Combined Hybridization and Evaluation of High-Lysine Rice: Nutritional and Physicochemical Qualities and Field Performance.

Rice, as a major food crop, provides necessary energy and nutrition for humans and livestock. However, its nutritional value is affected by lysine. Using point mutation, we previously obtained AK2 (aspartokinase) and DHDPS1 (dihydrodipicolinate synthase) genes insensitive to lysine feedback inhibition and constructed transgenic lines AK2-52 and DHDPS1-22, which show increased lysine synthesis, as well as Ri-12, which shows decreased lysine degradation by inhibiting rice lysine ketoglutarate reductase/saccharopine dehydrogenase (LKR/SDH) activity. In this study, further transgenic lines were hybridized and evaluated. The lysine content of mature seeds from pyramid lines PRD and PRA increased 32.5- and 29.8-fold, respectively, compared with the wild-type, while the three-gene pyramiding line PRDA had a moderate lysine content. The total lysine, total free lysine, and total protein contents of PRD and PRA also increased and had no obvious impact on the physical and chemical quality, seed appearance, and main agronomic traits. Meanwhile, comparative analysis with polygenic polymeric lines GR containing bacterial AK (lysC) and DHDPS (dapA) genes revealed differences in the way bacterial and endogenous rice AK and DHDPS regulate lysine biosynthesis. These results provide a reference for further evaluation and commercialization of high-lysine transgenic rice.

Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia.

Rapid screening and early treatment of lung infection are essential for effective control of many epidemics such as Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the potential correlation between lung infection and the change of back skin temperature distribution. Based on these findings, we propose to use low-cost, portable and rapid thermal imaging in combination with image-processing algorithms and machine learning analysis for non-invasive and safe detection of pneumonia. The proposed method was tested in 69 subjects (30 normal adults, 11 cases of fever without pneumonia, 19 cases of general pneumonia and 9 cases of COVID-19) where both RGB and thermal images were acquired from the back of each subject. The acquired images were processed automatically in order to extract multiple location and shape features that distinguish normal subjects from pneumonia patients at a high accuracy of 93 % . Furthermore, daily assessment of two pneumonia patients by the proposed method accurately predicted the clinical outcomes, coincident with those of laboratory tests. Our pilot study demonstrated the technical feasibility of portable and intelligent thermal imaging for screening and therapeutic assessment of pneumonia. The method can be potentially implemented in under-resourced regions for more effective control of respiratory epidemics.

Enhanced Strength and Plasticity of CoCrNiAl0.1Si0.1 Medium Entropy Alloy via Deformation Twinning and Microband at Cryogenic Temperature.

The synthesis of lightweight yet strong-ductile materials has been an imperative challenge in alloy design. In this study, the CoCrNi-based medium-entropy alloys (MEAs) with added Al and Si were manufactured by vacuum arc melting furnace subsequently followed by cool rolling and anneal process. The mechanical responses of CoCrNiAl0.1Si0.1 MEAs under quasi-static (1 × 10-3 s-1) tensile strength showed that MEAs had an outstanding balance of yield strength, ultimate tensile strength, and elongation. The yield strength, ultimate tensile strength, and elongation were increased from 480 MPa, 900 MPa, and 58% at 298 K to 700 MPa, 1250 MPa, and 72% at 77 K, respectively. Temperature dependencies of the yield strength and strain hardening were investigated to understand the excellent mechanical performance, considering the contribution of lattice distortions, deformation twins, and microbands. Severe lattice distortions were determined to play a predominant role in the temperature-dependent yield stress. The Peierls barrier height increased with decreasing temperature, owing to thermal vibrations causing the effective width of a dislocation core to decrease. Through the thermodynamic formula, the stacking fault energies were calculated to be 14.12 mJ/m2 and 8.32 mJ/m2 at 298 K and 77 K, respectively. In conclusion, the enhanced strength and ductility at cryogenic temperature can be attributed to multiple deformation mechanisms including dislocations, extensive deformation twins, and microbands. The synergistic effect of multiple deformation mechanisms lead to the outstanding mechanical properties of the alloy at room and cryogenic temperature.

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology.

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlin-ear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)- based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial re-gression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

Extreme Antiscaling Performance of Slippery Omniphobic Covalently Attached Liquids.

Scale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates. Although lubricant-based surfaces are promising candidates for antiscaling, lubricant drainage inhibits their utilization. Here, we develop methodologies to deposit slippery omniphobic covalently attached liquids (SOCAL) on arbitrary substrates. Similar to lubricant-based surfaces, SOCAL has ultralow roughness and surface energy, enabling low nucleation rates and eliminating the need to replenish the lubricant. To enable SOCAL coating on metals, we investigated the surface chemistry required to ensure high-quality functionalization as measured by ultralow contact angle hysteresis (<3°). Using a multilayer deposition approach, we first electrophoretically deposit (EPD) silicon dioxide (SiO2) as an intermediate layer between the metallic substrate and SOCAL. The necessity of EPD SiO2 is to smooth (<10 nm roughness) as well as to enable the proper surface chemistry for SOCAL bonding. To characterize antiscaling performance, we utilized calcium sulfate (CaSO4) scale tests, showing a 20× reduction in scale deposition rate than untreated metallic substrates. Descaling tests revealed that SOCAL dramatically decreases scale adhesion, resulting in rapid removal of scale buildup. Our work not only demonstrates a robust methodology for depositing antiscaling SOCAL coatings on metals but also develops design guidelines for the creation of antifouling coatings for alternate applications such as biofouling and high-temperature coking.