bluuebunny/crossref_metadata_2025_split huggingface dataset:
B - 91 Characterizing Memory Profiles of Movement Disorders: the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Recognition Subtests
Authors: Hua Michelle , Cedeño K Brianalysse Nicolena , Kline Julia , Seenaraine Elizabeth , Cation Bailey , Schoenberg Michael R 
Published: September 2024

Abstract

Objective
This study explores the utility of novel recognition memory composites integrating recently developed recognition subtests from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Duff et al., 2021) in characterizing memory profiles across Mild Cognitive Impairment (MCI) and Normal Cognitive Study (NCS) groups in patients with movement disorders.

Methods
Twenty patients with movement disorders (M = 69.4 years, SD = 6.3) were administered the RBANS including additional Story and Figure Recognition subtests as part of a comprehensive neuropsychological evaluation at an academic medical center. Samples were stratified by MCI (n = 15) and NCS (n = 5) groups based on clinician diagnoses. Z-score based composites were created for individual recognition measures (total, hits, false positives, and discriminability indices, d’) and “Total Delayed Recall” memory composites across list, story, and figure subtests.

Results
Mann–Whitney U test analyses revealed significant differences between MCI and NCS groups on Story recognition d’ performance, U = 9.50, p = &lt;0.05. Within the MCI group, a paired samples t-test revealed significantly worse performance on recognition d’ compared to “Total Delayed Recall” (M = 1.63, SD = 2.22); t(14) = 2.84, p = &lt;0.05.

Conclusions
Greater impairment in recognition discriminability compared to delayed recall suggests the involvement of memory processes that extend beyond retrieval deficits typically observed in movement disorders characterized by subcortical/frontosubcortical profiles. Further investigations are warranted in establishing implications of recognition memory composites in differentiating among MCI subtypes and neurodegenerative disease progression within movement disorder populations, as findings may have been influenced by unequal variances between groups.
B - 97 On the Rebound: Resilience and Subjective Cognitive Concerns in Cancer
Authors: Zarrella Giuliana V , Parsons Michael W , Hall Daniel L , Park Elyse R , Keeley Jared W , Rybarczky Bruce , Braun Sarah 
Published: September 2024

Abstract

Objective
There is a connection between resilience and subjective cognitive symptoms in medical populations. However, there is limited understanding of this relationship in cancer survivors, and no studies examine whether subjective cognition may change from pre- to post-resilience training in these patients. This study examined the relationship between subjective cognitive function and resilience in non-CNS cancer survivors who participated in a resiliency training intervention and investigated potential influences of change in subjective cognition.

Method
Adult cancer survivors at Massachusetts General Hospital (N = 275, Mage = 55.2, 87.3% female) participated in the Stress Management and Resilience Training-Relaxation Response Resilience Program (SMART-3RP) and completed self-report measures assessing subjective cognitive function, resilience, depression, and worry at pre-post the group intervention. Cross-sectional relationships were evaluated using hierarchical regression modeling. Repeated measures ANCOVA assessed differences in subjective cognition from pre-post, controlling for age and time since diagnosis. Mediation modeling assessed whether mental health symptoms change alongside subjective cognition and account for the relationship with resilience.

Results
Results revealed a significant relationship between subjective cognitive function and resilience at baseline (β = 0.35, p &lt; 0.001) and a significant change post-SMART-3RP (F(1, 77) = 6.09, p = 0.016). Patients experienced a decrease in cognitive concerns while making gains in resilience (β = 0.39, p &lt; 0.001). Changes in depression and anxiety did not mediate the relationship between resilience and subjective cognitive function (p’s &gt; 0.05).

Conclusion
It may be possible to refer survivors to programs, such as the SMART-3RP, to manage their subjective concerns, in the absence of objective cognitive deficits.
B - 106 Exploring the Impact of Screentime Use on Children’s Development at Aged 2 to 12: a Systematic Review
Authors: Medina Kimberly M , Martínez Karla , Muñiz Ruth Y , Ocasio Idanara T 
Published: September 2024

Abstract

Objective
Children’s use of digital media and the internet has become part of their daily lives, thus increasing their average screentime (ST). Research warns about the impact of excessive ST on children’s development. This study aimed to identify the impact of ST on the development of children between the ages of 2 and 12.

Data Selection
A systematic review was conducted to gather and identify the impact of excessive ST (more than two hours a day) in children aged 2 to 12. The search was limited to the databases APA PsyInfo, PubMed, COBIMET, and Google Scholar. The studies considered were published in English and Spanish between 2019 and 2023. The search terms used included “screentime,” “digital media”, “children,” “smartphones,” “tablets,” and “development.” After review, a sample of nine studies was selected for analysis, considering their publication, sample, and main findings. The quality assessment was carried out using the JBI Critical Appraisal Checklist for Quasi-Experimental/Analytical Cross-Sectional Studies (Tufanaru, et al., 2020).

Data Synthesis
This systematic review identified that excessive ST negatively affects children’s development.

Conclusions
This study’s findings underscore the importance of limiting ST to less than two hours a day due to the potential for cognitive and motor skills impairment and socio-emotional dysregulation. Professionals should strive to educate parents on better practices for the appropriate use of digital screens in children from ages 2 to 12. Public policy should raise awareness, create programs, and establish policies that regulate advertising aimed at children on smart devices, as this could increase ST.
B - 119 Comparing the Keystrokes (Ks) Test, an Online Assessment of Attention, Processing Speed, and Executive Functioning, with Established Neuropsychological Measures
Authors: Lopez Michael , Fulton John , Stuart Elizabeth , Kim Sahra , Hussey-Zommers Megan , Thomas Aaron , Kapoor Arunima , Conrad Alexis , Kristinsson Hayley , Chen Patrick 
Published: September 2024

Abstract

Objective
Despite technological advances, few accessible computerized neuropsychological measures exist. The KeyStrokes (KS) test was developed to provide an online assessment of simple attention, processing speed, and executive functioning.

Methods
25 participants from a mixed clinical sample of patients (M age = 52.6, SD = 15.7; 72% females) completed the KS test, which includes six conditions: two response time trials, three inhibition trials, and one inhibition/switching trial. Participants also completed the following: WAIS-IV digit span, symbol search, coding, matrix reasoning, reliable digit span, the Reynolds Interference Task (RIT), the Trail Making Test part A (TMT-A) and part B (TMT-B), the Grooved Pegboard Test (GPT), and the BDI-II and BAI.

Results
The internal consistency of the KS was excellent, α = 0.958. Correlations computed for the six subtests were all positive, strong (all above r &gt; 0.68), and significant (p &lt; 0.001). Higher scores on all KS subtests were associated with higher scores of each of the WAIS-IV subtests and on the RIT (p &lt; 0.05). Negative correlations were seen for TMT-A and TMT-B, as well as the GPT and all KS subtests (p &lt; 0.05). Multiple regression analyses demonstrated that age significantly predicted performance (p &lt; 0.001), while education did not.

Conclusion
These findings show strong convergent and discriminant validity with established measures. Based on our results, the KS appears to be a viable option for a new assessment particularly with the strong correlation data between the KS and multiple well-established neuropsychological tests.
B - 121 Feasibility of a Remote Neuropsychological Protocol in a Cohort Based Epidemiological Study: the Framingham Heart Study
Authors: Gurnani Ashita , Muller Emma , Walsh Lindsey , Devine Sherral , Sunderaraman Preeti , Gifford Katherine , Tao Qiushan , Liu Yulin , Ang Ting Fang Alvin , Mez Jesse , Au Rhoda 
Published: September 2024

Abstract

Objective
Research demonstrates adequate consistency between in-person and tele-np assessments with the latter typically omitting visually based measures and limiting capture of process-oriented scores. This study presents a novel tele-np protocol designed at the Framingham Heart Study (FHS) allowing for capture of visual and Boston Process Approach (BPA) data.

Methods
Beginning in 2021, FHS participants completed a tele-np protocol via HIPAA-compliant Zoom that includes tests similar to those administered in-person assessing all cognitive domains including visual memory and visuospatial functioning. Records forms, digital pen for BPA capture, and a label for return of materials, were mailed to participants. Regression models were used to compare in-person and tele-np results.

Results
The tele-np protocol consisting of 15 tests was administered to 169 participants across 30 states and three countries. Participants were 71.8 years on average (SD = 9.98), well-educated (84.6% ≥ college-educated), Caucasian (86.4%), and 53% male. Mean completion time of the tele-np protocol is 77.7 minutes (SD = 13.2). 76% of participants were deemed to be cognitively intact and 24% cognitively impaired based on their tele-np and previous in-person data. With the exception of a test of processing speed (β = −5.24 [−7.58,-2.89], p &lt; 0.001), raw scores across most tests did not significantly differ based on mode of administration (tele-np vs. in-person) after adjusting for age, sex, education, and years between in-person and tele-np evaluations.

Conclusion
Preliminary findings indicate that the current tele-np protocol yields reliable results. Tele-np offers a cost-effective approach to improve accessibility and retention of participants, especially those out of state who may have otherwise been excluded from data collection.
B - 133 Validation of the PCL-5 Extreme Symptom Scale Embedded Symptom Validity Index in aCross-Cultural Forensic Sample
Authors: Snodgrass Makenna , Schroeder Ryan , Spector Jack , Bieu Rachel 
Published: September 2024

Abstract

Objective
Embedded symptom validity indices have recently been developed for the PTSD Checklist for DSM-5 (PCL-5). One of these, the PCL-5 Extreme Symptom (PES) scale is derived by summing extreme responses on the PCL-5 (i.e., endorsed scores of 3 or 4). Preliminary findings support its effectiveness within a clinical sample. In the current study, we aimed to cross-validate the scale with a cross-cultural forensic sample.

Method
Macedonian and Kosovar contractors (N = 79) who were previously employed at military bases in Afghanistan and Iraq were evaluated between 2018 and 2023. These individuals underwent defense psychological examinations related to a pursuit of federal workers compensation claims for PTSD. Evaluations were performed with translated instruments and assistance from certified interpreters. There were two sets of analyses performed in the current study. Examinees were first determined to be valid or invalid responders based on their Trauma Symptom Inventory-II Atypical Response Scale (ATR) scores. For the second set of analyses, validity status was determined using a more conservative criterion of failing 2+ validity tests.

Results
Using receiver operating characteristic analyses, the area under the curve was found to be acceptable (0.73) for both sets of analyses. When invalidity was categorized with the ATR, a PES cutoff of 18 resulted in 100% specificity and 15% sensitivity. When invalidity was defined by failure of 2+ validity tests, the PES cutoff was able to be adjusted to 17 resulting in 90% specificity and 26% sensitivity.

Conclusion
Results of the current study cross-validate the PES with a cross-cultural forensic sample.
scHyper: reconstructing cell–cell communication through hypergraph neural networks
Authors: Li Wenying , Wang Haiyun , Zhao Jianping , Xia Junfeng , Sun Xiaoqiang 
Published: September 2024

Abstract
Cell–cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand–receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell–cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects
Authors: Wang Xiaoyu , Li Fuyi , Zhang Yiwen , Imoto Seiya , Shen Hsin-Hui , Li Shanshan , Guo Yuming , Yang Jian , Song Jiangning 
Published: September 2024

Abstract
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.
STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature
Authors: Mehryary Farrokh , Nastou Katerina , Ohta Tomoko , Jensen Lars Juhl , Pyysalo Sampo 
Published: September 2024

Abstract

Motivation
Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature.

Results
To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction subnetwork of the STRING database. This corpus comprises 1287 documents with ∼3500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model’s capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database.

Availability and implementation
We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/.
Chemical reaction enhanced graph learning for molecule representation
Authors: Li Anchen , Casiraghi Elena , Rousu Juho 
Published: September 2024

Abstract

Motivation
Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL.

Results
In this article, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction.

Availability and implementation
The code is publicly available at https://github.com/coder-ACAC/RLM.
Scientific collaborative within-group conduct, data-sharing, and publication agreements
Authors: Dodds Walter K 
Published: September 2024

Abstract
As science becomes ever more collaborative, the mechanisms for working in large and more diverse groups become more necessary. In the present article, I explore the utility of within-group collaboration agreements on participant conduct toward other project participants, within-group data sharing, and authorship of published manuscripts for research groups. Such agreements can solidify the expectations of the interactions among collaborators, potential rewards, and a feeling of security for those involved in the projects. They could also lead to more productive and satisfying research, as well as improving the training of future scientists.
A microRNA diagnostic biomarker for amyotrophic lateral sclerosis
Authors: Banack Sandra Anne , Dunlop Rachael A , Mehta Paul , Mitsumoto Hiroshi , Wood Stewart P , Han Moon , Cox Paul Alan 
Published: September 2024

Abstract
Blood-based diagnostic biomarkers for amyotrophic lateral sclerosis will improve patient outcomes and positively impact novel drug development. Critical to the development of such biomarkers is robust method validation, optimization and replication with adequate sample sizes and neurological disease comparative blood samples. We sought to test an amyotrophic lateral sclerosis biomarker derived from diverse samples to determine if it is disease specific. Extracellular vesicles were extracted from blood plasma obtained from individuals diagnosed with amyotrophic lateral sclerosis, primary lateral sclerosis, Parkinson’s disease and healthy controls. Immunoaffinity purification was used to create a neural-enriched extracellular vesicle fraction. MicroRNAs were measured across sample cohorts using real-time polymerase chain reaction. A Kruskal–Wallis test was used to assess differences in plasma microRNAs followed by post hoc Mann–Whitney tests to compare disease groups. Diagnostic accuracy was determined using a machine learning algorithm and a logistic regression model. We identified an eight-microRNA diagnostic signature for blood samples from amyotrophic lateral sclerosis patients with high sensitivity and specificity and an area under the curve calculation of 98% with clear statistical separation from neurological controls. The eight identified microRNAs represent disease-related biological processes consistent with amyotrophic lateral sclerosis. The direction and magnitude of gene fold regulation are consistent across four separate patient cohorts with real-time polymerase chain reaction analyses conducted in two laboratories from diverse samples and sample collection procedures. We propose that this diagnostic signature could be an aid to neurologists to supplement current clinical metrics used to diagnose amyotrophic lateral sclerosis.
Receptive-field nonlinearities in primary auditory cortex: a comparative perspective
Authors: Homma Natsumi Y , See Jermyn Z , Atencio Craig A , Hu Congcong , Downer Joshua D , Beitel Ralph E , Cheung Steven W , Najafabadi Mina Sadeghi , Olsen Timothy , Bigelow James , Hasenstaub Andrea R , Malone Brian J , Schreiner Christoph E 
Published: September 2024

Abstract
Cortical processing of auditory information can be affected by interspecies differences as well as brain states. Here we compare multifeature spectro-temporal receptive fields (STRFs) and associated input/output functions or nonlinearities (NLs) of neurons in primary auditory cortex (AC) of four mammalian species. Single-unit recordings were performed in awake animals (female squirrel monkeys, female, and male mice) and anesthetized animals (female squirrel monkeys, rats, and cats). Neuronal responses were modeled as consisting of two STRFs and their associated NLs. The NLs for the STRF with the highest information content show a broad distribution between linear and quadratic forms. In awake animals, we find a higher percentage of quadratic-like NLs as opposed to more linear NLs in anesthetized animals. Moderate sex differences of the shape of NLs were observed between male and female unanesthetized mice. This indicates that the core AC possesses a rich variety of potential computations, particularly in awake animals, suggesting that multiple computational algorithms are at play to enable the auditory system’s robust recognition of auditory events.