bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive assembly of monoclonal and mixed antibody sequences
Authors: Jiang Wenbin , Xiong Yueting , Xiao Jin , Wang Jingyi , Jiang Zhenjian , Luo Ling , Yuan Quan , Xia Ningshao , Yu Rongshan 
Published: August 2024

AbstractThe elucidation of antibody sequence information is crucial for understanding antigen binding and advancing therapeutic and research applications. However, completede novoassembly of monoclonal antibody sequences remains challenging due to accuracy and robustness limitations. To address this issue, we introduce Fusion, an innovativede novoassembler that integrates overlapping peptides and template information into complete sequences using a beam search strategy. We demonstrate Fusion’s performance by reconstructing multiple human and murine antibodies with highest accuracy (100% and over 99%, respectively). Biological validation of the recombinantly expressed AFS98 antibody with unknown sequences further supports its effectiveness. Furthermore, current methods are applicable only to traditional monoclonal antibody sequencing assembly, presenting a significant bottleneck in achieving higher throughput. In contrast, Fusion can assemble peptide sequences from mixtures of two or three monoclonal antibodies into complete individual sequences with the same accuracy as traditional sequencing, significantly enhancing throughput. To our knowledge, this is the first study enabling high-throughput sequencing of multiple antibodies using only bottom-up mass spectrometry. The duration, expense, and reagent consumption of mass spectrometry detection are comparable to those required for sequencing a single monoclonal antibody. In summary, Fusion’s superior performance in handling the complex antibody sequencing represents a significant advancement in antibody research.
RGAST: Relational Graph Attention Network for Spatial Transcriptome Analysis
Authors: Gong Yuqiao , Yu Zhangsheng 
Published: August 2024

AbstractRecent advancements in spatially resolved transcriptomics have provided a powerful means to comprehensively capture gene expression patterns while preserving the spatial context of the tissue microenvironment. Accurately deciphering the spatial context of spots within a tissue necessitates the careful utilization of their spatial information, which in turn requires feature extraction from complex and detailed spatial patterns. In this study, we present RGAST (Relational Graph Attention network for Spatial Transcriptome analysis), a framework designed to learn low-dimensional representations of spatial transcriptome (ST) data. RGAST is the first to consider gene expression similarity and spatial neighbor relationships simultaneously in constructing a heterogeneous graph network in ST analysis. We further introduce a cross-attention mechanism to provide a more comprehensive and adaptive representation of spatial transcriptome data. We validate the effectiveness of RGAST in different downstream tasks using diverse spatial transcriptomics datasets obtained from different platforms with varying spatial resolutions. Our results demonstrate that RGAST enhances spatial domain identification accuracy by approximately 10% compared to the second method in 10X Visium DLPFC dataset. Furthermore, RGAST facilitates the discovery of spatially variable genes, uncovers spatially resolved cell-cell interactions, enables more precise cell trajectory inference and reveals intricate 3D spatial patterns across multiple sections of ST data. Our RGAST method is available as a Python package on PyPI athttps://pypi.org/project/RGAST, free for academic use, and the source code is openly available from our GitHub repository athttps://github.com/GYQ-form/RGAST.
Unveiling Fine-scale Spatial Structures and Amplifying Gene Expression Signals in Ultra-Large ST slices with HERGAST
Authors: Gong Yuqiao , Yuan Xin , Jiao Qiong , Yu Zhangsheng 
Published: August 2024

SummaryWe propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large ST data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conque framework specially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential oversmoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulation, HERGAST consistently outperformed other methods across all settings with more than 10% average gaining. In real-world data, HERGAST’s high-precision spatial clustering enabled finding SPP1+ macrophages intermingled in tumors in colorectal cancer, while the enhanced gene expression signal enabled discovering unique spatial expression pattern of key genes in breast cancer.
Explainable AI in Healthcare: Systematic Review of Clinical Decision Support Systems
Authors: Aziz Noor A. , Manzoor Awais , Mazhar Qureshi Muhammad Deedahwar , Qureshi M. Atif , Rashwan Wael 
Published: August 2024

ABSTRACTThis systematic review examines the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in Clinical Decision Support Systems (CDSS), highlighting significant advancements and identifying persistent challenges. Utilising the PRISMA protocol, we searched major indexed databases such as Scopus, Web of Science, PubMed, and the Cochrane Library, to analyse publications from January 2000 to April 2024. This timeframe captures the progressive integration of XAI in CDSS, offering a historical and technological overview. The review covers the datasets, application areas, machine learning models, explainable AI methods, and evaluation strategies for multiple XAI methods.Analysing 68 articles, we uncover valuable insights into the strengths and limitations of current XAI approaches, revealing significant research gaps and providing actionable recommendations. We emphasise the need for more public datasets, advanced data treatment methods, comprehensive evaluations of XAI methods, and interdisciplinary collaboration. Our findings stress the importance of balancing model performance with explainability and enhancing the usability of XAI tools for medical practitioners. This research provides a valuable resource for healthcare professionals, researchers, and policymakers seeking to develop and evaluate effective, ethical decision-support systems in clinical settings.
Fine-tuning of conditional Transformers for the generation of functionally characterized enzymes
Authors: Nicolini Marco , Saitto Emanuele , Jimenez Franco Ruben Emilio , Cavalleri Emanuele , Mesiti Marco , Galeano Alfonso Aldo Javier , Malchiodi Dario , Paccanaro Alberto , Robinson Peter N. , Casiraghi Elena , Valentini Giorgio 
Published: August 2024

AbstractWe introduceFinenzyme, a Protein Language Model (PLM) that employs a multifaceted learning strategy based on transfer learning from a decoder-based Transformer, conditional learning using specific functional keywords, and fine-tuning to model specific Enzyme Commission (EC) categories. UsingFinenzyme, we investigate the conditions under which fine-tuning enhances the prediction and generation of EC categories, showing a two-fold perplexity improvement in EC-specific categories compared to a generalist model. Our extensive experimentation shows thatFinenzymegenerated sequences can be very different from natural ones while retaining similar tertiary structures, functions and chemical kinetics of their natural counterparts. Importantly, the embedded representations of the generated enzymes closely resemble those of natural ones, thus making them suitable for downstream tasks. Finally, we illustrate howFinenzymecan be used in practice to generate enzymes characterized by specific functions using in-silico directed evolution, a computationally inexpensive PLM fine-tuning procedure significantly enhancing and assisting targeted enzyme engineering tasks.
Dimensions and barriers for digital (in)equity and digital divide: a systematic integrative review
Authors: Raihan Mohammad M.H. , Subroto Sujoy , Chowdhury Nashit , Koch Katharina , Ruttan Erin , Turin Tanvir C. 
Published: August 2024

PurposeThis integrative review was conducted to provide an overview of existing research on digital (in)equity and the digital divide in developed countries.Design/methodology/approachWe searched academic and grey literature to identify relevant papers. From 8464 academic articles and 183 grey literature, after two levels of screening, 31 articles and 54 documents were selected, respectively. A thematic analysis was conducted following the steps suggested by Braun and Clarke and results were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.FindingsThe results showed that most articles and papers were either from Europe or North America. Studies used a range of research methods, including quantitative, qualitative and mixed methods. The results demonstrated four major dimensions of the digital divide among various vulnerable groups, including digital literacy, affordability, equity-deserving group-sensitive content and availability or access to infrastructure. Among vulnerable groups, low-income people were reported in the majority of the studies followed by older adults, racial and ethnic minorities, newcomers/new immigrants and refugees, Indigenous groups, people with disabilities and women. Most reported barriers included lack of access to the internet, digital skills, language barriers and internet costs.Originality/valueTo the best of our knowledge, there have been limited attempts to thoroughly review the literature to better understand the emerging dimensions of digital equity and the digital divide, identifying major vulnerable populations and their unique barriers and challenges. This review demonstrated that understanding intersectional characteristics (age, gender, disability, race, ethnicity, Indigenous identity and immigration status) and their interconnections is crucial for analyzing the dynamics of digital (in)equity and divide.
Simulation of coupled groundwater flow and contaminant transport using quintic B-spline collocation method
Authors: Bahar Ersin , Gurarslan Gurhan 
Published: August 2024

PurposeThe purpose of this study is to introduce a new numerical scheme with no stability condition and high-order accuracy for the solution of two-dimensional coupled groundwater flow and transport simulation problems with regular and irregular geometries and compare the results with widely acceptable programs such as Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW) and Modular Three-Dimensional Multispecies Transport Model (MT3DMS).Design/methodology/approachThe newly proposed numerical scheme is based on the method of lines (MOL) approach and uses high-order approximations both in space and time. Quintic B-spline (QBS) functions are used in space to transform partial differential equations, representing the relevant physical phenomena in the system of ordinary differential equations. Then this system is solved with the DOPRI5 algorithm that requires no stability condition. The obtained results are compared with the results of the MODFLOW and MT3DMS programs to verify the accuracy of the proposed scheme.FindingsThe results indicate that the proposed numerical scheme can successfully simulate the two-dimensional coupled groundwater flow and transport problems with complex geometry and parameter structures. All the results are in good agreement with the reference solutions.Originality/valueTo the best of the authors' knowledge, the QBS-DOPRI5 method is used for the first time for solving two-dimensional coupled groundwater flow and transport problems with complex geometries and can be extended to high-dimensional problems. In the future, considering the success of the proposed numerical scheme, it can be used successfully for the identification of groundwater contaminant source characteristics.
Central budget allocation regime and total factor productivity in Vietnam: a decomposition approach
Authors: Le Dao Van , Tran Tuyen Quang 
Published: August 2024

PurposeThis study explores the effect of local budget retention rate changes (RER) on total factor productivity (TFP) and its components in Vietnam.Design/methodology/approachThe study employs a two-system generalized method of moments (GMM) estimator and data from 2012 to 2019 across all 63 provinces/cities of Vietnam.FindingsThe study finds that local budget retention rates significantly influence public investment, affecting scale and allocation efficiency. The reallocation of budgets between regions and from the central government to local levels incurs certain costs, often resulting in economically robust provinces experiencing reductions in their retention rates.Practical implicationsRecognizing the challenges of immediate structural budget changes due to cultural and historical factors, the study suggests a more gradual policy approach. It emphasizes the importance of policy predictability, as abrupt reductions in the retention rate lead to higher costs than gradual reductions, thus implementing budget policies with a clearer timeline. This study provides insight into local budget allocation regimes and their impact on productivity in transitioning countries.Originality/valueFirst, the study provides fresh evidence of the impact of retention rate changes on TFP and its components in Vietnam. Second, the study provides insights into the mechanisms of the nexus of increased budget spending, capital efficiency and, most importantly, attaining improvement in education. We also offer further insights into inefficient budget allocation agents in Vietnam, especially in large cities, which should alert scholars to explore this topic further in the future.
Bachpan Bachao Andolon: saving childhood!
Authors: Nafees Lubna , Mehdi Mokhalles Mohammad , Kapoor Shivani 
Published: August 2024

Learning outcomes
The case study aims to provide students with an understanding of child labour in India and how the Bachpan Bachao Andolan (BBA) movement is facilitating the eradication of child labour in India. The key objectives are to understand the challenges of eradicating child labour in India, assess the approaches to rescue and rehabilitation for the protection of a child in India, analyse the personality traits of a leader and evaluate the charismatic leadership of a leader.

Case overview/synopsis
The case study discussed child labour and the movement started by Kailash Satyarthi to protect children through the BBA or Save the Childhood Movement in India. He was awarded the Nobel Peace Prize in 2014 for his contribution to creating an environment to abolish child labour. Headquartered in New Delhi, BBA was established in 1980 to eliminate child slavery and violence against children (BBA.org.in, 2017). Satyarthi’s effort brought regulatory changes in the Indian constitution in the form of “Education as a fundamental right” (The Novel Prize, 2023). As per census 2011, more than 10 million children were involved in various industries in India (Drishtiias.com, 2019). Satyarthi and his BBA were far behind the mission they were to achieve. How should he move to eliminate child labour from India? Will he be able to create any impact on citizens, society and government through his new approaches?

Complexity academic level
The case study is ideal for courses on understanding strategy, leadership, personality traits and labour laws. This case study is designed for use in undergraduate and graduate early-stage programmes. The main purpose of this case study is to be used in programmes leading to a Master of Business Administration or a Bachelor of Business Administration. This case scenario focused on the BBA movement in India and its aim to eradicate child labour in India. It discussed the child labour scenario in Indian industries and how Satyarthi started the BBA movement to protect the future of underprivileged children in India. The case study discusses the challenges faced in rescuing and rehabilitation of children in India. The authors applied the big five model to analyse the personality of Satyarthi in the case context. The authors also used the concept and components of charismatic leadership to evaluate the traits of charismatic leaders from a case perspective. This case study will benefit students by focusing on traits related to personality and charismatic leadership concepts. Students will get the opportunity to explore the practical and theoretical concepts that interplay in this study.

Supplementary materials
Teaching notes are available for educators only.

Subject code
CSS 6: Human resource management.
Farmers’ access to agricultural information sources: a study of Lakadong turmeric farmers
Authors: Wann Tiplut , Khongtim Jiarlimon 
Published: August 2024

Purpose
The aim of this study is to examine the availability, utilization and effectiveness of various information sources in enhancing the knowledge and practices of Lakadong turmeric farmers.

Design/methodology/approach
This study focuses on 125 Lakadong turmeric farmers in Myntkung and Biar villages, Meghalaya, India. A purposive sampling technique was used to select participants, ensuring the representation of various ages, experiences and agricultural practices. A mixed-methods approach was used, including structured interviews and focus group discussions, to gather information on the farmers.

Findings
The study found that most of the Lakadong turmeric farmers (35.2%) needed information on farming techniques and practices and 93.6% of farmers approached their friends, relatives or fellow farmers to access any information. For most farmers, 77 (62%) relied on word-of-mouth to get information. After using the information effectively and efficiently, most farmers (46.4%) agreed that there was an increase in Lakadong turmeric yields, improved crop quality and reduced pest infestation.

Research limitations/implications
The study highlights informational gaps in the agricultural sector, empowering farmers to explore alternative sources. It suggests that understanding farmers’ unique needs can improve agricultural extension agencies’ programs and resources. Policymakers should invest in digital literacy programs and regional information resources. The study also suggests integrating information literacy training into agricultural operations, fostering collaboration among farmers, institutions and information providers. The geographical indication tag on Lakadong turmeric can also empowers local communities by fostering economic opportunities through the promotion and protection of this prized spice.

Originality/value
Existing research tends to focus more broadly on agricultural information dissemination or other crops, leaving a notable gap in the literature regarding the unique informational needs and access challenges faced by Lakadong turmeric farmers.
Chatbot integration for online shopping: a bibliometric review and future research agenda
Authors: Sharma Namita , Arora Meenal , Tandon Urvashi , Mittal Amit 
Published: August 2024

Purpose
This study aims to conduct a comprehensive analysis of the current body of existing literature on chatbots and online shopping. Additionally, this study identifies and emphasize the future research agenda and emerging trends within this domain.

Design/methodology/approach
A thorough investigation was conducted on a set of 147 publications sourced from the Scopus database spanning the years 2016 to 2023 by using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The analysis included bibliometric techniques through VOSviewer, including science mapping and performance analysis of the literature under investigation.

Findings
The findings of the study indicate a systematic impression of prevailing scientific research on integration of Chatbot in online shopping. A majority of publications were contributed by developing countries specifically Asian regions. There has been a notable rise in research collaborations over the course of time. Further, themes were identified through keyword co-occurrence for exploration of future trends in the domain.

Practical implications
This study identifies and analyzes the patterns in the existing literature on chatbot and online shopping, with the objective of enhancing e-retailers comprehension of this particular topic area. The research findings hold significance for both researchers and organizations in their efforts to enhance strategy design.

Originality/value
This study uses bibliometric analysis to examine the literature on chatbots and online shopping, aiming to develop a systematic comprehension of the research field. This study makes a valuable contribution to the current scholarly discourse and provides support for future scholars in their investigations.
Examining the methods of converting the threats caused by sanctions on Iran’s petrochemical industry to economic opportunities and investigating the growth rate of Iran’s petrochemical industry during sanction
Authors: Ghazanfari Shabankareh Mohammad , Shesh Bulookii Ali , Kord Mojtaba 
Published: August 2024

Purpose
This study aims to examine the methods of converting the threats caused by sanctions on Iran’s petrochemical industry to economic opportunities and investigating the growth rate of Iran’s petrochemical industry during sanctions.

Design/methodology/approach
The present research was descriptive-exploratory and the research method was based on a mixed research strategy. In the qualitative part, two methods of content analysis and semistructured interview were used and in the quantitative part, the survey method was used. The participants of the qualitative part were the content of all related published researches that have been published in the past 20 years and to conduct a semistructured interview with experts that were selected by nonprobability sampling method. In the quantitative part, the statistical population, there were including managers, supervisors and experts active in the Iran’s petrochemical industry (especially in Asaluyeh area of Iran), the sampling method was nonprobability of the type available.

Findings
The findings of the research showed that the potential risks resulting from the sanctions on Iran’s petrochemical industry from the review of the selected researches include 5 main risks (marketing, financial, supply and procurement, technological and production) and 26 related subrisks. Also, the ranking of the mentioned risks showed that among the main risks, the highest rank is related to the financial risk and among the subrisks, the increase in transaction costs and disruption in the implementation of contractual obligations have the highest rank.

Research limitations/implications
The investigation of the growth rate of Iran’s petrochemical industry during the sanctions in terms of performance (production, export and foreign exchange) showed that the amount of production has gone through a growing trend during the period under review (it has grown by more than 68%). Also, the amount of exports in the investigated time period has been growing (it has grown by more than 70%) and finally, the amount of foreign exchange in this time period has experienced growth (more than 58% growth).

Originality/value
Introducing and examining methods of turning threats into economic opportunities in Iran’s petrochemical industry under the shadow of sanctions, which can be used in other active industries under sanctions in the energy field.