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The development and utilization of perovskites are beneficial to improve or even change the optical properties of devices and obtain fascinating performances such as higher photoelectric conversion efficiency, better thermal stability, higher external quantum efficiency, more excellent remodeling, and flexibility. So, there are many articles on perovskite reviews having been reported from synthesis, properties to various applications (such as optoelectronic devices, electrical memristor, etc.). Based on the reported review of perovskites, this study will make a further supplement to the research progress of perovskites in visible light communication (VLC), optical neuromorphic devices, and highlight huge development prospects in these emerging fields in recent years. First, we briefly reviewed the preparation methods of common perovskite materials, followed by the optical and electrical characteristics. Then, the specific applications of optical properties based on perovskite materials are emphatically investigated, in addition to traditional photovoltaic devices, especially the latest cutting-edge fields of information encryption and decryption, VLC as well as optical memristive devices for photonic synapse and photonic neuromorphic computing. Finally, the main conclusions and prospects are given. Perovskite-based optical memristive devices are enabled to assist photonic neuromorphic calculations, showing huge potential application prospects in intelligent integrated chip fusing sensing, storage, and computing.

Verticillium wilt, caused by the soilborne fungus Verticillium dahliae, poses a serious threat to the health of more than 200 plant species worldwide. Although plant rhizosphere‐associated microbiota can influence plant resistance to V. dahliae, empirical evidence underlying Verticillium wilt resistance of perennial trees is scarce. In this study, we systemically investigated the effect of the soil microbiota on the resistance of smoke trees (Cotinus coggygria) to Verticillium wilt using field, greenhouse and laboratory experiments. Comparative analysis of the soil microbiota in the two stands of smoke trees suggested that Bacillus represented the most abundant and key microbial genus related to potential disease suppression. Smoke tree seedlings were inoculated with isolated Bacillus strains, which exhibited disease suppressiveness and plant growth‐promoting properties. Furthermore, repletion of Bacillus agents to disease conducive soil significantly resulted in reduced incidence of smoke tree wilt and increased resistance of the soil microbiota to V. dahliae. Finally, we explored a more effective combination of Bacillus agents with the fungicide propiconazole to combat Verticillium wilt. The results establish a foundation for the development of an effective control for this disease. Overall, this work provides a direct link between Bacillus enrichment and disease resistance of smoke trees, facilitating the development of green control strategies and measurements of soil‐borne diseases. Summary Statement: Bacillus was the key microbial genus responsible for Verticillium wilt disease suppression and plant growth‐promoting and the combination of B. subtilis with a fungicide represented a promising effective control to combat Verticillium wilt. [ABSTRACT FROM AUTHOR]

Detecting circulating tumor cells has exhibited great significance in treating cancers since its concentration is an index strongly associated with the development and transfer of the tumor. However, the present commercial method for CTC detection is still expensive, because special antibodies and complicated devices must be used for cell separation and imaging. Hence, it is quite necessary to apply alternative materials and methods to decrease the cost of CTC detection. In this article, we coated a cellulose acetate membrane with nanoparticles formed by the polymerization of melamine and furfural, creating a surface with nanoscale roughness for the highly efficient capture of the sparse CTCs in a blood sample. Subsequently, the CTCs on the surface can be quantitatively detected by colorimetry with the aid of a COF-based nanozyme. The detection limit (LOD) can be as low as 3 cells/mL, which is the lowest LOD among the colorimetric methods to our knowledge. Considering the low cost of fabricating the membrane for CTC capture and the robustness of nanozymes compared with natural enzymes, this CTC detection approach displays great potential to decrease the financial burden of commercial CTC detection. [ABSTRACT FROM AUTHOR]

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations. [ABSTRACT FROM AUTHOR]

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes. [ABSTRACT FROM AUTHOR]

Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention. [ABSTRACT FROM AUTHOR]

Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by analyzing the data from a recent landmark brain development and child health study. We propose a nonparametric, Bayesian supervised heterogeneity analysis to uncover neurodevelopmental subtypes with distinct effect mechanisms. We impose stochastic block structures to identify network-based functional phenotypes and develop a variational expectation-maximization algorithm to facilitate an efficient posterior computation. Through integrating resting-state and task-related functional connectomes, we dissect heterogeneous effect mechanisms on children's fluid intelligence from the functional network phenotypes, including Fronto-parietal Network and Default Mode Network, under different cognitive states. Based on extensive simulations, we further confirm the superior performance of our method on uncovering brain-to-behavior relationships. for this article are available online including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Risk for childhood asthma is conferred by alleles within the 17q21 locus affecting ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3) expression. ORMDL3 inhibits sphingolipid de novo synthesis. Although the effects of 17q21 genotypes on sphingolipid synthesis in human asthma remain unclear, both decreased sphingolipid synthesis and ORMDL3 overexpression are linked to airway hyperreactivity. To characterize the relationship of genetic asthma susceptibility with sphingolipid synthesis, we analyzed asthma-associated 17q21 genotypes (rs7216389, rs8076131, rs4065275, rs12603332, and rs8067378) in both children with asthma and those without asthma, quantified plasma and whole-blood sphingolipids, and assessed sphingolipid de novo synthesis in peripheral blood cells by measuring the incorporation of stable isotopelabeled serine (substrate) into sphinganine and sphinganine-1-phosphate. Whole-blood dihydroceramides and ceramides were decreased in subjects with the 17q21 asthma-risk alleles rs7216389 and rs8076131. Children with nonallergic asthma had lower dihydroceramides, ceramides, and sphingomyelins than did controls. Children with allergic asthma had higher dihydroceramides, ceramides, and sphingomyelins compared with children with nonallergic asthma. Additionally, de novo sphingolipid synthesis was lower in children with asthma compared with controls. These findings connect genetic 17q21 variations that are associated with asthma risk and higher ORMDL3 expression to lower sphingolipid synthesis in humans. Altered sphingolipid synthesis may therefore be a critical factor in asthma pathogenesis and may guide the development of future therapeutics., Introduction Asthma in children is characterized by airway hyperreactivity and a strong genetic predisposition. The asthma-associated susceptibility locus 17q21 is strongly linked to childhood-onset asthma (1), and asthma-associated genotypes in [...]

Smoke tree (Cotinus coggygria) is an important ornamental tree that represents the autumnal landscape of red leaves in Northern China, especially in Beijing. However, Verticillium wilt, caused by the fungus (Verticillium dahliae), has resulted in a high mortality rate for smoke trees, posing a serious threat to the highly valued landscape of red leaves in Beijing. To explore an efficient control measure for Verticillium wilt, we systematically analyzed the applicability and efficacy of multiple treatments for three consecutive years in Xiangshan Park and Badaling Forest Park. From 2021 to 2023, diseased smoke trees in Xiangshan Park were subjected to three application methods (agent irrigation, trunk injection, or a combination of the two) and five candidate agents, namely Bacillus subtilis, azoxystrobin, propiconazole, carbendazim, and prochloraz. Analyses of the data for three consecutive years revealed a decreasing trend in the annual disease incidence rate. Specifically, the combined application of agent irrigation and trunk injection exhibited the highest control effect and a significant improvement in the landscape of red leaves in Beijing. Furthermore, the combination of propiconazole via irrigation plus the trunk injection of carbendazim and prochloraz had the greatest control effect. These suppressive measurements were further used and demonstrated to be effective in Badaling Forest Park. Overall, our study provides an effective disease management means for controlling Verticillium wilt in smoke trees. [ABSTRACT FROM AUTHOR]

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., a set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicate that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H 1 homological distance between rest and motor task was observed at both the whole-brain and sub-circuit consolidated levels, which suggested the self-similarity property of human brain functional connectivity unraveled by a homological kernel. Furthermore, at the whole-brain level, the rest–task differentiation was found to be most prominent between rest and different tasks at different homological orders: (i) Emotion task ( H 0 ), (ii) Motor task ( H 1 ), and (iii) Working memory task ( H 2 ). At the functional sub-circuit level, the rest–task functional dichotomy of the default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both the task and subject domains, which paves the way for subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study the non-localized coordination patterns of localized structures stretching across complex network fibers. [ABSTRACT FROM AUTHOR]