Picturing One\u27s Self: Camera Use in Zoom Classes during the COVID-19 Pandemic

Starting from the spring of 2020, higher institutions in the US underwent a rapid shift from in-person classes to emergency remote education, in response to the COVID-19 outbreak. Under this circumstance, a variety of video conferencing tools (e.g., Zoom) have been adopted for distance education, which pose a set of new challenges arising from synchronous online classes. Among these, one significant issue was students\u27 unwillingness to open cameras, resulting in a lack of non-verbal cues that instructors could rely on to gauge students\u27 understanding and adjust their teachings. Towards addressing this issue, our qualitative study aims at investigating the rationales behind students\u27 camera avoidance. Through a series of semi-structured interviews on undergraduate students in the U.S, we identified prominent factors -- namely the class size, lecture style, level of interactivity and privacy concerns -- that influenced students\u27 motivation for opening their cameras. At the same time, we uncovered several difficulties, such as heightened self-awareness, feeling of minority and academic perspective, that discouraged students from opening camera, with more substantial impacts on international students. We conclude with actionable insights into the design of online classes, video-conferencing platforms and camera technology that can promote camera usage, thereby contributing to scalable and inclusive interventions for facilitating the transition into remote education

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma.Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates.Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils.Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients

Ferroptosis-related gene HIC1 in the prediction of the prognosis and immunotherapeutic efficacy with immunological activity

BackgroundHypermethylated in Cancer 1 (HIC1) was originally confirmed as a tumor suppressor and has been found to be hypermethylated in human cancers. Although growing evidence has supported the critical roles of HIC1 in cancer initiation and development, its roles in tumor immune microenvironment and immunotherapy are still unclear, and no comprehensive pan-cancer analysis of HIC1 has been conducted.MethodsHIC1 expression in pan-cancer, and differential HIC1 expression between tumor and normal samples were investigated. Immunohistochemistry (IHC) was employed to validate HIC1 expression in different cancers by our clinical cohorts, including lung cancer, sarcoma (SARC), breast cancer, and kidney renal clear cell carcinoma (KIRC). The prognostic value of HIC1 was illustrated by Kaplan-Meier curves and univariate Cox analysis, followed by the genetic alteration analysis of HIC1 in pan-cancer. Gene Set Enrichment Analysis (GSEA) was conducted to illustrate the signaling pathways and biological functions of HIC1. The correlations between HIC1 and tumor mutation burden (TMB), microsatellite instability (MSI), and the immunotherapy efficacy of PD-1/PD-L1 inhibitors were analyzed by Spearman correlation analysis. Drug sensitivity analysis of HIC1 was performed by extracting data from the CellMiner™ database.ResultsHIC1 expression was abnormally expressed in most cancers, and remarkable associations between HIC1 expression and prognostic outcomes of patients in pan-cancer were detected. HIC1 was significantly correlated with T cells, macrophages, and mast cell infiltration in different cancers. Moreover, GSEA revealed that HIC1 was significantly involved in immune-related biological functions and signaling pathways. There was a close relationship of HIC1 with TMB and MSI in different cancers. Furthermore, the most exciting finding was that HIC1 expression was significantly correlated with the response to PD-1/PD-L1 inhibitors in cancer treatment. We also found that HIC1 was significantly correlated with the sensitivity of several anti-cancer drugs, such as axitinib, batracylin, and nelarabine. Finally, our clinical cohorts further validated the expression pattern of HIC1 in cancers.ConclusionsOur investigation provided an integrative understanding of the clinicopathological significance and functional roles of HIC1 in pan-cancer. Our findings suggested that HIC1 can function as a potential biomarker for predicting the prognosis, immunotherapy efficacy, and drug sensitivity with immunological activity in cancers

piR-hsa-211106 Inhibits the Progression of Lung Adenocarcinoma Through Pyruvate Carboxylase and Enhances Chemotherapy Sensitivity

Although the importance of PIWI-interacting RNAs (piRNAs) in cancer has recently been recognized, studies on the role and functional mechanism of piRNAs in lung adenocarcinoma (LUAD) development and progression are limited. In this study, we identified 10 differently expressed piRNAs in LUAD tissues compared to normal tissues, among which, piR-hsa-211106 expression levels were downregulated in LUAD tissues and cell lines. Furthermore, the effects of piR-hsa-211106 on the malignant phenotypes and chemosensitivity of LUAD cells were detected by gain- and loss-of-function analyses in vitro and in vivo, which showed that piR-hsa-211106 inhibited LUAD cell proliferation, tumor growth, and migration, but promoted apoptosis. Moreover, our finding indicated that piR-hsa-211106 is a potential therapeutic target that synergistically imparts anticancer effects with a chemotherapeutic agent for LUAD-cisplatin. Further mechanistic investigation indicated that piR-hsa-211106 could bind to pyruvate carboxylase (PC) by RNA pull down and RNA immunoprecipitation assays and inhibited PC mRNA and protein expression. Our study demonstrates that piR-hsa-211106 inhibits LUAD progression by hindering the expression and function of PC and enhances chemotherapy sensitivity, suggesting that piR-hsa-211106 is a novel diagnostic and therapeutic target for LUAD

Discussion on compact mechanism of air-stream and synchro-formed clamp plate impact molding

Applying the air impact molding method to mold the complicated pattern with wider opening surface and deeper concave, there always exist vaulted phenomenon and lower compactibility of sand mold over the entrance and the concave regions. Using the air-stream and synchro-formed clamp plate impact molding, however, this problem will be preferably solved. In this paper, the compact mechanism of the new molding method and the effect of some configuration factors, such as the area flowed by compressed air and the highness of the protruding block displacement around the diffluent clamp plate, on the compactibility of sand mold were discussed

Bio-inhibitive effect of an algal symbiotic bacterium on corrosion of magnesium in marine environment

It is a longstanding and challenging task to develop sustainable environment-friendly and cost-effective corrosion-protection technologies for Mg alloys, especially under marine conditions in which corrosion can normally be significantly accelerated by bacterial activity. However, this paper reports on the corrosion of highly active Mg interestingly inhibited by an algal-symbiotic bacterium Bacillus altitudinis. The corrosion of Mg in the presence of the bacterium drastically reduced by one order of magnitude after 14 days of immersion. This means that the algal-symbiotic bacterium widely available in natural ocean environments may be employed as a green and sustainable inhibitor in the marine industry. Based on electrochemical measurements, surface analyses and microbe experiments, a combined inhibition mechanism is proposed in the paper to interpret the interesting corrosion behavior of Mg

A Homo-Modal Framework Based on Optical Flow and Distance Correlation for Micro-Expression Recognition

Micro-expression&nbsp;recognition&nbsp;(MER) is challenging because extracting locally subtle changes in micro-expressions (MEs) is extremely difficult.&nbsp;Optical&nbsp;flow&nbsp;describes the variations between frames, which can effectively suppress facial identity information while characterizing ME movements well. Thus, several&nbsp;optical&nbsp;flow-based&nbsp;methods have been proposed to recognize MEs. However, these approaches using architectures with&nbsp;one&nbsp;branch&nbsp;for&nbsp;one&nbsp;input or multiple branches&nbsp;for&nbsp;multiple inputs do not reveal discriminative features, which leads to inferior performance. This paper proposes a novel homo-modal&nbsp;framework&nbsp;based&nbsp;on&nbsp;optical&nbsp;flow&nbsp;for&nbsp;the MER problem, termed homo-modal attention refinement network with&nbsp;Distance&nbsp;Correlation&nbsp;(HARN-DC). Concretely, HARN-DC consists of three components, i.e., an expression feature learning module, an expression-dilated feature learning module, and a classification branch. First, two identical structural Inception networks with a channel-wise attention module are designed to learn parallel global and local expression features&nbsp;based&nbsp;on&nbsp;the same ME&rsquo;s&nbsp;optical&nbsp;flow&nbsp;images. Second, to expand ME representations, a dilated loss incorporating&nbsp;Distance&nbsp;Correlation&nbsp;is proposed to amplify the differences between the two branches&rsquo; features. Last, the emotion categories are predicted via a fusion of expression-dilated features in the classification branch. Extensive experiments conducted&nbsp;on&nbsp;the composite database published by MEGC 2019 validate the effectiveness of HARN-DC under leave-one-subject-out (LOSO) cross-validation and composite database evaluation (CDE) protocol. The results indicate that our proposed approach can generate discriminative features and yield promising performance gains. Moreover, the results also show that HARN-DC is competitive with comparable state-of-the-art methods&nbsp;on&nbsp;MER.</p

Evaluation Model for the Scope of DC Interference Generated by Stray Currents in Light Rail Systems

Electrochemical corrosion caused by stray currents reduces the lifespan of buried gas pipelines and the safety of light rail systems. Determining the scope of stray current corrosion will help prevent the corrosion of existing buried pipelines and provide an effective reference for new pipeline siting. In response to this problem, in this paper the surface potential gradient was used to evaluate the scope of stray current corrosion. First, an analytical model for the scope of the stray current corrosion combined with distributed parameters and the electric field generated by a point current source was put forward. Second, exemplary calculations were conducted based on the proposed model. Sensitivity of the potential gradient was analyzed with an example of the transition resistance, and the dynamic distribution of surface potential gradient under different locomotive operation modes was also analyzed in time-domain. Finally, the scope was evaluated at four different intervals with the parameters from the field test to judge whether the protective measures need to be taken in areas with buried pipelines and light rail systems nearby or not

An Elimination Method of Temperature-Induced Linear Birefringence in a Stray Current Sensor

In this work, an elimination method of the temperature-induced linear birefringence (TILB) in a stray current sensor is proposed using the cylindrical spiral fiber (CSF), which produces a large amount of circular birefringence to eliminate the TILB based on geometric rotation effect. First, the differential equations that indicate the polarization evolution of the CSF element are derived, and the output error model is built based on the Jones matrix calculus. Then, an accurate search method is proposed to obtain the key parameters of the CSF, including the length of the cylindrical silica rod and the number of the curve spirals. The optimized results are 302 mm and 11, respectively. Moreover, an effective factor is proposed to analyze the elimination of the TILB, which should be greater than 7.42 to achieve the output error requirement that is not greater than 0.5%. Finally, temperature experiments are conducted to verify the feasibility of the elimination method. The results indicate that the output error caused by the TILB can be controlled less than 0.43% based on this elimination method within the range from −20 °C to 40 °C