bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electroconvulsive Therapy in a Severely Depressed, Bipolar I Disorder Patient, Who Is a 3-Time Liver Transplant Recipient Due to Primary Sclerosing Cholangitis
Authors: Ilic Thomas D. , McCarthy Caitlin J. , Steele Ian 
Published: November 2024

Abstract
Patients with bipolar I disorder who develop major depressive episodes with psychotic features and suicide ideations following severe episodes of mania can be difficult to treat with medications alone. Electroconvulsive therapy (ECT) is a treatment modality that can be quite helpful to these patients and lead to remission of depressive symptoms also while providing protection against mania. However the literature about using ECT in medically complex patients is sparse, which can limit guidance and knowledge on the specific risks of the procedure in specialized patient populations. We report in our own case the use of ECT to treat a major depressive episode in a young adult with bipolar I disorder who also has a history of 3 liver transplants in his childhood due to primary sclerosing cholangitis. Despite prior case reports relating the safety of ECT in liver transplant recipients, we discuss that our patient had a large intracranial hemorrhage during his first ECT that required 2 decompressive craniotomies and extensive physical rehabilitation. This case serves to expand the limited literature on the use of ECT in patients with history of liver transplants and to increase understanding of risks of the procedure in his specialized patient population.
CAIRS: A causal artificial intelligence recommendation system for digital mental health
Authors: Varidel Mathew , An Victor , Hickie Ian B. , Cripps Sally , Marchant Roman , Scott Jan , Crouse Jacob J. , Poulsen Adam , O’Dea Bridianne , Iorfino Frank 
Published: November 2024

AbstractDigital mental health tools have the prospect to enhance and expand access to care for those in need. Some tools provide interventional recommendations to individuals, typically using simple static rule-based systems (e.g., if-else statements) or by incorporating predictive artificial intelligence. However, interventional recommendations require a decision based on the comparison of future outcomes under different interventions, which requires causal considerations. Here we develop CAIRS, a causal artificial intelligence recommendation system that provides personalised interventional recommendations using an individual’s current presentation and the learned dynamics between domains to identify and rank intervention targets that have the greatest impact on future outcomes. Our approach was applied to longitudinal data of multiple mental health and related domains at two timepoints (1 week - 6 months from baseline) collected from a digital mental health tool. In our example, psychological distress was found to be the key influential domain that affected multiple domains (e.g., personal functioning, social connection), and thus was typically the preferred target in complex cases where multiple domains were unhealthy. Our approach is broadly applicable to recommendation contexts where causal considerations are important, and the framework could be incorporated within a live app to enhance digital mental health tools.
Deep Learning of Cellular Metabolic Flux Distributions Predicts Lifespan
Authors: Hilsabeck Tyler A.U. , Rea Shane L. 
Published: November 2024

ABSTRACTIt is a common observation that individuals within a species age at different rates. Variation in both genetics and environmental interaction are generally thought responsible. Surprisingly, even genetically identical organisms cultured under environmentally homogeneous conditions age at different rates, implying a more fundamental cause of aging. Here we have examined the basis for lifespan variance in haploid, single-celled yeast ofSaccharomyces cerevisiae. The probabilistic nature of metabolism means metabolites often, but not always, follow the same route through the metabolic network. We speculate redundancy in metabolic pathway choice is sufficient to explain lifespan variance. To interrogate the reaction flux space ofS. cerevisiaewe used a model of its intermediary metabolism, comprising 1,150 genes, 4,058 reactions, and 2,742 metabolites (yeast GEM_v8.5.0). We restricted traffic through the metabolic network by knocking out each of the 1,150 genes, then generated a total of 406,500 flux distributions spanning the solution space of the resulting 812 viable mutants. We collected replicative life span (RLS) data for the 812 viable mutants, corresponding to 66,400 individual cells. Four approaches were then employed to test whether reaction flux configuration could be used to predict lifespan: Principal Component Analysis (PCA) in conjunction with non-linear modeling of RLS; deep learning of RLS using either a Regression Neural Network (RNN) or a Classification Neural Network (CfNN); and deep learning using a convolutional neural network (CNN) following conversion of flux distributions to pixelated images. The four approaches reveal a core network of highly correlated reactions controlling aging rate that is sufficient to explain all lifespan variance. It includes biosynthetic pathways encompassing ceramides, monolysocardiolipins, phosphoinositides, porphyrin and glycerolipids. Our data lead to two novel conclusions. First, variance in the replicative lifespan ofS. cerevisiaeis an emergent property of its metabolic network. Second, there is convergence among metabolic configurations toward three meta-stable flux states – one associated with extended life, another with shortened life, and a third with wild type life span.One Sentence SummaryTraffic routes and rates through the metabolic network ofS. cerevisiaefully account for variance in replicative lifespan.
Scaling down for efficiency: Medium-sized protein language models perform well at transfer learning on realistic datasets
Authors: Vieira Luiz C. , Handojo Morgan L. , Wilke Claus O. 
Published: November 2024

Protein language models (pLMs) can offer deep insights into evolutionary and structural properties of proteins. While larger models, such as the 15 billion parameter model ESM-2, promise to capture more complex patterns in sequence space, they also present practical challenges due to their high dimensionality and high computational cost. We systematically evaluated the performance of various pLMs across multiple biological datasets to assess the impact of model size on transfer learning. Surprisingly, we found that larger models not necessarily outperform smaller ones, in particular when data is limited. Medium-sized models, such as ESM-2 650M and ESM C 600M, demonstrated consistently good performance, falling only slightly behind their larger counterparts—ESM-2 15B and ESM C 6B—despite being many times smaller. Additionally, we compared various methods of compressing embeddings prior to transfer learning, and we found that mean embeddings consistently outperformed other compression methods. In summary, ESM C 600M with mean embeddings offers an optimal balance between performance and efficiency, making it a practical and scalable choice for transfer learning in realistic biological applications.Significance StatementThis work challenges the common belief that larger language models always yield better results, here in the context of protein biochemistry. By systematically comparing transformer models of different sizes in transfer learning tasks, we demonstrate that medium size models, such as ESM C 600M, frequently perform as well as or better than larger variants, especially when data is limited. These findings provide an efficient strategy for machine learning-based protein analysis. Smaller and more efficient models help democratize cutting-edge AI approaches, making them more accessible to researchers with limited computational resources.
Insights into the conformational dynamics of the cytoplasmic domain of metal-sensing sensor histidine kinase ZraS
Authors: Mahapatra Nilima , Mahanta Pranjal , Pandey Shubhant , Acharya Rudresh 
Published: November 2024

ABSTRACTZraS is a metal sensor integral to ZraPSR, a two-component signaling system found in enterobacters. It belongs to a family of bifunctional sensor histidine kinases (SHKs) and is speculated to sense zinc-induced stress on the bacterial envelope. Information on the structure-function relationship of sensor kinases is elusive due to the lack of full-length structures, intrinsically dynamic behavior, and difficulty trapping them in active state conformations. While the kinase domains of a few sensor histidine kinases are well-characterized, they exhibit significant functional diversity attributed to their modular multi-domain arrangement in the cytoplasmic region, combined with other signal transducing elements such as simple helices, HAMP and PAS domains. We report the crystal structure of the entire cytoplasmic region ofEscherichia coliZraS (EcZraS-CD) resolved at resolution 2.49 Å, comprising of a unique helical linker and the kinase domain. In the asymmetric unit, four molecules of ZraS assemble as homodimers trapped as two ligand-bound occluded conformers. Our analysis using these conformers show that modulation of the dimer bundle through segmental helical bending, sliding, and rotation leads to reorganization of the dimerization interface during kinase activation. Further, our analysis reveals the significance of aromatic amino acid interactions and loop residues at the dimer base in regulating the directionality of rotation during autophosphorylation. We also performed an invitro coupled assay to determine ATPase activity. Overall, our findings provide structure-based mechanistic insights into the process of autophosphorylation in trans-acting sensor histidine kinases.
Implication of S-1 biomarkers on aging and prognosis in elderly patients with lung cancer treated by adjuvant S-1 chemotherapy: Accompanying results of the Setouchi Lung Cancer Group Study 1201
Authors: Soh Junichi , Yamamoto Hiromasa , Okumura Norihito , Suzuki Hiroyuki , Nakata Masao , Fujiwara Toshiya , Gemba Kenichi , Sano Isao , Fujinaga Takuji , Kataoka Masafumi , Terasaki Yasuhiro , Fujimoto Nobukazu , Kataoka Kazuhiko , Kosaka Shinji , Yamashita Motohiro , Inokawa Hidetoshi , Inoue Masaaki , Nakamura Hiroshige , Yamashita Yoshinori , Takahashi Yuta , Torigoe Hidejiro , Sato Hiroki , Tomida Shuta , Hotta Katsuyuki , Yoshioka Hiroshige , Morita Satoshi , Matsuo Keitaro , Sakamoto Junichi , Date Hiroshi , Toyooka Shinichi 
Published: November 2024

ABSTRACTBACKGROUNDSThe management of elderly patients presents several challenges due to age-related declines; however recent recommendations advocate for age not being the sole determinant for adjuvant treatment decisions in patients with non-small cell lung cancer (NSCLC). Aging may alter expression levels of 5-fluorouracil (5-FU) biomarkers.METHODSExpression changes with aging were explored using The Cancer Genome Atlas (TCGA) database. 5-FU-related biomarker expressions, including thymidylate synthase (TS), dihydropyrimidine dehydrogenase (DPD), orotate phosphoribosyltransferase, epidermal growth factor receptor (EGFR), and excision repair cross-complementation group-1 (ERCC1), were assessed by the quantitative reverse-transcription PCR assays in 89 NSCLCs elderly patients (≥ 75 years old) receiving adjuvant S-1, an oral fluoropyrimidine agent, therapy in a SLCG1201 trial.RESULTS:TCGA database analysis (n=955) indicated decreasedTSexpression with aging, particularly in those 75 or older. In the SCLG1201 trial, univariate analysis revealed that highEGFRand lowTSexpressions correlated with favorable recurrence-free survival (RFS)(p=0.0264) and overall survival (OS)(p=0.0365), respectively. Multivariable analysis confirmed pathological stage as an independent prognostic factor for both RFS and OS (p=0.0052 and 0.0352, respectively).EGFRmutant tumors (n=27) showed significantly higherDPD(p=0.0066) andEGFR(p<0.0001) expressions, and lowerTS(p=0.0125) andERCC1(p=0.0015) expressions.CONCLUSIONDespite pathological stage being an independent prognostic factor, highEGFRand lowTSexpressions may predict better clinical outcomes in elderly NSCLC patients using adjuvant S-1. The age-related decrease in TS expression supports the potential benefit of 5-FU-based therapy in them compared to younger patients. Further research is warranted to validate these clinical implications.
Separating three variability and noise sources in the response fluctuation of brain stimulation
Authors: Ma K. , Goetz S. M. 
Published: November 2024

AbstractMotor-evoked potentials (MEPs) are among the few directly observable responses to suprathreshold brain stimulation and serve a variety of applications. If the MEP size is graphed over the stimulation strength, they form an input–output (IO), recruitment, or dose–response curve. Previous statistical models with two variability sources inherently consider the small MEPs at the low plateau as part of the neural recruitment properties. However, recent studies demonstrated that small MEP responses are contaminated and over-shadowed by background noise of mostly technical quality and suggested that the recruitment curve should continue below this noise level. This work intends to separate physiological variability from background noise and improve the description of recruitment behaviour. We developed a model with three variability sources and a logarithmic logistic function without a lower plateau. Compared to previous models, we incorporated an additional source for background noise from amplifiers, electrode impedance, and remote bioelectric activity, which form the obesrved low-side plateau. Compared to the dual-variability source modes, our approach better described IO characteristics, evidenced by lower Bayesian Information Criterion scores across all subjects and pulse shapes. The model independently extracted hidden variability information across the stimulated neural system and isolated it from background noise, which led to an accurate estimation of the IO curve parameters. This new model offers a robust tool to analyse brain stimulation IO curves in clinical and experimental neuroscience, reducing the risk of spurious results from inappropriate statistical methods. By providing a more accurate representation of MEP responses and variability sources, this approach advances our understanding of cortical excitability and may improve the assessment of neuromodulation effects.
Two-dimensional synchrotron beam characterization from a single interferogram
Authors: Nikolic Bojan , Carilli Christopher L. , Thyagarajan Nithyanandan , Torino Laura , Iriso Ubaldo 
Published: November 2024

Double-aperture young interferometry is widely used in accelerators to provide a one-dimensional beam measurement. We improve this technique by combining and further developing techniques of nonredundant, two-dimensional, aperture masking, and self-calibration from astronomy. Using visible synchrotron radiation, tests at the ALBA synchrotron show that this method provides an accurate two-dimensional beam transverse characterization, even from a single 1 ms interferogram. The nonredundancy of the aperture mask in the technique enables it to be resistant to spatial phase fluctuations that might be introduced by vibration of optical components or in the laboratory atmosphere.

Published by the American Physical Society
2024
Feasibility study of true muonium discovery with the CERN-SPS H4 positron beam
Authors: Gargiulo Ruben , Palmisano Stefano , Di Meco Elisa 
Published: November 2024

True muonium (μ+μ−) is one of the heaviest and smallest electromagnetic bound states not containing hadrons, and has never been observed so far. In this work we show that the spin-1 TM state (ortho-TM) can be observed at a discovery level of significance in three months at the CERN SPS North-Area H4A beam line, using 43.7 GeV secondary positrons. In this way, by impinging the positrons on multiple thin low-Z targets, ortho-TM, which decays predominantly to e+e−, can be produced from e+e−→TM interaction on resonance (s∼2mμ).

Published by the American Physical Society
2024
Level of financial disclosures for listed insurance companies using ISO 31000: empirical evidence from Jordan and Palestine
Authors: Al-Khazaleh Suhaib , Badwan Nemer , Qubbaj Ihab , Almashaqbeh Mohammad 
Published: November 2024

PurposeIn light of the complex risk and transparency regulations, this paper investigates the factors influencing the level of risk management disclosure by insurance firms in Jordan and Palestine. The characteristics examined were ownership structure, which covers public, institutional and management ownership on risk management disclosure (RMD) utilizing ISO 31000, as well as profitability, leverage, liquidity and firm size.Design/methodology/approachTo achieve the aim of this study, a quantitative research methodology was used. Based on the total number of observations, 232 purposeful annual observations for the study sample were collected between 2016 and 2023 for 10 insurance companies listed on the Palestine stock exchange (Palestinian companies) with 80 observations and 19 companies listed on the Amman stock exchange (Jordanian companies) with 152 observations. This study uses panel data regression with fixed effects models. By employing the 2SLS approach, we comprehensively address the main endogeneity concerns and problems in risk management disclosure RMD of insurance firms in Jordan and Palestine.FindingsThe results show that risk management disclosure is significantly influenced by the liquidity and size of an organization. Furthermore, RMD is not significantly affected by profitability, debt, public ownership, institutional ownership or liquidity, whereas business size has a favorable influence.Research limitations/implicationsThe findings of this study may not be generalizable to firms in other countries because of the limitations of insurance firms in Palestine and Jordan. Study replication in future studies should consider the potential for bias and differences in data interpretation when utilizing qualitative methodologies to evaluate RMD.Practical implicationsThe practical implications emphasize how crucial it is for investors, practitioners and stakeholders to choose firms that are large and have little liquidity because this is linked to high levels of risk management transparency. This knowledge can offer investors an important direction for assessing possible risks and transparency in risk management within the insurance sector framework. The study recommends that the governments of Palestine and Jordan enact laws requiring risk management disclosure according to the ISO 31000:2018 standard, especially in the insurance industry.Originality/valueThis study contributes to the literature by illuminating the relationship between firm size, liquidity and risk management disclosure in insurance companies operating in Jordan and Palestine. Therefore, investors should choose large, relatively liquid companies with strong risk management disclosure. This study offers theoretical insights that may be used as a guide for other research, improving the understanding of the variables influencing risk management disclosure in insurance companies and advancing scientific understanding.
Internal corporate venturing: riding the relational rollercoaster through sensemaking and sensegiving reconfiguration
Authors: Tunstall Richard , Pittaway Luke , Van Rensburg Deryck J. , Corbett Andrew C. 
Published: November 2024

PurposeInternal corporate venturing is a vehicle for firms to realize strategic and financial goals through entrepreneurial ventures. Prior research presents a strategic process in which individual managers make rational choices based on their formal roles and top-down corporate objectives. Recent work has challenged this by adopting a relational approach using a macro-level perspective highlighting cultural and institutional logics. This study augments and develops this relational approach by contributing a micro-level perspective by focusing on managers engaged in developing ventures in large organizations. The data show how internal corporate venturing (ICV) actors use discursive practices to make sense of their relationship contexts and develop interpretive repertoires to give sense to their decisions and shape their future strategies. The data illustrate how corporate venturing actors make sense of their uncertain experience and develop insider-outsider strategies by balancing three competing interpretive repertories, which form the basis of strategies supporting an entrepreneurial future in an organizational context.Design/methodology/approachForty-two interviews were conducted with ICV actors, including senior directors of corporate venturing units in multinational corporations and their venture project leaders. The authors conducted a micro-level study through an interpretive sensemaking analysis of managers' “talk.” Interviews are considered through three lenses: “functional talk” (why they said it), “interpretive themes” (what they said) and “interpretive repertoires” (how they said it).FindingsThe perceived challenges experienced by the participants through their relationships were identified. Participants emphasized balancing project and organizational role risk in pursuing venture development, leading to a perceived dependent trust relationship between supporters. Three interpretive repertories were identified through which participants positioned their explanations of their relationship contexts in ICV. Participants used these to discursively frame their corporate venturing practices and position their future strategies.Originality/valueA new framework of corporate venture sensemaking and sensegiving reconfiguration is provided to explain how managers discursively resolve conflicting relationship pressures while maintaining personal positioning. The paper shows how conflicting interpretive repertoires and personal interpretations are generated through a discursive practice comprising sensemaking and sensegiving reconfiguration processes to shape their future strategies. The paper contributes to theory by explicating the relational perspective of ICV at the micro-level and demonstrates how this is influenced by the discursive practices of managers leading the ICV activity.
On a cold standby repairable deteriorating system subject to random shocks and stochastic lead time via phase type quasi-renewal processes
Authors: Shenbagam R. , Yedida Sarada 
Published: November 2024

PurposeThe objective of this article is to investigate a shock model for a cold standby repairable deteriorating system with non-negligible phase type quasi-renewal process maintenance times and threshold with spare.Design/methodology/approachThis paper aims to assess the significance of a property of phase type quasi-renewal processes, it is easy to evaluate the theory concept of a cold standby repairable deteriorating system subject to random shocks and stochastic lead time via phase type quasi-renewal processes.FindingsThe current research work obtained an optimal replacement policy N* analytically and numerically for a cold standby repairable deteriorating system with spare using a phase type quasi-renewal process.Originality/valueThe study of a random threshold shock model for a cold standby repairable deteriorating system with spare via-phase type quasi-renewal processes has not been reported anywhere in the literature. In addition, in such a context, an optimal replacement policy N* has not been obtained analytically. The current research paper is to bridge the gap in the literature. The numerical example presented indicates that a maintenance policy suggested in this article aids in generating a significantly.