bluuebunny/crossref_metadata_2025_split huggingface dataset:
In silico tool for Predicting, Designing and Scanning IL-2 inducing peptides
Authors: Mehta Naman Kumar , Lathwal Anjali , Kumar Rajesh , Kaur Dilraj , Raghava Gajendra P. S. 
Published: June 2021

AbstractInterleukin-2 (IL-2) based immunotherapy has been approved for treating certain types of cancer, as IL-2 plays a crucial role in regulating the immune system. In this study, we developed a method for predicting IL-2-inducing peptides. Our method was trained, tested, and validated on a main dataset containing 6,574 experimentally validated Major histocompatibility complex (MHC) binders, including 3,429 IL-2-inducing and 3,145 non-inducing peptides. A primary analysis of IL-2 inducing and non-inducing peptides revealed that certain residues, such as alanine and leucine, are more abundant in IL-2-inducing peptides. Initially, we developed alignment-based methods, which demonstrated high precision but limited coverage. Subsequently, we developed artificial intelligence-based models, including machine learning (ML), deep learning (DL), and large language models (LLM), to predict IL-2-inducing peptides. Our Extra Tree-based model, developed using dipeptide composition and peptide length, achieved a maximum AUC of 0.82. Finally, we constructed ensemble models that combined artificial intelligence and alignment-based methods. Our best ensemble model, which integrates the Extra Tree-based model with MERCI, achieved the highest AUC of 0.84 and an MCC of 0.51 on the main dataset. One limitation of the main dataset is that both IL-2-inducing and non-inducing peptides are MHC binders. To address this limitation, we created two additional datasets: Alternate Dataset 1, consisting of 3,429 IL-2-inducing peptides and 3,429 non-inducing peptides (MHC non-binders), and Alternate Dataset 2, consisting of 3,429 IL-2-inducing peptides and 3,439 non-inducing peptides (MHC binders + MHC non-binders). Our best ensemble model achieved AUCs of 0.9 and 0.8 with MCCs of 0.61 and 0.44 on Alternate Datasets 1 and 2, respectively. To assist the scientific community, we have integrated the best models from this study into a standalone software and web server, IL2pred, which enables users to predict, scan, and design IL-2-inducing peptides (https://webs.iiitd.edu.in/raghava/il2pred/).HighlightsDevelopment of alignment-based techniques for predicting IL-2-inducing peptides.Identification of amino acid patterns associated with IL-2 induction.Implementation of machine learning, deep learning, and LLM-based models for peptide prediction.Ensemble approaches combining alignment-based and AI-driven methods and integration into the webserver.
BehaviorDEPOT: a tool for automated behavior classification and analysis in rodents
Authors: Gabriel Christopher J , Zeidler Zachary , Jin Benita , Guo Changliang , Wu Anna , Delaney Molly , Cheung Jovian , DiFazio Lauren E. , Sharpe Melissa J. , Aharoni Daniel , Wilke Scott A. , DeNardo Laura A. 
Published: June 2021

AbstractQuantitative descriptions of animal behavior are essential to understand the underlying neural substrates. Many behavioral analyses are performed by hand or with expensive and inflexible commercial software that often fail on animals with attached head implants, such as those used for in vivo optogenetics and calcium imaging. With the development of machine learning algorithms that can estimate animal positions across time and space, it is becoming easier for users with no prior coding experience to perform automated animal tracking in behavioral video recordings. Yet classifying discrete behaviors based on positional tracking data remains a significant challenge. To achieve this, we must start with reliable ground truth definitions of behavior, a process that is hindered by unreliable human annotations. To overcome these barriers, we developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a MATLAB-based application comprising six independent modules and a graphical user interface. In the Analysis Module we provide hard-coded classifiers for freezing and rearing. Optionally applied spatiotemporal filters allow users to analyze behaviors in varied experimental designs (e.g. cued tasks or optogenetic manipulations). Even inexperienced users can generate organized behavioral data arrays that can be seamlessly aligned with neurophysiological recordings for detailed analyses of the neural substrates. Four additional modules create an easy-to-use pipeline for establishing reliable ground-truth definitions of behaviors as well as custom behavioral classifiers. Finally, our Experiment Module runs fear conditioning experiments using an Arduino-based design that interfaces with commercialhardware and significantly reduces associated costs. We demonstrate the utility and flexibility of BehaviorDEPOT in widely used behavioral assays including fear conditioning, avoidance, and decision-making tasks. We also demonstrate the robustness of the BehaviorDEPOT freezing classifier across multiple camera types and in mice and rats wearing optogenetic patch cables and head-mounted Miniscopes. BehaviorDEPOT provides a simple, flexible, automated pipeline to move from pose tracking to reliably quantifying a wide variety of task-relevant behaviors.
Linking spatial self-organization to community assembly and biodiversity
Authors: Bera Bidesh K. , Tzuk Omer , Bennett Jamie J. R. , Meron Ehud 
Published: June 2021

AbstractDrier climates impose environmental stresses on plant communities that may result in community reassembly and threatened ecosystem services, but also may trigger self-organization in spatial patterns of biota and resources, which act to relax these stresses. The complex relationships between these counteracting processes – community reassembly and spatial self-organization – have hardly been studied. Using a spatio-temporal model of dryland plant communities and a trait-based approach, we study the response of such communities to imposed water stress of increasing degrees. We first show that spatial patterning acts to reverse shifts from fast-growing species to stress-tolerant species, as well as to reverse functional-diversity loss. We then show that spatial re-patterning buffers the impact of further stress on community structure. Finally, we identify multistability ranges of uniform and patterned community states and use them to propose forms of non-uniform ecosystem management that integrate the need for provisioning ecosystem services with the need to preserve community structure.
When to stop computing and start investing
Authors: Aguilar Sean R. , Kosheleva Olga 
Published: June 2021

PurposeThe purpose of the study is to analyze when — while predicting the future price of a financial instrument — we should stop computations and start using this information for the actual investment.Design/methodology/approachWe derive the explicit formulas explaining how the resulting gain depends on the duration of computations.FindingsWe provide an algorithm that enables us to decide the computation time that leads to the largest possible gain.Originality/valueTo the best of our knowledge, this is the first solution to the problem. Following our recommendations will allow investors to select the computation time for which the resulting gain is the largest possible.
On subcopula estimation for discrete models
Authors: Tasena Santi 
Published: June 2021

PurposeTo discuss subcopula estimation for discrete models.Design/methodology/approachThe convergence of estimators is considered under the weak convergence of distribution functions and its equivalent properties known in prior works.FindingsThe domain of the true subcopula associated with discrete random variables is found to be discrete on the interior of the unit hypercube. The construction of an estimator in which their domains have the same form as that of the true subcopula is provided, in case, the marginal distributions are binomial.Originality/valueTo the best of our knowledge, this is the first time such an estimator is defined and proved to be converged to the true subcopula.
TOPSIS model based on entropy and similarity measure for market segment selection and evaluation
Authors: Duong Truong Thi Thuy , Thao Nguyen Xuan 
Published: June 2021

PurposeThe paper aims to propose a practical model for market segment selection and evaluation. The paper carries out a technique of order preference similarity to the ideal solution (TOPSIS) approach to make an operation systematic dealing with multi-criteria decision- making problem.Design/methodology/approachIntroducing a multi-criteria decision-making problem based on TOPSIS approach. A new entropy and new similarity measure under neutrosopic environment are proposed to evaluate the weights of criteria and the relative closeness coefficient in TOPSIS model.FindingsThe outcomes show that the TOPSIS model based on new entropy and similarity measure is effective for evaluation and selection market segment. Profitability, growth of the market, the likelihood of sustainable differential advantages are the most important insights of criteria.Originality/valueThis paper put forward an effective multi-criteria decision-making dealing with uncertain information.
Impact of idiosyncratic and macroeconomic risks on capital structure: evidence from SADC countries
Authors: Marcelle Amelot Lydie Myriam , Subadar Agathee Ushad 
Published: June 2021

PurposeThe purpose of this study is to investigate the impact of idiosyncratic and macroeconomic risks on capital structure on SADC countries.Design/methodology/approachEmploying data from the African Financials database, the analysis is conducted over a ten year period spanning from 2009 to 2018 for 309 companies. Unit Root Fisher Chi-Square test and Granger Causality test were employed to test for unidirectional and bidirectional relationships cross-sectionally. To resolve endogeneity issues, System GMM was used as main topology for panel regression analysis.FindingsThe study confirmed that companies become risk averse when there is an increase in idiosyncratic and macroeconomic risk and therefore take less leverage. According to the perking order theory, a higher variability in earnings shows that the bankruptcy probability amplifies. Hence, institutions with high income employ more internal finance during periods of high idiosyncratic and macroeconomic uncertainty thereby lowering leverage. A positive significant and statistically relationship is also confirmed between idiosyncratic risk and leverage in Botswana, South Africa and Tanzania. Companies with higher leverage make riskier investment in line with the trade-off theory. In short, executives from the SADC region consider more importance to fluctuations in risk while accelerating or diminishing leverage in their capital structure.Originality/valueThe study is among one of the pioneering work underpinning the idiosyncratic risk and macroeconomic risk on capital structure and relying on a large number of companies across the SADC region. In this respect, it adds contribution to the existing literature on risks and capital structure to the socio-economic goals of the SADC region.
Social network analysis applications in sustainable construction and built environment management: a review
Authors: Hewa Welege Nandun Madhusanka , Pan Wei , Kumaraswamy Mohan 
Published: June 2021

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.
Climatic conditions and the resilience of buildings along Lagos coastline
Authors: Faremi Olajide Julius , Ajayi Oluranti Olupolola , Zakariyyah Kudirat Ibilola , Adenuga Olumide Afolarin 
Published: June 2021

PurposeThe study investigates the extent to which defects in coastline buildings are influenced by the climatic conditions within the coastal zones.Design/methodology/approachThe study conducted both desk study and field survey. The primary data for the study were collected through a cross-sectional survey of facilities and maintenance managers of randomly selected coastline buildings. Of the 120 self-administered structured questionnaires, 102 were successfully retrieved representing an 85% response rate. Data collected were analysed using charts, relative prevalence index and Spearman's rho correlation visualization technique.FindingsSaltwater intrusion, ocean overflow, extreme rainfall, debris flow, floods and droughts are the prevalent climatic conditions along the coastline. Steel corrosion, foundation settlement, spalling of concrete and fading of finishes are prevalent defects in coastline buildings. The result shows a positive significant correlation between climatic conditions and defects in coastline buildings.Research limitations/implicationsThe study compliments literature on buildings resilience and maintenance management, and also provides a basis for streamlining future research on coastline buildings.Practical implicationsThe results provide information on climatic conditions and prevalent defects that should be considered during the design and construction of coastline buildings. The information provided could assist construction stakeholders in improving the resilience of coastline buildings.Originality/valueThe study established that coastline buildings are vulnerable to a rapid rate of defect and deterioration which threatens the sustainability of coastline cities. It suggests measures that could improve the resilience of the elements and components of coastline buildings and consequently enhance the safety of life and property, and improve the physical and economic performance of coastline buildings.
Living-transforming disaster relief shelter: a conceptual approach for sustainable post-disaster housing
Authors: Ghanbarzadeh Ghomi Sara , Wedawatta Gayan , Ginige Kanchana , Ingirige Bingunath 
Published: June 2021

PurposeThe purpose of this paper is to investigate the performance of post-disaster housing reconstruction projects, propose the conceptual living-transforming disaster relief shelter (LTFDR-shelter) approach where temporary shelter is incrementally transformed into a more permanent dwelling by using living technologies and investigate its applicability to provide sustainable post-disaster housing following natural-hazard-induced disasters.Design/methodology/approachA questionnaire survey with 120 household recipients of three Sri Lankan post-disaster housing projects was employed to explore how the post-disaster housing projects have performed against the occupants' expectations. Furthermore, the new proposed LTFDR-shelter conceptual approach's applicability to address the existing issues found in the study was investigated.FindingsThe paper evaluates and identifies the physical and technical, and socio-economic performance issues of post-disaster housing and discusses the applicability of the proposed LTFDR-shelter conceptual approach as an efficient tool to adequately improve the identified factors integrating three phases of relief, rehabilitation and reconstruction employing living technology.Research limitations/implicationsAlthough the study's scope was limited to the occupant view of the performance of post-disaster housing in Sri Lanka, the findings and conceptual LTFDR-shelter approach could be of particular relevance to other developing countries affected by similar disasters. Further research is recommended to investigate and develop this concept in depth.Originality/valueThis study lays the conceptual foundation for a new theoretical approach in post-disaster housing, which encourages more interdisciplinary collaborations and empirical investigations that potentially enhance post-disaster housing performance and facilitates the application of living technology in the built environment.
An enhanced spatial smoothing algorithm for coherent signals DOA estimation
Authors: Qi Bingbing , Liu Dunge 
Published: June 2021

PurposeThe existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.Design/methodology/approachIn this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.FindingsTheoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.Originality/valueThe proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.
Learning from plants: a new framework to approach water-harvesting design concepts
Authors: Jalali Sara , Aliabadi Morteza , Mahdavinejad Mohammadjavad 
Published: June 2021

PurposeThis paper's main objective is to focus on the water-harvesting ability of plants and try to implement a solution-based method to outline a plant-inspired design framework.Design/methodology/approachThe current paper aims to provide a step-by-step approach to the biological-inspired design by looking deeply at plants' mechanisms and features to harvest water and conduct a method to learn them in an organized way.FindingsIn addition to the proposed framework, the fundamental water-harvesting principles of plants including increasing condensation, reducing transpiration and facilitating transportation have been extracted by investigating several adaptable plants. The relevant factors related to each of these three principles are introduced and can potentially ease the process of bio-inspiration as it contributes to the findability and understandability of a particular biologic strategy. As a result, this framework can be used to the formation of novel designs in different disciplines. In this process, the development of an architectural design concept is presented as an example.Originality/valueThe current global issue about the shortage of water leads researchers to learn adaptability from nature and increase the demands of using bio-inspired strategies. The novelty of this study is to introduce a water-harvesting design path, which has been presented using a four-step-plant-to-design process. Learning from plants' water-harvesting strategies will contribute to efficiency in different disciplines. The findings of this study have important implications for developing bio-inspired water-harvesting materials and systems. Moreover, the findings add substantially to the understanding of water-harvesting architecture and play an important role in bridging the gap between theory and practice.
Conditions for spin-off creation at Swiss universities of applied sciences – a gender sensitive approach
Authors: Schneider Noemi , Blaese Richard , Liebig Brigitte 
Published: June 2021

PurposeThe promotion of research-based entrepreneurship is considered a crucial task for universities and policymakers in many Western countries. Research has shown that the university environment plays a decisive role in the spin-off activities of researchers. Although the number of science-based spin-offs has increased in recent years, women are still an exception when it comes to developing spin-off ventures. In turn, there is a lack of knowledge regarding the university environment that supports entrepreneurship from a gender perspective.Design/methodology/approachBased on the theoretical framework of the “Entrepreneurial University,” this contribution examines the formal and informal conditions for academic entrepreneurship using the example of Swiss universities of applied sciences (UAS). Based on a cross-sectional dataset of 1,551 researchers from various disciplines who were surveyed in 2019, linear regressions and logistic regression models were used to test gender-specific differences in the perception of organizational conditions concerning the entrepreneurial exploitation of research.FindingsThe results demonstrated significant differences in the perception of formal and informal conditions in higher education. First, they show gender differences in the perception of informal entrepreneurial support in universities; in particular, female researchers received less informal support for spin-off projects. For example, women hardly viewed commercial use of research and development knowledge as a career option and considered the existence of entrepreneurial role models at universities to be low. Second, further analyses highlighted that also formal support offerings were less known among female researchers.Originality/valueThe study highlights organizational barriers for female researchers regarding the development of spin-off creation at UAS, including the different formal and informal conditions for female academics in comparison to their male counterparts.