The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs.

Socioeconomic impact of small-scale irrigation on household’s livelihood improvement in Bena-Tsemay district of South Omo zone, Southern Ethiopia

The study investigated the socioeconomic impacts of irrigated agriculture and factors affecting the decision of agro-pastoralists to participate in irrigation during 2017-2018. The result depends on cross-sectional data collected from a sample of 120 households of which 90 irrigation users and 30 non-users using a combination of purposive and random sampling. The data were analyzed using descriptive statistics and logistic regression to assess factors that affect participation in irrigation. The logistic regression model revealed that age, credit access, extension contact, distance to water, and labor force significantly affected the decision of given agro-pastoralists to participate in irrigation practices at less than 5% probability levels. This indicates that the explanatory variables included in the model influence the decision of agro-pastoralists to participate in irrigation practices. Therefore, the provision of credit service to allow rapid progress in introducing technologies like tractors for farming practices and frequent extension contact with irrigation users could enhance the productivity in the area. Int. J. Agril. Res. Innov. Tech. 11(2): 139-146, Dec 2021

NucleoMap: a computational tool for identifying nucleosomes in ultra-high resolution contact maps

Although poorly positioned nucleosomes are ubiquitous in the prokaryote genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling profiling of nucleosomes in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome binding preferences, read density, and pairing information, NucleoMap precisely locates nucleosomes in both eukaryotic and prokaryotic genomes and outperforms existing nucleosome identification methods in sensitivity and specificity. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also predict two tetra-nucleosome folding structures in human embryonic stem cells using machine learning methods and analysis their distribution at different structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the original data's contact distance profile.

Knowledge, practices of food safety of establishment owners, food processors, and customers to prevent the Covid-19 pandemic at foodservice businesses in Son La city in 2020

Ensuring food safety for foodservice businesses is extremely necessary during the current complicated situation of the Covid-19 pandemic. This study has evaluated the knowledge and practices on food safety of establishment owners, food processors, and customers to prevent the Covid-19 pandemic at foodservice businesses in Son La city in 2020. The results show that over 75 % of establishment owners knew regulations to ensure food safety in the prevention of the Covid-19 pandemic, and 100 % of establishment owners were trained in disseminating and guiding pandemic prevention documents. Food processors who have good knowledge and practices of regulations of the Ministry of Health on ensuring food safety to prevent Covid-19, such as wearing masks when working, keeping contact distance with food, washing hands, disinfecting correctly, and do not gather in large numbers in production facilities have reached a high rate of over 90 %. All customers know about the 5K regulations. It is necessary to strengthen the propaganda to ensure food safety to prevent the Covid-19 pandemic so that the subjects can better understand the regulations of the Government, the Ministry of Health, Departments on ensuring food safety and prevention of the Covid-19 pandemic, and good practices of the above regulations.

Disruption of metapopulation structure effectively reduces Tasmanian devil facial tumour disease spread at the expense of abundance and genetic diversity

Metapopulation structure (i.e. the spatial arrangement of local populations and corridors between them) plays a fundamental role in the persistence of wildlife populations, but can also drive the spread of infectious diseases. While the disruption of metapopulation connectivity can reduce disease spread, it can also impair host resilience by disrupting gene flow and colonisation dynamics. Thus, a pressing challenge for many wildlife populations is to elucidate whether the benefits of disease management methods that reduce metapopulation connectivity outweigh the associated risks. Directly transmissible cancers are clonal malignant cell lines capable to spread through host populations without immune recognition, when susceptible and infected hosts become in close contact. Using an individual-based metapopulation model we investigate the effects of the interplay between host dispersal, disease transmission rate and inter-individual contact distance for transmission (determining within-population mixing) on the spread and persistence of a transmissible cancer, Tasmanian devil facial tumour disease (DFTD), from local to regional scales. Further, we explore population isolation scenarios to devise management strategies to mitigate disease spread. Disease spread, and the ensuing population declines, are synergistically determined by individuals' dispersal, disease transmission rate and within-population mixing. Low to intermediate transmission rates can be magnified by high dispersal and inter-individual transmission distance. Once disease transmission rate is high, dispersal and inter-individual contact distance do not impact the outcome of the disease transmission dynamics. Isolation of local populations effectively reduced metapopulation-level disease prevalence but caused severe declines in metapopulation size and genetic diversity. The relative position of managed (i.e. isolated) populations within the metapopulation had a significant effect on disease prevalence, highlighting the importance of considering metapopulation structure when implementing metapopulation-scale disease control measures. Our findings suggests that population isolation is not an ideal management method for preventing disease spread in species inhabiting already fragmented landscapes, where genetic diversity and extinction risk are already a concern, such as the Tasmanian devil.

Optimization of a CNT-based SiGe Thin Film Solar Cell Structure

In this paper, a SiGe thin film solar cell structure based on the carbon nanotube (CNT) and with a back surface field (BSF) layer is proposed. The efficiency of this structure is 40.36%, which is higher than conventional structures without CNT layer. We optimize this structure by changing the base layer thickness and determining the ratio of the width of the upper contact to the width of the entire cell. The cell efficiency after this optimization reaches 41.08%. Furthermore, the performance of this cell is evaluated using two types of CNT layers with sheet resistances of 128 Ω/□ and 76 Ω/□. The results of numerical simulation show that the SiGe thin film solar cell using CNT layer with 128 Ω/□ sheet resistance has better performance parameters. Finally, the number of metal electrodes above the cell is optimized due to the shading effect and we show that the contact distance in the presence of CNT layer can be increased up to 2000 µm.

Limits and potential of combined folding and docking using PconsDock.

In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilizing deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSA). In CASP14, the best method could predict the structure of most proteins with impressive accuracy. The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most of the earlier studies have not used the latest DL methods for inter-chain contact distance predictions. In this paper, we showed for the first time that using one of the best DL-based residue-residue contact prediction methods (trRosetta), it is possible to simultaneously predict both the tertiary and quaternary structures of some protein pairs, even when the structures of the monomers are not known. Straightforward application of this method to a standard dataset for protein-protein docking yielded limited success, however, using alternative methods for MSA generating allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins and thus this function can be used to evaluate the quality of the resulting docking models. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods, however, no a priori structural information for the individual proteins is needed. Moreover, the results of traditional and fold-and-dock approaches are complementary and thus a combined docking pipeline should increase overall docking success significantly. The dock-and-fold pipeline helped us to generate the best model for one of the CASP14 oligomeric targets, H1065.

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

Protein Model Accuracy Estimation Empowered by Deep Learning and Inter-residue Distance Prediction in CASP14

The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (CASP13) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). During the 2020 CASP14 experiment, we developed and tested several EMA predictors that used deep learning with the new features based on inter-residue distance/contact predictions as well as the existing model quality features. The average global distance test (GDT-TS) score loss of ranking CASP14 structural models by three multi-model MULTICOM EMA predictors (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) is 0.073, 0.079, and 0.081, respectively, which are ranked first, second, and third places out of 68 CASP14 EMA predictors. The single-model EMA predictor (MULTICOM-DEEP) is ranked 10th place among all the single-model EMA methods in terms of GDT-TS score loss. The results show that deep learning and contact/distance predictions are useful in ranking and selecting protein structural models.

pyconsFold: A fast and easy tool for modelling and docking using distance predictions

MotivationContact predictions within a protein has recently become a viable method for accurate prediction of protein structure. Using predicted distance distributions has been shown in many cases to be superior to only using a binary contact annotation. Using predicted inter-protein distances has also been shown to be able to dock some protein dimers.ResultsHere we present pyconsFold. Using CNS as its underlying folding mechanism and predicted contact distance it outperforms regular contact prediction based modelling on our dataset of 210 proteins. It performs marginally worse than the state of the art pyRosetta folding pipeline but is on average about 20 times faster per model. More importantly pyconsFold can also be used as a fold-and-dock protocol by using predicted inter-protein contacts to simultaneously fold and dock two protein chains.Availability and implementationpyconsFold is implemented in Python 3 with a strong focus on using as few dependencies as possible for longevity. It is available both as a pip package in Python 3 and as source code on GitHub and is published under the GPLv3 [email protected] materialInstall instructions, examples and parameters can be found in the supplemental notes.Availability of dataThe data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.