bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spiking neural networks compensate for weight drift in organic neuromorphic device networks
Authors: Felder Daniel , Linkhorst John , Wessling Matthias 
Published: April 2023

Abstract
Organic neuromorphic devices can accelerate neural networks and integrate with biological systems. Devices based on the biocompatible and conductive polymer PEDOT:PSS are fast, require low amounts of energy and perform well in crossbar simulations. However, parasitic electrochemical reactions lead to self-discharge and the fading of the learned conductance states over time. This limits a neural network’s operating time and requires complex compensation mechanisms. Spiking neural networks (SNNs) take inspiration from biology to implement local and always-on learning. We show that these SNNs can function on organic neuromorphic hardware and compensate for self-discharge by continuously relearning and reinforcing forgotten states. In this work, we use a high-resolution charge transport model to describe the behavior of organic neuromorphic devices and create a computationally efficient surrogate model. By integrating the surrogate model into a Brian 2 simulation, we can describe the behavior of SNNs on organic neuromorphic hardware. A biologically plausible two-layer network for recognizing

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pixel MNIST images is trained and observed during self-discharge. The network achieves, for its size, competitive recognition results of up to 82.5%. Building a network with forgetful devices yields superior accuracy during training with 84.5% compared to ideal devices. However, trained networks without active spike-timing-dependent plasticity quickly lose their predictive performance. We show that online learning can keep the performance at a steady level close to the initial accuracy, even for idle rates of up to 90%. This performance is maintained when the output neuron’s labels are not revalidated for up to 24 h. These findings reconfirm the potential of organic neuromorphic devices for brain-inspired computing. Their biocompatibility and the demonstrated adaptability to SNNs open the path towards close integration with multi-electrode arrays, drug-delivery devices, and other bio-interfacing systems as either fully organic or hybrid organic-inorganic systems.
Surface ID: a geometry-aware system for protein molecular surface comparison
Authors: Riahi Saleh , Lee Jae Hyeon , Sorenson Taylor , Wei Shuai , Jager Sven , Olfati-Saber Reza , Zhou Yanfeng , Park Anna , Wendt Maria , Minoux Hervé , Qiu Yu 
Published: April 2023

Abstract

Motivation
A protein can be represented in several forms, including its 1D sequence, 3D atom coordinates, and molecular surface. A protein surface contains rich structural and chemical features directly related to the protein’s function such as its ability to interact with other molecules. While many methods have been developed for comparing the similarity of proteins using the sequence and structural representations, computational methods based on molecular surface representation are limited.

Results
Here, we describe “Surface ID,” a geometric deep learning system for high-throughput surface comparison based on geometric and chemical features. Surface ID offers a novel grouping and alignment algorithm useful for clustering proteins by function, visualization, and in silico screening of potential binding partners to a target molecule. Our method demonstrates top performance in surface similarity assessment, indicating great potential for protein functional annotation, a major need in protein engineering and therapeutic design.

Availability and implementation
Source code for the Surface ID model, trained weights, and inference script are available at https://github.com/Sanofi-Public/LMR-SurfaceID.
Antagonism between DDX6 and PI3K-AKT signaling is an oocyte-intrinsic mechanism controlling primordial follicle growth
Authors: Kato Yuzuru , Saga Yumiko 
Published: April 2023

Abstract
Oocyte maturation and subsequent ovulation during the reproductive lifespan ensure long-term reproduction in mammalian females. This is achieved by tight regulation for the maintenance and growth of primordial follicles. However, the underlying mechanisms remain unsolved. We herein report that posttranscriptional gene regulation mediated by an RNA helicase, DEAD-box helicase 6 (DDX6), and phosphoinositide-3-kinase (PI3K)-AKT signaling exhibits an antagonistic interaction in mouse primordial follicles. DDX6 forms P-body-like cytoplasmic foci in oocytes, which colocalize to a P-body component, DCP1A. Interestingly, the P-body-like granules predominantly assemble in primordial follicles, but disperse once follicle growth is initiated, suggesting that they play a role in the maintenance of primordial follicles. Oocyte-specific knockout of Ddx6 using Gdf9-iCre revealed that Ddx6-deficient oocytes are defective in foci assembly and are abnormally enlarged, resulting in premature depletion of primordial follicles. These results indicate that DDX6 is required to maintain primordial follicles. The abnormal oocyte enlargement is because of enhanced PI3K-AKT signaling, a pivotal signaling pathway in the growth of primordial follicles. Conversely, the forced activation of PI3K-AKT signaling by knocking out Pten disassembles P-body-like granules in primordial follicles. These data suggest that DDX6 and PI3K-AKT signaling mutually antagonize the assembly of P-body-like granules and the growth of primordial follicles. We propose this mutual antagonism as an oocyte-intrinsic mechanism controlling the maintenance and growth of primordial follicles, ensuring the longevity of female reproduction.
Eosinophilic myocarditis during treatment of acute myeloid leukaemia: cardiac magnetic resonance in the very early phase mimicking triple-vessel coronary artery disease: a case report
Authors: Salihu Adil , Stadelmann Raphael , Solimando Emilie , Schwitter Juerg 
Published: April 2023

Abstract

Background
Chemotherapy of acute myeloid leukaemia (AML) can cause a broad spectrum of cardiotoxic effects. Cardiac magnetic resonance (CMR) is key for the diagnosis of eosinophilic myocarditis (EM) defined by the presence of sub-endocardial necrosis and fibrosis. This case report describes the picture of severe triple-vessel ischaemia due to infiltration of eosinophilia without atherosclerotic coronary artery disease (CAD).

Case summary
A 57-year-old woman was diagnosed with AML requiring chemotherapy. Three days after initiation of chemotherapy, the patient presented with chest pain and new left ventricular (LV) dysfunction and hyper-eosinophilia. A CMR examination initially was compatible with severe triple-vessel ischaemia. Tissue characterization by CMR was not done due to severe dyspnoea promoting the differential diagnosis of triple-vessel CAD or chemotherapy-induced triple-vessel coronary spasm. However, invasive coronary angiography excluded obstructive CAD. Severe LV dysfunction and troponin elevation persisted arguing against coronary vasospasm. Chemotherapy induced a massive increase in blood eosinophils, and EM was considered as most likely diagnosis. Immunosuppressive treatment improved the patient’s status and a CMR later on confirmed the diagnosis of EM.

Discussion
Chemotherapy-induced massive eosinophilia can cause widespread coronary micro-vascular infiltration mimicking severe triple-vessel CAD. Early CMR did not evaluate tissue composition, and EM was not considered which delayed adequate treatment. A complete CMR assessment is key to establish the correct diagnosis.
An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function
Authors: Katsushika Susumu , Kodera Satoshi , Sawano Shinnosuke , Shinohara Hiroki , Setoguchi Naoto , Tanabe Kengo , Higashikuni Yasutomi , Takeda Norifumi , Fujiu Katsuhito , Daimon Masao , Akazawa Hiroshi , Morita Hiroyuki , Komuro Issei 
Published: April 2023

Abstract

Aims
The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability.

Methods and results
We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model’s decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5–6 leads, low voltage in I/II/V4–6 leads, Q wave in V3–6 leads, ventricular activation time prolongation in I/V5–6 leads, S-wave prolongation in V2–3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists’ ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02).

Conclusion
We visually interpreted the model’s decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application.
Epicardial adipose tissue and obstructive coronary artery disease in acute chest pain: the EPIC-ACS study
Authors: Jehn Stefanie , Roggel Anja , Dykun Iryna , Balcer Bastian , Al-Rashid Fadi , Totzeck Matthias , Risse Joachim , Kill Clemens , Rassaf Tienush , Mahabadi Amir A 
Published: April 2023

Abstract

Aims
We tested the hypothesis that epicardial adipose tissue (EAT) quantification improves the prediction of the presence of obstructive coronary artery disease (CAD) in patients presenting with acute chest pain to the emergency department.

Methods and results
Within this prospective observational cohort study, we included 657 consecutive patients (mean age 58.06 ± 18.04 years, 53% male) presenting to the emergency department with acute chest pain suggestive of acute coronary syndrome between December 2018 and August 2020. Patients with ST-elevation myocardial infarction, haemodynamic instability, or known CAD were excluded. As part of the initial workup, we performed bedside echocardiography for quantification of EAT thickness by a dedicated study physician, blinded to all patient characteristics. Treating physicians remained unaware of the results of the EAT assessment. The primary endpoint was defined as the presence of obstructive CAD, as detected in subsequent invasive coronary angiography. Patients reaching the primary endpoint had significantly more EAT than patients without obstructive CAD (7.90 ± 2.56 mm vs. 3.96 ± 1.91 mm, P &lt; 0.0001). In a multivariable regression analysis, a 1 mm increase in EAT thickness was associated with a nearby two-fold increased odds of the presence of obstructive CAD [1.87 (1.64–2.12), P &lt; 0.0001]. Adding EAT to a multivariable model of the GRACE score, cardiac biomarkers and traditional risk factors significantly improved the area under the receiver operating characteristic curve (0.759–0.901, P &lt; 0.0001).

Conclusion
Epicardial adipose tissue strongly and independently predicts the presence of obstructive CAD in patients presenting with acute chest pain to the emergency department. Our results suggest that the assessment of EAT may improve diagnostic algorithms of patients with acute chest pain.
Early-warning signals of impending speciation
Authors: Sinitambirivoutin Maïdie , Nosil Patrik , Flaxman Samuel , Feder Jeffrey , Gompert Zachariah , Dakos Vasilis 
Published: April 2023

Abstract
Species formation is a central topic in biology, and a large body of theoretical work has explored the conditions under which speciation occurs, including whether speciation dynamics are gradual or abrupt. In some cases of abrupt speciation, differentiation slowly builds up until it reaches a threshold, at which point linkage disequilibrium (LD) and divergent selection enter a positive feedback loop that triggers accelerated change. Notably, such abrupt transitions powered by a positive feedback have also been observed in a range of other systems. Efforts to anticipate abrupt transitions have led to the development of “early warning signals” (EWS), that is, specific statistical patterns preceding abrupt transitions. Examples of EWS are rising autocorrelation and variance in time-series data due to the reduction of the ability of the system to recover from disturbances. Here, we investigate whether speciation dynamics in theoretical models also exhibit EWS. Using a model of genetic divergence between two populations, we search for EWS before gradual and abrupt speciation events. We do so using six different metrics of differentiation: the effective migration rate, the number of selected loci, the mean fitness of our studied population, LD, FST, and Dabs, a metric analogous to DXY. We find evidence for EWS, with a heterogeneity in their strength among differentiation metrics. We specifically identify FST and the effective migration rate as the most reliable EWS of upcoming abrupt speciation events. Our results provide initial insights into potential EWS of impending speciation and contribute to efforts to generalize the mechanisms underlying EWS.
Reading the Sensory Trajectory of Unmoored Existence and the Poetic Encoding of Wounds in Zsuzsa Bánk’s Der Schwimmer (2002)
Authors: Chauhan Shivani 
Published: April 2023

Abstract
Zsuzsa Bánk’s Der Schwimmer (2002) is a meditation on the trials of flight and dislocation. Underpinned by complex memory dynamics, the narrative reconstructs the fate of several ‘unmoored’ figures whose physical or psychological experiences of liminality exacerbate their exile. Drawing on David Howes’s concepts of ‘emplacement’ and ‘displacement’, the primary objective of this essay is to examine the poetic encoding of sensory tropes underlying the core subjects of memory, trauma and migration in the novel. Firstly, rather than reading this novel primarily as a socio-political document, this article highlights the aesthetic qualities of Der Schwimmer while offering deep insights into the subject-specific experiences of belonging and displacement. Secondly, a focus on the literary interweaving of sensory phenomena illuminates a new and under-researched aspect of transnational writing. Ultimately, this article argues that a critical focus on the sensory can open up innovative ways of understanding the aesthetic organization of Der Schwimmer.
Informal Caregiving Networks of Older Adults With Dementia Superimposed on Multimorbidity: A Social Network Analysis Study
Authors: Song Mi-Kyung , Paul Sudeshna , Happ Mary Beth , Lea Janice , Pirkle James L , Turberville-Trujillo Linda 
Published: April 2023

Abstract

Background and Objectives
Caregiving is a social process and commonly involves more than a single caregiver, especially for older adults with multimorbidity, including dementia. This study was to characterize informal caregiving networks of older adults with dementia superimposed on multimorbidity (e.g., end-stage kidney disease) and to examine the relationships of network properties to outcomes of caregivers and older adults.

Research Design and Methods
An egocentric social network survey was conducted. Up to 3 family caregivers of older adults on dialysis who had moderate-to-severe irreversible cognitive impairment with or without a documented diagnosis of dementia were recruited from 11 dialysis centers in 2 states. Caregivers completed a social network survey about individuals providing caregiving to the older adult and measures of caregiving burden and rewards, depression, and financial hardship. Older adults’ emergency department visits and hospital admissions during the past 12 months were abstracted from the medical records.

Results
A total of 76 caregiver informants of 46 older adults (78% Black) participated in the study. Of the 46 older adults, 65% had a multimember network (median size of 4). As the network density (the proportion of ties between members among all possible ties) increased, primary caregivers’ financial hardship decreased whereas nonprimary caregivers’ financial hardship increased. Further, for every 1-unit increase in mean degree (the average number of connections among members), there was a nearly fourfold increase in the odds of no hospital admission during the prior year for the older adult.

Discussion and Implications
The network dynamics of informal caregiving networks may have an impact on the well-being of caregivers and older adults with dementia, but confirmatory longitudinal studies are needed.
LMNglyPred: prediction of human N-linked glycosylation sites using embeddings from a pre-trained protein language model
Authors: Pakhrin Subash C , Pokharel Suresh , Aoki-Kinoshita Kiyoko F , Beck Moriah R , Dam Tarun K , Caragea Doina , KC Dukka B 
Published: April 2023

Abstract
Protein N-linked glycosylation is an important post-translational mechanism in Homo sapiens, playing essential roles in many vital biological processes. It occurs at the N-X-[S/T] sequon in amino acid sequences, where X can be any amino acid except proline. However, not all N-X-[S/T] sequons are glycosylated; thus, the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In this regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem that has not been extensively addressed by the existing methods, especially in regard to the creation of negative sets and leveraging the distilled information from protein language models (pLMs). Here, we developed LMNglyPred, a deep learning-based approach, to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained pLM. LMNglyPred produces sensitivity, specificity, Matthews Correlation Coefficient, precision, and accuracy of 76.50, 75.36, 0.49, 60.99, and 75.74 percent, respectively, on a benchmark-independent test set. These results demonstrate that LMNglyPred is a robust computational tool to predict N-linked glycosylation sites confined to the N-X-[S/T] sequon.
The death and life of Jang Nayeon: a case for personality rights in the digital layers of reality
Authors: Boothe Amber 
Published: April 2023

Abstract
Inspired by the story of Jang Ji-sung and the virtual resurrection of her daughter, Nayeon, this article explores how the legal issues that arise when people (living or deceased) are digitally cloned and rendered into virtual or augmented reality ground a compelling argument for why UK law should recognize a broader suite of personality rights. Technologies once confined to the two-dimensional realm of print media or locked within the aspect ratio of a digital display are now manifesting in three dimensions. In the same way, that technological advancements in photography gave rise to the current formulation of privacy and publicity rights, innovations in virtual and augmented reality [collectively known as extended reality (XR) technology] foreshadow the next paradigm shift. Utilizing a series of hypothetical scenarios that illustrate the unique challenges XR technology poses, this article reveals how the UK’s lack of personality rights is becoming ever more problematic in the case of the living, and wholly indefensible in the case of the dead.
Amplicon Sequencing Minimal Information (ASqMI): Quality and Reporting Guidelines for Actionable Calls in Biodefense Applications
Authors: Keenum Ishi , Player Robert , Kralj Jason , Servetas Stephanie , Sussman Michael D , Russell Joseph A , Stone Jennifer , Chandrapati Sailaja , Sozhamannan Shanmuga 
Published: April 2023

Abstract

Background
Accurate, high-confidence data is critical for assessing potential biothreat incidents. In a biothreat event, false-negative and -positive results have serious consequences. Worst case scenarios can result in unnecessary shutdowns or fatalities at an exorbitant monetary and psychological cost, respectively. Quantitative PCR assays for agents of interest have been successfully used for routine biosurveillance. Recently, there has been increased impetus for adoption of amplicon sequencing (AS) for biosurveillance because it enables discrimination of true positives from near-neighbor false positives, as well as broad, simultaneous detection of many targets in many pathogens in a high-throughput scheme. However, the high sensitivity of AS can lead to false positives. Appropriate controls and workflow reporting can help address these challenges.

Objectives
Data reporting standards are critical to data trustworthiness. The standards presented herein aim to provide a framework for method quality assessment in biodetection.

Methods
We present a set of standards, Amplicon Sequencing Minimal Information (ASqMI), developed under the auspices of the AOAC INTERNATIONAL Stakeholder Program on Agent Detection Assays for making actionable calls in biosurveillance applications. In addition to the first minimum information guidelines for AS, we provide a controls checklist and scoring scheme to assure AS run quality and assess potential sample contamination.

Results
Adoption of the ASqMI guidelines will improve data quality, help track workflow performance, and ultimately provide decision makers confidence to trust the results of this new and powerful technology.

Conclusion
AS workflows can provide robust, confident calls for biodetection; however, due diligence in reporting and controls are needed. The ASqMI guideline is the first AS minimum reporting guidance document that also provides the means for end users to evaluate their workflows to improve confidence.

Highlights
Standardized reporting guidance for actionable calls is critical to ensuring trustworthy data.