Current Chinese Science - Current Issue

Introduction: A delayed detached eddy simulation in Open FOAM was performed to study flow separation of a circular cylinder with roundness imperfection up to 4% of its diameter at Reynolds numbers of 100, 3900, and 10⁴ in normal flow. Methods: The flow was considered to be Newtonian and incompressible. The separation position was determined independently based on surface pressure distribution and skin friction. Results: Results show that the patterns of these distributions depend on both Reynolds number and roundness imperfection level, and flow separation in an imperfectly round cylinder may be induced by either an adverse pressure gradient or a Gentle Bend (GB) introduced by the roughness. For the separation point determined by the pressure-based method, its accuracy can be affected by the characteristic of pressure distribution near the separation point at low Reynolds numbers, and, thus, its physical validity needs to be verified by flow visualization at high Reynolds numbers. Conclusion: The skin friction-based method can accurately predict separation point for both perfectly and imperfectly round cylinders without additional information. When the roundness imperfection ratio reaches 2% and the Reynolds number reaches 3900, both approaches indicate that the flow separation point converges to the location of GB on the cylinder surface and the two sets of predicted separation points agree well.

In recent years, long non-coding RNAs (lncRNAs) have played important roles in various biological processes. Mutations and regulation of lncRNAs are closely associated with many human cancers. Predicting potential lncRNA-cancer associations helps to understand cancer's pathogenesis and provides new ideas and approaches for cancer prevention, treatment and diagnosis. Predicting lncRNA-cancer associations based on computational methods helps systematic biological studies. In particular, machine learning methods have received much attention and are commonly used to solve these problems. Therefore, many machine learning computational models have been proposed to improve the prediction performance and achieve accurate diagnosis and effective treatment of cancer. This review provides an overview of existing models for predicting lncRNA-cancer associations by machine learning methods. The evaluation metrics of each model are briefly described, analyzed the advantages and limitations of these models are analyzed. We also provide a case study summary of the two cancers listed. Finally, the challenges and future trends of predicting lncRNA-cancer associations with machine learning methods are discussed.

Background: Hilsa shad (Tenualosa ilisha) is a popular anadromous fish in Bangladesh known to cause allergies. Despite recognized allergenicity, there is a paucity of research at the molecular level on hilsa allergen. Methods: Muscle transcriptomes of hilsa from freshwater, brackish, and deep sea habitats were sequenced using Illumina NovaSeq 6000 and assembled. BLASTx analysis of the Allergen Online database identified potential allergens. The molecular docking study investigated parvalbumin's interaction with human IgE. Results: An analysis of hilsa muscle transcriptomes revealed 28 known fish allergens, including parvalbumin, tropomyosin, including parvalbumin, tropomyosin, filamin C, creatine kinase-2, aldolase A, triosephosphate isomerase B, and Glyceraldehyde-3-phosphate Dehydrogenase (G3PD). Creatine kinase showed significantly higher abundance (p < 0.05) and habitat variation (freshwater vs. brackish water). In silico analysis suggested upregulation of Sal s 2 enolase and Equ c 6 lysozyme in freshwater and brackish water compared to the deep sea. Docking studies identified a potential B-cell epitope in parvalbumin that interacts with human IgE. Conclusion: This study has unveiled 28 potential allergens in hilsa, including habitat-specific variations. The parvalbumin-IgE interaction has been suggested as a mechanism for allergies. The findings have illuminated fish allergy in Bangladesh and paved the way for further investigation.

Introduction: Enhancing the photoelectrochemical response of TiO2 nanotube arrays (TNA) is crucial to improve the efficiency of solar energy utilization. In this work, TNA was prepared electrochemically by anodization at single voltages of 20 V, 30 V and 40 V as well as a special two-step voltage of 30 V-20 V, 30 V-40 V, respectively. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the morphology and crystalline structure of the sample. Methods: The photoelectrochemical response was measured by electrochemical potentiostatic technique. The results show TNA evenly aligns with increasing the anodization voltage. Results: However, there is TiO2 that does not form TNA and is dispersed as fragments on TNA surface at a higher voltage, which adversely affects TNA's photoelectrochemical properties. Conclusion: During the process of anodization, the oxidation current changes due to the switch in voltage. A two-step voltage method enhances pure TNA's photoelectrochemical response to visible light.