bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid characterization of silicon photomultipliers for noble liquid experiments
Authors: Chana B. , Mahtab M. , Retière F. , Viel S. 
Published: March 2023

Abstract
Silicon photomultipliers (SiPMs) are emerging as the photodetector technology to be used in upcoming noble liquid experiments. Newly developed SiPMs sensitive to vacuum ultraviolet (VUV) light will be directly used for the readout of scintillation photons (λ = 175 nm) from liquid xenon in future tonne-scale experiments, such as nEXO, searching for neutrinoless double beta decay in 136Xe. In this research project, VUV-SiPMs from two different vendors are characterized using current–voltage (IV) and pulse-level measurements performed at TRIUMF, from room temperature to liquid xenon temperature. These data are analysed to extract the SiPM’s features such as breakdown voltage, gain, crosstalk, afterpulsing and dark noise rates. The IV and pulse-level results are compared. A method is proposed for rapid quality control of large numbers of SiPM using IV measurements.
The design and test results of A Giga-Bit Cable Receiver (GBCR) for the ATLAS Inner Tracker Pixel Detector
Authors: Zhang L. , Gong D. , Liu T. , Chen C. , Deng B. , Hou S. , Huang G. , Huang X. , Liu C. , Moreira P. , Sun Q. , Sun X. , Kulis S. , Ye J. , Zhang W. 
Published: March 2023

AbstractThis paper presents the design and test results of a Gigabit Cable Receiver ASIC called GBCR for the HL-LHC upgrade of the ATLAS Inner Tracker (ITk) pixel detector. Three prototypes (GBCR1, GBCR2, and GBCR3) have been designed in the CERN-identified 65 nm CMOS technology. GBCR receives seven (GBCR2) or six (GBCR3) channels (RX) each at 1.28 Gbps from the front-end readout chip RD53B via flex cables up to 1 meter and Twinax cables up to 5 meters and sends the equalized and retimed signals to lpGBT. Both GBCR2 and GBCR3 ASICs have two transmitting channels (TX) that pre-emphasize the signals from lpGBT before sending them to RD53B through the same cables. No Single-Event Upset (SEU) is observed in any tested channels of GBCR2 in a 400 MeV proton beam. The extrapolated bit error rate for the future HL-LHC application is below 8 × 10−16, significantly below the specified BER criterion. GBCR3 is designed to improve the immunity to single-event-upset by applying the Triple Modular Redundancy (TMR) technology to all RX channels. The retimed signals from GBCR3 have less total jitter than those from GBCR2 (35 ps versus 79 ps). Each receiver channel of GBCR3 consumes 75% more power than that of GBCR2.
The importance of interactions between snow, permafrost and vegetation dynamics in affecting terrestrial carbon balance in circumpolar regions
Authors: Xu Yiming , Zhuang Qianlai 
Published: March 2023

Abstract
Permafrost dynamics can drastically affect vegetation and soil carbon dynamics in northern high latitudes. Vegetation has significant influences on the energy balance of soil surface by impacting the short-wave radiation, long-wave radiation and surface sensible heat flux, affecting soil thermal dynamics, in turn, inducing vegetation shift, affecting carbon cycling. During winter, snow can also significantly impact soil temperature due to its insulative effect. However, these processes have not been fully modeled to date. To quantify the interactions between vegetation, snow, and soil thermal dynamics and their impacts on carbon dynamics over the circumpolar region (45–90° N), we revise a sophisticated ecosystem model to improve simulations of soil temperature profile and their influences on vegetation, ecosystem carbon pools and fluxes. We find that, with warmer soil temperature in winter and cooler soil temperature in summer simulated with the revised model considering vegetation shift and snow effects, the region will release 1.54 Pg C/year to the atmosphere for present-day and 66.77–87.95 Pg C in 2022–2100. The canopy effects due to vegetation shift, however, will get more carbon sequestered into the ecosystem at 1.00 Pg C/year for present day and 36.09–44.32 Pg C/year in 2022–2100. This study highlights the importance to consider the interactions between snow, vegetation shift and soil thermal dynamics in simulating carbon dynamics in the region.
Naimark dilations of qubit POVMs and joint measurements
Authors: Pellonpää Juha-Pekka , Designolle Sébastien , Uola Roope 
Published: March 2023

Abstract
Measurement incompatibility is one of the cornerstones of quantum theory. This phenomenon appears in many forms, of which the concept of non-joint measurability has received considerable attention in the recent years. In order to characterise this non-classical phenomenon, various analytical and numerical methods have been developed. The analytical approaches have mostly concentrated on the qubit case, as well as to scenarios involving sets of measurements with symmetries, such as position and momentum or sets of mutually unbiased bases. The numerical methods can, in principle, decide any finite-dimensional and discrete joint measurability problem, but they naturally have practical limitations in terms of computational power. These methods exclusively start from a given set of measurements and ask whether the set possesses incompatibility. Here, we take a complementary approach by asking which measurements are compatible with a given measurement. It turns out, that this question can be answered in full generality through a minimal Naimark dilation of the given measurement: the set of interest is exactly those measurements that have a block-diagonal representation in such dilation. We demonstrate the use of the technique through various qubit examples, leading to an alternative characterisation of all compatible pairs of binary qubit measurements, which retrieves the celebrated Busch criterion. We further apply the technique to special examples of trinary and continuous qubit measurements.
Hybrid quantum-classical dynamics of pure-dephasing systems
*
Authors: Manfredi Giovanni , Rittaud Antoine , Tronci Cesare 
Published: March 2023

Abstract
We consider the interaction dynamics of a classical oscillator and a quantum two-level system for different pure-dephasing Hamiltonians of the type

H
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q
,
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)
=

H
C

(
q
,
p
)
1
+

H
I

(
q
,
p
)

σ
ˆ

z

. This type of systems represents a severe challenge for popular hybrid quantum–classical descriptions. For example, in the case of the common Ehrenfest model, the classical density evolution is shown to decouple entirely from the pure-dephasing quantum dynamics. We focus on a recently proposed hybrid wave equation that is based on Koopman’s wavefunction description of classical mechanics. This model retains quantum–classical correlations whenever a coupling potential is present. Here, several benchmark problems are considered and the results are compared with those arising from fully quantum dynamics. A good agreement is found for a series of study cases involving harmonic oscillators with linear and quadratic coupling, as well as time-varying coupling parameters. In all these cases the classical evolution coincides exactly with the oscillator dynamics resulting from the fully quantum description. In the special case of time-independent coupling involving a classical oscillator with varying frequency, the quantum Bloch rotation exhibits peculiar features that escape from the hybrid description. In addition, nonlinear corrections to the harmonic Hamiltonian lead to an overall growth of decoherence at long times, which is absent in the fully quantum treatment.
Information parity increases on functional brain networks under influence of a psychedelic substance
Authors: Viol Aline , Viswanathan Gandhimohan M , Soldatkina Oleksandra , Palhano-Fontes Fernanda , Onias Heloisa , de Araujo Draulio , Hövel Philipp 
Published: March 2023

Abstract
The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex network theory has greatly contributed to understand brain process in different states of mind. We study a non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These findings suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This could be interpreted as a mechanism to maintain the network functional resilience.
ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning
Authors: Jia Xiaoti , Zhao Pei , Li Fuyi , Qin Zhaohui , Ren Haoran , Li Junzhou , Miao Chunbo , Zhao Quanzhi , Akutsu Tatsuya , Dou Gensheng , Chen Zhen , Song Jiangning 
Published: March 2023

Abstract
Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying Khib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that Khib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify Khib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific Khib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific Khib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting Khib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available Khib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.
Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma
Authors: Yan Zi-Jun , Yu Chu-Ting , Chen Lei , Wang Hong-Yang 
Published: March 2023

AbstractImmune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.
Machine learning on protein–protein interaction prediction: models, challenges and trends
Authors: Tang Tao , Zhang Xiaocai , Liu Yuansheng , Peng Hui , Zheng Binshuang , Yin Yanlin , Zeng Xiangxiang 
Published: March 2023

AbstractProtein–protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.
NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes
Authors: He Weiming , Yang Jian , Jing Yi , Xu Lian , Yu Kang , Fang Xiaodong 
Published: March 2023

AbstractSummaryLarge-scale comparative genomic studies have provided important insights into species evolution and diversity, but also lead to a great challenge to visualize. Quick catching or presenting key information hidden in the vast amount of genomic data and relationships among multiple genomes requires an efficient visualization tool. However, current tools for such visualization remain inflexible in layout and/or require advanced computation skills, especially for visualization of genome-based synteny. Here, we developed an easy-to-use and flexible layout tool, NGenomeSyn [multiple (N) Genome Synteny], for publication-ready visualization of syntenic relationships of the whole genome or local region and genomic features (e.g. repeats, structural variations, genes) across multiple genomes with a high customization. NGenomeSyn provides an easy way for its users to visualize a large amount of data with a rich layout by simply adjusting options for moving, scaling, and rotation of target genomes. Moreover, NGenomeSyn could be applied on the visualization of relationships on non-genomic data with similar input formats.Availability and implementationNGenomeSyn is freely available at GitHub (https://github.com/hewm2008/NGenomeSyn) and Zenodo (https://doi.org/10.5281/zenodo.7645148).
The logarithmic transformation in bivariate allometry
Authors: Packard Gary C 
Published: March 2023

AbstractThe field of biological allometry has been dominated since early in the last century by the logarithmic transformation, which is widely perceived to be necessary for the proper analysis of bivariate data relating the size of a structure or the intensity of a process to some measure of body size. Some investigators argue that transformation is needed to align the analysis with underlying theory; others assert that transformation is required to describe multiplicative growth in living substance; and still other workers believe that transformation is necessary to accommodate multiplicative variation in the response variable (heteroscedasticity) and/or a lognormal distribution for residuals from the fitted equation. None of these beliefs is true. Moreover, constraints imposed by logarithmic transformation typically result in data being ‘fitted’ to a predetermined statistical model instead of a model being fitted to the data, thereby leading in many instances to erroneous perceptions of pattern in the data, misinterpretation of the findings and misdirection for future research. Robust statistical models with different functional form and different assumptions about random error can be fitted directly to the original data by non-linear regression, thereby obviating transformation altogether. The utility of the regression protocol is illustrated in a re-analysis of published data.
Loss of age-related laminar differentiation of intracortical myelin in bipolar disorder
Authors: Suh Jee Su , Rowley Christopher D , Sehmbi Manpreet , Tardif Christine L , Minuzzi Luciano , Bock Nicholas A , Frey Benicio N 
Published: March 2023

AbstractAge-related changes of intracortical myelin in bipolar disorder (BD) have been observed to deviate from the quadratic age curve observed in healthy controls (HC), but it is unclear if this holds at varying cortical depths. From BD (n = 44; age range = 17.6–45.5 years) and HC (n = 60; age range = 17.1–45.8 years) participants, we collected 3T T1-weighted (T1w) images with strong intracortical contrast. Signal values were sampled from 3 equivolume cortical depths. Linear mixed models were used to compare age-related changes in the T1w signal between depths and between groups at each depth. In HC, the age-related changes were significantly different between the superficial one-fourth depth and the deeper depths in the right ventral somatosensory (t = −4.63; FDRp = 0.00025), left dorsomedial somatosensory (t = −3.16; FDRp = 0.028), left rostral ventral premotor (t = −3.16; FDRp = 0.028), and right ventral inferior parietal cortex (t = −3.29; FDRp = 0.028). BD participants exhibited no differences in the age-related T1w signal between depths. Illness duration was negatively correlated with the T1w signal at the one-fourth depth in the right anterior cingulate cortex (rACC; rho = −0.50; FDRp = 0.029). Physiological age-related and depth-specific variation in the T1w signal were not observed in BD. The T1w signal in the rACC may reflect lifetime disease burden in the disorder.
A fast algorithm to approximate the spectral density of locally tree-like networks with assortativity
Authors: Guzman Grover E C , Fujita André 
Published: March 2023

AbstractGraphs have become crucial for representing and examining biological, social and technological interactions. In this context, the graph spectrum is an exciting feature to be studied because it encodes the structural and dynamic characteristics of the graph. Hence, it becomes essential to efficiently compute the graph’s spectral distribution (eigenvalue’s density function). Recently, some authors proposed degree-based methods to obtain the spectral density of locally tree-like networks in linear time. The bottleneck of their approach is that they assumed that the graph’s assortativity is zero. However, most real-world networks, such as social and biological networks, present assortativity. Consequently, their spectral density approximations may be inaccurate. Here, we propose a method that considers assortativity. Our algorithm’s time and space complexities are $\mathscr{O}(d_{\max}^{2})$, where $d_{\max}$ is the largest degree of the graph. Finally, we show our method’s efficacy in simulated and empirical networks.
Overcoming professional barriers encountered by women in interventional cardiology: an EAPCI statement
Authors: Buchanan Gill Louise , Paradies Valeria , Karam Nicole , Holmvang Lene , Mamas Mamas A , Mehilli Julinda , Capodanno Davide , Capranzano Piera , Appelman Yolande , Manzo-Silberman Stéphane , Kunadian Vijay , Mauri Josepa , Shuepke Stefanie , Petronio Anna Sonia , Kaluzna-Oleksy Marta , Gilard Martine , Morice Marie Claude , Barbato Emanuele , Dudek Dariusz , Chieffo Alaide 
Published: March 2023

Abstract
Despite the increasing proportion of female medical and nursing students, there is still a significant under-representation of women working as healthcare providers in interventional cardiology, with very few of them reaching senior leadership, academic positions, or acting principal investigators, as well as actively involved in company advisory boards. In this position paper, we will describe the current status of women working in interventional cardiology across Europe. We will also provide an overview of the most relevant determinants of the under-representation of women at each stage of the interventional cardiology career path and offer practical suggestions for overcoming these challenges.
Circulating ketone bodies and cardiovascular outcomes: the MESA study
Authors: Shemesh Elad , Chevli Parag Anilkumar , Islam Tareq , German Charles A , Otvos James , Yeboah Joseph , Rodriguez Fatima , deFilippi Christopher , Lima Joao A C , Blaha Michael , Pandey Ambarish , Vaduganathan Muthiah , Shapiro Michael D 
Published: March 2023

Abstract

Aims
Ketone bodies (KB) are an important alternative metabolic fuel source for the myocardium. Experimental and human investigations suggest that KB may have protective effects in patients with heart failure. This study aimed to examine the association between KB and cardiovascular outcomes and mortality in an ethnically diverse population free from cardiovascular disease (CVD).

Methods and results
This analysis included 6796 participants (mean age 62 ± 10 years, 53% women) from the Multi-Ethnic Study of Atherosclerosis. Total KB was measured by nuclear magnetic resonance spectroscopy. Multivariable-adjusted Cox proportional hazard models were used to examine the association of total KB with cardiovascular outcomes. At a mean follow-up of 13.6 years, after adjusting for traditional CVD risk factors, increasing total KB was associated with a higher rate of hard CVD, defined as a composite of myocardial infarction, resuscitated cardiac arrest, stroke, and cardiovascular death, and all CVD (additionally included adjudicated angina) [hazard ratio, HR (95% confidence interval, CI): 1.54 (1.12–2.12) and 1.37 (1.04–1.80) per 10-fold increase in total KB, respectively]. Participants also experienced an 87% (95% CI: 1.17–2.97) increased rate of CVD mortality and an 81% (1.45–2.23) increased rate of all-cause mortality per 10-fold increase in total KB. Moreover, a higher rate of incident heart failure was observed with increasing total KB [1.68 (1.07–2.65), per 10-fold increase in total KB].

Conclusion
The study found that elevated endogenous KB in a healthy community-based population is associated with a higher rate of CVD and mortality. Ketone bodies could serve as a potential biomarker for cardiovascular risk assessment.