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Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics affects health outcomes. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The proposed KRV framework improves statistical power by capturing intrinsic structure within the genetic and microbiome data while reducing the multiple-testing burden. We apply the covariate-adjusted KRV test to the Hispanic Community Health Study/Study of Latinos in a genome-wide association analysis (first gene-level, then variant-level) for microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in previous association studies and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. Our findings highlight the value of the KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition.

This paper investigates the robust consensus problem for heterogeneous second-order multi-agent systems with uncertain parameters. Based on the sliding mode control method, novel robust consensus protocols are designed for the linear multi-agent systems with uncertain parameters and a class of uncertain nonlinear multi-agent systems. Finally, numerical simulations are given to verify the effectiveness of the proposed protocols.

This paper proposes an efficient iterative algorithm for determining the position and velocity of a moving target in di stributed MIMO radar systems. We first formulate the localizatio n problem as a constrained weighted least squares (CWLS) probl em, which is essentially a quadratically constrained quadratic pro gramming (QCQP) problem due to its quadratic equality constrai nts. Then, the QCQP problem is transformed into a linearly const rained quadratic programming (LCQP) problem through approx imating each non-convex constraint as a linear constraint, and it i s solved iteratively based on QR decomposition. The closed-form solution in each iteration for solving the LQCP problem makes th e proposed method have high computational efficiency. The perfo rmance of the proposed method is validated via numerical simulations.

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Most existing strategies for mitigating batch effects rely on approaches designed for genomic analysis, failing to address the zero-inflated and over-dispersed microbiome data. Strategies tailored for microbiome data are restricted to association testing, failing to allow other analytic goals such as visualization. We develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. It is a fundamental advancement in the field because it is the first comprehensive method that accommodates the complex distributions of microbial read counts, and it generates batch-removed zero-inflated read counts that can be used in and benefit all usual subsequent analyses. We apply ConQuR to real microbiome data sets and demonstrate its state-of-the-art performance in removing batch effects while preserving or even amplifying the signals of interest.

BackgroundStaphylococcus aureus is a leading cause of infectious morbidity and mortality in neonates. Few data exist on the association of the nasal microbiome and susceptibility to neonatal S. aureus colonization and infection.MethodsWe performed 2 matched case–control studies (colonization cohort—neonates who did and did not acquire S. aureus colonization; bacteremia cohort—neonates who did [colonized neonates] and did not [controls] acquire S. aureus colonization and neonates with S. aureus bacteremia [bacteremic neonantes]). Neonates in 2 intensive care units were enrolled and matched on week of life at time of colonization or infection. Nasal samples were collected weekly until discharge and cultured for S. aureus, and the nasal microbiome was characterized using 16S rRNA gene sequencing.ResultsIn the colonization cohort, 43 S. aureus–colonized neonates were matched to 82 controls. At 1 week of life, neonates who acquired S. aureus colonization had lower alpha diversity (Wilcoxon rank-sum test P < .05) and differed in beta diversity (omnibus MiRKAT P = .002) even after adjusting for birth weight (P = .01). The bacteremia cohort included 10 neonates, of whom 80% developed bacteremia within 4 weeks of birth and 70% had positive S. aureus cultures within a few days of bacteremia. Neonates with bacteremia had an increased relative abundance of S. aureus sequences and lower alpha diversity measures compared with colonized neonates and controls.ConclusionsThe association of increased S. aureus abundance and decrease of microbiome diversity suggest the need for interventions targeting the nasal microbiome to prevent S. aureus disease in vulnerable neonates.

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

The application of biological nitrogen and phosphorus removal processes in high-altitude areas faces severe challenges due to low temperature, low atmosphere pressure and low oxygen concentration. In this study, a simultaneous nitrification, denitrification and phosphorus removal (SNDPR) system was operated under low atmosphere pressure. The chemical oxygen demand (COD) concentrations in influent were decreased from 300 mg/L (stage I) to 200 mg/L (stage II), corresponding to the low COD concentration of sewage in high-altitude areas. The removal of COD and total phosphate was efficient at the H1 reactor (72 kPa). The removal rates of COD and total phosphate were 94.08% (stage I), 90.66% (stage II) and 98.43% (stage I), 99.34% (stage II), respectively, which were similar to L1 (100 kPa). The removal rates of total inorganic nitrogen and simulation nitrification and denitrification were from 81.21% (stage I) and 59.48% (stage I) to 72.86% (stage II) and 31.95% (stage II), respectively, which were also improved compared to L1. Cycle experiment results indicated that the activity of phosphorus accumulating organisms was enhanced, while the ammonia oxidation process was inhibited under low atmosphere pressure.

Colloidal quantum dot (QD), a solution‐processable nanoscale optoelectronic building block with well‐controlled light absorption and emission properties, has emerged as a promising material system capable of interacting with various photonic structures. Integrated QD/photonic structures have been successfully realized in many optical and optoelectronic devices, enabling enhanced performance and/or new functionalities. In this review, the recent advances in this research area are summarized. In particular, the use of four typical photonic structures, namely, diffraction gratings, resonance cavities, plasmonic structures, and photonic crystals, in modulating the light absorption (e.g., for solar cells and photodetectors) or light emission (e.g., for color converters, lasers, and light emitting diodes) properties of QD‐based devices is discussed. A brief overview of QD‐based passive devices for on‐chip photonic circuit integration is also presented to provide a holistic view on future opportunities for QD/photonic structure‐integrated optoelectronic systems., The recent advances of the integrated quantum dot (QD)/photonic structures in many optoelectronic and optical devices for performance enhancement and new functionalities are summarized in this review. The use of four typical photonic structures applied in either modulating the light absorption or light emission properties of QD‐based devices is discussed, and the innovative QD‐based on‐chip photonic circuit is briefly overviewed.

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

Laser-induced graphene (LIG) has emerged as a promising and versatile method for high-throughput graphene patterning; however, its full potential in creating complex structures and devices for practical applications is yet to be explored. In this study, an in-situ growing LIG process that enables to pattern superhydrophobic fluorine-doped graphene on fluorinated ethylene propylene (FEP)-coated polyimide (PI) is demonstrated. This method leverages on distinct spectral responses of FEP and PI during laser excitation to generate the environment preferentially for LIG formation, eliminating the need for multistep processes and specific atmospheres. The structured and water-repellant structures rendered by the spectral-tuned interfacial LIG process are suitable as the electrode for the construction of a flexible droplet-based electricity generator (DEG), which exhibits high power conversion efficiency, generating a peak power density of 47.5 W m-2 from the impact of a water droplet 105 µL from a height of 25 cm. Importantly, the device exhibits superior cyclability and operational stability under high humidity and various pH conditions. The facile process developed can be extended to realize various functional devices.

Background Staphylococcus aureus is a leading cause of infectious morbidity and mortality in neonates. Few data exist on the association of the nasal microbiome and susceptibility to neonatal S. aureus colonization and infection. Methods We performed 2 matched case–control studies (colonization cohort—neonates who did and did not acquire S. aureus colonization; bacteremia cohort—neonates who did [colonized neonates] and did not [controls] acquire S. aureus colonization and neonates with S. aureus bacteremia [bacteremic neonantes]). Neonates in 2 intensive care units were enrolled and matched on week of life at time of colonization or infection. Nasal samples were collected weekly until discharge and cultured for S. aureus, and the nasal microbiome was characterized using 16S rRNA gene sequencing. Results In the colonization cohort, 43 S. aureus–colonized neonates were matched to 82 controls. At 1 week of life, neonates who acquired S. aureus colonization had lower alpha diversity (Wilcoxon rank-sum test P < .05) and differed in beta diversity (omnibus MiRKAT P = .002) even after adjusting for birth weight (P = .01). The bacteremia cohort included 10 neonates, of whom 80% developed bacteremia within 4 weeks of birth and 70% had positive S. aureus cultures within a few days of bacteremia. Neonates with bacteremia had an increased relative abundance of S. aureus sequences and lower alpha diversity measures compared with colonized neonates and controls. Conclusions The association of increased S. aureus abundance and decrease of microbiome diversity suggest the need for interventions targeting the nasal microbiome to prevent S. aureus disease in vulnerable neonates.

Monolithic integration of nanostructured metalenses with broadband light transmission and good charge transport can simultaneously enhance the sensitivity, speed, and efficiency of photodetectors. The realization of built-in broadband metalenses in perovskite photodetectors, however, has been largely challenged by the limited choice of materials and the difficulty in nanofabrication. Here a new type of broadband-transmitting built-in TiO

Background One mechanism that can explain the link between processed meat consumption and colorectal cancer (CRC) is the production of carcinogenic N-nitroso compounds (NOCs) in the gastrointestinal tract. Oral and gut microbes metabolize ingested proteins (a source of secondary and tertiary amines and amides) and can reduce nitrate to nitrite, generating potentially carcinogenic NOCs. Objective We evaluated whether nitrate/nitrite in processed meat or water influences the fecal or salivary microbiota. Design In this dietary intervention study, 63 volunteers consumed diets high in conventional processed meats for two weeks, switched to diets high in poultry for two weeks, and then consumed phytochemical-enriched conventional processed or low-nitrite processed meat diets for two weeks. During the intervention, they drank water with low nitrate concentrations and consumed a healthy diet with low antioxidants. Then the volunteers drank nitrate-enriched water for 1 week, in combination with one of the four different diets. We measured creatinine-adjusted urinary nitrate levels and characterized the oral and fecal microbiota using 16S rRNA amplicon sequencing. Results Using linear mixed models, we found that, compared to baseline, urinary nitrate levels were reduced during the phytochemical-enriched low-nitrite meat diet (p-value = 0.009) and modestly during the poultry diet (p-value = 0.048). In contrast, urinary nitrate increased after 1-week of drinking nitrate-enriched water (p-value

Exploring highly efficient catalysts for the electrochemical hydrogen evolution reaction (HER) is highly demanded in the sustainable production of hydrogen. MoS2 is recognized as a potential candidate catalyst for HER, but its active site is mainly located at the edges, which is extremely limited. Here, we have investigated the catalytic performance of HER in the MoS2 basal planes during the structural transition from the 2H to the 1T' phase. Different kinds of 2H/1T' structural interfaces are considered, and the adsorbed H free energies (ΔGH) on these surfaces were calculated. The active site for H adsorption is on the top of S atoms at the 2H/1T' phase boundary. The zigzag 2H/1T' interfaces exhibit an optimal performance for the Volmer reaction with the ΔGH being very close to zero. The Volmer-Heyrovsky reaction is dominantly preferred to the Volmer-Tafel reaction. Our study provides a new picture to boost up the active sites of the basal plane for HER on MoS2, and this electrocatalytic mechanism is also applicable for other transition metal dichalcogenide materials.

For many cerebrovascular diseases both blood pressure (BP) and hemodynamic changes are important clinical variables. In this paper, we describe the development of a novel approach to noninvasively and simultaneously monitor cerebral hemodynamics, BP, and other important parameters at high temporal resolution (250 Hz sampling rate). In this approach, cerebral hemodynamics are acquired using near infrared spectroscopy based sensors and algorithms, whereas continuous BP is acquired by superficial temporal artery tonometry with pulse transit time based drift correction. The sensors, monitoring system, and data analysis algorithms used in the prototype for this approach are reported in detail in this paper. Preliminary performance tests demonstrated that we were able to simultaneously and noninvasively record and reveal cerebral hemodynamics and BP during people’s daily activity. As examples, we report dynamic cerebral hemodynamic and BP fluctuations during postural changes and micturition. These preliminary results demonstrate the feasibility of our approach, and its unique power in catching hemodynamics and BP fluctuations during transient symptoms (such as syncope) and revealing the dynamic features of related events.

BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.

Objective: To fight the “silent killer” hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability. Methods: In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR ( n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer ( n = 20) and the arterial line ( n = 4). Results: The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node. Conclusion: The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion. Significance: This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

Na-ion batteries (NIBs) offer a low-cost solution for energy storage applications. However, the cathode materials of NIBs are still limited by their low energy density, exhibiting either small capacity or low operation voltage. In this work we explored a high-voltage cathode material, vanadium fluorophosphate Na3V2(PO4)2O2F. We prepared the material in both nano-size and micro-size by a hydrothermal method, adopting ethylene glycol and water as the solvent, respectively. The morphology and physiochemical properties of the nano-sized and micro-sized Na3V2(PO4)2O2F are systematically characterized and the battery cycling performances are examined in different voltage windows. Interestingly, we find that the battery capacity and rate capability are highly dependent on the cathode morphology while the cyclability of the electrode is mainly affected by the operation voltage window. The nano-sized Na3V2(PO4)2O2F can achieve an energy density of 459 Wh kg−1 with good cyclability within a voltage window of 3–4.5 V while the micro-sized Na3V2(PO4)2O2F delivers a much poorer electrochemical performance due to its coarse morphology. By widening the operation voltage window to 1–4.5 V, an energy density of 660 Wh kg−1 can be obtained from the nano-sized Na3V2(PO4)2O2F electrode at the initial cycles, but it decreases significantly after tens of cycles. We analyzed the rate capability of Na3V2(PO4)2O2F by considering multi Na+ ion intercalation reactions and found the large voltage window induced additional Na+ ion intercalation reaction occurs through a much slower kinetic process as compared to other reactions activated in the narrower voltage window (e.g., 3–4.5 V). The model of the intercalation reactions as well as the possible reasons of the degraded cycling performance in wide operation voltage windows are discussed based on the battery charging-discharging behavior, electrochemical impedance analysis, ex-situ morphology measurements and previously established material models. The findings validate the potential of Na3V2(PO4)2O2F as a high energy density cathode material for NIB, but also call for new crystal structure design of Na3V2(PO4)2O2F to achieve high energy density and good cyclability at the same time.

Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene-environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one-by-one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP-by-SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Thus, in this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function andwe model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. Extensive simulations and real data analysis show that our proposed method has correct type I error and can have higher power than score-based approaches under many situations.

BackgroundWhether human immunodeficiency virus (HIV) infection impacts gut microbial α-diversity is controversial. We reanalyzed raw 16S ribosomal RNA (rRNA) gene sequences and metadata from published studies to examine α-diversity measures between HIV-uninfected (HIV–) and HIV-infected (HIV+) individuals.MethodsWe conducted a systematic review and individual level meta-analysis by searching Embase, Medline, and Scopus for original research studies (inception to 31 December 2017). Included studies reported 16S rRNA gene sequences of fecal samples from HIV+ patients. Raw sequence reads and metadata were obtained from public databases or from study authors. Raw reads were processed through standardized pipelines with use of a high-resolution taxonomic classifier. The χ2 test, paired t tests, and generalized linear mixed models were used to relate α-diversity measures and clinical metadata.ResultsTwenty-two studies were identified with 17 datasets available for analysis, yielding 1032 samples (311 HIV–, 721 HIV+). HIV status was associated with a decrease in measures of α-diversity (P < .001). However, in stratified analysis, HIV status was associated with decreased α-diversity only in women and in men who have sex with women (MSW) but not in men who have sex with men (MSM). In analyses limited to women and MSW, controlling for HIV status, women displayed increased α-diversity compared with MSW.ConclusionsOur study suggests that HIV status, sexual risk category, and gender impact gut microbial community α-diversity. Future studies should consider MSM status in gut microbiome analyses.

Heterovalent doping of halide perovskite nanocrystals (NCs), offering potential tunability in optical and electrical properties, remains a grand challenge. Here, we report for the first time a controlled doping of monovalent Ag+ into CsPbBr3 NCs via a facile room-temperature synthesis method. Our results suggest that Ag+ ions act as substitutional dopants to replace Pb2+ ions in the perovskite NCs, shifting the Fermi level down toward the valence band and in turn inducing a heavy p-type character. Field effect transistors fabricated with Ag+-doped CsPbBr3 NCs exhibit 3 orders of magnitude enhancement in hole mobility at room temperature, compared with undoped CsPbBr3 NCs. Low-temperature electrical studies further confirm the influence of Ag+ doping on the charge-carrier transport. This work demonstrates the tunability of heterovalent doping on the electrical properties of halide perovskite NCs, shedding light on their future applications in versatile optoelectronic devices.

Hg2+ is highly toxic and hazardous and widely found in polluted water. To remove mercury ions from wastewater, there is an urgent need to investigate and develop new adsorbents. Herein, we synthesized three novel thioether-based metal-organic frameworks (MOFs) through a facile diffusion method or a solvothermal strategy, i.e. [(ZnCl2)3(L1)2·χ(solvent)]n (1), [(Cu2I3O2)4(CH4N0.5)4(L1)4(DMA)4·3(H2O)·χ(solvent)]n (2) and [(CuBr2)2(L2)2 CH3CN·χ(solvent)]n (3), where L1 = 1,3,5-tris((pyridin-4-ylthio)methyl)benzene and L2 = 2,4,6-trimethoxy-1,3,5-tris((pyridin-4-ylthio)methyl)benzene. The obtained thioether-based MOFs were characterized by single-crystal X-ray diffraction, Fourier transform infrared spectroscopy, elemental analysis and thermogravimetric analysis. Further studies revealed that they could remove Hg2+ from water. They have high adsorptivity (up to 362 mg g-1) and are highly efficient in removing Hg2+ (up to 95%). Besides, these MOFs can be recycled and can selectively remove Hg2+ from water in the presence of other metal ions. Consequently, these MOFs are highly promising candidates for the selective absorption and removal of mercury ions from water.

Two-dimensional (2D) materials are expected to be utilized as electrodes for alkali metal ion batteries due to their exceptional properties, but the larger size of K ions has been supposed to induce structural collapses and low charge-discharge efficiency. In this work, we propose transition metal dichalcogenide (TMD) materials as the anode electrodes for potassium ion batteries (PIBs). K ions can stably be adsorbed on most of the TMD materials with strong adsorption energies, and the structural phase transition from the 2H phase to the 1T phase can further enhance the K adsorption. It is surprising that, the diffusion barriers for K ions on TMD monolayers are low enough (less than 0.05 eV) to allow K ions to freely migrate. Among the TMD materials that we consider here, both VS2 and TiS2 exhibit extraordinary properties with good electronic conductivity, fast K diffusion, optimal open circuit voltage and high theoretical K storage capacity, which are promising anode materials for K ion batteries.

While valence-pure copper alkynyl nanoclusters show near-infrared TADF, the mixed-valence ones exhibit semiconductivity., We report herein that copper alkynyl nanoclusters show metal-core dependent properties via a charge-transfer mechanism, which enables new understanding of their structure–property relationship. Initially, nanoclusters 1 and 2 bearing respective Cu(i)15 (C1) and Cu(i)28 (C2) cores were prepared and revealed to display near-infrared (NIR) photoluminescence mainly from the mixed alkynyl → Cu(i) ligand-to-metal charge transfer (LMCT) and cluster-centered transition, and they further exhibit thermally activated delayed fluorescence (TADF). Subsequently, a vanadate-induced oxidative approach to in situ generate a nucleating Cu(ii) cation led to assembly of 3 and 4 featuring respective [Cu(ii)O6]@Cu(i)47 (C3) and {[Cu(ii)O4]·[VO4]2}@Cu(i)46 (C4) cores. While interstitial occupancy of Cu(ii) triggers inter-valence charge-transfer (IVCT) from Cu(i) to Cu(ii) to quench the photoluminescence of 3 and 4, such a process facilitates charge mobility to render them semiconductive. Overall, metal-core modification results in an interplay between charge-transfer processes to switch TADF to semiconductivity, which underpins an unusual structure–property correlation for designed synthesis of metal nanoclusters with unique properties and functions.

This paper investigates the moving target localization problem in distributed multiple-input multiple-output (MIMO) radar systems using bistatic range (BR) and bistatic range rate (BRR) measurements, which can be obtained via some local sensor signal processing methods. In the first stage, the cost function of the localization problem is re-formulated by augmenting the unknown vector, and then initial estimates of the target position and velocity are given. In the second stage, the estimation error terms of the first stage are estimated by the weighted least square (WLS) technique. To the end, the target position and velocity are refined by subtracting the estimation errors from the outputs of the first stage. Numerical simulation results corroborate the theoretical developments.

Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (eg, various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at https://github.com/hk1785/mMeta.

Graphical abstract, Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical presentations, ranging from asymptomatic cases to severe pneumonia or even death. In severe COVID-19 cases, an increased level of proinflammatory cytokines has been observed in the bloodstream, forming the so-called “cytokine storm”. Generally, nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) inflammasome activation intensely induces cytokine production as an inflammatory response to viral infection. Therefore, the NLRP3 inflammasome can be a potential target for the treatment of COVID-19. Hence, this review first introduces the canonical NLRP3 inflammasome activation pathway. Second, we review the cellular/molecular mechanisms of NLRP3 inflammasome activation by SARS-CoV-2 infection (e.g., viroporins, ion flux and the complement cascade). Furthermore, we describe the involvement of the NLRP3 inflammasome in the pathogenesis of COVID-19 (e.g., cytokine storm, respiratory manifestations, cardiovascular comorbidity and neurological symptoms). Finally, we also propose several promising inhibitors targeting the NLRP3 inflammasome, cytokine products and neutrophils to provide novel therapeutic strategies for COVID-19.

A transmission-mode high sensitivity quantitative phase microscope (QPM) is developed for profiling transparent thin film structures. The QPM system is implemented with a common-path interferometry design and a high well-depth camera, which has allowed us to achieve an optical path length difference sensitivity of around 50 picometers. A frame averaging method can be used to further improve the sensitivity. To account for multiple interference within thin films, a transmission matrix model is developed to achieve accurate height profile reconstruction. With the correction model, the profiling accuracy can be improved from 20.6% to 4.0% for a MoS2 thin film with a thickness of around 25 nm

Background Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates. Methods As an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy. Results Thorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives. Conclusions We present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis.

Perovskite light emitting diodes suffer from poor operational stability, exhibiting a rapid decay of external quantum efficiency within minutes to hours after turn-on. To address this issue, we explore surface treatment of perovskite films with phenylalkylammonium iodide molecules of varying alkyl chain lengths. Combining experimental characterization and theoretical modelling, we show that these molecules stabilize the perovskite through suppression of iodide ion migration. The stabilization effect is enhanced with increasing chain length due to the stronger binding of the molecules with the perovskite surface, as well as the increased steric hindrance to reconfiguration for accommodating ion migration. The passivation also reduces the surface defects, resulting in a high radiance and delayed roll-off of external quantum efficiency. Using the optimized passivation molecule, phenylpropylammonium iodide, we achieve devices with an efficiency of 17.5%, a radiance of 1282.8 W sr−1 m−2 and a record T50 half-lifetime of 130 h under 100 mA cm−2., Perovskite light emitting diodes suffer from operational stability, showing rapid decay of performance within minutes to hours after turn-on. Here, the authors investigate how the steric and Coulomb interaction of ammonium passivation molecules with varying alkyl chain length can improve device stability by suppressing iodide ion migration.

Hepatocellular carcinoma (HCC) is a common malignant tumor with insidious onset and rapid progression. Its treatment is often difficult owing to tumor resistance. In this study, we aimed to understand the different biological characteristics, gene expression profiles, and drug resistance mechanisms of HCC cells cultured under different conditions. A conventional adherence method and a liquid overlay technique were used to prepare two‐ and three‐dimensional cultures of Bel‐7402 and 5‐fluorouracil (5‐Fu)‐resistant Bel‐7402 (Bel‐7402/5‐Fu) cells. Morphological characteristics were assessed via microscopy, and cell cycle distribution and apoptotic rate were obtained using flow cytometry. Cell sensitivity to different concentrations of drugs was detected with 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assays. Gene expression profiles and signal transduction pathways of Bel‐7402 and Bel‐7402/5‐Fu cells under different culture conditions were determined using gene chips. Cells in three‐dimensional culture were suspended and they grew into dense multicellular spheroid (MCS) structures, aggregating with each other. In contrast to cells in the two‐dimensional culture, cell cycle arrest was observed in MCSs. The sensitivity of Bel‐7402 cells in the two‐dimensional culture to drugs at high concentrations was significantly higher than that of cells in the three‐dimensional culture (p, Biological features, gene expression profile, and mechanisms of drug resistance of two‐ and three‐dimensional hepatocellular carcinoma cell cultures.

Background. As a member of the exon junction complex (EJC), RNA-binding motif protein 8A (RBM8A) plays a crucial role in the maintenance of mRNA and multiple activities of an organism. Immunotherapy has been proven to be a staple type of cancer treatment. However, the role of RBM8A and immunity across cancer types is unclear. Objective. This study aims to visualize the expression, prognosis, mutations, and coexpressed gene results of RBM8A across cancer types and to explore the link between RBM8A expression and immunity. Methods. In this study, data were collected from multiple online databases. We analyzed the data using the HPA, UALCAN Database, COSMIC, cBioPortal, Cancer Regulome tools, Kaplan–Meier Plotter, and TIMER website. Results. For the expression of RBM8A in normal tissues, higher expression of RBM8A was observed in immune-related cells than in nonimmune organs. The expression level of RBM8A was related to tumor type. Missense mutations in RBM8A were found in most tumors and affected the prognosis of carcinomas with coexpressed genes. RBM8A was strongly associated with immune-infiltrating cells and immune checkpoint inhibitors, especially in LIHC. Conclusions. RBM8A is a gene worth exploring and may be a unique immune target in the future.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called ‘Strengthening The Organization and Reporting of Microbiome Studies’ (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.