bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards H&amp;E Referenced Multiplex Immunofluorescence Interpretation: Spatial Co-localization, Cell Feature Validation, and Virtual H&amp;E Generation
Authors: Wang Chen , Jiang Jun , Moore Raymond , Novotny Brenna , Guo Ruifeng , Fogarty Zachary , Liu Yuanhang , Goode Ellen , Winham Stacey , Markovic Svetomir 
Published: January 2025

Abstract

Multiplexed Immunofluorescence (MxIF) enables detailed immune cell phenotyping, providing critical insights into cell behavior within the tumor immune microenvironment (TIME). However, signal integrity can be compromised due to the complex cyclic staining processes inherent to MxIF. Hematoxylin and Eosin (H&E) staining, on the other hand, offers complementary information through its depiction of cell morphology and texture patterns and is often visually cross-referenced with MxIF in clinical settings. In this study, we proposed a novel framework to align H&E and MxIF images for precise cross-modal cell feature validation. Using cell detection outputs from each modality as anchors, we formulated the multimodal image registration problem as point set alignment. Coherent Point Drift (CPD) is employed for initial alignment, followed by Graph Matching (GM) for refinement. Evaluations on ovarian cancer tissue microarrays (TMAs) demonstrate that our method achieves high alignment accuracy, enabling reliable validation of cell-level features across modalities for both restained and serial sections. Our results indicate that restained H&E enhances confidence in findings derived from MxIF. Additionally, we demonstrated the feasibility of generating high-quality virtual H&E images from MxIF data when restained H&E is unavailable, offering a viable alternative for integrated multimodal analysis.
BHASHABLEND: Bridging Transcription and Translation for Multilingual Video Content
Authors: Tripathi Ayush , Yadav Vanshika , Chauhan Tanishq , Abidi Ali Imam 
Published: January 2025

Abstract

Translating video content into multiple languages is feasible with existing solutions but remains challenging. This work outlines a sophisticated advanced system that satisfies quality and accessibility improvements in multilingual video translation. The proposed method includes extracting audio from video, transcribing the audio using an innovative speech recognition model, and translating the transcribed text into various languages. The system uses Google’s Translation API and Text-to-Speech library, ensuring synchronization with the original video. The BhashaBlend model achieved a strong word error rate of 12.4%, significantly better than many major ASR systems: Google at 15.82%, and Microsoft at 16.51%. The model's performance was powerful for languages with the simplest phonetic realization, such as German, English, and Spanish, proving its dependability in delivering video dubbing. This highlights the potential of the model to produce results where excessive lingual complexity is involved and points towards the high applicability scope of BhashaBlend in language-polyvalent applications.
A replicating RNA vaccine protects against lethal clade 2.3.4.4b influenza A H5N1 virus challenge in cynomolgus macaques
Authors: Hawman David , Griffin Amanda , Okumura Atsuhi , Leventhal Shanna , McCarthy Natalie , Agumamidi Mahati , Chorabik Michael , Altynova Ekaterina , Lewis Matthew , Hinkley Troy , Stone E. Taylor , Warner Nikole , Lovaglio Jamie , Smith Brian , Hanley Patrick , Saturday Greg , Shaia Carl , Clancy Chad , Rosenke Kyle , Erasmus Jesse , Feldmann Heinz 
Published: January 2025

Abstract

In early 2024, clade 2.3.4.4b highly pathogenic avian influenza (HPAI) A H5N1 virus was detected in United States dairy cattle. While so far the public health threat of contemporary clade 2.3.4.4b H5N1 virus strains remains low, continued circulation in mammals and frequent spillover into humans poses a threat of pandemic H5N1. The United States and other countries have stockpiled vaccines and have plans in place to rapidly produce vaccines should a pandemic H5N1 virus emerge. However, the continued antigenic drift of clade 2.3.4.4b H5N1 antigens compared to historical antigens used by stockpiled vaccines has raised questions of whether these vaccines will confer protection or if stockpiles need to be updated. We recently evaluated a replicating RNA (repRNA) vaccine against lethal contemporary 2.3.4.4b H5N1 virus challenge in mice and found that a homologous but not historical H5 hemagglutinin (HA)-based vaccine conferred protection. Here we further evaluated the protective capacity of a repRNA expressing the contemporary 2.3.4.4b HA or a repRNA expressing a historical H5 HA (A/Vietnam/1203/2004) in a recently developed lethal non-human primate challenge model. We found that both vaccines conferred robust protection against lethal 2.3.4.4b H5N1 virus challenge, protecting against clinical disease and death, reducing viral loads and signs of respiratory illness. Our data show the repRNA platform can elicit protective immunity against lethal respiratory influenza disease and that historical H5 HAs elicit cross-protective immunity.
Mean–Variance Portfolio Optimization Using Ensemble Learning-Based Cryptocurrency Price Prediction
Authors: Safari Mojtaba , Nakharutai Nawapon , Chiawkhun Phisanu , Phetpradap Parkpoom 
Published: January 2025

Abstract

The success of portfolio construction largely relies on accurately forecasting future asset performance. Advances in machine learning present significant opportunities to integrate prediction theory into portfolio selection. However, some studies indicate that relying on a single prediction model may lead to biased forecasts or suboptimal portfolio outcomes due to individual algorithms' inherent limitations and assumptions. This study proposes an ensemble learning approach for portfolio optimization by combining advanced predictive analytics with classical mean-variance (MV) models as a two-stage methodology. To do so, seven machine learning models, including LSTM, BLSTM, GRU, DMLP, RF, XGBoost, and SVR, are combined using ensemble approaches such as ridge regression (RR) and principal component regression (PCR). Next, the future insights are integrated into the MV framework to optimize asset allocations, aiming to balance risk and return effectively. An empirical analysis of top cryptocurrencies showed that ensemble learning improves prediction accuracy. Furthermore, the integration of forecasting results with the MV framework outperformed traditional approaches in terms of risk-adjusted returns and portfolio stability.
Neurocognitive and QoL Profiles as a Tools for Informed Decisions in Brain Metastasis Management: Analysis from A Prospective Randomized Placebo-Controlled Trial (CTRI/2022/01/039599).
Authors: Surendran Haripriya Parapparambil , Dutta Debnarayan , Unnikrishnan Mazhuvancherry Kesavan , Poornchary Narmadha Mukunthu , Chandran Dhanya , Kalavagunta Sruthi , Ayiramuthu Parasuraman , Mangalath Sabitha 
Published: January 2025

Abstract

Purpose:To evaluate neurocognitive function and quality of life (QoL) scores in brain metastasis (BM) patients as potential indicators for optimizing therapeutic interventions.
Methods:Neurocognitive function and QoL scores were collected from a prospective randomized trial (CTRI/2022/01/039599) involving BM patients (n=130). Neurocognitive function was assessed using the Addenbrooke's Cognitive Examination (ACE), and QoL was measured using EORTC QLQ-C30 and BN20 questionnaires prior to radiation therapy (RT). Predictors of neurocognitive decline and QoL outcomes were identified through descriptive and regression analyses to inform intervention strategies.
Results:At baseline, mean ACE scores were 75.19 ± 18.67. Among patients, 60% had normal cognitive function (ACE ≥ 75), while 28.5%, 6.2%, and 5.4% exhibited mild (1 SD decline), moderate (2 SDs decline), and severe cognitive impairment (ACE < 37), respectively. Significant predictors of impairment included poor performance status (ECOG 2; OR = 52.08, p < 0.001), education below graduation (OR = 0.002, p = 0.03), hypertension (OR = 52.08, p = 0.02), and lesions in the left temporal (OR = 71.4, p = 0.04) and occipital lobes (p = 0.03). Baseline mean global QoL score was 56.5 ± 32.5, with functional domain scores ≤60 in all patients.
Conclusion:At baseline, 60% of BM patients demonstrated normal cognition, while the remaining exhibited impairments, predominantly in memory, verbal fluency, and visuospatial abilities. Severe cognitive impairment correlated with poor ECOG status, lower education, hypertension, and left temporal lesions. Incorporating neurocognitive and QoL assessments into routine practice may aid in personalized intervention planning.
Trial registration number: CTRI/2022/01/039599
Principal component analysis for yield and quality traits of sugar beet varieties treated by proline under saline stress
Authors: Abu-Ellail Farrag Farghal Boraiy , Abdelkawy Ramy Nabil Farez , Sadek Karam Abdel 
Published: January 2025

Abstract

Background
In our study, saline soil stress negatively affects root and sugar yields. Therefore, six sugar beet varieties were evaluated using different levels of proline to decrease the harmful effect of salinity in 2022/2023 and 2023/2024.
Methods
Proline treatments (control, 50, 100, and 200 ppm) were sprayed in the main plots, and six sugar beet varieties were arranged within subplots, such as Smart Seza, Smart Meyra, BTS 3880, BTS 3740, SV 2003, and Wombat Smart during the two seasons.
Results
The elite varieties recording high mean values for growth and yield traits were Smart Seza and Smart Mayra, whereas the best varieties reflecting high mean values for quality traits were SV2003 and Smart Seza. The root diameter (RD), root length (RL), top fresh weight (TW), root fresh weight / plant (RW), extractable sugar (EXT), sucrose (SU), and sugar yield (SY) showed a positive relationship with the root yield of sugar beets, whereas potassium (K), α-amino (N), sodium (Na), and sucrose loss to molasses (SLM) showed a negative relationship with sugar yield. Results revealed that out of 12, only three principal components (PCs) showed 95.6% variability among the studied traits. Maximum variation was found for PC1; therefore, the selection of important characteristics that increase sugar yield under PC1 may be desirable.
Conclusions
A 200 ppm proline concentration sprayed on sugar beet varieties can be recommended to achieve high yield and quality sugar traits. In general, Smart Seza and Smart Mayra were the best varieties with high mean values for growth, yield, and quality attributes.
A Webshell Detection Method Based on Multi-Feature Fusion and Federated Learning
Authors: ZENG Qing-peng , CHAI Jiang-li , WU Jian-sheng 
Published: January 2025

Abstract

Webshell attacks have become a prevalent strategy in the arsenal of network intruders, enabling them to seize a measure of control over web servers and execute malicious operations. These incursions are particularly insidious due to their covert nature, with attackers perpetually devising fresh Webshell variants to sidestep security measures. Compounding the issue is the shortfall in information exchange and collaboration among server networks, which leads to a patchwork of detection capabilities against Webshell threats. This fragmentation hampers the development of a unified and potent defense architecture.This scholarly work introduces a cutting-edge Webshell detection technique founded on the principles of federated learning, tailored to overcome the aforementioned challenges. The proposed method amalgamates a diverse array of features from various tiers of Webshell analysis, including source code, Abstract Syntax Tree (AST) sequence features, and Opcode sequence features, along with a suite of statistical attributes such as code structure features, text obfuscation metrics, and indicators reflecting Cybersecurity expertise and experience. The research constructs a TextCNN-based neural network architecture specifically attuned to learning the malevolent behaviors exhibited by Webshell samples. In parallel, the study leverages the FedAvg algorithm from the realm of federated learning, coupled with the DP-SGD optimizer, to facilitate collaborative training across numerous participants while ensuring that data remains within their respective domains. This approach not only upholds data privacy but also forestalls the leak of sensitive information.The empirical findings from the experiments conducted on the AMWD’22 dataset are resoundingly positive. The proposed method’s model achieved an impressive accuracy rate of 99.47%, complemented by an F-1 score of 99.67%, signifying superior detection capabilities when compared to alternative models. Furthermore, the rigorous design of federated learning experiments for comparative analysis has successfully validated that the model’s accuracy can be enhanced from an already high baseline of 98.01% to an even more remarkable 99.01%, all while ensuring that data remains within its original domain, thus preserving privacy and security.
Research on enterprise network public opinion guiding decision-making under crisis anticipation
Authors: Wang Jiakun , Guo Xiaotong , Chen Huiying 
Published: January 2025

Abstract

In the context of the rapid popularization of social media platforms and the frequent occurrence of crisis events, determining the optimal network public opinion information (referred as public opinion) guidance strategy is of great significance for assisting enterprises actively manage crises and promoting their sustainable development. Considering the randomness of crisis outbreaks and the complexity of the derivative public opinion propagation process, this paper innovatively proposes a differential game propagation model of public opinion under the anticipation of crisis. Subsequently, it focuses on the strategy choices of various subjects in different decision-making scenarios before and after the crisis, identifies the key parameters influencing the evolution of public opinion, and determines the optimal decision-making mode for enterprises. The results indicate that enterprises can achieve optimal public opinion guidance by choosing a centralized decision-making mode after the crisis and a decentralized decision-making mode before the crisis. Compared with the probability of crisis occurrence, enterprises’ strategy choices are more sensitive to changes in the benefit distribution coefficient between the media and netizens. The incentives provided by enterprises to media are significantly superior to those provided to netizens in promoting the propagation of public opinion.
Advancing Arecanut Quality Grading: A Comparative Analysis of YOLO Models with Hyperparameter Optimization
Authors: D Dhanush Ghate , H Saishma , M Adithya , Ghate Sudeep D 
Published: January 2025

Abstract

Arecanut grading is essential for maintaining quality, fair pricing, and efficient trade. Manual grading methods, dependent on subjective human assessment, are prone to errors, inconsistencies, and inefficiencies, particularly in large-scale operations.Automating this process is vital for improving accuracy and scalability. The You Only Look Once (YOLO) deep learning method autonomously evaluates arecanuts by training on 2,000 high-resolution photos uniformly categorized into four quality ratings. We split the dataset into 80% for training, 20% for validation, and used a separate curated test set to evaluate generalization. Then evaluated YOLOv8 and YOLOv11 models in nano, small, and medium configurations. The optimization process involved tuning batch size, learning rate, and weight decay through grid search and applying data augmentation techniques.The YOLOv8 nano model achieved the highest accuracy of 98.25%, with a precision of 0.98, a recall of 0.98, and a processing time of 220.19 ms per image. In contrast, YOLOv11 models exhibited lower accuracy due to overlapping feature misclassifications. While the results highlight the potential of YOLO models in automating agricultural grading, the study is constrained by dataset size and single-perspective imaging, limiting its generalizability. Future work will focus on expanding datasets, incorporating advanced imaging technologies, and improving model transparency for practical deployment. These results demonstrate the potential of YOLO models in automating agricultural grading, offering a scalable, efficient, and sustainable solution for arecanut classification in real-world applications.
The Impact of Housing Insecurity on Hospitalized Patients with Diagnosis of Cirrhosis: A Comparative Analysis Using Data from the National Inpatient Sample (2016-2021)
Authors: Akambase Joseph A , Ali Yasmin , Goble Spencer R 
Published: January 2025

Abstract

Purpose
Patients experiencing homelessness are disproportionately affected by cirrhosis due to socioeconomic barriers, housing insecurity, and healthcare access challenges. However, the impact of homelessness on clinical outcomes and healthcare utilization among hospitalized cirrhosis patients has not been well-characterized.
Methods
We conducted a cross-sectional study using the National Inpatient Sample (2016–2021) to analyze hospitalizations of adults with cirrhosis, comparing outcomes between those with and without homelessness. Demographic, clinical, and hospital-level characteristics were assessed, along with outcomes such as mortality and AMA discharges. Healthcare utilization metrics, including length of stay (LOS) and cost, were also evaluated, with multivariable regression used to adjust for confounders
Results
Among 4,579,858 hospitalizations for cirrhosis, 109,640 (2.4%) involved homeless patients, who were younger (mean 53.5 vs. 60.6 years, p < 0.001) and predominantly male (80.4% vs. 58.9%, p < 0.001). Homeless patients had higher rates of alcohol use (73.5% vs. 30.9%, p < 0.001), opioid use disorder (11.8% vs. 3.6%, p < 0.001), and psychiatric comorbidities (62% vs. 37.4%, p < 0.001). Hispanic and Native American patients were overrepresented, while white patients were underrepresented. Mortality was lower in homeless patients ([aOR] 0.46, 95% CI: 0.42–0.50, p < 0.001). However, AMA discharges were significantly higher (9.6% vs. 2.7%, p < 0.001). Homeless patients had longer hospital stays (mean 7.3 vs. 6.2 days, p < 0.001) but lower per-day hospitalization costs ($2,278 vs. $2,859, p < 0.001).
Conclusion
Homelessness is associated with distinct clinical and healthcare utilization patterns among hospitalized patients with cirrhosis. Despite lower mortality and procedural intervention rates, high AMA discharge rates and prolonged hospital stays underscore the challenges to safe discharge among patient with cirrhosis.
Mechanochemical Synthesis of Diclofenac Conjugates with Glucosamine and Chitosan Exhibiting COX-2 Selective Ulcer Safe Anti-inflammatory Activity
Authors: Afzal Sana , Iqbal Mohammad Saeed , Khan Abdul Haleem 
Published: January 2025

Introduction:
Non-steroidal anti-inflammatory drugs are associated with severe gastrointestinal irritation upon prolonged use, largely due to their carboxylic (--
COOH) functional group.

Aim:
To address this issue, we aimed to synthesize diclofenac conjugates with glucosamine and chitosan, converting the -COOH group into an amide (-CONH-) via a
mechanochemical, environmentally friendly method.

Method:
In this study, diclofenac acid was first converted to its acid chloride using
thionyl chloride under mechanochemical conditions and subsequently reacted with glucosamine base and chitosan. The resulting conjugates were evaluated for anti-inflammatory activity through the rat-paw edema test, along with ulcerogenicity, COX inhibition
assays, and cardiovascular assessment.

Result:
The mechanochemical approach provided high yields (>90%) and resulted in
conjugates that significantly reduced paw edema (62.3 ± 2.3% for diclofenac-glucosamine and 58.5 ± 1.6% for diclofenac-chitosan) compared to diclofenac sodium
(49.0 ± 1.3%) after 5 h. Notably, the conjugates were ulcer-safe, as no gastric lesions
were observed, unlike the multiple lesions detected in animals treated with diclofenac
sodium. Both conjugates also demonstrated a high degree of COX-2 selectivity and cardiovascular safety.

Conclusion:
This study highlights the potential of mechanochemical synthesis for efficient amide formation, avoiding the need for hydroxyl group protection.
Green Pseudo-Seven Component Synthesis of Tris-cyclohexylamino-oxo-benzene-1,3,5-tricarboxylates via Passerini Reaction under Aqueous Conditions
Authors: Azimi-Roshan Abbas , Kiyani Hamzeh , Mamaghani Manouchehr 
Published: January 2025

Abstract:
In this study, a simple, mild, and environmentally friendly process was used for the pseudo-seven
component reaction between readily available benzene-1,3,5-tricarboxylic acid, isocyano-cyclohexane, and
various aldehydes. This rapid method produced the tris-cyclohexylamino-oxo-benzene-1,3,5-tricarboxylates in
relatively acceptable and more reasonable reaction times (10-15 min) and high achievable efficiencies (85-
95%) by employing a Passerini reaction. Water was used as an eco-friendly and low-cost solvent for the green
synthesis of benzene tris-carboxamides. The reactions were carried out via a Passerini approach at 50ºC for
relatively shorter reaction times. The antibacterial action of the synthetic tris-carboxamides was investigated,
and some of them gave satisfactory and promising antibacterial results. Target tris-Passerini molecules recrystallized
from 96% EtOH. The structures of the resulting molecules were evaluated and confirmed with the help
of spectral data and elemental analyses.