bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of a seven-long non-coding RNA signature associated with Jab1/CSN5 in predicting hepatocellular carcinoma
Authors: Ma Weijie , Yao Ye , Xu Gang , Wu Xiaoling , Li Jinghua , Wang Ganggang , Chen Xi , Wang Kunlei , Chen Yirang , Guo Yonghua , Li Yongsheng , Tan Deli , Guo Hui , Liu Zhisu , Yuan Yufeng , Claret Francois X. 
Published: July 2021

AbstractHepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide, accounting for over 700,000 deaths each year. The lack of predictive and prognostic biomarkers for HCC, with effective therapy, remains a significant challenge for HCC management. Long non-coding RNAs (lncRNAs) play a key role in tumorigenesis and have clinical value as potential biomarkers in the early diagnosis and prediction of HCC. Jun activation domain-binding protein 1 (Jab1, also known as COP9 signalosome subunit 5, CSN5) is a potential oncogene that plays a critical role in the occurrence of HCC. Here, we performed a comprehensive analysis for Jab1/CSN5-associated lncRNAs to predict the prognosis of HCC. The differentially expressed (DE) lncRNAs between in HCC were analyzed based on the TCGA RNA-seq data. We detected 1031 upregulated lncRNAs in 371 HCC tissues and identified a seven-lncRNA signature strongly correlated with Jab1/CSN5 (SNHG6, CTD3065J16.9, LINC01604, CTD3025N20.3, KB-1460A1.5, RP13-582O9.7, and RP11-29520.2). We further evaluated the prognostic significance of these lncRNAs by GEPIA (http://gepia.cancer-pku.cn/). The expression data in 364 liver tumors indicated that this seven-lncRNA signature could better predict worse survival in HCC patients. Moreover, 35 clinical HCC samples were evaluated to assess the validity and reproducibility of the bioinformatic analysis. We found that the targeted lncRNAs were upregulated, with a strong association with Jab1/CSN5 and prognostic value in HCC. Functional enrichment analysis by Gene Ontology (GO) showed that these seven prognostic lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer. Overall, our findings demonstrate the clinical implication of Jab1/CSN5 with the seven‐lncRNAs in predicting survival for patients with HCC.
Visualizing the strongly reshaped skyrmion Hall effect in multilayer wire devices
Authors: Tan Anthony K. C. , Ho Pin , Lourembam James , Huang Lisen , Tan Hang Khume , Reichhardt Cynthia J. O. , Reichhardt Charles , Soumyanarayanan Anjan 
Published: July 2021

AbstractMagnetic skyrmions are nanoscale spin textures touted as next-generation computing elements. When subjected to lateral currents, skyrmions move at considerable speeds. Their topological charge results in an additional transverse deflection known as the skyrmion Hall effect (SkHE). While promising, their dynamic phenomenology with current, skyrmion size, geometric effects and disorder remain to be established. Here we report on the ensemble dynamics of individual skyrmions forming dense arrays in Pt/Co/MgO wires by examining over 20,000 instances of motion across currents and fields. The skyrmion speed reaches 24 m/s in the plastic flow regime and is surprisingly robust to positional and size variations. Meanwhile, the SkHE saturates at ∼22∘, is substantially reshaped by the wire edge, and crucially increases weakly with skyrmion size. Particle model simulations suggest that the SkHE size dependence — contrary to analytical predictions — arises from the interplay of intrinsic and pinning-driven effects. These results establish a robust framework to harness SkHE and achieve high-throughput skyrmion motion in wire devices.
Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease
Authors: Sahoo Debashis , Swanson Lee , Sayed Ibrahim M. , Katkar Gajanan D. , Ibeawuchi Stella-Rita , Mittal Yash , Pranadinata Rama F. , Tindle Courtney , Fuller Mackenzie , Stec Dominik L. , Chang John T. , Sandborn William J. , Das Soumita , Ghosh Pradipta 
Published: July 2021

AbstractModeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here we outline an unbiased AI-assisted approach for target identification and validation. A network was built in which clusters of genes are connected by directed edges that highlight asymmetric Boolean relationships. Using machine-learning, a path of continuum states was pinpointed, which most effectively predicted disease outcome. This path was enriched in gene-clusters that maintain the integrity of the gut epithelial barrier. We exploit this insight to prioritize one target, choose appropriate pre-clinical murine models for target validation and design patient-derived organoid models. Potential for treatment efficacy is confirmed in patient-derived organoids using multivariate analyses. This AI-assisted approach identifies a first-in-class gut barrier-protective agent in IBD and predicted Phase-III success of candidate agents.
Morphological screening of mesenchymal mammary tumor organoids to identify drugs that reverse epithelial-mesenchymal transition
Authors: Zhao Na , Powell Reid T. , Yuan Xueying , Bae Goeun , Roarty Kevin P. , Stossi Fabio , Strempfl Martina , Toneff Michael J. , Johnson Hannah L. , Mani Sendurai A. , Jones Philip , Stephan Clifford C. , Rosen Jeffrey M. 
Published: July 2021

AbstractThe epithelial-mesenchymal transition (EMT) has been implicated in conferring stem cell properties and therapeutic resistance to cancer cells. Therefore, identification of drugs that can reprogram EMT may provide new therapeutic strategies. Here, we report that cells derived from claudin-low mammary tumors, a mesenchymal subtype of triple-negative breast cancer, exhibit a distinctive organoid structure with extended “spikes” in 3D matrices. Upon a miR-200 induced mesenchymal-epithelial transition (MET), the organoids switch to a smoother round morphology. Based on these observations, we developed a morphological screening method with accompanying analytical pipelines that leverage deep neural networks and nearest neighborhood classification to screen for EMT-reversing drugs. Through screening of a targeted epigenetic drug library, we identified multiple class I HDAC inhibitors and Bromodomain inhibitors that reverse EMT. These data support the use of morphological screening of mesenchymal mammary tumor organoids as a platform to identify drugs that reverse EMT.
Self-supplied drinking water in low- and middle-income countries in the Asia-Pacific
Authors: Foster Tim , Priadi Cindy , Kotra Krishna Kumar , Odagiri Mitsunori , Rand Emily Christensen , Willetts Juliet 
Published: July 2021

AbstractThere is increasing awareness of household self-supply and the role it can play in securing water for domestic needs in low- and middle-income countries (LMICs), but its scale across the Asia-Pacific has not previously been quantified. This study analysed 77 datasets from 26 countries to estimate the prevalence of self-supplied drinking water, and its associated trends in LMICs in South Asia, Southeast Asia and the Pacific. When factoring in temporal trends, results suggest that >760 million people—or 31% of the population—relied on self-supply for their drinking water in these regions in 2018, with the number of users increasing by >9 million each year. Reliance on self-supply for drinking water is greater in rural areas than in urban areas (37% of rural population vs 20% of urban population), though results vary considerably between countries. Groundwater sources constitute the most common form of self-supply in South Asia and Southeast Asia, while rainwater collection is dominant in the Pacific. The results confirm the significance of self-supply in the Asia-Pacific and suggest that households are a major but often overlooked source of financing within the water sector. The findings raise important questions about how policy and practice should respond to this widespread phenomenon.
The impact of the COVID-19 epidemic on hospital admissions for alcohol-related liver disease and pancreatitis in Japan
Authors: Itoshima Hisashi , Shin Jung-ho , Takada Daisuke , Morishita Tetsuji , Kunisawa Susumu , Imanaka Yuichi 
Published: July 2021

AbstractDuring the coronavirus disease 2019 (COVID-19) pandemic, there have been health concerns related to alcohol use and misuse. We aimed to examine the population-level change in cases of alcohol-related liver disease and pancreatitis that required admission during the COVID-19 epidemic by interrupted time series (ITS) analysis using claims data. We defined the period from April 2020, when the Japanese government declared a state of emergency, as the beginning of the COVID-19 epidemic. This ITS analysis included 3,026,389 overall admissions and 10,242 admissions for alcohol-related liver disease or pancreatitis from 257 hospitals between July 2018 and June 2020. The rate of admissions per 1000 admissions during the COVID-19 epidemic period (April 2020–June 2020) was 1.2 times (rate ratio: 1.22, 95% confidence interval: 1.12–1.33) compared to the pre-epidemic period. Analyses stratified by sex revealed that the increases in admission rates of alcohol-related liver disease or pancreatitis for females were higher than for males during the COVID-19 epidemic period. The COVID-19 epidemic in Japan might associates an increase in hospital admissions for alcohol-related liver disease and pancreatitis. Our study could support the concern of alcohol consumption and health problems during the COVID-19 pandemic.
Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
Authors: Creagh Andrew P. , Lipsmeier Florian , Lindemann Michael , Vos Maarten De 
Published: July 2021

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.
Designing magnetic field sensor based on tapered photonic crystal fibre assisted by a ferrofluid
Authors: Taghizadeh Mostafa , Bozorgzadeh Forough , Ghorbani Marjan 
Published: July 2021

AbstractA novel magnetic field sensor is proposed based on the combination of in-line tapered photonic crystal fibre (PCF) Mach–Zehnder interferometer and magnetic nanoparticles. The sensor is theoretically investigated and experimentally realized. The effect of the mechanical strain and the magnetic field on the sensitivity of the sensor is studied. It is found that the proposed sensor shows a wavelength-sensitivity of $$-\,0.072\,\text {nm/mT}$$

-

0.072

nm/mT

and a strain-sensitivity of $$1\,\text {pm/}\upmu \,\epsilon \,$$

1

pm/
μ

ϵ

. To evaluate the effect of the magnetic nanoparticles on the output light intensity, the sensitivity response of the device has been measured under different magnetic field strengths for different length scales. The experimental results show refractive index changes of the magnetic nanoparticles-infiltrated PCF—acting as fibre cladding—under the applied magnetic field leads to variations of the interferometric output. The sensitivity of magnetic field measurement with the sensor with $$30\,\text {mm}$$

30

mm

and $$40\,\text {mm}$$

40

mm

PCF could reach up to $$0.021\,\text {dB/mT}$$

0.021

dB/mT

and $$0.017\,\text {dB/mT}$$

0.017

dB/mT

, respectively. The results show a very good linear response that is an essential requirement for the practical sensors. The proposed magnetic field sensor finds applications in various areas, such as optical sensing, military, power industry, and tunable photonic devices.
The impact of ossification spread on cervical spine function in patients with ossification of the posterior longitudinal ligament
Authors: Katsumi Keiichi , Hirai Takashi , Yoshii Toshitaka , Maki Satoshi , Mori Kanji , Nagoshi Narihito , Nishimura Soraya , Takeuchi Kazuhiro , Ushio Shuta , Furuya Takeo , Watanabe Kei , Nishida Norihiro , Watanabe Kota , Kaito Takashi , Kato Satoshi , Nagashima Katsuya , Koda Masao , Ito Kenyu , Imagama Shiro , Matsuoka Yuji , Wada Kanichiro , Kimura Atsushi , Ohba Tetsuro , Katoh Hiroyuki , Matsuyama Yukihiro , Ozawa Hiroshi , Haro Hirotaka , Takeshita Katsushi , Watanabe Masahiko , Matsumoto Morio , Nakamura Masaya , Yamazaki Masashi , Okawa Atsushi , Kawaguchi Yoshiharu 
Published: July 2021

AbstractOssification of the posterior longitudinal ligament (OPLL) is a progressive disease. The bridging of ossified lesions to the vertebral body gradually increases, thereby decreasing the mobility of the cervical spine; thus, cervical spine function may decrease over time. However, cervical spine function in patients with cervical OPLL has not been evaluated in large prospective studies. Therefore, we conducted a prospective multicenter study to clarify whether ossification spread can influence cervical spine function and quality of life (QOL) in patients with cervical OPLL. In total, 238 patients (162 men, 76 women; mean age, 63.9 years) were enrolled from 16 institutions. Each patient underwent whole spine computed tomography and was evaluated for cervical spine function and QOL using the Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ). In the multivariate regression analysis, a higher neck VAS score and a larger number of bridge formations of OPLL in the whole spine were significant predictors of adverse outcomes related to cervical spine function. This is the first prospective multicenter study to reveal the impact of ossification spread on cervical spine function. These findings are important to understand the natural course of OPLL and can serve as controls when evaluating postoperative cervical spine function.
Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
Authors: Sidhom John-William , Baras Alexander S. 
Published: July 2021

AbstractSARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.
An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images
Authors: Papadopoulos Alexandros , Topouzis Fotis , Delopoulos Anastasios 
Published: July 2021

AbstractDiabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert’s diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading.
Optical power limiter in the femtosecond filamentation regime
Authors: Agiotis Leonidas , Meunier Michel 
Published: July 2021

AbstractWe present the use of a power limiting apparatus to evaluate ultrafast optical nonlinearities of transparent liquids (water and ethanol) in the femtosecond filamentation regime. The setup has been previously employed for the same purpose, however, in a longer pulsewidth (> 20 ps) regime, which leads to an ambiguous evaluation of the critical power for self-focusing. The uncertainty originates from the existence of a threshold power for optical breakdown well below the critical power for self-focusing within this timeframe. Contrarily, using the proposed apparatus in the femtosecond regime, we observe for the first time a unique optical response, which features the underlying physics of laser filamentation. Importantly, we demonstrate a dependence of the optical transmission of the power limiter on its geometrical, imaging characteristics and the conditions under which a distinct demarcation for the critical power for self-focusing can be determined. The result is supported by numerical simulations, which indicate that the features of the observed power-dependent optical response of the power limiting setup are physically related to the spontaneous transformation of the laser pulses into nonlinear conical waves.
A computational and structural analysis of germline and somatic variants affecting the DDR mechanism, and their impact on human diseases
Authors: Magraner-Pardo Lorena , Laskowski Roman A. , Pons Tirso , Thornton Janet M. 
Published: July 2021

AbstractDNA-Damage Response (DDR) proteins are crucial for maintaining the integrity of the genome by identifying and repairing errors in DNA. Variants affecting their function can have severe consequences since failure to repair damaged DNA can result in cells turning cancerous. Here, we compare germline and somatic variants in DDR genes, specifically looking at their locations in the corresponding three-dimensional (3D) structures, Pfam domains, and protein–protein interaction interfaces. We show that somatic variants in metastatic cases are more likely to be found in Pfam domains and protein interaction interfaces than are pathogenic germline variants or variants of unknown significance (VUS). We also show that there are hotspots in the structures of ATM and BRCA2 proteins where pathogenic germline, and recurrent somatic variants from primary and metastatic tumours, cluster together in 3D. Moreover, in the ATM, BRCA1 and BRCA2 genes from prostate cancer patients, the distributions of germline benign, pathogenic, VUS, and recurrent somatic variants differ across Pfam domains. Together, these results provide a better characterisation of the most recurrent affected regions in DDRs and could help in the understanding of individual susceptibility to tumour development.
Poly (red DSBR)/Al-ZnO modified carbon paste electrode sensor for dopamine: a voltammetric study
Authors: Shashikumara J. K. , Swamy B. E. Kumara , Sharma S. C. , Hariprasad S. A. , Mohanty Kaustubha 
Published: July 2021

AbstractIn the present work, the ZnO and Al-ZnO nanoflakes (NFs) were synthesized by the co-precipitation process. The synthesized NFs were characterized by X-ray diffraction and field emission scanning electron microscopy. Energy dispersive X-ray spectrometer was explored for the elemental chemical compositions. The prepared NFs were taken for the modification of the electrode and developed the modified electrode for the electrochemical analysis of the dopamine (DOA) at pH 7.4. The Al-ZnO modified carbon paste electrode (MCPE) was electropolymerised by using textile dye red DSBR. The Po-RD/Al-ZnO MCPE exhibited good electrochemical sensor properties towards the electrochemical detection of DOA. Several factors such as the impact of speed rate (υ), pH and concentration of the DOA were analyzed at the modified electrode. The great sensitivity was established to the fast electron-transfer kinetics and surface coverage of the DOA on the electrode. The prepared electrode exhibits satisfactory stability at the ambient conditions. The detection limit of 0.58 μM was achieved for the DOA. The decorated sensor was stable, sensitive, selective, and reproducible and used in the analytical applications.