bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reference gene selection for expression studies in the reproductive axis tissues of Magang geese at different reproductive stages under light treatment
Authors: Zhang Bei-Bei , Shen Xu , Li Xiu-Jin , Tian Yun-Bo , Ouyang Hong-Jia , Huang Yun-Mao 
Published: April 2021

AbstractIn quantitative PCR research, appropriate reference genes are key to determining accurate mRNA expression levels. In order to screen the reference genes suitable for detecting gene expression in tissues of the reproductive axis, a total of 420 (males and females = 1:5) 3-year-old Magang geese were selected and subjected to light treatment. The hypothalamus, pituitary and testicular tissues were subsequently collected at different stages. Ten genes including HPRT1, GAPDH, ACTB, LDHA, SDHA, B2M, TUBB4, TFRC, RPS2 and RPL4 were selected as candidate reference genes. The expression of these genes in goose reproductive axis tissues was detected by real-time fluorescent quantitative PCR. The ΔCT, geNorm, NormFinder and BestKeeper algorithms were applied to sort gene expression according to stability. The results showed that ACTB and TUBB4 were the most suitable reference genes for the hypothalamic tissue of Magang goose in the three breeding stages; HPRT1 and RPL4 for pituitary tissue; and HPRT1 and LDHA for testicular tissue. For all three reproductive axis tissues, ACTB was the most suitable reference gene, whereas the least stable reference gene was GAPDH. Altogether, these results can provide references for tissue expression studies in geese under light treatment.
Development of machine learning model for diagnostic disease prediction based on laboratory tests
Authors: Park Dong Jin , Park Min Woo , Lee Homin , Kim Young-Jin , Kim Yeongsic , Park Young Hoon 
Published: April 2021

AbstractThe use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
Real-time coronary artery stenosis detection based on modern neural networks
Authors: Danilov Viacheslav V. , Klyshnikov Kirill Yu. , Gerget Olga M. , Kutikhin Anton G. , Ganyukov Vladimir I. , Frangi Alejandro F. , Ovcharenko Evgeny A. 
Published: April 2021

AbstractInvasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.
Design approach of perforated labyrinth-based acoustic metasurface for selective acoustic levitation manipulation
Authors: Xu Zhike , Qin Ling , Xu Wei , Fang Shuhua , Wang Jiyao 
Published: April 2021

AbstractThis paper proposes a metasurface design approach with perforated labyrinthine path coil structure to manipulate the acoustic transmission with inexpensive materials. The medium in the labyrinthine path coils in this design is air, but not limited to air. A systematic approach has been proposed for the unit cell design of acoustic metamaterials with adjustable resonance peak frequencies and bandgap width. The theory demonstrates that the length of pipe segments determines resonance peak frequencies and the cross-sectional area ratio adjusts the bandgap width. The proposed design approach uses an equivalent pipe circuit based analytical model to design the high transmission (high pass) and high reflection (low pass) unit cell. The simulation and experiment has been performed to evaluate the validity of the theory. Although there exists some assumptions in the theory, the theory still has enough accuracy to guide the metasurface design illustrated by the simulation and experiment results.
Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
Authors: Jain Aashish , Terashi Genki , Kagaya Yuki , Maddhuri Venkata Subramaniya Sai Raghavendra , Christoffer Charles , Kihara Daisuke 
Published: April 2021

AbstractProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.
Assessing the potential for deep learning and computer vision to identify bumble bee species from images
Authors: Spiesman Brian J. , Gratton Claudio , Hatfield Richard G. , Hsu William H. , Jepsen Sarina , McCornack Brian , Patel Krushi , Wang Guanghui 
Published: April 2021

AbstractPollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.
Interactions of genetic variants and prenatal stress in relation to the risk for recurrent respiratory infections in children
Authors: Korhonen Laura S. , Lukkarinen Minna , Kantojärvi Katri , Räty Panu , Karlsson Hasse , Paunio Tiina , Peltola Ville , Karlsson Linnea 
Published: April 2021

AbstractGenetic variants may predispose children to recurrent respiratory infections (RRIs) but studies on genotype-environment interaction are rare. We hypothesized that the risk for RRIs is elevated in children with innate immune gene variants, and that prenatal exposure to maternal psychological distress further increases the risk. In a birth cohort, children with RRIs (n = 96) were identified by the age of 24 months and compared with the remaining cohort children (n = 894). The risk for RRIs in children with preselected genetic variants and the interaction between maternal distress during pregnancy and child genotype were assessed with logistic regression. TheIL6minor allele G was associated with elevated risk for RRIs (OR 1.55; 95% CI 1.14–2.12). Overall, there was no interaction between maternal psychological distress and child genotype. Exploratory analyses showed that, the association between the variant type ofIL6and the risk for RRIs was dependent on prenatal exposure to maternal psychological distress in males (OR 1.96; 95% CI 1.04–3.67). Our study didn’t find genotype-environment interaction between prenatal maternal distress and child genotype. Exploratory analyses suggest sex differences in gene-environment interaction related to susceptibility to RRIs.
Effect of PM2.5 pollution on perinatal mortality in China
Authors: Li Guangqin , Li Lingyu , Liu Dan , Qin Jiahong , Zhu Hongjun 
Published: April 2021

AbstractUsing ArcGIS to analyze satellite derived PM2.5estimates, this paper obtains the average concentration and maximum concentration of fine particulate matter (PM2.5) in China's 31 provinces from 2002 to 2015. We adopt fixed effects model and spatial Durbin model to investigate the association between PM2.5and perinatal mortality rates. The results indicate that PM2.5has a significantly positive association with perinatal mortality rates. A 1% increase of log-transformed average concentration and maximum concentrations of PM2.5is associated with 1.76‰ and 2.31‰ increase of perinatal mortality rates, respectively. In spatial econometrics analysis, we find PM2.5has significant spatial autocorrelation characteristics. The concentrations of log-transformed average and maximum PM2.5increase 1% is associated with a 2.49% increase in a 2.49‰ and 2.19‰ increase of perinatal mortality rates, respectively. The potential mechanism is that air pollution has an impact on infant weight to impact perinatal mortality rates.
Enhanced degradation of anthraquinone dyes by microbial monoculture and developed consortium through the production of specific enzymes
Authors: Mohanty Swati Sambita , Kumar Arvind 
Published: April 2021

AbstractThe current study investigates the decolorization of Indanthrene Blue RS dye and the optimization of process parameters needed for effective decolorization by the bacterial consortium. The pure culture of strain TS8, PMS, and NCH has been isolated from the textile wastewater sample collected from local textile processing units outlet and dye contaminated soil from Odisha, India. A bacterial consortium-BP of Bacillus flexus TS8 (BF), Proteus mirabilis PMS (PM), and Pseudomonas aeruginosa NCH (PA) were developed. The physicochemical parameters were optimized to attain maximum decolorization efficacy. Degradation of Indanthrene Blue RS and the formation of metabolites were confirmed through UV–vis spectroscopy, FT-IR, and GC–MS analysis. The developed consortium-BP showed an enhanced decolorization of Indanthrene Blue RS dye with an Average decolorization rate of 11,088 µg h−1 within 9 h compared to the individual strains under aerobic conditions. The supplementation of agricultural residual wastes showed increased decolorization efficiency of consortium-BP. Higher reduction in TOC and COD removal (≥ 80%) determined the mineralization of Indanthrene Blue RS by consortium-BP. Significant induction of various oxidoreductive enzymes in consortium-BP compared to that of Individual strains indicates their involvement in the overall decolorization and degradation process, with the higher protein concentration in the intracellular enzymes. Studies on the phytotoxicity effect revealed the non-toxic nature of the degraded products formed on mineralization of Indanthrene Blue RS by consortium-BP. This study represents a new approach for enhanced biodegradation using consortium-BP in treating textile wastewaters containing anthraquinone dyes.
Establishing an immunocompromised porcine model of human cancer for novel therapy development with pancreatic adenocarcinoma and irreversible electroporation
Authors: Hendricks-Wenger Alissa , Aycock Kenneth N. , Nagai-Singer Margaret A. , Coutermarsh-Ott Sheryl , Lorenzo Melvin F. , Gannon Jessica , Uh Kyungjun , Farrell Kayla , Beitel-White Natalie , Brock Rebecca M. , Simon Alexander , Morrison Holly A. , Tuohy Joanne , Clark-Deener Sherrie , Vlaisavljevich Eli , Davalos Rafael V. , Lee Kiho , Allen Irving C. 
Published: April 2021

AbstractNew therapies to treat pancreatic cancer are direly needed. However, efficacious interventions lack a strong preclinical model that can recapitulate patients’ anatomy and physiology. Likewise, the availability of human primary malignant tissue for ex vivo studies is limited. These are significant limitations in the biomedical device field. We have developed RAG2/IL2RG deficient pigs using CRISPR/Cas9 as a large animal model with the novel application of cancer xenograft studies of human pancreatic adenocarcinoma. In this proof-of-concept study, these pigs were successfully generated using on-demand genetic modifications in embryos, circumventing the need for breeding and husbandry. Human Panc01 cells injected subcutaneously into the ears of RAG2/IL2RG deficient pigs demonstrated 100% engraftment with growth rates similar to those typically observed in mouse models. Histopathology revealed no immune cell infiltration and tumor morphology was highly consistent with the mouse models. The electrical properties and response to irreversible electroporation of the tumor tissue were found to be similar to excised human pancreatic cancer tumors. The ample tumor tissue produced enabled improved accuracy and modeling of the electrical properties of tumor tissue. Together, this suggests that this model will be useful and capable of bridging the gap of translating therapies from the bench to clinical application.
Activation of matrix metalloproteinases and FoxO3a in HaCaT keratinocytes by radiofrequency electromagnetic field exposure
Authors: Kim Ju Hwan , Kang Dong-Jun , Bae Jun-Sang , Lee Jai Hyuen , Jeon Sangbong , Choi Hyung-Do , Kim Nam , Kim Hyung-Gun , Kim Hak Rim 
Published: April 2021

AbstractAs the skin is the largest body organ and critically serves as a barrier, it is frequently exposed and could be physiologically affected by radiofrequency electromagnetic field (RF-EMF) exposure. In this study, we found that 1760 MHz RF-EMF (4.0 W/kg specific absorption rate for 2 h/day during 4 days) exposure could induce intracellular reactive oxygen species (ROS) production in HaCaT human keratinocytes using 2′,7′-dichlorofluorescin diacetate fluorescent probe analysis. However, cell growth and viability were unaffected by RF-EMF exposure. Since oxidative stress in the skin greatly influences the skin-aging process, we analyzed the skin senescence-related factors activated by ROS generation. Matrix metalloproteinases 1, 3, and 7 (MMP1, MMP3, and MMP7), the main skin wrinkle-related proteins, were significantly increased in HaCaT cells after RF-EMF exposure. Additionally, the gelatinolytic activities of secreted MMP2 and MMP9 were also increased by RF-EMF exposure. FoxO3a (Ser318/321) and ERK1/2 (Thr 202/Tyr 204) phosphorylation levels were significantly increased by RF-EMF exposure. However, Bcl2 and Bax expression levels were not significantly changed, indicating that the apoptotic pathway was not activated in keratinocytes following RF-EMF exposure. In summary, our findings show that exposure to 1760 MHz RF-EMF induces ROS generation, leading to MMP activation and FoxO3a and ERK1/2 phosphorylation. These data suggest that RF-EMF exposure induces cellular senescence of skin cells through ROS induction in HaCaT human keratinocytes.
A meta-analysis comparing short-term weight and cardiometabolic changes between olanzapine/samidorphan and olanzapine
Authors: Srisurapanont Manit , Suttajit Sirijit , Likhitsathian Surinporn , Maneeton Benchalak , Maneeton Narong 
Published: April 2021

AbstractThis study compared weight and cardiometabolic changes after short-term treatment of olanzapine/samidorphan and olanzapine. Eligible criteria for an included trial were ≤ 24 weeks, randomized controlled trials (RCTs) that compared olanzapine/samidorphan and olanzapine treatments in patients/healthy volunteers and reported weight or cardiometabolic outcomes. Three databases were searched on October 31, 2020. Primary outcomes included weight changes and all-cause dropout rates. Standardized mean differences (SMDs) and risk ratios (RRs) were computed and pooled using a random-effect model. This meta-analysis included four RCTs (n = 1195). The heterogeneous data revealed that weight changes were not significantly different between olanzapine/samidorphan and olanzapine groups (4 RCTs, SDM = − 0.19, 95% CI − 0.45 to 0.07, I2 = 75%). The whole-sample, pooled RR of all-cause dropout rates (4 RCTs, RR = 1.02, 95% CI 0.84 to 1.23, I2 = 0%) was not significant different between olanzapine/samidorphan and olanzapine groups. A lower percentage of males and a lower initial body mass index were associated with the greater effect of samidorphan in preventing olanzapine-induced weight gain. Current evidence is insufficient to support the use of samidorphan to prevent olanzapine-induced weight gain and olanzapine-induced cardiometabolic abnormalities. Samidorphan is well accepted by olanzapine-treated patients.
Characteristics of fixation patterns and their relationship with visual function of patients with idiopathic macular holes after vitrectomy
Authors: Liu Yuyan , Wang Ying , Dong Yi , Liang Dongqing , Xie Shiyong , Xiao Bo , Chu Yanhua , Han Quanhong 
Published: April 2021

AbstractTo analyze the relationships between the fixation location and the visual function of idiopathic macular hole (IMH) patients with macular integrity assessment (MAIA) examination preoperatively and 3 months postoperatively. This was a retrospective case analysis. Forty-three eyes of 43 patients diagnosed with IMH were included in this study. The best corrected visual acuity (BCVA) assessments, optical coherence tomography (OCT) and MAIA examinations were performed before surgery and 1 week, 1 month and 3 months after surgery. The relationships between MAIA parameters and visual acuity were assessed by correlation analysis. Grouping by fixation location with the foveola (2°) as the centre, the locations could be divided into five groups, including foveolar, temporal, nasal, inferior and superior fixation. The mean macular sensitivity (MMS) of the macular area was correlated with the BCVA in the IMH patients before and 3 months after surgery (before surgery P = 0.00, after surgery P = 0.00). The MMS could be used as a good indicator for evaluating visual function in IMH patients. There was a significant difference in fixation location before and after the operation (P = 0.01). The preoperative fixation location of IMH patients was mainly in the superior area, while postoperatively moved to the foveola and nasal areas. Paying attention to the changes of fixation locations in IMH patients may provide new clues for further improving postoperative visual function.
Optical analysis of the behavior of sealants under mechanical, thermal and chemical stress
Authors: Erbe Christina , Deckers Florian , Schmidtmann Irene , Heider Julia , Wehrbein Heinrich 
Published: April 2021

AbstractRegarding their resistance five sealants were tested in vitro after experiencing mechanical, thermal and chemical stress. Included for testing were two fluoride varnishes: Fluor Protector [FP] (Ivoclar Vivadent) and Protecto CaF2 Nano One-Step Seal [PN] (BonaDent) and three fluoride-composite filled sealants (with acid etch technique): Clinpro XT Varnish [CP] (3 M Espe), Pro Seal [PS] & Light Bond [LB] (Reliance Orthodontic Products) and a positive control group [CG] Tetric EvoFlow (Ivoclar Vivadent). The sealants were applied on 180 bovine teeth (n = 10/ sealer) in a standardized manner after bracket bonding. Mechanical pressure and its effect by simulating different time points and standardized electric cleaning protocol was tested first. Followed by thermal burden due to varying thermal stress and thirdly change in pH stress imitating chemical exposure were examined separately. A digital microscope and a grid incisal and apical to the brackets (n = 32 fields) was used to standardize the optical analysis. Material loss due to mechanical stress compared to CG (score 0.00) was CP (1.2%), FP (21.5%), LB (22.2%) and PN (81.1%). No significant difference to CG presented PS. Material loss due to thermal stress was CP (0.5%), PS (2%), FP (2.6%), LB (3.1%) and PN (39.9%). Material loss due to chemical stress was FP (1.8%), PS (2.1%), LB (5.5%) and PN (39.6%). No significant difference to CG presented CP. Only PS and CP had optically provable, good resiliance to mechanical, thermal and chemical stress. Significantly poorer outcomes in particular showed PN.