Biallelic mutations in valyl-tRNA synthetase gene VARS are associated with a progressive neurodevelopmental epileptic encephalopathy.

Aminoacyl-tRNA synthetases (ARSs) function to transfer amino acids to cognate tRNA molecules, which are required for protein translation. To date, biallelic mutations in 31 ARS genes are known to cause recessive, early-onset severe multi-organ diseases. VARS encodes the only known valine cytoplasmic-localized aminoacyl-tRNA synthetase. Here, we report seven patients from five unrelated families with five different biallelic missense variants in VARS. Subjects present with a range of global developmental delay, epileptic encephalopathy and primary or progressive microcephaly. Longitudinal assessment demonstrates progressive cortical atrophy and white matter volume loss. Variants map to the VARS tRNA binding domain and adjacent to the anticodon domain, and disrupt highly conserved residues. Patient primary cells show intact VARS protein but reduced enzymatic activity, suggesting partial loss of function. The implication of VARS in pediatric neurodegeneration broadens the spectrum of human diseases due to mutations in tRNA synthetase genes

Isolates of Liao Ning Virus from Wild-Caught Mosquitoes in the Xinjiang Province of China in 2005

Liao ning virus (LNV) is related to Banna virus, a known human-pathogen present in south-east Asia. Both viruses belong to the genus Seadornavirus, family Reoviridae. LNV causes lethal haemorrhage in experimentally infected mice. Twenty seven isolates of LNV were made from mosquitoes collected in different locations within the Xinjiang province of north-western China during 2005. These mosquitoes were caught in the accommodation of human patients with febrile manifestations, or in animal barns where sheep represent the main livestock species. The regions where LNV was isolated are affected by seasonal encephalitis, but are free of Japanese encephalitis (JE). Genome segment 10 (Seg-10) (encoding cell-attachment and serotype-determining protein VP10) and Seg-12 (encoding non-structural protein VP12) were sequenced for multiple LNV isolates. Phylogenetic analyses showed a less homogenous Seg-10 gene pool, as compared to segment 12. However, all of these isolates appear to belong to LNV type-1. These data suggest a relatively recent introduction of LNV into Xinjiang province, with substitution rates for LNV Seg-10 and Seg-12, respectively, of 2.29×10−4 and 1.57×10−4 substitutions/nt/year. These substitution rates are similar to those estimated for other dsRNA viruses. Our data indicate that the history of LNV is characterized by a lack of demographic fluctuations. However, a decline in the LNV population in the late 1980s - early 1990s, was indicated by data for both Seg-10 and Seg-12. Data also suggest a beginning of an expansion in the late 1990s as inferred from Seg-12 skyline plot

Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models

Remote sensing estimation of crop nitrogen content allows real-time monitoring of growth to develop scientific methods. However, most of the current remote sensing estimates of crop nitrogen contents have limitations in accurately reflecting the vertical distribution of nutrients in plants. Firstly, the original hyperspectrum is first-order differential (FD), second-order differential (SD), and continuous removal (CR), and the corresponding sensitive bands were screened by correlation analysis in this paper. Then, the spectral reflectance, vegetation indices, and wavelet coefficients were used as input features to construct models for estimating nitrogen content of flag leaf, upper 1 leaf, upper 2 leaf, upper 3 leaf, and upper 4 leaf based on partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR), respectively. The results showed that the accuracy of nitrogen content prediction based on wavelet coefficients was the highest. The combination of MLR and SVM with wavelet coefficients had high accuracy and robustness in the prediction of nitrogen content at different leaf positions. Additionally, the prediction accuracy of nitrogen gradually increased as the leaf position of winter wheat increased. The study can provide technical support for remote sensing estimation of nutrient elements at vertical leaf position of crops. The study can provide a reference for prediction of other crops

Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models

Remote sensing estimation of crop nitrogen content allows real-time monitoring of growth to develop scientific methods. However, most of the current remote sensing estimates of crop nitrogen contents have limitations in accurately reflecting the vertical distribution of nutrients in plants. Firstly, the original hyperspectrum is first-order differential (FD), second-order differential (SD), and continuous removal (CR), and the corresponding sensitive bands were screened by correlation analysis in this paper. Then, the spectral reflectance, vegetation indices, and wavelet coefficients were used as input features to construct models for estimating nitrogen content of flag leaf, upper 1 leaf, upper 2 leaf, upper 3 leaf, and upper 4 leaf based on partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR), respectively. The results showed that the accuracy of nitrogen content prediction based on wavelet coefficients was the highest. The combination of MLR and SVM with wavelet coefficients had high accuracy and robustness in the prediction of nitrogen content at different leaf positions. Additionally, the prediction accuracy of nitrogen gradually increased as the leaf position of winter wheat increased. The study can provide technical support for remote sensing estimation of nutrient elements at vertical leaf position of crops. The study can provide a reference for prediction of other crops

Thermosensitive Microgels-Decorated Magnetic Graphene Oxides for Specific Recognition and Adsorption of Pb(II) from Aqueous Solution

Herein, we report a novel type of smart graphene oxide nanocomposites (MGO@PNB) with excellent magnetism and high thermosensitive ion-recognition selectivity of lead ions (Pb2+). The MGO@PNB are fabricated by immobilizing superparamagnetic Fe3O4 nanoparticles (NPs) and poly­(N-isopropylacrylamide-co-benzo-18-crown-6 acrylamide) thermosensitive microgels (PNB) onto graphene oxide (GO) nanosheets using a simple one-step solvothermal method and mussel-inspired polydopamine chemistry. The PNB are composed of cross-linked poly­(N-isopropylacrylamide) (PNIPAM) chains with numerous appended 18-crown-6 units. The 18-crown-6 units serve as hosts that can selectively recognize and capture Pb2+ from aqueous solution, and the PNIPAM chains act as a microenvironmental actuator for the inclusion constants of 18-crown-6/Pb2+ host–guest complexes. The loaded Fe3O4 NPs endow the MGO@PNB with convenient magnetic separability. The fabricated MGO@PNB demonstrate remarkably high ion-recognition selectivity of Pb2+ among the coexisting metal ions because of the formation of stable 18-crown-6/Pb2+ inclusion complexes. Most interestingly, the MGO@PNB show excellent thermosensitive adsorption ability toward Pb2+ due to the incorporation of PNIPAM functional chains on the GO. Further thermodynamic studies indicate that the adsorption of Pb2+ onto the MGO@PNB is a spontaneous and endothermic process. The adsorption kinetics and isotherm data can be well described by the pseudo-second-order kinetic model and the Langmuir isotherm model, respectively. Most importantly, the Pb2+-loaded MGO@PNB can be more easily regenerated by alternatively washing with hot/cold water than the commonly used regeneration methods. Such multifunctional graphene oxide nanocomposites could be used for specific recognition and removal of Pb2+ from water environment

Effect and Mechanism of Mycobacterium tuberculosis Lipoprotein LpqH in NLRP3 Inflammasome Activation in Mouse Ana-1 Macrophage

The study is aimed at investigating the role and mechanism of LpqH of Mycobacterium tuberculosis in the activation of NLRP3 inflammasome in mouse Ana-1 macrophages. ExPASy-ProtParam, PHYRE2, ABCpred, and SYFPEITHI were used to predict and analyze the physicochemical properties, protein structure, and B cell/T cell-associated epitopes of LpqH protein. The recombinant LpqH protein was purified, and its immunoreactivity was analyzed with western blot. The LPS-treated mouse Ana-1 macrophages were incubated with purified LpqH protein directly. The expression of NLRP3, ASC, and caspase-1 protein was detected by western blot. The secretion of IL-1β was detected by ELISA, and LDH was detected by a kit. Cell death was detected by flow cytometry. LpqH consisted of 159 amino acids and was a hydrophobic protein with stable properties. Its secondary structure contained 47% random coils, 53% β-sheets, and 3% α-helix. The tertiary structure showed a relatively loose spatial conformation. Additionally, it had 8 B cell epitopes (score>0.8) and 10 CTL cell epitopes (score≥20). The recombinant LpqH, which had strong immunoreactivity, significantly increased the levels of NLRP3, ASC, and caspase-1 p20 (P0.05). Meanwhile, LpqH protein did not cause additional cell death (P>0.05). LpqH protein has good immunogenicity and can activate the NLRP3 inflammasome through the potassium efflux pathway without causing cell death

Modulating the Oxygen Reduction Reaction Performance via Precisely Tuned Reactive Sites in Porphyrin-Based Covalent Organic Frameworks

Covalent organic frameworks (COFs) have emerged as promising electrocatalysts due to their controllable architectures, highly exposed molecular active sites, and ordered structures. In this study, a series of porphyrin-based COFs (TAPP-x-COF) with various transition metals (Co, Ni, Fe) were synthesized via a facile post-metallization strategy under solvothermal synthesis. The resulting porphyrin-based COFs showed oxygen reduction reaction (ORR) activity with a trend in Co > Fe > Ni. Among them, TAPP-Co-COF exhibited the best ORR activity (E1/2 = 0.66 V and jL = 4.82 mA cm−2) in alkaline media, which is comparable to those of Pt/C under the same conditions. Furthermore, TAPP-Co-COF was employed as a cathode in a Zn-air battery, demonstrating a high power density of 103.73 mW cm–2 and robust cycling stability. This work presents a simple method for using COFs as a smart platform to fabricate efficient electrocatalysts

Influence of Oxygen Vacancy Behaviors in Cooling Process on Semiconductor Gas Sensors: A Numerical Analysis

The influence of oxygen vacancy behaviors during a cooling process in semiconductor gas sensors is discussed by the numerical analysis method based on the gradient-distributed oxygen vacancy model. A diffusion equation is established to describe the behaviors of oxygen vacancies, which follows the effects of diffusion and exclusion in the cooling process. Numerical analysis is introduced to find the accurate solutions of the diffusion equation. The solutions illustrate the oxygen vacancy distribution profiles, which are dependent on the cooling rate as well as the temperature interval of the cooling process. The gas-sensing characteristics of reduced resistance and response are calculated. Both of them, together with oxygen vacancy distribution, show the grain size effects and the re-annealing effect. It is found that the properties of gas sensors can be controlled or adjusted by the designed cooling process. The proposed model provides a possibility for sensor characteristics simulations, which may be beneficial for the design of gas sensors. A quantitative interpretation on the gas-sensing mechanism of semiconductors has been contributed