bluuebunny/crossref_metadata_2025_split huggingface dataset:
TooT-PLM-P2S: Incorporating Secondary Structure Information into Protein Language Models
Authors: Ghazikhani Hamed , Butler Gregory 
Published: August 2024

AbstractIn bioinformatics, modeling the protein space to better predict function and structure has benefitted from Protein Language Models (PLMs). Their basis is the protein’s amino acid sequence and self-supervised learning. Ankh is a prime example of such a PLM. While there has been some recent work on integrating structure with a PLM to enhance predictive performance, to date there has been no work on integrating secondary structure rather than three-dimensional structure. Here we present TooT-PLM-P2S that begins with the Ankh model pre-trained on 45 million proteins using self-supervised learning. TooT-PLM-P2S builds upon the Ankh model by initially using its pre-trained encoder and decoder. It then undergoes an additional training phase with approximately 10,000 proteins and their corresponding secondary structures. This retraining process modifies the encoder and decoder, resulting in the creation of TooT-PLM-P2S. We then assess the impact of integrating secondary structure information into the Ankh model by comparing Ankh and TooT-PLM-P2S on eight downstream tasks including fluorescence and solubility prediction, sub-cellular localization, and membrane protein classification. For both Ankh and TooT-PLM-P2S the downstream tasks required task-specific training. Few of the results showed statistically significant differences. Ankh outperformed on three of the eight tasks, TooT-PLM-P2S did not outperform on any task for the primary metric. TooT-PLM-P2S did outperform for the precision metric for the task of discriminating membrane proteins from non-membrane proteins. This study requires future work with expanded datasets and refined integration methods.
An Automated High-throughput Affinity Capture-Mass Spectrometry Platform with Data- Independent Acquisition
Authors: Jing Hui , Richardson Paul L. , Potts Gregory K. , Senaweera Sameera , Marin Violeta L , McClure Ryan , Banlasan Adam , Tang Hua , Kath James E. , Ma Renze , Williams Jon D. 
Published: August 2024

AbstractAffinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process has limited its use in high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small molecule target enrichment in 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded coverage of ∼380 kinases, a >60% increase compared to using a data-dependent acquisition (DDA)-MS method and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.
Identification of the Polo-like kinase substrate required for homologous synapsis inC. elegans
Authors: Gold Ariel L. , Hurlock Matthew E. , Guevara Alicia M. , Isenberg Lilah Y. Z. , Kim Yumi 
Published: August 2024

ABSTRACTThe synaptonemal complex (SC) is a zipper-like protein structure that aligns homologous chromosome pairs and regulates recombination during meiosis. Despite its conserved appearance and function, how synapsis occurs between chromosome axes remains elusive. Here, we demonstrate that Polo-like kinases (PLKs) phosphorylate a single conserved residue in the disordered C-terminal tails of two paralogous SC subunits, SYP-5 and SYP-6, to establish an electrostatic interface between the SC central region and chromosome axes inC. elegans. While SYP-5/6 phosphorylation is dispensable for the ability of SC proteins to self-assemble, local phosphorylation by PLKs at the pairing center is crucial for SC elongation between homologous chromosome axes. Additionally, SYP-5/6 phosphorylation is essential for asymmetric SC disassembly and proper PLK-2 localization after crossover designation, which drives chromosome remodeling required for homolog separation during meiosis I. This work identifies a key regulatory mechanism by which localized PLK activity mediates the SC-axis interaction through phosphorylation of SYP-5/6, coupling synapsis initiation to homolog pairing.
Data-driven determination of plant growth stages for improved weather index insurance design
Authors: Zou Jing , Odening Martin , Okhrin Ostap 
Published: August 2024

PurposeThis paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.Design/methodology/approachUsing the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.FindingsOur results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.Originality/valueThis research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.
The art of deception: humanizing AI to outsmart detection
Authors: Ayub Taseef , Ahmad Malla Rayees , Khan Mashood Yousuf , Ganaie Shabir Ahmad 
Published: August 2024

Purpose
The study aims to investigate the influence of HIX.AI, an artificial intelligence (AI) tool that humanizes the generated content, on the detection capabilities of AI-generated text detectors.

Design/methodology/approach
The study investigates the reliability of six AI-generated content detection tools by passing ten essays, five each generated using Chat Generative Pre-Trained Transformer (ChatGPT) and Bard (Gemini) before and after passing through HIX.AI, which humanizes the AI-generated content.

Findings
The study found that the selected AI-generated text detectors identified the generated content with inconsistencies. Some of the essays were falsely identified as human-written by a few detectors, indicating that the detectors are unreliable. Post-HIX.AI application found that all the essays were passed as human-written except two, which identified as AI-generated and mixed content by two separate detectors.

Practical implications
The findings present the evolving field of AI-generated text detectors and the tools that can bypass the detectors highlighting the difficulties in identifying the generated content in the presence of the humanization tool. Passing the generated content as human-written has serious consequences, especially in academics. Hence, the study recommends more robust detectors to distinguish human-written and AI-generated content accurately.

Originality/value
The study contributes to the existing literature on AI text detectors and highlights the challenges that humanization tools pose in identifying AI-generated text by AI text detectors.
Serial mediation effect of service innovation and word of mouth on mutual fund investment decision through mobile customer relationship management
Authors: Deb Suman Kumar , Jain Ruchi , Marwah Sanjiv , Deb Varsha 
Published: August 2024

Purpose
The purpose of this paper is to examine the effect of mobile customer relationship management (mCRM), service innovation (SI) and word of mouth (WOM) on the investment decisions (IDs) of mutual fund investors.

Design/methodology/approach
This paper presents a new model for impacting the IDs of mutual fund investors. To verify the suggested model, Partial Least Squares with Structural Equation Modelling are used. For analysis, a survey questionnaire is designed, and data inputs were invited from more than 400 online mutual fund investors in Delhi NCR.

Findings
The outcomes reveal that the ID of mutual fund investors is significantly influenced by WOM. WOM, in turn, is significantly impacted by mCRM applications through SI playing a mediating role.

Research limitations/implications
The limitation with reference to survey respondents was that only online mutual fund investors using mCRM applications were considered. Moreover, this study was conducted in Delhi NRC, and a limited sample was considered.

Practical implications
The result from this research helps the financial organisation to consider SI in their mCRM application as one of the main concerned areas for increased WOM that directly influences the ID of mutual fund investors.

Originality/value
This study highlighted the impact of SI and WOM on the mutual fund investors’ decision, who use mCRM application. The outcome may contribute to the theoretical framework of IDs concerning mCRM applications. The results of this research offer practical implications for financial organisations in strategising their marketing and product development plans in the context of mutual funds. Also, the mutual fund ID through mCRM application is positively influenced by SI and WOM using both constructs as sequential mediating tools. This research offers new insights into mCRM application for mutual fund investors and financial organisations in India.
How to promote resilience in the supply chain in the context of COVID-19? An exploratory study using the Delphi method
Authors: Santos Devisson Mesquita dos , Lopes Fernanda Leandra Leal , Melo André Cristiano Silva , Nunes Denilson Ricardo de Lucena , Rampasso Izabela Simon , Martins Vitor William Batista 
Published: August 2024

PurposeThis paper is dedicated to elaborating, proposing and validating an action plan to enhance the mitigation of risks generated by the COVID-19 pandemic in the electric sector supply chain, aiming to promote a more resilient supply chain.Design/methodology/approachFor this, a systematic review of the literature was carried out to prepare an action plan that was validated by a group of experts, through the Delphi methodology.FindingsAs a result, an action plan was obtained, with 18 actions subdivided into 13 resilience elements and related to 20 main risks arising from the pandemic. The actions oriented to the development of relationships among supply chain members, promotion of a culture oriented to learning and problem solving, contingency plan, safety stock and risk management were pointed as those capable of generating resilience in the chain analyzed in the moment of crisis.Originality/valueThe results achieved can contribute to the expansion of debates in the area of resilient supply chain management, as well as contribute to supply chain managers in their elaboration and definition of actions that aim to make the supply chain more resilient. It is noteworthy that no similar study was found in the literature considering the specificities of supply chain management in the Brazilian Amazon region.
To protect and to serve? Comparing students’ perceptions of the legitimacy of the campus police in urban and suburban contexts
Authors: Jacobsen Shannon K. 
Published: August 2024

PurposeGiven that previous research examining students’ perceptions of the legitimacy of the campus police (CP) has largely taken place on a single campus and utilized quantitative methods, the purpose of this qualitative study was to understand how community context shapes students’ interactions with and perceptions of the CP in two distinct campus settings.Design/methodology/approachThis study draws on in-depth interviews with 70 undergraduates, including 36 attending a university in a relatively safe, affluent suburban area and 34 attending a university in a high crime, disadvantaged urban area.FindingsAlthough participants at both universities appreciated the presence and visibility of the CP, some Suburban U students felt that rather than being protected from outside threats, they were instead policed for engaging in behaviors that they deemed typical of the college experience. Alternatively, Urban U students expressed the opposite of their CP, providing evidence that brief and positive officer-initiated encounters can enhance perceptions of legitimacy, particularly in settings characterized by higher crime rates.Research limitations/implicationsThese findings have implications for how the CP can build trust and legitimacy among students to enhance campus safety and foster future compliance and mutual respect.Originality/valueThis study extends the findings of previous research in two important ways. First, it compares two starkly different campus contexts to understand how community setting and local crime rates influence students’ perceptions of the CP, and second, it utilizes both a comparative and qualitative research design.
Food safety, voluntary recall and firm reputation
Authors: Yu Jianyu , Cai Dongling , Gong Qiang , Mo Jiaying 
Published: August 2024

AbstractProduct recalls are direct remedies for producers in case of food safety problems. Unlike in the United States or other developed countries, in China, voluntary recalls are rarely documented in the food sector. The purpose of this paper is to address two questions: (1) why do firms adopt different recall strategies in different countries; (2) under what circumstances can voluntary recall help firms build up their food safety reputation? Based on the theory of collective reputation, we develop a dynamic model to incorporate firms' recall strategy and investigate the impact of such a strategy on industry collective reputation. The model takes into account production hazards: producers' lapses in food safety despite good‐faith efforts. Our results show that voluntary recall helps a firm to maintain a good historical record. Hence, firms are likely to achieve a high level of collective reputation under voluntary recall. However, a firm is willing to initiate voluntary recall only if the collective reputation is high enough. This explains the current situation in China: as consumers show little trust (belief in collective reputation is low), firms cannot recover the recall loss and thus have no incentive to initiate a voluntary recall.
Leveraging NLP for crisis communication management: A case study of news media analysis of the COVID‐19 pandemic in two Nordic countries
Authors: Mykkänen Markus , Patron Anri , Badham Mark , Valentini Chiara 
Published: August 2024

AbstractThe news media plays a vital role in influencing public perceptions about topics, issues and crises. They also act as important intermediaries between organizations and public, enabling organizations to shape how people think about crisis topics and actors. Monitoring news coverage and assessing the news media's agenda‐setting role in a crisis can help organizations respond more effectively to emerging situations. When crises are prolonged and affect many countries, media analysis can become a tedious task for crisis managers. This study demonstrates how natural language processing (NLP) methods can be utilized in news media analysis of crisis situations, such as an extended cross‐national pandemic. Specifically, it demonstrates the possibilities of using NLP to identify and compare the salience of diverse crisis topics and how the media treat these topics and crisis actors (first‐ and second‐level agenda‐setting) across countries, news outlets, and time. The COVID‐19 pandemic serves as an illustrative case study to showcase the application of NLP techniques to provide insights into public perceptions of a major health crisis shaped by the news media in two Nordic countries (Finland and Sweden). Findings show the suitability of NLP methods to detect nuanced differences in news media coverage and offer relevant knowledge of how public perceptions and responsibility attribution fluctuate across time and countries.
A stepped wedge cluster randomized trial to evaluate the effectiveness of a multisite family caregiver skills training program
Authors: Van Houtven Courtney Harold , Coffman Cynthia J. , Decosimo Kasey , Grubber Janet M. , Dadolf Joshua , Sullivan Caitlin , Tucker Matthew , Bruening Rebecca , Sperber Nina R. , Stechuchak Karen M. , Shepherd‐Banigan Megan , Boucher Nathan , Ma Jessica E. , Kaufman Brystana G. , Colón‐Emeric Cathleen S. , Jackson George L. , Damush Teresa M. , Christensen Leah , Wang Virginia , Allen Kelli D. , Hastings Susan N. 
Published: August 2024

AbstractObjectiveTo assess the effects of an evidence‐based family caregiver training program (implementation of Helping Invested Families Improve Veteran Experiences Study [iHI‐FIVES]) in the Veterans Affairs healthcare system on Veteran days not at home and family caregiver well‐being.Data Sources and Study SettingParticipants included Veterans referred to home‐ and community‐based services with an identified caregiver across 8 medical centers and confirmed family caregivers of eligible Veterans.Study DesignIn a stepped wedge cluster randomized trial, sites were randomized to a 6‐month time interval for starting iHI‐FIVES and received standardized implementation support. The primary outcome, number of Veteran “days not at home,” and secondary outcomes, changes over 3 months in measures of caregiver well‐being, were compared between pre‐ and post‐iHI‐FIVES intervals using generalized linear models including covariates.Data Collection/Extraction MethodsPatient data were extracted from the electronic health record. Caregiver data were collected from 2 telephone‐based surveys.Principal FindingsOverall, n = 898 eligible Veterans were identified across pre‐iHI‐FIVES (n = 327) and post‐iHI‐FIVES intervals (n = 571). Just under one fifth (17%) of Veterans in post‐iHI‐FIVES intervals had a caregiver enroll in iHI‐FIVES. Veteran and caregiver demographics in pre‐iHI‐FIVES intervals were similar to those in post‐iHI‐FIVES intervals. In adjusted models, the estimated rate of days not at home over 6‐months was 42% lower (rate ratio = 0.58 [95% confidence interval: 0.31–1.09; p = 0.09]) post‐iHI‐FIVES compared with pre‐iHI‐FIVES. The estimated mean days not at home over a 6‐month period was 13.0 days pre‐iHI‐FIVES and 7.5 post‐iHI‐FIVES. There were no differences between pre‐ and post‐iHI‐FIVES in change over 3 months in caregiver well‐being measures.ConclusionsReducing days not at home is consistent with effectiveness because more time at home increases quality of life. In this study, after adjusting for Veteran characteristics, we did not find evidence that implementation of a caregiver training program yielded a reduction in Veteran's days not at home.
Assessing and supporting children and young people with probable or diagnosed fetal alcohol spectrum disorder: The experience of clinicians working within child and adolescent mental health services
Authors: Tanfield Yasmin , Burrows Kathy , Middlemiss Megan 
Published: August 2024

AbstractBackgroundFetal alcohol spectrum disorders (FASD) are a group of conditions that occur due to prenatal alcohol exposure (PAE), which impacts physical, behavioral, and cognitive ability. The literature demonstrates that healthcare professionals lack knowledge and understanding of FASD, resulting in children and young people (CYP) often getting misdiagnosed with neurodevelopmental disorders or the diagnosis of FASD missed, increasing their risk of experiencing secondary mental health difficulties. Child and Adolescent Mental Health Services (CAMHS) are the commissioned service to diagnose neurodevelopmental conditions and support CYP with mental health difficulties, therefore, it is likely that CYP with probable or diagnosed FASD will present in CAMHS. There is currently no research exploring the awareness and understanding of FASD within these services.MethodsConstructivist grounded theory was utilized to explore the barriers and facilitators clinicians experience when assessing and supporting CYP with probable or diagnosed FASD within CAMHS. A sample of 12 CAMHS clinicians from an NHS Mental Health Trust situated in the Northeast of England were interviewed. Interviews were transcribed and analyzed and grounded theory techniques were utilized to generate an end model.ResultsThe end model was developed on a box analogy with four categories. ‘Unable to Open the Box’ captures barriers CAMHS clinicians experience when exploring FASD, ‘Things that Help Open the Box’ captures facilitators CAMHS clinicians experience when exploring FASD, ‘Asking Others About the Box’ captures systemic influences CAMHS clinicians may experience when exploring FASD, and ‘Making the Box Easier to Open in Future’ captures how we can support CAMHS clinicians moving forward to explore FASD.ConclusionsThis model provides new insights into the barriers and facilitators CAMHS clinicians experience when assessing and supporting CYP with probable or diagnosed FASD, highlighting key clinical implications. Recommendations for future research are outlined to expand the knowledge base for this area.
Prognostic mutations identified by whole‐exome sequencing and validation of the Molecular International Prognostic Scoring System in myelodysplastic syndromes after allogeneic haematopoietic stem cell transplantation
Authors: Wang Hong , Li Xueqian , Qi Jiaqian , Liu Hong , Chu Tiantian , Xu Xiaoyan , Qiu Huiying , Fu Chengcheng , Tang Xiaowen , Ruan Changgeng , Wu Depei , Han Yue 
Published: August 2024

SummaryIn this study, we used the whole‐exome sequencing (WES) approach to obtain genomic profiles from 92 marrow samples of myelodysplastic syndrome (MDS) patients before haematopoietic stem cell transplantation. We identified 129 mutations in 45 driver genes. Fifty‐five patients (59.8%) carried at least 1 driver mutation. The splicing factor U2AF1 was the most frequently mutated in the cohort (21 cases, 23%), followed by BCOR (9 cases, 10%), ASXL1 (8 cases, 9%), TET2 (6 cases, 7%), NPM1 (5 cases, 5%), RUNX1 (5 cases, 5%), and SETBP1 (5 cases, 5%). WES also identified 49 possible oncogenic variants in six genes (PIEZO1, LOXHD1, MYH13, DNAH5, DPH1, and USH2A) that were associated with overall survival (OS) or relapse‐free survival (RFS) in MDS after transplantation. Multivariate analysis showed mutations in DNAH5 and USH2A to be independent risk factors for OS. Mutations in DNAH5 and LOXHD1 were risk factors for worse RFS. The Molecular International Prognostic Scoring System retained its independent prognostic significance for RFS after multivariate analysis.