bluuebunny/crossref_metadata_2025_split huggingface dataset:
Caputo–Fabrizio fractional model of MHD second grade fluid with Newtonian heating and heat generation
Authors: Sehra  , Iftikhar Wajeeha , Haq Sami Ul , Jan Saeed Ullah , Khan Ilyas , Mohamed Abdullah 
Published: December 2022

AbstractIn this research article the heat transfer of generalized second grade fluid is investigated with heat generation. The fluid flow is analyzed under the effects of Magneto hydrodynamics over an infinite vertical flat plate. The Newtonian heating phenomenon has been adopted at the boundary. For this purpose the problem is divided into two compartments i.e. momentum equation and energy equations. Some specific dimensionless parameters are defined to convert the model equations into dimensionless system of equations. The solutions for dimensionless energy and momentum equations are obtained by using the Laplace transform technique. From obtained results by neglecting magneto hydrodynamic effects and heat source some special case are achieved which are already published in literature. The case for which the fractional parameter approaches to the classical order is also discussed and it has been observed that it is convergent. Finally, the influences of different physical parameters are sketched graphically. It has been observed that for increasing values of Prandtl number the velocity and temperature decreases, for increasing values of Grashof number the velocity of the fluid increases. Also it has been investigated that for increasing values of fractional parameter the velocity and temperature of the fluid increases.
Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics
Authors: Cui Jingyang , Zhang Guanghua , Chen Zhenguo , Yu Naiwen 
Published: December 2022

AbstractUser and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.
Self-supervised and semi-supervised learning for road condition estimation from distributed road-side cameras
Authors: Garcea Fabio , Blanco Giacomo , Croci Alberto , Lamberti Fabrizio , Mamone Riccardo , Ricupero Ruben , Morra Lia , Allamano Paola 
Published: December 2022

AbstractMonitoring road conditions, e.g., water build-up due to intense rainfall, plays a fundamental role in ensuring road safety while increasing resilience to the effects of climate change. Distributed cameras provide an easy and affordable alternative to instrumented weather stations, enabling diffused and capillary road monitoring. Here, we propose a deep learning-based solution to automatically detect wet road events in continuous video streams acquired by road-side surveillance cameras. Our contribution is two-fold: first, we employ a convolutional Long Short-Term Memory model (convLSTM) to detect subtle changes in the road appearance, introducing a novel temporally consistent data augmentation to increase robustness to outdoor illumination conditions. Second, we present a contrastive self-supervised framework that is uniquely tailored to surveillance camera networks. The proposed technique was validated on a large-scale dataset comprising roughly 2000 full day sequences (roughly 400K video frames, of which 300K unlabelled), acquired from several road-side cameras over a span of two years. Experimental results show the effectiveness of self-supervised and semi-supervised learning, increasing the frame classification performance (measured by the Area under the ROC curve) from 0.86 to 0.92. From the standpoint of event detection, we show that incorporating temporal features through a convLSTM model both improves the detection rate of wet road events (+ 10%) and reduces false positive alarms ($$-$$- 45%). The proposed techniques could benefit also other tasks related to weather analysis from road-side and vehicle-mounted cameras.
Assessing nitrogen dynamics model and the role of artificial lagoon in effluent loading of shrimp farms in Gomishan wetland, southern Caspian Sea
Authors: Ghodrati Fateme , Ghorbani Rasoul , Agh Naser , Hedayati Aliakbar , Naddafi Rahmat , Jalali Ali , Shiroudmirzaei Fakhrie 
Published: December 2022

AbstractShrimp farming increases the nutrients, especially nitrogen in the water resources reducing water quality. This study was conducted to evaluate the nitrogen dynamics in white shrimp (Litopenaeus vannamei) farms and the role of artificial lagoon (24 ha) in reducing nitrogen levels in Gomishan coastal wetland, the eastern Caspian Sea. The results indicated that at the end of the 4-month breeding period, the amounts of nitrogen and phosphorus introduced into Gomishan wetland were calculated as to 220.157 and 39 tons, respectively in a breeding area covering 830 hectares. Nitrogen values (based on nitrate) calculated based on the relationship between the basin and the discharge of the outlet channel of the site at the time of complete emptying of the farms, were calculated to be approximately 121.8 tons per breeding time that it had an important role in eliminating about 45% of nutrient pollution and reducing the concentration of dissolved nitrogen. Moreover, nitrogen isotopic trace was observed in shrimp samples, in similar levels in the samples of both shrimp pond and lagoon, which emphasizes the role of feeding from natural food, especially benthic fauna. Overall, due to the decline of Caspian Sea water level, Gomishan coastal wetland is drying, and the output of shrimp farms is currenly the only source supplying water for the wetland. Hence, appropriate management strategies could minimize the amounts of nutrients into the natural water whilst aiding wetland’s survaival.
Improved algorithm for determining cable saddle pre-offsets considering the coupling effect of tower and splay saddles
Authors: Deng Xiaokang , Zhao Huiru 
Published: December 2022

AbstractBy analysing the mechanical and geometrical relations between the main cable, tower, and splay saddles, and considering the coupling effect of the tower and splay saddles, an improved algorithm is proposed to determine the cable saddles pre-offsets of suspension bridges. The equilibrium relationship of the cable saddles, the compatible deformation condition, and the basic equation of the main cable shape are considered to establish several coupled non-linear equations up to 19, and the tower and splay saddle pre-offsets are obtained by solving the above equations with the Newton–Raphson method. This paper presents the initial value selection principle and the constraint conditions for solving the cable saddle pre-offsets of the plane cable suspension bridge and the calculation process ensures convergence. The calculation example demonstrates that the improved algorithm without an exact initial value can achieve excellent convergence.
A reverse phase protein array based phospho-antibody characterization approach and its applicability for clinical derived tissue specimens
Authors: Wang Nan , Zhang Li , Ying Qi , Song Zhentao , Lu Aiping , Treumann Achim , Liu Zhaojian , Sun Tao , Ding Zhiyong 
Published: December 2022

AbstractSystematic quantification of phosphoprotein within cell signaling networks in solid tissues remains challenging and precise quantification in large scale samples has great potential for biomarker identification and validation. We developed a reverse phase protein array (RPPA) based phosphor-antibody characterization approach by taking advantage of the lysis buffer compatible with alkaline phosphatase (AP) treatment that differs from the conventional RPPA antibody validation procedure and applied it onto fresh frozen (FF) and formalin-fixed and paraffin-embedded tissue (FFPE) to test its applicability. By screening 106 phospho-antibodies using RPPA, we demonstrated that AP treatment could serve as an independent factor to be adopted for rapid phospho-antibody selection. We also showed desirable reproducibility and specificity in clincical specimens indicating its potential for tissue-based phospho-protein profiling. Of further clinical significance, using the same approach, based on melanoma and lung cancer FFPE samples, we showed great interexperimental reproducibility and significant correlation with pathological markers in both tissues generating meaningful data that match clinical features. Our findings set a benchmark of an efficient workflow for phospho-antibody characterization that is compatible with high-plex clinical proteomics in precison oncology.
Comparisons of outcomes and complications of immediate sequential bilateral cataract surgery and unilateral cataract surgery in a tertiary hospital in South Korea
Authors: Hong Suji , Park Wonkyung , Eom Youngsub , Kim Hyo Myung , Song Jong Suk 
Published: December 2022

AbstractWe investigated the proportions of immediate sequential bilateral cataract surgery (ISBCS) and unilateral cataract surgery during the coronavirus disease 2019 pandemic and compared visual outcomes between the two groups in a tertiary hospital in South Korea. We reviewed 441 cataract surgeries performed between March 1, 2021, and October 31, 2021, at Korea University Guro Hospital by a single surgeon (J.S.S). Medical records of demographics, preoperative visual acuity, corneal astigmatism, axial length, preoperative spherical equivalent, preoperative target (using Barrett’s Universal 2 formula), postoperative visual acuity, postoperative refractive error, and postoperative complications were evaluated. Among all patients, 322 (73.0%) eyes underwent ISBCS, and 119 (27.0%) eyes underwent unilateral cataract surgery. The preoperative corrective distance visual acuity (CDVA) was lower in the unilateral cataract surgery group (0.40 ± 0.45 logMAR) than the ISBCS group (0.28 ± 0.16 logMAR, P = 0.008), whereas there was no significant difference in postoperative CDVA between the two groups (0.06 ± 0.10 logMAR vs. 0.07 ± 0.16 logMAR, P = 0.63). There was also no difference in the absolute refractive error between the two groups (0.46 ± 0.37 diopters [D] vs. 0.42 ± 0.38 D, P = 0.63). The preoperative CDVA (P = 0.000) was the significant factor influencing absolute refractive error (r = 0.191, P < 0.001). There was no difference in complications between the two groups, although two patients in the ISBCS group complained of postoperative strabismus.
Eco-friendly salt/alkali-free exhaustion dyeing of cotton fabric with reactive dyes
Authors: Aysha Tarek S. , Ahmed Nahed S. , El-Sedik Mervat S. , Youssef Yehya A. , El-Shishtawy Reda M. 
Published: December 2022

AbstractThe textile-wet process enormously consumes a large volume of water and chemicals, and thus awareness of cleaner production has been growing to protect the environment from the industrial effluents. In this context, reactive dyeing of cellulosic materials such as cotton fabrics is a major sector of textile coloration that necessitates the use of a large amount of sodium sulfate or sodium chloride and alkali to exhaust and fix the dye molecules with cellulosic macromolecules, respectively. However, the remaining salt and alkali in the effluent badly affect the environment. For this purpose, the use of trisodium nitrilotriacetate (TNA) in reactive dyeing of cotton fabrics was hypothesized to have a double benefit, one as an exhausting agent (organic salt) and the second as a fixing agent (organic base). Thus, the exhaust dyeing characteristics of cotton fabrics using C.I. Reactive Yellow 145 (RY145) was optimized under different conditions of TNA concentration, alkali concentration, temperature, and dyeing time. The color strength and the primary and secondary exhaustion values were also investigated with an eye on those values obtained using the conventional dyeing method. The characterization of effluent samples with RY 145 taken after dyeing using TNA compared with conventional dyeing indicated an efficient reduction of COD, BOD, and TDS values by 99, 97, and 97%, respectively. The new dyeing method was implemented using C.I. Reactive Black 5 (RB5), C.I. Reactive Blue 160 (RB160), and C.I. Reactive Red 24 (RR24) to reveal good dyeability and fastness properties comparable with those obtained using the conventional method. The overall results obtained suggest the suitability of TNA as an environmentally friendly agent suitable as an exhausting and fixing agent of cellulosic fabrics.
Validation of SNP markers for thermotolerance adaptation in Ovis aries adapted to different climatic regions using KASP-PCR technique
Authors: Astuti Putri Kusuma , Ilie Daniela Elena , Gavojdian Dinu , Wanjala George , Badaoui Bouabid , Ohran Husein , Pasic-Juhas Eva , Bagi Zoltán , Jávor András , Kusza Szilvia 
Published: December 2022

AbstractA study on 51 SNPs belonging to 29 genes related to heat stress was carried out in 720 sheep from 17 different breeds adapted to different climates from Hungary, Bosnia and Herzegovina, Morocco and Romania, using Kompetitive Allele-Specific Polymerase Chain Reaction. Genotype frequency and the Hardy–Weinberg equilibrium were calculated, followed by a clustering using the Principal Component Analysis. We analyzed the polymorphisms in the following genes analyzed: HSPA12A,HSP90AA1,IL33,DIO2,BTNL2,CSN2,ABCG1,CSN1S1,GHR,HSPA8,STAT3, and HCRT. We emphasized on HSPA12A and HSPA8 genes as they were successfully genotyped in all studied flocks in which genotype frequency patterns were identified. Contrary to previous findings, the A allele for HSPA8 SNP was not observed in the heat tolerant breeds, being found exclusively in cold-tolerant breeds. The principal component analysis could not clearly differentiate the breeds, while plot concentration was slightly varied among the three groups, with HSP90AA1 and IL33 SNPs’ loading values significantly contributing to PC1 and PC2. We confirmed previous works that the HSPA12A,HSPA8,HSP90AA1 and IL33 SNPs are potential candidate markers for thermotolerance adaptation in sheep. This research contributes to the genetic variability of SNPs for thermotolerance adaptability in sheep.
Gas sensing response of ion beam irradiated Ga-doped ZnO thin films
Authors: Ramola R. C. , Negi Sandhya , Singh Ravi Chand , Singh Fouran 
Published: December 2022

AbstractThe ion beam induced modified gallium doped ZnO thin films are studied for their gas sensing applications. The Ag9+ and Si6+ irradiated gallium doped zinc oxide thin films were exposed to various concentrations of ethanol and acetone gas for gas sensing applications. The Ag9+ ion irradiated Ga-doped ZnO thin was optimized at different operating temperature. It was observed that gas sensing response for both ethanol and acetone gas increases with increasing Ag9+ ion fluence. This indicates that the swift heavy ions have improved the sensitivity of Ga-doled ZnO thin film by reducing the particle size. The Si6+ ion irradiated Ga-doped ZnO thin films were also exposed to ethanol and acetone gas for gas sensing applications. In comparison to Ag9+ ion irradiated thin film, the film irradiated with Si6+ ion beam exhibits a greater sensing response to both ethanol and acetone gas.
Validation of a novel iPhone application for evaluating near functional visual acuity
Authors: Hanyuda Akiko , Kubota Miyuki , Kubota Shunsuke , Masui Sachiko , Yuki Kenya , Tsubota Kazuo , Negishi Kazuno 
Published: December 2022

AbstractMonitoring dynamic changes in near vision is important for early detection of presbyopia. This study assessed the accuracy and reliability of a new smartphone-based application, the Smart Vision Check (SVC), compared with those of a conventional device (AS-28; Kowa, Aichi, Japan), for measuring near functional visual acuity (NFVA). We enrolled 115 healthy volunteers aged ≥ 20 years with bilateral best-corrected visual acuity of ≥ 20/25. The SVC was designed for use on an Apple iPhone SE2 to measure NFVA by tapping on the orientation icon manually. Conventional FVA was measured using the AS-28 with − 2.50 D added to the best distance correction at baseline. There was no significant difference in NFVA-related measurements between the AS-28 and SVC (P > 0.05). The Spearman correlation coefficients of NFVA measurements between the two devices were over 0.60 (P < 0.001). The Bland–Altman plot indicated minimal bias with limits of agreements of ± 0.34 logMAR for NFVA with habitual correction when comparing the AS-28 and SVC. The intraclass correlation coefficient of the repeated SVC-measured NFVA was 0.915 (95% CI 0.800–0.969). In summary, the SVC has the potential to evaluate NFVA in a relatively easy manner. Applied clinically, the SVC can be useful for presbyopia screening.
A large language model for electronic health records
Authors: Yang Xi , Chen Aokun , PourNejatian Nima , Shin Hoo Chang , Smith Kaleb E. , Parisien Christopher , Compas Colin , Martin Cheryl , Costa Anthony B. , Flores Mona G. , Zhang Ying , Magoc Tanja , Harle Christopher A. , Lipori Gloria , Mitchell Duane A. , Hogan William R. , Shenkman Elizabeth A. , Bian Jiang , Wu Yonghui 
Published: December 2022

AbstractThere is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at:https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.
Single-cell transcriptomics of the goldfish retina reveals genetic divergence in the asymmetrically evolved subgenomes after allotetraploidization
Authors: Kon Tetsuo , Fukuta Kentaro , Chen Zelin , Kon-Nanjo Koto , Suzuki Kota , Ishikawa Masakazu , Tanaka Hikari , Burgess Shawn M. , Noguchi Hideki , Toyoda Atsushi , Omori Yoshihiro 
Published: December 2022

AbstractThe recent whole-genome duplication (WGD) in goldfish (Carassius auratus) approximately 14 million years ago makes it a valuable model for studying gene evolution during the early stages after WGD. We analyzed the transcriptome of the goldfish retina at the level of single-cell (scRNA-seq) and open chromatin regions (scATAC-seq). We identified a group of genes that have undergone dosage selection, accounting for 5% of the total 11,444 ohnolog pairs. We also identified 306 putative sub/neo-functionalized ohnolog pairs that are likely to be under cell-type-specific genetic variation at single-cell resolution. Diversification in the expression patterns of several ohnolog pairs was observed in the retinal cell subpopulations. The single-cell level transcriptome analysis in this study uncovered the early stages of evolution in retinal cell of goldfish after WGD. Our results provide clues for understanding the relationship between the early stages of gene evolution after WGD and the evolution of diverse vertebrate retinal functions.
Effect of bacterial DNA enrichment on detection and quantification of bacteria in an infected tissue model by metagenomic next-generation sequencing
Authors: Lazarevic Vladimir , Gaïa Nadia , Girard Myriam , Mauffrey Florian , Ruppé Etienne , Schrenzel Jacques 
Published: December 2022

Abstract
Before implementing metagenomic next-generation sequencing (mNGS) in the routine diagnostic laboratory, several challenges need to be resolved. To address strengths and limitations of mNGS in bacterial detection and quantification in samples with overwhelming host DNA abundance, we used the pig muscle tissue spiked with a home-made bacterial mock community, consisting of four species from different phyla. From the spiked tissue, we extracted DNA using: (i) a procedure based on mechanical/chemical lysis (no bacterial DNA enrichment); (ii) the Ultra-Deep Microbiome Prep (Molzym) kit for bacterial DNA enrichment; and (iii) the same enrichment kit but replacing the original proteinase K treatment for tissue solubilization by a collagenases/thermolysin digestion and cell filtration. Following mNGS, we determined bacterial: ‘host’ read ratios and taxonomic abundance profiles. We calculated the load of each mock-community member by combining its read counts with read counts and microscopically-determined cell counts of other co-spiked bacteria. In unenriched samples, bacterial quantification and taxonomic profiling were fairly accurate but at the expense of the sensitivity of detection. The removal of ‘host’ DNA by the modified enrichment protocol substantially improved bacterial detection in comparison to the other two extraction procedures and generated less distorted taxonomic profiles as compared to the original enrichment protocol.
Synthesis of 1,1-Disubstituted Allenylic Silyl Ethers through Iron-Catalyzed Regioselective C(sp2)–H Functionalization of Allenes
Authors: Wang Yi-Ming , Ding Ruiqi , Wang Yidong 
Published: December 2022

AbstractWe report a synthesis of allenylic silyl ethers through iron-catalyzed functionalization of the C(sp2)–H bonds of monosubstituted alkylallenes. In the presence of a cyclopentadienyliron dicarbonyl based catalyst and triisopropylsilyl triflate as a silylation agent, a variety of aryl aldehydes were suitable coupling partners in this transformation, furnishing a collection of 1,1-disubstituted allenylic triisopropylsilyl ethers as products in moderate to excellent yields as a single regioisomer. Lithium bistriflimide was identified as a critical additive in this transformation. The optimized protocol was scalable, and the products were amenable to further transformation to give a number of unsaturated, polyfunctional derivatives.