bluuebunny/crossref_metadata_2025_split huggingface dataset:
Both eyes open: Vigilant Incentives help auditors improve AI safety
Authors: Bova Paolo , Stefano Alessandro Di , Han The Anh 
Published: April 2024

Abstract
Auditors can play a vital role in ensuring that tech companies develop and deploy AI systems safely, taking into account not just immediate, but also systemic harms that may arise from the use of future AI capabilities. However, to support auditors in evaluating the capabilities and consequences of cutting-edge AI systems, governments may need to encourage a range of potential auditors to invest in new auditing tools and approaches. We use evolutionary game theory to model scenarios where the government wishes to incentivise auditing but cannot discriminate between high and low-quality auditing. We warn that it is alarmingly easy to stumble on ‘Adversarial Incentives’, which prevent a sustainable market for auditing AI systems from forming. Adversarial Incentives mainly reward auditors for catching unsafe behaviour. If AI companies learn to tailor their behaviour to the quality of audits, the lack of opportunities to catch unsafe behaviour will discourage auditors from innovating. Instead, we recommend that governments always reward auditors, except when they find evidence that those auditors failed to detect unsafe behaviour they should have. These ‘Vigilant Incentives’ could encourage auditors to find innovative ways to evaluate cutting-edge AI systems. Overall, our analysis provides useful insights for the design and implementation of efficient incentive strategies for encouraging a robust auditing ecosystem.
Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation
Authors: Askanazi Evan , Grinberg Ilya 
Published: April 2024

Abstract
Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for uncertainty quantification (UQ) of ML predictions has not been available. Focusing on the intuitive and computationally efficient similarity-based UQ, we show that a simple metric based on Euclidean feature space distance and sampling density together with the decorrelation of the features using Gram–Schmidt orthogonalization allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. To demonstrate the generality of the method, we apply it to support vector regression models for various small data sets in materials science and other fields. We also show that this metric is a more effective UQ tool than the standard approach of using the average distance of k nearest neighbors (k = 1–10) in features space for similarity evaluation. Our method is computationally simple, can be used with any ML learning method and enables analysis of the sources of the ML prediction errors. Therefore, it is suitable for use as a standard technique for the estimation of ML prediction reliability for small data sets and as a tool for data set design.
Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning
Authors: Halaly Raz , Tsur Elishai Ezra 
Published: April 2024

Abstract
Model predictive control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic (brain-inspired) spiking neural network for continuous adaptive non-linear MPC. Utilizing real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.15% median prediction error reduction with 5 spiking neurons and up to 96.08% with 5,000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.
The Springs of the Arno
Authors: Havely Nick 
Published: April 2024

Abstract
Chapter 2 follows the journey from the sanctuary of La Verna in the east of the Casentino to the Passo del Muraglione on the ridge between Forlì and Florence. Beginning with an account of one of the longest sections of the walk between La Verna and the hill-resort of Badia Prataglia, it then describes another ancient sanctuary, Camaldoli, and the views of its many visitors: from humanists in the fifteenth century to tourists in the eighteenth and nineteenth centuries and escaping prisoners of war in the twentieth century. It discusses the motives and perceptions of those who traced Dante’s ‘footsteps’ in the Casentino, especially around Monte Falterona and the source of the Arno, and shows through two concluding ‘diversions’ how easy it is to get lost around Falterona and the Muraglione Pass.
Hormonal profiles in dormant turions of 22 aquatic plant species: do they reflect functional or taxonomic traits?
Authors: Adamec Lubomír , Plačková Lenka , Bitomský Martin , Doležal Karel 
Published: April 2024

Abstract

Background and Aims
Turions are vegetative, dormant overwintering organs formed in aquatic plants in response to unfavourable ecological conditions. Contents of cytokinin (CK), auxin metabolites and abscisic acid (ABA) as main growth and development regulators were compared in innately dormant autumnal turions of 22 aquatic plant species of different functional ecological or taxonomic groups with those in non-dormant winter apices in three aquatic species and with those in spring turions of four species after their overwintering.

Methods
The hormones were analysed in miniature turion samples using ultraperformance liquid chromatography coupled with triple quadrupole mass spectrometry.

Key Results
In innately dormant turions, the total contents of each of the four main CK types, biologically active forms and total CKs differed by two to three orders of magnitude across 22 species; the proportion of active CK forms was 0.18–67 %. Similarly, the content of four auxin forms was extremely variable and the IAA proportion as the active form was 0.014–99 %. The ABA content varied from almost zero to 54 µmol kg−1 dry weight and after overwintering it usually significantly decreased. Of all functional traits studied, hormone profiles depended most on the place of turion sprouting (surface vs bottom) and we suggest that this trait is crucial for turion ecophysiology.

Conclusions
The key role of ABA in regulating turion dormancy was confirmed. However, the highly variable pattern of the ABA content in innately dormant and in overwintered turions indicates that the hormonal mechanism regulating the innate dormancy and its breaking in turions is not uniform within aquatic plants.
MerCat2: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from omics data
Authors: Figueroa Jose L , Redinbo Andrew , Panyala Ajay , Colby Sean , Friesen Maren L , Tiemann Lisa , White Richard Allen 
Published: April 2024

Abstract

Motivation
MerCat2 (“Mer—Catenate2”) is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs k-mer counting of any length k, resulting in feature abundance counts tables, quality control reports, protein feature metrics, and graphical representation (i.e. principal component analysis (PCA)).

Results
MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons.

Availability and implementation
MerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: mamba create -n mercat2 -c conda-forge -c bioconda mercat2
For antibody sequence generative modeling, mixture models may be all you need
Authors: Parkinson Jonathan , Wang Wei 
Published: April 2024

Abstract

Motivation
Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity.

Results
In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing &gt;400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature.

Availability and implementation
All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.
Leptin receptor reactivation restores brain function in early-life Lepr-deficient mice
Authors: Fernandes Caroline , Forny-Germano Leticia , Andrade Mayara M , Lyra E Silva Natalia M , Ramos-Lobo Angela M , Meireles Fernanda , Tovar-Moll Fernanda , Houzel Jean Christophe , Donato Jose , De Felice Fernanda G 
Published: April 2024

Abstract
Obesity is a chronic disease caused by excessive fat accumulation that impacts the body and brain health. Insufficient leptin or leptin receptor (LepR) is involved in the disease pathogenesis. Leptin is involved with several neurological processes, and it has crucial developmental roles. We have previously demonstrated that leptin deficiency in early life leads to permanent developmental problems in young adult mice, including an imbalance in energy homeostasis, alterations in melanocortin and the reproductive system and a reduction in brain mass. Given that in humans, obesity has been associated with brain atrophy and cognitive impairment, it is important to determine the long-term consequences of early-life leptin deficiency on brain structure and memory function.
Here, we demonstrate that leptin-deficient (LepOb) mice exhibit altered brain volume, decreased neurogenesis and memory impairment. Similar effects were observed in animals that do not express the LepR (LepRNull). Interestingly, restoring the expression of LepR in 10-week-old mice reverses brain atrophy, in addition to neurogenesis and memory impairments in older animals.
Our findings indicate that leptin deficiency impairs brain development and memory, which are reversible by restoring leptin signalling in adulthood.
NK-like CD8 T cell: one potential evolutionary continuum between adaptive memory and innate immunity
Authors: Wang Qiulei , Chen Shaodan , Guo Zhenhong , Xia Sheng , Zhang Minghui 
Published: April 2024

Summary
CD8 T cells are crucial adaptive immune cells with cytotoxicity to fight against pathogens or abnormal self-cells via major histocompatibility complex class I-dependent priming pathways. The composition of the memory CD8 T-cell pool is influenced by various factors. Physiological aging, chronic viral infection, and autoimmune diseases promote the accumulation of CD8 T cells with highly differentiated memory phenotypes. Accumulating studies have shown that some of these memory CD8 T cells also exhibit innate-like cytotoxicity and upregulate the expression of receptors associated with natural killer (NK) cells. Further analysis shows that these NK-like CD8 T cells have transcriptional profiles of both NK and CD8 T cells, suggesting the transformation of CD8 T cells into NK cells. However, the specific induction mechanism underlying NK-like transformation and the implications of this process for CD8 T cells are still unclear. This review aimed to deduce the possible differentiation model of NK-like CD8 T cells, summarize the functions of major NK-cell receptors expressed on these cells, and provide a new perspective for exploring the role of these CD8 T cells in health and disease.
The neural representations of valence transformation in indole processing
Authors: Zou Laiquan , Qi Yue , Shen Lei , Huang Yanyang , Huang Jiayu , Xia Zheng , Fan Mingxia , Fan Wu , Chai Guo-bi , Shi Qing-zhao , Zhang Qidong , Yan Chao 
Published: April 2024

Abstract
Indole is often associated with a sweet and floral odor typical of jasmine flowers at low concentrations and an unpleasant, animal-like odor at high concentrations. However, the mechanism whereby the brain processes this opposite valence of indole is not fully understood yet. In this study, we aimed to investigate the neural mechanisms underlying indole valence encoding in conversion and nonconversion groups using the smelling task to arouse pleasantness. For this purpose, 12 conversion individuals and 15 nonconversion individuals participated in an event–related functional magnetic resonance imaging paradigm with low (low-indole) and high (high-indole) indole concentrations in which valence was manipulated independent of intensity. The results of this experiment showed that neural activity in the right amygdala, orbitofrontal cortex and insula was associated with valence independent of intensity. Furthermore, activation in the right orbitofrontal cortex in response to low-indole was positively associated with subjective pleasantness ratings. Conversely, activation in the right insula and amygdala in response to low-indole was positively correlated with anticipatory hedonic traits. Interestingly, while amygdala activation in response to high-indole also showed a positive correlation with these hedonic traits, such correlation was observed solely with right insula activation in response to high-indole. Additionally, activation in the right amygdala in response to low-indole was positively correlated with consummatory pleasure and hedonic traits. Regarding olfactory function, only activation in the right orbitofrontal cortex in response to high-indole was positively correlated with olfactory identification, whereas activation in the insula in response to low-indole was negatively correlated with the level of self-reported olfactory dysfunction. Based on these findings, valence transformation of indole processing in the right orbitofrontal cortex, insula, and amygdala may be associated with individual hedonic traits and perceptual differences.
Undernourished Household Contacts Are at Increased Risk of Tuberculosis (TB) Disease, but not TB Infection—a Multicenter Prospective Cohort Analysis
Authors: Sinha Pranay , Ezhumalai Komala , Du Xinyi , Ponnuraja Chinnaiyan , Dauphinais Madolyn Rose , Gupte Nikhil , Sarkar Sonali , Gupta Amita , Gaikwad Sanjay , Thangakunam Balamugesh , Paradkar Mandar , Christopher Devasahayam J , Mave Vidya , Viswanathan Vijay , Ellner Jerrold J , Kornfeld Hardy , Horsburgh C R , Padmapriyadarsini Chandrasekaran , Gupte Akshay 
Published: April 2024

Abstract
Undernutrition is the leading risk factor for tuberculosis (TB) globally and in India. This multicenter prospective cohort analysis from India suggests that undernutrition is associated with increased risk of TB disease but not TB infection among household contacts of persons with TB.
Social media for cardiac imagers: a review
Authors: Schuuring Mark J , Anwer Shehab , Petersen Steffen E , Moharem-Elgamal Sarah , Muraru Denisa 
Published: April 2024

Abstract
Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram, and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as digital object identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care.
Intramural hematoma extending from a dissection within an implanted stent: a case report treated with fenestration using a cutting balloon
Authors: Okura Takeshi , Okabe Toshitaka , Isomura Naoei , Ochiai Masahiko 
Published: April 2024

Abstract

Background
Dissection after balloon dilation or stent implantation is a common complication of percutaneous coronary intervention. In general, coronary stent implantation for coronary artery dissection is safe when the dissection is completely covered by the stent, particularly when dissection occurs during pre-dilation. However, here, we report a case of severe restenosis caused by a pre-dilation hematoma that extended after stent implantation.

Case summary
A 76-year-old man was diagnosed with angina on exertion and underwent percutaneous coronary intervention in the right coronary artery. After pre-dilation with a cutting balloon, non-flow-limiting dissection occurred. An everolimus-eluting stent was implanted, completely sealing the dissection, and intravascular ultrasound revealed adequate stent expansion without stent edge dissection. Two weeks after the procedure, confirmatory coronary angiography revealed severe restenosis extending from the distal stent edge to the distal right coronary artery. Intravascular ultrasound revealed a hematoma extending from the middle of the stent to the distal segment.

Discussion
The patient had been on steroids for a long time. The cutting balloon used for pre-dilation may have created a deep dissection reaching the tunica media, already rendered vulnerable by steroids, potentially leading to injury to the vasa vasorum. The intramural hematoma from the bleeding vasa vasorum might have been the underlying cause of this phenomenon, as evidenced by its increase in size despite the entry of the dissection being completely sealed. Cardiologists should be aware of this possibility.
IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning
Authors: Yin Hengchuang , Wu Shufang , Tan Jie , Guo Qian , Li Mo , Guo Jinyuan , Wang Yaqi , Jiang Xiaoqing , Zhu Huaiqiu 
Published: April 2024

Abstract

Background
The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.

Findings
We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals.

Conclusions
IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.
Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing
Authors: Wei Chen , Wang Weikai , Hu Zhihao , Huang Zhuoli , Lu Ye , Zhou Wenwen , Liu Xiaoying , Jin Xin , Yin Jianhua , Li Guibo 
Published: April 2024

Abstract
Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.