bluuebunny/crossref_metadata_2025 huggingface dataset:
Exam Review Versus Categorical Feedback: Which Improves Scores by Doctor of Physical Therapy Students?
Authors: Huebner Bethany , Jackson Barbara , Danzl Megan , Pitt Jason
Published: December 2023

Introduction.

Feedback after computer-based assessments is essential to the learning process. Options include knowledge of results (KR), knowledge of correct results (KCR), and elaborated feedback (EF). This study aimed to identify whether specific or broader forms of postassessment feedback optimize physical therapist students' computer-based assessment scores.

Review of Literature.

Health care educators have various types of feedback they can provide to students. Educators may consider the need for exam integrity and increasing student engagement when determining what type of feedback to deliver. Elaborated feedback seems to be most effective in higher-order learning objectives than KCR or KR to improve learning outcomes and can be delivered in clear and engaging ways for students while maintaining exam integrity.

Subjects.

Forty-nine first-year Doctor of Physical Therapy students.

Methods.

This study was a quasi-experimental design. Students were given a feedback form postassessment: secure exam review with note sheet (KCR) or Strengths and Opportunities Report (EF). Students repeated assessments on the same content (mix of repeat and related items). Exam scores and question performance were analyzed with linear and logistic regression. Students were surveyed about feedback preferences.

Results.

After adjusting for differences in the initial exam scores and graduate Grade Point Average, KCR improved retake exam scores (confidence interval [CI95] = 0.07–1.82 percentage points). Knowledge of correct results was more effective than EF for repeated questions (RR = 1.53, CI95 = 1.12–2.09, P = .0031). Knowledge of correct results and EF were similarly effective for related questions (RR = 1.01, CI95 = 0.76–1.33, P = .9997). Most students (89.75%) preferred KCR.

Discussion and Conclusion.

Compared with EF, KCR provided modest improvements on follow-up exams, specifically on repeated questions. Because students are unlikely to encounter repeated questions on licensure exams, EF may be an excellent choice to balance exam integrity and feedback quality.
Evaluation of the Performance of a Card Game to Introduce Students to Interprofessional Collaboration
Authors: Joseph Antony R. , Wright Valerie M. , Watkins Susan M. , Goddard Stacy E. , Mast Daniel D.
Published: December 2023

Background:

Gamification is an approach that can be used to introduce interprofessional collaboration in nursing and health science. Card games are an effective and convenient way to educate students about clinical professions.

Purpose:

We compared the perception of an experimental group of students who played an educational card game to a control group that played an uninstructive card game.

Methods:

College students (n = 148) from nursing and health science majors consented to play a 30-minute card game and complete a 13-item survey.

Results:

Perceptions of the card game were significantly better for students in the experimental group who played the interprofessional collaboration game than for those in the control group (t = 10.33, P < .001). Survey subscales were rated significantly higher for respondents who played the interprofessional card game.

Conclusions:

The use of an innovative card game teaching strategy significantly increased the perception of interprofessional collaboration among college students.
Immunohistochemical Expression of CK14 and Bcl-2 in Odontogenic Keratocyst and Its Variants
Authors: Krishnan Reshma Poothakulath , Pandiar Deepak , Sagar Sandra
Published: December 2023

The odontogenic keratocysts (OKCs) are aggressive cystic jaw lesions with a high epithelial turnover rate and increased propensity for recurrence. Sometimes, the characteristic histopathological features of OKCs are either completely lost or seen focally due to previous marsupialization or inflammation. This research aimed to determine whether specific patterns of CK14 and Bcl-2 staining could assist in diagnosing OKCs with altered epithelial features and provide clues in elucidating their aggressive nature. CK14 expression was restricted to basal and suprabasal layers near satellite cysts and in areas showing subepithelial split. The entire epithelial lining showed CK14 expression in areas of inflammation and after marsupialization. The typical basal/suprabasal staining of Bcl-2 was lost in areas of inflammation and intensity is decreased in OKCs after marsupialization. These new findings could offer a hint into the biological nature and pathogenesis of OKCs. Because of its therapeutic consequences and high recurrence rate, proper recognition and diagnosis are essential for treatment planning.
Biparametric Quantitative MRI for Prostate Cancer Detection
Authors: Uyanik Meltem , Vigneswaran Hari T. , Hale Graham R. , Gann Peter , Magin Richard , Abern Michael R.
Published: December 2023

Objectives:

This study sought to prospectively investigate a novel quantitative biparametric prostate magnetic resonance imaging (MRI) protocol to detect prostate cancer (PCa) in biopsy-naïve men. Secondarily, this study reports the accuracy of fractional order calculus (FROC) diffusion and quantitative T2 compared with the Prostate Imaging Reporting & Data System (PI-RADS).

Methods:

This prospective pilot study (NCT04175730) enrolled 50 prostate biopsy-naïve men who met eligibility criteria. All men received 3T MRI with T2 and diffusion-weighted imaging (DWI) (b-values: 50–4,000 s/mm2). Men with PI-RADS lesions ≥3 underwent targeted and systematic prostate biopsy, omitting systematic biopsy cores in peripheral zone lesions. DWI series images were fit to signal decay to calculate ADC (mm2/s) and the FROC model for coefficient DF (mm2/s). The primary end point was detection of Gleason grade group ≥2 (GG≥2) PCa. Receiver operating characteristic regression and area under the curve (AUC) were reported.

Results:

Forty-eight men underwent MRI and biopsy. Mean age was 61.5 years (56–68), 29% were White, 52% were African American, mean PSA was 6.0 ng/mL (4.9–8.0), and mean PSA density was 0.14 ng/mL2. In total, 61 PI-RADS ≥3 lesions were targeted for biopsy. GG≥2 PC was found in 7% (1/14) of PI-RADS 3 lesions, 28% (10/36) of PI-RADS 4 lesions, and 36% (4/11) of PI-RADS 5 lesions. The AUC for detection of GG≥2 PC was 0.63 (0.5–0.76) for PI-RADS, 0.82 (0.68–0.96) for ADC, and 0.87 (0.77–0.97) for the FROC model.

Conclusion:

This small prospective pilot study demonstrates the feasibility of a novel quantitative biparametic MRI protocol to detect prostate cancer in biopsy-naïve men.
LIRAGLUTIDE ALLEVIATES ACUTE LUNG INJURY AND MORTALITY IN PNEUMONIA-INDUCED SEPSIS THROUGH REGULATING SURFACTANT PROTEIN EXPRESSION AND SECRETION
Authors: Guo Junping , Chen Xinghua , Wang Cole , Ruan Feng , Xiong Yunhe , Wang Lijun , Abdel-Razek Osama , Meng Qinghe , Shahbazov Rauf , Cooney Robert N. , Wang Guirong
Published: December 2023

Glucagon-like peptide 1 (GLP-1) analogs are used to treat type 2 diabetes, and they can regulate insulin secretion, energy homeostasis, inflammation, and immune cell function. This study sought to determine whether the GLP-1 analog liraglutide exerts a beneficial action in an acute lung injury model of pneumonia-induced sepsis. Methods: Wild-type FVB/NJ mice (n = 114) were infected by intratracheal injection with Pseudomonas aeruginosa Xen5 (4 × 104 CFU/mouse) or an equal volume (50 μL) of saline (control) with or without a subcutaneous injection of liraglutide (2 mg/kg, 30 min after infection). Mice were killed 24 h after infection. Lung tissues and BALF were analyzed. In separate experiments, the dynamic growth of bacteria and animal mortality was monitored using in vivo imaging system within 48 h after infection. In addition, primary lung alveolar type II cells isolated from mice were used to study the mechanism of liraglutide action. Result: Liraglutide improved survival (P < 0.05), decreased bacterial loads in vivo, and reduced lung injury scores (P < 0.01) in septic mice. Liraglutide-treated mice showed decreased levels of inflammatory cells (P < 0.01) and proinflammatory cytokines (TNF-α and IL-6) (P < 0.01) in the lung compared with septic controls. Liraglutide significantly increased pulmonary surfactant proteins (SP-A and SP-B) expression/secretion (P < 0.01) and phospholipid secretion (P < 0.01) in vivo. Primary alveolar type II cells pretreated with liraglutide improved SP-A and SP-B expression after LPS exposure (P < 0.01). Conclusion: Liraglutide attenuates mortality and lung inflammation/injury in pneumonia-induced sepsis. The increased surfactant expression/secretion and anti-inflammatory effects of liraglutide represent potential mechanisms by GLP-1 agonists potentiate host defense and maintain alveolar respiratory function in acute lung injury.
Correlations betweenper capitagreen tea consumption and COVID-19 morbidity/mortality: comparing the strength before and after Omicron emergence
Authors: Storozhuk Maksim
Published: December 2023

Purpose

There is rapidly growingin vitropharmacological evidence suggesting preventive/therapeutic potential of green tea and its constituent EGCG in battle with COVID-19. The aim of this study was to analyze time course of previously reported correlations betweenper capitagreen tea consumption and COVID-19 morbidity/mortality with a focus on comparing periods of time before (year 2021) and after (years 2022-2024) emergence of Omicron variant.

Methods

Correlations betweenper capitagreen tea consumption and COVID-19 morbidity/mortality were calculated using multiple regression models in a subset (n=84) of countries/territories worldwide with HDI above 0.55. Strength of the correlations for several dates in 2021 - 2024 was compared.

Results

Higherper capitagreen tea consumption was associated with lower COVID-19 morbidity and mortality. Statistically significant correlations were observed in 2021, 2022, 2023 and 2024. As compared to 2021, the strength of both correlations was decreased during 2022-2024 period. On average normalized to 2021 strength of correlation betweenper capitagreen tea consumption and COVID-19 morbidity decreased to 0.60, while normalized strength of correlation betweenper capitagreen tea consumption and COVID-19 mortality decreased to 0.83.

Conclusion

Overall, the results of this study are in line within vitropharmacological evidence. Though indirectly, these results at epidemiological level support the idea that green tea consumption may have not only preventive, but also therapeutic value in relation to COVID-19. Nevertheless, because of limitations of this study, this idea still should be considered as a hypothesis requiring further assessment.
The Applications of Laser Interstitial Thermal Therapy and Machine Learning in Neurosurgery: A Systematic Review
Authors: Bouras Andrew , Patel Dhruv , Chetla Nitin
Published: December 2023

Background

The incorporation of Machine Learning (ML) into Laser Interstitial Thermal Therapy (LITT) represents a significant advancement in minimally invasive neurosurgery, particularly for treating brain tumors, vascular malformations, and epileptogenic foci. This systematic review focuses on evaluating the integration and impact of ML in enhancing the efficacy, precision, and outcomes of LITT in neurosurgical procedures.

Methods

An exhaustive search was conducted in major scientific databases for studies from 2015 to 2023 that specifically focused on the application of ML in LITT. The review assessed the development and implementation of ML algorithms in surgical planning, outcome prediction, and postoperative evaluation in LITT. Rigorous inclusion criteria were applied to select studies, and a combination of meta-analysis and qualitative synthesis was used to analyze the data.

Results

The review synthesizes findings from a range of studies, including retrospective analyses and initial clinical trials. It highlights the role of ML in enhancing the selection criteria for LITT, optimizing surgical approaches, and improving patient-specific outcome predictions. While LITT showed favorable results in treating non-resectable lesions, the integration of ML was found to potentially refine these outcomes further. However, challenges such as the need for larger sample sizes, standardization of ML algorithms, and validation of these methods in clinical settings were noted.

Conclusions

The integration of ML into LITT procedures marks a promising frontier in neurosurgery, offering potential improvements in surgical accuracy and patient outcomes. The evidence suggests a need for continued development and rigorous testing of ML applications in LITT. Future research should focus on the refinement and validation of ML algorithms for wider clinical adoption, ensuring that technological advancements align with patient safety and treatment efficacy.
A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Authors: Lee Arthur S. , Ayers Lauren J. , Kosicki Michael , Chan Wai-Man , Fozo Lydia N. , Pratt Brandon M. , Collins Thomas E. , Zhao Boxun , Rose Matthew F. , Sanchis-Juan Alba , Fu Jack M. , Wong Isaac , Zhao Xuefang , Tenney Alan P. , Lee Cassia , Laricchia Kristen M. , Barry Brenda J. , Bradford Victoria R. , Lek Monkol , MacArthur Daniel G. , Lee Eunjung Alice , Talkowski Michael E. , Brand Harrison , Pennacchio Len A. , Engle Elizabeth C.
Published: December 2023

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN)cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ∼86,000 cMNs and related cell types, identifying ∼250,000 accessible regulatory elements with cognate gene predictions for ∼145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in anin vivotransgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genesMAFB, PHOX2A, CHN1,andEBF3– as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.
CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation
Authors: Bilal Shakir , Zaatour Wajdi , Otano Yilian Alonso , Saha Arindam , Newcomb Kenneth , Soo Kim , Kim Jun , Ginjala Raveena , Groen Derek , Michael Edwin
Published: December 2023

The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen transmission in real-world communities. Developing such computational systems must also overcome the cold-start problem related to the inevitable scarce early data and extant knowledge regarding a novel pathogen’s transmissibility and virulence, while addressing changing population behavior and policy options as a pandemic evolves. Here, we describe how we have coupled advances in the construction of digital or virtual models of real-world cities with an agile, modular, agent-based model of viral transmission and data from navigation and social media interactions, to overcome these challenges in order to provide a new simulation tool, CitySEIRCast, that can model viral spread at the sub-national level. Our data pipelines and workflows are designed purposefully to be flexible and scalable so that we can implement the system on hybrid cloud/cluster systems and be agile enough to address different population settings and indeed, diseases. Our simulation results demonstrate that CitySEIRCast can provide the timely high resolution spatio-temporal epidemic predictions required for supporting situational awareness of the state of a pandemic as well as for facilitating assessments of vulnerable sub-populations and locations and evaluations of the impacts of implemented interventions, inclusive of the effects of population behavioral response to fluctuations in case incidence. This work arose in response to requests from county agencies to support their work on COVID-19 monitoring, risk assessment, and planning, and using the described workflows, we were able to provide uninterrupted bi-weekly simulations to guide their efforts for over a year from late 2021 to 2023. We discuss future work that can significantly improve the scalability and real-time application of this digital city-based epidemic modelling system, such that validated predictions and forecasts of the paths that may followed by a contagion both over time and space can be used to anticipate the spread dynamics, risky groups and regions, and options for responding effectively to a complex epidemic.
Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents
Authors: Kowalewski Joel , Boyle Sean M. , Arvidson Ryan , Ejercito Jadrian , Ray Anandasankar
Published: December 2023

The rational discovery of behaviorally active odorants is impeded by a lack of understanding on how the olfactory system generates percept or valence for a volatile chemical. In previous studies we showed that chemical informatics could be used to model prediction of ligands for a large repertoire of odorant receptors inDrosophila(Boyle et al., 2013). However, it remained difficult to predict behavioral valence of volatiles since the activities of a large ensembles of odor receptors encode odor information, and little is known of the complex information processing circuitry. This is a systems-level challenge well-suited for Machine-learning approaches which we have used to model olfaction in two organisms with completely unrelated olfactory receptor proteins: humans (∼400 GPCRs) and insects (∼100 ion-channels). We use chemical structure-based Machine Learning models for prediction of valence in insects and for 146 human odor characters. Using these predictive models, we evaluate a vast chemical space of >10 million compoundsin silico.Validations of human and insect behaviors yield very high success rates. The discovery of desirable fragrances for humans that are highly repulsive to insects offers a powerful integrated approach to discover new insect repellents.
The significance of social interactions in synchronized swarming flight in a termite
Authors: Mizumoto Nobuaki , Nozaki Tomonari
Published: December 2023

In social insect colonies, individuals of working caste coordinate their actions to manage various collective tasks. Such collective behaviors are observed not only in working castes but also in reproductives. During certain seasons, newly emerged winged reproductives (alates) fly from the nest to disperse and find mating partners in a synchronized manner. Although this “swarming” behavior is one of the collective behaviors that involve the greatest number of individuals in social insects, underlying social interactions remain unexplored. Here, we show that synchronized flight among colony members results from collective decision-making. Our simple simulation model suggests that social interactions in a large group of alates enable synchronized flight within a nest under fluctuating environmental conditions. This model is supported by empirical experiments using a termiteReticulitermes kanmonensis. Under the semi-field environment with fluctuating temperatures, alates within the same colony synchronized their dispersal flight under higher air temperatures, while dispersal flight was suppressed under lower temperatures. Furthermore, termites could synchronize their dispersal flights even under laboratory conditions with constant temperature, indicating that environmental cues are not always necessary for synchronization. In either case, higher synchronization happened with a larger number of alates. All these results demonstrate that both environmental and social factors interplay to enable the synchronized swarming flight of social insects.
Refinement of a technique for collecting and evaluating the osmolality of haemolymph fromDrosophilalarvae
Authors: Kurio Misato , Tsukasa Yuma , Uemura Tadashi , Usui Tadao
Published: December 2023

ex vivophysiological experiments using small insect models such asDrosophilalarvae have become increasingly useful to address fundamental biological questions. To perform such experiments, various artificial saline solutions have been developed, but their osmolality varies significantly from one to the next. Such a variation of osmolality stems, in part, from the difficulty of determining the true value of haemolymph osmolality inDrosophilalarvae. Thus, there is a pressing need to refine protocols for collecting and measuring the osmolality of the larval haemolymph. Two major obstacles are thought to impede the accurate analysis of haemolymph collected from small insects: melanin formation and gut-derived contamination. Here, we greatly refined existing haemolymph collecting methods, evaluated the purity of the collected haemolymph under melanin-free conditions, and concluded that the true value of haemolymph osmolality is close to 306.0 mOsm kg−1inDrosophilalarvae.
Convergent Transcriptomic Evidence Reveals the Dysfunctional Quantitative Mechanism of Synaptic Plasticity Control in ASD
Authors: Kong Chao , Bing Zhi-Tong , Yang Lei , Huang Zi-Gang , Wang Wen-Xu
Published: December 2023

A prominent endophenotype in Autism Spectrum Disorder (ASD) is synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigated the postsynaptic signal transduction network in glutamatergic neurons and integrated transcriptomics to unveil the malfunction of translation control.

We devised an innovative and highly dependable pipeline to transform our acquired signal transduction network into a mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employed Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD.

The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncovered distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.

Importantly, our work has introduced a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.
Assess Alzheimer’s Disease via Plasma Extracellular Vesicle-derived mRNA
Authors: Phu Pham Le Hoang , Chang Ching-Fang , Tuchez Katherine , Chen Yuchao
Published: December 2023

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern, particularly due to the increasing aging population. Recently, it has been revealed that extracellular vesicles (EVs) originating from neurons play a critical role in AD pathogenesis and progression. These neuronal EVs can cross the blood-brain barrier and enter peripheral circulation, offering a less invasive means for assessing blood-based AD biomarkers. In this study, we analyzed plasma EV-derived messenger RNA (mRNA) from 82 subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls, using next-generation sequencing (NGS) to profile their gene expression for functional enrichment and pathway analysis. Based on the differentially expressed genes identified in both MCI and AD groups, we established a diagnostic model by implementing a machine learning classifier. The refined model demonstrated an average diagnostic accuracy over 98% and showed a strong correlation with different AD stages, suggesting the potential of plasma EV-derived mRNA as a promising non-invasive biomarker for early detection and ongoing monitoring of AD.
An accurate and rapidly calibrating speech neuroprosthesis
Authors: Card Nicholas S. , Wairagkar Maitreyee , Iacobacci Carrina , Hou Xianda , Singer-Clark Tyler , Willett Francis R. , Kunz Erin M. , Fan Chaofei , Vahdati Nia Maryam , Deo Darrel R. , Srinivasan Aparna , Choi Eun Young , Glasser Matthew F. , Hochberg Leigh R. , Henderson Jaimie M. , Shahlaie Kiarash , Brandman David M. , Stavisky Sergey D.
Published: December 2023

Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40’s with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.
Evolutionary trends of alternative splicing
Authors: de la Fuente Rebeca , Díaz-Villanueva Wladimiro , Arnau Vicente , Moya Andrés
Published: December 2023

Alternative splicing is the post-transcriptional mechanism by which many different isoforms are generated from a single gene, contributing to increasing spatio-temporal transcriptome complexity. We propose a novel genome-level measure of alternative splicing, which associates it with a single value for each species. Thus, a comparative analysis of species spanning the whole tree of life has revealed certain evolutionary trends in alternative splicing, prevalence in specific lineages, and relation to genome compositional structures.
GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach
Authors: Zhu Cheng , Zhang Chengyun , Shang Tianfeng , Zhang Chenhao , Zhai Silong , Su Zhihao , Duan Hongliang
Published: December 2023

The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.
Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses
Authors: Neethirajan Suresh
Published: December 2023

This study leverages Convolutional Neural Networks (CNN) and Mel Frequency Cepstral Coefficients (MFCC) to analyze the vocalization patterns of laying hens, focusing on their responses to both visual (umbrella opening) and auditory (dog barking) stressors at different ages. The aim is to understand how these diverse stressors, along with the hens’ age and the timing of stress application, affect their vocal behavior. Utilizing a comprehensive dataset of chicken vocal recordings, both from stress-exposed and control groups, the research enables a detailed comparative analysis of vocal responses to varied environmental stimuli. A significant outcome of this study is the distinct vocal patterns exhibited by younger chickens compared to older ones, suggesting developmental variations in stress response. This finding contributes to a deeper understanding of poultry welfare, demon-strating the potential of non-invasive vocalization analysis for early stress detection and aligning with ethical live-stock management practices. The CNN model’s ability to distinguish between pre- and post-stress vocalizations highlights the substantial impact of stressor application on chicken vocal behavior. This study not only sheds light on the nuanced interactions between stress stimuli and animal behavior but also marks a significant advancement in smart farming. It paves the way for real-time welfare assessments and more informed decision-making in poultry management. Looking forward, the study suggests avenues for longitudinal research on chronic stress and the application of these methodologies across different species and farming contexts. Ultimately, this research represents a pivotal step in integrating technology with animal welfare, offering a promising approach to transforming welfare assessments in animal husbandry.
Parallel genetic screens identify nuclear envelope homeostasis as a key determinant of telomere entanglement resolution in fission yeast
Authors: Nageshan Rishi Kumar , Krogan Nevan , Cooper Julia Promisel
Published: December 2023

In fission yeast lacking the telomere binding protein, Taz1, replication stalls at telomeres, triggering deleterious downstream events. Strand invasion from onetaz1Δtelomeric stalled fork to another on a separate (non-sister) chromosome leads to telomere entanglements, which are resolved in mitosis at 32°C; however, entanglement resolution fails at ≤20°C, leading to cold-specific cell lethality. Previously, we found that loss of the mitotic function of Rif1, a conserved DNA replication and repair factor, suppresses cold sensitivity by promoting resolution of entanglements without affecting entanglement formation. To understand the underlying pathways of mitotic entanglement resolution, we performed a series of genomewide synthetic genetic array screens to generate a comprehensive list of genetic interactors oftaz1Δ andrif1Δ. We modified a previously described screening method to ensure that the queried cells were kept in log phase growth. In addition to recapitulating previously identified genetic interactions, we find that loss of genes encoding components of nuclear pore complexes (NPCs) promotes telomere disentanglement and suppressestaz1Δcold sensitivity; we attribute this to more rapid anaphase midregion nuclear envelope (NE) breakdown in the absence of these NPC components. Moreover, loss of genes involved in lipid metabolism reverses the ability ofrif1+ deletion to suppresstaz1Δcold sensitivity, again pinpointing NE modulation. Arif1+ separation-of-function mutant that specifically loses Rif1’s mitotic functions yields similar genetic interactions. Genes promoting membrane fluidity were enriched in a paralleltaz1+synthetic lethal screen at permissive temperature, cementing the idea that the cold specificity oftaz1Δlethality stems from altered NE homeostasis.
MnM: a machine learning approach to detect replication states and genomic subpopulations for single-cell DNA replication timing disentanglement
Authors: Josephides Joseph M. , Chen Chun-Long
Published: December 2023

We introduce MnM, an efficient tool for characterising single-cell DNA replication states and revealing genomic subpopulations in heterogeneous samples, notably cancers. MnM uses single-cell copy-number data to accurately perform missing-value imputation, classify cell replication states and detect genomic heterogeneity, which allows to separate somatic copy-number alterations from copy-number variations due to DNA replication. By applying our machine learning methods, our research unveils critical insights into chromosomal aberrations and showcases ubiquitous aneuploidy in tumorigenesis. MnM democratises single-cell subpopulation detection which, in hand, enables the extraction of single-cell DNA replication timing (scRT) profiles from genomically-heterogenous subpopulations detected by DNA content and issued from single samples. By analysing over 119,000 human single cells from cultured cell lines, patient tumours as well as patient-derived xenograft samples, the copy-number and replication timing profiles issued in this study lead to the first multi-sample subpopulation-disentangled scRT atlas and act as data contribution for further cancer research. Our results highlight the necessity of studyingin vivosamples to comprehensively grasp the complexities of DNA replication, given that cell lines, while convenient, lack dynamic environmental factors. This tool offers to advance our understanding of cancer initiation and progression, facilitating further research in the interface of genomic instability and replication stress.

GRAPHICAL ABSTRACT
The role of digital public services in promoting international tourism: empirical evidence from European regions
Authors: Ha Le Thanh
Published: December 2023

Purpose

The present study investigates a nexus between digital public services (DPS) and international tourism empirically.

Design/methodology/approach

This article analyzes the nexus of DPS and international tourism by using the international sample of 23 European countries in the span of nearly 10 years from 2011 to 2019. Various econometric techniques, including the panel-corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model, are employed to confirm the author’s findings. Furthermore, the autoregressive distributed lag (ARDL) method is applied to measure the short- and long-run effects of DPS on international tourism developments.

Findings

Tourism is positively influenced by digitalization, implying that the enhancement of digital public service usage results in the development of the tourism industry. However, when looking at the effect of DPS in the short term, a negative impact can be found on tourism, as the density reported in the previous analysis stated a negative response to the tourism density. This effect spans the course of several facets, such as international tourism arrivals, international tourism receipt, international tourism, receipts (% of total exports) and global tourism expenditure (% of total imports). Although the result is unfavorable in the short term, digitalization promises great prospects for tourism in the long term. Notably, an improvement in economic growth, financial development as well a reduction in the pervasiveness of corruption and an improvement of environmental quality are transmission channels through which DPS have favorable influences on tourism activities.

Practical implications

The author’s findings are vital for managers and policymakers to establish a comprehensive grasp of digitalization's role in deciding tourist adoption. This is because digitalization has been proven to play a role in determining tourism adoption.

Originality/value

The present study is the first to examine the relationship between DPS and international tourism empirically. The author is also the first to distinguish the effects of digitalization in the short and long run.
Different forms of supplier involvement, knowledge orchestration capability, technological uncertainty and new product development performance: a test of three-way interaction
Authors: Zhang Cailin , Li Suicheng
Published: December 2023

Purpose

This paper examines the role of purchasing in facilitating early supplier involvement in new product development (NPD) in contexts of technological uncertainty (TU). Taking a purchasing perspective, it develops a moderate model to explain the effects of supplier involvement on NPD performance and whether and how knowledge orchestration capability (KOC) and TU affect these relationships. Additionally, KOC drivers are defined.

Design/methodology/approach

A total of 317 usable questionnaires from Chinese high-technology firms were collected. Moderated multiple regression (MMR) was used to test all hypotheses. Resource orchestration theory (ROT) was the adopted theoretical lens.

Findings

Two forms of supplier involvement (as knowledge source and co-creator) were found to distinctly affect NPD performance and have potential substitutive relationships. Purchasing KOC positively moderates the relationships between forms of supplier involvement on NPD performance. TU strengthens the moderating role of purchasing KOC. Furthermore, purchasing status and supply complexity are important antecedents for purchasing KOC.

Practical implications

These findings serve as a blueprint for involving purchasing in technologically uncertain NPD projects and improve supplier NPD integration. Additionally, management should recognize the purchasing function's role and empower it to identify ideas, knowledge and solutions within supply networks.

Originality/value

This research contributes to the ROT by examining the role of purchasing KOC on supplier involvement in NPD performance, especially under TU. Moreover, it demonstrates significant and positive relations between purchasing department status and external supply complexity on its KOC.
A big data analytics method for the influence of WeChat public platform articles on blood donation behavior
Authors: Guo Xinyu , Chen Xu , Liang Xiaoke
Published: December 2023

Purpose

The purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.

Design/methodology/approach

This paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.

Findings

The results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.

Originality/value

First, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.
An empirical analysis of the dynamic impact of DeFi on GCC foreign exchange forward markets: portfolio implication
Authors: Tien Ho Thuy , Dang Nguyen Mau Ba , Hung Ngo Thai
Published: December 2023

Purpose

This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

Design/methodology/approach

This study applies the GARCH-DECO model and cross-quantilogram framework.

Findings

The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

Practical implications

For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

Originality/value

To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.
Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries
Authors: Singh Vikram , Sharma Nirbhay , Sharma Somesh Kumar
Published: December 2023

Purpose

Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

Design/methodology/approach

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

Findings

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

Practical implications

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

Originality/value

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.
The heterogeneous impacts of climate finance on environmental sustainability and social welfare in developing countries
Authors: Gong Xueting , Borojo Dinkneh Gebre , Yushi Jiang
Published: December 2023

Purpose

Due to their limited capacity for adaptation and dependence on natural resources for economic growth, developing countries (DCs) tend to be more prone to climate change. It is argued that climate finance (CF) is a significant financial innovation to mitigate the negative effects of climate variation. However, the heterogeneous impacts of CF on environmental sustainability (ES) and social welfare (SW) have been masked. Thus, this study aims to investigate the heterogeneous effects of CF on ES and SW in 80 CF receipt DCs from 2002 to 2018. This study also aims to investigate the effects of CF on ES and SW based on population size, income heterogeneity and the type of CF.

Design/methodology/approach

The method of moments quantile regression (MMQR) with fixed effects is utilized. Alternatively, the fully modified least square (FMOLS) and dynamic least square (DOLS) estimators are used for the robustness test.

Findings

The findings revealed that DCs with the lowest and middle quantiles of EF, carbon dioxide (CO2) emissions and human development exhibit large beneficial impacts of CF on ES and SW. In contrast, the positive effects of CF on ES breakdown for countries with the largest distributions of EF and CO2 emissions. Besides, the impacts of CF on ES and SW depend on income heterogeneity, population size and the type of CF.

Practical implications

This study calls for a framework to integrate CF into all economic development decisions to strengthen climate-resilient SW and ES in DCs.

Originality/value

To the best of the authors’ knowledge, this is the first study to investigate the effects of CF on ES and SW in a wide range of DCs. Thus, it complements existing related literature focusing on the effects of CF on ES and SW.