bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Hydrophilicity of Wheat Starch via Sodium Dodecyl Sulfate Treatment
Authors: Li Yu , Peng Zhangchi , Wu Dianxing , Shu Xiaoli 
Published: March 2022

AbstractStarch is an important multifunctional ingredient for food, cosmetics, fuels, and pharmaceuticals. Naturally, starch is hydrophilicity, while its’ absorbability varies among botanical sources and physical or chemical modifications. In this study, fractioned A‐ and B‐type wheat starch granules show different physiochemical properties, B‐type granules have greater water and oil absorption capacity. After removing surface proteins with sodium dodecyl sulfate (SDS), starch granule morphology and crystalline structure are not apparently altered, except some shallow indentations are observed on the exterior of the starch granules. However, treatment with SDS affects the starch gelatinization, increases the swelling power, and solubility of starch significantly. Meanwhile, starch after removal of surface protein exhibits higher hydrophilicity and lower lipophilicity, especially for A‐type granules with larger size, the increases in water absorbability are more pronounced. This study gives us new insights in improving starch hydrophilicity through removing starch granule‐associated proteins, which may promote the application of starch in absorbent materials and desiccant.
Main effect and epistatic QTL affecting spike shattering and association with plant height revealed in two spring wheat (Triticum aestivum L.) populations
Authors: Bokore Firdissa E. , Cuthbert Richard D. , Knox Ron E. , Campbell Heather L. , Meyer Brad , N’Diaye Amidou , Pozniak Curtis J. , DePauw Ron 
Published: March 2022

Abstract
Key message
A major QTL on chromosome arm 4BS was associated with reduced spike shattering and reduced plant height in coupling phase, and a second major QTL associated with reduced spike shattering was detected on chromosome arm 5AL in the same wheat variety Carberry.

Abstract
Spike shattering can cause severe grain yield loss in wheat. Development of cultivars with reduced shattering but having easy mechanical threshability is the target of wheat breeding programs. This study was conducted to determine quantitative trait loci (QTL) associated with shattering resistance, and epistasis among QTL in the populations Carberry/AC Cadillac and Carberry/Thatcher. Response of the populations to spike shattering was evaluated near Swift Current, SK, in four to five environments. Plant height data recorded in different locations and years were used to determine the relationship of the trait with spike shattering. Each population was genotyped and mapped with the wheat 90 K Illumina iSelect SNP array. Main effect QTL were analyzed by MapQTL 6, and epistatic interactions between main effect QTL were determined by QTLNetwork 2.0. Correlations between height and shattering ranged from 0.15 to 0.49. Carberry contributed two major QTL associated with spike shattering on chromosome arms 4BS and 5AL, detected in both populations. Carberry also contributed two minor QTL on 7AS and 7AL. AC Cadillac contributed five minor QTL on 1AL, 2DL, 3AL, 3DL and 7DS. Nine epistatic QTL interactions were identified, out of which the most consistent and synergistic interaction, that reduced the expression of shattering, occurred between 4BS and 5AL QTL. The 4BS QTL was consistently associated with reduced shattering and reduced plant height in the coupling phase. The present findings shed light on the inheritance of shattering resistance and provide genetic markers for manipulating the trait to develop wheat cultivars.
Forgetful updating and stubborn decision-makers
Authors: Hu Tai-Wei 
Published: March 2022

AbstractA decision-maker receives an informative signal each period and is randomly required to make a terminal action based on the signals received so far. The decision-maker is restricted to use a (stochastic) finite automaton no larger than a given size to process information. In contrast to the existing literature that focuses on very low probability of termination, I consider information structures with a (nearly) revealing signal, in which analytical solutions are available for all probability values of termination. Results from that model reveal two robust predictions regarding constrained optimal behaviour. First, it is optimal to ignore small (in terms of informativeness) signals. Second, when deterministic schemes are optimal, big signals with similar strengths should be treated similarly; otherwise, randomization takes a lexicographic order according to the strengths of the signals. I also identify a new behavioural bias, information stubbornness, according to which the decision maker does not respond to further informative signals after seeing a nearly revealing signal. As a result, the decision-maker can persistently choose the wrong action even after an unbounded number of informative signals.
Gesellschaftliche Perspektiven einer fachspezifischen KI für automatisierte Entscheidungen
Authors: Puppe Frank 
Published: March 2022

ZusammenfassungDie künstliche Intelligenz (KI) entwickelt sich rasant und hat bereits eindrucksvolle Erfolge zu verzeichnen, darunter übermenschliche Kompetenz in den meisten Spielen und vielen Quizshows, intelligente Suchmaschinen, individualisierte Werbung, Spracherkennung, -ausgabe und -übersetzung auf sehr hohem Niveau und hervorragende Leistungen bei der Bildverarbeitung, u. a. in der Medizin, der optischen Zeichenerkennung, beim autonomen Fahren, aber auch beim Erkennen von Menschen auf Bildern und Videos oder bei Deep Fakes für Fotos und Videos. Es ist zu erwarten, dass die KI auch in der Entscheidungsfindung Menschen übertreffen wird; ein alter Traum der Expertensysteme, der durch Lernverfahren, Big Data und Zugang zu dem gesammelten Wissen im Web in greifbare Nähe rückt. Gegenstand dieses Beitrags sind aber weniger die technischen Entwicklungen, sondern mögliche gesellschaftliche Auswirkungen einer spezialisierten, kompetenten KI für verschiedene Bereiche der autonomen, d. h. nicht nur unterstützenden Entscheidungsfindung: als Fußballschiedsrichter, in der Medizin, für richterliche Entscheidungen und sehr spekulativ auch im politischen Bereich. Dabei werden Vor- und Nachteile dieser Szenarien aus gesellschaftlicher Sicht diskutiert.
A novel gyrovirus in a common pheasant (Phasianus colchicus) with poult enteritis and mortality syndrome
Authors: Fehér Enikő , Bali Krisztina , Kaszab Eszter , Ihász Katalin , Jakab Szilvia , Nagy Borbála , Ursu Krisztina , Farkas Szilvia L. , Bányai Krisztián 
Published: March 2022

AbstractA novel gyrovirus was detected in an intestinal specimen of a common pheasant that died due to poult enteritis and mortality syndrome. The genome of the pheasant-associated gyrovirus (PAGyV) is 2353 nucleotides (nt) long and contains putative genes for the VP1, VP2, and VP3 proteins in an arrangement that is typical for gyroviruses. Gyrovirus-specific motifs were identified in both the coding region and the intergenic region of the PAGyV genome. The VP1 of PAGyV shares up to 67.6% pairwise nt sequence identity with reference sequences and forms a distinct branch in the phylogenetic tree. Thus, according to the recently described species demarcation criteria, PAGyV belongs to a novel species in the genus Gyrovirus, family Anelloviridae, for which we propose the name "Gyrovirus phaco 1".
Reducing uncertainty in seismic assessment of multiple masonry buildings based on monitored demolitions
Authors: Martakis Panagiotis , Reuland Yves , Imesch Marco , Chatzi Eleni 
Published: March 2022

AbstractA significant part of the existing building stock in regions of low to moderate seismic hazard has been designed without modern seismic considerations and is, in the meantime, exceeding its design life span. The assessment of seismic performance poses an engineering challenge, due to unknown material properties, undocumented structural interventions and the scarcity of event-based information. Operational modal analysis has been applied in some cases to verify model assumptions beyond visual inspection. However, masonry buildings exhibit amplitude-dependent stiffness even at very low response amplitudes, raising questions about the validity of such methods. Planned demolitions provide engineers with the opportunity to leverage higher-amplitude vibrations generated during demolition activities to better understand the dynamic behaviour of existing buildings. This paper introduces a Bayesian model-updating framework, which aims at reducing uncertainty in seismic analysis, by fusing dynamic measurements with best-practice structural models. The proposed hybrid framework is applied to nine real masonry buildings, representative of existing residential buildings, as typically encountered in Switzerland, that have been monitored during controlled demolition. A vast reduction in prediction uncertainty is achieved through data-driven model updating, additionally exposing intra- and inter-typological differences in terms of seismic capacity and ductility. In addition, differences between updated model predictions and typical engineering assumptions and generic typological curves are discussed. Overall, this contribution demonstrates, applies and discusses the practical benefits of a straightforward methodology for fusing monitoring data into the seismic evaluation of existing masonry structures.
How to Use Metallurgical Analysis for Fire Investigation: The Case of AISI 316 Stainless Steel
Authors: Casaroli Andrea , Boniardi Marco , Gerosa Riccardo , Rivolta Barbara 
Published: March 2022

AbstractIn this work, fire simulations were carried out on AISI 316 austenitic stainless steel sheets (similar to EN X5CrNiMo17-12-2), usually employed for the production of flue pipes. The thermal alteration induced by the fire was simulated by heating samples at different temperatures (from 20°C to 1050°C) and for different exposure times (1 h and 6 h) in a laboratory furnace. The metallurgical investigation concerned the surface coloring by photographic analysis; the morphology and chemical composition of the surface oxides by SEM and EDS; the microstructure as a function of temperature by LOM. The influence of the different parameters was discussed by a statistical ANOVA approach. The obtained results allowed to associate each temperature to one or more alterations in color, to the morphology and the chemical composition assumed by the surface oxides, as well as to the microstructure of the steel.
An active semi-supervised deep learning model for human activity recognition
Authors: Bi Haixia , Perello-Nieto Miquel , Santos-Rodriguez Raul , Flach Peter , Craddock Ian 
Published: March 2022

AbstractHuman activity recognition (HAR), which aims at inferring the behavioral patterns of people, is a fundamental research problem in digital health and ambient intelligence. The application of machine learning methods in HAR has been investigated vigorously in recent years. However, there are still a number of challenges confronting the task, where one significant barrier lies in the longstanding shortage of annotations. To address this issue, we establish a new paradigm for HAR, which integrates active learning and semi-supervised learning into one framework. The main idea is to reduce the annotation cost by actively selecting the most informative samples for annotation, as well as leveraging the unlabelled instances in a semi-supervised way. In particular, we propose to utilize the massive unlabelled data via temporal ensembling of convolutional neural networks (CNN), which yields robust consensus predictions by aggregating the outputs of the training networks on different epochs. We conducted extensive experiments on three public benchmark datasets. The proposed method achieves Macro F1 values of 0.76, 0.45 and 0.91 in a low annotation scenario on PAMAP2, USCHAD and UCIHAR datasets respectively, outperforming a multitude of state-of-the-art deep models. The ablation study proves the effectiveness of the two components of the framework, i.e., active learning-based sample selection and semi-supervised model training with temporal ensembling, in alleviating the issue of insufficient labels. Cross-validation and statistical significance experiments further demonstrate the robustness and generalization ability of the proposed method. The source codes are available at https://github.com/HaixiaBi1982/ActSemiCNNAct.
Open Reading Frame-3a gene of the 2019 novel coronavirus inhibits the occurrence and development of colorectal cancer
Authors: Shuwen Han , Yinhang Wu , Jing Mao , Gong Chen , Xiaohui Hou , Xi Yang , Wei Wu 
Published: March 2022

AbstractIntestinal microecology is composed of bacteria, fungi and viruses. As a part of intestinal microecology, viruses participate in the occurrence and development of colorectal cancer. The 2019-nCoV was detected in stool samples from patients during COVID-19, suggesting that the 2019-nCoV may be associated with intestinal microecology. However, the relationship of the 2019-nCoV and CRC is unclear. The aim of this study is to explore the role of Open Reading Frame-3a (ORF3a) of the 2019-nCoV in CRC. After the pCDH-CMV-MCS-EF1-Puro vector that provides high expression of ORF3a was transfected into the SW480 CRC cell line, immunofluorescence was used to determine the localization of ORF3a in SW480 cells. The proliferation, migration, invasion, apoptosis, and cell cycle progression of SW480 cells were measured using the Cell Counting Kit-8 (CCK-8), wound healing, Transwell assay, flow cytometry, the TUNEL assay, and propidium iodide single staining. The results showed that ORF3a inhibited the proliferation, invasion, and migration of SW480 cells and induced their apoptosis after 24, 48, 72 h. Meanwhile, ORF3a inhibited the cell cycle and blocked SW480 CRC cells in the G1 phase. In in vivo experiments, high ORF3a expression was associated with decreased tumor volume, tumor weight, relative tumor volume, and tumor activity. ORF3a inhibited the growth and induced apoptosis and necrosis of tumor tissues. Based on this, we demonstrated that ORF3a might play a role in CRC, providing a new direction for the prevention and treatment of CRC.
miR-93-5p suppresses ovarian cancer malignancy and negatively regulate CCND2 by binding to its 3′UTR region
Authors: Chen Guotong , Yan Yiwei , Qiu Xiaojv , Ye Chengfeng , Jiang Xingmei , Song Shuo , Zhang Yibo , Chang Huanhuan , Wang Leqi , He Xuehuan , Tang Lingrong , Zhang Qingyu , Zhang Ying 
Published: March 2022

AbstractOvarian cancer is the most fatal gynecological cancer worldwide, yet the fundamental mechanism of malignancy acquisition in ovarian cancer remains unknown. miRNA has been implicated to a variety of diseases, including cancer initiation and progression. Cyclin-D2 (CCND2) is ubiquitously implicated in cancer uncontrol cell proliferation. Bioinformatic research revealed that CCND2 is a candidate gene for miR-93-5p with a binding site in its 3′UTR region in the current study. Using our ovarian cancer sample, we verified that miR-93-5p is negatively correlated with CCND2 mRNA and protein levels. Luciferase report assay revealed miR-93-5p inhibits CCND2 production through binding to the 3′UTR region. The expression of miR-93-5p in ovarian cancer patient samples was then determined, and a survival analysis was performed. Our findings showed that miR-93-5p is downregulated in ovarian cancer and is a favorable predictive factor in ovarian cancer patient. CCK8 assay, wound healing assay and flow cytometry-based cell cycle and apoptotic cell analyses were employed here. We found that miR-93-5p suppresses ovarian cancer cell proliferation and migration while enhances cell death. Our research certified that miR-93-5p reduces ovarian cancer malignancy by targeting CCND2.
Was lockdown life worth living?
Authors: Lawford-Smith Holly 
Published: March 2022

AbstractLockdowns in Australia have been strict and lengthy. Policy-makers appear to have given the preservation of quantity of lives strong priority over the preservation of quality of lives. But thought-experiments in population ethics suggest that this is not always the right priority. In this paper, I'll discuss both negative impacts on quantity of lives caused by the lockdowns themselves, including an increase in domestic violence, and negative impacts on quality of lives caused by lockdowns, in order to raise the question of whether we each had reason to choose quantity over quality in our own lives in a way that would justify the lockdowns we had.
Flight delay forecasting and analysis of direct and indirect factors
Authors: Wang Fujun , Bi Jun , Xie Dongfan , Zhao Xiaomei 
Published: March 2022

AbstractThe accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short‐term memory network of delay prediction with an attention mechanism (LSTM‐AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM‐AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM‐AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM‐AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM‐AM combined with runway and apron flow control can reduce or eliminate delays of one flight.
Genome-wide mining of B-type cytokinin response regulators in wheat reveals the involvement of
Authors: Hu Yifeng , Cui Hongmei , Xia Pengliang , Liu Gensen , Wu Xingyang , Li Yiting , Yang Yan , Zeng Fansong , Li Yan , Ma Dongfang 
Published: March 2022

Context Cytokinin response regulators (RRs) are important components of the two component signal systems that are involved in the regulation of plant growth and development, and in the response to abiotic stresses. Plant cytokinin response regulators (RR) were divided into type A and type B. A-type RR proteins act as negative feedback to regulate cytokinin signals, while B-type RRs could regulate A-type RR gene expression, and B-type RR genes have proved to play important roles in regulating cytokinin signal transduction in various biological processes. Aims We aimed to explore and analyse B-type RR genes in wheat in a preliminary fashion. Methods Using bioinformatics methods, wheat type B RR genes were identified, and type B Triticum aestivum RR (TaRR) genes were analysed using quantitative real-time polymerase chain reaction. In order to further analyse the function of TaRR, staining experiments were performed. Key results Twenty-nine B-type TaRR genes were identified in the wheat genome, divided into three groups according to their phylogenetic relationships. Chromosome mapping showed that 29 TaRRs were evenly distributed on 12 chromosomes, while there were no genes located on the other nine chromosomes, which may have experienced gene loss during evolution. The polymerase chain reaction results showed that TaRRs were significantly up-regulated under polyethylene glycol treatments. Under sodium chloride stress, TaRRs were up-regulated to varying degrees, reaching the maximum at 24 h. The study also found that the expression pattern of TaRRs was different in the root and leaf under different abiotic stresses. In addition, staining experiments also showed that TaRR5.1-6A could induce the self-defence function of leaves. Conclusions These results form the basis for further exploring the role of B-type TaRR genes in plant response to drought stress and salt stress. Implications This study lays the molecular biology foundation for the functional study of the B-type TaRR genes.
Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings
Authors: Chandna Arjun , Mahajan Raman , Gautam Priyanka , Mwandigha Lazaro , Gunasekaran Karthik , Bhusan Divendu , Cheung Arthur T L , Day Nicholas , Dittrich Sabine , Dondorp Arjen , Geevar Tulasi , Ghattamaneni Srinivasa R , Hussain Samreen , Jimenez Carolina , Karthikeyan Rohini , Kumar Sanjeev , Kumar Shiril , Kumar Vikash , Kundu Debasree , Lakshmanan Ankita , Manesh Abi , Menggred Chonticha , Moorthy Mahesh , Osborn Jennifer , Richard-Greenblatt Melissa , Sharma Sadhana , Singh Veena K , Singh Vikash K , Suri Javvad , Suzuki Shuichi , Tubprasert Jaruwan , Turner Paul , Villanueva Annavi M G , Waithira Naomi , Kumar Pragya , Varghese George M , Koshiaris Constantinos , Lubell Yoel , Burza Sakib 
Published: March 2022

Abstract

Background
In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed.

Methods
We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 &lt; 94%; respiratory rate &gt; 30 BPM; SpO2/FiO2 &lt; 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort.

Results
In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72–0.74) and calibration (calibration slopes: 1.01–1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone.

Conclusions
We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.