bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining Human Coronavirus Spike Protein Sequences and Evolutionary Distances May Give Insights into the Origins of SARS-CoV-2.
Authors: Wainwright Felicity , Misirli Goksel , Andras Peter 
Published: November 2024

Abstract

Introduction Since the emergence of the human coronavirus SARS-CoV-2, and the consequent COVID-19 pandemic, the nature of the virus’ origin has been hotly debated. The possibility that it was artificially engineered in a laboratory setting has been considered, as has the possibility that it simply evolved naturally from existing known human coronaviruses. In this paper, we provide a comparative approach that involves examining the evolutionary distance between all known human coronaviruses by using genetic sequences of their spike proteins and proteases and determining where SARS-CoV-2 fits within this evolutionary picture to provide insights into the origins of the virus. A similar distance-based approach has been applied to related organisms using different protein sequence data to test if a similar trend exists. Materials and Methods We utilised existing sequence data (previously deposited in online databases) of the seven known human coronavirus spike proteins, then mapped them against each other in RStudio using the BLOSUM62 Substitution Matrix to determine the genetic similarity between the spike proteins and ascertain whether the similarity scores for SARS-CoV-2 were within the expected range based upon values for the other coronaviruses. The similarity scores were normalised and then a frequency distribution was obtained. We then repeated this process for selected influenza neuraminidases and norovirus capsid proteins respectively, to compare distribution patterns across the three virus types. Finally, the frequency distribution scores were analysed using a Kolmogorov-Smirnov test. The process detailed above was then repeated in order to compare genetic similarity between human coronavirus protease sequences; proteases are essential for viral replication. We examined both cysteine proteases encoded within the coronavirus genome - the main protease (3CLpro) and the papain-like protease (PLpro). Results By implementing a Kolmogorov-Smirnov test, we demonstrate primarily that the frequency distribution for SARS-CoV-2 is not significantly different to that of the other six known human coronaviruses (D = 0.4, p-value = 0.4195). We also demonstrate that the frequency distributions for coronavirus spike proteins, influenza neuraminidases, and norovirus capsid proteins are all significantly different, demonstrating that each virus type has followed a different evolutionary pattern. Conclusions Based on the similarity scores of SARS-CoV-2 upon comparing spike protein sequences to those of the other six known human coronaviruses, the results suggest that SARS-CoV-2 is sufficiently similar to the other coronaviruses to indicate that it evolved naturally, as opposed to arising due to laboratory manipulation. This supports existing hypotheses theorising that SARS-CoV-2 developed naturally. Our results also show that the different virus types follow clearly different evolutionary paths.
Comparative Outcomes of Intralesional Collagenase Injection for Peyronie's Disease: Acute vs. Stable Phase Treatment
Authors: KUCHAKULLA MANISH , Bhambhvani Hriday , Wald Gal , Basourakos Spyridon , Lewicki Patrick , Gal Jonathan , Kashanian James 
Published: November 2024

Abstract

Collagenase clostridium histolyticum (CCH) was FDA-approved for treating Peyronie’s Disease, demonstrating significant curvature improvement in patients during the stable phase of disease. In this study, we aimed to assess the safety and effectiveness of intralesional CCH injections during the acute phase of Peyronie's Disease. We retrospectively evaluated all men who received intralesional collagenase for Peyronie’s Disease at a single academic institution between July 2017 and January 2023, focusing on patient demographics, comorbidities, pre- and post-treatment curvature, and adverse events. Patients in the acute phase had symptoms for ≤ 12 months with penile pain and/or curvature changes. A total of 100 patients treated with intralesional CCH injections for Peyronie's disease were included. Percent change in penile curvature was not significantly different between acute and stable phases (37.02% vs. 34.01%, p = 0.57). A total of 55 patients (76%) in the stable phase and 23 patients (82%) in the active phase showed improvement in curvature, with no significant differences in curvature outcomes or adverse events between the two groups. This study contributes to the growing evidence that intralesional collagenase treatment during the acute phase of Peyronie's disease is safe and effective.
Fast Noisy Long Read Alignment with Multi-Level Parallelism
Authors: Xia Zeyu , Yang Canqun , Peng Chenchen , Guo Yifei , Guo Yufei , Tang Tao , Cui Yingbo 
Published: November 2024

Abstract

Background:
The advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment tools inapplicable. Additionally, a single CPU's performance bottleneck restricts the effectiveness of alignment algorithms for SMRT sequencing.
Methods:
To address these challenges, we introduce ParaHAT, a parallel alignment algorithm for noisy long reads. ParaHAT utilizes vector-level, thread-level, process-level, and heterogeneous parallelism. We redesign the dynamic programming matrices layouts to eliminate data dependency in the base-level alignment, enabling effective vectorization. We further enhance computational speed through heterogeneous parallel technology and implement the algorithm for multi-node computing using MPI, overcoming the computational limits of a single node.
Conclusion:
Performance evaluations show that ParaHAT got a 5.39x speedup in base-level alignment, with a parallel acceleration ratio and weak scalability metric of 94.61 and 98.98% on 128 nodes, respectively.
Integrating Multiple Slack Bus Operations and Metaheuristic Techniques for Power Flow Optimization
Authors: Dahal Swaechchha , Hegglid Gunne John , Nøland Jonas Kristiansen , Chhetri Bhupendra Bimal , Mishra Sambeet , Øyvang Thomas 
Published: November 2024

Abstract

The increasing complexity of modern energy grids amplifies the importance of realistic power flow studies in power system analysis. This study implements a Multiple Slack Bus Operation (MSO) framework to enhance the realism and efficiency of optimal power flow (OPF) analysis. This paper introduces a comparative evaluation of three metaheuristic algorithms—Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO)—within the MSO framework. The algorithms are assessed based on their effectiveness in system loss minimization, line loading optimization, generator voltage angle adjustment, and generation distribution changes. Utilizing the Reduced Nordic 44 model and IEEE benchmark test systems at various load conditions, the findings reveal that the GWO algorithm, when integrated with the MSO framework, achieves the most significant reduction in total system losses. Specifically, the implementation of MSO alone reduced system losses by 5%, and its combination with GWO led to an additional 8.3% decrease. This study investigates the application of metaheuristic algorithms within a multiple slack bus context, highlighting their potential to enhance power network efficiency and suggesting broader applications for future power flow optimization strategies.
Deep Rank to Match Model via Multi-task Learning for Online Large-scale Recommender System
Authors: Ding XiaoPing , Jiang Yang , Xu JiaLi , Fu GuiJie , Li Xiang 
Published: November 2024

Abstract

Large-scale recommender systems consist of two stages: matching and ranking. The matching stage retrieves candidate items relevant to user interests, while the ranking stage predicts the probabilities of user interactions with these items. Traditional matching models retrieve items with high relevance but not necessarily good business indicators like CTR. Integrating the ranking task into the matching stage using multi-task learning (MTL) addresses this issue. Challenges include dealing with the inconsistency of sample space and training objectives between matching and ranking, and building an MTL framework that meets computational cost requirements. We propose the Deep Rank to Match via Multi-Task Learning (DRMMTL) model to tackle these challenges. DRMMTL introduces the ranking task into the matching stage and uses a dual-tower structure to ensure computational efficiency and consistency of final scores. Offline evaluation on real-world datasets and online A/B tests validate DRMMTL's superiority, showing significant performance improvements over traditional matching models. DRMMTL has been successfully deployed on our e-commerce recommendation platform, handling major online traffic.
A Deep Learning Approach for Detection of Object-Based Forgery in Videos Using Motion Residuals Footprints
Authors: Raj Mrinal , Bakas Jamimamul , Jha Ashutosh 
Published: November 2024

Abstract

Nowadays, with the rapidly evolving digital technology, mul-timedia content has a significant influence on our day-to-day lives. Videocontent plays a crucial role in many cases, especially in the legal indus-try. For example, CCTV-captured videos are now accepted as prime evi-dence of any crime scene in a courtroom. However, surveillance videos canbe easily manipulated using publicly available image and video process-ing tools. This paper introduces a deep learning-based model to detectobject-based forgery in surveillance videos. In this research, we exploitmotion residuals to capture the footprints generated due to forgery oper-ations. A VGG-19 network is used to learn the extracted motion residualsand detect the forged frames. The proposed model was tested for effec-tiveness through a comprehensive experiment, achieving an F1-score of98.88%.
Fungal lignocellulolytic enzymes: an in silico and full factorial design approach
Authors: Candeo Esteffany de Souza , Scheufele Fabiano Bisinella , Pena Aline de Cassia Campos , Dequigiovanni Gabriel , Linde Giani Andrea , Mata Gerardo , Colauto Nelson Barros , Schaker Patricia Dayane Carvalho 
Published: November 2024

Abstract

Efficient degradation of lignocellulosic biomass is key for the production of value-added products, contributing to sustainable and renewable solutions. This study employs a two-step approach to evaluate lignocellulolytic enzymes of Ceratocystis paradoxa, Colletotrichum falcatum, and Sporisorium scitamineum. First, an in silico genomic analysis was conducted to predict the potential enzyme groups produced by these fungi. Second, a 2³ full factorial design of solid-state cultivation was employed to investigate the cultivation conditions that optimize enzyme activity. In silico analysis of phytopathogen genomes identified proteins with the potential for biomass degradation. Cellulase and phenoloxidase activities were assessed in culture medium and solid-state cultivation. A 2³ full factorial design was employed for solid-state cultivation to evaluate the cellulose, endoglucanase, and laccase activities. In silico analysis shows that C. falcatum has the most diverse enzyme set for lignocellulosic biomass degradation. In vitro assays corroborate this, demonstrating that C. falcatum produces the highest enzyme quantities, except for cellulase, where C. paradoxa outperforms it. Both C. paradoxa and C. falcatum exhibit cellulase and phenoloxidase activities, but only C. falcatum shows laccase activity. Most favorable enzyme production in solid-state cultivation occurred with 85–95 g 100 g− 1 bagasse moisture and 5 g 100 g− 1 yeast extract, with four-day cultivation period needed for cellulase and endoglucanase in C. paradoxa and 12 days for endoglucanase and laccase in C. falcatum. The in silico and in vitro assays demonstrated that C. falcatum can produce a diverse enzyme set, including laccase, cellulase, and endoglucanase, making it a promising candidate for enzymatic industrial applications.
Analog performance investigation of 10 nm Junctionless GAA FETs using Machine learning methods and deep learning analysis
Authors: Ouchen R. , Berghout T. , Djeffal F. , Ferhati H. 
Published: November 2024

Abstract

With the continuous downscaling of analog CMOS-based circuits, the sensitivity of nanoelectronic devices to design parameter variations has significantly increased. In this paper, we introduce a novel design approach that combines numerical simulations with Machine Learning (ML) analysis to explore the key design parameters of ultra-low scale Junctionless Gate-All-Around (JL GAA) Field-Effect Transistors (FETs). Accurate 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the I-V characteristics of 10 nm JL GAA FET devices. The influence of variations in device geometry and doping concentration on analog Figures-of-Merit (FoMs), such as intrinsic gain (Av) and cut-off frequency, is thoroughly analyzed. The use of high-k dielectric materials is also explored for improving the frequency response in high-speed analog circuits. By leveraging ML techniques, the study identifies the optimal design parameters that enhance key analog performance metrics, enabling efficient prediction and optimization of device behavior. Our results highlight the importance of channel radius and channel doping in enhancing the intrinsic gain and frequency response in analog devices. Moreover, the investigated JL GAA FETs exhibit high intrinsic gain and frequency performances, making them ideal candidates for high-speed and high-gain analog circuits. The integration of machine learning techniques further streamlines the optimization process, leading to the identification of optimal parameters that maximize analog performance of the device.
Improving the efficiency and performance of Rb 2 SnI 6 -based perovskite solar cells through comprehensive optimization: A numerical study
Authors: Alam Minhaz Ul , Shifat Md. Kamrul Islam , Modak Jibon Krisha , Tarekuzzaman Md. , Haque Md. Ismail , Rasheduzzaman Md. , Qader Md Abdul , Islam Riazul , Arafat Yasir , Hasan Md. Zahid 
Published: November 2024

Abstract

In this investigation, we have studied the optimal performance of perovskite solar cells (PSCs) based on tin halide (Rb2SnI6), using the SCAPS-1D software (solar capacitance simulator). The SCAPS 1D was employed to improve the device in the Rb2SnI6-based PSC, which utilized tungsten disulfide (WS2) as the electron transport layer and cadmium telluride (CdTe) as the hole transport layer (HTL). To identify the most suitable electron transport layer (ETL), we initially investigated WS2, SnS2, PCBM, and C60. The ITO/WS2/ Rb2SnI6/CdTe/Ni structure proved to be the most effective ETL after extensive investigation, demonstrating a power conversion efficiency (PCE) of 24.95%, a Voc of 1.0896 V, a Jsc of 44.6795 mA cm2 and an FF of 82.71%. Subsequently, we evaluated the impact of the absorber thickness, ETL thickness, and defect density on the device's effectiveness in the Rb2SnI6, WS2, and CdTe layers. We further investigated the effect of adjusting the interfacial defect densities at the CdTe/Rb2SnI6 and Rb2SnI6/WS2 interfaces to optimize the device's capabilities further. Additionally, we examined the proposed PSCs' quantum efficiency (QE), current density-voltage (J-V), shunt resistance, series resistance, capacitance-voltage, working temperature, and generation-recombination parameters. The results of these simulations provide valuable information for the excellent scientific fabrication of an inorganic PSC that is based on Rb2SnI6.
Detailed Analysis and Comparison of Geospace Timeline Data for Space Weather Monitoring
Authors: Chowdhury Aniruddha Chowdhury Aniruddha 
Published: November 2024

Abstract

This paper delves into the intricacies of geospace timeline data over multiple periods, scrutinizing solar wind parameters and geomagnetic indices. Through an in-depth examination of variations in magnetic field strength, solar wind density, speed, temperature, and geomagnetic activity indices (Kp and DST), this research aims to provide a comprehensive understanding of solar-terrestrial interactions. By comparing data from the latest 1 day, 3 days, and 7 days, we aim to enhance our grasp of these interactions to improve space weather forecasting capabilities. The analysis period is from July 26, 2024, to August 2, 2024, and the data was collected from the Space Weather Prediction Center (SWPC) located in Boulder, Colorado.
Causal associations between fibroblast growth factors and breast cancer: evidence from two-sample Mendelian randomization analysis
Authors: Feng Fubin , Sun Mengxuan , Yao Yan , Li Huayao , Song Linqi , Sun Changgang 
Published: November 2024

Abstract

Previous studies have suggested a potential association between fibroblast growth factors and breast cancer, but the evidence for the relationship between specific fibroblast growth factors with breast cancer is limited and controversial. To explore the interactions between 13 fibroblast growth factors (FGFs) and 3 fibroblast growth factor receptors (FGFRs) with breast cancer, we conducted a mendelian randomization study based on GWAS summary statistics of European ancestry. The IVW analysis showed that for overall breast cancer, FGF20 showed a genetically protective effect, FGF4 can genetically promote the risk of breast cancer. FGF1 and FGF7 were consistently associated with increased risk of ER + breast cancer, however FGF20 decreased the risk of ER + breast cancer. FGF23 promote the risk of ER- breast cancer. In the reverse MR study, ER + breast cancer tended to exhibit elevated levels of FGF7 and decreased levels of FGFR2. Our study results indicate that only specific types of FGFs and FGFRs may have a causal relationship with breast cancer. This research provides a new perspective on the mechanisms of action of different types of FGFs and FGFRs in breast cancer, and offers potential genetic support for personalized medicine and precision therapy.
Schizolepidopsis gerriennei sp. nov.: an Early Jurassic ovulate cone from Belgium
Authors: De Brito Léa , Fischer Valentin , Godefroit Pascal , Prestianni Cyrille 
Published: November 2024

Abstract

UnambiguousPinaceae taxa are described from the Late Jurassic onwards. This fossil record does not, however, match the molecular estimates of deep divergence within Pinaceae. In recent years, new anatomical observations and revisions have reassigned several species of Schizolepis Braun to the emended genus Schizolepidopsis Doweld, 2001 Emend. Domogatskaya Et Herman. This genus is currently regarded as an early representative of Pinaceae, notably sharing bisporangiate scales, winged seeds, and a more or less constricted cone with scales arranged helically around the axis. This would strongly push the earliest Pinaceae to the early Mesozoic or even the latest Paleozoic. Here, we describe an ovulate cone from the Lower Jurassic of Belgium. Despite its partial preservation, the use of micro-CT allowed the reconstruction of its morphology in details. This reconstruction highlights several characters (ovulate cone with helically arranged scales, constrained, bilobed and bisporangial scales with probable winged seeds) diagnostic of the genus Schizolepidopsis, and constituting a new species, which we name Schizolepidopsis gerriennei sp. nov. This description emphasizes several anatomical characters previously rarely noted for this genus. This ovulate cone represents the first occurrence of this genus in the Early Jurassic of Western Europe, which further completes the picture of its distribution and diversity and supports a much deeper origin of Pinaceae that usually conceived.
Study on meson cloud contributions to octet and decuplet baryon masses
Authors: Ge Yan , Gui Shuai , Liu Shixing , Liu Xuyang 
Published: November 2024

Abstract

In the present work, we explore the effect of the meson cloud dressing in the octet and decuplet baryon masses within the perturbative chiral quark model by considering both the ground and excited quark propagators. The total results on the octet and decuplet baryon masses agree excellently with the experimental data. In addition, the study shows clearly that π, K and η meson clouds contributing to the baryon masses are significantly influential, reducing about 40% of three quark-core values. And the study also reveals that the excited-states in the intermediate quark propagators play an extremely important role, they are twice as larger as those from the ground-state quark propagator.
Impact of Disaster Experience and Family Cancer on Well-being in Japan: A Cross-sectional Web-based Survey
Authors: Hara Akemi , Ozaki Akihiko , Takebayashi Yoshitake , Kotera Yasuhiro , Saito Hiroaki , Sawano Toyoaki , Ito Naomi , Gonda Kenji , Wada Masahiro , Tsubokura Masaharu , Ohira Hiromichi , Murakami Michio 
Published: November 2024

Abstract

Background:
Disasters and family cancer experiences both significantly impact well-being, especially in disaster-prone areas like Japan. Disruptions in medical care and increased emotional strain make coping with cancer even more challenging. This study explores how these factors together affect well-being in Japan, highlighting the need for further research to better understand and support affected families.
Methods:
A web-based survey was conducted among 2,179 adults in Japan in 2019, including residents of Fukushima and other areas affected by large-scale disasters. Well-being was the outcome variable, assessed using a five-component measure. Multivariable multivariate linear regression models were used to analyze the associations between disaster experiences and having a family member with cancer and well-being (outcome variable). Confounder variables, including age, income, and access to healthcare, were adjusted for in the models to provide a clearer understanding of the relationships between these factors and well-being.
Results:
Among 2,179 respondents, 78.6% reported no disaster experiences, 17.1% experienced damages but were in recovery, and 4.4% had unrecovered damages, with Fukushima residents disproportionately affected. In the subgroup with cancer-affected family members, higher satisfaction with cancer care (FAMCARE Scale) was positively associated with well-being (e.g., positive emotion: B = 0.41, 95% CI: 0.26 to 0.56, p < 0.001), while no significant relationship was observed between disaster damage and well-being in this subgroup. Unrecovered disaster damages were associated with significantly lower well-being across all measures in the overall population (e.g., positive emotion: B = -0.28, 95% CI: -0.43 to -0.12, p < 0.001). Psychological distress and subjective health status were strong predictors of well-being across all groups.
Conclusions:
Unresolved disaster damage negatively impacts well-being, while high-quality cancer care can positively influence well-being for families affected by cancer. These findings emphasize the need for integrated approaches to disaster recovery and healthcare that consider the multifaceted nature of well-being.