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Abstract Conservation Agriculture (CA) is an innovative approach that promotes sustainable farming while enhancing soil health. However, residue management challenges often hinder its adoption, causing farmers to burn crop leftovers in fields. This study aimed to evaluate the effectiveness of various furrow openers under simulated soil bin conditions. Three types of furrow openers were examined: single disk (SD), Inverted T-type furrow opener with a plain rolling coulter (ITRC), and double disc (DD) furrow opener. Tests were conducted at different forward speeds (1.5, 2, and 2.5 km h−1) and with three straw densities (1, 2, and 3 t ha−1) at a consistent working depth of 5 cm. Draft measurements were obtained using load cells connected to an Arduino-based data-logging system. Results indicated that draft requirements increased with forward speed and straw density, while straw-cutting efficiency decreased with these factors. Average draft values for SD, ITRC, and DD were 290.3 N, 420 N, and 368.5 N, respectively, and straw-cutting efficiencies were 53.62%, 59.47%, and 74.89%, respectively. The DD furrow opener showed the highest straw-cutting efficiency (81.36%) at a working speed of 1.5 km h−1 and a straw density of 1 t ha−1, demonstrating optimal performance compared to other furrow openers.

Abstract Despite the fact that several technologies have been developed to assist healthcare workers in reducing errors and improving accuracy in illness diagnosis, there is still substantial ambiguity regarding the accurate disease diagnosis based on symptoms. The goal of this work is to establish a group decision-making problem in an uncertain situation to assist medical practitioners in generating accurate illness predictions based on symptoms. This study proposes a novel distance measure for Pythagorean fuzzy sets that incorporates the inherent uncertainty of complex, uncertain data by incorporating indeterminacy in the computation. First, we establish the proposed Mabala distance measure by describing it’s properties. Then, the suggested distance measure is applied to solve group decision-making problems in uncertain situations. A case study of disease analysis based on symptoms is presented to illustrate the decision-making procedure involving four medical professionals, five symptoms, and five probable diseases. Furthermore, We have presented two cases of disease analysis using non-standard and standard Pythagorean fuzzy soft matrices. The results suggest that the proposed Mabala’s distance measure has great potential for improving disease analysis. The proposed Mabala distance measure is compared to five existing distance measures using an identical data set of prospective disease symptoms. The comparative analysis indicates that the suggested Mabala distance measure’s result almost coincides with the results of the other distance measurements. A set of sensitivity analysis is provided to analyze the durability and consistency of the proposed distance measurements across different input scenarios.

The lack of terrestrial 4G and 5G communication infrastructure in rural and remote areas aggravates the digital divide. Satellite communication is important to achieve the universal goal of connecting the digitally unconnected population through global coverage. In this paper, first, we discuss the feasibility of hosting web services in satellites through several examples of radiation-hardened processors and computational boards. Next, we propose a Space-Based Hosting Service (SBHS) approach to deploy the content-servers or copies of terrestrial content-servers in satellites to achieve the low-latency service requirements. For SBHS, we develop mathematical models for communication, queuing, as well as computation, and use B+ tree to estimate computational delay and computational energy consumption of the content-server. Further, an optimization algorithm using Markov Decision Process (MDP) is designed to schedule computational cores in Low Earth Orbit (LEO) satellites to minimize queuing delay and computational energy consumption. The efficacy of our SBHS is evaluated using extensive simulations with the Wikipedia use-case in the Iridium-NEXT satellite constellation model under varying traffic conditions from different countries. The results show that SHBS is able to achieve less than 10 ms round trip latency worldwide and minimum end-to-end delays of 9.19 ms and 24.29 ms, respectively, for text-based and multimedia-based traffic. The 92.51% and 88.66% reduction in delay for text and multimedia traffic, respectively, compared to the benchmark schemes underlines the benefits of hosting content-servers in space for achieving ultra-low latency.

Abstract Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD maps. Our framework uses a quaternion residual block self-attention network (QRBSA) to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD maps. In QRBSA, quaternion-valued convolution effectively learns local relations in orientation space, while self-attention in the quaternion domain captures long-range correlations. We apply our framework to 3D data collected from commercially relevant titanium alloys, showing both qualitatively and quantitatively that our method can predict missing samples (EBSD information between sparsely sectioned mapping points) as compared to high-resolution ground truth 3D EBSD maps.

Coronaviruses (family Coronaviridae, genera Alphacoronavirus, Betacoronavirus) are dangerous viral pathogens that have caused three outbreaks of severe respiratory diseases, SARS, MERS, COVID-19. In Russia, data on coronaviruses in natural reservoirs are limited, as investigations began only during the COVID-19 pandemic.The aim. To study the diversity of coronaviruses among rodents and insectivores in the Republic of Altai.Materials and methods. Rodents (n = 67) and shrews (n = 52) were captured in 2022. Samples were analyzed by reverse transcription-polymerase chain reaction followed by sequencing.Results and conclusions. Four samples from rodents (Myodes rutilus, M. glareolus, Apodemus peninsulae, A. agrarius) and two samples from an insectivore (Crocidura sibirica) were positive for coronaviruses, among which three different coronaviruses were detected. Rodent-borne coronaviruses are classified in the genus Betacoronavirus, subgenera Embecovirus, and have shown host associated clustering. The nucleotide sequences of Siberian coronaviruses from rodents were identical for closely related species (M. rutilus and M. glareolus, A. agrarius and A. peninsulae) and close (> 94 % homology) to previously published sequences in each of the groups of carriers found in the territory Novosibirsk region, Europe and China. The coronavirus identified from the insectivore, possibly belonging to a new subgenera of the family Coronaviridae, has also been assigned to the genus Betacoronavirus.Conclusion. Five species of natural carriers of three different coronaviruses were detected in the Altai Republic. A high level of identity of coronaviruses genomes from rodents has been revealed, indicating a relatively low rate of their evolution.

Extensive spread of tick-borne diseases poses a significant problem for public health and the health of the population living in endemic areas.The aim of the study was to search, analyze genetic material and identify new viral agents of the Phenuiviridae family in taiga ticks collected in Asian regions of Russia using the method of high throughput sequencing.Materials and methods. The study involved 1460 taiga ticks collected in suburban areas of the Tomsk, Yekaterinburg and Primorsky Territory. The genetic material isolated from ticks was sequenced using Illumina technology followed by phylogenetic analysis.Results and discussion. Analysis of the sequencing results made it possible to detect extended nucleotide sequences of the L-gene fragment characteristic of the Phenuiviridae family viruses. We were able to identify 20 nucleotide sequences the length of 250 bp on average in homogenates of Ixodes persulcatus ticks. Eighteen isolates have been identified as members of the genus Uukuvirus and two isolates have been assigned to the genus Phlebovirus, Phenuiviridae family. Phylogenetic analysis has shown that all isolates of the genus Uukuvirus fall under the cluster of Tacheng tick virus 2 belonging to the species Tacheng uukuvirus. They form a separate phylogenetic group which is closely related to two Romanian variants of 2019. Tacheng tick virus 2 was detected in all three surveyed regions of the Asian part of Russia. Two Tomsk isolates of phlebovirus were classified as Sara tick phlebovirus and they clustered with two isolates of phleboviruses from Karelia. Thus, the genetic material of Tacheng tick virus 2 and Sara tick phlebovirus belonging to two genera of the family Phenuiviridae was found in I. persulcatus ticks collected in three geographically different regions of the Asian part of Russia.

Abstract We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.

Numerical modelling is often used for climate impact studies in water resources management. It is, however, not yet highly accessible to many students of hydrology in Central Asia. One big hurdle for new learners is the scripting requirement for the preparation of relevant data prior to the actual modelling. We present a robust, open-source workflow and comprehensive teaching material that aim at bridging the gap between theoretical hydrological modelling know-how and applied modelling for climate impact studies in Central Asia. The teaching material has been refined over 2 consecutive years and is being taken up by several professors teaching hydrological modelling in Central Asia.

Abstract In computer vision, single-image super-resolution (SISR) has been extensively explored using convolutional neural networks (CNNs) on optical images, but images outside this domain, such as those from scientific experiments, are not well investigated. Experimental data is often gathered using non-optical methods, which alters the metrics for image quality. One such example is electron backscatter diffraction (EBSD), a materials characterization technique that maps crystal arrangement in solid materials, which provides insight into processing, structure, and property relationships. We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps. This approach includes quaternion-based orientation recognition, loss functions that consider rotational effects and crystallographic symmetry, and an inference pipeline to convert network output into established visualization formats for EBSD maps. The ability to generate physically accurate, high-resolution EBSD maps with super-resolution enables high-throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.

The photoluminescence from self-assembled long-wavelength InAs/GaAs quantum dots was investigated at 15 K under hydrostatic pressure up to 9 GPa. Photoemission from both the ground and the first excited states in large InAs dots was observed. The pressure coefficients of the two emissions were 69 and 72 meV/GPa, respectively. A nonlinear elasticity theory was used to interpret the significantly small pressure coefficients of the large dots. The sequential quenching of the ground and the excited state emissions with increasing pressure suggests that the excited state emissions originate from the optical transitions between the first excited electron states and the first excited hole states. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The total demand for and uptake of nutrients by vegetation is rarely quantified or compared across vegetation types. Here, we describe different nutrient use and allocation strategies in neotropical savanna (cerrado) and transitional forest (cerradão) tree communities composed of different species, report leaf nutrient resorption and calculate ecosystem-level nutrient use efficiency. We couple net primary productivity (NPP) estimates with nutrient stoichiometry to quantify nutrient demand and nutrient flows at the whole-stand scale for different components of vegetation biomass. Species from the two vegetation communities showed similar mean nutrient concentrations and nutrient resorption efficiency, except for wood P concentration that was fourfold higher in cerrado than cerradão species. The cerradão showed higher canopy NPP, while fine roots and wood NPP were similar for the two vegetation types. Nutrient requirement in the two vegetation types was dominated by the demands of the canopy, with canopy resorption generally contributing more than 50 % of the total canopy demand for nutrients, while less than 35 % of N, P, K, Ca and Mg were allocated to wood or fine roots. Proportionally, cerrado showed higher nutrient demand from fine roots (over 35 % of the total nutrient demand) and for the wood component (over 13 % of the total nutrient demand), while ∼ 60 %–70 % of the cerradão nutrient demand was allocated to the canopy. The proportional difference in nutrient allocation to the different biomass components suggests cerrado species allocate less nutrients to a given fine root biomass, but more nutrients to a given wood biomass. Our findings suggest that cerradão species are more limited in P and K than cerrado species, inducing higher resorption to compensate for low uptake. Moreover, we found that N uptake for cerradão was higher with lower N use efficiency, i.e. the amount of production per nutrient unit, leading higher N demand compared to the cerrado. This difference in nutrient dynamics explains how similar soils and the same climate dominated by savanna vegetation can also support forest-like formations. Tree species composition is likely the major factor regulating nutrient use, limiting vegetation transitions and influencing nutrient demand at landscape scales.

Introduction: Machine learning (ML) applications for studying asteroid resonant dynamics are a relatively new field of study. Results from several different approaches are currently available for asteroids interacting with the z2, z1, M1:2, and ν6 resonances. However, one challenge when using ML to the databases produced by these studies is that there is often a severe imbalance ratio between the number of asteroids in librating orbits and the rest of the asteroidal population. This imbalance ratio can be as high as 1:270, which can impact the performance of classical ML algorithms, that were not designed for such severe imbalances.Methods: Various techniques have been recently developed to address this problem, including cost-sensitive strategies, methods that oversample the minority class, undersample the majority one, or combinations of both. Here, we investigate the most effective approaches for improving the performance of ML algorithms for known resonant asteroidal databases.Results: Cost-sensitive methods either improved or had not affect the outcome of ML methods and should always be used, when possible. The methods that showed the best performance for the studied databases were SMOTE oversampling plus Tomek undersampling, SMOTE oversampling, and Random oversampling and undersampling.Discussion: Testing these methods first could save significant time and efforts for future studies with imbalanced asteroidal databases.

Deep learning techniques are very prominent in processing remotely sensed synthetic aperture radar (SAR) images for real-time, high-impact applications, such as image classification, object detection, and semantic segmentation. The accuracy of deep learning models, such as convolutional neural networks (CNNs), depends on the quality of the input data. Compared to the model-centric approach, where the model parameters are optimized during training, the data-centric approach can enhance the performance accuracy as data quality is improved before training the models. Improving the data quality of SAR images is challenging as SAR image properties are different from optical images. Image fusion techniques proved to enhance the quality of SAR images when combined with optical images. Many fusion techniques exist for combining SAR and optical images in the classical domain. This paper proposes a novel approach to using quantum computing for the image fusion of SAR and optical images. Eight different quantum techniques are used to process and fuse the images. We designed and created a dataset for land-use classification by collecting data using the Google Earth Engine. The quality metric measurements show that the quality of SAR images has improved by using the proposed quantum processing techniques. In addition, performance evaluation of the deep learning CNNs on the dataset was carried out for all quantum processing techniques. Our approach improved the classification accuracy from 82.64%, with only SAR images for training, to 95.36% using the proposed image fusion techniques.

Pathogenic hantaviruses, belonging to the family Hantaviridae, genus Orthohantavirus, are widely spread in many regions of the world and cause hemorrhagic fever with renal syndrome (HFRS) in Europe and Asia. In the European Russia, the most active HFRS foci are located in the optimum habitat area of natural reservoir of the Puumala virus (PUUV), bank voles (Myodes glareolus), – in the Middle Volga and Cis-Urals. The largest number of cases of HFRS was registered in the Volga Federal District. In 2019, an outbreak of HFRS was registered among the residents of Saratov and the Saratov Region, the number of cases was 2702. Objective of the study was genetic identification of hantaviruses from HFRS patients and rodent carriers and phylogenetic analysis of full-size genomes from natural hosts during HFRS outbreak in the Saratov Region, 2019. Materials and methods. Blood samples of 8 HFRS patients from Saratov and 3 lung samples of bank voles captured in the suburb of Saratov were analyzed using reverse transcription polymerase chain reaction, followed by sequencing and phylogenetic analysis. Results and discussion. A total of 6 viral RNA isolates from HFRS patients were genetically typed, full-length RNA-isolate genomes were obtained for 3 natural carriers. Our data indicate that PUUV virus was associated with HFRS outbreak in Saratov. Genetic analysis revealed that the virus belonged to RUS lineage of PUUV, related most closely to strains from Udmurtia and Tatarstan and differed from strains circulating in the territory of Bashkortostan and Samara Region.

Aim: This study aimed to determine the food safety and protein adequacy of meats from various animals in the Shuchinsk- Burabay resort zone. Materials and Methods: Samples of meat were collected from markets "Zhomart" and "Kausar." Two hundred and ninety-eight samples of meat were obtained: beef - 166, horse - 42, pork - 67, mutton - 8, and poultry - 15. From each carcass, part of the carcass, point samples were taken to form an average sample and conduct research. Analyses used inductively coupled plasma mass spectrometry and liquid chromatography. The determination of amino acid composition was carried out following the chromatographic method for determining the amino acid composition of meat proteins. Results: The quality of meats from the resort zone was lower than recommended due to the reduced content of essential amino acids, including valine, lysine, isoleucine, and phenylalanine. Concentrations of heavy metals and radionuclides did not exceed maximum permissible limits, and trace concentrations of arsenic, cadmium, and lead were detected in all meat samples, except beef. The latter meat showed increased concentrations of mercury. Conclusion: In terms of quality indicators, all types of meat met standards; however, pork protein displayed the most favorable amino acid composition, both for content of essential amino acids and the ratio of amounts of essential to non-essential amino acids. For content of heavy metals, poultry and pork meats were safest.

Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.

The aim of the present study is to explore the transformation of (1→3)(1→4)-β-D-glucans of rye biomass by Aspergills niger and accumulation of (1→3)(1→6)-β-D-glucans in the microbial cell wall.Biomass from rye grain was obtained as a result of enzymatic hydrolysis of grain grinding of Omsk region of non-standard quality with grain impurity content of 45 ± 2 % by preparations (1→4)-β-glucanolytic, (1→3)-β-glucanolytic, (1→4)-xylanolytic and (1→4)-amylolytic action. Fermentation of hydrolysates, sucrosemineral and molasses medium by A. niger was carried out by a batch process under aerobic conditions. Determined the content of β-glucans, amino-nitrogen, glucose, disaccharides in grinding grain rye, rye biomass, the biomass of A. niger, the supernatants by colorimetric methods. Determination of chitin in biomass and qualitative determination of chitosan in supernatants of hydrolysates was carried out using chitosan sulfate sample and subsequent microscopy.The results of the research showed that (1→3)(1→4)-β-D-glucans in grain grinding are 10.2 ± 0.2 % in terms of dry matter, which exceeds the content of polysaccharide in the grain of standard quality by 1.5 – 3 times. In rye biomass revealed their smaller amount, 6.4 ± 0.5 %, apparently, due to the action of (1→4)and (1→3)-β-glucanase, (1→4)-xylanase and (1→4)-amylase. In microbial mass A. niger content of (1→3)(1→6)-β-Dglucans were at the level of 21.7 ± 0.7 %.On the basis of the obtained results, it was concluded that it is possible to use rye grain of non-standard quality, with a high content of grain impurities and a low proportion of starch polysaccharides, as a source of β-glucancontaining substrate for biosynthesis (1→3)(1→6)-β-D-glucans by A. niger having advantages over (1→3) (1→4)-β-D-glucans of plant origin. They are functionally more active and have a wide range of applications, namely as food additives in the manufacture of a wide range of products: for the enrichment of fibers, increasing the shelf life of products due to its water-binding properties, as thickeners, emulsifying and fat-reducing microingredients, stabilizers of creamy emulsions, textureformers, flavor enhancers.

Abstract Background Ovarian hyperstimulation syndrome remains a serious complication during in vitro fertilization cycles if high dose human chorionic gonadotropin (hCG) is used to trigger ovulation in high responder patients. Though much of this risk is mitigated with trigger using gonadotropin releasing-hormone (GnRH) agonist alone, it may result in lower birth rates. GnRH-agonist trigger and adjuvant low dose hCG has been proposed to improve birth rates, but timing of this hCG support to corpus luteum function has never been fully described. In this randomized, prospective trial, we explore differences in live birth rates and incidence of ovarian hyperstimulation syndrome (OHSS) in high-responder patients undergoing in vitro fertilization (IVF) receiving low dose hCG at the time of GnRH-agonist (dual trigger) or hCG adjuvant at the time of oocyte retrieval. Does the timing of hCG support make a difference? Results Thirty-four subjects high-responder patients were randomized to receive low-dose hCG at the time of GnRH-agonist trigger (Group 1) and 37 received low-dose hCG at the time of oocyte retrieval (Group 2). There were no differences in the baseline characteristics and outcome of ovarian stimulation between the two groups. There were no differences in the live birth rates between Group 1 and Group 2 by intention-to-treat (14/34, 41.2% versus 21/37, 56.8%, p = 0.19) or per-protocol (14/26, 53.8% versus 19/31, 61.3%, p = 0.57) analyses. There was a slightly higher incidence of OHSS in Group 2 compared to Group 1 although the difference was not statistically significant (3/31, 9.7% versus 1/26, 3.8%). All the cases of OHSS in Group 2 were moderate while the one case of OHSS in Group 1 was mild. Conclusions For high responder patients receiving GnRH-agonist trigger, low dose hCG supplementation allowed high pregnancy rates after fresh embryo transfer, regardless of whether it was given at the time of trigger or at oocyte retrieval. Dual trigger may be preferable to reduce the risk of OHSS.

Objective of the study was investigation of hantaviruses in a new natural reservoir - insectivores, their diversity and geographic distribution in the Western and Eastern Siberia. Materials and methods. Sorex shrews (71 specimens) were captured in Altai Republic, Altai, and Krasnoyarsk Territories, and Omsk Region. All samples were analyzed for hantavirus RNA using reverse transcription polymerase chain reaction with subsequent sequencing. Results and conclusions. A total of 12 viral RNA isolates from two Sorex species (S. araneus and S. roboratus) were genetically typed. Sequence analysis of the partial L and S segments identified two distinct hantaviruses. Kenkeme (KKMV) virus was detected in a flat-skulled shrew (S. roboratus) in Altai Territory. This virus was previously found in geographically remote sites in the Sakha Republic and China. New foci of Seewis virus circulation among common shrews (S. araneus), were found in the Omsk Region, Altai and Krasnoyarsk Territories. Previously published and new findings suggest that hantaviruses among shrews are widely distributed over the Russian Federation territory. The widespread circulation of SWSV and KKMV throughout Siberia is an important argument for investigation of their role in human infections.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean.

The aim of the work is to study the inhibitory activity of the Streptomyces lucensis VKPM Ac-1743 and Streptomyces violaceus VKPM Ac-1734 strains stored at –18 °C in a 15 % glycerol solution and in a 0.9 % sodium chloride solution.The object of the study was the actinomycete strains of S. lucensis VKPM Ac-1743 and S. violaceus VKPM Ac-1734 producers of glycosidase inhibitor, which is a micro-ingredient for creating products with a low glycemic index. The storage time was nine months. Bookmark storage was performed by flushing with a taped agar starchcontaining environment of Capek. For comparison, we studied the properties of cultures stored at +4 °C without cryoprotectant.Deep cultivation of Streptomyces strains was carried out in a periodic manner on a medium containing corn starch hydrolyzate under the Multitron incubator shaker (INFORS,Switzerland).Inhibitory activity was determined in inactivated native solutions by a colorimetric method with respect to pancreatic α-amylase, a test glycosidase involved in carbohydrate metabolism, and whose activity was chosen as a criterion for assessing the hypoglycemic action of the inhibitor. The proteinase activity of the inactivated native solution was determined by a modified method using a casein substrate.The experimental data were processed using the methods of mathematical statistics and Excel XP programs.As a result of the research, it was established that the actinomycete strains of S. lucensis and S. violaceus, the producers of glycosidase inhibitors, can maintain inhibitory activity during low-temperature storage for nine months.For the S. lucensis actinomycete strain, stored in a 15 % glycerol solution at – 18 °C, the maximum inhibitory activity is 48 hours in the biotechnological process and is (3686 ± 300) IU/cm3 native solution, and for the violaceus strain – (3150 ± 200) IU/cm³ of the native solution, respectively.For the strain of actinomycete S. lucensis, stored in 0.9 % sodium chloride solution at a temperature of – 18 °C, the maximum inhibitory activity accounts for 72 h of the biotechnological process and is (2600 ± 200) IU/cm³ of the native solution, and for the strain S. violaceus the maximum inhibitory activity accounts for 24 hours of the biotechnological process and is (3530 ± 200) IU/cm³ of the native solution.At a storage temperature of +4 °C, the inhibitory activity for the strain of actinomycete S. lucensis is (560 ± 20) IU/cm³ of native solution, and for the strain of S. violaceus – (1747 ± 100) IU/cm³ of native solution, respectively. On the basis of the data obtained, it can be concluded that a temperature of –18 °C is preferred for long-term storage.During the cultivation of Streptomyces strains, proteinase activity ranged from (0,012 ± 0,001) U/cm³ to (0,072 ± 0,002) U/cm³.The obtained data can be applied in further studies to develop conditions for long-term storage of collection crops.

The essence of the gold loan and its role in the formation of the market of the precious metals have been considered. The gold mining companies are stated to be the main users of the borrowed gold. The main uses of the gold loans have been presented, including uses by gold mining companies, for the development of the mining industry and refinancing the debt. The modern problems of the functioning of the market for gold loans have been considered. The role of Central and commercial banks in the development of the market for gold loans has been reflected. The mechanism of the operation of the gold loan has been represented. The benefits of the gold credit for the gold mining industry compared to a conventional loan have been reflected.

The results of an analysis of the use of trade operations in the market of precious metals have been enumerated. We gave the characteristic of the market objects (standard and small bars) and accounts (allocated and unallocated), which are used for accounting precious metals in the market. The assignment for the subsoil use of the operations such as “spot” and “swap” types, as well as deposit, optional, futures and forward transactions, has been presented. The actions of a gold mining company, within the situation of an expected fall of prices, and a company that consumes gold (precious metals), in terms of a projected increase of prices, have been considered. The formulae for determining the forward price of the precious metals and the cost of the forward contract for subsoil users have been proposed.

Corruption, being linked to institutions of power, affects the efficiency of using public funds in a country. This happens, in particular, due to distortions in the structure of public expenditure, worsening quality and decreasing accessibility of public services, and overpricing of public services. The consequences of corruption that arise in different areas of human activity often become subject of scientific investigations performed by reputable scientists. However, the issue of the impact of corruption on public spending efficiency in the countries with different levels of democratic development has not been researched. The aim of this study is to determine the impact of corruption on the efficiency of public spending across the world (in 166 countries for the period from 2004 until 2017). The research is carried out by applying regression analysis to indicators characterizing the level of corruption and the efficiency of public spending obtained from large-scale survey data collected by major international organizations and aggregated in the World Bank’s “World Development Indicators” database. The analysis is also performed by using k-means clustering method to group countries into 4 clusters by the level of democracy and to build refined one-factor econometric models for each of them. The study revealed strong correlation relationship between corruption and public spending efficiency. It has been determined that a unit increase in corruption perceptions (a decrease in corruption) leads to a marginal increase in the efficiency of public spending by 0.931 units in the simple linear regression model and by 0.807 units in the multiple regression model. The study also showed that the impact of corruption on public spending varies depending on the level of democracy in a country. In the countries with low democracy levels, a unit decrease in corruption increases the efficiency of public spending by 0.923 units, whereas a similar decrease in corruption in the countries with high democracy levels will increase public spending efficiency only by 0.701 units. The findings of this study allow us to determine with higher accuracy the effects of corruption reduction measures on the efficiency of public spending.

Sampling along a precipitation gradient in tropical South America extending from ca. 0.8 to 2.0 m a−1, savanna soils had consistently lower exchangeable cation concentrations and higher C / N ratios than nearby forest plots. These soil differences were also reflected in canopy averaged leaf traits with savanna trees typically having higher leaf mass per unit area but lower mass-based nitrogen (Nm) and potassium (Km). Both Nm and Km also increased with declining mean annual precipitation (PA), but most area-based leaf traits such as leaf photosynthetic capacity showed no systematic variation with PA or vegetation type. Despite this invariance, when taken in conjunction with other measures such as mean canopy height, area-based soil exchangeable potassium content, [K]sa , proved to be an excellent predictor of several photosynthetic properties (including 13C isotope discrimination). Moreover, when considered in a multivariate context with PA and soil plant available water storage capacity (θP) as covariates, [K]sa also proved to be an excellent predictor of stand-level canopy area, providing drastically improved fits as compared to models considering just PA and/or θP. Neither calcium, nor magnesium, nor soil pH could substitute for potassium when tested as alternative model predictors (ΔAIC > 10). Nor for any model could simple soil texture metrics such as sand or clay content substitute for either [K]sa or θP. Taken in conjunction with recent work in Africa and the forests of the Amazon Basin, this suggests – in combination with some newly conceptualised interacting effects of PA and θP also presented here – a critical role for potassium as a modulator of tropical vegetation structure and function.

Through interpretations of remote-sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands located mostly within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related to tree canopy cover in a similar way for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the relative contribution of middle and lower strata of woody vegetation. Herbaceous layer cover declined as woody cover increased. This pattern of understorey grasses and herbs progressively replaced by shrubs as the canopy closes over was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna–forest-species discontinuum is observed compared to that inferred when trees of a basal diameter > 0.1 m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy-cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater amount of above-ground biomass than in South America. Up to three times as much above-ground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna–forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, consistent across all three continents coexistence was found to be confined to a well-defined edaphic–climate envelope with soil and climate the key determinants of the relative location of forest and savanna stands. Moreover, when considered in conjunction with the appropriate water availability metrics, it emerges that soil exchangeable cations exert considerable control on woody canopy-cover extent as measured in our pan-continental (forest + savanna) data set. Taken together these observations do not lend support to the notion of alternate stable states mediated through fire feedbacks as the prime force shaping the distribution of the two dominant vegetation types of the tropical lands.

Matrix metalloproteinases (MMPs), which degrade extracellular proteins as part of a variety of physiological processes, and their inhibitors have been implicated in the dental caries process. Here we investigated 28 genetic variants spanning the MMP10, MMP14, and MMP16 genes to detect association with dental caries experience in 13 age- and race-stratified (n=3,587) samples from 6 parent studies. Analyses were performed separately for each sample, and results were combined across samples by meta-analysis. Two SNPs (rs2046315 and rs10429371) upstream of MMP16 were significantly associated with caries in an individual sample of white adults and via meta-analysis across 8 adult samples after gene-wise adjustment for multiple comparisons. Noteworthy is SNP rs2046315 (p=8.14×10-8) association with caries in white adults. This SNP was originally nominated in a genome-wide-association study (GWAS) of dental caries in a sample of white adults and yielded associations in a subsequent GWAS of surface level caries in white adults as well. Therefore, in our study, we were able to recapture the association between rs2046315 and dental caries in white adults. Although we did not strengthen evidence that MMPs 10, 14, and 16 influence caries risk, MMP16 is still a likely candidate gene to pursue.

Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.

Tropical tree height-diameter (H:D) relationships may vary by forest type and region making large-scale estimates of above-ground biomass subject to bias if they ignore these differences in stem allometry. We have therefore developed a new global tropical forest database consisting of 39 955 concurrent H and D measurements encompassing 283 sites in 22 tropical countries. Utilising this database, our objectives were: 1. to determine if H:D relationships differ by geographic region and forest type (wet to dry forests, including zones of tension where forest and savanna overlap). 2. to ascertain if the H:D relationship is modulated by climate and/or forest structural characteristics (e.g. stand-level basal area, A). 3. to develop H:D allometric equations and evaluate biases to reduce error in future local-to-global estimates of tropical forest biomass. Annual precipitation coefficient of variation (PV), dry season length (SD), and mean annual air temperature (TA) emerged as key drivers of variation in H:D relationships at the pantropical and region scales. Vegetation structure also played a role with trees in forests of a high A being, on average, taller at any given D. After the effects of environment and forest structure are taken into account, two main regional groups can be identified. Forests in Asia, Africa and the Guyana Shield all have, on average, similar H:D relationships, but with trees in the forests of much of the Amazon Basin and tropical Australia typically being shorter at any given D than their counterparts elsewhere. The region-environment-structure model with the lowest Akaike's information criterion and lowest deviation estimated stand-level H across all plots to within amedian −2.7 to 0.9% of the true value. Some of the plot-to-plot variability in H:D relationships not accounted for by this model could be attributed to variations in soil physical conditions. Other things being equal, trees tend to be more slender in the absence of soil physical constraints, especially at smaller D. Pantropical and continental-level models provided less robust estimates of H, especially when the roles of climate and stand structure in modulating H:D allometry were not simultaneously taken into account.