Identification of a peripheral blood long non-coding RNA (Upperhand) as a potential diagnostic marker of coronary artery disease

Background: Long non-coding RNAs (lncRNAs) have been confirmed to be involved in the pathologi­cal processes of multiple diseases. However, the characteristic expression of lncRNAs in peripheral blood of coronary artery disease (CAD) patients and whether some of these lncRNAs can be used as diagnostic biomarkers for CAD requires further investigation. Methods: Six healthy and CAD individuals were selected for microarray analysis, and 5 differentially expressed lncRNAs were selected and confirmed in the second cohort consisting of 30 control individu­als and 30 CAD patients with different SYNTAX scores. Upperhand were verified in the third cohort consisting of 115 controls and 137 CAD patients. Results: Thirty one lncRNAs were differentially expressed between the two groups, among whom, 25 were upregulated in the CAD group and 6 were downregulated. Four of the selected five lncRNAs were significantly upregulated in the CAD group, and Upperhand had the largest area under the curve (AUC). The diagnostic value of Upperhand was tested further, and it remained having a high diagnostic value. Conclusions: The expression level of Upperhand in peripheral blood of CAD patients is significantly higher than in control individuals, and is correlated with severity of CAD. Upperhand is a potential diagnostic biomarker of CAD, and when combined with TCONS_00029157, diagnostic value slightly increased

On the Origin of Tibetans and Their Genetic Basis in Adapting High-Altitude Environments

Since their arrival in the Tibetan Plateau during the Neolithic Age, Tibetans have been well-adapted to extreme environmental conditions and possess genetic variation that reflect their living environment and migratory history. To investigate the origin of Tibetans and the genetic basis of adaptation in a rigorous environment, we genotyped 30 Tibetan individuals with more than one million SNP markers. Our findings suggested that Tibetans, together with the Yi people, were descendants of Tibeto-Burmans who diverged from ancient settlers of East Asia. The valleys of the Hengduan Mountain range may be a major migration route. We also identified a set of positively-selected genes that belong to functional classes of the embryonic, female gonad, and blood vessel developments, as well as response to hypoxia. Most of these genes were highly correlated with population-specific and beneficial phenotypes, such as high infant survival rate and the absence of chronic mountain sickness

Experimental and Numerical Studies of Cloud Cavitation Behavior around a Reversible S-Shaped Hydrofoil

The S-shaped hydrofoil is often used in the design of reversible machinery due to its centrally symmetrical camber line. The objective of this paper is to study the influence of cloud cavitation on the flow structure and the unsteady characteristics of lift and drag around an S-shaped hydrofoil via experimental tests and numerical simulations. In the experimental component, the tests were carried out in a cavitation tunnel and a high-speed camera was used to record the cavitation details around the S-shaped hydrofoil with different cavitation numbers. The experimental results show that sheet cavitation gradually transforms into cloud cavitation with a decrease in the inlet cavitation number, the maximum cavity length increases faster after the occurrence of cloud cavitation, and the shedding cycle time of cloud cavitation gradually increases with a decrease in the inlet cavitation number. In the numerical component, the numerical results are in good agreement with the experimental data. The numerical results show that the movement of the re-entrant jet is the main factor for the formation of the cloud cavitation around the S-shaped hydrofoil. The shedding cloud cavity induces the U-shaped vortex structure around the S-shaped hydrofoil, and it produces a higher vorticity distribution around the cavity. The periodic motion of cloud cavity causes the unsteady fluctuation of the lift–drag coefficient of the S-shaped hydrofoil, and because of the unique pressure distribution characteristics of the S-shaped hydrofoil, the lift and drag coefficient appeared as two peaks in one typical cycle of cloud cavitation

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces `blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS.Comment: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 202

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

The Analysis of Cavitation Flow and Pressure Pulsation of Bi-Directional Pump

A bi-directional pump is designed by using S-shaped hydrofoil, is the most convenient way to achieve bi-directional operation. In this paper, high-speed photography is used to visualize the flow field characteristics of the bidirectional pump under different cavitation numbers, and the flow field changes caused by cavitation are quantitatively analyzed in combination with the pressure pulsation sensor. The results show that the operation efficiency of the bidirectional pump in reverse operation is lower than that in forward operation. Tip clearance cavitation occurs on both suction and pressure surfaces of the impeller under reverse operation and large flow. In reverse operation, the influence of guide vane on the main frequency of pressure pulsation in the impeller is obvious. The quasi-periodic vertical cavitation flow phenomenon increases the amplitude of pressure pulsation in the impeller and becomes the main component of the internal flow in the bidirectional axial flow pump

The “L-Sandwich” Strategy for True Coronary Bifurcation Lesions: A Randomized Clinical Trial

Background. This study explored the efficacy of the “L-sandwich” strategy, which involves the implantation of stents in the main vessel (MV) and shaft of the side branch (SB) with a drug-coated balloon (DCB) applied to the SB ostium, for coronary true bifurcation lesions. Methods and Results. Of 99 patients with true bifurcation lesions, 38 patients underwent the “L-sandwich” strategy (group A), 32 patients underwent a two-stent strategy (group B), and 29 patients underwent a single-stent + DCB strategy (group C). Angiography outcomes (late lumen loss [LLL], minimum lumen diameter [MLD]), and clinical outcomes (major adverse cardiac events [MACEs]) were analyzed. At 6 months, the MLD of the SB ostium in groups A and B were similar (P>0.05) and group A larger than group C (P0.05), and patients in the other groups had no MACEs. Conclusions. The “L-sandwich” strategy was feasible for the treatment of true coronary bifurcation lesions. It is a simpler procedure with similar acute lumen gain than the two-stent strategy, results in a larger SB lumen than the single-stent + DCB strategy, and it can also be used as a remedy for dissection following the single-stent + DCB strategy