bluuebunny/crossref_metadata_2025_split huggingface dataset:
GP73 is a TBC-domain Rab GTPase-activating protein contributing to the pathogenesis of non-alcoholic fatty liver disease without obesity
Authors: Peng Yumeng , Zeng Qiang , Wan Luming , Ma Enhao , Li Huilong , Yang Xiaopan , Zhang Yanhong , Huang Linfei , Lin Haotian , Feng Jiangyue , Xu Yixin , Li Jingfei , Liu Muyi , Liu Jing , Lin Changqin , Sun Zhiwei , Cheng Gong , Zhang Xuemiao , Liu Jialong , Li Dongrui , Wei Meng , Mo Yunhai , Mu Xuetao , Deng Xiaowei , Zhang Dandan , Dong Siqing , Huang Hanqing , Fang Yi , Gao Qi , Yang Xiaoli , Wu Feixiang , Zhong Hui , Wei Congwen 
Published: December 2021

AbstractThe prevalence of non-obese nonalcoholic fatty liver disease (NAFLD) is increasing worldwide with unclear etiology and pathogenesis. Here, we show GP73, a Golgi protein upregulated in livers from patients with a variety of liver diseases, exhibits Rab GTPase-activating protein (GAP) activity regulating ApoB export. Upon regular-diet feeding, liver-GP73-high mice display non-obese NAFLD phenotype, characterized by reduced body weight, intrahepatic lipid accumulation, and gradual insulin resistance development, none of which can be recapitulated in liver-GAP inactive GP73-high mice. Common and specific gene expression signatures associated with GP73-induced non-obese NAFLD and high-fat diet (HFD)-induced obese NAFLD are revealed. Notably, metformin inactivates the GAP activity of GP73 and alleviates GP73-induced non-obese NAFLD. GP73 is pathologically elevated in NAFLD individuals without obesity, and GP73 blockade improves whole-body metabolism in non-obese NAFLD mouse model. These findings reveal a pathophysiological role of GP73 in triggering non-obese NAFLD and may offer an opportunity for clinical intervention.
Tumor evolution selectively inactivates the core microRNA machinery for immune evasion
Authors: Song Tian-Yu , Long Min , Zhao Hai-Xin , Zou Miao-Wen , Fan Hong-Jie , Liu Yang , Geng Chen-Lu , Song Min-Fang , Liu Yu-Feng , Chen Jun-Yi , Yang Yu-Lin , Zhou Wen-Rong , Huang Da-Wei , Peng Bo , Peng Zhen-Gang , Cang Yong 
Published: December 2021

AbstractCancer cells acquire genetic heterogeneity to escape from immune surveillance during tumor evolution, but a systematic approach to distinguish driver from passenger mutations is lacking. Here we investigate the impact of different immune pressure on tumor clonal dynamics and immune evasion mechanism, by combining massive parallel sequencing of immune edited tumors and CRISPR library screens in syngeneic mouse tumor model and co-culture system. We find that the core microRNA (miRNA) biogenesis and targeting machinery maintains the sensitivity of cancer cells to PD-1-independent T cell-mediated cytotoxicity. Genetic inactivation of the machinery or re-introduction of ANKRD52 frequent patient mutations dampens the JAK-STAT-interferon-γ signaling and antigen presentation in cancer cells, largely by abolishing miR-155-targeted silencing of suppressor of cytokine signaling 1 (SOCS1). Expression of each miRNA machinery component strongly correlates with intratumoral T cell infiltration in nearly all human cancer types. Our data indicate that the evolutionarily conserved miRNA pathway can be exploited by cancer cells to escape from T cell-mediated elimination and immunotherapy.
Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas
Authors: Lim Woo Hyeon , Park Joon Sik , Park Jaeseok , Choi Seung Hong 
Published: December 2021

AbstractTemporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm3; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm3; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (Ktrans, Vp, and Ve). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95th percentile Ktrans and Ve from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.
Dynamic tracking of a magnetic micro-roller using ultrasound phase analysis
Authors: Pane Stefano , Iacovacci Veronica , Ansari Mohammad Hasan Dad , Menciassi Arianna 
Published: December 2021

AbstractMicrorobots (MRs) have attracted significant interest for their potentialities in diagnosis and non-invasive intervention in hard-to-reach body areas. Fine control of biomedical MRs requires real-time feedback on their position and configuration. Ultrasound (US) imaging stands as a mature and advantageous technology for MRs tracking, but it suffers from disturbances due to low contrast resolution. To overcome these limitations and make US imaging suitable for monitoring and tracking MRs, we propose a US contrast enhancement mechanism for MR visualization in echogenic backgrounds (e.g., tissue). Our technique exploits the specific acoustic phase modulation produced by the MR characteristic motions. By applying this principle, we performed real-time visualization and position tracking of a magnetic MR rolling on a lumen boundary, both in static flow and opposing flow conditions, with an average error of 0.25 body-lengths. Overall, the reported results unveil countless possibilities to exploit the proposed approach as a robust feedback strategy for monitoring and tracking biomedical MRs in-vivo.
Multivariate modelling of milk fatty acid profile to discriminate the forages in dairy cows’ ration
Authors: Riuzzi Giorgia , Davis Hannah , Lanza Ilaria , Butler Gillian , Contiero Barbara , Gottardo Flaviana , Segato Severino 
Published: December 2021

AbstractAlthough there are many studies on the importance of fatty acids (FA) in our diet and on the influence of dairy diets on FA metabolism, only a few investigate their predictive capacity to discriminate the type, amount and conservation method of farm forages. This research quantifies differences in milk FA concentrations and, using a supervised factorial discriminant analysis, assesses potential biomarkers when replacing maize with other silages, grass/lucerne hays or fresh grass. The statistical modelling identified three main clusters of milk FA profiles associated with silages, hays and fresh grass as dominant roughages. The main implication of a dairy cow feeding system based on poliphytic forages from permanent meadows is enhancing milk’s nutritional quality due to an increase in beneficial omega-3 polyunsaturated FA, conjugated linoleic acids and odd chain FA, compared to feeding maize silage. The study also identified a small but powerful and reliable pool of milk FA that can act as biomarkers to authenticate feeding systems: C16:1 c-9, C17:0, C18:0, C18:3 c-9, c-12, c-15, C18:1 c-9, C18:1 t-11 and C20:0.
Epidemiological study in a small rural area of Veneto (Italian region) during Sars-Cov-2 Pandemia
Authors: Bassanello Marco , Pasini Luciano , Senzolo Marco , Gambaro Andrea , Roman Marco , Coli Ugo , D’Aquino Maurizio 
Published: December 2021

AbstractThe emergence of severe acute respiratory syndrome type 2 coronavirus (SARS-CoV-2) and its complications have demonstrated the devastating impact of a new infectious pathogen. The organisational change promulgated by the isolation of affected communities is of extreme importance to achieve effective containment of the contagion and good patient care. The epidemiological study of the population of a small rural community in the North East of Italy revealed how much the virus had circulated during Spring, 2020, and how contagion has evolved after a prolonged lockdown. In the 1st phase, NAAT (Nucleic Acid Amplification Testing) was performed in cases with more or less severe symptoms and a study was performed to trace the infection of family members. Only 0.2% of the population tested positive on NAAT, via nasopharyngeal swab during this 1st phase. In the 2nd phase a random sample of the general population were tested for circulating anti-Sars-Cov-2 immunoglobulins. This showed that approximately 97.9% of the population were negative, while 2.1% (with positive IgG at a distance) of the population had contracted the virus in a mildly symptomatic or asymptomatic form. The main symptom in subjects who developed immunity was fever. Antibodies were found in subjects with forced coexistence with quarantined or infected subjects. The mutual spatial distance by categories has shown higher relative prevalence of IgG positive and IgM negative cases in close proximity but also far from the infected, with respect to an intermediate distance. This suggests that subjects living in thinly populated areas could come in contact with the virus more likely due to intentional/relational proximity, while those living nearby could also be infected through random proximity.
Procalcitonin metabolomics in the critically ill reveal relationships between inflammation intensity and energy utilization pathways
Authors: Kobayashi Hirotada , Amrein Karin , Lasky-Su Jessica A. , Christopher Kenneth B. 
Published: December 2021

AbstractProcalcitonin is a biomarker of systemic inflammation and may have importance in the immune response. The metabolic response to elevated procalcitonin in critical illness is not known. The response to inflammation is vitally important to understanding metabolism alterations during extreme stress. Our aim was to determine if patients with elevated procalcitonin have differences in the metabolomic response to early critical illness. We performed a metabolomics study of the VITdAL-ICU trial where subjects received high dose vitamin D3or placebo. Mixed-effects modeling was used to study changes in metabolites over time relative to procalcitonin levels adjusted for age, Simplified Acute Physiology Score II, admission diagnosis, day 0 25-hydroxyvitamin D level, and the 25-hydroxyvitamin D response to intervention. With elevated procalcitonin, multiple members of the short and medium chain acylcarnitine, dicarboxylate fatty acid, branched-chain amino acid, and pentose phosphate pathway metabolite classes had significantly positive false discovery rate corrected associations. Further, multiple long chain acylcarnitines and lysophosphatidylcholines had significantly negative false discovery rate corrected associations with elevated procalcitonin. Gaussian graphical model analysis revealed functional modules specific to elevated procalcitonin. Our findings show that metabolite differences exist with increased procalcitonin indicating activation of branched chain amino acid dehydrogenase and a metabolic shift.
Sequence variations, flanking region mutations, and allele frequency at 31 autosomal STRs in the central Indian population by next generation sequencing (NGS)
Authors: Dash Hirak Ranjan , Kaitholia Kamlesh , Kumawat R. K. , Singh Anil Kumar , Shrivastava Pankaj , Chaubey Gyaneshwer , Das Surajit 
Published: December 2021

AbstractCapillary electrophoresis-based analysis does not reflect the exact allele number variation at the STR loci due to the non-availability of the data on sequence variation in the repeat region and the SNPs in flanking regions. Herein, this study reports the length-based and sequence-based allelic data of 138 central Indian individuals at 31 autosomal STR loci by NGS. The sequence data at each allele was compared to the reference hg19 sequence. The length-based allelic results were found in concordance with the CE-based results. 20 out of 31 autosomal STR loci showed an increase in the number of alleles by the presence of sequence variation and/or SNPs in the flanking regions. The highest gain in the heterozygosity and allele numbers was observed in D5S2800, D1S1656, D16S539, D5S818, and vWA. rs25768 (A/G) at D5S818 was found to be the most frequent SNP in the studied population. Allele no. 15 of D3S1358, allele no. 19 of D2S1338, and allele no. 22 of D12S391 showed 5 isoalleles each with the same size and with different intervening sequences. Length-based determination of the alleles showed Penta E to be the most useful marker in the central Indian population among 31 STRs studied; however, sequence-based analysis advocated D2S1338 to be the most useful marker in terms of various forensic parameters. Population genetics analysis showed a shared genetic ancestry of the studied population with other Indian populations. This first-ever study to the best of our knowledge on sequence-based STR analysis in the central Indian population is expected to prove the use of NGS in forensic case-work and in forensic DNA laboratories.
Yolk–shell structured magnetic mesoporous silica: a novel and highly efficient adsorbent for removal of methylene blue
Authors: Mirbagheri Reza , Elhamifar Dawood , Shaker Masoumeh 
Published: December 2021

AbstractIn this study, a novel magnetic mesoporous silica with yolk–shell structure (Fe3O4@Void@m.SiO2) was successfully synthesized via a polymer-template assisted method. The Fe3O4@Void@m.SiO2 was characterized by using FT-IR, EDS, SEM, TEM, VSM, PXRD and nitrogen adsorption–desorption analyses. The Fe3O4@Void@m.SiO2 nanocomposite showed high efficiency in adsorption of an organic dye and water pollutant called methylene blue (MB) with 98.2% removal capability. Furthermore, the effect of different parameters in the adsorption of MB was investigated. Different models of kinetic were examined and compared with each other. The recoverability and reusability of designed Fe3O4@Void@m.SiO2 material were also studied under applied conditions.
Effect of minerals and heavy metals in sand samples of Ponnai river, Tamil Nadu, India
Authors: Chandrasekaran A. , Senthil Kumar C. K. , Sathish V. , Manigandan S. , Tamilarasi A. 
Published: December 2021

AbstractRiver sand samples have been collected from Ponnai river, Tamil Nadu, India for characterization of minerals and heavy metals by different spectroscopic techniques. Initially, the samples were subjected by Fourier Transform-Infra Red (FT-IR) spectroscopic technique and infra-red absorption bands values are observed in the range of 515–520, 695–700, 775–780 cm−1 which shows the presence of quartz in all the samples. Similarly, infra-red peaks were absorbed for feldspar, kaolinite, calcite, gibbsite and organic carbon and confirmed by X-Ray diffraction (XRD) technique. Additionally, zircon, aragonite, magnetite and kyanite minerals were identified in the samples using only the XRD method. The concentration of heavy metals such as Pb, Cr, Zn, Ni, Hg, As, Mn, Cu has been determined by flame atomic absorption spectrometry (FAAS). An average metal concentration measured in mg kg−1 were: Pb 0.12, As 0.15, Hg 0.13, Cu 2.80, Zn 10.15 Cr 12.70, Ni 2.86 and Mn 104.94 and hence found in the order of Mn > Cr > Zn > Ni > Cu > As > Hg > Pb. These average values do not exceed the world average value and hence potentially do not affect the quality of sand in the river. In addition to that, presences of heavy metals are confirmed by scanning electron microscope equipped with energy dispersive X-ray spectrometry (SEM/EDS) analysis. In order to understand the possible natural and anthropogenic sources of heavy metals, multivariate statistical techniques such as Pearson correlation, principal component and cluster analysis were performed. Results obtained from the statistical techniques were good agreement with each other.
Facial Emotions over Static Facial Images Using Deep Learning Techniques with Hysterical Interpretation
Authors: ViswanathReddy Dr A , Aswini Reddy A , Bindyashree C A 
Published: December 2021

Abstract
Recognition of facial expression has many potential applications that have attracted the researcher’s attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger, and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. Face Detection, Extraction, Classification, and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determine which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial-and-error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results.
Design of Decision Based Recursive Weighted Median Filter With Exponential Weights
Authors: Ganesh Motepalli Siva Rama , Kadali Kalyan Sagar , Bhukya Ramu , Palleswari Y.T.R. , Siva Asapu , Pragaspathy S 
Published: December 2021

Abstract
The prescribed algorithm for removing impulse noise effectively even under high noise densities without causing any loss of image details. Hence a cascaded section of median filters that, involves an Decision-based Median Filter followed by a Recursive Weighted Median (RWM) Filter employing exponential weights are used. The median controlled algorithm is employed to calculate the exponential weights. In the algorithms that where proposed in earlier which involves a cascaded section of the median with the RWM filters provided lesser Peak Signal/Noise Ratio (PSNR) and greater Mean Square Error(MSE) values. Hence the output appeared to be distorted for higher noise levels. These drawbacks have been eliminated in this proposed algorithm.
A Peer Review of Hybrid Electric Vehicle Based on Step-Up Multi-input Dc-Dc Converter and renewable energy source
Authors: Babu Jarapala Ramesh , Nayak Manas Ranjan , Mangu B. 
Published: December 2021

Abstract
Due to the rapid increase of environmental pollution caused by automobiles. To decrease pollution and to save our resources, there is an alternator to use an electric vehicle instead of a gasoline engine. The main drawback of a gasoline engine of compared to the electric vehicle can polluter noise efficiency durability. When it comes to durability, efficiency, and acceleration capabilities of electric vehicles, they are more impressive. The electric vehicles involve HEVs and BEVs. Generally, ultra-capacitor, solar Photovoltaic (PV) system, batters, regenerative braking systems and flywheel are utilized in HEVs as energy storage devices. All energy storage devices are linked to this distinct dc-dc converter scheme for raising input sources’ voltage. In past few decades, most HEVs have incorporated multi-input converters in order to enhance their reliability and efficiency. There are several distinct multi-input dc-dc converter schemas utilized in HEVs. This research discusses their current and future trends as well as energy storage devices.
Analysis and implementation of realtime stock prediction using reinforcement frameworks
Authors: Pragathi DrYVS Sai , Phani Narasimham M V S , Ramana Murthy B V 
Published: December 2021

Abstract
Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.
RETRACTED: Artificial Neural Network Based shafts surface pressures Analysis
Authors: Murthy B V Ramana , Padmakar Vuppu , Chandrika B N S M , Lanka Satya Prasad 
Published: December 2021

AbstractThis paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.
Recovery of residual public spaces to improve the quality of life of the inhabitants of San Borja, Lima
Authors: Facho O , Cama T , Esenarro D , Livia J , Cuetoand C , Ramos D 
Published: December 2021

Abstract
The present research aims to propose a model for the recovery of residual public spaces to improve the quality of life of the district of San Borja’s inhabitants. San Borja is in the process of densification and requires a more significant number of public spaces that offer, in addition to vegetation, public places for active and passive recreation, such as spaces for sports and games, walking pets, and relaxation. These needs have increased due to the confinement caused by the COVID-19 pandemic. Also, it was noted that the median strip, or central reservation of the avenue, can be recovered for people to use. Therefore, a four-phase study was carried out that included reviewing the literature and observing two cases. In conclusion, a model was proposed to recover the public space of the median strips of San Borja Norte Avenue and San Borja Sur Avenue to improve the quality of life of the inhabitants of San Borja, which can be replicated in other avenues with residual spaces with similar characteristics.
Voice Controlled Guided System for Wheelchair with Collision Detection and Avoidance
Authors: Chhowara Purvi , GhansyamRathod  
Published: December 2021

Abstract
Many people are suffering from temporary or permanent disabilities due to accidents or any pre-illness that may introduce a wheelchair for the person as essential. The use of a wheelchair independently can be derived from the severity of the disability. But in the case of severe situations when a person relies on somebody else to handle the movement of a wheelchair. This paper aims to provide a solution to this problem by using voice command to guide the wheelchair eliminating the reliability of another person.