Highly Photosensitive Lead Sulfide Thin Films Grown by H2 S Free MOCVD Using a Single Source Metal-Organic Precursor.

High-quality lead sulfide (PbS) films are deposited on selected substrate chemistries by an H2 S-free metal-organic chemical vapor deposition (MOCVD) process using a single-source metal-organic complex (Pb(dmampS)2 ). The complex is synthesized via a salt metathesis reaction between PbCl2  and lithium 1-(dimethylamino)-2-methylpropane-2-thiolate (Li(dmampS)) in diethyl ether. Subsequent film deposition is conducted by a simple thermolysis process in the absence of H2 S, yet chemical and structural analysis confirm chemically stoichiometric and homogenous films. Mechanistic studies with electron impact mass spectroscopy (EIMS) and gas chromatography mass spectroscopy (GCMS) suggest the selective cleavage of C-S bonds in the complex as the reason for the facile PbS formation with negligible impurity incorporation. The high crystallinity, low hole concentrations, and charge transport properties comparable and in many cases superior to films produced by atomic layer deposition (ALD) testify to the quality of the films. Lastly, rigid and flexible photodetectors fabricated with the PbS films exhibit considerably high photocurrents, reliable switching characteristics, and high sensitivity over a broad spectral bandwidth, highlighting the potential for realizing practical broadband photodetectors.

Creating Tunable Mesoporosity by Temperature-Driven Localized Crystallite Agglomeration.

A new synthetic approach for tunable mesoporous metal-organic frameworks (MeMs) is developed. In this approach, mesopores are created in the process of heat conversion of highly mosaic metal-organic framework (MOF) crystals with non-interpenetrated low-density nanocrystallites into MOF crystals with two-fold interpenetrated high-density nanocrystallites. The two-fold interpenetration reduces the volume of the nanocrystallites in the mosaic crystal, and the accompanying localized agglomeration of the nanocrystallites results in the formation of mesopores among the localized crystallite agglomerates. The pore size can be easily modulated from 7 to 90 nm by controlling the heat treatment conditions, that is, the aging temperature and aging time. Various proteins can be encapsulated in the MeM, and immobilized enzymes show catalyst activity comparable to that of the free native enzymes. Immobilized ß-galactosidase is recyclable and the enzyme activity of the immobilized catalase is maintained after exposure to high temperatures and various organic solvents.

atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding.

SUMMARY: Understanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms. AVAILABILITY AND IMPLEMENTATION: atSNP Search is freely available at http://atsnp.biostat.wisc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting gene expression in massively parallel reporter assays: A comparative study.

In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

atSNP: transcription factor binding affinity testing for regulatory SNP detection.

MOTIVATION: Genome-wide association studies revealed that most disease-associated single nucleotide polymorphisms (SNPs) are located in regulatory regions within introns or in regions between genes. Regulatory SNPs (rSNPs) are such SNPs that affect gene regulation by changing transcription factor (TF) binding affinities to genomic sequences. Identifying potential rSNPs is crucial for understanding disease mechanisms. In silico methods that evaluate the impact of SNPs on TF binding affinities are not scalable for large-scale analysis. RESULTS: We describe A: ffinity T: esting for regulatory SNP: s (atSNP), a computationally efficient R package for identifying rSNPs in silico. atSNP implements an importance sampling algorithm coupled with a first-order Markov model for the background nucleotide sequences to test the significance of affinity scores and SNP-driven changes in these scores. Application of atSNP with >20 K SNPs indicates that atSNP is the only available tool for such a large-scale task. atSNP provides user-friendly output in the form of both tables and composite logo plots for visualizing SNP-motif interactions. Evaluations of atSNP with known rSNP-TF interactions indicate that SNP is able to prioritize motifs for a given set of SNPs with high accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/keleslab/atSNP. CONTACT: keles@stat.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adaptive Estimation with Partially Overlapping Models.

In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A priori knowledge of such structure enables efficient estimation of all model parameters. However, in practice, this structure may be unknown. We propose adaptive composite M-estimation (ACME) for partially overlapping models using a composite loss function, which is a linear combination of loss functions defining the individual models. Penalization is applied to pairwise differences of parameters across models, resulting in data driven identification of the overlap structure. Further penalization is imposed on the individual parameters, enabling sparse estimation in the regression setting. The recovery of the overlap structure enables more efficient parameter estimation. An oracle result is established. Simulation studies illustrate the advantages of ACME over existing methods that fit individual models separately or make strong a priori assumption about the overlap structure.

Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Photostimulated Pyrothermoelectric Coupling in Two-Dimensional Tin Monoselenide Enabling Zero-Biased Multimodal Transducers.

Despite the advancement of the Internet of Things (IoT) and portable devices, the development of zero-biased sensing systems for the dual detection of light and gases remains a challenge. As an emerging technology, direct energy conversion driven by intriguing physical properties of two-dimensional (2D) materials can be realized in nanodevices or a zero-biased integrated system. In this study, we unprecedentedly attempted to exploit the photostimulated pyrothermoelectric coupling of two-dimensional SnSe for use in zero-biased multimodal transducers for the dual detection of light and gases. We synthesized homogeneous, large-area 6 in SnSe multilayers via a rational synthetic route based on the thermal decomposition of a solution-processed single-source precursor. Zero-biased SnSe transducers for the dual monitoring of light and gases were realized by exploiting the synergistic coupling of the photostimulated pyroelectric and thermoelectric effects of SnSe. The extracted photoresponsivity at 532 nm and NO2 gas responsivity of the SnSe-based transducers corresponded to 1.07 × 10-6 A/W and 13263.6% at 0 V, respectively. To bring universal applicability of the zero-biased SnSe transducers, the wide operation bandwidth photoelectrical properties (visible to NIR) and dynamic current responses toward two NO2/NH3 gases were systematically evaluated.

Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Evaluation of tin nitride (Sn3N4) via atomic layer deposition using novel volatile Sn precursors.

Novel Sn precursors, Sn(tbip)2, Sn(tbtp)2, and Sn(tbta)2, were synthesized and characterized using various analytical techniques and density functional theory calculations. These precursors contained cyclic amine ligands derived from iminopyrrolidine. X-ray crystallography revealed the formation of monomeric SnL2 with distorted seesaw geometry. Thermogravimetric analysis demonstrated the exceptional volatility of all complexes. Sn(tbtp)2 showed the lowest residual weight of 2.7% at 265 °C. Sn3N4 thin films were successfully synthesized using Sn(tbtp)2 as the Sn precursor and NH3 plasma. The precursor exhibited ideal characteristics for atomic layer deposition, with a saturated growth per cycle value of 1.9 Å cy-1 and no need for incubation when the film was deposited at 150-225 °C. The indirect optical bandgap of the Sn3N4 film was approximately 1-1.2 eV, as determined through ultraviolet-visible spectroscopy. Therefore, this study suggests that the Sn3N4 thin films prepared using the newly synthesized Sn precursor are suitable for application in thin-film photovoltaic devices.

Wafer-Scale Production of Two-Dimensional Tin Monoselenide: Expandable Synthetic Platform for van der Waals Semiconductor-Based Broadband Photodetectors.

A synthetic platform for industrially applicable two-dimensional (2D) semiconductors that addresses the paramount issues associated with large-scale production, wide-range photosensitive materials, and oxidative stability has not yet been developed. In this study, we attained the 6 in. scale production of 2D SnSe semiconductors with spatial homogeneity using a rational synthetic platform based on the thermal decomposition of solution-processed single-source precursors. The long-range structural and chemical homogeneities of the 2D SnSe layers are manifested using comprehensive spectroscopic analyses. Furthermore, the capability of the SnSe-based photodetectors for broadband photodetection is distinctly verified. The photoresponsivity and detectivity of the SnSe-based photodetectors are 5.89 A W-1 and 1.8 × 1011 Jones at 532 nm, 1.2 A W-1 and 3.7 × 1010 Jones at 1064 nm, and 0.14 A W-1 and 4.3 × 109 Jones at 1550 nm, respectively. The minimum rise times for the 532 and 1064 nm lasers are 62 and 374 µs, respectively. The photoelectrical analysis of the 5 × 5 SnSe-based photodetector array reveals 100% active devices with 95.06% photocurrent uniformity. We unequivocally validated that the air and thermal stabilities of the photocurrent yielded from the SnSe-based photodetector are determined to be >30 d in air and 160 °C, respectively, which are suitable for optoelectronic applications.

Glutathione reductase from Brassica rapa affects tolerance and the redox state but not fermentation ability in response to oxidative stress in genetically modified Saccharomyces cerevisiae.

To determine whether the exogenous expression of glutathione reductase (GR) from Brassica rapa subsp. pekinensis (BrGR) can reduce the deleterious effects of unfavorable conditions, we constructed a transgenic Saccharomyces cerevisiae strain bearing the GR gene cloned into the yeast expression vector, pVTU260. BrGR expression was confirmed by semi reverse transcriptase-polymerase chain reaction (RT-PCR) analysis, immunoblotting analysis and an enzyme assay. Ectopic BrGR-expression improved cellular glutathione (GSH) homeostasis after higher GSH accumulation in the transgenic yeast than in the wild-type yeast under H(2)O(2)-induced oxidative stress. The BrGR-expressing yeast strain induced the activation of metabolic enzymes (Hxt, G6PDH, GAPDH and Ald), antioxidant systems (Gpx, Trx2, Trx3, Trr1, Tsa1 and porin) and molecular chaperones (Hsp104, Hsp90, Hsp70, Hsp42, Hsp26, Grp, Sti1 and Zpr1), which led to lower oxidative protein damage after a reduction in the level of cellular ROS in the BrGR-expressing yeast strain exposed to H(2)O(2) than in the wild-type yeast strain. BrGR-expression increased the ability to adapt and recover from H(2)O(2)-induced oxidative stress and various stressors, including heat shock, menadione, tert-butyl hydroperoxide, heavy metals, sodium dodecyl sulfate, ethanol and NaCl, but did not affect fermentation capacity. These results suggest that ectopic BrGR expression confers acquired tolerance by improving proteostasis and redox homeostasis through co-activation of various cell rescue proteins against ROS-induced oxidative stress in yeast cells.

Nanoscale Visualization of the Electron Conduction Channel in the SiO/Graphite Composite Anode.

Conductive atomic force microscopy (C-AFM) is widely used to determine the electronic conductivity of a sample surface with nanoscale spatial resolution. However, the origin of possible artifacts has not been widely researched, hindering the accurate and reliable interpretation of C-AFM imaging results. Herein, artifact-free C-AFM is used to observe the electron conduction channels in Si-based composite anodes. The origin of a typical C-AFM artifact induced by surface morphology is investigated using a relevant statistical method that enables visualization of the contribution of artifacts in each C-AFM image. The artifact is suppressed by polishing the sample surface using a cooling cross-section polisher, which is confirmed by Pearson correlation analysis. The artifact-free C-AFM image was used to compare the current signals (before and after cycling) from two different composite anodes comprising single-walled carbon nanotubes (SWCNTs) and carbon black as conductive additives. The relationship between the electrical degradation and morphological evolution of the active materials depending on the conductive additive is discussed to explain the improved electrical and electrochemical properties of the electrode containing SWCNTs.

Over-Expression of Dehydroascorbate Reductase Improves Salt Tolerance, Environmental Adaptability and Productivity in Oryza sativa.

Abiotic stress induces reactive oxygen species (ROS) generation in plants, and high ROS levels can cause partial or severe oxidative damage to cellular components that regulate the redox status. Here, we developed salt-tolerant transgenic rice plants that overexpressed the dehydroascorbate reductase gene (OsDHAR1) under the control of a stress-inducible sweet potato promoter (SWPA2). OsDHAR1-expressing transgenic plants exhibited improved environmental adaptability compared to wild-type plants, owing to enhanced ascorbate levels, redox homeostasis, photosynthetic ability, and membrane stability through cross-activation of ascorbate-glutathione cycle enzymes under paddy-field conditions, which enhanced various agronomic traits, including root development, panicle number, spikelet number per panicle, and total grain yield. dhar2-knockdown plants were susceptible to salt stress, and owing to poor seed maturation, exhibited reduced biomass (root growth) and grain yield under paddy field conditions. Microarray revealed that transgenic plants highly expressed genes associated with cell growth, plant growth, leaf senescence, root development, ROS and heavy metal detoxification systems, lipid metabolism, isoflavone and ascorbate recycling, and photosynthesis. We identified the genetic source of functional genomics-based molecular breeding in crop plants and provided new insights into the physiological processes underlying environmental adaptability, which will enable improvement of stress tolerance and crop species productivity in response to climate change.

New Volatile Tantalum Imido Precursors with Carboxamide Ligands.

A series of Ta(V) t Bu-imido/N-alkoxy carboxamide complexes, TaCl2(N t Bu)(pyridine)(edpa) (1), TaCl(N t Bu)(edpa)2 (2), Ta(N t Bu)(edpa)3 (3), TaCl2(N t Bu)(pyridine)(mdpa) (4), and Ta(N t Bu)(mdpa)3 (5), were successfully synthesized by metathesis reactions between Ta(N t Bu)Cl3(py)2 and several equivalents of Na(edpa) (edpaH = N-ethoxy-2,2-dimethylpropanamide) and Na(mdpa) (mdpaH = N-methoxy-2,2-dimethylpropanamide). Furthermore, complexes 3 and 5 were simply transformed to new dimeric structures [Ta(µ2-O)(edpa)3]2 (6) and [Ta(µ2-O)(mdpa)3]2 (7) with the elimination of the N t Bu imido group by air exposure. Compounds 1-7 were characterized by 1H and 13C nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, elemental analysis, thermogravimetric analysis (TGA), and single-crystal X-ray diffraction. Single-crystal X-ray diffraction analysis revealed that complexes 3 and 5 have a distorted pentagonal bipyramidal geometry around the central Ta atom, with three monoanionic bidentate N-alkoxy carboxamide ligands and one t Bu imido ligand saturating the coordination of tantalum ions. TGA revealed that complexes 3 and 5 had superior thermal characteristics and stability. These complexes could potentially be applied as precursors for tantalum oxide thin films.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants.

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .

Identification of direct transcriptional targets of NFATC2 that promote ß cell proliferation.

The transcription factor NFATC2 induces ß cell proliferation in mouse and human islets. However, the genomic targets that mediate these effects have not been identified. We expressed active forms of Nfatc2 and Nfatc1 in human islets. By integrating changes in gene expression with genomic binding sites for NFATC2, we identified approximately 2200 transcriptional targets of NFATC2. Genes induced by NFATC2 were enriched for transcripts that regulate the cell cycle and for DNA motifs associated with the transcription factor FOXP. Islets from an endocrine-specific Foxp1, Foxp2, and Foxp4 triple-knockout mouse were less responsive to NFATC2-induced ß cell proliferation, suggesting the FOXP family works to regulate ß cell proliferation in concert with NFATC2. NFATC2 induced ß cell proliferation in both mouse and human islets, whereas NFATC1 did so only in human islets. Exploiting this species difference, we identified approximately 250 direct transcriptional targets of NFAT in human islets. This gene set enriches for cell cycle-associated transcripts and includes Nr4a1. Deletion of Nr4a1 reduced the capacity of NFATC2 to induce ß cell proliferation, suggesting that much of the effect of NFATC2 occurs through its induction of Nr4a1. Integration of noncoding RNA expression, chromatin accessibility, and NFATC2 binding sites enabled us to identify NFATC2-dependent enhancer loci that mediate ß cell proliferation.

Expression of yeast cyclophilin A (Cpr1) provides improved stress tolerance in Escherichia coli.

Cyclophilins contain the conserved activity of cis-trans peptidyl-prolyl isomerase that is implicated in protein folding and function as molecular chaperones. The yeast cyclophilin A gene (cpr1) was subcloned to the prokaryotic expression vector pKM260. It was found that the expression of Cpr1 drastically increased the cell viability of E. coli BL21 in the presence of abiotic stress conditions, such as cadmium, copper, hydrogen peroxide, heat, and SDS. Thus, this study illustrates the importance of Cpr1 as a molecular chaperone that improved cellular stress responses when E. coli cells were exposed to adverse conditions, and it also shows the possibility of increasing the stability of E. coli strains utilized for the production of recombinant proteins.

Ensemble estimation and variable selection with semiparametric regression models.

We consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function may be difficult to maximize, but the components are easy to maximize. It covers settings where the nuisance parameter may be estimated at different rates in the component likelihoods. As a motivating example we consider proportional hazards regression with prospective doubly censored data, in which the likelihood factors into a current status data likelihood and a left-truncated right-censored data likelihood. Variable selection is important in such regression modelling, but the applicability of existing techniques is unclear in the ensemble approach. We propose ensemble variable selection using the least squares approximation technique on the unpenalized ensemble estimator, followed by ensemble re-estimation under the selected model. The resulting estimator has the oracle property such that the set of nonzero parameters is successfully recovered and the semiparametric efficiency bound is achieved for this parameter set. Simulations show that the proposed method performs well relative to alternative approaches. Analysis of an AIDS cohort study illustrates the practical utility of the method.

Metal-insulator transition-induced adsorption-resistant behavior of small Au nanoparticles.

Smaller nonmetallic nanoparticles are more inert: Metal-insulator transition of Au nanoparticles on silica is closely related to the metal-support charge transfer, which has a strong influence on chemisorption reactivity of Au. Smaller nonmetallic Au nanoparticles are more resistant towards butanethiol chemisorption [picture and graph: see text].The size-dependent variation of the electronic and chemical properties of Au nanoparticles formed on native Si oxide surfaces is investigated using synchrotron radiation photoemission spectroscopy and ultraviolet photoelectron spectroscopy. The adsorption reactivity toward butanethiol adsorption initially increases with decreasing particle size; however, the reactivity of Au nanoparticles becomes gradually lower below a size of approximately 0.8 nm. The photoemission spectral changes suggest a metal-insulator transition, accompanied by negative charge transfer from the nanoparticles to the support, which may be the source of the chemical inertness of small Au nanoparticles.