bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the Causes of the Decline in the Iraqi Agricultural Sector’s Contribution to the GDP
Authors: Drebee Hayder Abbas , Abdul Razak Nor Azam , Shaybth Razzaq Thiab 
Published: August 2022

Abstract
The agricultural sector is one of the most important economic sectors in Iraq. It is ranked second after the crude oil sector in terms of its contribution to the gross domestic product (GDP). However, its contribution to the GDP declined from 26.93% in 1991 to 10.86% in 2004, 7.23% in 2010, and 4.77% in 2020. This decline can be attributed to many determinants of agricultural production that had a significant impact; therefore, this study aimed to investigate the determinants of agricultural production in Iraq during the period 2004:1- 2020:4 using the Autoregressive Distributed Lag Model (ARDL). The study concluded that there is a short and long-run equilibrium relationship between the GDP and the cultivated area, corruption, inflation, agricultural investment, agricultural production requirements, the availability of water, and employment in agricultural, on the other hand. The results indicated the significant impact of these variables on agricultural GDP in the long run. The main variables contributing to the agricultural GDP are water availability, corruption, and employment in agricultural. The estimates of the short-run parameters would correspond in terms of signs and significance with the results of the long-run estimates, even if the parameters’ values varied in different proportions. It indicates that the Iraqi agricultural sector suffers from a lack of optimal use of agricultural resources. Therefore, decision-makers in Iraq must adopt an effective agricultural policy by setting plans for the optimal use of water and paying attention to the human element to be qualified to carry out employment in agricultural and attract investment in the agricultural sector to develop it. It is also necessary to put in place financial and economic policies that reduce the adverse effects of inflationary pressures in the short and long terms, combat rampant corruption in the country, and preserve agricultural lands and reclamation of unsuitable lands.
Hybrid extracellular vesicles-liposome incorporated advanced bioink to deliver microRNA
Authors: Elkhoury Kamil , Chen Mo , Koçak Polen , Enciso-Martínez Eduardo , Bassous Nicole Joy , Lee Myung Chul , Byambaa Batzaya , Rezaei Zahra , Li Yang , Ubina López María Elizabeth , Gurian Melvin , Sobahi Nebras , Hussain Mohammad Asif , Sanchez-Gonzalez Laura , Leijten Jeroen , Hassan Shabir , Arab-Tehrany Elmira , Ward Jennifer Ellis , Shin Su Ryon 
Published: August 2022

Abstract
In additive manufacturing, bioink formulations govern strategies to engineer 3D living tissues that mimic the complex architectures and functions of native tissues for successful tissue regeneration. Conventional 3D-printed tissues are limited in their ability to alter the fate of laden cells. Specifically, the efficient delivery of gene expression regulators (i.e. microRNAs (miRNAs)) to cells in bioprinted tissues has remained largely elusive. In this study, we explored the inclusion of extracellular vesicles (EVs), naturally occurring nanovesicles (NVs), into bioinks to resolve this challenge. EVs show excellent biocompatibility, rapid endocytosis, and low immunogenicity, which lead to the efficient delivery of miRNAs without measurable cytotoxicity. EVs were fused with liposomes to prolong and control their release by altering their physical interaction with the bioink. Hybrid EVs-liposome (hEL) NVs were embedded in gelatin-based hydrogels to create bioinks that could efficiently encapsulate and deliver miRNAs at the target site in a controlled and sustained manner. The regulation of cells’ gene expression in a 3D bioprinted matrix was achieved using the hELs-laden bioink as a precursor for excellent shape fidelity and high cell viability constructs. Novel regulatory factors-loaded bioinks will expedite the translation of new bioprinting applications in the tissue engineering field.
Nano TiC particles modulate wear resistance and impact toughness of 40Cr alloy steel gradient-reinforced layer fabricated by laser melting deposition
Authors: Wang Xi , Yang Weiguang , Zhou Ti , Zhou Hai , Liu He 
Published: August 2022

Abstract
Alloy steel components can be subjected to serious damage from a variety of conditions during the industrial production procedures, such as wear and fracture failure. Therefore, the preparation of gradient-reinforced layers on the surface of the alloy steel was considered an effective technique to improve the performance. Along these lines, a 40Cr alloy steel, which was commonly used in industry, was systematically investigated in this work. The nano TiC ceramic material was selected in the hard phase. In this work, continuous-wave laser was used to fabricate gradient reinforced layers, which provided a technical reference for the development of protective reinforcement layers for alloy steels with excellent mechanical properties. A dense structure was formed inside the nano TiC gradient reinforced layer, which has a lower friction coefficient (0.25) and wear loss weight (23 mg). The height of the surface material loss under a heavy load wear environment (187 μm) was lower than that of a bare 40Cr alloy steel sample (1116 μm). The impact energy of the nano TiC gradient reinforced layer (75.27 J cm−2) was higher than that of a bare 40Cr alloy steel sample (15.25 J cm−2). Both the wear behavior and impact toughness strengthening mechanism of the nano TiC gradient reinforced layer were revealed.
Ecological Momentary Assessment of Associations Between Social Interactions and Physical Activity Outcomes Among Women in Midlife With CVD Risk Conditions
Authors: Arigo Danielle , Brown Megan M , Shank Faith , Young Chelsie M 
Published: August 2022

Abstract

Background
Risk for cardiovascular disease is particularly high among women in midlife (ages 40–60). Moderate-to-vigorous-intensity physical activity (PA) can protect against risk during this time, and research is needed to understand contributors to PA motivation and behavior in daily life.

Purpose
This study used ecological momentary assessment to examine: (a) within-person associations between social interactions (both quantity and quality) and PA outcomes (motivation and behavior) among women in midlife, and (b) variability in within-person associations across days.

Methods
Women ages 40–60 with one or more cardiovascular disease risk conditions (e.g., hypertension; n = 75; MAge = 51.6 years, MBMI = 34.0 kg/m2) wore waistband accelerometers and completed five surveys per day for 10 days.

Results
Controlling for social interactions overall, at times when women reported a higher number of positive interactions, they reported higher PA motivation; this association was negative for both the number and valence of negative interactions. At times when women experienced a higher number of interactions overall, they engaged in fewer subsequent minutes of moderate-to-vigorous-intensity PA, though reports of positive and negative interactions were not associated with moderate-to-vigorous-intensity PA behavior. Importantly, the direction of these within-person associations differed between days of observation (positive associations on ~50% of days and negative associations on ~50% of days).

Conclusions
Findings show that social interactions influence PA motivation and behavior among women in midlife but that these influences are inconsistent, suggesting the need for careful consideration of how to engage social interactions to promote PA in this group.
Long-Term Fine Particulate Matter Concentrations and Prevalence of Severe Acute Respiratory Syndrome Coronavirus 2: Differential Relationships by Socioeconomic Status Among Pregnant Individuals in New York City
Authors: Casey Joan A , Kioumourtzoglou Marianthi-Anna , Ogburn Elizabeth L , Melamed Alexander , Shaman Jeffrey , Kandula Sasikiran , Neophytou Andreas , Darwin Kristin C , Sheffield Jeanne S , Gyamfi-Bannerman Cynthia 
Published: August 2022

Abstract
We aimed to determine whether long-term ambient concentrations of fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) were associated with increased risk of testing positive for coronavirus disease 2019 (COVID-19) among pregnant individuals who were universally screened at delivery and whether socioeconomic status (SES) modified this relationship. We used obstetrical data collected from New-York Presbyterian Hospital/Columbia University Irving Medical Center in New York, New York, between March and December 2020, including data on Medicaid use (a proxy for low SES) and COVID-19 test results. We linked estimated 2018–2019 PM2.5 concentrations (300-m resolution) with census-tract–level population density, household size, income, and mobility (as measured by mobile-device use) on the basis of residential address. Analyses included 3,318 individuals; 5% tested positive for COVID-19 at delivery, 8% tested positive during pregnancy, and 48% used Medicaid. Average long-term PM2.5 concentrations were 7.4 (standard deviation, 0.8) μg/m3. In adjusted multilevel logistic regression models, we saw no association between PM2.5 and ever testing positive for COVID-19; however, odds were elevated among those using Medicaid (per 1-μg/m3 increase, odds ratio = 1.6, 95% confidence interval: 1.0, 2.5). Further, while only 22% of those testing positive showed symptoms, 69% of symptomatic individuals used Medicaid. SES, including unmeasured occupational exposures or increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to concurrent social and environmental exposures, may explain the increased odds of testing positive for COVID-19 being confined to vulnerable pregnant individuals using Medicaid.
Recommendations for Using Causal Diagrams to Study Racial Health Disparities
Authors: Howe Chanelle J , Bailey Zinzi D , Raifman Julia R , Jackson John W 
Published: August 2022

Abstract
There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations
Authors: Hu Jing , Gao Jie , Fang Xiaomin , Liu Zijing , Wang Fan , Huang Weili , Wu Hua , Zhao Guodong 
Published: August 2022

Abstract
Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical–gene–tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure–gene and gene–gene associations and a coarse-granularity transformer encoder to extract chemical–chemical and chemical–cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.
Managing Vocational Work, Achieving and Sustaining Work Performance: Support and Self-management amongst Young Autistic Adults in the Context of Vocational Support Interventions in Sweden
Authors: Bertilsdotter Rosqvist Hanna , Hultman Lill , Hallqvist Johan 
Published: August 2022

Abstract
In this article, we explore experiences of support and self-management amongst young autistic adults in the context of vocational support interventions in Sweden. We analyse how young autistic men use different strategies to manage their vocational work and the support they need to maintain, achieve and sustain their work performance. Data consist of eleven interviews with 4 autistic young adult men in different work environments where vocational support interventions are implemented to different degrees. One finding concludes that the interviewees are affected by and try to adapt to neurotypical norms and expectations about working life and adulthood. Although individualised coping strategies can be helpful, it is important for employers and formal support persons to understand and acknowledge that individual emotional and problem-solving coping strategies are demanding and need to be combined with adaptations in the working environment. Another finding concludes how work managers act as gatekeeper in the vocational support system the young autistic men aspire to access and in which they need to manage their work performance. Thus, social workers must provide structured and well-coordinated formal work support by both involving the autistic clients’ employers, work managers and informal networks.
Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom
Authors: Vihta1 Karina Doris , Pouwels Koen B , Peto1 Tim EA , Pritchard Emma , House Thomas , Studley Ruth , Rourke Emma , Cook Duncan , Diamond Ian , Crook1 Derrick , Clifton David A , Matthews Philippa C , Stoesser Nicole , Eyre David W , Walker Ann Sarah ,   the COVID-19 Infection Survey team
Published: August 2022

Abstract

Background
The SARS-CoV-2 Delta variant has been replaced by the highly transmissible Omicron BA.1 variant, and subsequently by Omicron BA.2. It is important to understand how these changes in dominant variants affect reported symptoms, while also accounting for symptoms arising from other co-circulating respiratory viruses.

Methods
In a nationally representative UK community study, the COVID-19 Infection Survey, we investigated symptoms in PCR-positive infection episodes vs. PCR-negative study visits over calendar time, by age and vaccination status, comparing periods when the Delta, Omicron BA.1 and BA.2 variants were dominant.

Results
Between October-2020 and April-2022, 120,995 SARS-CoV-2 PCR-positive episodes occurred in 115,886 participants, with 70,683 (58%) reporting symptoms. The comparator comprised 4,766,366 PCR-negative study visits (483,894 participants); 203,422 (4%) reporting symptoms. Symptom reporting in PCR-positives varied over time, with a marked reduction in loss of taste/smell as Omicron BA.1 dominated, maintained with BA.2 (44%/45% 17 October 2021, 16%/13% 2 January 2022, 15%/12% 27 March 2022). Cough, fever, shortness of breath, myalgia, fatigue/weakness and headache also decreased after Omicron BA.1 dominated, but sore throat increased, the latter to a greater degree than concurrent increases in PCR-negatives. Fatigue/weakness increased again after BA.2 dominated, although to a similar degree to concurrent increases in PCR-negatives. Symptoms were consistently more common in adults aged 18-65 years than in children or older adults.

Conclusions
Increases in sore throat (also common in the general community), and a marked reduction in loss of taste/smell, make Omicron harder to detect with symptom-based testing algorithms, with implications for institutional and national testing policies.
Association of Antibody Immunity With Cryptococcal Antigenemia and Mortality in a South African Cohort With Advanced Human Immunodeficiency Virus Disease
Authors: Yoon Hyunah , Wake Rachel M , Nakouzi Antonio S , Wang Tao , Agalliu Ilir , Tiemessen Caroline T , Govender Nelesh P , Jarvis Joseph N , Harrison Thomas S , Pirofski Liise-anne 
Published: August 2022

Abstract

Background
Asymptomatic cryptococcal antigenemia (positive blood cryptococcal antigen [CrAg]) is associated with increased mortality in individuals with human immunodeficiency virus (HIV) even after adjusting for CD4 count and despite receiving antifungal treatment. The association of antibody immunity with mortality in adults with HIV with cryptococcal antigenemia is unknown.

Methods
Cryptococcal capsular glucuronoxylomannan (GXM)- and naturally occurring β-glucans (laminarin, curdlan)-binding antibodies were measured in blood samples of 197 South Africans with HIV who underwent CrAg screening and were followed up to 6 months. Associations between antibody titers, CrAg status, and all-cause mortality were sought using logistic and Cox regression, respectively.

Results
Compared with CrAg-negative individuals (n = 130), CrAg-positive individuals (n = 67) had significantly higher IgG1 (median, 6672; interquartile range [IQR], 4696–10 414 vs 5343, 3808–7722 μg/mL; P = .007), IgG2 (1467, 813–2607 vs 1036, 519–2012 μg/mL; P = .01), and GXM-IgG (1:170, 61–412 vs 1:117, 47–176; P = .0009) and lower curdlan-IgG (1:47, 11–133 vs 1:93, 40–206; P = .01) titers. GXM-IgG was associated directly with cryptococcal antigenemia adjusted for CD4 count and antiretroviral therapy use (odds ratio, 1.64; 95% confidence interval [CI], 1.21 to 2.22). Among CrAg-positive individuals, GXM-IgG was inversely associated with mortality at 6 months adjusted for CD4 count and tuberculosis (hazard ratio, 0.50; 95% CI, .33 to .77).

Conclusions
The inverse association of GXM-IgG with mortality in CrAg-positive individuals suggests that GXM-IgG titer may have prognostic value in those individuals. Prospective longitudinal studies to investigate this hypothesis and identify mechanisms by which antibody may protect against mortality are warranted.
Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal
Authors: Vagliano Iacopo , Chesnaye Nicholas C , Leopold Jan Hendrik , Jager Kitty J , Abu-Hanna Ameen , Schut Martijn C 
Published: August 2022

ABSTRACTBackgroundThe number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability.MethodsWe searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines.ResultsForty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in 39 studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the US-derived Medical Information Mart for Intensive Care (MIMIC) data set was commonly used. Reproducibility was limited as data and code were usually unavailable.ConclusionsFlexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.
ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets
Authors: Yilmazer Hakan , Özel Selma Ayşe 
Published: August 2022

Abstract
In this paper, we introduced two novel collaborative filtering techniques for recommendation systems in cases of various cold-start situations and incomplete datasets. The first model establishes an asymmetric weight matrix between items without using item meta-data and eradicates the disadvantages of neighborhood approaches by automatic determination of threshold values. Our first model, z-scoREC, is also regarded as a pure deep-learning model because it performs like a vanilla auto-encoder in transforming column vectors with z-score normalization similar to batch normalization. With the second model, ImposeSVD, we aimed to enhance the shortcomings of the PureSVD in cases of cold-start and incomplete data by preserving its straightforward implementation and non-parametric form. The ImposeSVD model relies on the z-scoREC, produces synthetic new predictions for the users by decomposing the latent factors from the imposed matrix. We evaluated our method on the well-known datasets and found out that our method was outperforming similar approaches in the specific scenarios including recommendations for cold-start users, strength in cold-start systems, and diversification of long-tail item recommendations in lists. Our z-scoREC model also outperformed familiar neighbor-based approaches when operated as a recommender system and gave a closer appearance to the decomposition methods despite its simple and rigid cost framework.