bluuebunny/crossref_metadata_2025_split huggingface dataset:
Palm kernel meal improves the production and conidia quality of Cordyceps javanica in conventional solid-state fermentation
Authors: Ordaz-Hernández Armando , Montesinos-Matías Roberto , Mellín-Rosas Marco A. , Pérez-Aguirre Teresa , Loera Octavio , Angel-Cuapio Alejandro 
Published: June 2023

Abstract
Palm kernel meal was incorporated into rice, and its effect on conidiation by Cordyceps javanica under solid-state fermentation was determined. The addition of 5% palm kernel meal increased the conidial yield by up to 40%, without compromising conidia viability and hydrophobicity. In addition, conidia caused higher levels of mortality by mycosis against Diaphorina citriadults (90%), relative to conidia harvested from rice (52%). The conidia recovered from rice/palm kernel meal mixtures also retained viability greater than 90% after storage for 10 months at 4 °C, while the conidia produced on rice reached 80%. Thus, conidia produced in the presence of palm kernel meal can be consumed immediately or in the medium term. Some advantages of palm kernel meal as a co-substrate are also analyzed in the conventional production system of C. javanica. These results are attractive for improving the mycoinsecticide production process, with excellent cost-benefit and minimal changes in infrastructure and process.
Dual-Transformer Head End-to-End Person Search Network
Authors: Feng Cheng , Han Dezhi , Chen Chongqing 
Published: June 2023

Abstract
Person Search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and CNN (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through the ResNet backbone network to extract features, the RPN proposes possible bounding boxes. The box head then extracts more efficient features within these bounding boxes for detection. Following this, the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds. Extensive experiments on two widely used benchmark datasets, CUHK-SYSU and PRW, achieve state-of-the-art performance levels, 94.9 mAP and 95.3 top-1 score on the CUHK-SYSU dataset, and 51.6 mAP and 87.6 top-1 score on the PRW dataset, which demonstrates the advantages of this paper’s approach.
Association of hepatitis B virus infection with head and neck cancer: a propensity-matched study
Authors: Zhang lanlan , Wang Weihong 
Published: June 2023

Abstract

Objective This work focused on investigating the relation between hepatitis B virus (HBV) infection with head and neck cancer (HNC), identifying the associated risk factors and providing a reference for preventing and treating HNC.
Methods Retrospective analysis was conducted on 1,572 HNC cases who were hospitalized between January 2009 and December 2020. Meanwhile, 58,409 individuals with non-oncological illnesses from the departments of oral and maxillofacial surgery, psychiatry, neurology and cardiology were recruited as controls. R software was utilized for data processing. Clinical data were processed using SPSS 22.0, while baseline radiotherapy data were balanced with 1:4 propensity score matching (PSM).
Results In this study, 1:4 PSM was completed in 1,572 HNC patients and 6,288 controls. In comparison with controls, HNC cases had a markedly increased HBsAg positivity rate (5.9% vs 3.5%, p<0.001). Additionally, the HBsAb positivity rate of HNC cases remarkably decreased relative to controls (52.9% vs 58.7%, p<0.001).
Conclusions HBV infection was positively related to HNC, while HBsAb was negatively correlated with HNC.
Spontaneous low-temperature conversion of ortho-azidocarbonyl compounds to 2,1-benzisoxazoles
Authors: Villanueva-Kasis Oscar , de Loera Denisse , Vega-Rodríguez Sarai , Leyva Elisa 
Published: June 2023

Abstract
We have observed the spontaneous conversion of two ortho-azidocarbonyl compounds to the corresponding 2,1-benzisoxazoles, which are also called anthranils. These systems comprehend the 2-azidobenzaldehyde and the 2-azido-4,5-dimethoxybenzaldehyde, obtained from halogenated starting materials via a nucleophilic aromatic substitution with sodium azide. Upon the formation of the organic azide, the molecule undergoes a rapid cyclization and release of gaseous nitrogen to afford the 2,1-benzisoxazole. The ortho-azidocarbonyl compounds that persisted were stored in the freezer, well-sealed and sheltered from light. Even under those conditions, the compounds eventually transformed to their corresponding 2,1-benzisoxazoles, as seen via Fourier-transform Infrared Spectroscopy (FTIR). A series of computational studies were performed at the B3LYP/6-311G(d,p) level of theory to confirm the spontaneous formation of the 2,1-benzisoxazoles from the corresponding ortho-azidocarbonyl compounds. The methoxy groups do not influence the kinetics of the reaction, but the formation of the 2,1-benzisoxazole seems to be thermodynamically favored by the presence of the methoxy group.
The Expression of Autophagy Markers in IVIG- resistant Kawasaki Disease and the Establishment of Prediction Model
Authors: Yang Zhou , Wu Yali , Yuan Chun-hui , Yin Wei , Wang Baoxiang , Ding Yan 
Published: June 2023

Abstract
BACKGROUND
The aim of this study was to find early predictors of Intravenous Immunoglobulin-Resistant Kawasaki Disease
METHODS
Patients diagnosed with Kawasaki disease were enrolled in this study. Univariate analysis and multiple logistic regression were used to analyze the clinical characteristics and laboratory findings of patients in both groups before IVIG treatment. Independent predictors of Intravenous Immunoglobulin-Resistant Kawasaki Disease were analyzed, and a prediction model for children with Intravenous Immunoglobulin-Resistant Kawasaki Disease was constructed.
Results
A total of 108 children (67 males and 41 females) with C-globulin-sensitive Kawasaki disease and 31 children (20 males and 11 females) with C-globulin-naïve Kawasaki disease participated in this study .Compared with the IVIG-sensitive group, the duration of hospitalization, ALT, AST, GLB, r-GT, IgG, PCT, ESR were elevated in the Intravenous Immunoglobulin-Resistant Kawasaki Disease group, and ATG16L1, LC3II, BECN1, RBC, HGB, ALB, A/G, and CK were significantly lower (P < 0.05). mRNA expression of ESR, BECN1, and LC3II were independent risk factors for IVIG non-responsive Kawasaki disease. A logistic regression model and scoring system were established, and the cut-off values of independent risk factors were derived from ROC curves: ESR ≥ 79.5mm/h, BECN1 ≤ 0.645, LC3II ≤ 0.481. A new scoring system was established according to the respective regression coefficients as follows: ESR ≥ 79.5mm/h (1 point), BECN1 ≤ 0.645 (1 point). LC3II ≤ 0.481 (2 points), 0–1 as low risk for IVIG non-response, and ≥ 2 as high risk. Applied to this group of study subjects, the sensitivity was 87.10%, specificity 83.33%, Youden index 0.70, AUC 0.9.
CONCLUSIONS
Autophagy markers ATG16L1, BECN1, and LC3II are down-regulated in the expression of C globule nonresponsive Kawasaki disease. ESR, BECN1, and LC3II mRNAs are independent risk factors for C globule nonresponsive Kawasaki disease and may be involved in the development of C globule nonresponsive Kawasaki disease. This study established a new model that can be used to predict C globule nonresponsive Kawasaki disease, and future validation in a larger population is needed.
Weakly Supervised Cross-domain Person Re-Identification Algorithm based on Small Sample Learning
Authors: Li Huiping , Wang Yan , Zhu Lingwei , Wang Wenchao , Yin Kangning , Li Ye , Yin Guangqiang 
Published: June 2023

Abstract
Person Re-identification (Re-ID) algorithms based on deep learning are developing rapidly, and supervised learning methods ensure a steady improvement in algorithm performance by virtue of massive amounts of data. However, in the real environment, data collection and labeling of the current scene is time-consuming and laborious, and the actual performance of the algorithmic model is often poor. Therefore, the design of the model should focus on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data since, weak cross-domain capability and low accuracy encountered in Re-ID in weakly supervised scenarios. First, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, an residual compensation and fusion attention (RCFA) module is designed with the help of residual compensation and fused attention module, based on which a model base framework is built to explore the impact brought by different insertion positions. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data.
The present value of human life losses associated with COVID-19 and likely productivity losses averted through COVID-19 vaccination in Madagascar
Authors: MUSANGO Laurent , MANDROSOVOLOLONA Vatsiharizandry , Fidiniaina RANDRIATSARAFARA Mamy , Malala RANARISON Volahanta , Joses KIRIGIA Muthuri , Arsène RATSIMBASOA Claude 
Published: June 2023

Abstract

Background As of 3 March 2023, Madagascar had conducted 525,461 COVID-19 tests, which revealed a total of 67,865 cases, comprising 66,433 recovered cases, ten active cases, and 1,422 deaths. To date, no study has attempted to estimate the total present value of human lives lost \(\left({TPVHL}_{MADAGASCAR}\right)\), productivity losses, and potential total productivity losses (or indirect costs) averted through COVID-19 vaccination for use in advocacy. The study reported in this paper attempted to bridge those information gaps. Methods A human capital model (HCM) was used to estimate the \({TPVHL}_{MADAGASCAR}\), i.e., sum of the discounted value of human life losses amongst persons in seven age groups \(\left({PVHL}_{i}\right)\): 1 = under 5 years, 2 = 5 to 14 years, 3 = 15 to 24 years, 4 = 25 to 34 years, 5 = 35 to 44 years, 6 = 45 to 59 years, and 7 = 60 years and older. Each age group \({PVHL}_{i=1,\dots ,7}\) was obtained through the multiplication of discount factor, undiscounted year of life, non-health gross domestic product (GDP) per capita, and age group’s number of COVID-19 deaths. To test the robustness of the results, the HCM was re-run five times, assuming (i) a 5% discount rate, (ii) a 10% discount rate, (iii) Africa’s highest average life expectancy at birth of 78.76 years (Algeria females), (iv) the world highest life expectancy of 88.17 years (Hong Kong females), and (v) projected excess COVID-19 mortality of 11,418.66. The projections of productivity losses averted through COVID-19 vaccination were done assuming 100%, 70%, 52.4%, and 8.266% coverage. Results The 1,422 human lives lost due to COVID-19 had a \({TPVHL}_{MADAGASCAR}\) of Int$ 46,485,587.15; and a mean of Int$ 32,690.28 per human life. Re-run of the model with (i) discount rates of 5% and 10% reduced \({TPVHL}_{MADAGASCAR}\) by 23.26% and 53%, respectively; (ii) average life expectancies of 78.76 years and 88.17 years grew \({TPVHL}_{MADAGASCAR}\) by 23.72% and 39.49%, respectively; (iii) projected excess COVID-19 mortality of 11,418.66 augmented \({TPVHL}_{MADAGASCAR}\) by 703%. Vaccinating 70% of the target population could potentially save the country Int$ 965,088,931 (1.71% of GDP). Conclusions COVID-19 pandemic has cost Madagascar substantive health and productivity losses. Potentially, the losses could be significantly reduced by optimizing COVID-19 vaccination coverage of the target population.
Effect of prior breast cancer on survival of female patients with primary liver cancer: Development of a competing risk model nomogram
Authors: he jun , Chen Xiangmei , Wang Yu , Chen Wenxiang , Zhou Jianyin 
Published: June 2023

Abstract
Background
The impact of prior breast cancer on subsequent primary liver cancer (PLC) survival remains poorly understood. Moreover, traditional prediction models struggle to accurately predict cancer-specific survival (CSS) for PLC cases that have a history of breast cancer. We aimed to investigate the role of prior breast cancer on subsequent PLC survival and construct a CSS prediction nomogram for PLC cases with a history of breast cancer.
Methods
We obtained data on female PLC patients between 2005 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. To minimize the impact of confounding bias, we employed propensity score matching (PSM) to match each patient with prior breast cancer to 10 patients without a history of breast cancer. Univariate, as well as multivariate COX survival and CSS analyses, were conducted to investigate the effect of prior breast cancer on subsequent PLC survival. Additionally, a competing risk model nomogram was built to predict PLC-specific survival.
Results
Our survival analyses revealed that prior breast cancer did not significantly affect overall survival (OS) among PLC cases. However, it served as a prognostic factor for predicting favorable outcomes in PLC-specific survival. A history of prior breast cancer reduced PLC-specific mortality by 0.26-fold (HR = 0.74, 95% CI: 0.88–0.96, p = 0.023). Furthermore, the analysis of concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that our model had good predictive power and outperformed conventional prediction models. According to decision curve analysis (DCA), our constructed nomogram had good clinical significance.
Conclusions
Prior breast cancer is beneficial to PLC-specific survival in PLC patients. The constructed competing risk model nomogram demonstrated good predictive ability for PLC-specific survival.
Supporting rehabilitation practice for COVID-19 recovery: A descriptive qualitative analysis of healthcare worker perspectives.
Authors: Laake Tanis Van , Hitch Danielle 
Published: June 2023

Abstract
Objective The study aimed to explore the perspective of healthcare workers on the resources they need to provide quality rehabilitation for people recovering from COVID-19 infection.Methods A descriptive qualitative approach using reflexive thematic analysis was employed. Focus groups and interviews were performed with 9 healthcare workers (1 Intensive Care Unit Physiotherapist, 1 Respiratory Therapist and 7 Occupational Therapists) with experience treating patients recovering from COVID-19 infection within hospital and in the community. Verbatim transcripts were coded and analysed for themes.Results Healthcare workers perceive an urgent need for resources about recovery from COVID-19 infection to support quality care. They particularly want detailed guidance about potential trajectories of recovery, and what should be expected over time for people with Long COVID. Many observed that this group of patients required higher oxygen requirement and experienced slower recovery than others with serious respiratory conditions. Supports for quality care do exist but are not perceived as sufficient by participants. They want any new resources developed to be accessible, quick to access and targeted to specific healthcare worker roles and services settings. Participants identified several barriers to accessing practice supports, including the time poor nature of healthcare and financial costs to both patients and healthcare workers.Conclusion Healthcare workers perceive an urgent need for the development of resources to support quality rehabilitation for people recovering from COVID-19 infection, to support best outcomes for this group of patients. Any resources developed must consider identified barriers to their access and use to maximise their impact.
Cytotoxic effects of dimethyl-celecoxib (DMC) on bone tumor cell line MG63 and cell line HEK293
Authors: Naji Tahereh , Fatehi Maryam , Ahmadi Rahim , Ghahramani Morteza , Khalaji Mahla 
Published: June 2023

Abstract
Dimethyl-celecoxib (DMC), is a celecoxib (CXB) derivative has no inhibitory function on cyclooxygenase-2 (COX-2) and displays antitumor properties. This substance can be helpful in advancing the treatment of COX-2-indipentent cancers. In the current study, we assayed the efficacy of DMC on MG63 bone tumor cell line and Human embryonic kidney (HEK293) cell line. The cellular viability, nitrogen monoxide content, and Inducible nitric oxide synthases (iNOS) gene expression were measured respectively with MTT (The MTT assay is a colorimetric assay for assessing cell metabolic activity.) Griess reaction, and real-time Reverse transcription polymerase chain reaction (RT-PCR) procedures .IC50 was determined by MTT assay, and iNOS gene expression was evaluated by RT-PCR. Also, monoxide nitrogen production was monitored by a Griess test and finally one-way Anova (Analysis of Variance (ANOVA) is a statistical formula used to compare variances across the means (or average) of different groups.) and T-test were used to analyze the data. Results showed that Dimethylcelloxib at concentrations of: 62.5, 125, 250, 500 µg /ml at 48 h significantly decreased the survival rate of MG63 and HEK293 tumor cells (P < 0.001). It was found that iNOS gene expression decreased significantly (P < 0.001) and production of monoxide nitrogen molecule had a significant increase (P < 0.01 and P < 0.001). since Dimethylceloxib reduced iNOS gene expression, it was expected to decrease nitrogen monoxide, but this drug showed its cytotoxic effects through increased nitrogen monoxide production.
A window to the future: effects of climate change on the distribution patterns of Iranian Zygaenidae and their host plants
Authors: Noori Sajad , Hofmann Axel , Rödder Dennis , Husemann Martin , Rajaei Hossein 
Published: June 2023

Abstract

Climate change has been suggested as an important human-induced driver for the ongoing sixth mass extinction. As a common response to climate change, and particularly global warming, species move toward higher latitudes or shift uphill. Furthermore, rapid climate change impacts the biotic interactions of species. Especially, in Zygaenid moths which are highly specialized in both habitat and host plant preferences. Iranian Zygaenidae are relatively well-known and represent a unique fauna with a high endemism rate (46%) in the whole Palearctic; as such they are a good model to study the impact of climate change on future distributions. In this study, species distribution modeling (SDM) and ensembles of small models (ESMs) were used to investigate the impact of climate change on the future distribution of endemic and non-endemic species of zygaenids, as well as their larval host plants. Three different climate scenarios were applied to predict the probable responses of the species to different climate change intensities. Our results suggest that the central and southern parts of the country will be impacted profoundly by climate change compared to the northern regions. Beyond this, most endemic species will experience an altitudinal shift from their current range, while non-endemic species may move towards higher latitudes. Considering that the regions with higher diversity of zygaenids are limited to mountainous areas, mainly within the Irano-Anatolian biodiversity hotspot, the identification of their local high diversity regions for conservation practices has a high priority.
quantms: A cloud-based pipeline for proteomics reanalysis enables the quantification of 17521 proteins in 9,502 human samples.
Authors: Dai Chengxin , Pfeuffer Julianus , Wang Hong , Sachsenberg Timo , Demichev Vadim , Kohlbacher Oliver , Perez-Riverol Yasset 
Published: June 2023

Abstract
Public proteomics data is rapidly increasing, creating a computational challenge for large-scale reanalysis. Here, we introduce quantms, an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 56 of the largest datasets, comprising 26801 instrument files from 9502 human samples, to quantify 17521 based on 1.02 million unique peptides. Based on standard file formats improves the reproducibility and deposition of the data to ProteomeXchange.
Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed
Authors: Beyene Tsegamlak Diriba , Zimale Fasikaw Atanaw , Gebrekristos Sirak Tekleab , Nedaw Dessie 
Published: June 2023

Abstract

A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources of data through combining algorithms. The main objective of this study is to assess the accuracy and importance of the fused multistage approach of bias correction. Accordingly, two versions of resampled and spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) estimates were merged with ground measurements using a conditional merging procedure. Results of applied performance measures (i.e. seven) on corrected and merged CHIRP SREs show that the Percent of Detection (POD) and Percent Volume Error (PVE) have improved. Depending on the combination of coupled stations for validation, up to 70 and 50% PVE improvement was achieved at some stations for wet and dry periods, respectively. Moreover, the bias-corrected and conditionally merged CHIRP SREs have outperformed the estimates by resampling CHIRP with station dataset (CHIRPS) over the sparsely populated western part of the watershed. However, the devised method was limited in considering dry-day events during bias correction, which in turn has affected the performance of the bias correction of the CHIRPS product. Finally, future research should concentrate on such methods of fusing to understand the benefits of various approaches and produce more precise rainfall records.
Novel Pyrimidin-4-yl-3-amino-pyrrolo[3,4-c]pyrazoles as Protein
Kinase C Inhibitors for Treating Diseases
Authors: De Surya K. 
Published: June 2023

Abstract:
This patent describes the series of compounds and their pharmaceutically acceptable
salts, such as compound K7 (as a representative potent compound). These protein
kinase C selective inhibitors are useful for treating diabetes mellitus and its complications,
cancer, ischemia, inflammation, central nervous system disorders, cardiovascular disease,
Alzheimer's disease, dermatological disease, virus diseases, inflammatory disorders, or
diseases in which the liver is a target organ.