, we show that the proposed dissemination scheme improves QoS and QoE, and reduces the average disconnection time.

One of the main strategies to control the spread of infectious animal diseases is the implementation of movement restrictions. This paper shows a loss in efficiency of the movement restriction policy (MRP) when behavioral responses of farmers are taken into account. Incorporating the strategic behavior of farmers in an epidemiologic model reveals that the MRP can trigger premature animal sales by farms at high risk of becoming infected that significantly reduce the efficacy of the policy. The results are validated in a parameterized network via Monte Carlo simulations and measures to mitigate the loss of efficiency of the MRP are discussed. Financial aid to farmers can be justified by public health concerns, not only for equity. This paper contributes to developing an interdisciplinary analytical framework regarding the expansion of infectious diseases combining economic and epidemiologic dimensions.

Outlier removal is a fundamental data processing task to ensure the quality of scanned point cloud data (PCD), which is becoming increasing important in industrial applications and reverse engineering. Acquired scanned PCD is usually noisy, sparse and temporarily incoherent. Thus the processing of scanned data is typically an ill-posed problem. In the paper, we present a simple and effective method based on two geometrical characteristics constraints to trim the noisy points. One of the geometrical characteristics is the local density information and another is the deviation from the local fitting plane. The local density based method provides a preprocessing step, which could remove those sparse outlier and isolated outlier. The non-isolated outlier removal in this paper depends on a local projection method, which placing those points onto objects. There is no doubt that the deviation of any point from the local fitting plane should be a criterion to reduce the noisy points. The experimental results demonstrate the ability to remove the noisy point from various man-made objects consisting of complex outlier.

Background Over the last decade, implementation research and a science of global health delivery have emerged as important vehicles to improve the effectiveness of interventions. Efforts to control neglected tropical diseases (NTD) operate in challenging circumstances and with marginalized populations, making attention to context-specific details particularly relevant. Socio-anthropological insights have much to offer a science of NTD delivery. In this paper, an accessible and actionable framework for understanding NTD intervention effectiveness, based on socio-anthropological research, is presented and its utility for program planning and monitoring and evaluation is outlined. Methodology/Principal findings The framework was developed inductively by comparatively analyzing three rapid ethnographic studies undertaken in Eastern Africa (2010–2013) on three different large-scale NTD interventions: rabies elimination in Tanzania, sleeping sickness control in Uganda and the prevention of parasitic worms in Zambia. The framework includes five “intervention domains” where the effectiveness of these interventions was negotiated and determined at the local level. This involves: 1) the terrain of intervention (including seasonality and geographical variability); 2) community agency (including local knowledge, risk perceptions, behaviors, leadership and social pressure); 3) the strategies and incentives of field staff (skills, motivations, capabilities and support); 4) the socio-materiality of technology (characteristics of intervention tools and the adoption process itself); and 5) the governance of interventions (policy narratives, available expertise, bureaucracy, politics and the utilization of knowledge). The paper illustrates the importance of each of these domains by drawing on the case study research, presenting lessons learnt and practical recommendations for how such insights could improve intervention delivery. Conclusions/Significance To help close the gap between efficacy and effectiveness in NTD programs, it is important that field staff: 1) generate meaningful knowledge about contextual factors; 2) use this knowledge to tailor field strategies; and 3) create routine mechanisms to account for the dynamic process of implementation itself. The framework presented here offers a simple analytical tool to strengthen these knowledge-to-action relationships existing project planning tools, drawing on the insights of socio-anthropology., Author summary Many efficacious tools exist to control NTDs, but effectively moving these tools and approaches from the boardroom to the village is a complicated socio-political process. In the era of Sustainable Development Goals, global health has become more focused on improving the delivery of existing interventions. Greater attention to implementation research, including the value of social science perspectives, has followed in an effort to build a science of global health delivery. This paper presents an accessible and actionable socio-anthropological framework for understanding the effectiveness factors of NTD interventions. The framework was developed by comparatively analyzing three large-scale NTD interventions in Eastern Africa: rabies elimination in Tanzania, sleeping sickness control in Uganda and the prevention of parasitic worms in Zambia. The framework includes five “intervention domains” where the effectiveness of these interventions was determined: 1) the terrain of intervention; 2) community agency; 3) the strategies and incentives of field staff; 4) the socio-materiality of technology; and 5) the governance of interventions. The paper illustrates the importance of each of these domains, presenting lessons learnt and practical recommendations. As a flexible analytical tool, the framework could be integrated into the planning and implementation process itself, bringing the insights of socio-anthropological approaches into an emerging science of NTD delivery.

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands.

The exposure to Non-Ionizing-Electromagnetic Fields (NI-EMFs) is often indicated as a cofactor responsible for the fertility reduction, which has been described in recent years. Despite the great interest in this topic and the research effort in exploring it, to date, there are no reliable data. Therefore, we carried out a scientometric analysis of the scientific literature published in peer reviewed Journals concerning this topic to better understand the reasons of this partial failure. To this aim, we identified and analysed 104 papers, published in last 26 years in peer-reviewed Journals, present in ISI Web of Knowledge Core Collection. Then, we analysed the impact of the Journals in which the papers were published as well as that of the single papers, the paper citation dynamics, the keywords citation busts, the geographical localization of citations and the co-authorship dynamics of the Authors. As a result, we found that different animal models (rodent, rabbit, guinea pig, and swine) and different experimental approaches (epidemiological vs. experimental studies) have the same impact, highlighting the lack of universally adopted standard in research activity. The analysis of the temporal trend in keywords and the high differences in citations between the different countries (also in those belonging to the same geographical and socio-economical area) pointed out the difficulties in approaching this branch of study. Lastly, it was evident that the Authors did not behave as a connected community, but as unconnected clusters of very small size. In conclusion, based on the results of our analysis, we think that important efforts must be undertaken to adopt more standardized models and to improve the research quality and the information exchange within the scientific community, with the aim of improving the reliability and usefulness of the results of research regarding the effect of NI-EMFs on fertility.

International audience; Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator.

Contradictory findings among scientific studies that address a particular issue may impede the conversion of science to management implementation. A systematic review of peer-reviewed studies to generate a single outcome may overcome this problem. The contentious topic of the impact that a megaherbivore such as the savanna elephant have for other species and their environment can benefit from such an approach. After some 68 years, 367 peer-reviewed papers covered the topic and 51 of these papers provided sufficient data to be included in a meta-analysis. We separated the direct impact that elephants had on trees and herbs from the indirect effects on other vertebrates, invertebrates, and soil properties. Elephants have an impact on tree structure and abundance but no overall negative cascading effects for species that share space with them. Primary productivity explained a small amount of variation of elephant impact on vegetation. Elephant numbers (density), study duration, rainfall, tree cover, and the presence of artificial water and fences failed to describe patterns of impact. We conclude that published information do not support the calls made for artificially manipulating elephant numbers to ameliorate elephant impact, and call for the management of space use by elephants to maintain savanna heterogeneity.

15 páginas, 2 figuras, 2 tablas, Background Scientific literature on cystic echinococcosis (CE) reporting data on risk factors is limited and to the best of our knowledge, no global evaluation of human CE risk factors has to date been performed. This systematic review (SR) summarizes available data on statistically relevant potential risk factors (PRFs) associated with human CE., Methodology/Principal Findings Database searches identified 1,367 papers, of which thirty-seven were eligible for inclusion. Of these, eight and twenty-nine were case-control and cross-sectional studies, respectively. Among the eligible papers, twenty-one were included in the meta-analyses. Pooled odds ratio (OR) were used as a measure of effect and separately analysed for the two study designs. PRFs derived from case-control studies that were significantly associated with higher odds of outcome were ªdog free to roamº (OR 5.23; 95% CI 2.45±11.14), ªfeeding dogs with visceraº (OR 4.69; 95% CI 3.02±7.29), ªslaughter at homeº (OR 4.67; 95% CI 2.02±10.78) or at ªslaughterhousesº (OR 2.7; 95% CI 1.15±6.3), ªdog ownershipº (OR 3.54; 95% CI 1.27±9.85), ªliving in rural areasº (OR 1.83; 95% CI 1.16±2.9) and ªlow incomeº (OR 1.68; 95% CI 1.02±2.76). Statistically significant PRFs from cross-sectional studies with higher odds of outcome were ªage >16 yearsº (OR 6.08; 95% CI 4.05±9.13), ªliving in rural areasº (OR 2.26; 95% CI 1.41±3.61), ªbeing femaleº (OR 1.38; 95% CI 1.06± 1.8) and ªdog ownershipº (OR 1.37; 95% CI 1.01±1.86). Conclusions/Significance Living in endemic rural areas, in which free roaming dogs have access to offal and being a dog-owner, seem to be among the most significant PRFs for acquiring this parasiticinfection. Results of data analysed here may contribute to our understanding of the PRFs for CE and may potentially be useful in planning community interventions aimed at controlling CE in endemic areas., Conclusions/Significance Living in endemic rural areas, in which free roaming dogs have access to offal and being a dog-owner, seem to be among the most significant PRFs for acquiring this parasiticinfection. Results of data analysed here may contribute to our understanding of the PRFs for CE and may potentially be useful in planning community interventions aimed at controlling CE in endemic areas., This research received funding from the European Community's Seventh Framework Programme under the grant agreement 602051 (Project HERACLES: Human cystic Echinococcosis ReseArch in CentraL and Eastern Societies; http://www.heracles-fp7.eu/).

Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.

On 5th December 2012, a scientific article reviewing a change in the feeding behaviour of the European catfish, one of the largest freshwater fish, was published in the American scientific journal, PLOS ONE, an open access journal, which also allows the mass publication of pictures and videos. Within a few days following the publication of this article, it was relayed by numerous web sites and generated a media craze. In this paper, we analyse the circulation of this scientific information in the sphere of Web-based media during the two months following its publication, by revealing the citation mechanisms of the original article and the logic of the Internet users participating in its diffusion. In addition, since the circulation of its informational content travelled beyond linguistic and geographical boundaries, we chose to compare the citation modalities and intertextual relationships of documents in the three countries where the article spread the most widely, namely: France, the United States and Great Britain. Even though our study shows that the media circulation of scientific papers operates in a traditional way, the intertextual analysis underlines the grand variety of participants (such as journalists, non-scientists, fishermen, technology enthusiasts and Internet users) involved in the diffusion of this information, each of them mobilizing different intertextual strategies, according to their various targets. They all transformed, reformulated and appropriated the scientific information according to their own, unique interests. This study also emphasizes the importance of journalistic websites as opinion relays. They were the first diffusers involved in spreading the information but this role was rarely acknowledged by the Internet users - through citations, for example. In contrast, we observed that amateurs' communities (communities of practices and communities of interest of fishermen or of buzz fans), which only became involved in a second temporal phase of the spreading, preferred to build up their credibility through citations of the original article. Finally, this research helps to rethink the mechanisms of the circulation of scientific information in the Web-based media, highlighting both the variety and the inventiveness of the interactions between the academic and public spheres.

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

The Late Mesolithic in Southern Europe is dated to the 7th and the first part of the 6th millennia BCE and is marked by profound changes which are mostly evident in the technical know-how and tool-kit of the last hunter-fisher-gatherer societies. The significance of this phase also relates to the fact that it precedes the Early Neolithic, another period of major transformations of human societies. Nonetheless, the Late Mesolithic still remains a poorly known age in this area. A burial discovered at Mondeval de Sora (Northern Italy) in 1987, represents a unique window into this period. In this paper, we provide a detailed analysis of more than 50 lithic and osseous artifacts associated with this burial. We highlight important contextual data regarding the techno-economic dimension and the notion of personal burial possessions. Based on the association and location of some items, we propose a new interpretation of the social status of this individual and the possible impact of technological innovation on the social organization and symbolic sphere of Late Mesolithic groups.

Projectile technology is commonly viewed as a significant contributor to past human subsistence and, consequently, to our evolution. Due to the allegedly central role of projectile weapons in the food-getting strategies of Upper Palaeolithic people, typo-technological changes in the European lithic record have often been linked to supposed developments in hunting weaponry. Yet, relatively little reliable functional data is currently available that would aid the detailed reconstruction of past weapon designs. In this paper, we take a use-wear approach to the backed tool assemblages from the Recent and Final Gravettian layers (Levels 3 and 2) of Abri Pataud (Dordogne, France). Our use of strict projectile identification criteria relying on combinations of low and high magnification features and our critical view of the overlap between production and use-related fractures permitted us to confidently identify a large number of used armatures in both collections. By isolating lithic projectiles with the strongest evidence of impact and by recording wear attributes on them in detail, we could establish that the hunting equipment used during the Level 3 occupations involved both lithic weapon tips and composite points armed with lithic inserts. By contrast, the Level 2 assemblage reflects a heavy reliance on composite points in hunting reindeer and other game. Instead of an entirely new weapon design, the Level 2 collection therefore marks a shift in weapon preferences. Using recent faunal data, we discuss the significance of the observed diachronic change from the point of view of prey choice, seasonality, and social organisation of hunting activities. Our analysis shows that to understand their behavioural significance, typo-technological changes in the lithic record must be viewed in the light of functional data and detailed contextual information.

Our knowledge of the recolonization of north-west Europe at the end of the Last Glacial Maximum depends to a large extent on finds from Gough’s Cave (Somerset, UK). Ultra-high resolution radiocarbon determinations suggest that the cave was occupied seasonally by Magdalenian hunters for perhaps no more than two or three human generations, centred on 12,600 BP (~14,950–14,750 cal BP). They left behind a rich and diverse assemblage of Magdalenian lithic and osseous artefacts, butchered animal bones, and cannibalised human remains. The faunal assemblage from Gough’s Cave is one of the most comprehensively studied from any Magdalenian site, yet new and unexpected discoveries continue to be made. Here, we record previously unrecognized flint-knapping tools that were identified during a survey of the Gough’s Cave faunal collection at the Natural History Museum (London). We identified bones used as hammers and teeth manipulated as pressure-flakers to manufacture flint tools. Most of the pieces appear to be ad hoc (single-use?) tools, but a horse molar was almost certainly a curated object that was used over an extended period to work many stone tools. This paper explores how these knapping tools were used to support a more nuanced understanding of Magdalenian stone-tool manufacturing processes. Moreover, we provide a standard for identifying minimally-used knapping tools that will help to establish whether retouchers and other organic stone-working tools are as rare in the Magdalenian archaeological record as current studies suggest.

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.

The APOBEC3 (A3) genes encode cytidine deaminase proteins with potent antiviral and anti-retroelement activity. This locus is characterized by duplication, recombination, and deletion events that gave rise to the seven A3s found in primates. These include three single deaminase domain A3s (A3A, A3C, and A3H) and four double deaminase domain A3s (A3B, A3D, A3F, and A3G). The most potent of the A3 proteins against HIV-1 is A3G. However, it is not clear if double deaminase domain A3s have a generalized functional advantage to restrict HIV-1. In order to test whether superior restriction factors could be created by genetically linking single A3 domains into synthetic double domains, we linked A3C and A3H single domains in novel combinations. We found that A3C/A3H double domains acquired enhanced antiviral activity that is at least as potent, if not better than, A3G. Although these synthetic double domain A3s package into budding virions more efficiently than their respective single domains, this does not fully explain their gain of antiviral potency. The antiviral activity is conferred both by cytidine-deaminase dependent and independent mechanisms, with the latter correlating to an increase in RNA binding affinity. T cell lines expressing this A3C-A3H super restriction factor are able to control replicating HIV-1ΔVif infection to similar levels as A3G. Together, these data show that novel combinations of A3 domains are capable of gaining potent antiviral activity to levels similar to the most potent genome-encoded A3s, via a primarily non-catalytic mechanism., Author summary Antiviral genes are encoded by all organisms to help protect them from viral infections, including proteins encoded by primates to protect them from viruses similar to HIV-1. These antiviral proteins are also called “restriction factors”. Some restriction factors are broadly acting, while others are very specific. During the course of evolution, some of these genes have expanded into multiple copies and rearranged in different versions to give them new activities. In this paper, we validated the hypothesis that one particular antiviral gene family, called the APOBEC3 family, has the capability of making novel combinations of antiviral human genes with as great, or greater, potency against HIV-1 as the most potent natural member of this family. By combining parts of the APOBEC3 proteins into novel combinations, we created potent antiviral versions that act through a mechanism distinct from existing APOBEC3 proteins.

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

Spinner dolphins on Hawai‘i Island’s west coast (Stenella longirostris longirostris) rest by day in protected bays that are increasingly popular for recreation. Because more frequent interactions of people with these dolphins is likely to reduce rest for dolphins and to explain recent decline in dolphin abundance, the National Oceanic and Atmospheric Administration (NOAA) proposed stricter rules regarding interactions with spinner dolphins near the main Hawaiian Islands and plans to increase enforcement. Simultaneous investment in public education about both interaction rules and their biological rationale has been and is likely to be relatively low. To test the hypothesis that more educational signage will reduce human-generated disturbance of dolphins, a paper questionnaire was distributed to 351 land-based, mostly unguided visitors at three dolphin resting bays on Hawai‘i Island’s west coast. Responses indicated that visitors wanted to see dolphins, were ignorant of interaction rules, were likely to read signs explaining rules and their biological rationales, and were likely to follow known rules. Therefore, investment in effective educational signage at dolphin resting bays is recommended as one way to support conservation of spinner dolphins on Hawai‘i Island’s west coast and similar sites in the Hawaiian archipelago.

Surgery in humans is continuously evolving and promoted minimally invasive treatment. On the other hand, despite the importance of the 3Rs principles for experimental animals is well documented, no reports describe specific methodologies for implementing "refinement" in practice. Here, we describe a new technique, the "Ohta Method" for caudal arthrocentesis in the pursuit of the 3Rs for animal experiments and the development of innovative methods for investigating systemic organ arteries through minimally invasive procedures. This procedure requires only a percutaneous puncture of the caudal artery without any injury to the limb or body trunk. In addition, it does not cut down the artery, making hemostasis easier and recovering arterial damage easier. We will show multiple organ artery angiographies in marmoset for the first time in the world. The principle described in this paper could also be applied to many other small animals, such as rats. Moreover, using this method, multiple doses of the drug or cells can be administered to the target organ at the time of therapeutic intervention, thereby enabling the establishment of more sophisticated and complex therapeutic intervention studies as translational research.

Background In recent years, the price of pork in China continues to fluctuate at a high level. The forecast of pork price becomes more important. Single prediction models are often used for this work, but they are not accurate enough. This paper proposes a new method based on Census X12-GM(1,1) combination model. Methods Monthly pork price data from January 2014 to December 2020 were obtained from the State Statistics Bureau(Mainland China). Census X12 model was adopted to get the long-term trend factor, business cycle change factor and seasonal factor of pork price data before September 2020. GM (1,1) model was used to fit and predict the long-term trend factor and business cycle change factor. The fitting and forecasting values of GM(1,1) were multiplied by the seasonal factor and empirical seasonal factor individually to obtain the fitting and forecasting values of the original monthly pork price series. Results The expression of GM(1,1) model for fitting and forecasting long-term trend factor and and business cycle change factor was X(1)(k) = −1704.80e−0.022(k−1) + 1742.36. Empirical seasonal factor of predicted values was 1.002 Using Census X12-GM(1,1) method, the final forecast values of pork price from July 2020 to December 2020 were 34.75, 33.98, 33.23, 32.50, 31.78 and 31.08 respectively. Compared with ARIMA, GM(1,1) and Holt-Winters models, Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of Census X12-GM(1,1) method was the lowest on forecasting part. Conclusions Compared with other single model, Census X12-GM(1,1) method has better prediction accuracy for monthly pork price series. The monthly pork price predicted by Census X12-GM(1,1) method can be used as an important reference for stakeholders.

There are strong interactions between an economic system and its ecological context. In this sense, livestock have been an integral part of human economies since the Neolithic, contributing significantly to the creation and maintenance of agricultural anthropized landscapes. For this reason, in the frame of the ERC-StG project ’ZooMWest’ we collected and analyzed thousands of zooarchaeological data from NE Iberia. By considering these data in comparison with ecological indicators (archaeobotanical remains) and archaeological evidence (settlement characteristics and their distribution) this paper seeks to characterize changes in animal production and the relationship between people, livestock, and their environment. These methods allow for an investigation of the topic at different scales (site, zone, territory) with a broad diachronic perspective, and for consideration of orography and cultural traditions alongside climatic factors. Through this integration of various streams of evidence, we aim to better understand the structure of ancient economic systems and the way they conditioned human decision-making on animal production. Results show a shifting relationship with the territory between the Bronze Age and Late Antiquity, in which market requirements and an economic model with a higher degree of integration increasingly influenced husbandry strategies. These processes are reflected in changes in land use and forms of territorial occupation, although along different rhythms and trajectories., This work was financially supported by the ERC-Starting Grant ZooMWest–Zooarchaeology and Mobility in the Western Mediterranean: Husbandry production from the Late Bronze Age to the Late Antiquity (award number 716298), funded by the European Research Council Agency (ERCEA) under the direction of Sílvia Valenzuela-Lamas. In cooperation with PID2019-110022GB -100 MOBICEX (Ministerio de Ciencia e Innovación, ES); 2017 SGR 995-GRC-Archaeology of Social Dynamics (ASD)-CSIC (Generalitat de Catalunya, ES). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript., We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space., Author summary Cortical circuits are characterized by a high degree of structural and dynamical complexity, and this biological reality is reflected in the large number of parameters in even semi-realistic cortical models. A fundamental task of computational neuroscience is to understand how these parameters govern network dynamics. While some neuronal parameters can be measured in vivo, many remain poorly constrained due to limitations of available experimental techniques. Computational models can address this problem by relating difficult-to-measure parameters to observable quantities, but to do so one must overcome two challenges: (1) the computational expense of mapping a high dimensional parameter space, and (2) extracting biological insights from such a map. This study aims to address these challenges in the following ways: First, we propose a parsimonious data-informed algorithm that efficiently predicts spontaneous cortical activity, thereby speeding up the mapping of parameter landscapes. Second, we show that lateral inhibition provides a basis for conceptualizing cortical parameter space, enabling us to begin to make sense of its geometric structure and attendant scaling relations. We illustrate our approach on a biologically realistic model of the monkey primary visual cortex.

Sheep remains constitute the main archaeozoological evidence for the presence of Early Neolithic human groups in the highlands of the Southern Pyrenees but understanding the role of herding activities in the Neolithisation process of this mountain ecosystem calls for the analysis of large and well-dated faunal assemblages. Cova de Els Trocs (Bisaurri, Huesca, Spain), a cave located at 1564 m a.s.l on the southern slopes of the Central Pyrenees, is an excellent case study since it was seasonally occupied throughout the Neolithic (ca. 5312–2913 cal. BC) and more than 4000 caprine remains were recovered inside. The multi-proxy analytical approach here presented has allowed us to offer new data elaborating on vertical mobility practices and herd management dynamics as has not been attempted up until now within Neolithic high-mountain sites in the Iberian Peninsula. For the first time, δ18O and δ13C stable isotope analyses offer direct evidence on both the regular practice of altitudinal movements of sheep flocks and the extended breeding season of sheep. Autumn births are recorded from the second half of the fifth millennium cal. BC onwards. Age-at-death distributions illustrate the progressive decline in caprine perinatal mortality together with the rising survival rate of individuals older than six months of age and the larger frequency of adults. This trend alongside the ‘off-season’ lambing signal at the implementation of husbandry techniques over time, probably aiming to increase the size of the flocks and their productivity. Palaeoparasitological analyses of sediment samples document also the growing reliance on herding activities of the human groups visiting the Els Trocs cave throughout the Neolithic sequence. In sum, our work provides substantial arguments to conclude that the advanced herding management skills of the Early Neolithic communities arriving in Iberia facilitated the anthropisation process of the subalpine areas of the Central Pyrenees., This paper was supported by several projects awarded to MR-G: ‘Los Caminos del Neolítico’ (HAR2009-09027) and ‘Los Caminos del Neolítico II’ (HAR2013-46800 P) were granted by the National R&D&I Plan/Ministry of Economy, Industry and Competitiveness (https://www.ciencia.gob.es/portal/site/MICINN/) and co-financed by the Government of Aragón (https://www.aragon.es/), and ‘La Memoria del Camino: Ciencia y divulgación de las primeras rutas pecuarias neolíticas en el Pirineo-MEDELCA’ (FCT-2015-9947) was financed by the Spanish Foundation for Science and Technology (FECYT) (https://www.fecyt.es/). The archaeozoological research was carried out within the framework of the project awarded to MM-G ‘… y la oveja domesticó al pastor: Señales genómicas y arqueozoológicas de los primeros ovinos durante la neolitización de la península ibérica’ (HAR2016-75914-R) by the National R&D&I Plan/Ministry of Economy, Industry and Competitiveness (https://www.ciencia.gob.es/portal/site/MICINN/). AH was awarded a PhD grant by the Université Fédérale de Toulouse ANR-11-IDEX-0002-02 (https://www.univ-toulouse.fr/). Support of the publication fee was granted by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

Livestock manure emits reduced sulfur compounds and methane, which affect nature and the climate. These gases are efficiently mitigated by addition of a tannic acid-sodium fluoride combination inhibitor (TA-NaF), and to some extent by acidification. In this paper, TA-NaF treatment was performed on swine manure to study the treatment influence on methanogenic pathways and sulfur transformation pathways in various laboratory experiments. Stable carbon isotope labeling revealed that both untreated and TA-NaF treated swine manures were dominated by hydrogenotrophic methanogenesis. However, in supplementary experiments in wastewater sludge, TA-NaF clearly inhibited acetoclastic methanogenesis, whereas acidification inhibited hydrogenotrophic methanogenesis. In swine manure, TA-NaF inhibited s-amino acid catabolism to a larger extent than sulfate reduction. Conversely, acidification reduced sulfate reduction activity more than s-amino acid degradation. TA-NaF treatment had no significant effect on methanogenic community structure, which was surprising considering clear effects on isotope ratios of methane and carbon dioxide. Halophile sulfate reducers adapted well to TA-NaF treatment, but the community change also depended on temperature. The combined experimental work resulted in a proposed inhibition scheme for sulfur transformations and methanogenic pathways as affected by TA-NaF and acidification in swine manure and in other inocula.

The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.

Vehicle-tree collisions are the most common type of road crash with fixed obstacle in Czech Republic. Based on the literature review and using real world in-depth crash data, this paper aims to define factors, which significantly influence the injury severity of single vehicle-tree crashes. In-depth data provide a comprehensive view to the failure on the system infrastructure—human—vehicle related to crash, the in-depth crash database include very detailed information related to infrastructure, vehicle, human failure and crash participants characteristics and their medical condition and also crash reconstruction. Multinomial logistic regression and generalized linear mixed model were used to determine the individual effect of each predictor. The statistically significant variables were the day period, trunk diameter and impact speed. Using multinomial logistic regression shows also vehicle age as statistically significant. Obtained results can help to efficiently direct countermeasures not only on the road infrastructure—e.g. speed reduction in selected locations with specified tree character. However, the emphasis should be also focused on driver behaviour.

Aim/Goal/Purpose Population surveys underrepresent people with severe mental ill health. This paper aims to use multiple regression analyses to explore perceived social support, loneliness and factor associations from self-report survey data collected during the Covid-19 pandemic in a sample of individuals with severe mental ill health. Design/Methodology/Approach We sampled an already existing cohort of people with severe mental ill health. Researchers contacted participants by phone or by post to invite them to take part in a survey about how the pandemic restrictions had impacted health, Covid-19 experiences, perceived social support, employment and loneliness. Loneliness was measured by the three item UCLA loneliness scale. Findings In the pandemic sub-cohort, 367 adults with a severe mental ill health diagnosis completed a remote survey. 29–34% of participants reported being lonely. Loneliness was associated with being younger in age (adjusted OR = -.98, p = .02), living alone (adjusted OR = 2.04, p = .01), high levels of social and economic deprivation (adjusted OR = 2.49, p = .04), and lower perceived social support (B = -5.86, p < .001). Living alone was associated with lower perceived social support. Being lonely was associated with a self-reported deterioration in mental health during the pandemic (adjusted OR = 3.46, 95%CI 2.03–5.91). Practical implications Intervention strategies to tackle loneliness in the severe mental ill health population are needed. Further research is needed to follow-up the severe mental ill health population after pandemic restrictions are lifted to understand perceived social support and loneliness trends. Originality Loneliness was a substantial problem for the severe mental ill health population before the Covid-19 pandemic but there is limited evidence to understand perceived social support and loneliness trends during the pandemic.

Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal results. The proposed versions are compared against original Butterfly Optimization Algorithm (BOA), Grey Wolf Optimizer (GWO), Moth-flame Optimization (MFO), Particle warm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA) using CEC 2014 benchmark functions and 4 different real-world engineering problems namely: welded beam engineering design, tension/compression spring, pressure vessel design, and Speed reducer design problem. Furthermore, the proposed approches have been applied to feature selection problem using 5 UCI datasets. The results show the superiority of the third version (CLSOBBOA) in achieving the best results in terms of speed and accuracy.

According to thevariance hypothesis, variety-seeking or exploration is a critical condition for improving learning and performance over time. Extant computational learning models support this hypothesis by showing how individuals who are exposed to diverse knowledge sources are more likely to find superior solutions to a particular problem. Yet this research provides no precise guidelines about how broadly individuals should search. Our goal in this paper is to elucidate the conditions under which variety-seeking in organizations is beneficial. To this end, we developed a computational model in which individuals learn as they interact with other individuals, and update their knowledge as a result of this interaction. The model reveals how the type of learning environment (performance landscape) in which the learning dynamic unfolds determines when the benefits of variety-seeking outweigh the costs. Variety-seeking is performance-enhancing only when the knowledge of the chosen learning targets (i.e., individuals to learn from) provide useful information about the features of the performance landscape. The results further suggest that superior knowledge might be available locally, i.e., in the proximity of an individual’s current location. We also identify the point beyond which variety-seeking causes a sharp performance decline and show how this point depends on the type of landscape in which the learning dynamic unfolds and the degree of specialization of individual knowledge. The presence of this critical point explains why exploration becomes very costly. The implications of our findings for establishing the boundaries of exploration are discussed.

The hog industry is the core industry in the field of agriculture and animal husbandry in China, which development is related to the improvement of people’s quality of life. The production of the hog industry has been restricted by environmental regulations, which needs to reduce costs and improve efficiency. Based on the data of 29 provinces from 2008 to 2019, this paper aims to use the stochastic frontier analysis method to calculate the cost efficiency of fattening pigs, sows, and piglets in three stages of pig production and focuses on the impact of environmental regulation policies on cost efficiency. The study found that the cost efficiency of fattening pigs, sows, and piglets in China were 0.77, 0.79, and 0.53, respectively, and the efficiency losses were 23%, 21%, and 47%, respectively. The impact of environmental regulation policies on the cost efficiency of fattening pigs showed an ’ N ’ trend, and the impact on the cost efficiency of sows and piglets showed an inverted ’ N ’ trend. For fattening pigs, increasing the intensity of environmental regulation, and exceeding the second inflection point of the ’ N ’ curve can achieve the dual goals of environmental governance and cost reduction and efficiency increase. For sows, reducing the intensity of environmental regulation appropriately can avoid cost-efficiency loss. For piglets, environmental regulation policies have not effectively incentives the cost efficiency of piglets. In addition, raising the level of technology investment in fattening pigs and sows can achieve cost efficiency gains, and can farmers use emerging financial product tools such as ’ insurance + futures ’ to avoid market risks and efficiency losses.

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics., Author summary Machine learning algorithms have proven to be effective for tools for detection and classification tasks in many fields, however, these processes are generally data-hungry and their use in marine acoustics has been limited by a lack of large labeled datasets for algorithms to learn from. In underwater acoustic recordings, many signals generated by animals, human activities and physical processes mingle together, and their sounds can change depending on ocean temperatures, locations, and behavior. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. This paper presents a process which combines unsupervised and supervised learning phases and expert oversight to generate and use large datasets for acoustic classification of marine mammal and human-generated signals. Unsupervised learning is used to automatically generate the large training datasets needed to teach a supervised learning algorithm to correctly classify seven different signal types commonly recorded in the Southern California Bight. Using this process, researchers with large unlabeled acoustic datasets can begin to take advantage of widespread advances in machine learning.

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data., Author summary Perceptual systems are designed to make sense of fragmented sensory data by inferring common, latent causes. Seeing a cluster of insects might allow us to infer the presence of a common food source, whereas the same number of insects scattered over a larger area of land might not evoke the same suspicions. The ability to reliably make this inference based on statistical information about the environment is surprisingly non-trivial: making the best possible inference requires making full use of the probabilistic information provided by the sensory data, which would require considering a combinatorially explosive number of hypothetical world states. In this paper, we test human subjects on their ability to perform a causal detection task: subjects are asked to judge whether an underlying cause of clustering is present or absent, based on the spatial distribution of those items. We show that subjects do not reason optimally on this task, and that particular computational short cuts (“committing” to certain world states over others, rather than representing them all) might underlie perceptual decision-making in these causal detection schemes.