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To investigate whether the polymerase chain reaction (PCR) on the BI gene of Toxoplasma gondii could contribute to the diagnosis of cerebral toxoplasmosis in patients with AIDS, we retrospectively tested CSF samples from 20 patients with AIDS suspected of having cerebral toxoplasmosis for the presence of T. gondii. Suspicion of cerebral toxoplasmosis was based on accepted criteria. Nine patients with AIDS with IgG antibodies to T. gondii but who were not suspected of having cerebral toxoplasmosis and four patients with AIDS seronegative for T. gondii served as negative control patients. T. gondii was demonstrated by PCR in the CSF from 13 of the 20 patients with AIDS suspected of having cerebral toxoplasmosis but was not demonstrated in the CSF samples from the nine control patients seropositive for T. gondii and the four control patients seronegative for T. gondii. The data were statistically evaluated. This study shows the value of PCR for the detection of T. gondii in CSF for the diagnosis of cerebral toxoplasmosis in patients with AIDS. [ABSTRACT FROM PUBLISHER]

Microscopic tests, molecular dynamics (MD) simulation, and two-piece healing (TPH) approaches were employed to investigate the interfacial intrinsic healing behavior and mechanisms of graphene-modified asphalt (GMA) binders at different aging levels. A vacuum layer 10-Å thick was injected between two molecules of asphalt binder to resemble a pattern of cracks. The mean squared displacement (MSD), molecular diffusion coefficient (D), and surface free energy (SFE) of asphalt molecules were utilized to evaluate the micro intrinsic healing behavior of asphalt binders. The macro intrinsic healing capacity of asphalt binders was determined using the intrinsic healing function [Rh(t)] developed from the TPH test. The GMA binder had a higher healing efficiency than the base binder in the short-term healing condition according to the density of the MD simulations, the SFE of the sessile drop method, and the initial healing rate (R0) of the TPH experiment. GMA binder had higher potential for instantaneous healing than the base binder because of its greater SFE in general. According to an analysis of parameters MSD, D , and Rh(t) quantified by MD simulations and TPH tests, the GMA binder had a greater capacity for long-term healing than the base binder after a long-term aging process. An R2 of 0.87 was obtained from the regression analysis of R0 against SFE, and an R2 of 0.93 was obtained from the regression analysis of Rh(3,600)−R0 against D. The experimental findings at the macroscopic scale and the simulation outcomes at the microscopic level were mutually supportive. [ABSTRACT FROM AUTHOR]

Lymph node metastasis, the initial step in distant metastasis, represents a primary contributor to mortality in patients diagnosed with oral squamous cell carcinoma (OSCC). However, the underlying mechanisms of lymph node metastasis in OSCC remain incompletely understood. Here, the transcriptomes of 56 383 single cells derived from paired tissues of six OSCC patients are analyzed. This study founds that CXCR4+ epithelial cells, identified as highly malignant disseminated tumor cells (DTCs), exhibited a propensity for lymph node metastasis. Importantly, a distinct subset of tumor‐associated macrophages (TAMs) characterized by exclusive expression of phosphoprotein 1 (SPP1) is discovered. These TAMs may remodel the metastatic lymph node microenvironment by potentially activating fibroblasts and promoting T cell exhaustion through SPP1‐CD44 and CD155‐CD226 ligand‐receptor interactions, thereby facilitating colonization and proliferation of disseminated tumor cells. The research advanced the mechanistic understanding of metastatic tumor microenvironment (TME) and provided a foundation for the development of personalized treatments for OSCC patients with metastasis. [ABSTRACT FROM AUTHOR]

Rotavirus species A (RVA) is a major cause of acute viral gastroenteritis in young humans and diverse animal species. The study of the genetic characteristics of RVAs that infect rabbits (Oryctolagus cuniculus) (lapine strain [LRV]) has been limited, and, to date, the most common and epidemiologically important combinations of G/P genotypes in rabbits have been reported to be G3 P[14] and G3 P[22]. In this study, a rotavirus species A detected from an outbreak of enteritis in a Mexican commercial rabbitry was genotypically characterized. Based on sequence and phylogenetic analysis of the VP7 and VP4 genes, the strain identified in this study (C-3/15) demonstrated a G3 P[8] genotype of rotavirus, which had not previously been reported in rabbits. Moreover, both genes were closely related to human, not lapine, rotaviruses. The G3 genotype has been reported in a wide variety of hosts, including humans and rabbits, whereas the P[8] genotype has only been reported in humans. Because this combination of genotypes has never been identified in rabbits, it is proposed that the finding presented here is possibly the result of an interspecies transmission event. This is the first work to study the molecular characteristics of rotaviruses in rabbits in Mexico, as well as the identification of human G3 and P[8] genotypes in a rabbit with enteric disease. [ABSTRACT FROM AUTHOR]

The family Picornaviridae contains well-known human pathogens (e.g., poliovirus, coxsackievirus, rhinovirus, and parechovirus). In addition, this family contains a number of viruses that infect animals, including members of the genus Cardiovirus such as Encephalomyocarditis virus (EMCV) and Theiler's murine encephalomyelits virus (TMEV). The latter are important murine pathogens that cause myocarditis, type 1 diabetes and chronic inflammation in the brains, mimicking multiple sclerosis. Recently, a new picornavirus was isolated from humans, named Saffold virus (SAFV). The virus is genetically related to Theiler's virus and classified as a new species in the genus Cardiovirus, which until the discovery of SAFV did not contain human viruses. By analogy with the rodent cardioviruses, SAFV may be a relevant new human pathogen. Thus far, SAFVs have sporadically been detected by molecular techniques in respiratory and fecal specimens, but the epidemiology and clinical significance remained unclear. Here we describe the first cultivated SAFV type 3 (SAFV-3) isolate, its growth characteristics, full-length sequence, and epidemiology. Unlike the previously isolated SAFV-1 and -2 viruses, SAFV-3 showed efficient growth in several cell lines with a clear cytopathic effect. The latter allowed us to conduct a large-scale serological survey by a virus-neutralization assay. This survey showed that infection by SAFV-3 occurs early in life (>75% positive at 24 months) and that the seroprevalence reaches >90% in older children and adults. Neutralizing antibodies were found in serum samples collected in several countries in Europe, Africa, and Asia. In conclusion, this study describes the first cultivated SAFV-3 isolate, its full-length sequence, and epidemiology. SAFV-3 is a highly common and widespread human virus causing infection in early childhood. This finding has important implications for understanding the impact of these ubiquitous viruses and their possible role in acute and/or chronic disease.

With the rapid development of entity recognition technology, animal recognition has gradually become essential in modern society, supporting labour‐intensive agriculture and animal husbandry tasks. Severe problems such as maintaining biodiversity can also benefit from animal identification technology. However, certain invasive recognition systems have resulted in permanent harm to animals, while noninvasive identification methods also exhibit certain drawbacks. This paper conducts a systematic literature review (SLR), presenting a comprehensive overview of various animal recognition technologies and their applications. Specifically, it examines methodologies such as deep learning, image processing and acoustic analysis used for different animal characteristics and identification purposes. The contribution of machine learning to animal feature extraction is highlighted, emphasising its significance for animal taxonomy and wild species monitoring. Additionally, this review addresses the challenges and limitations of current technologies, including data scarcity, model accuracy and computational requirements, and suggests opportunities for future research to overcome these obstacles. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate detection of wildlife, particularly small and hidden animals, is crucial for conservation efforts. Traditional image-based methods often struggle in complex environments. This study introduces a novel approach that combines image and text data to improve detection accuracy. By incorporating textual information about animal characteristics and leveraging a Concept Enhancement Module (CEM), our model can better understand and locate animals, even in challenging conditions. Experimental results demonstrate a significant improvement in detection accuracy, achieving an average precision of 95.8% on a challenging wildlife dataset. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This represents a substantial improvement compared to existing state-of-the-art methods. Our multimodal approach offers a promising solution for enhancing wildlife monitoring and conservation efforts. Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. [ABSTRACT FROM AUTHOR]

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model's ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R2 of 0.98 for variable cattle herd densities. These results highlight that a model with high AP0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate identification of individual Amur tigers is vital for their conservation, as it helps us understand their population and distribution. Existing identification methods often fall short in accuracy, and our study focuses on creating a more accurate method for identifying individual Amur tigers using advanced deep learning techniques. We improved an existing neural network model called InceptionResNetV2 by adding features like dropout layers and dual-attention mechanisms to better capture the unique stripe patterns of each tiger and reduce errors during training. We tested our model on a large dataset of tiger images and found it to be highly effective, achieving an average recognition accuracy of over 95% for different body parts, with left stripes reaching the highest 99.37%. This method significantly outperforms previous models and provides a reliable tool for wildlife researchers and conservationists to monitor and protect Amur tigers. By improving the ability to track individual tigers, our research offers practical benefits for preserving this endangered species and enhancing wildlife management practices globally. Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and right stripe areas from images of 107 individual Amur tigers, achieving a high average classification accuracy of 97.3%. By introducing a dropout layer and a dual-attention mechanism, we enhance the InceptionResNetV2 model to better capture the stripe features of individual tigers at various granularities and reduce overfitting during training. Experimental results demonstrate that our model outperforms other classic models, offering optimal recognition accuracy and ideal loss changes. The average recognition accuracy for different body part features is 95.36%, with left stripes achieving a peak accuracy of 99.37%. These results highlight the model's excellent recognition capabilities. Our research provides a valuable and practical approach to the individual identification of rare and endangered animals, offering significant potential for improving conservation efforts. [ABSTRACT FROM AUTHOR]

Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well‐suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase. [ABSTRACT FROM AUTHOR]

Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time‐consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest‐entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north‐eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest‐entrances along the cliff with the colony. For this, we used three CNN architectures, U‐Net, ResUnet, and DeepLabv3. The U‐Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest‐entrances across the colony, with a mean absolute error of 2.7 nest‐entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. We show that the strong changes observed in the distribution of nest‐entrances are a measurable effect of a long record of human‐induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation. [ABSTRACT FROM AUTHOR]

The 'landscape of fear' concept offers valuable insights into wildlife behaviour, yet its practical integration into habitat management for conservation remains underexplored. In this study, conducted in the subtropical monsoon grasslands of Bardia National Park, Nepal, we aimed to bridge this gap through a multi‐year, landscape‐scale experimental investigation in Bardia National Park, Nepal. The park has the highest density of tigers (with an estimated density of ~7 individuals per 100 km2) in Nepal, allowing us to understand the effect of habitat management on predation risk and resource availability especially for three cervid species: chital (Axis axis), swamp deer (Rucervus duvaucelii) and hog deer (Axis porcinus). We used plots with varying mowing frequency (0–4 times per year), size (ranging from small: 49 m2 to large: 3600 m2) and artificial fertilisation type (none, phosphorus, nitrogen) to assess the trade‐offs between probable predation risk and resources for these cervid species, which constitute primary prey for tigers in Nepal. Our results showed distinct responses of these deer to perceived predation risk within grassland habitats. Notably, these deer exhibited heightened use of larger plots, indicative of a perceived sense of safety, as evidenced by the higher occurrence of pellet groups in the larger plots (mean = 0.1 pellet groups m−2 in 3600 m2 plots vs. 0.07 in 400 m2 and 0.05 in 49 m2 plots). Furthermore, the level of use by the deer was significantly higher in larger plots that received mowing and fertilisation treatments compared to smaller plots subjected to similar treatments. Of particular interest is the observation that chital and swamp deer exhibited greater utilisation of the centre (core) areas within the larger plots (mean = 0.21 pellet groups m−2 at the centre vs. 0.13 at the edge) despite the edge (periphery) also provided attractive resources to these deer. In contrast, hog deer did not display any discernible reaction to the experimental treatments, suggesting potential species‐specific variations in response to perceived predation risk arising from management interventions. Our findings emphasise the importance of a sense of security as a primary determinant of habitat selection for medium‐sized deer within managed grassland environments. These insights carry practical implications for park managers, providing a nuanced understanding of integrating the 'landscape of fear' into habitat management strategies. This study emphasises that the 'landscape of fear' concept can and should be integrated into habitat management to maintain delicate predator–prey dynamics within ecosystems. [ABSTRACT FROM AUTHOR]

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development. [ABSTRACT FROM AUTHOR]

In Wire Arc Additive Manufacturing (WAAM), variable layer heights enable the non-parallel or non-planar slicing of parts. In researching variable layer heights, this paper documents printing strategies for a reference geometry whose key features are non-orthogonal growth and unsupported overhangs. The complexity of 3D printing with welding requires parameter optimization to control the deposition of molten material. Thus, 3D printing with welding requires the precise deposition of molten material. Currently, there is no standard solution for the customization of process parameters and intelligent collection of data from sensors. To address this gap in technology, this research develops an Internet of Things (IoT)-enabled, distributed communication protocol to control process parameters, synchronize commands, and integrate device data. To intelligently collect sensor information, this research creates a query-able database during pre-planning and production. This contributes to fundamental research in WAAM by documenting strategies for printing variable layer heights, the customization of control parameters, and the collection of data through a WAAM Information Model (WIM). [ABSTRACT FROM AUTHOR]

Simple Summary: Species recognition is a key part of understanding biodiversity and can help us to better conserve and manage biodiversity. Traditional species recognition methods require large amounts of image data to train the recognition model, but obtaining image data of rare and endangered species is a challenge. However, Contrastive Language–Image Pre-training (CLIP), a generalized artificial intelligence model, can perform classification by calculating the similarity between images and text without the need for training data. Taking advantage of this and considering the unique geographic distribution pattern of species, we propose a CLIP-based species recognition method that can recognize species based on geographic distribution knowledge. This study is the first to combine geographic distribution knowledge with species recognition, which can lead to more effective recognition of rare and endangered species. Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large amounts of well-labeled, high-quality image data, the collection of which is challenging for rare and endangered species. In addition, recognition methods designed based on specific species have poor generalization ability and are difficult to adapt to new species recognition scenarios. To address these issues, zero-shot species recognition based on Contrastive Language–Image Pre-training (CLIP) has become a research hotspot. However, previous studies have primarily utilized visual descriptive information and taxonomic information of species to improve zero-shot recognition performance, and the use of geographic distribution characteristics of species to improve zero-shot recognition performance has not been explored. To fill this gap, we proposed a CLIP-driven zero-shot species recognition method that incorporates knowledge of the geographic distribution of species. First, we designed three prompts based on the species geographic distribution statistical data. Then, the latitude and longitude coordinate information attached to each image in the species dataset was converted into addresses, and they were integrated together to form the geographical distribution knowledge of each species. Finally, species recognition results were derived by calculating the similarity after acquiring features by the trained CLIP image encoder and text encoder. We conducted extensive experiments on multiple species datasets from the iNaturalist 2021 dataset, where the zero-shot recognition accuracies of mammals, mollusks, reptiles, amphibians, birds, and insects were 44.96%, 15.27%, 17.51%, 9.47%, 28.35%, and 7.03%, an improvement of 2.07%, 0.48%, 0.35%, 1.12%, 1.64%, and 0.61%, respectively, as compared to CLIP with default prompt. The experimental results show that the fusion of geographic distribution statistical data can effectively improve the performance of zero-shot species recognition, which provides a new way to utilize species domain knowledge. [ABSTRACT FROM AUTHOR]

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Layered double hydroxides (LDHs) are a vital tool in many different areas, such as drug delivery, catalysis, anion exchange (materials), polymer processing, etc. Conventionally, LDHs are synthesized in a batch process that consists of particle generation and ripening, where product properties are manipulated for stability and the optimal uptake of genetic material. Continuous processing and intensive mixing holds high promise for improved particle generation and characteristic control. In this contribution, an iterative method, using the mentioned particle generation as a use case, was applied to quickly generate a continuous process optimization platform for continuous, plugging-free particle generation with the required characteristics. Assisted by rapid prototyping and additive manufacturing, a vortex mixer was produced that delivers satisfactory long-term results. [ABSTRACT FROM AUTHOR]

The discovery that extracellular vesicles (EVs) serve as carriers of virus particles calls for a reevaluation of the release strategies of non-enveloped viruses. Little is currently known about the molecular mechanisms that determine the release and composition of EVs produced by virus-infected cells, as well as conservation of these mechanisms among viruses. We previously described an important role for the Leader protein of the picornavirus encephalomyocarditis virus (EMCV) in the induction of virus-carrying EV subsets with distinct molecular and physical properties. EMCV L acts as a 'viral security protein' by suppressing host antiviral stress and type-I interferon (IFN) responses. Here, we tested the ability of functionally related picornavirus proteins of Theilers murine encephalitis virus (TMEV L), Saffold virus (SAFV L), and coxsackievirus B3 (CVB3 2Apro), to rescue EV and EV-enclosed virus release when introduced in Leader-deficient EMCV. We show that all viral security proteins tested were able to promote virus packaging in EVs, but that only the expression of EMCV L and CVB3 2Apro increased overall EV production. We provide evidence that one of the main antiviral pathways counteracted by this class of picornaviral proteins, i.e. the inhibition of PKR-mediated stress responses, affected EV and EV-enclosed virus release during infection. Moreover, we show that the enhanced capacity of the viral proteins EMCV L and CVB3 2Apro to promote EV-enclosed virus release is linked to their ability to simultaneously promote the activation of the stress kinase P38 MAPK. Taken together, we demonstrate that cellular stress pathways involving the kinases PKR and P38 are modulated by the activity of non-structural viral proteins to increase the release EV-enclosed viruses during picornavirus infections. These data shed new light on the molecular regulation of EV production in response to virus infection. Author summary: During infection, virus particles can be packaged by host cells within extracellular vesicles (EVs), nanosized vesicles that serve as vehicles for cell-to-cell communication. These EVs can shield virus particles from antibodies, and alter the uptake of viruses by susceptible cells and cells carrying-out immune surveillance. However, despite the potential consequences of EV-enclosed virus release on disease progression, little is known about the virus and host factors regulating this process. Here, we investigated the role of virus-encoded host-modulating proteins in the packaging of non-enveloped viruses in EVs, focusing on the picornavirus virus family. We previously demonstrated that the picornavirus encephalomyocarditis virus alters the number and type of EVs released by infected cells to promote EV-enclosed virus release, and that this depended on the activity of the viral Leader protein. Using a panel of knockout cells, inhibitors, and recombinant virus constructs, we here show that this alteration of EV release depends on modulation of the pathways downstream of two host stress kinases, PKR and P38 MAPK, and is conserved in the 2A protease of distantly related pathogen coxsackievirus B3. These data shed light on the molecular regulation of EV-enclosed virus release, and pave the way to find inhibitors of EV-mediated virus spread. [ABSTRACT FROM AUTHOR]

Efficient animal detection is essential for biodiversity protection. Unmanned aerial vehicles (UAVs) have been widely used because of their low costs and minimal environmental intrusion. However, using UAVs for practical animal detection poses two challenges: (a) the UAV's fly highly to avoid disturbing animals, resulting in small object detection problems; (b) the limited processing power of UAVs makes large state‐of‐the‐art (SOTA) methods (e.g., You Only Look Once V7, YOLOv7) difficult to deploy. This work proposes the WILD‐YOLO based on YOLOv7 to deal with the two problems. To detect small objects, WILD‐YOLO improves upon YOLOv7 by adding a small object detection head in the head part. To enable real‐time animal detection in field environments with UAVs, the lighten FasterNet and GhostNet have been used to significantly reduce the model size. Compared to YOLOv7, WILD‐YOLO significantly reduces the number of parameters, making it suitable for lightweight deployment on UAVs. Additionally, comparisons with other lightweight models such as YOLOv7‐tiny, YOLOv5‐s, YOLOv4‐s and MobilenetV2 on the datasets are conducted. The experimental results demonstrate that this proposed WILD‐YOLO method outperforms other approaches and has great potential for effective detection of wildlife in complex environments encountered by UAVs. [ABSTRACT FROM AUTHOR]

The restructuring of the national economy, the processes of transformation taking place in it, actualize the problems of controlled vector development of complex hierarchical systems in the economy. The purpose of this study is to analyze the theoretical and methodological aspects of the development of complex hierarchical systems (CIS) in the economy in the context of unpredictability and stochasticity of the external and internal environment, as well as the uneven development of socioeconomic processes that require controlled vector development. It is the controllability of the vector development of the CIS that makes it possible to ensure the security of the processes of non-linear development of the CIS. The object of the research is the processes of non-linear development of the CIS in the conditions of unpredictability, stochasticity of the external environment and uneven development of socioeconomic processes caused by globalization transformations of the world economy. The study used general and special methods to achieve the goal and solve the tasks. The general methods include the abstract and logical method, which was used to explain the theoretical foundations of the security of the development of complex hierarchical systems, and the methods of theoretical generalization, system and behavioral-economic analysis, which helped to formulate the qualitative goals and objectives of the CIS, identify the problems of managing controlled dynamic processes, conduct a critical analysis of development concepts and find out the genesis of approaches. Special research methods include system-structural analysis, which was used for the formation, selection and implementation of hypotheses, construction of forecasts, and the system approach and methods of analysis and synthesis, which helped to identify and aggregate the qualitative characteristics of the concept of management of secured development of CIS. As a result of the application of these methods, a synergistic approach to the management of CIS processes was achieved and the problems related to stochasticity and uncertainty of the external and internal environment, uneven development of socioeconomic processes and other factors affecting the security of the development of the intellectual economy are addressed. The influence of endogenous and exogenous factors on the development of CIS is substantiated. A methodology based on process-functional management has been proposed to analyze the level of development of the CIS, which can help to increase the efficiency of the use of production resources. The conception of modeling mechanisms for the development of information security systems (ISS) based on the transformation of management processes and investment processes has been developed. [ABSTRACT FROM AUTHOR]

The infection of the central nervous system (CNS) with neurotropic viruses induces neuroinflammation and is associated with the development of neuroinflammatory and neurodegenerative diseases, including multiple sclerosis and epilepsy. The activation of the innate and adaptive immune response, including microglial, macrophages, and T and B cells, while required for efficient viral control within the CNS, is also associated with neuropathology. Under healthy conditions, resident microglia play a pivotal role in maintaining CNS homeostasis. However, during pathological events, such as CNS viral infection, microglia become reactive, and immune cells from the periphery infiltrate into the brain, disrupting CNS homeostasis and contributing to disease development. Theiler's murine encephalomyelitis virus (TMEV), a neurotropic picornavirus, is used in two distinct mouse models: TMEV-induced demyelination disease (TMEV-IDD) and TMEV-induced seizures, representing mouse models of multiple sclerosis and epilepsy, respectively. These murine models have contributed substantially to our understanding of the pathophysiology of MS and seizures/epilepsy following viral infection, serving as critical tools for identifying pharmacological targetable pathways to modulate disease development. This review aims to discuss the host–pathogen interaction during a neurotropic picornavirus infection and to shed light on our current understanding of the multifaceted roles played by microglia and macrophages in the context of these two complexes viral-induced disease. [ABSTRACT FROM AUTHOR]

This paper discusses additive manufacturing (AM) and topology optimization (TO) and their relationship with industrial revolution 4.0. An overview of different AM techniques is given, along with the importance of design for manufacturing and assembly in progressing AM. The potential of AM to build complicated geometries with great precision has attracted a lot of interest in recent years. TO, one of the major enabling technologies in AM, has been essential in building compliant systems with improved performance across numerous industries. The development of hybrid mechanisms that integrate both compliant and stiff pieces because of improvements in "TO" algorithms has improved their usefulness and efficiency. Augmented realty and digital twins (DTs) have been used with "TO" to improve product design visualization and collaboration. Synergies between IN 4.0, TO, and AM have been discussed along with their cross-domain relevance. Machine learning involvement for more robust integration of IN 4.0 with TO and AM have also been discussed. The development of the Digital Triad, which combines DTs, digital threads, and digital trust to enable effective and secure data sharing and cooperation, is the result of the convergence of internet-of-things, cloud computing, and big data analytics. However, concerns about data privacy and cybersecurity still need to be resolved. The use of machine learning algorithms for cyberattack detection and mitigation as well as secure block chain-based frameworks for managing intellectual property rights are just a few of the frameworks and tactics that researchers have suggested to lower cybersecurity risks in AM systems. The establishment of new standards and guidelines for the cybersecurity of AM systems is anticipated to result from ongoing research in this area. [ABSTRACT FROM AUTHOR]

In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-convolution and is gradually becoming the most commonly used segmentation architecture in the medical field. The encoder of U-Net is mainly used to capture the context information in the image, which plays an important role in the performance of the semantic segmentation algorithm. However, it is unstable for U-Net with simple skip connection to perform unstably in global multi-scale modelling, and it is prone to semantic gaps in feature fusion. Inspired by this, in this work, we propose a Deep Tensor Low Rank Channel Cross Fusion Neural Network (DTLR-CS) to replace the simple skip connection in U-Net. To avoid space compression and to solve the high rank problem, we designed a tensor low-ranking module to generate a large number of low-rank tensors containing context features. To reduce semantic differences, we introduced a cross-fusion connection module, which consists of a channel cross-fusion sub-module and a feature connection sub-module. Based on the proposed network, experiments have shown that our network has accurate cell segmentation performance. [ABSTRACT FROM AUTHOR]

Although structural design complexities do not potentially pose challenges to many additive manufacturing technologies, several manufacturing constraints should be considered in the design process. One critical constraint is a structure's unsupported or overhanging features. If these features are not reduced or eliminated, they can cause a decline in part surface quality, inhibit print success, or increase production time and cost due to support printing and removal. To eliminate these features, a new post-topology optimization strategy is proposed. The design problem is first topologically optimized, then boundary identification and overhang detection are carried out. Next, additional support-free struts subject to a specified thickness and angle are introduced to support previously detected infeasible features. This addition can increase the structure's volume; therefore, an optional volume correction stage is introduced to obtain a new but lower volume fraction which will be used in the final topology optimization, boundary identification, and overhang elimination stages. Experimental and numerical load–displacement relationships are established for varying overhang angle thresholds and minimum feature sizes. [ABSTRACT FROM AUTHOR]

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One of the applications of mechanical metamaterials is in car tires, as a non-pneumatic tire (NPT). Therefore, to find a suitable cell to replace the pneumatic part of the tire, three different solution methods were used, including topology optimization of the cubic unit cell, cylindrical unit cell, and fatigue testing cylindrical sample (FTCS). First, to find the mechanical properties, a tensile test was conducted for materials made of polylactic acid (PLA) and then, the optimization was done based on the weight and overhang control for the possibility of manufacturing with 3D printers, as constraints, besides, the objective of minimum compliance. In the optimization of the cubic unit cell, the sample with a minimum remaining weight of 35% was selected as the optimal sample. However, for the cylindrical unit cell, a sample with a weight limit of 20% was the most optimal state. In contrast, in the FTCS optimization, a specimen with lower remaining weight equal to 60% of the initial weight was selected. After obtaining the answer, five cells in the FTCS and two mentioned cells were evaluated under compressive testing. The samples were also subjected to bending fatigue loadings. The results demonstrated that cellular structures with 15% of lower weight than the optimized samples had the same fatigue lifetime. In the compressive test, the line slope of the specimens with cellular structures in the elastic region of the force-displacement diagram was reduced by 37%, compared to the completely solid samples. However, the weight of these samples decreased by 59%. Furthermore, the fracture surface was also investigated by field-emission scanning electron microscopy. It was observed that a weak connection between the layers was the cause of failure. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) is the process of joining materials by selectively depositing them layer upon layer for the purpose of manufacturing parts or assemblies which are based on a 3D digital model. The nature of these processes results in the morphing of complex component geometries, enabling a high degree of design freedom and resulting in lightweight structures with increased performance. These processes, however, experience many limitations regarding manufacturability. The aim of this study is to develop a method and tool that optimizes the design of a component to avoid overhanging geometries and the need for supports during the Additive Manufacturing process. A workflow consisting of steps for topology optimization, orientation optimization, material addition, and machine code generation is described and implemented using Rhinoceros 3D and Grasshopper software. The proposed workflow is compared to a conventional workflow regarding manufacturing Key Performance Indicators (KPIs) such as part volume, support volume, and build time. A significant reduction is observed regarding all the KPIs by using the proposed method. Examining the results from both the conventional workflow and the proposed one, it is clear that the latter has unquestionable advantages in terms of effectiveness. In the particular case study presented, a total volume reduction of around 80% is observed. The reduction in the total volume (including the required support volume) leads to a significant reduction in the material used as well as in the build time, consequently resulting in cost reduction. [ABSTRACT FROM AUTHOR]

Identifying playing styles in football is highly valuable for achieving effective performance analysis. While there is extensive research on team styles, studies on individual player styles are still in their early stages. Thus, the aim of this systematic review was to provide a comprehensive overview of the existing literature on player styles and identify research areas required for further development, offering new directions for future research. Following the PRISMA guidelines for systematic reviews, we conducted a search using a specific strategy across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus). Inclusion and exclusion criteria were applied to the initial search results, ultimately identifying twelve studies suitable for inclusion in this review. Through thematic analysis and qualitative evaluation of these studies, several key findings emerged: (a) a lack of a structured theoretical framework for player styles based on their positions within the team formation, (b) absence of studies investigating the influence of contextual variables on player styles, (c) methodological deficiencies observed in the reviewed studies, and (d) disparity in the objectives of sports science and data science studies. By identifying these gaps in the literature and presenting a structured framework for player styles (based on the compilation of all reported styles from the reviewed studies), this review aims to assist team stakeholders and provide guidance for future research endeavors. [ABSTRACT FROM AUTHOR]

Background: Saffold virus (SAFV), which belongs to the genus Cardiovirus of the family Picornaviridae, is associated with acute respiratory or gastrointestinal illnesses in children; it is also suspected to cause severe diseases, such as acute flaccid paralysis and aseptic meningitis. However, the understanding of the mechanism of its pathogenicity is still limited due to the many unknowns about its lifecycle; for example, the cellular receptor for its infection remains to be determined. A system to monitor SAFV infection in vitro and in vivo is required in order to accelerate research on SAFV. Results: We generated a recombinant SAFV expressing green fluorescent protein (GFP) or UnaG, a novel fluorescent protein derived from Japanese eel. HeLa cells infected by either GFP or UnaG-expressing SAFV showed a bright green fluorescent signal, enabling convenient monitoring of SAFV infection. However, the expression of GFP but not UnaG was quickly lost during virus passaging due to the difference in genetic stability in the SAFV virus genome; the UnaG gene was stably maintained in the virus genome after at least five passages. Conclusions: SAFV infection of cultured cells can easily be monitored using UnaG-expressing SAFV, which is superior to GFP in terms of genetic stability in the virus genome. This virus could be a useful tool for SAFV research, such as comparing the susceptibility of various cells to SAFV infection and evaluating the effects of antivirals on SAFV infection in high-throughput screening. [ABSTRACT FROM AUTHOR]

In the last decade, drones have become an affordable technology offering highly mobile aerial platforms that can carry a range of sensory equipment into hitherto uncharted areas. Drones have thus become a widely applicable tool for surveying animal populations and habitats to assist conservation efforts or to study the behavioural ecology of species by monitoring individual and group behaviour.Here, we review current applications for drone surveys and the potential of recently developed computer algorithms for automatic species detection and individual tracking in drone footage. We further review which factors are reportedly associated with animal disturbance during drone presentations and how drones may be used to study anti‐predator behaviour.Drone surveys of species and their environments allow scientists to create digital terrain models of habitats, estimate species abundance, monitor individual behaviour and study the composition, spatial organization and movement of groups. As drones can influence the behaviour of many bird and mammal species directly, they also provide an experimental tool to study animal responses to novel situations, including the drone itself.We conclude that the combined use of drones and automated detection software can assist population estimates and opens new possibilities to study individual and collective behaviour. With regard to drone‐related disturbance and their potential use as predator models, we recommend to interpret results against the background of population‐specific predation pressure and sources of anthropogenic disturbance. [ABSTRACT FROM AUTHOR]

The preparation and characterization of three deep eutectic solvents (DESs) using N, N, N-trimethyl butylsulphonate ammonium hydrosulfate (DES1-0) as hydrogen bond acceptor (HBA) and urea as hydrogen bond donor (HBD) are presented. DES1-0 and three DESs are used as electrolytes to test the performance for fuel cell. The results show that the designed DESs have high performance. The maximum power density (MPD) with the four samples follows the order at 30 °C and 50 °C: DES2-1 > DES1-0 > DES1-1 > DES1-2. DES with DES1-0 to urea in 2:1 ratio (DES2-1) shows the highest performance, and the MPD of 80.20 mW·cm−2 has been achieved at 50 °C. DES2-1 increases the proton diffusion coefficient and speeds up proton transfer rates, so it can provide higher performance than single DES1-0 ionic liquid. In addition, the equivalent circuit model shows that the electrode process of the four samples is in the control of both charge transfer and diffusion processes. Only DES which have high capacitance and low resistance can achieve high performance for fuel cell. DES2-1 has the largest electric double layer capacitance at electrode/electrolyte interface (Cd) and the smallest Warburg impedance resistance (W0) among all electrolytes in this paper, and it has the low solution resistance (Ru) and charge transfer resistance (Rct); therefore, its performance in fuel cell is the highest. [ABSTRACT FROM AUTHOR]

In the context of additive manufacturing, the adjustment of process data to individual geometric features offers the potential to further increase manufacturing speed and quality, while being widely underestimated in recent research. Unfortunately, the current non-uniformd at a handling in the CAD-CAM-Link results in adown stream data loss, that prevents the availability of geometric knowledge from being present at any time to apply the more advanced approaches of adaptive slicing and tool path generation. Automatic detection of various geometric entities would be beneficial for classifying partial surfaces and volumetric ranges to gain customized informational insights of geometric parameterization. In this work, an enhanced approach of geometric deep learning for the analysis of voxelized engineering parts will be presented to align the inference representations to modeling paradigms for complex design models like architected materials. Although the baseline voxel representation offers distinct advantages in detection accuracy, it comes with an adversely large memory footprint. The geometry discretization leads to high resolutions needed to capture various detail levels that prevent the analysis of fine-grained objects. To achieve efficient usage of three-dimensional (3D) deep learning techniques, we propose a 3D-convolutional neural network-based feature recognition approach using signed distance field data to limit the needed resolution. These implicit geometric data leverage the advantages of volumetric convolution while alleviating their disadvantages through the use of the continuous signed distance function. When analyzing computer-aided design data for geometric primitive features, a common application task in surface reconstruction of reverse engineering the proposed methodology, achieves a detection accuracy that is in line with the accuracy values achieved by comparable algorithms. This enables the recognition offine-grained surface instances. The unambiguous shape information extracted could be used in subsequent adaptive slicing algorithms to achieve individual geometry-based hatch generation. [ABSTRACT FROM AUTHOR]