A High-Voltage-Specialized Direct-Current Triboelectric Nanogenerator for Air Purification.

Human health and the environment face significant challenges of air pollution, which is predominantly caused by PM2.5 or PM10 particles. Existing control methods often require elevated energy consumption or bulky high-voltage electrical equipment. To overcome these limitations, a self-powered, convenient, and compact direct current high-voltage triboelectric nanogenerator based on triboelectrification and electrostatic breakdown effects is proposed. By optimizing the structure-design of the direct current triboelectric nanogenerator and corresponding output voltage, it can easily achieve an output voltage of over 3 kV with a high charge density of 320 µC m-2 . A power management circuit is designed to overcome the influence of third domain self-breakdown, optimize 92.5% amplitude of voltage shake, and raise 5% charge utilization ratio. With a device size as tiny as 2.25 cm3 , it can continuously drive carbon nanowires to generate negative ions that settle dust within 300 s. This compact, simple, efficient, and safe high-voltage direct current triboelectric nanogenerator represents a promising sustainable solution. It offers efficient dust mitigation, fostering cleaner environments, and enhancing overall health.

A COMPASS histone H3K4 trimethyltransferase pentamer transactivates drought tolerance and growth/biomass production in Populus trichocarpa.

Histone H3 lysine-4 trimethylation (H3K4me3) activating drought-responsive genes in plants for drought adaptation has long been established, but the underlying regulatory mechanisms are unknown. Here, using yeast two-hybrid, bimolecular fluorescence complementation, biochemical analyses, transient and CRISPR-mediated transgenesis in Populus trichocarpa, we unveiled in this adaptation a regulatory interplay between chromatin regulation and gene transactivation mediated by an epigenetic determinant, a PtrSDG2-1-PtrCOMPASS (complex proteins associated with Set1)-like H3K4me3 complex, PtrSDG2-1-PtrWDR5a-1-PtrRbBP5-1-PtrAsh2-2 (PtrSWRA). Under drought conditions, a transcription factor PtrAREB1-2 interacts with PtrSWRA, forming a PtrSWRA-PtrAREB1-2 pentamer, to recruit PtrSWRA to specific promoter elements of drought-tolerant genes, such as PtrHox2, PtrHox46, and PtrHox52, for depositing H3K4me3 to promote and maintain activated state of such genes for tolerance. CRISPR-edited defects in the pentamer impaired drought tolerance and elevated expression of PtrHox2, PtrHox46, or PtrHox52 improved the tolerance as well as growth in P. trichocarpa. Our findings revealed the identity of the underlying H3K4 trimethyltransferase and its interactive arrangement with the COMPASS for catalysis specificity and efficiency. Furthermore, our study uncovered how the H3K4 trimethyltransferase-COMPASS complex is recruited to the effector genes for elevating H3K4me3 marks for improved drought tolerance and growth/biomass production in plants.

A Rotating Triboelectric Nanogenerator Driven by Bidirectional Swing for Water Wave Energy Harvesting.

Due to the simple installation and convenient maintenance, the floating water wave energy harvesting devices have significant economic advantages. Mass power density is the most important index to evaluate the advancement of floating wave energy harvesting devices. Herein, a self-adaptive rotating triboelectric nanogenerator (SR-TENG) with a compound pendulum and a functional gear-set is provided for wave energy harvesting. First, a compound pendulum structure with a low center of gravity and high moment of inertia is obtained by the geometric design and mechanical study. Besides, compared with the previous triboelectric nanogenerator with one-way clutch, SR-TENG can harvest twice the kinetic energy utilization through the functional gear-set. Importantly, depending on the structure design, the SR-TENG obtains the average mass power density of 45.18 mW kg-1 under low frequency driving conditions, which is about 10 times the reference electromagnetic generator with a similar structure and size. This result shows that the SR-TENG has a significant advantage in small water wave energy harvesting. These findings provide an important example of the floating water wave energy harvesting devices.

Deterministic Effect of pH on Shaping Soil Resistome Revealed by Metagenomic Analysis.

Soil is recognized as the major reservoir of antibiotic resistance genes (ARGs), harboring the most diverse naturally evolved ARGs on the planet. Multidrug resistance genes are a class of ARGs, and their high prevalence in natural soil ecosystems has recently raised concerns. Since most of these genes express proton motive force (PMF) driven efflux pumps, studying whether soil pH is a determinant for the selection of multidrug efflux pump genes and thus shaping the soil resistome are of great interest. In this study, we collected 108 soils with pH values ranging from 4.37 to 9.69 from multiple ecosystems and profiled the composition of ARGs for metagenomes and metagenome-assembled genomes. We observed the multidrug efflux pump genes enriched in the acidic soil resistome, and their abundances have significant soil pH dependence. This reflects the benefits of high soil proton activity on the multidrug efflux pump genes, especially for the PMF-driven inner membrane transferase. In addition, we preliminary indicate the putative microbial participants in pH shaping the soil resistome by applying ecological analyzing tools such as stepwise regression and random forest model fitting. The decisive influence of proton activity on shaping the resistome is more impactful than any other examined factors, and as the consequence, we revisited the influence of edaphic factors on the soil resistome; i.e., the deterministic selection of resistance mechanisms by edaphic factors could lead to the bottom-up shaping of the ARG composition. Such natural developing mechanisms of the resistome are herein suggested to be considered in assessing human-driven ARG transmissions.

Accurate luminance and chromaticity controls of digital colors using CIE-based RGBW algorithms.

Emerging high brightness of color displays and high signal-to-noise ratio of camera sensors require an addition of white (W) subpixels to ordinary red, green, and blue (RGB) subpixels. Conventional algorithms converting RGB signals to RGBW signals suffer from reduced chroma of highly saturated colors and complicated coordinate transformations between RGB color spaces and color spaces defined by the Commission internationale de l'éclairage (CIE). In this work, we developed a complete set of RGBW algorithms to digitally code a color in the CIE-based color spaces, making complicated processes including color space transformations and white balancing become largely unnecessary. The analytic three-dimensional gamut can be obtained so that the maximal hue and luminance of a digital frame could be simultaneously obtained. Exemplary applications in adaptive controls of the colors of an RGB display in accordance with the W component of background light validate our theory. The algorithm opens an avenue toward accurate manipulations of digital colors for RGBW sensors and displays.

Cross-Domain Object Detection by Dual Adaptive Branch.

The object detection task usually assumes that the training and test samples obey the same distribution, and this assumption is not valid in reality, therefore the study of cross-domain object detection is proposed. Compared with image classification, the cross-domain object detection task presents the greater challenge, which requires both accurate classification and localization of samples in the target domain. The teacher-student framework (the student model is supervised by pseudo-labels from the teacher model) has produced a large accuracy improvement in cross-domain object detection. Feature-level adversarial training is used in the student model, which allows features in the source and target domains to share a similar distribution. However, the direction and gradient of the weights can be divided into domain-specific and domain-invariant features, and the purpose of domain adaptive is to focus on the domain-invariant features while eliminating interference from the domain-specific features. Inspired by this, we propose a teacher-student framework named dual adaptive branch (DAB), which uses domain adversarial learning to address the domain distribution. Specifically, we ensure that the student model aligns domain-invariant features and suppresses domain-specific features in this process. We further validate our method based on multiple domains. The experimental results demonstrate that our proposed method significantly improves the performance of cross-domain object detection and achieves the competitive experimental results on common benchmarks.

Adaptive model training strategy for continuous classification of time series.

The classification of time series is essential in many real-world applications like healthcare. The class of a time series is usually labeled at the final time, but more and more time-sensitive applications require classifying time series continuously. For example, the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS). Different from the existing single-shot classification, the key of CCTS is to model multiple distributions simultaneously due to the dynamic evolution of time series. But the deep learning model will encounter intertwined problems of catastrophic forgetting and over-fitting when learning multi-distribution. In this work, we found that the well-designed distribution division and replay strategies in the model training process can help to solve the problems. We propose a novel Adaptive model training strategy for CCTS (ACCTS). Its adaptability represents two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays important samples adaptive to their contribution to the model. Experiments on four real-world datasets show that our method outperforms all baselines.

Population genomics reveals moderate genetic differentiation between populations of endangered Forest Musk Deer located in Shaanxi and Sichuan.

BACKGROUND: Many endangered species exist in small, genetically depauperate, or inbred populations, hence promoting genetic differentiation and reducing long-term population viability. Forest Musk Deer (Moschus berezovskii) has been subject to illegal hunting for hundreds of years due to the medical and commercial values of musk, resulting in a significant decline in population size. However, it is still unclear to what extent the genetic exchange and inbreeding levels are between geographically isolated populations. By using whole-genome data, we reconstructed the demographic history, evaluated genetic diversity, and characterized the population genetic structure of Forest Musk Deer from one wild population in Sichuan Province and two captive populations from two ex-situ centers in Shaanxi Province. RESULTS: SNP calling by GATK resulted in a total of 44,008,662 SNPs. Principal component analysis (PCA), phylogenetic tree (NJ tree), ancestral component analysis (ADMIXTURE) and the ABBA-BABA test separated Sichuan and Shaanxi Forest Musk Deer as two genetic clusters, but no obvious genetic differentiation was observed between the two captive populations. The average pairwise FST value between the populations in Sichuan and Shaanxi ranged from 0.05-0.07, suggesting a low to moderate genetic differentiation. The mean heterozygous SNPs rate was 0.14% (0.11%-0.15%) for Forest Musk Deer at the genomic scale, and varied significantly among three populations (Chi-square = 1.22, p < 0.05, Kruskal-Wallis Test), with the Sichuan population having the lowest (0.11%). The nucleotide diversity of three populations varied significantly (p < 0.05, Kruskal-Wallis Test), with the Sichuan population having the lowest genetic θπ (1.69 × 10-3). CONCLUSIONS: Genetic diversity of Forest Musk Deer was moderate at the genomic scale compared with other endangered species. Genetic differentiation between populations in Sichuan and Shaanxi may not only result from historical biogeographical factors but also be associated with contemporary human disturbances. Our findings provide scientific aid for the conservation and management of Forest Musk Deer. They can extend the proposed measures at the genomic level to apply to other musk deer species worldwide.

Dimerization of PtrMYB074 and PtrWRKY19 mediates transcriptional activation of PtrbHLH186 for secondary xylem development in Populus trichocarpa.

Wood formation is controlled by transcriptional regulatory networks (TRNs) involving regulatory homeostasis determined by combinations of transcription factor (TF)-DNA and TF-TF interactions. Functions of TF-TF interactions in wood formation are still in the early stages of identification. PtrMYB074 is a woody dicot-specific TF in a TRN for wood formation in Populus trichocarpa. Here, using yeast two-hybrid and bimolecular fluorescence complementation, we conducted a genome-wide screening for PtrMYB074 interactors and identified 54 PtrMYB074-TF pairs. Of these pairs, 53 are novel. We focused on the PtrMYB074-PtrWRKY19 pair, the most highly expressed and xylem-specific interactor, and its direct transregulatory target, PtrbHLH186, the xylem-specific one of the pair's only two direct TF target genes. Using transient and CRISPR-mediated transgenesis in P. trichocarpa coupled with chromatin immunoprecipitation and electrophoretic mobility shift assays, we demonstrated that PtrMYB074 is recruited by PtrWRKY19 and that the PtrMYB074-PtrWRKY19 dimers are required to transactive PtrbHLH186. Overexpressing PtrbHLH186 in P. trichocarpa resulted in retarded plant growth, increased guaiacyl lignin, a higher proportion of smaller stem vessels and strong drought-tolerant phenotypes. Knowledge of the PtrMYB074-PtrWRKY19-PtrbHLH186 regulation may help design genetic controls of optimal growth and wood formation to maximize beneficial wood properties while minimizing negative effects on growth.

The AREB1 Transcription Factor Influences Histone Acetylation to Regulate Drought Responses and Tolerance in Populus trichocarpa.

Plants develop tolerance to drought by activating genes with altered levels of epigenetic modifications. Specific transcription factors are involved in this activation, but the molecular connections within the regulatory system are unclear. Here, we analyzed genome-wide acetylated lysine residue 9 of histone H3 (H3K9ac) enrichment and examined its association with transcriptomes in Populus trichocarpa under drought stress. We revealed that abscisic acid-Responsive Element (ABRE) motifs in promoters of the drought-responsive genes PtrNAC006, PtrNAC007, and PtrNAC120 are involved in H3K9ac enhancement and activation of these genes. Overexpressing these PtrNAC genes in P trichocarpa resulted in strong drought-tolerance phenotypes. We showed that the ABRE binding protein PtrAREB1-2 binds to ABRE motifs associated with these PtrNAC genes and recruits the histone acetyltransferase unit ADA2b-GCN5, forming AREB1-ADA2b-GCN5 ternary protein complexes. Moreover, this recruitment enables GCN5-mediated histone acetylation to enhance H3K9ac and enrich RNA polymerase II specifically at these PtrNAC genes for the development of drought tolerance. CRISPR editing or RNA interference-mediated downregulation of any of the ternary members results in highly drought-sensitive P trichocarpa Thus, the combinatorial function of the ternary proteins establishes a coordinated histone acetylation and transcription factor-mediated gene activation for drought response and tolerance in Populus species.

Biofertilizer-induced response to cadmium accumulation in Oryza sativa L. grains involving exogenous organic matter and soil bacterial community structure.

The accumulation of cadmium (Cd) in Oryza sativa L., the world's most significant staple crop, is a health threat to millions of people. The objective of this study was to evaluate the effectiveness of commercially available biofertilizers (with high (BF2) and low organic matter (OM) content (BF1)) on Cd accumulation in two types of soils and to determine the bacterial community responses by high-throughput sequencing. The study was conducted in the form of pot experiment in greenhouse in 2018. Four treatments were set: BF1, BF2, organic fertilizer (OF), and control (CK) and the amendments were applied before the rice cultivation. The results showed that the addition of biofertilizers immobilized or mobilized Cd in soils, depending on the soil type and the OM content in biofertilizers. The exogenous OM in biofertilizers was the driving factor for the difference in pH and Cd accumulation in rice grains. The application of biofertilizers with high OM content was effective in reducing Cd accumulation in the rice grains (19.7% lower than CK) by significantly increasing soil pH (from 6.02 to 6.67) in acid silt loam soil (TZ). The consumption of acid fermentation products by soil chemoorganotrophs and the complexation of organic anions in the biofertilizer treatment tended to buffer the pH drop in the drainage and decrease the Cd availability. However, in the weak acid silty clay loam soil (SX), the addition of biofertilizer with high OM significantly increased Cd accumulation in rice grains (21.9% higher than CK), probably owing to the release of acid substances, resulting from the significant increase of the predominant bacteria Chloroflexi. The addition of biofertilizer with low OM content did not significantly change Cd accumulation in rice grains or affect the soil microbial structures in both soils. In conclusion, the effects of biofertilizer on rice Cd accumulation were related to the OM content and soil bacterial community. Biofertilizers with high organic matter may not be suitable for amendments in the paddy soils with high clay content to reduce Cd accumulation in rice grains.

Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment.

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority.

Predicting auditory feedback control of speech production from subregional shape of subcortical structures.

Although a growing body of research has focused on the cortical sensorimotor mechanisms that support auditory feedback control of speech production, much less is known about the subcortical contributions to this control process. This study examined whether subregional anatomy of subcortical structures assessed by statistical shape analysis is associated with vocal compensations and cortical event-related potentials in response to pitch feedback errors. The results revealed significant negative correlations between the magnitudes of vocal compensations and subregional shape of the right thalamus, between the latencies of vocal compensations and subregional shape of the left caudate and pallidum, and between the latencies of cortical N1 responses and subregional shape of the left putamen. These associations indicate that smaller local volumes of the basal ganglia and thalamus are predictive of slower and larger neurobehavioral responses to vocal pitch errors. Furthermore, increased local volumes of the left hippocampus and right amygdala were predictive of larger vocal compensations, suggesting that there is an interplay between the memory-related subcortical structures and auditory-vocal integration. These results, for the first time, provide evidence for differential associations of subregional morphology of the basal ganglia, thalamus, hippocampus, and amygdala with neurobehavioral processing of vocal pitch errors, suggesting that subregional shape measures of subcortical structures can predict behavioral outcome of auditory-vocal integration and associated neural features. Hum Brain Mapp 39:459-471, 2018. © 2017 Wiley Periodicals, Inc.

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units.

We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.

Neuronal seipin knockout facilitates Aß-induced neuroinflammation and neurotoxicity via reduction of PPARγ in hippocampus of mouse.

BACKGROUND: A characteristic phenotype of congenital generalized lipodystrophy 2 (CGL2) that is caused by loss-of-function of seipin gene is mental retardation. Seipin is highly expressed in hippocampal pyramidal cells and astrocytes. Neuronal knockout of seipin in mice (seipin-KO mice) reduces the hippocampal peroxisome proliferator-activated receptor gamma (PPARγ) level without the loss of pyramidal cells. The down-regulation of PPARγ has gained increasing attention in neuroinflammation of Alzheimer's disease (AD). Thus, the present study focused on exploring the influence of seipin depletion on ß-amyloid (Aß)-induced neuroinflammation and Aß neurotoxicity. METHODS: Adult male seipin-KO mice were treated with a single intracerebroventricular (i.c.v.) injection of Aß25-35 (1.2 nmol/mouse) or Aß1-42 (0.1 nmol/mouse), generally a non-neurotoxic dose in wild-type (WT) mice. Spatial cognitive behaviors were assessed by Morris water maze and Y-maze tests, and hippocampal CA1 pyramidal cells and inflammatory responses were examined. RESULTS: The Aß25-35/1-42 injection in the seipin-KO mice caused approximately 30-35 % death of pyramidal cells and production of Hoechst-positive cells with the impairment of spatial memory. In comparison with the WT mice, the number of astrocytes and microglia in the seipin-KO mice had no significant difference, whereas the levels of IL-6 and TNF-α were slightly increased. Similarly, the Aß25-35/1-42 injection in the seipin-KO mice rather than the WT mice could stimulate the activation of astrocytes or microglia and further elevated the levels of IL-6 and TNF-α. Treatment of the seipin-KO mice with the PPARγ agonist rosiglitazone (rosi) could prevent Aß25-35/1-42-induced neuroinflammation and neurotoxicity, which was blocked by the PPARγ antagonist GW9962. In the seipin-KO mice, the level of glycogen synthase kinase-3ß (GSK3ß) phosphorylation at Tyr216 was elevated, while at Ser9, it was reduced compared to the WT mice, which were corrected by the rosi treatment but were unaffected by the Aß25-35 injection. CONCLUSIONS: Seipin deficiency in astrocytes increases GSK3ß activity and levels of IL-6 and TNF-α through reducing PPARγ, which can facilitate Aß25-35/1-42-induced neuroinflammation to cause the death of neuronal cells and cognitive deficits.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing.

Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.

Reassessing type 2 diabetes in adolescents and its management strategies based on insulin resistance.

With changes in lifestyle behaviors, including dietary structure and habits, the prevalence of Youth-onset Type 2 Diabetes Mellitus (YODM) has increased 2 to 3 times compared to 30 years ago. YODM patients experience complications earlier, progress faster, and exhibit more severe symptoms. However, limited and inconclusive direct evidence, coupled with poor patient compliance, poses challenges in the clinical management of YODM. Apart from the continuous decline in pancreatic ß-cell function and quantity, tissue-specific insulin resistance (IR) is also a typical characteristic of YODM. The main mechanisms of IR in YODM involve different aspects such as obesity, dietary imbalance, abnormal substance metabolism, chronic inflammation, oxidative stress, and hormonal fluctuations during adolescence. For the comprehensive management of YODM, besides achieving good control of blood glucose levels, it may be necessary to apply the most appropriate methods considering the uniqueness of the patient population and the specifics of the disease. Early identification and detection of the disease are crucial. Precise screening of patients with well-functioning pancreatic insulin ß-cells, primarily characterized by IR and obesity, represents the population most likely to achieve diabetes remission or reversal through lifestyle modifications, medications, or even surgical interventions. Additionally, considering potential emotional disorders or the impact of adolescent hormones in these patients, health education for patients and caregivers is essential to make them aware of the long-term benefits of well-controlled blood glucose. In conclusion, adopting comprehensive management measures to achieve diabetes remission or reversal is the ideal goal. Controlling high blood glucose, obesity, and other risk factors related to diabetes complications is the next priority to delay the occurrence and progression of complications. A comprehensive perspective on IR provides insights and references for identifying YODM and its management strategies.

Dynamic interfacial electrostatic energy harvesting via a single wire.

Spontaneously occurred electrostatic breakdown releases enormous energy, but harnessing the energy remains a notable challenge due to its irregularity and instantaneity. Here, we propose a revolutionary method that effectively harvests the energy of dynamic interfacial electrostatic breakdown by simply imbedding a conductive wire (diameter, 25 micrometers) beneath dielectric materials to regulate the originally chaotic and distributed electrostatic energy resulted from contact electrification into aggregation, effectively transforming mechanical energy into electricity. A point-charge physical model is proposed to explain the power generation process and output characteristics, guide structural design, and enhance output performance. Furthermore, a quantified triboelectric series including 72 dielectric material pairs is established for materials choice and optimization. In addition, a high voltage of over 10 kilovolts is achieved using polytetrafluoroethylene and polyethylene terephthalate. This work opens a door for effectively using electrostatic energy, offering promising applications ranging from novel high-voltage power sources, smart clothing, and internet of things.

Insights into growth stages and genotypes in airborne Pb accumulation in Oryza sativa L. grains: Utilizing isotope fingerprinting alongside a model study.

Atmospheric exposure is an important pathway of accumulation of lead (Pb) in Oryza sativa L. grains. In this study, source contributions of soil, early atmospheric exposure, and late atmospheric exposure, along with their bioaccumulation ratios were examined both in the pot and field experiments using stable Pb isotope fingerprinting technology combined with a three-compartment accumulation model. Furthermore, genotype differences in airborne Pb accumulation among four field-grown rice cultivars were investigated using the partial least squares path model (PLS-PM) linking rice Pb accumulation to agronomic traits. The findings revealed that during the late growth period, the air-foliar-grain transfer of Pb was crucial for rice Pb accumulation. Approximately 69-82% of the Pb found in polished rice was contributed by atmospheric source, with more than 80% accumulating during the late growth stage. The air accumulation ratios of rice grains were genotype-specific and estimated to be 0.364-1.062 m3/g during the late growth. Notably, grain size exhibited the highest standardized total effects on the airborne Pb concentrations in the polished rice, followed by leaf Pb and the upward translocation efficiency of Pb. The present study indicates that mitigating the health risks associated with Pb in rice can be achieved by controlling atmospheric Pb levels during the late growth stage and choosing Japonica inbred varieties characterized by large grain size.