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.

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.

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.

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.

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.

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.

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.

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.

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/ .

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.

ASR Enhances Environmental Stress Tolerance and Improves Grain Yield by Modulating Stomatal Closure in Rice.

Abscisic acid-, stress-, and ripening-induced (ASR) genes are involved in responding to abiotic stresses, but their precise roles in enhancing grain yield under stress conditions remain to be determined. We cloned a rice (Oryza sativa) ASR gene, OsASR1, and characterized its function in rice plants. OsASR1 expression was induced by abscisic acid (ABA), salt, and drought treatments. Transgenic rice plants overexpressing OsASR1 displayed improved water regulation under salt and drought stresses, which was associated with osmolyte accumulation, improved modulation of stomatal closure, and reduced transpiration rates. OsASR1-overexpressing plants were hypersensitive to exogenous ABA and accumulated higher endogenous ABA levels under salt and drought stresses, indicating that OsASR1 is a positive regulator of the ABA signaling pathway. The growth of OsASR1-overexpressing plants was superior to that of wild-type (WT) plants under paddy field conditions when irrigation was withheld, likely due to improved modulation of stomatal closure via modified ABA signaling. The transgenic plants had higher grain yields than WT plants for four consecutive generations. We conclude that OsASR1 has a crucial role in ABA-mediated regulation of stomatal closure to conserve water under salt- and drought-stress conditions, and OsASR1 overexpression can enhance salinity and drought tolerance, resulting in improved crop yields.

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.

atSNPInfrastructure, a case study for searching billions of records while providing significant cost savings over cloud providers.

We explore the feasibility of a database storage engine housing up to 307 billion genetic Single Nucleotide Polymorphisms (SNP) for online access. We evaluate database storage engines and implement a solution utilizing factors such as dataset size, information gain, cost and hardware constraints. Our solution provides a full feature functional model for scalable storage and query-ability for researchers exploring the SNP's in the human genome. We address the scalability problem by building physical infrastructure and comparing final costs to a major cloud provider.

Gene network underlying the response of harvested pepper to chilling stress.

Cold storage is an effective postharvest control strategy to maintain the freshness of vegetables by suppressing respiration. However, subtropical plants including pepper (Capsicum annuum L.) undergo chilling injury. To better understand the molecular mechanisms involved in preventing chilling injury, transcriptome profiling analysis of peppers stored in a cold chamber and treated with 50µM methyl jasmonate (MeJA) and 1µLL-1 1-methylcyclopropene as an ethylene reaction inhibitor was performed. A total of 240, 470, and 290 genes were upregulated and 184, 291, and 219 genes down-regulated in cold-, MeJA- and 1-methylcyclopropene-treated peppers, respectively. MeJA-treated peppers had significant transcriptome changes compared to cold- and 1-MCP-treated peppers after 24h of storage. MeJA treatment upregulated the genes for peroxidase and catalase related to stress responses, as well as the ethylene-responsive factor (ERF) family and MAP kinase involved in ethylene signaling factors in peppers. Functional analysis revealed that in comparison with wild type plants, ERF1-expressing plants showed a higher antioxidant capacity and enhanced expression levels of oxidative stress-related and jasmonic acid synthesis-related genes during chilling storage conditions. Additionally, ERFs and JA biosynthesis gene expression in peppers during long-term cold storage was upregulated by MeJA. Thus, MeJA enables peppers to respond to cold stress and ethylene signaling, and this could help to prevent chilling injury. Our results suggest that ethylene signaling and JA synthesis share the reactive oxygen species (ROS) scavenger-mediated stress adaption system during chilling stress in pepper. In addition, these findings provide a global insight into the genetic basis for preventing chilling injury in subtropical crops.

Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data.

Although genome-wide association studies (GWAS) have been successful at finding thousands of disease-associated genetic variants (GVs), identifying causal variants and elucidating the mechanisms by which genotypes influence phenotypes are critical open questions. A key challenge is that a large percentage of disease-associated GVs are potential regulatory variants located in noncoding regions, making them difficult to interpret. Recent research efforts focus on going beyond annotating GVs by integrating functional annotation data with GWAS to prioritize GVs. However, applicability of these approaches is challenged by high dimensionality and heterogeneity of functional annotation data. Furthermore, existing methods often assume global associations of GVs with annotation data. This strong assumption is susceptible to violations for GVs involved in many complex diseases. To address these issues, we develop a general regression framework, named Annotation Regression for GWAS (ARoG). ARoG is based on a finite mixture of linear regressions model where GWAS association measures are viewed as responses and functional annotations as predictors. This mixture framework addresses heterogeneity of effects of GVs by grouping them into clusters and high dimensionality of the functional annotations by enabling annotation selection within each cluster. ARoG further employs permutation testing to evaluate the significance of selected annotations. Computational experiments indicate that ARoG can discover distinct associations between disease risk and functional annotations. Application of ARoG to autism and schizophrenia data from Psychiatric Genomics Consortium led to identification of GVs that significantly affect interactions of several transcription factors with DNA as potential mechanisms contributing to these disorders.

Ultra-widefield fundus autofluorescence in age-related macular degeneration.

BACKGROUND: Establish accuracy and reproducibility of subjective grading in ultra-widefield fundus autofluorescence (FAF) imaging in patients with age-related macular degeneration (AMD), and determine if an association exists between peripheral FAF abnormalities and AMD. METHODS: This was a prospective, single-blinded case-control study. Patients were consecutively recruited for the study. Patients were excluded if there was a history of prior or active ocular pathology other than AMD or image quality was insufficient for analysis as determined by two independent graders. Control patients were those without any evidence of AMD or other ophthalmic disease apart from cataract. Using the Optos 200Tx (Optos, Marlborough, MA, USA), a ResMax central macula and an ultra-widefield peripheral retina image was taken for each eye in both normal color and short wavelength FAF. Ultra-widefield photographs were modified to mask the macula. Each ResMax and ultra-widefield image was independently graded by two blinded investigators. RESULTS: There were 28 AMD patients and 11 controls. There was a significant difference in the average age between AMD patients and control groups (80 versus 64, respectively P<0.001). There was moderate, statistically significant agreement between observers regarding image interpretation (78.4%, K = 0.524, P<0.001), and 69.0% (K = 0.49, P<0.001) agreement between graders for FAF abnormality patterns. Patients with AMD were at greater risk for peripheral FAF abnormalities (OR: 3.43, P = 0.019) and patients with FAF abnormalities on central macular ResMax images were at greater risk of peripheral FAF findings (OR: 5.19, P = 0.017). CONCLUSION: Subjective interpretation of FAF images has moderate reproducibility and validity in assessment of peripheral FAF abnormalities. Peripheral FAF abnormalities are seen in both AMD and control patients. Those with AMD, poor visual acuity, and macular FAF abnormalities are at greater risk.

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.