Experimental Investigation of Bubble Oscillation and Leaping Driven by Thermocapillary Effects with Non-condensable Gas

Boiling phase-change plays a crucial role in heat transfer as it can dissipate higher heat fluxes than single phase. Bubble nucleation, growth, motion (oscillation or leaping), coalescence and departure, govern the boiling and the heat transfer performance on wires. Many factors affect the bubble mechanisms and interactions taking place, which include the nature of the fluid, experimental conditions, thermocapillary effects, etc. This work investigates the bubble oscillation and leaping phenomena caused by thermocapillary effects in the presence of oxygen and air as non-condensable gases during boiling on a platinum micro-wire. More in particular, the bubble oscillation performance is compared under various bulk temperatures and heat fluxes for two different non-condensable gases. It is observed that for a similar fluid bulk temperature, the lower the heat flux the longer the bubble displacement. Moreover, bubble oscillation phenomenon is influenced by the concentration of non-condensable gas dissolved in the liquid showing larger harmonic periods and shorter waiting times with decrease in the contact line pinning force by approximately 7–44% in the presence of air when compared to nitrogen. Last during oscillations, bubble leaping phenomenon was observed as a consequence of the interaction between the jet flows above the oscillating bubble

Direct radical functionalization of native sugars

Naturally occurring (native) sugars and carbohydrates contain numerous hydroxyl groups of similar reactivity1, 2. Chemists, therefore, rely typically on laborious, multi-step protecting-group strategies3 to convert these renewable feedstocks into reagents (glycosyl donors) to make glycans. The direct transformation of native sugars to complex saccharides remains a notable challenge. Here we describe a photoinduced approach to achieve site- and stereoselective chemical glycosylation from widely available native sugar building blocks, which through homolytic (one-electron) chemistry bypasses unnecessary hydroxyl group masking and manipulation. This process is reminiscent of nature in its regiocontrolled generation of a transient glycosyl donor, followed by radical-based cross-coupling with electrophiles on activation with light. Through selective anomeric functionalization of mono- and oligosaccharides, this protecting-group-free ‘cap and glycosylate’ approach offers straightforward access to a wide array of metabolically robust glycosyl compounds. Owing to its biocompatibility, the method was extended to the direct post-translational glycosylation of proteins

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.Comment: version 2.0; 28 pages, 6 figure

Tissue factor/FVIIa activates Bcl-2 and prevents doxorubicin-induced apoptosis in neuroblastoma cells

<p>Abstract</p> <p>Background</p> <p>Tissue factor (TF) is a transmembrane protein that acts as a receptor for activated coagulation factor VII (FVIIa), initiating the coagulation cascade. Recent studies demonstrate that expression of tumor-derived TF also mediates intracellular signaling relevant to tumor growth and apoptosis. Our present study investigates the possible mechanism by which the interaction between TF and FVIIa regulates chemotherapy resistance in neuroblastoma cell lines.</p> <p>Methods</p> <p>Gene and siRNA transfection was used to enforce TF expression in a TF-negative neuroblastoma cell line and to silence endogenous TF expression in a TF-overexpressing neuroblastoma line, respectively. The expression of TF, Bcl-2, STAT5, and Akt as well as the phosphorylation of STAT5 and Akt in gene transfected cells or cells treated with JAK inhibitor and LY294002 were determined by Western blot assay. Tumor cell growth was determined by a clonogenic assay. Cytotoxic and apoptotic effect of doxorubicin on neuroblastoma cell lines was analyzed by WST assay and annexin-V staining (by flow cytometry) respectively.</p> <p>Results</p> <p>Enforced expression of TF in a TF-negative neuroblastoma cell line in the presence of FVIIa induced upregulation of Bcl-2, leading to resistance to doxorubicin. Conversely, inhibition of endogenous TF expression in a TF-overexpressing neuroblastoma cell line using siRNA resulted in down-regulation of Bcl-2 and sensitization to doxorubicin-induced apoptosis. Additionally, neuroblastoma cells expressing high levels of either endogenous or transfected TF treated with FVIIa readily phosphorylated STAT5 and Akt. Using selective pharmacologic inhibitors, we demonstrated that JAK inhibitor I, but not the PI3K inhibitor LY294002, blocked the TF/FVIIa-induced upregulation of Bcl-2.</p> <p>Conclusion</p> <p>This study shows that in neuroblastoma cell lines overexpressed TF ligated with FVIIa produced upregulation of Bcl-2 expression through the JAK/STAT5 signaling pathway, resulting in resistance to apoptosis. We surmise that this TF-FVIIa pathway may contribute, at least in part, to chemotherapy resistance in neuroblastoma.</p

Thermochemical sulfate reduction in fossil Ordovician deposits of the Majiang area: Evidence from a molecular-marker investigation

The main reservoirs of Majiang fossil deposits consist of the Silurian Wengxiang group, dominantly sandstones, and the Ordovician Honghuayuan formation, dominantly carbonate rocks, and the Lower Cambrian Niutitang Formation mudstones serve as the major source rocks. Thermochemical sulfate reduction (TSR) might have taken place in the Paleozoic marine carbonate oil pools, as indicated by high concentrations of dibenzothiophenes in the extracts (MDBT=0.27-4.32 µg/g extract, and MDBT/MPH= 0.71-1.38). Hydrocarbons in the Pojiaozhai Ordovician carbonate reservoirs have undergone severe TSR and are characterized by higher quantities of diamondoids and MDBT and heavier isotopic values (δ13C=-28.4‰). The very large amounts of dibenzothiophenes might be products of reactions between biphenyls and sulfur species associated with TSR

Optimal Logistics Control of an Omnichannel Supply Chain

This paper aims to find the best way to control logistics in an omnichannel supply chain (OSC). For this purpose, two steps of work were carried out around case-based reasoning (CBR). In the first step, the combined feedback which proved stability was selected to control logistics in the single node, while the variational method and the virtual siphon were combined to determine the optimal control curve. There is a linear part and a nonlinear part in the combined feedback. The new method of storing data mode is &ldquo;data turning to picture&rdquo;. In the second step, image features were extracted by the hybrid method of SURF-GoogLeNet and used for case matching via the grey cloud method. SURF-GoogLeNet was firstly used to update the weight proportion of the defect points in the whole image via the speeded up robust features (SURF) method and secondly to self-extract features using the GoogLeNet method. Finally, the effectiveness of the proposed methods was verified through experiments. The research findings shed new light on the management of supply chains

Social Capital and Sleep Quality in Individuals Who Self-Isolated for 14 Days During the Coronavirus Disease 2019 (COVID-19) Outbreak in January 2020 in China

BACKGROUND: From the end of December 2019, coronavirus disease 2019 (COVID-19) began to spread in central China. Social capital is a measure of social trust, belonging, and participation. This study aimed to investigate the effects of social capital on sleep quality and the mechanisms involved in people who self-isolated at home for 14 days in January 2020 during the COVID-19 epidemic in central China. MATERIAL AND METHODS: Individuals (n=170) who self-isolated at home for 14 days in central China, completed self-reported questionnaires on the third day of isolation. Individual social capital was assessed using the Personal Social Capital Scale 16 (PSCI-16) questionnaire. Anxiety was assessed using the Self-Rating Anxiety Scale (SAS) questionnaire, stress was assessed using the Stanford Acute Stress Reaction (SASR) questionnaire, and sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Path analysis was performed to evaluate the relationships between a dependent variable (social capital) and two or more independent variables, using Pearson’s correlation analysis and structural equation modeling (SEM). RESULTS: Low levels of social capital were associated with increased levels of anxiety and stress, but increased levels of social capital were positively associated with increased quality of sleep. Anxiety was associated with stress and reduced sleep quality, and the combination of anxiety and stress reduced the positive effects of social capital on sleep quality. CONCLUSIONS: During a period of individual self-isolation during the COVID-19 virus epidemic in central China, increased social capital improved sleep quality by reducing anxiety and stress

The Effects of Social Support on Sleep Quality of Medical Staff Treating Patients with Coronavirus Disease 2019 (COVID-19) in January and February 2020 in China

BACKGROUND: Coronavirus disease 2019 (COVID-19), formerly known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 2019 novel coronavirus (2019-nCoV), was first identified in December 2019 in Wuhan City, China. Structural equation modeling (SEM) is a multivariate analysis method to determine the structural relationship between measured variables. This observational study aimed to use SEM to determine the effects of social support on sleep quality and function of medical staff who treated patients with COVID-19 in January and February 2020 in Wuhan, China. MATERIAL AND METHODS: A one-month cross-sectional observational study included 180 medical staff who treated patients with COVID-19 infection. Levels of anxiety, self-efficacy, stress, sleep quality, and social support were measured using the and the Self-Rating Anxiety Scale (SAS), the General Self-Efficacy Scale (GSES), the Stanford Acute Stress Reaction (SASR) questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Social Support Rate Scale (SSRS), respectively. Pearson’s correlation analysis and SEM identified the interactions between these factors. RESULTS: Levels of social support for medical staff were significantly associated with self-efficacy and sleep quality and negatively associated with the degree of anxiety and stress. Levels of anxiety were significantly associated with the levels of stress, which negatively impacted self-efficacy and sleep quality. Anxiety, stress, and self-efficacy were mediating variables associated with social support and sleep quality. CONCLUSIONS: SEM showed that medical staff in China who were treating patients with COVID-19 infection during January and February 2020 had levels of anxiety, stress, and self-efficacy that were dependent on sleep quality and social support