bluuebunny/crossref_metadata_2025_split huggingface dataset:
ADM: adaptive graph diffusion for meta-dimension reduction
Authors: Feng Junning , Liang Yong , Yu Tianwei 
Published: November 2024

Abstract
Dimension reduction is essential for analyzing high-dimensional data, with various techniques developed to address diverse data characteristics. However, individual methods often struggle to capture all intricate patterns and complex structures simultaneously. To overcome this limitation, we introduce ADM (Adaptive graph Diffusion for Meta-dimension reduction), a novel meta-dimension reduction method grounded in graph diffusion theory. ADM integrates results from multiple dimension reduction techniques, leveraging their individual strengths while mitigating their specific weaknesses.ADM utilizes dynamic Markov processes to transform Euclidean space results into an information space, revealing intrinsic nonlinear manifold structures that are hard to capture by conventional methods. A critical advancement in ADM is its adaptive diffusion mechanism, which dynamically selects optimal diffusion time scales for each sample, enabling effective representation of multi-scale structures. This approach generates robust, high-quality low-dimensional representations that capture both local and global data structures while reducing noise and technique-specific distortions. We demonstrate ADM’s efficacy on simulated and real-world datasets, including various omics data types. Results show that ADM provides clearer separation between biological groups and reveals more meaningful patterns compared to existing methods, advancing the analysis and visualization of complex biological data.
A versatile pipeline to identify convergently lost ancestral conserved fragments associated with convergent evolution of vocal learning
Authors: Li Xiaoyi , Zhu Kangli , Zhen Ying 
Published: November 2024

Abstract
Molecular convergence in convergently evolved lineages provides valuable insights into the shared genetic basis of converged phenotypes. However, most methods are limited to coding regions, overlooking the potential contribution of regulatory regions. We focused on the independently evolved vocal learning ability in multiple avian lineages, and developed a whole-genome-alignment-free approach to identify genome-wide Convergently Lost Ancestral Conserved fragments (CLACs) in these lineages, encompassing noncoding regions. We discovered 2711 CLACs that are overrepresented in noncoding regions. Proximal genes of these CLACs exhibit significant enrichment in neurological pathways, including glutamate receptor signaling pathway and axon guidance pathway. Moreover, their expression is highly enriched in brain tissues associated with speech formation. Notably, several have known functions in speech and language learning, including ROBO family, SLIT2, GRIN1, and GRIN2B. Additionally, we found significantly enriched motifs in noncoding CLACs, which match binding motifs of transcriptional factors involved in neurogenesis and gene expression regulation in brain. Furthermore, we discovered 19 candidate genes that harbor CLACs in both human and multiple avian vocal learning lineages, suggesting their potential contribution to the independent evolution of vocal learning in both birds and humans.
BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction
Authors: Tan Yuejun , Xie Linhai , Yang Hong , Zhang Qingyuan , Luo Jinyuan , Zhang Yanchun 
Published: November 2024

Abstract
Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full combination space of genetic perturbations and cell types. Consequently, a bunch of computational models have been proposed to predict unseen combinations based on existing data. Among them, generative models, e.g. variational autoencoder and diffusion models, have the superiority in capturing the perturbed data distribution, but lack a biologically understandable foundation for generalization. On the other side of the spectrum, Gene Regulation Networks or gene pathway knowledge have been exploited for more reasonable generalization enhancement. Unfortunately, they do not reach a balanced processing of the two data modalities, leading to a degraded fitting ability. Hence, we propose a dual-stream architecture. Before the information from two modalities are merged, the sequencing data are learned with a generative model while three types of knowledge data are comprehensively processed with graph networks and a masked transformer, enforcing a deep understanding of single-modality data, respectively. The benchmark results show an approximate 20% reduction in terms of mean squared error, proving the effectiveness of the model.
RNADiffFold: generative RNA secondary structure prediction using discrete diffusion models
Authors: Wang Zhen , Feng Yizhen , Tian Qingwen , Liu Ziqi , Yan Pengju , Li Xiaolin 
Published: November 2024

Abstract
Ribonucleic acid (RNA) molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of RNA secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for RNA structure prediction, including energy-based and learning-based approaches, often depict RNA secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce RNADiffFold, an innovative generative prediction approach of RNA secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to refine the contact maps starting from a noise-infused state progressively. We also devise a potent conditioning mechanism that harnesses features extracted from RNA sequences to steer the model toward generating an accurate secondary structure. These features encompass one-hot encoded sequences, probabilistic maps generated from a pre-trained scoring network, and embeddings and attention maps derived from RNA foundation model. Experimental results on both within- and cross-family datasets demonstrate RNADiffFold’s competitive performance compared with current state-of-the-art methods. Additionally, RNADiffFold has shown a notable proficiency in capturing the dynamic aspects of RNA structures, a claim corroborated by its performance on datasets comprising multiple conformations.
VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network
Authors: Zhang Tianjiao , Zhang Xiang , Wu Zhenao , Ren Jixiang , Zhao Zhongqian , Zhang Hongfei , Wang Guohua , Wang Tao 
Published: November 2024

Abstract
Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell–cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell–cell communication. Furthermore, other methods for analyzing cell–cell communication mainly focus on individual tissue sections, neglecting cell–cell communication across multiple tissue layers, and fail to comprehensively elucidate cell–cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell–cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell–cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell–cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell–cell communication.
Association of proprotein convertase subtilisin/kexin type-9 inhibitors with risk of nonmelanoma skin cancer: a retrospective cohort study
Authors: Li Cheng-Yuan , Wang Wei-Ting , Ma Sheng-Hsiang , Lo Li-Wei , Wu Chen-Yi , Chang Wei-Chuan , Chen Yi-Ju , Chen Tai-Li 
Published: November 2024

Abstract

Background
Growing evidence has shown that cholesterol metabolism abnormalities involve carcinogenesis. Proprotein convertase subtilisin/kexin type-9 (PCSK9) inhibitors have been reported to inhibit tumour progression and prevent ultraviolet-related skin damage.

Objectives
To investigate the association of PCSK9 inhibitors with the risk of nonmelanoma skin cancer (NMSC).

Methods
This retrospective cohort study analysed data from the US Collaborative Network in the TriNetX database. Adults aged ≥ 40 years with atherosclerotic cardiovascular disease (ASCVD) under statin therapy between 2016 and 2022 were identified. A target trial design was used to compare the risk of NMSC, including basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), in patients also treated with PCSK9 inhibitors or continuing statin treatment (the control group). Each head-to-head comparison involved propensity score matching. Hazard ratios (HRs) were estimated using Cox proportional hazard models. Stratified analyses based on age, sex, Fitzpatrick skin type and immune status were also performed.

Results
A total of 73 636 patients with ASCVD were analysed. Compared with the control group, patients with ASCVD initiating PCSK9 inhibitors had lower risks of developing NMSC [HR 0.78, 95% confidence interval (CI) 0.71–0.87], BCC (HR 0.78, 95% CI 0.69–0.89) and cSCC (HR 0.79, 95% CI 0.67–0.93). Subanalyses revealed a reduced risk of NMSC with each PCSK9 inhibitor, namely evolocumab and alirocumab. Stratified analyses showed similar results in patients aged 65–79 years, those older than 80 years and in men.

Conclusions
Our study indicated that patients with ASCVD taking PCSK9 inhibitors have a lower risk of incident NMSC than those not taking PCSK9 inhibitors.
Abnormal neurovascular coupling exists in patients with peritoneal dialysis and hemodialysis: evidence from a multi-mode MRI study
Authors: Yin Xiayan , Yang Wenbo , Song Lijun , Liu Jiamin , Li Mingan , Yang Zhenghan , Liu Wenhu , Wang Zhenchang , Wang Hao , Guo Weikang 
Published: November 2024

ABSTRACT

Background
Neurovascular coupling (NVC), as indicated by a comprehensive analysis of the amplitude of low-frequency fluctuation (ALFF) and cerebral blood flow (CBF), provides mechanistic insights into neurological disorders. Patients undergoing peritoneal dialysis (PD) and hemodialysis (HD) often face cognitive impairment, the causes of which are not fully understood.

Methods
ALFF was derived from functional magnetic resonance imaging, and CBF was quantified using arterial spin labeling in a cohort comprising 58 patients with PD, 60 patients with HD and 62 healthy controls. Voxel-based global analysis for both ALFF and CBF, alongside region-based analyses of ALFF-CBF coupling coefficients, were conducted. Additionally, the study explored the correlation between clinical laboratory indices and imaging metrics.

Results
Compared with HC, NVC was reduced in the bilateral medial superior frontal gyrus (SFGmed), insula, posterior cingulate cortex (PCC) and caudate (CAU) among dialysis patients. Furthermore, the PD group exhibited lower NVC in the bilateral SFGmed, bilateral PCC and left CAU compared with the HD group. Within the PD group, sodium level was negatively correlated with the ALFF-CBF coupling coefficient in the right insula. Additionally, a positive correlation emerged between the ALFF-CBF coupling coefficient in bilateral SFGmed and the dialysis adequacy.

Conclusion
While Montreal Cognitive Assessment scores did not significantly differ between patients with PD and HD, PD group demonstrated poorer NVC in the bilateral SFGmed, bilateral PCC and left CAU. Sodium level and dialysis adequacy may affect NVC in patients with PD.
Elevated cardiac troponin secondary to heterophile antibodies: a case series highlighting an underrecognized differential
Authors: Millhouse James , Kamalanathan Harish , Jayasinghe Rohan 
Published: November 2024

Abstract

Background
Heterophile antibody presence confounds troponin assay results, causing falsely elevated troponin levels. This rare phenomenon is an important differential to consider when evaluating patients with suspected acute coronary syndrome. We present a case series of three patients with similar clinical presentations where the presence of heterophile antibodies was confirmed.

Case summary
We reviewed three patients from our hospital who presented with chest pain in a 12-month period. All patients were males aged 50–70. All patients had elevated troponin, and there was clinical concern for acute coronary syndrome in two patients. Two patients underwent coronary angiography during admission, and the third had a recent angiogram within the last 6 months. No obstructive lesions were found, and no alternative diagnoses were identified. Ultimately, the presence of heterophile antibodies was confirmed in all three patients.

Discussion
Heterophile antibody presence is an important differential to consider in patients with unexplained troponin elevation. Once the presence of heterophile antibodies is confirmed, this aids in clinician decision-making and helps to guide investigations and treatment in future.
Artificial intelligence and jobs: evidence from US commuting zones
Authors: Bonfiglioli Alessandra , Crinò Rosario , Gancia Gino , Papadakis Ioannis 
Published: November 2024

Abstract
We study the effect of Artificial Intelligence (AI) on employment across US commuting zones (CZs) over the period 2000–2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across CZs and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution and for those in STEM occupations. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.
Targeting fibroblast phenotype switching in cardiac remodelling as a promising antifibrotic strategy
Authors: Kel Alexander , Thum Thomas , Kunduzova Oksana 
Published: November 2024

Abstract
Myocardial fibrosis, a common feature of heart disease, remains an unsolved clinical challenge. Fibrosis resolution requires activation of cardiac fibroblasts exhibiting context-dependent beneficial and detrimental dichotomy. Here, we explored the hypothesis of fibroblast reversible transition between quiescence and activated myofibroblastic states as a manifestation of cell phenotypic switching in myocardial remodelling. In support, gene regulatory networks executing conversion of cardiac fibroblasts to myofibroblasts and vice versa in fibrosis resolution are reconstructed using TRANSPATH database. In a scenario of fibroblast activation triggered by transforming growth factor β, a cardinal mediator of tissue fibrosis, signalling cascades governing entry into or exit from specific fibroblast statures in cardiac fibrotic remodelling were dissected. It is suggested that fibroblast phenotypic switching constitutes the central gait toward guiding cell state-gating strategies to counteract adverse cardiac fibrosis, a devastating disorder with no approved therapeutic option.
The benefit of surgery during systematic therapy for gastrointestinal stromal tumor liver metastasis: a SEER-based retrospective study
Authors: Hu Bozhi , Ye Yingjiang , Gao Zhidong 
Published: November 2024

Abstract

Background
The liver is the most common site of gastrointestinal stromal tumor (GIST) metastasis. Most patients who develop metastases gradually develop multiline drug resistance during long-term systematic treatment. We aimed to evaluate the benefit of surgery during the systematic treatment of GIST liver metastases.

Methods
Data on GISTs with liver metastasis were retrieved from the Surveillance, Epidemiology, and End Results database. This study included 607 patients, of whom 380 patients were treated with chemotherapy alone (Chemo group) and 227 patients underwent surgery in addition to chemotherapy (Chemo&Surg group). The primary outcomes were cancer-specific survival (CSS) and overall survival (OS). Propensity score matching (PSM) was performed to balance the baseline factors.

Results
According to the multivariate analysis, surgery benefitted both CSS and OS (P &lt; 0.001). After PSM, surgical resection still showed significant benefits in terms of both CSS and OS (P &lt; 0.001). Surgery combined with chemotherapy increased the median CSS by at least 63 months and the median OS by at least 76 months. Subgroup analysis of the Chemo&Surg group revealed that the timing of surgery was not an independent influencing factor for either CSS or OS.

Conclusions
We found that performing additional surgery, in addition to systematic therapy, for GIST liver metastasis resulted in improved CSS and OS. These benefits were not affected by the timing of surgery during systemic treatment.
Implicit multiparameter full waveform inversion of multioffset ground penetrating radar data
Authors: Sun Jian , Liu Ying , Lin Yuzhao , Xing Lei , Liu Huaishan 
Published: November 2024

SUMMARY
Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is one of the key methods for reconstructing high-resolution near-surface images and understanding changes in the near-surface environment. However, FWI faces great challenges due to the ill-posed nature of the optimization problem and its high sensitivity to the quality of the initial model. In this paper, we propose the implicit full waveform inversion (IFWI) algorithm for GPR that addresses these limitations by using an implicit and continuous function built by deep neural networks to represent multiple subsurface parameters. In this way, IFWI can be thought of as training a physically constrained neural network representation, allowing it to exploit the frequency principle in deep learning optimization, that is first recovering the low-frequency components of the subsurface parameters and large-scale structures, and then their high-frequency and fine-grained information, which makes IFWI an automated multiscale inversion approach without manual frequency band selection. Numerical experiments are conducted to verify the effectiveness and performance of IFWI relative to FWI. The results demonstrate that IFWI can accurately reconstruct subsurface images and anomalies even with a suboptimal initial model, while FWI and multiscale FWI struggle to provide useful information due to being trapped in local minima. Consequently, IFWI exhibits less dependence on the initial model compared to FWI and multiscale FWI. To better understand the behaviour of IFWI and its sensitivity to neural network architecture, we comprehensively discuss the influences of these hyperparameters on IFWI, providing initial selection guidelines and practical guidance for adjustments.
Making a World of Climate Insecurity: The Threat Multiplier Frame and the US National Security Community
Authors: Cullum Rob 
Published: November 2024

Abstract
How do dominant framings of climate change and security shape the future trajectory of climate security? This article answers these questions by analyzing the dominant threat multiplier framing of climate change and security in the American national security context. Using the emergent worldmaking concept, it argues that American national security organizations’ intellectual and physical responses to questions of climate security risk creating a dismal and limited future in which national security systems adopt a reactive and managerial posture toward the effects of climate change. The key effect of this frame is to reorient questions of climate and security around the priorities and agendas of national security organizations rather than at the sources of need and instability.
Postmating prezygotic isolation occurs at two levels of divergence in Drosophila recens and D. subquinaria
Authors: Duitsman Andrew P , Bomar Adam M , Powell Jerbrea , Dyer Kelly A 
Published: November 2024

Abstract
Identifying the presence and strength of reproductive isolating barriers is necessary to understand how species form and then remain distinct in the face of ongoing gene flow. Here, we study reproductive isolation at two stages of the speciation process in the closely related mushroom-feeding species Drosophila recens and Drosophila subquinaria. We assess 3 isolating barriers that occur after mating, including the number of eggs laid, the proportion of eggs laid that hatched, and the number of adult offspring from a single mating. First, all 3 reproductive barriers are present between D. recens females and D. subquinaria males, which are at the late stages of speciation but still produce fertile daughters through which gene flow can occur. There is no evidence for geographic variation in any of these traits, concurrent with patterns of behavioural isolation. Second, all 3 of these reproductive barriers are strong between geographically distant conspecific populations of D. subquinaria, which are in the early stages of speciation and show genetic differentiation and asymmetric behavioural discrimination. The reduction in the number of eggs laid is asymmetric, consistent with patterns in behavioural isolation, and suggests the evolution of postmating prezygotic isolation due to cascade reinforcement against mating with D. recens. In summary, not only may postmating prezygotic reproductive barriers help maintain isolation between D. recens and D. subquinaria, but they may also drive the earliest stages of isolation within D. subquinaria.
A rare complication of infantile hemangioma: Kasabach–Merritt phenomenon
Authors: Caravantes Ricardo A , Toralla José Manuel , Saenz Daniela 
Published: November 2024

Abstract
Infantile hemangiomas are the most common type of vascular tumors, affecting ~5% of infants within the first weeks of life. In rare instances, these tumors can lead to Kasabach–Merritt phenomenon (KMP), a life-threatening consumptive coagulopathy characterized by thrombocytopenia, microangiopathic hemolytic anemia, and hypofibrinogenemia. In the present case, a 20-month-old patient is diagnosed with KMP. This case report highlights the challenges in diagnosis and management, reinforcing the importance of multidisciplinary approach.