bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mode-locked laser with multiple timescales in a microresonator-based nested cavity
Authors: Aadhi A. , Alamgir Imtiaz , Di Lauro Luigi , Fischer Bennet , Perron Nicolas , Dmitriev Pavel , Mazoukh Celine , Roztocki Piotr , Rimoldi Cristina , Chemnitz Mario , Eshaghi Armaghan , Viktorov Evgeny A. , Kovalev Anton V. , Little Brent E. , Chu Sai T. , Moss David J. , Morandotti Roberto 
Published: March 2024

Mode-locking techniques have played a pivotal role in developing and advancing laser technology. Stable fiber-cavity configurations can generate trains of pulses spanning from MHz to GHz speeds, which are fundamental to various applications in micromachining, spectroscopy, and communications. However, the generation and exploitation of multiple timescales in a single laser cavity configuration remain unexplored. Our work demonstrates a fiber-cavity laser configuration designed to generate and control pulse trains from nanosecond to picosecond timescales with a broadband output and a low mode-locking threshold. Our approach exploits a frequency mode-locking mechanism that simultaneously drives the modes of an integrated microring resonator nested within an external fiber-loop cavity, guaranteeing ultra-stable operation. By selectively filtering the nested cavity modes, we can transition from nanosecond pulses to pulse burst trains in which nanosecond and picosecond components coexist. Our laser configuration produces a train of pulses with durations of 5.1 ns and 3.1 ps at repetition rates of 4.4 MHz and 48.7 GHz, with time-bandwidth products close to the transform-limited values of 0.5 and 0.46, respectively. Moreover, in the absence of frequency modulation, we demonstrate the generation of comb spectra with an adjustable central wavelength. Our findings have the potential to significantly contribute to the development of cutting-edge technologies and applications, harnessing the distinct advantages of mode-locked pulses across various scientific and engineering disciplines.
Fluorinated organic ammonium salt passivation for high-efficiency and stable inverted CsPbI2Br perovskite solar cells
Authors: Liu Xin , She Xingchen , Wang Lang , Li Wei , Zhang Wen , Wang Shu , Wangyang Peihua , Wang Zhijun , Li Jie , Cui Xumei , Lan Mu , Liu Liqin , Sun Hui , Zhang Jun , Yang Dingyu 
Published: March 2024

All-inorganic CsPbI2Br inverted perovskite solar cells (PSCs) have drawn increasing attention because of their outstanding thermal stability and compatible process with tandem cells. However, relatively low open circuit voltage (Voc) has lagged their progress far behind theoretical limits. Herein, we introduce phenylmethylammonium iodide and 4-trifluoromethyl phenylmethylammonium iodide (CFPMAI) on the surface of a CsPbI2Br perovskite film and investigate their passivation effects. It is found that CFPMAI with a –CF3 substituent significantly decreases the trap density of the perovskite film by forming interactions with the under-coordinated Pb2+ ions and effectively suppresses the non-radiative recombination in the resulting PSC. In addition, CFPMAI surface passivation facilitates the optimization of energy-level alignment at the CsPbI2Br perovskite/[6,6]-phenyl C61 butyric acid methyl ester interface, resulting in improved charge extraction from the perovskite to the charge transport layer. Consequently, the optimized inverted CsPbI2Br device exhibits a markedly improved champion efficiency of 14.43% with a Voc of 1.12 V, a Jsc of 16.31 mA/cm2, and a fill factor of 79.02%, compared to the 10.92% (Voc of 0.95 V) efficiency of the control device. This study confirms the importance of substituent groups on surface passivation molecules for effective passivation of defects and optimization of energy levels, particularly for Voc improvement.
Algorithmic power and scientific knowledge
Authors: Signorini Inês 
Published: March 2024

Abstract
This paper critically revisits traditional perspectives on technology within academic and scientific writing
studies. It aims to comprehend the intricate, emerging, and dynamic sociotechnical configurations that underlie contemporary
scientific practices. These practices increasingly involve language, text, and literacy practices, seen as products of the
collaboration between humans and machines. The paper draws on empirical research on influential institutional metadiscourses in
high-impact scientific writing produced and/or disseminated by public universities and a research institute in the State of São
Paulo (Brazil), whose local policies of globalization are driven by international university rankings. I use a qualitative content
analysis approach grounded in socio-anthropological, socio-semiotic, and pragmatic studies of linguistic ideologies to shed light
on how ideological and socio-semiotic processes support the metapragmatics of scientific writing in university policy documents.
This metapragmatics is utterly alien to the role of performative sociotechnical infrastructures in the production, distribution,
and hierarchization of scientific texts. Additionally, these documents do not account for the diverse conditions and restrictions
that shape the production and circulation of academic knowledge in geopolitically marginal and equally diverse regions within the
country, including those within São Paulo.
Gromov–Hausdorff variational principles and measure stability
*
Authors: Lee Keonhee , Morales C A , Nguyen Ngocthach 
Published: March 2024

Abstract
Variational formulae for the Gromov–Hausdorff distances of compact metric spaces and their continuous maps are obtained. Also, a notion of µ-topological GH-stability on metric-measure spaces with probability µ is given. We prove that every expansive map with the weak µ-shadowing property (Lee and Rojas 2022 Russ. J. Nonlinear Dyn.
18 297–307) is µ-topologically GH-stable.
Spin-fluctuation-induced sign reversal of the spin Hall angle in Pt100−x
Co
x
alloys near the Curie temperature
Authors: Chang Yuhan , Wang Ruoshui , Ga Yonglong , Cui Baoshan , Xie Hongfei , Zhang Xu , Zhang Jianrong , Yan Ze , Cao Yang , Guo Xi , Xu Hengyi , Zuo Yalu , Jia Chenglong , Yang Hongxin , Xue Desheng , Xi Li 
Published: March 2024

Abstract
The spin Hall effect (SHE), typically emerging in non-magnetic metals with strong spin-orbit couplings, has attracted significant attention for its ability to convert a charge current into a spin current, a key feature in power-efficient spintronic devices. Recently, an enhanced SHE has been detected in the magnetic alloys, where the spin Hall conductivity is strongly modified by the dynamical and thermal spin fluctuations. We find that the spin Hall angle (

θ

SH

) in Pt100−x
Co
x
alloys dramatically changes at the Curie temperature, which is positive in the paramagnetic phase akin to Pt, while negative in the ferromagnetic phase. Such intriguing behavior of

θ

SH

stemming from individual and collective fluctuations in the magnetic moments is further substantiated with the full-fledged Monte Carlo simulations. Our work broadens insights into the SHE and highlights the importance of spin fluctuations for the spin-current generation near the ferromagnetic instability point of magnetic alloys.
Detecting the quantum phase transition from the perspective of quantum information in the Aubry–André model
Authors: Wei 韦 Geng-Biao 庚彪 , Ye 叶 Liu 柳 , Wang 王 Dong 栋 
Published: March 2024

Abstract
We investigate the effectiveness of entropic uncertainty, entanglement and steering in discerning quantum phase transitions (QPTs). Specifically, we observe significant fluctuations in entropic uncertainty as the driving parameter traverses the phase transition point. It is observed that the entropic uncertainty, entanglement and quantum steering, based on the electron distribution probability, can serve as indicators for detecting QPTs. Notably, we reveal an intriguing anticorrelation relationship between entropic uncertainty and entanglement in the Aubry–André model. Moreover, we explore the feasibility of detecting a QPT when the period parameter is a rational number. These observations open up new and efficient avenues for probing QPTs.
Mass suppression effect in QCD radiation and hadron angular distribution in jet*
Authors: Jiang 蒋 Chuan-Hui 传辉 , Li 李 Hai Tao 海涛 , Li 李 Shi-Yuan 世渊 , Si 司 Zong-Guo 宗国 
Published: March 2024

Abstract
The finite mass of the heavy quark suppresses the collimated radiations; this is generally referred to as the dead cone effect. In this paper, we study the distribution of hadron multiplicity over the hadron opening angle with respect to the jet axis for various jet flavors. The corresponding measurement can be the most straightforward and simplest approach to explore the dynamical evolution of the radiations in the corresponding jet, which can expose the mass effect. We also propose a transverse energy-weighted angular distribution, which sheds light on the interplay between perturbative and non-perturbative effects in the radiation. Through Monte-Carlo simulations, our calculations show that the dead cone effect can be clearly observed by finding the ratio between the b and light-quark (inclusive) jets; this is expected to be measured at the LHC in the future.
B − L model with D
4 × Z
4 × Z
2 symmetry for fermion mass hierarchies and mixings*
Authors: Vien V. V. 
Published: March 2024

Abstract
We constructed a gauge

model with

symmetry to explain the quark and lepton mass hierarchies and their mixings with realistic CP phases via the type-I seesaw mechanism. Six quark mases, three quark mixing angles, and the CP phase in the quark sector take the central values whereas Yukawa couplings in the quark sector are diluted in a range of difference of three orders of magnitude by the perturbation theory at the first order. Concerning the neutrino sector, a small neutrino mass is achieved by the type-I seesaw mechanism. Both inverted and normal neutrino mass hierarchies are consistent with the experimental data. The predicted sum of neutrino masses for normal and inverted hierarchies, the effective neutrino masses, and the Dirac CP phase are also consistent with recently reported limits.
Gas gains over 104 and optimisation using 55Fe X-rays in low pressure SF6 with a novel Multi-Mesh ThGEM for directional dark matter searches
Authors: McLean A.G. , Spooner N.J.C. , Crane T. , Eldridge C. , Ezeribe A.C. , Marcelo Gregorio R.R. , Scarff A. 
Published: March 2024

Abstract
The Negative Ion Drift (NID) gas SF6 has favourable properties for track reconstruction in directional Dark Matter (DM) searches utilising low pressure gaseous Time Projection Chambers (TPCs). However, the electronegative nature of the gas means that it is more difficult to achieve significant gas gains with regular Thick Gaseous Electron Multipliers (ThGEMs). Typically, the maximum attainable gas gain in SF6 and other Negative Ion (NI) gas mixtures, previously achieved with an 55Fe X-ray source or electron beam, is on the order of 103 [1,2,3,4]; whereas electron drift gases like CF4 and similar mixtures are readily capable of reaching gas gains on the order of 104 or greater [5,9,7,8,6]. In this paper, a novel two stage Multi-Mesh ThGEM (MMThGEM) structure is presented. The MMThGEM was used to amplify charge liberated by an 55Fe X-ray source in 40 Torr of SF6. By expanding on previously demonstrated results [10], the device was pushed to its sparking limit and stable gas gains up to ˜50000 were observed. The device was further optimised by varying the field strengths of both the collection and transfer regions in isolation. Following this optimisation procedure, the device was able to produce a maximum stable gas gain of ˜90000. These results demonstrate an order of magnitude improvement in gain with the NID gas over previously reported values and ultimately benefits the sensitivity of a NITPC to low energy recoils in the context of a directional DM search.
Ion entropy and accurate entropy-based FDR estimation in metabolomics
Authors: An Shaowei , Lu Miaoshan , Wang Ruimin , Wang Jinyin , Jiang Hengxuan , Xie Cong , Tong Junjie , Yu Changbin 
Published: March 2024

Abstract
Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target–decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.
Biolinguistic graph fusion model for circRNA–miRNA association prediction
Authors: Guo Lu-Xiang , Wang Lei , You Zhu-Hong , Yu Chang-Qing , Hu Meng-Lei , Zhao Bo-Wei , Li Yang 
Published: March 2024

AbstractEmerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA–miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.
Inference of differential gene regulatory networks using boosted differential trees
Authors: Galindez Gihanna , List Markus , Baumbach Jan , Völker Uwe , Mäder Ulrike , Blumenthal David B , Kacprowski Tim 
Published: March 2024

Abstract

Summary
Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn’s disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Availability and implementation
BoostDiff is available at https://github.com/scibiome/boostdiff_inference.
AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification
Authors: Kim Jung , Naqvi Ammar S , Corbett Ryan J , Kaufman Rebecca S , Vaksman Zalman , Brown Miguel A , Miller Daniel P , Phul Saksham , Geng Zhuangzhuang , Storm Phillip B , Resnick Adam C , Stewart Douglas R , Rokita Jo Lynne , Diskin Sharon J 
Published: March 2024

Abstract

Summary
With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics—Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting.

Availability and implementation
AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.
MolFeSCue: enhancing molecular property prediction in data-limited and imbalanced contexts using few-shot and contrastive learning
Authors: Zhang Ruochi , Wu Chao , Yang Qian , Liu Chang , Wang Yan , Li Kewei , Huang Lan , Zhou Fengfeng 
Published: March 2024

Abstract

Motivation
Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models.

Results
This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm’s effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery.

Availability and implementation
We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.
Uncertainty-aware single-cell annotation with a hierarchical reject option
Authors: Theunissen Lauren , Mortier Thomas , Saeys Yvan , Waegeman Willem 
Published: March 2024

Abstract

Motivation
Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is currently no consensus on best practices.

Results
We evaluate three annotation approaches (i) full rejection, (ii) partial rejection, and (iii) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a method’s rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships.

Availability and implementation
Code is freely available at https://github.com/Latheuni/Hierarchical_reject and https://doi.org/10.5281/zenodo.10697468.
A rapid and accurate method for evaluating the degradation of pan-Akt in cells by PROTACs using NanoLuc luciferase
Authors: Ji Xiaojun , Miao Lei , Wu Yebin , Zhao Tingli , Si Yaxuan , Tan Xiaoyun , Zhou Qiuhua , Zuo Rui , Pei Junjie , Wu Jian , Ma Changyou , Ma Zhongjun , Xu Dan 
Published: March 2024

Abstract
Proteolysis targeting chimera (PROTAC) is a protein degradation technique that has been increasingly used in the development of new drugs in recent years. Akt is a classical serine/threonine kinase, and its role outside of the kinase has gradually gained attention in recent years, making it one of the proteins targeted by PROTACs. Currently, there are many methods used for the evaluation of intracellular protein degradation, but each has its own advantages or disadvantages. This study aimed to investigate the feasibility of evaluating the degradation of pan-Akt proteins in cells by PROTACs (MS21 and MS170) using the NanoLuc luciferase method. After conducting a thorough comparison between this method and the classical western blot assay in various cells, as well as testing the stability of the experiments between multiple batches, we found that NanoLuc luciferase is a highly accurate, stable, low-cost and easy-to-operate method for the evaluation of intracellular pan-Akt degradation by PROTACs with a short cycle time and high cellular expandability. Given the numerous advantages of this method, it is hypothesized that it could be extended to evaluate the degradation of more target proteins of PROTACs. In summary, the NanoLuc luciferase is a suitable method for early protein degradation screening of PROTAC compounds.
Cementoplasty to cryoablation: review and current status
Authors: Tan Jin Rong , Yan Yet Yen , Sheikh Adnan , Ouellette Hugue , Mallinson Paul , Munk Peter L 
Published: March 2024

Abstract
Recent advances in percutaneous image-guided techniques have empowered interventional radiologists with diverse treatment options for the management of musculoskeletal lesions. Of note, there is growing utility for cementoplasty procedures, with indications ranging from stabilization of bone metastases to treatment of painful vertebral compression fractures. Likewise, cryoablation has emerged as a viable adjunct in the treatment of both primary and secondary bone and soft tissue neoplasms. These treatment options have been progressively incorporated into the multidisciplinary approach to holistic care of patients, alongside conventional radiotherapy, systemic therapy, surgery, and analgesia. This review article serves to outline the indications, technical considerations, latest developments, and evidence for the burgeoning role of cementoplasty and cryoablation in the musculoskeletal system, with an emphasis on pain palliation and tumour control.