Augmented photocatalysis induced by 1T-MoS2 bridged 2D/2D MgIn2S4@1T/2H-MoS2 Z-scheme heterojunction: mechanistic insights into H2O2 and H2 evolution.

In the realm of composite photocatalysts, the fusion of the co-catalyst effect with interfacial engineering is recognized as a potent strategy for facilitating the segregation and migration of photo-induced charge carriers. Herein, an innovative mediator-based Z-scheme hybrid, i.e. MIS@1T/2H-MoS2, has been well designed by pairing MIS with 1T/2H-MoS2via a facile hydrothermal strategy as a competent photocatalyst for H2O2 and H2 generation. The co-catalyst, i.e. metallic 1T-phase bridging between semiconducting 2H-MoS2 and MIS, serves as a solid state electron mediator in the heterostructure. Morphological findings revealed the growth of 1T/2H-MoS2 nanoflowers over MIS microflowers, verifying the close interaction between MIS and 1T/2H-MoS2. By virtue of accelerated e-/h+ pair separation and migration efficiency along with a proliferated density of active sites, the MMoS2-30 photocatalyst yields an optimum H2O2 of 35 µmol h-1 and H2 of 370 µmol h-1 (ACE of 5.9%), which is 3 and 2.7 fold higher than pristine MIS. This obvious enhancement can be attributed to photoluminescence and electrochemical aspects that substantiate the diminished charge transfer resistance along with improved charge carrier separation, representing a good example of a noble metal-free photocatalyst. The proposed Z-scheme charge transfer mechanism is aided by time-resolved photoluminescence (TRPL), XPS, radical trapping experiments, and EPR analysis. Overall, this endeavour provides advanced insights into the architecture of noble metal-free Z-scheme heterostructures, offering promising prospects in photocatalytic applications.

ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets.

Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of p values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.

Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks.

A key aspect of how the brain learns and enables decision-making processes is through synaptic interactions. Electrical transmission and communication in a network of synapses are modulated by extracellular fields generated by ionic chemical gradients. Emulating such spatial interactions in synthetic networks can be of potential use for neuromorphic learning and the hardware implementation of artificial intelligence. Here, we demonstrate that in a network of hydrogen-doped perovskite nickelate devices, electric bias across a single junction can tune the coupling strength between the neighboring cells. Electrical transport measurements and spatially resolved diffraction and nanoprobe X-ray and scanning microwave impedance spectroscopic studies suggest that graded proton distribution in the inhomogeneous medium of hydrogen-doped nickelate film enables this behavior. We further demonstrate signal integration through the coupling of various junctions.

Low-energy quasi-circular electron correlations with charge order wavelength in Bi2Sr2CaCu2O8+δ.

Most resonant inelastic x-ray scattering (RIXS) studies of dynamic charge order correlations in the cuprates have focused on the high-symmetry directions of the copper oxide plane. However, scattering along other in-plane directions should not be ignored as it may help understand, for example, the origin of charge order correlations or the isotropic scattering resulting in strange metal behavior. Our RIXS experiments reveal dynamic charge correlations over the qx-qy scattering plane in underdoped Bi2Sr2CaCu2O8+δ. Tracking the softening of the RIXS-measured bond-stretching phonon, we show that these dynamic correlations exist at energies below approximately 70 meV and are centered around a quasi-circular manifold in the qx-qy scattering plane with radius equal to the magnitude of the charge order wave vector, qCO. This phonon-tracking procedure also allows us to rule out fluctuations of short-range directional charge order (i.e., centered around [qx = ±qCO, qy = 0] and [qx = 0, qy = ±qCO]) as the origin of the observed correlations.

Nanoarchitecture of a Ti3C2@TiO2 Hybrid for Photocatalytic Antibiotic Degradation and Hydrogen Evolution: Stability, Kinetics, and Mechanistic Insights.

Designing of a visible-light-driven semiconductor-based heterojunction with suitable band alignment and well-defined interfacial contact is considered to be an effective strategy for the transformation of solar-to-chemical energy and environmental remediation. In this context, MXenes have received tremendous attention in the research community due to their merits of abundant derivatives, elemental composition, excellent metallic conductivity, and surface termination groups. Meanwhile, a facile synthetic strategy for MXene-derived TiO2 nanocomposites with stable framework and higher photocatalytic activity under visible-light irradiation still remains a challenge for researchers. Herein, we report a novel synthetic strategy of preparing a two-dimensional Ti3C2@TiO2 nanohybrid by a facile reflux method under acidic conditions. In this oxidation reaction, protonation of the hydroxyl terminal group of MXene creates Ti more electrophilic and susceptible to an oxidative nucleophilic addition reaction with the presence of both water and oxygen. The physicochemical properties of the nanohybrid Ti3C2@TiO2 were verified by varieties of characterization techniques. High-resolution transmission electron microscopy and X-ray photoelectron spectroscopy analysis specifically elucidated the intimate interfacial interaction between Ti3C2 and TiO2. The optimized Ti3C2@TiO2-48 h photocatalyst exhibited the highest tetracycline hydrochloride (TCH, 90% in 90 min) degradation efficiency in comparison to pristine TiO2 with a rate constant (k) of 0.02463 min-1. The major contribution of •O2- and •OH radicals throughout photocatalytic TCH degradation was confirmed by the trapping experiment. Moreover, the photocatalyst showed the highest hydrogen generation rate of 140.8 µmol h-1 along with an apparent conversion efficiency of 2.2%. The excellent photocatalytic activity of Ti3C2@TiO2 originated from the superior electrical conductivity of cocatalyst Ti3C2, which facilitated spatial photogenerated e-/h+ separation and transfer at the Ti3C2 MXene@TiO2 interface. Overall, this research work will describe a promising protocol of designing MXene-derived photocatalysts toward efficient environmental remediation and wastewater treatment applications.

Architecture and Kinetic Studies of Photocatalytic H2O2 Generation and H2 Evolution through Regulation of Spatial Charge Transfer via Z-Scheme Path over a (001) Facet Engineered TiO2@MXene/B-g-C3N4 Ternary Hybrid.

Spatial charge separation and migration are the critical shortcomings dominating the core energy conversion corridors of photocatalytic systems. Here, a biomimetic multi-interfacial architecture providing strong coupled interaction and rapid charge transmission for photostable and competent photocatalytic H2O2 production and H2 evolution is proposed. The triple-hybrid all-solid-state Z-scheme system was formed with the (001) facet exposed TiO2 nanosheets derived from MXene layers and B-g-C3N4 nanosheets (M/(001)TiO2@BCN) through an electrostatic self-assembly strategy with intimate electronic interaction due to Ti orbital modulation and proper stacking among the hybrids. The metallic and highly conductive MXene layers act as solid state electron mediators in the Z-scheme heterojunction that promote electron-hole separation and migration efficiency. Specifically, the MTBCN-12.5 composite provides optimum yield of H2O2 up to 1480.1 µmol h-1 g-1 and a H2 evolution rate of 408.4 µmol h-1 (with ACE 6.7%), which are 4 and 20 fold greater than the pristine BCN, respectively. The enhanced photocatalytic performance is systematically identified by the increased surface area, higher cathodic and anodic current densities of -1.01 and 2.27 mA cm-2, delayed charge recombination as supported by PL and EIS measurement, and excellent photostability. The Z-scheme charge transfer mechanism is validated by time-resolved photoluminescence (TRPL) analysis, cyclic voltametric analysis, and the radical trapping experiment as detected by PL analysis. This research marks a substantial advancement and establishes the foundation for future design ideas in accelerating charge transfer.

TiMEG: an integrative statistical method for partially missing multi-omics data.

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Querying multiple sets of P-values through composed hypothesis testing.

MOTIVATION: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as a set of P-values resulting from previous analyses, that need to be combined flexibly to explore complex hypotheses, while guaranteeing a low proportion of false discoveries. RESULTS: We introduce the generic concept of composed hypothesis, which corresponds to an arbitrary complex combination of simple hypotheses. We rephrase the problem of testing a composed hypothesis as a classification task and show that finding items for which the composed null hypothesis is rejected boils down to fitting a mixture model and classifying the items according to their posterior probabilities. We show that inference can be efficiently performed and provide a thorough classification rule to control for type I error. The performance and the usefulness of the approach are illustrated in simulations and on two different applications. The method is scalable, does not require any parameter tuning, and provided valuable biological insight on the considered application cases. AVAILABILITY AND IMPLEMENTATION: The QCH methodology is available in the qch package hosted on CRAN. Additionally, R codes to reproduce the Einkorn example are available on the personal webpage of the first author: https://www6.inrae.fr/mia-paris/Equipes/Membres/Tristan-Mary-Huard. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Emergence of quenched disorder as a dominant control for complex phase diagram of rare-earth nickelates.

Competing interactions in complex materials tend to induce multiple quantum phases of comparable energetics close to the ground state stability. This requires novel strategies and tools to segregate such phases with desired control to manipulate the properties relevant for contemporary technologies. Here, we show 'quenched disorder (QD)' as a predominant control parameter to realize a broad range of the quantum phases of bulkRNiO3(R= rare-earth ion) phase diagram in a LaxEu1-xNiO3compounds by systematic introduction of QD. Using static and terahertz dynamic transport studies on epitaxial thin films, we demonstrate various phases such as Fermi to non-Fermi liquid crossover, bad metallic behavior, quantum criticality, preservation of orbital and charge order symmetry and increased electronic inhomogeneity responsible for Maxwell-Wagner type of dielectric response, etc. The underlying mechanisms are unveiled by the anomalous responses of microscopic quantities such as scattering rate, plasma frequency, spectral weight, effective mass, and disorder. The results and methodology implemented here can be a generic pursuit of disorder based unified control to extract quantum phases submerged in competing energetics in all complex materials.

Determination of critical community size from an HIV/AIDS model.

After an epidemic outbreak, the infection persists in a community long enough to engulf the entire susceptible population. Local extinction of the disease could be possible if the susceptible population gets depleted. In large communities, the tendency of eventual damp down of recurrent epidemics is balanced by random variability. But, in small communities, the infection would die out when the number of susceptible falls below a certain threshold. Critical community size (CCS) is considered to be the mentioned threshold, at which the infection is as likely as not to die out after a major epidemic for small communities unless reintroduced from outside. The determination of CCS could aid in devising systematic control strategies to eradicate the infectious disease from small communities. In this article, we have come up with a simplified computation based approach to deduce the CCS of HIV disease dynamics. We consider a deterministic HIV model proposed by Silva and Torres, and following Nåsell, introduce stochasticity in the model through time-varying population sizes of different compartments. Besides, Metcalf's group observed that the relative risk of extinction of some infections on islands is almost double that in the mainlands i.e. infections cease to exist at a significantly higher rate in islands compared to the mainlands. They attributed this phenomenon to the greater recolonization in the mainlands. Interestingly, the application of our method on demographic facts and figures of countries in the AIDS belt of Africa led us to expect that existing control measures and isolated locations would assist in temporary eradication of HIV infection much faster. For example, our method suggests that through systematic control strategies, after 7.36 years HIV epidemics will temporarily be eradicated from different communes of island nation Madagascar, where the population size falls below its CCS value, unless the disease is reintroduced from outside.

Digital- to Analog-Type Terahertz Modulation Controlled by Mosaicity of the Substrate Template in Rare-Earth Nickelate Thin Films.

The extreme sensitivity of the metal-insulator (M-I) transition in RNiO3 (R = rare-earth ion) nickelates to various extrinsic and intrinsic factors rely on mechanisms driving structure-property relations. Here, we demonstrate a unique way to control the M-I transition of epitaxial Pr0.5Sm0.5NiO3 thin films using a mosaic template of the LaAlO3(100) substrate; two sets of epitaxial films were deposited on highly oriented crystals and mosaic (with multiple crystallites) crystals. While the former films exhibit a robust and sharp M-I transition, the films on the mosaic substrate show distinctively much more subtle and broad transition, albeit same factors suggesting compositional purity. Terahertz (THz) dynamic conductivity too behaves very differently for the two types of films; Drude dynamics dominate the conductivity of highly crystalline films, whereas disorder-driven Drude-Smith conductivity prevails in mosaic films. Using this mosaic structure-controlled M-I transition and conductivity dynamics, we propose to implement these two templates of films for digital and analog THz transmission amplitude modulators.

Tight clustering for large datasets with an application to gene expression data.

This article proposes a practical and scalable version of the tight clustering algorithm. The tight clustering algorithm provides tight and stable relevant clusters as output while leaving a set of points as noise or scattered points, that would not go into any cluster. However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is applicable to data sets of very large size and deduce the properties of the proposed algorithm. We validate our algorithm with extensive simulation study and multiple real data analyses including analysis of real data on gene expression.

A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease.

To decipher the genetic architecture of human disease, various types of omics data are generated. Two common omics data are genotypes and gene expression. Often genotype data for a large number of individuals and gene expression data for a few individuals are generated due to biological and technical reasons, leading to unequal sample sizes for different omics data. Unavailability of standard statistical procedure for integrating such datasets motivates us to propose a two-step multi-locus association method using latent variables. Our method is powerful than single/separate omics data analysis and it unravels comprehensively deep-seated signals through a single statistical model. Extensive simulation confirms that it is robust to various genetic models as its power increases with sample size and number of associated loci. It provides p-values very fast. Application to real dataset on psoriasis identifies 17 novel SNPs, functionally related to psoriasis-associated genes, at much smaller sample size than standard GWAS.

Family-based genome-wide association of inflammation biomarkers and fenofibrate treatment response in the GOLDN study.

In this paper we analyzed whole-genome genetic information provided by GAW20 from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study for family data. Lipid levels such as triglycerides (TGs) and high-density lipoprotein (HDL) are measured at different time points before and after administration of an anti-inflammatory drug fenofibrate. Apart from that, the data contain some covariates and whole-genome genotype information. We propose 2 novel approaches based on Henderson's iterative mixed model to identify associated loci corresponding to (a) inflammatory biomarkers like TGs and HDLs together over time, and (b) the response to fenofibrate treatment. We developed a mixed-model approach using both TG and HDL phenotypes at all 4 time points for a genetic association study whereas we used TGs only to study genetic association with response to the drug. We expect that use of complete family data in a longitudinal framework under a single model involving the appropriate correlation structures would be able to exploit the maximum possible information contained in the sample. Our analysis of whole-genome single nucleotide polymorphisms (SNPs) and genomic regions corresponding to drug treatment finds no significant locus after multiple correction. Arguably, the moderately small sample size of the data set, as compared to the sample size usually used in genome-wide association studies (GWAS), could be a reason for such a result. Nevertheless, we report the top 20 SNPs associated with the phenotypes, and the top 20 SNPs and genomic regions associated with a response to fenofibrate treatment. Application of our methods to larger GWAS and further functional validation of the reported top SNPs and genomic regions might provide important biological insight into the genetic constitution of the trait.

Cation disorder and epitaxial strain modulated Drude-Smith type terahertz conductivity and Hall-carrier switching in Ca1-x Ce x RuO3 thin films.

The CaRuO3 is a non-Fermi liquid pseudo-cubic perovskite with a magnetic ground state on the verge of phase transition and it lies in the vicinity of the quantum critical point. To understand the sensitivity of its ground state, the effects of subtle aliovalent chemical disorder on the static and high frequency dynamic conductivity in the coherently strained structures were explored. The Ce-doped Ca1-x Ce x RuO3 (0 ⩽ x ⩽ 0.1) thin films were deposited on LaAlO3 (1 0 0) and SrTiO3 (1 0 0) substrates and studies for low-energy terahertz (THz) carrier dynamics, dc transport and Hall effect. These compositions exhibited a very effective and unusual Hall-carrier switching in both compressive and tensile strain induced epitaxial thin films. The dc resistivity depicts a switching from a non-Fermi liquid to a Fermi liquid behavior without any magnetic phase transition. A discernible and gradual crossover from Drude to Drude-Smith THz dynamic optical conductivity was observed while traversing from pure to 10% Ce-doped CaRuO3 films. Overall, a nearly Fermi liquid behavior, effective carrier switching and unusual features in THz conductivity, were all novel features realized for the first time in physically and/or chemically modified CaRuO3. These new phases highlight the novel subtleties and versatility of the systems lying near the quantum critical point.