bluuebunny/crossref_metadata_2025_split huggingface dataset:
Habitat Suitability modeling of Urial (Ovis orientalis arkal) in the Samelghan plain by using Maximum Entropy Method
Authors: Naqibzadeh Abbas , Sarhangzadeh Jalil , Sotoudeh Ahad , Mashkur Marjan , Thomalsky Judith 
Published: November 2021

AbstractHabitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively surveyed. The basis of these models’ work is to minimize the relationship between species distribution and biotic and abiotic environments. For some species, there is information about presence and absence that allows the use of a variety of standard statistical methods, however, the absence data is not available for most species. Nowadays, the methods that need presence-only data are expanded. One of these methods is the Maximum Entropy (MaxEnt) modeling. The purpose of this study is to model the habitat of Urial (Ovis orientalis arkal) in the Samelghan plain in the North East of Iran with the MaxEnt method. This algorithm uses the Jackknife plot and percent contribution values to determine the significance of the variables. The results showed that variables such as southern aspects, Juniperus-Acer, Artemisia-Perennial plants, slope 0-5%, and asphalt road were the most important factors affecting the species’ habitat selection. The area under curve (AUC) Receiver Operating Characteristic (ROC) showed an excellent model performance. Suitable habitat was classified based on the threshold value (0.0513) and the ROC, which based on the results 28% of the area was a suitable habitat for Urial.
Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy in a plant-pollinator network
Authors: Cantwell-Jones Aoife , Larson Keith , Ward Alan , Bates Olivia K. , Cox Tara , Brannlund Frida , Gibbons Charlotte , Richardson Ryan , Tylianakis Jason M. , Johansson Jacob , Gill Richard J. 
Published: November 2021

ABSTRACTFunctional overlap between species (redundancy) shapes competitive and mutualistic interactions, determining community responses to perturbations. Most studies view functional redundancy as static, even though individuals within species vary in traits over seasonal or spatial gradients. Consequently, we lack knowledge on trait turnover within species, how functional redundancy spatiotemporally varies, and when and where interaction networks are vulnerable to functional loss. Studying an Arctic bumblebee community, we investigated how body-size turnover over elevation and season shapes their host-plant interactions, and test how sensitive networks are to sequentially losing body-size groups. With trait turnover being larger than species, we found: i) late-season networks were less specialised when nodes comprised functionally similar bumblebees; ii) removal of bumblebee-body-size groups over species accelerated coextinction of host plants, with the magnitude varying in space and time. We demonstrate functional redundancy can vary spatiotemporally, and functional loss impacts interaction partners more than expected from species loss alone.
DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification
Authors: Fotis C. , Alevizos G. , Meimetis N. , Koleri C. , Gkekas T. , Alexopoulos LG. 
Published: November 2021

AbstractMotivationThe analysis and comparison of compounds’ transcriptomic signatures can help elucidate a compound’s Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound’s effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs.ResultsWe created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds’ gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.Availability and ImplementationThe source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM.ContactEmail for correspondence: leo@mail.ntua.gr.Supplementary informationAccompanying supplementary material are available online.
Several different sequences are implicated in bloodstream-form-specific gene expression in Trypanosoma brucei
Authors: Tshitenge Tania Bishola , Reichert Lena , Liu Bin , Clayton Christine 
Published: November 2021

AbstractThe parasite Trypanosoma brucei grows as bloodstream forms in mammalian hosts, and as procyclic forms in tsetse flies. In trypanosomes, gene expression regulation depends heavily on post-transcriptional mechanisms. Both the RNA-binding protein RBP10 and glycosomal phosphoglycerate kinase PGKC are expressed only in mammalian-infective forms. RBP10 targets procyclic-specific mRNAs for destruction, while PGKC is required for bloodstream-form glycolysis. Developmental regulation of both is essential: expression of either RBP10 or PGKC in procyclic forms inhibits their proliferation. We show that the 3′-untranslated region of the RBP10 mRNA is extraordinarily long - 7.3kb - and were able to identify six different sequences, scattered across the untranslated region, which can independently cause bloodstream-form-specific expression. The 3′-untranslated region of the PGKC mRNA, although much shorter, still contains two different regions, of 125 and 153nt, that independently gave developmental regulation. No short consensus sequences were identified that were enriched either within these regulatory regions, or when compared with other mRNAs with similar regulation, suggesting that more than one regulatory RNA-binding protein is important for repression of mRNAs in procyclic forms. We also identified regions, including an AT repeat, that increased expression in bloodstream forms, or suppressed it in both forms. Trypanosome mRNAs that encode RNA-binding proteins often have extremely extended 3′-untranslated regions. We suggest that one function of this might be to act as a fail-safe mechanism to ensure correct regulation even if mRNA processing or expression of trans regulators is defective.
Dual-modal metabolic analysis reveals hypothermia-reversible uncoupling of oxidative phosphorylation in neonatal brain hypoxia-ischemia
Authors: Sun Naidi , Sun Yu-Yo , Cao Rui , Chen Hong-Ru , Wang Yiming , Fugate Elizabeth , Smucker Marchelle R. , Kuo Yi-Min , Grant P. Ellen , Lindquist Diana M. , Kuan Chia-Yi , Hu Song 
Published: November 2021

AbstractHypoxia-ischemia (HI), which disrupts the oxygen supply-demand balance in the brain by impairing blood oxygen supply and the cerebral metabolic rate of oxygen (CMRO2), is a leading cause of neonatal brain injury. However, it is unclear how post-HI hypothermia helps to restore the balance, as cooling reduces CMRO2. Also, how transient HI leads to secondary energy failure (SEF) in neonatal brains remains elusive. Using photoacoustic microscopy, we examined the effects of HI on CMRO2in awake 10-day-old mice, supplemented by bioenergetic analysis of purified cortical mitochondria. Our results show that while HI suppresses ipsilateral CMRO2, it sparks a prolonged CMRO2-surge post-HI, associated with increased mitochondrial oxygen consumption, superoxide emission, and reduced mitochondrial membrane potential necessary for ATP synthesis—indicating oxidative phosphorylation (OXPHOS) uncoupling. Post-HI hypothermia prevents the CMRO2-surge by constraining oxygen extraction fraction, reduces mitochondrial oxidative stress, and maintains ATP and N-acetylaspartate levels, resulting in attenuated infarction at 24 hours post-HI. Our findings suggest that OXPHOS-uncoupling induced by the post-HI CMRO2-surge underlies SEF and blocking the surge is a key mechanism of hypothermia protection. Also, our study highlights the potential of optical CMRO2-measurements for detecting neonatal HI brain injury and guiding the titration of therapeutic hypothermia at the bedside.
The evolutionary history of human spindle genes includes back-and-forth gene flow with Neandertals
Authors: Peyrégne Stéphane , Kelso Janet , Peter Benjamin M. , Pääbo Svante 
Published: November 2021

AbstractProteins associated with the spindle apparatus, a cytoskeletal structure that ensures the proper segregation of chromosomes during cell division, experienced an unusual number of amino acid substitutions in modern humans after the split from the ancestors of Neandertals and Denisovans. Here, we analyze the history of these substitutions and show that some of the genes in which they occur may have been targets of positive selection. We also find that the two changes in the kinetochore scaffold 1 (KNL1) protein, previously believed to be specific to modern humans, were present in some Neandertals. We show that the KNL1 gene of these Neandertals shared a common ancestor with present-day Africans about 200,000 years ago due to gene flow from the ancestors (or relatives) of modern humans into Neandertals. Subsequently, some non-Africans inherited this modern human-like gene variant from Neandertals, but none inherited the ancestral gene variants. These results add to the growing evidence of early contacts between modern humans and archaic groups in Eurasia and illustrate the intricate relationships among these groups.
Multi-task representations in human cortex transform along a sensory-to-motor hierarchy
Authors: Ito Takuya , Murray John D. 
Published: November 2021

AbstractHuman cognition recruits diverse neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multi-task representations across human cortex using functional MRI during 26 cognitive tasks in the same subjects. We measured the representational similarity across tasks within a region, and the alignment of representations between regions. We found a cortical topography of representational alignment following a hierarchical sensory-association-motor gradient, revealing compression-then-expansion of multi-task dimensionality along this gradient. To investigate computational principles of multi-task representations, we trained multi-layer neural network models to transform empirical visual to motor representations. Compression-then-expansion organization in models emerged exclusively in a training regime where internal representations are highly optimized for sensory-to-motor transformation, and not under generic signal propagation. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multi-task representations across human cortex to support flexible cognition.
The FORCE panel: An all-in-one SNP marker set for confirming investigative genetic genealogy leads and for general forensic applications
Authors: Tillmar Andreas , Sturk-Andreaggi Kimberly , Daniels-Higginbotham Jennifer , Thomas Jacqueline Tyler , Marshall Charla 
Published: December 2021

AbstractThe FORensic Capture Enrichment (FORCE) panel is an all-in-one SNP panel for forensic applications. This panel of 5,422 markers encompasses common, forensically relevant SNPs (identity, ancestry, phenotype, X- and Y-chromosomal SNPs), a novel set of 3,931 autosomal SNPs for extended kinship analysis, and no clinically relevant/disease markers. The FORCE panel was developed as a custom hybridization capture assay utilizing ∼20,000 baits to target the selected SNPs. Five non-probative, previously identified World War II (WWII) cases were used to assess the kinship panel. Each case included one bone sample and associated family reference DNA samples. Additionally, seven reference quality samples, two 200-year-old bone samples, and four control DNAs were processed for kit performance and concordance assessments. SNP recovery after capture resulted in a mean of ∼99% SNPs exceeding 10X coverage for reference and control samples, and 44.4% SNPs for bone samples. The WWII case results showed that the FORCE panel could predict 1st to 5th degree relationships with strong statistical support (likelihood ratios over 10,000 and posterior probabilities over 99.99%). To conclude, SNPs will be important for further advances in forensic DNA analysis. The FORCE panel shows promising results and demonstrates the utility of a 5,000 SNP panel for forensic applications.
Prediction of cold rolling gas based on EEMD-LSTM deep learning technology
Authors: Zhai Hui , Xiong Wei , Li Fujin , Yang Jie , Su Dongyan , Zhang Yongjun 
Published: November 2021

Purpose
The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.

Design/methodology/approach
In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.

Findings
The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.

Originality/value
A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.
Understanding dynamic distribution capabilities to enhance supply chain performance: a dynamic capability view
Authors: Kazmi Syeda Wajiha , Ahmed Waqar 
Published: November 2021

PurposeThe purpose of this paper is to evaluate and understand the factors that contribute to the enhancement in the process of supply chain activities, specifically among manufacturing industries.Design/methodology/approachThis paper follows the quantitative approach by disseminating a structured questionnaire to supply chain practitioners working in manufacturing industries. A sample of 109 responses is gathered from senior employees involved in sales and operations planning. Exploratory and confirmatory factor analyses are performed to evaluate the reliability and validity of the model. Lastly, SmartPLS is used to test the hypotheses proposed in the study.FindingsThe findings revealed that demand sensing and managing practices positively impact supply chain performance by creating dynamic distribution capabilities. It is observed that distribution capabilities integrate both efficient and effective handling of resources. However, demand management practice has insignificant relationship with supply chain performance.Research limitations/implicationsThe authors suggest that supply chain management must expand in firms, and importance must be given as supply chain performance can provide competitive advantage in order to sustain in today's competitive market.Practical implicationsThe findings can help decision-makers working in the planning process by identifying the right tools to sense the market and react accordingly.Originality/valueThis paper is an initial effort to understand the dynamic distribution capabilities by establishing the link with the studied variables, especially in the manufacturing industries in Pakistan.
Does doing good pay off?
Authors: Marwan Syed , Haneef Mohamed Aslam 
Published: November 2021

Purpose
The purpose of this paper is to examine the world’s first social impact bond (SIB) and the lessons that can be learned for the Islamic finance industry to fulfil its true objectives.

Design/methodology/approach
The Peterborough SIB was recently announced to be successful in achieving its targeted social and investment outcomes, reducing recidivism by 9 per cent and paying back investors a 3 per cent pa return. The paper compares Peterborough SIB with socially responsible investment (SRI) sukuk in terms of form and substance, and finds that there are various lessons from the Peterborough SIB that can be useful for future development of Islamic financial products.

Findings
Innovative social financial tools such as SIB exemplify the true spirit of risk sharing and social responsibility, which is arguably missing in current practices of the Islamic finance industry. With the growing interest towards SRI strategies and increase in socially motivated investors, such financial tools may not only help the sustainable growth of the Islamic finance industry, but also fill in the gap between its theory and practice.

Practical implications
As such, the paper also proposes a social impact sukuk model which integrates the key aspects learned from Peterborough SIB. This includes prioritising social impact, measurable success indicators, data and management systems, flexible contracts, third sector integration, risk sharing and fostering the culture of innovation.

Originality/value
The findings can offer some practical insights in dealing with the issue of Islamic finance practice being overly concerned with its formal adherence with Islamic legal rules whilst neglecting its true fundamental values.
Reciprocity, self-interest and reputation: debt vs equity contracts
Authors: Shah Syed Munawar , Abdul-Majid Mariani 
Published: November 2021

Purpose
The purpose of this paper is to examine whether reputation element affects the decision relative performance of trust, bonus and incentive contracts using social laboratory experiments.

Design/methodology/approach
The study conducts the following lab experiments bonus–incentive treatment without reputation, bonus–incentive treatment with reputation and trust–incentive treatment with reputation.

Findings
The study finds that the reputation and fairness concerns, in contrast to self-interest, may have a decisive impact on the actual and optimal choices in the reciprocity-based contracts. The principal pays higher salaries in the bonus contract as compared to an incentive contract.

Originality/value
The study contributes to the behavioral economic literature in the following dimensions. The existing literature on lab experiments considers a bonus contract as better than the debt contract; however, it does not consider the trust contract better than the debt contract.
Economic growth and financial performance of Islamic banks: a CAMELS approach
Authors: Ledhem Mohammed Ayoub , Mekidiche Mohammed 
Published: November 2021

PurposeThe purpose of this paper is to investigate the link between the financial performance of Islamic finance and economic growth in all of Malaysia, Indonesia, Brunei, Turkey and Saudi Arabia within the endogenous growth model framework.Design/methodology/approachThis study applied dynamic panel system GMM to estimate the impact of the financial performance of Islamic finance on economic growth using quarterly data (2014:1-2018:4). CAMELS system parameters were employed as variables of the financial performance of Islamic finance and gross domestic product (GDP) as a proxy of economic growth. The sample contained all Islamic banks working in the five countries.FindingsThe findings demonstrated that the only significant factor of the financial performance of Islamic finance, which affects the endogenous economic growth, is profitability through return on equity (ROE). The experimental findings also indicated the necessity of stimulating other financial performance factors of Islamic finance to achieve a significant contribution to economic growth.Practical implicationsThe analysis in this paper would fill the literature gap by investigating the link between financial performance of Islamic finance and economic growth, as this study serves as a guide for the academians, researchers and decision-makers who want to achieve economic growth through stimulating Islamic finance in the banking sector. However, this study may well be extended to investigate the link between the financial performance of Islamic finance and economic growth over the Z-score model as another measure for the financial performance of Islamic finance.Originality/valueThis paper is the first that investigates the link between financial performance of Islamic finance and economic growth empirically using CAMELS parameters within the endogenous growth model to provide robust information about this link based on a sample of the top pioneer Islamic finance countries.