bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smith homomorphisms and 𝑠𝑝𝑖𝑛^{ℎ} structures
Authors: Debray Arun , Krulewski Cameron 
Published: December 2024

In this article, we answer two questions of Buchanan–McKean [KSp-characteristic classes determine Spinh cobordism, https://arxiv.org/ abs/2312.08209, 2023] about bordism for manifolds with

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bordism, and we provide a geometric explanation for the isomorphism

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\Omega _{4k}^{\mathrm {Spin}^c}\otimes \mathbb {Z}[1/2] \cong \Omega _{4k}^{\mathrm {Spin}^h}\otimes \mathbb {Z}[1/2]

. Our proofs use the general theory of twisted spin structures and Smith homomorphisms that we developed joint with Devalapurkar, Liu, Pacheco-Tallaj, and Thorngren [The Smith fiber sequence and invertible field theories, https://arxiv.org/abs/2405.04649, 2024], specifically that the Smith homomorphism participates in a long exact sequence with explicit, computable terms.
GUI-Equipped Waveform Simulator
Authors: Essick John 
Published: December 2024

Abstract
Readers write a digitized waveform simulation program with advanced graphical user interface (GUI) features, including numeric input controls, a pull-down menu, and an embedded waveform plot. The completed program creates waveforms with sine, cosine, DC level, square, sawtooth, triangle, and user-defined shapes. In writing this program, three Python “utility” modules are created to carry out the following tasks: creation of waveform data with a desired shape, creation of a GUI with interactive controls, and creation of a GUI-embedded plot to display waveform data. These three modules are used in programs created throughout the remainder of the book. The GUI-equipped program created as a Do It Yourself project allows for interactive study of arbitrary-shape waveform generation via the summation of sine waves, while a Use It! example demonstrates the need for multithread execution in programs that carry out simultaneous GUI and time-intensive data-acquisition processes.
Data Analysis: Fast Fourier Transform
Authors: Essick John 
Published: December 2024

Abstract
Using SciPy functions, readers write Python programs to investigate the proper use of the fast Fourier transform (FFT). After a quick introduction to FFT properties, the theory of the discrete Fourier transform and discrete inverse Fourier transform is presented, including the mathematics behind how these quantities are used to produce discrete complex-amplitude and frequency spectra from a given input waveform. Then, readers write a program in which SciPy’s complex Fourier transform function is applied to various sinusoidal inputs, demonstrating that this function produces the expected complex-amplitude spectra. Finally, a program is written that simply plots the magnitude of the complex-amplitude as a function of frequency, and the effects of spectral leakage, windowing, and aliasing for input frequencies above the Nyquist frequency are explored. By applying this understanding of the FFT, along with the use of a DAQ device, readers construct a computer-based spectrum analyzer as a Do It Yourself project. A Use It! example investigates another of SciPy’s frequency-related signal-processing capabilities—digital filtering.
Applying Author Styles
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
In the previous chapter we gave each pre element a gray background. It looks OK, and it is good to have defaults, but sites want a say in how they look. Websites do that with Cascading Style Sheets (CSS), which allow web authors (and, as we’ll see, browser developers) to define how a web page ought to look.
Handling Buttons and Links
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
Our browser is still missing the key insight of hypertext: documents linked together by hyperlinks. It lets us watch the waves, but not surf the web. So in this chapter, we’ll implement hyperlinks, an address bar, and the rest of the browser interface—the part of the browser that decides which page we are looking at.
Sending Information to Servers
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
So far, our browser has seen the web as read only—but when you post on Facebook, fill out a survey, or search Google, you’re sending information to servers as well as receiving information from them. In this chapter, we’ll start to transform our browser into a platform for web applications by building out support for HTML forms, the simplest way for a browser to send information to a server.
Making Content Accessible
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
So far, we’ve focused on making the browser an effective platform for developing web applications. But ultimately, the browser is a user agent. That means it should assist the user in whatever way it can to access and use web applications. Browsers therefore offer a range of accessibility [1] features that take advantage of declarative UI and the flexibility of HTML and CSS to make it possible to interact with web pages by touch, keyboard, or voice.
A Changing Landscape
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
The web is a dynamic, ever-changing place. The first web browser, in 1989, did not support colors, images, styling, or scripting. Three decades of market forces, implementation quirks, and the ever-expanding reach of the web then made browsers what they are today. Those forces are as strong as ever. Browsers continue to evolve!
Cell-specific priors rescue differential gene expression in spatial spot-based technologies
Authors: Nahman Ornit , Few-Cooper Timothy J , Shen-Orr Shai S 
Published: December 2024

Abstract
Spatial transcriptomics (ST), a breakthrough technology, captures the complex structure and state of tissues through the spatial profiling of gene expression. A variety of ST technologies have now emerged, most prominently spot-based platforms such as Visium. Despite the widespread use of ST and its distinct data characteristics, the vast majority of studies continue to analyze ST data using algorithms originally designed for older technologies such as single-cell (SC) and bulk RNA-seq—particularly when identifying differentially expressed genes (DEGs). However, it remains unclear whether these algorithms are still valid or appropriate for ST data. Therefore, here, we sought to characterize the performance of these methods by constructing an in silico simulator of ST data with a controllable and known DEG ground truth. Surprisingly, our findings reveal little variation in the performance of classic DEG algorithms—all of which fail to accurately recapture known DEGs to significant levels. We further demonstrate that cellular heterogeneity within spots is a primary cause of this poor performance and propose a simple gene-selection scheme, based on prior knowledge of cell-type specificity, to overcome this. Notably, our approach outperforms existing data-driven methods designed specifically for ST data and offers improved DEG recovery and reliability rates. In summary, our work details a conceptual framework that can be used upstream, agnostically, of any DEG algorithm to improve the accuracy of ST analysis and any downstream findings.
A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks
Authors: Liu Yong , Zhong Le , Yan Bin , Chen Zhuobin , Yu Yanjia , Yu Dan , Qin Jing , Wang Junwen 
Published: December 2024

Abstract
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF–gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning–based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes. However, these methods do not significantly improve predictions of TGIs due to some limitations regarding constructing underlying interactive structures linking regulatory components. In this study, we introduce a deep learning framework, DeepTGI, that encodes gene expression profiles from single-cell and/or bulk transcriptomic data and predicts TGIs with high accuracy. Our approach could fuse the features extracted from Auto-encoder with self-attention mechanism and other networks and could transform multihead attention modules to define representative features. By comparing it with other models or methods, DeepTGI exhibits its superiority to identify more potential TGIs and to reconstruct the GRNs and, therefore, could provide broader perspectives for discovery of more biological meaningful TGIs and for understanding transcriptional gene regulatory mechanisms.
Breaking free from references: a consensus-based approach for community profiling with long amplicon nanopore data
Authors: Stock Willem , Rousseau Coralie , Dierickx Glen , D'hondt Sofie , Amadei Martínez Luz , Dittami Simon M , van der Loos Luna M , De Clerck Olivier 
Published: December 2024

Abstract
Third-generation sequencing platforms, such as Oxford Nanopore Technology (ONT), have made it possible to characterize communities through the sequencing of long amplicons. While this theoretically allows for an increased taxonomic resolution compared to short-read sequencing platforms such as Illumina, the high error rate remains problematic for accurately identifying the community members present within a sample. Here, we present and validate CONCOMPRA, a tool that allows the detection of closely related strains within a community by drafting and mapping to consensus sequences. We show that CONCOMPRA outperforms several other tools for profiling bacterial communities using full-length 16S rRNA gene sequencing. Since CONCOMPRA does not rely on a sequence database for profiling communities, it is applicable to systems and amplicons for which little to no reference data exists. Our validation test shows that the amplification of long PCR products is likely to produce chimeric byproducts that inflate alpha diversity and skew community structure, stressing the importance of chimera detection. CONCOMPRA is available on GitHub (https://github.com/willem-stock/CONCOMPRA).
GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering
Authors: Cui Lubin , Guo Guiliang , Ng Michael K , Zou Quan , Qiu Yushan 
Published: December 2024

Abstract
Single-cell multi-omics refers to the various types of biological data at the single-cell level. These data have enabled insight and resolution to cellular phenotypes, biological processes, and developmental stages. Current advances hold high potential for breakthroughs by integrating multiple different omics layers. However, singlecell multi-omics data usually have different feature dimensions and direct or indirect relationships. How to keep the data structure of these different data and extract hidden relationships is a major challenge for omics data integration, and effective integration models are urgently needed. In this paper, we propose an irregular tensor decomposition model (GSTRPCA) based on tensor robust principal component analysis (TRPCA). We developed a weighted threshold model for the decomposition of irregular tensor data by combining low-rank and sparsity constraints, which requires that the low-dimensional embeddings of the data remain lowrank and sparse. The major advantage of the GSTRPCA algorithm is its ability to keep the original data structure and explore hidden related features among omics data. For GSTRPCA, we also designed an effective algorithm that theoretically guarantees global convergence for the tensor decomposition. The computational experiments on irregular tensor datasets demonstrate that GSTRPCA significantly outperformed the state-of-the-art methods and hence confirm the superiority of GSTRPCA in clustering single-cell multiomics data. To our knowledge, this is the first tensor decomposition method for irregular tensor data to keep the data structure and hence improve the clustering performance for single-cell multi-omics data. GSTRPCA is a Matlabbased algorithm, and the code is available from https://github.com/GGL-B/GSTRPCA.
scPAS: single-cell phenotype-associated subpopulation identifier
Authors: Xie Aimin , Wang Hao , Zhao Jiaxu , Wang Zhaoyang , Xu Jinyuan , Xu Yan 
Published: December 2024

Abstract
Despite significant advancements in single-cell sequencing analysis for characterizing tissue sample heterogeneity, identifying the associations between cell subpopulations and disease phenotypes remains a challenging task. Here, we introduce scPAS, a new bioinformatics tool designed to integrate bulk data to identify phenotype-associated cell subpopulations within single-cell data. scPAS employs a network-regularized sparse regression model to quantify the association between each cell in single-cell data and a phenotype. Additionally, it estimates the significance of these associations through a permutation test, thereby identifying phenotype-associated cell subpopulations. Utilizing simulated data and various single-cell datasets from breast carcinoma, ovarian cancer, and atherosclerosis, as well as spatial transcriptomics data from multiple cancers, we demonstrated the accuracy, flexibility, and broad applicability of scPAS. Evaluations on large datasets revealed that scPAS exhibits superior operational efficiency compared to other methods. The open-source scPAS R package is available at GitHub website: https://github.com/aiminXie/scPAS.
A likelihood approach to incorporating self-report data in HIV recency classification
Authors: Yang Wenlong , Liu Danping , Bao Le , Li Runze 
Published: December 2024

Abstract
Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent versus long-term) could be determined from self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over 1 year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates individuals with known recency status based on testing histories and individuals whose recency status could not be determined and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi PHIA data, as well as on simulated data. Our model obtains more efficient and less biased parameter estimates and is relatively robust to potential reporting error and model misspecification.
Graphical model inference with external network data
Authors: Jewson Jack , Li Li , Battaglia Laura , Hansen Stephen , Rossell David , Zwiernik Piotr 
Published: December 2024

ABSTRACT
A frequent challenge when using graphical models in practice is that the sample size is limited relative to the number of parameters. They also become hard to interpret when the number of variables p gets large. We consider applications where one has external data, in the form of networks between variables, that can improve inference and help interpret the fitted model. An example of interest regards the interplay between social media and the co-evolution of the COVID-19 pandemic across USA counties. We develop a spike-and-slab prior framework that depicts how partial correlations depend on the networks, by regressing the edge probabilities, average partial correlations, and their variance on the networks. The goal is to detect when the network data relates to the graphical model and, if so, explain how. We develop computational schemes and software in R and probabilistic programming languages. Our applications show that incorporating network data can improve interpretation, statistical accuracy, and out-of-sample prediction.
Misidentified Victim-Survivors as Domestic and Family Violence Perpetrators: Plea Negotiations as a Tool for Justice?
Authors: Moffa Monique , Flynn Asher , Burns Kate Hutton 
Published: December 2024

Abstract
The misidentification of primary perpetrators in domestic and family violence (DFV) incidents has received increased attention in academic, practitioner and policy contexts. Yet limited research has explored the implications of misidentification for women in criminal proceedings, specifically how misidentification and subsequent criminalisation of women are linked to both erroneous or forced guilty pleas, and a reliance on plea negotiations to correct misidentification errors. Drawing on interviews with 21 legal defence practitioners, we document the strategies practitioners use in the plea negotiation process to advocate for women defendants who have been misidentified in DFV contexts. We also explore the barriers that women defendants face in accessing justice in a discretionary and managerialist criminal justice system.
Deglobalization: three scenarios
Authors: van Bergeijk Peter A G 
Published: December 2024

Abstract
The trade developments of the first quarter of the 21st century prompt speculation on how future generations will perceive them. Will they interpret this era as one where deglobalization took root and flourished, marked by a slowdown in world trade? Or will they view it as a mere pause in globalization’s upward trajectory? The literature explores these possibilities, noting the impact of events like the Financial Crisis, COVID-19 lockdowns and the Ukraine and Gaza wars on world trade. There is disagreement over whether globalization is receding or evolving. Three scenarios offer varied perspectives, from a bleak outlook of disintegrating international relations to a more optimistic view where deglobalization is seen as a temporary setback. These scenarios highlight the complexity and uncertainty surrounding (de)globalization, emphasizing the need for interdisciplinary analysis and a nuanced understanding of global trends.
Measuring and quantifying the effects of pyloric dilatation in patients with delayed emptying of the gastric conduit after Ivor-Lewis esophagectomy using EndoFlip™
Authors: Brunner Stefanie , Lorenz Florian , Dratsch Thomas , Krauss Dolores T , Eckhoff Jennifer A , Schröder Lorenz , Allo Gabriel , Jung Jin-On , Kasper Philipp , Fuchs Hans F , Schroeder Wolfgang , Bruns Christiane J , Goeser Tobias , Chon Seung-Hun 
Published: December 2024

Summary
The most common functional challenge after Ivor-Lewis esophagectomy is delayed emptying of the gastric conduit. One of the primary endoscopic treatment strategies is performing a pyloric dilatation. However, the effects of dilation have never been scientifically proven. A novel method to detect pyloric distensibility (DI) is the endoluminal functional lumen imaging probe (EndoFlip™). The purpose of this study is to analyze the effects of pyloric dilatation using an EndoFlip™ measurement. Forty-nine patients after Ivor-Lewis esophagectomy were included retrospectively from June 2021 to August 2023 at University Hospital Cologne, Germany. All patients suffered from early delayed emptying of the gastric conduit (DGCE). DI was measured before and after endoscopic dilatation using EndoFlip™ at 40, 45, and 50 mL balloon filling. The Student’s t-test and Chi-Squared test were used. All tests were two-sided, with statistical significance set at P ≤ 0.05. EndoFlip™ measurement and pyloric dilatation were feasible in all patients and no adverse events were recorded. DI proved to be smaller in patients before dilatation compared to patients after dilatation. For 40, 45, and 50 mL balloon filling, the mean DI was 5.0 versus 10.0, 4.5 versus 9.1, and 4.0 and 7.5 mm2/mmHg before versus after dilatation. The differences were significant in all balloon fillings. Endoscopic dilatation of the pylorus is the primary endoscopic treatment strategy in patients suffering from DGCE. Currently, the success of dilatation can only be measured with clinical data. This study could demonstrate that EndoFlip™ can be used as an additional diagnostic tool to rate the success of pyloric dilatation.
Ventricular septal defect characteristics influence presence of septal anatomical isthmuses in patients with repaired tetralogy of Fallot
Authors: Wallet Justin , Kimura Yoshitaka , Blom Nico A , Hazekamp Mark G , Bartelings Margot M , Jongbloed Monique R M , Zeppenfeld Katja 
Published: December 2024

Abstract

Aims
In repaired tetralogy of Fallot (rTOF), the septal anatomical isthmuses (AI), AI 3, between the ventricular septal defect (VSD) and pulmonary annulus, and AI 4, between the VSD and tricuspid annulus, are important ventricular tachycardia (VT) substrates when slow conducting. Our aim was to assess the influence of VSD characteristics, specifically the presence of muscular or fibrous tissue at its border, on the presence or absence of septal AIs, potentially related to VT.

Methods and results
All consecutive rTOF patients who underwent electroanatomical mapping between January 2005 and March 2023 with an available surgical report providing VSD details (n = 91) were included. The majority of patients had an outlet perimembranous VSD (n = 76, 84%), 6 (7%) an outlet muscular VSD, and 7 (8%) a doubly committed juxta-arterial VSD. In patients with an outlet perimembranous VSD, AI 3 was present in almost all (97%), whereas no AI 4 was observed. In patients with an outlet muscular VSD, AI 3 and AI 4 were present in 83% and 33%, respectively. In all patients with a doubly committed VSD, where the outlet septum is hypoplastic/fibrous, AI 3 was absent. Among patients with a doubly committed VSD with a muscular postero-inferior rim, 50% had AI 4, whereas none of those with a fibrous postero-inferior rim had AI 4.

Conclusion
Ventricular septal defect characteristics aid in determining the presence of septal AIs potentially related to VT. In patients with doubly committed VSDs, septal VT substrates are unlikely. Detailed knowledge of anatomical VSD characteristics is desirable for understanding VT in rTOF.