bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishment of the necessary material and technical base lays the groundwork for the use of innovation in the grain industry
Authors: Tleubayev Azat , Kakhaberi Gabelashvili , Rustembayev Bazarkhan , Shulenbayeva Faya , Lošťák Michal , Rakhimov Adil , Kassym Ruslan , Tlenshiyeva Akmaral 
Published: August 2022

Abstract
Here we show for the first time that, solution of pressing problems and further sustainable development of the grain economy is only possible with the necessary material and technical base, allowing the use of modern systems in the agro-industrial complex. The article considers the problems of deficiency of basic plant nutrient elements. On the basis of statistical data, descriptive analysis of the wheat production dynamics has been carried out, state of mineral fertilizers usage in the country is shown. The comparison of mineral fertilizers use in agriculture in Kazakhstan and other countries in the world has been made, from which it is determined that the domestic system of mineral fertilizers application ranks the last positions in the world and lags far behind the foreign countries. The statistical data demonstrate the low wheat yielding ability index, which is directly related to soil depletion. The statistical data coming from official sources and conducted field studies constitute the evidence basis. Purpose – to analyze the current state of mineral fertilizer use in Kazakhstan, justify the need for compulsory fertilizer dressing on all crop lands, elaborate recommendations for solving the existing problems. Methods – general scientific: 1) when analyzing statistical data, the expert evaluation method was used; 2) demonstration plots have been digitized, agrochemical soil analysis has been conducted, optimum rates of phosphorus and nitrogen content have been determined, wheat plantings in plots with different phosphorus and nitrogen content have been performed. Outcomes – problem of soil depletion has been represented, effect of different mineral fertilizers doses on the productivity of wheat has been established. Conclusion – implementation of precision agriculture technology systems is required, thereby the state needs to: 1) introduce a country-wide requirement for compulsory annual submission of agrochemical maps by agricultural producers, for the purpose of croplands status monitoring; 2) construction of new agrochemical laboratories and mineral fertilizer production facilities; 3) initiate the process for relevant personnel training.
Low–Molecular Weight Fucoidan Polysaccharide Inhibits Pulmonary Fibrosis by Regulating M1/M2 Macrophages Polarization
Authors: Zhang Bo , Yuan Yanli , Sun Meiying , Xue Tao , Liu YanJuan , Shao Fengli , Xin Jie , Wang Zhen , Li Xinpeng 
Published: August 2022

Abstract
In this study, sulfated polysaccharides extracted from Laminaria japonica were degraded by free radicals to obtain low–molecular weight fucoidan (LMWF). The effects of LMWF on bleomycin-treated pulmonary fibrosis (PF) mice were evaluated in precious research. In this study, the expression of M2 macrophage polarization biomarker arginase was detected by immunohistochemistry, and the expression of M1/M2 markers was detected by Western blot and qPCR. It was found that the expression of M1/M2 macrophage markers were significantly increased in the lung tissue of pulmonary fibrosis mice, and LMWF could effectively reduce its expression. Subsequently, in order to verify the mechanism of LMWF inhibiting pulmonary fibrosis by regulating macrophage polarization, MH-S cells were induced by LPS combined with IFN-γ and IL-4 to construct the in vitro macrophage polarization models, and the expression of biomarkers were analyzed by Western blot and qPCR. The results showed that LMWF could regulate M1 macrophage polarization through early anti-inflammatory effect and inhibit the development of pulmonary fibrosis by inhibiting M2 macrophage polarization in the later stage.
Kidney Function Impacts Plasma Alzheimer’s Biomarkers in a Cognitively Normal Multi- Ethnic Cohort
Authors: Hall James R. , Petersen Melissa , Johnson Leigh A , O’Bryant Sid 
Published: August 2022

Abstract
Background: Plasma biomarkers of Alzheimer’s disease (AD) can be cost-effective, non-invasive, scalable measures for screening into clinical research and trials. Little research has examined the impact of medical comorbidities on these biomarkers especially among underserved communities. The current study examined the impact of kidney functioning on plasma AD biomarkers among a multi-ethnic cohort. Methods: 1,328 cognitively unimpaired Mexican American (659) and non-Hispanic white (669) were examined. eGFR levels were categorized into eGFR1>=90 (normal kidney function), eGFR2 60-89 (mild kidney function loss) and eGFR3 <60 (moderate to severe kidney function loss). Aβ40, Aβ42, total tau and NfL were assayed using Simoa. Results: Mild and moderate/severe eGFR levels,were associated with all plasma biomarkers. For Mexican Americans, mild kidney and moderate/severe kidney function loss were associated with Aβ40, Aβ42, total tau and NfL. Among non-Hispanic whites, mild kidney function loss was associated with Aβ40 and Aβ42 whereas moderate/severe kidney function loss was associated with all biomarkers.Conclusions: Among cognitively normal adults, even mild kidney loss is associated with alterations in the plasma AD biomarkers. Additional work to determine how to best consider eGFR levels to avoid misdiagnosis and inappropriate referrals for more invasive, and costly, procedures based on plasma biomarker findings is needed.
Design of Smart Autonomous Solar Panel with Cascaded Sepic-Boost Converter for High Voltage Renewable Applications
Authors: Kumari Rubi , Pyakurel Pawan , Rai Bijay , Pandit Moumi , Sherpa Karma Sonam , bhattacharya dibyadeep 
Published: August 2022

Abstract
Solar energy is most admired alternative to traditional energy sources. Special attention is required to improve solar gathering techniques in mountainous regions like Sikkim, where sun availability is unreliable but irradiance level is high. This paper proposes a hardware model for smart autonomous self-adjusting solar panel whose output is fed to DC-DC Cascaded SEPIC-Boost Converter for high voltage renewable based applications. The developed renewable system is designed to adjust the panel in a position where maximum voltage is extracted. The output of the solar panel is fed to the DC-DC cascaded converter. A detailed circuit analysis is performed to evaluate the system's efficiency and voltage gain. The converter is examined with different duty cycles. The proposed converter generates 40 V DC output with 20 V DC input voltage from solar panel at 50% duty ratio and gives 90% efficiency. The feasibility of the converter is confirmed with results obtained from an experimental prototype.
GP38 as a Vaccine Target for Crimean-Congo Hemorrhagic Fever Virus
Authors: Schnell Matthias , Scher Gabrielle , Bente Dennis , Mears Megan , Cajimat Maria 
Published: August 2022

Abstract
Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is a tick-borne virus that causes severe hemorrhagic disease in humans. There is a great need for effective vaccines and therapeutics against CCHFV for humans, as none are currently internationally approved. Recently, a monoclonal antibody against the GP38 glycoprotein protected mice against lethal CCHFV challenge. To show that GP38 is required and sufficient for protection against CCHFV, we used three inactivated rhabdoviral-based CCHFV-M vaccines, with or without GP38 in the presence or absence of the other CCHFV glycoproteins. All three vaccines elicited strong antibody responses against the respective CCHFV glycoproteins. However, only vaccines containing GP38 showed protection against CCHFV challenge in mice; vaccines without GP38 were not protective. The results of this study establish the need for GP38 in vaccines targeting CCHFV-M and demonstrate the efficacy of a CCHFV vaccine candidate based on an established vector platform.
Frailty and Age Impact Immune Responses to Moderna COVID-19 mRNA Vaccine
Authors: Semelka Charles T. , DeWitt Michael E. , Blevins Maria W. , Holbrook Beth C. , Sanders John W. , Alexander-Miller Martha A. 
Published: August 2022

Abstract
BACKGROUND: Immune responses to COVID-19 mRNA vaccines have not been well characterized in frail older adults. We postulated that frailty is associated with impaired antibody and cellular mRNA vaccine responses.METHODS: We followed older adults in a retirement facility with longitudinal clinical and serological samples from the first Moderna mRNA-1273 vaccine dose starting in February 2021 through their 3rd (booster) vaccine dose. Outcomes were antibody titers, antibody avidity, and AIM+ T cell function and phenotype. Statistical analysis used antibody titers in linear mixed-effects linear regression with clinical predictors including, age, sex, prior infection status, and clinical frailty scale (CFS) score. T cell function analysis used clinical predictors and cellular phenotype variables in linear regression models.RESULTS: Participants (n=15) had median age of 90 years and mild, moderate, or severe frailty scores (n=3, 7, or 5 respectively). After 2 vaccine doses, anti-spike antibody titers were higher in 5-fold higher in individuals with mild frailty compared to severe frailty and 9-fold higher in individuals with prior COVID-19 infection compared to uninfected (p=0.02 and p<0.001). Following the booster, titers improved regardless of COVID-19 infection or frailty. Antibody avidity significantly declined following 2 vaccine doses regardless of frailty status, but reached maximal avidity after the booster. Spike-specific CD4+ T cell responses were modulated by frailty and terminally differentiated effector memory TEMRA cells, and spike-specific TFH cell responses were inversely correlated with age. Additionally, an immune-senescent memory T cell phenotype was correlated with frailty and functional decline.CONCLUSIONS: We described the separate influences of frailty and age on adaptive immune responses to the Moderna COVID-19 mRNA vaccine. Though overall antibody responses were robust, higher frailty diminished initial antibody quantity, and all older adults had impaired antibody avidity. Following the booster, antibody responses improved, overcoming the effects of age and frailty. CD4+ T cell responses were independently impacted by age, frailty, and burden of immune-senescence. Frailty was correlated with increased burden of immune-senescence, suggesting an immune-mediated mechanism for physiological decline.
Anatomic study of the innervation of Extensor Carpi Radialis Longus and Extensor Carpi Radialis Brevis. Interest in nerve transfers.
Authors: Collado Pablo Martínez , Español Guillermo-José Tarnawski , Espinel Anton Fornieles , Flores Júlia Benítez , Martí MªRosa Morro , Pérez Manuel Llusá 
Published: August 2022

Abstract
Purpose: This study pretends to describe the innervation pattern of Extensor Carpi Radialis Longus (ECRL) and Extensor Carpi Radialis Brevis (ECRB) to use ECRL branches as a first choice in nerve transfers to restore flexion and extension of wrist and fingers due to its easier identification and its redundant innervation.Methods: 9 cryopreserved and anonymous specimens were dissected to identify the radial nerve and describe the number of branches, distance between its origin and a line connecting both epicondyles, length of each branch and type of innervation of ECRL and ECRB according to Taylor’s classification.Results: 6 out of 9 specimens presented two branches from the radial nerve to innervate the ECRL (Taylor type 3). 3 out of 9 specimens only had one branch to innervate the ECRL, originating from the radial nerve (two of them classified as a Taylor type 1 and the other as a Taylor type 2). All the specimens had only one branch to innervate the ECRB (eight showing a Taylor type 1 pattern, and one classified as Taylor Type 2); in 7 out of 9 this branch emerges from the deep branch of the radial nerve, emerging in the other 3 specimens from the superficial branch of the radial nerve.Conclusion: The use of the ECRL branches could be considered as the first choice in nerve transfers to restore the flexion and extension of elbow, wrist and fingers in nerve injuries, because of its constant origin after the brachioradialis branch and in most of the cases its redundant innervation.
Modulation of backflow variability on the coherent changes in the Eurasian and North American winter temperatures and weakness in the CMIP6 Models
Authors: Sun Chenghu , Shi Xiaohui , Lu Qifeng , Li Weijing 
Published: August 2022

Abstract
The synchronization of winter temperature anomalies between Eurasia and North America was observed over northern Eurasia and North America at an inter-annual time scale. We found that this was associated with variations in the second leading mode of the winter atmospheric background flow (i.e., the 300 hPa meridional wind; V300-EOF2) over the North Atlantic-Arctic Oceans, and the surface air temperature (SAT) anomalies exhibited coherent changes over Eurasia and North America. The results suggested that this mode modulates the large-scale Rossby wave propagating across the mid-high latitude of the Eurasian and North American continents, and this also interferes with the climatological quasi-stationary wavenumber-2 to affect the stratosphere. In the positive phase of the V300-EOF2, the coordinated variation in the tropospheric and low stratospheric circulation facilitated the southward intrusion of cold air into Eurasia and North America simultaneously. We also evaluated the performance of the high top (HT) and low top (LT) of the CMIP6 models to capture the variation associated with this mode. The multi-model ensemble mean (MME) of the HT and LT models generally captured the spatial structures of the mode and reproduced coherent changes in the SATs over mid-latitude Eurasia and North America. The MME of the HT models showed a better performance than the LT models for simulating temperature variations over Eurasia, while they demonstrated poor performance over North America. There was a large spread in the simulation performance among the individual models, indicating great uncertainty in simulating SATs over the two regions. This result was primarily attributed to the different abilities to reproduce the tropospheric and stratospheric variations that affect the SATs. We also identified that the HT models did not substantively improve the simulation ability to represent the observed linear interference between the anomalous wave-2 and the climatological stationary wave-2 than that of the LT models, although a full stratosphere was included. These results have implications for further evaluation of the predictability of the backflow variability and concomitant Eurasian-North American cooling in the S2S project.
Prevalence of dysmenorrhea, associated factors, pain intensity, and its effect on daily academic activities among female undergraduate students of Hawassa University College of medicine and health sciences, Hawassa, Ethiopia
Authors: belayneh Wondu , Kassaye Zerai , Arusi Temesgen Tantu , Abera Netsanet , Hantalo Admassu , Melkamu Biruk , Gutulo Muluken 
Published: August 2022

Abstract
Background: Painful menstruation, known as dysmenorrhea, is a common gynecologic incident that affects mainly reproductive-age females with significant social, academic, and psychological negative impacts. Young females are one of the productive age who become future leaders and hope for the country as well as the community. Any problem which affects this age group will have a tremendous economic and psychological burden on the country and community. This study aims to assess the prevalence, associated risk factors of dysmenorrhea, pain intensity, and its effects on academic activities among Hawassa university students from April 1–30/2021Methods: A cross-sectional study was employed among Hawassa University students. Standardized self-administered questionnaires were used to get 348 individuals selected by systematic random sampling technique. A verbal multidimensional scoring system and Numerical Pain Rating Scale (NPRS) tools were used to the assessment of dysmenorrhea and the extent of pain severity. Data entered into Epi info version 7 and exported to SPSS version 21 for analysis. Pearson chi-square test was used to check the presence of an association between independent and outcome variables. Bivariate analysis and multivariate logistic regression were used for further assessment of the strength. The significance level was obtained using an odds ratio of 95%and CI a with p-value < 0. 05.Results: The prevalence of dysmenorrhea was 80% (277). Of these,39.7 % (110) have reported that daily activity was affected; analgesics were required and gave sufficient relief, and 12.6% (35) reported that analgesia has no effect and their daily activities are inhibited. It hurt academic activities such as loss of class concentration, study time, sleep disturbances, personal relationships, and absenteeism. Factors associated are being sexually active, family history of dysmenorrhea, and presence of depression Conclusion: Dysmenorrhea is prevalent in this study and has a significant impact on academic activities. Family history of dysmenorrhea, being sexually active, and presence of depression are associated factors.
Mediating effect of anxiety and depression between family function and hope in patients receiving maintenance hemodialysis: A cross-sectional study
Authors: Wang Xuefen , Xia Fuhai , Wang Guoqing 
Published: August 2022

Abstract
Objectives: The aim of this study was to explore the mediating effect of anxiety and depression on family function and hope in patients on maintenance hemodialysis (MHD), and to increase the knowledge about hope.Methods: The family APGAR index, hospital anxiety and depression scale, and Herth hope index were recorded using the self-reported questionnaires completed by 227 MHD patients.Results: The family function can directly predict hope, positively predict hope through depression (β = 0.052, p = 0.001), and positively predict hope through the chain mediating of anxiety and depression (β = 0.087, p = 0.001), according to chain mediation analysis. The total effect size was 28.31%. The total indirect effect value was 0.139, and the total effect value was 0.491.Conclusions: Our findings suggest that family function has a direct impact on MHD patients' hope, and that lowering anxiety and depression can help to feel more hopeful.
Restrictively Self-sampled and Compressed Local Differential Privacy in Federated Learning
Authors: Zhao Jianzhe , Li Yuchen , Zhang Ronglin , Song Wuganjing , Dong Rongrong , Matwin Stan 
Published: August 2022

Abstract
As a popular machine learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security than conventional machine learning. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Recently, local differential privacy (LDP) has worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel local differential privacy method in federated learning (SLDP-FL), which achieves the privacy amplification effect by the client self-sampling and provides compressed and private parameters in each iteration by a compressed LDP mechanism. Thereby, it improves the model performance as well as efficiency observably.Moreover, we theoretically analyze the relationship between the model accuracy and client self-sampling probability. A restrictive client self-sampling technology is proposed, which eliminates the randomness of self-sampling probability settings in existing studies and improves the utilization of the federated system. Comprehensive experiments on MNIST and Fashion-MNIST datasets show that the SLDP-FL optimizes the existing federated learning framework through compression mechanism and self-sampling technique with restrictive probability since it is superior to the current algorithms' accuracy and convergence and communication efficiency.
Predicting Future Diabetes Using Machine Learning Models in a High-Risk Pediatric Cohort
Authors: Sinha Madhumita , Arreola Elsa Vazquez 
Published: August 2022

Abstract
The acute rise in childhood obesity has been associated with an increased risk of metabolic dysfunction and early-onset diabetes. Metabolic syndrome (MetS), a cluster of risk factors predicting future cardiometabolic disease, is not well established in children. This study leverages data from a longitudinal observational study of diabetes (1965–2007) that enrolled pediatric and adult participants, to create machine learning-based models (ML) to predict future incident diabetes. Anthropometric, clinical, and laboratory data from baseline examinations of 2,049 nondiabetic children were obtained, and 17-metabolic risk predictor variables were included. Multiple classification schemes were explored, and an ensemble model was created by combining the 5-best algorithms with the highest predictive accuracy. The area under the receiver-operating-characteristic curve (AUROC) of the ensemble model was significantly higher than the logistic regression model in predicting future diabetes [0.88 (95% CI: 0.85–0.9) and 0.63 (95% CI: 0.57–0.69), (p < 0.001) ]. The predictor variable importance of the best performing Random Forest model identified additional risk predictors outside of the pediatric MetS components as recommended by the International Diabetes Federation. This study used consistent and well-structured clinical data to create ML-based models that predicted future incident diabetes in a pediatric cohort with a high degree of accuracy.
Quantum Ensemble for Classification
Authors: Macaluso Antonio , Clissa Luca , Lodi Stefano , Sartori Claudio 
Published: August 2022

Abstract
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and have a lower variance than the individual classifiers that make them up, but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain B transformations of the quantum state which encodes the training set in only log ( B ) operations. This implies an exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively to the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.