Recently Developed Research Articles

Methodological Considerations on the Use of Cohort Designs in Drug-Drug Interaction Studies in Pharmacoepidemiology

Abstract Purpose of Review The evidence regarding the clinical effects of drug-drug interactions (DDIs) is scarce and limited. Pharmacoepidemiologic studies could help fill in this important knowledge gap. Here, we review the pharmacoepidemiology of DDIs with a focus on cohort designs. We also highlight the decision-making process with respect to different aspects of cohort study design, potential biases that may arise during this decision process, and mitigation strategies. Recent Findings Considering the pharmacologic mechanism of the DDI of interest as well as of the object drug and the precipitant drug separately at the design stage of cohort studies for DDIs will help minimize major biases such as prevalent user bias and confounding by indication. Confounding by indication could also be mitigated by using control precipitants. Further, the correct assignment of the cohort entry date via the application of a time-varying exposure definition can help minimize immortal time bias and prevalent user bias. Minimization of these biases may also potentially be achieved with recently developed tools such as target trial emulation and the prevalent new-user design; however, more research is needed in the area. Summary Careful consideration of the underlying pharmacology and the specifics of study design will help minimize major biases in cohort studies that aim to assess the clinical effects of DDIs. Recent methodological developments from other areas of pharmacoepidemiology could further improve the internal validity of DDI studies.

A Slicing‐Free Perspective to Sufficient Dimension Reduction: Selective Review and Recent Developments

SummarySince the pioneering work of sliced inverse regression, sufficient dimension reduction has been growing into a mature field in statistics and it has broad applications to regression diagnostics, data visualisation, image processing and machine learning. In this paper, we provide a review of several popular inverse regression methods, including sliced inverse regression (SIR) method and principal hessian directions (PHD) method. In addition, we adopt a conditional characteristic function approach and develop a new class of slicing‐free methods, which are parallel to the classical SIR and PHD, and are named weighted inverse regression ensemble (WIRE) and weighted PHD (WPHD), respectively. Relationship with recently developed martingale difference divergence matrix is also revealed. Numerical studies and a real data example show that the proposed slicing‐free alternatives have superior performance than SIR and PHD.

Recent Advances and Developments in Injectable Conductive Polymer Gels for Bioelectronics.

Soft matter bioelectronics represents an emerging and interdisciplinary research frontier aiming to harness the synergy between biology and electronics for advanced diagnostic and healthcare applications. In this context, a whole family of soft gels have been recently developed with self-healing ability and tunable biological mimetic features to act as a tissue-like space bridging the interface between the electronic device and dynamic biological fluids and body tissues. This review article provides a comprehensive overview of electroactive polymer gels, formed by noncovalent intermolecular interactions and dynamic covalent bonds, as injectable electroactive gels, covering their synthesis, characterization, and applications. First, hydrogels crafted from conducting polymers (poly(3,4-ethylene-dioxythiophene) (PEDOT), polyaniline (PANi), and polypyrrole (PPy))-based networks which are connected through physical interactions (e.g., hydrogen bonding, π-π stacking, hydrophobic interactions) or dynamic covalent bonds (e.g., imine bonds, Schiff-base, borate ester bonds) are addressed. Injectable hydrogels involving hybrid networks of polymers with conductive nanomaterials (i.e., graphene oxide, carbon nanotubes, metallic nanoparticles, etc.) are also discussed. Besides, it also delves into recent advancements in injectable ionic liquid-integrated gels (iongels) and deep eutectic solvent-integrated gels (eutectogels), which present promising avenues for future research. Finally, the current applications and future prospects of injectable electroactive polymer gels in cutting-edge bioelectronic applications ranging from tissue engineering to biosensing are outlined.

Medicinal chemistry perspective of JAK inhibitors: synthesis, biological profile, selectivity, and structure activity relationship.

JAK-STAT signalling pathway was discovered more than quarter century ago. The JAK-STAT pathway protein is considered as one of the crucial hubs for cytokine secretion which mediates activation of different inflammatory, cellular responses and hence involved in different etiological factors. The various etiological factors involved are haematopoiesis, immune fitness, tissue repair, inflammation, apoptosis, and adipogenesis. The presence of the active mutation V617K plays a significant role in the progression of the JAK-STAT pathway-related disease. Consequently, targeting the JAK-STAT pathway could be a promising therapeutic approach for addressing a range of causative factors. In this current review, we provided a comprehensive discussion for the in-detail study of anatomy and physiology of the JAK-STAT pathway which contributes structural domain rearrangement, activation, and negative regulation associated with the downstream signaling pathway, relationship between different cytokines and diseases. This review also discussed the recent development of clinical trial entities. Additionally, this review also provides updates on FDA-approved drugs. In the current investigation, we have classified recently developed small molecule inhibitors of JAK-STAT pathway according to different chemical classes and we emphasized their synthetic routes, biological evaluation, selectivity, and structure-activity relationship.

Frictionless line-to-line contact: Comparison of mortar and unbiased formulation in combination with Lagrange-multiplier and penalty method

Recent developments in the beam-to-beam contact mechanics have prompted us to investigate further into the relations between different formulations and contact-force models. Specifically we address in this paper the recently developed mortar method for beams and develop its variant based on the penalty method. This allows an in-depth comparison between the two formulations. Furthermore, we design an unbiased approach and introduce a new discretisation technique developed specifically for this method. The unbiased line-to-line method is also combined both with the Lagrange-multiplier and the penalty method. All developed elements have been tested using the same examples to provide an objective comparison between formulations in terms of robustness and computational cost.

Gene and Cellular Therapies for Leukodystrophies.

Leukodystrophies are a heterogenous group of inherited, degenerative encephalopathies, that if left untreated, are often lethal at an early age. Although some of the leukodystrophies can be treated with allogeneic hematopoietic stem cell transplantation, not all patients have suitable donors, and new treatment strategies, such as gene therapy, are rapidly being developed. Recent developments in the field of gene therapy for severe combined immune deficiencies, Leber's amaurosis, epidermolysis bullosa, Duchenne's muscular dystrophy and spinal muscular atrophy, have paved the way for the treatment of leukodystrophies, revealing some of the pitfalls, but overall showing promising results. Gene therapy offers the possibility for overexpression of secretable enzymes that can be released and through uptake, allow cross-correction of affected cells. Here, we discuss some of the leukodystrophies that have demonstrated strong potential for gene therapy interventions, such as X-linked adrenoleukodystrophy (X-ALD), and metachromatic leukodystrophy (MLD), which have reached clinical application. We further discuss the advantages and disadvantages of ex vivo lentiviral hematopoietic stem cell gene therapy, an approach for targeting microglia-like cells or rendering cross-correction. In addition, we summarize ongoing developments in the field of in vivo administration of recombinant adeno-associated viral (rAAV) vectors, which can be used for direct targeting of affected cells, and other recently developed molecular technologies that may be applicable to treating leukodystrophies in the future.

Recent Development on the Extraction Process of Plants Essential Oil and its Effect on Chemical Composition: A Review

This study presents a thorough review on different innovative extraction techniques of different plant essential oil derived from its peel, leaves, seeds and shrubs. The review also entails the different pre-treatment processes that affects the quality and recovery of the plant essential oil. Also, discussed the recently developed green extraction method such as Microwave Assisted Hydrodistillation (MAHD), Solvent Free Microwave Extraction (SFME), Ultrasound Assisted Extraction (UAE), Supercritical Fluid Extraction (SFE) and Ohmic Heated Assisted Hydrodistillation (OHAD). The impact of these extraction process on the important chemical composition of essential oil is further analyzed in this review. The important parameters in extraction is also studied such as the water to material ratio, extraction temperature, power input and extraction time. Based on the different articles reviewed, it signifies that the innovative extraction method improves the quality and quantity of the essential oil yield. It also improved the number of component that can be extracted in the plant essential oil and its biological activities such as antioxidant and antimicrobial activities. Thus this review aims to emphasize the potential of the newly developed essential oil extraction techniques that would help to boon the pharmaceuticals, food and agriculture industry.

Salivary corticosterone reflects plasmatic levels in a wild seabird

Wild animals have been increasingly exposed to a wide range of stressors, mainly due to the intensification of human activities and habitat modifications. Consequently, new tools in order to assess the physiological and health status of wild animals have been developed. In particular, glucocorticoids have received a special attention. Primarily metabolic hormones, they are also used to evaluate the stress level of organisms. While historically measured in blood samples, new less-invasive methods have been recently developed to measure glucocorticoids in matrices such as faeces, hairs/feathers, or saliva. To date, measurements in saliva are still in their infancy despite the numerous advantages of the matrix: non-invasive, reflects the biologically active portion of glucocorticoids, allows to measure both baseline and stress-induced levels. In addition, most studies using saliva have been performed on domestic and captive animals, and recent development in wild animals have focused on mammals. Here, we show, for the first time, that saliva could also be reliably used in free-ranging birds, as glucocorticoid levels in saliva strongly correlated with plasma levels. This promising result opens new avenues for a non-invasive sampling method to assess health status of wild birds in conservation biology and ecology.

Ion incorporation into bone grafting materials.

The use of biomaterials in regenerative medicine has expanded to treat various disorders caused by trauma or disease in orthopedics and dentistry. However, the treatment of large and complex bone defects presents a challenge, leading to a pressing need for optimized biomaterials for bone repair. Recent advances in chemical sciences have enabled the incorporation of therapeutic ions into bone grafts to enhance their performance. These ions, such as strontium (for bone regeneration/osteoporosis), copper (for angiogenesis), boron (for bone growth), iron (for chemotaxis), cobalt (for B12 synthesis), lithium (for osteogenesis/cementogenesis), silver (for antibacterial resistance), and magnesium (for bone and cartilage regeneration), among others (e.g., zinc, sodium, and silica), have been studied extensively. This review aims to provide a comprehensive overview of current knowledge and recent developments in ion incorporation into biomaterials for bone and periodontal tissue repair. It also discusses recently developed biomaterials from a basic design and clinical application perspective. Additionally, the review highlights the importance of precise ion introduction into biomaterials to address existing limitations and challenges in combination therapies. Future prospects and opportunities for the development and optimization of biomaterials for bone tissue engineering are emphasized.

Recent Developments of the International Future Lab of Hydrogen Economy: Using Artificial Intelligence (AI) Based Machine Learning (ML) in r-SOC Development

The International Future Laboratory for Hydrogen Economy at the Technical University of Munich (TUM) has established a research-network model researching in the field of electrically assisted gasification, reversible solid oxide cells, and biocatalytic synthesis of hydrogen. Using experimental and numerical data, the current study elucidates the progress on reversible solid oxide cell technologies. Advanced artificial intelligence (AI)-based machine learning approaches are utilized to evaluate and optimize SOC-relevant processes. The SOC research is giving particular attention to the integrated approach of traditional numerical methods such as CFD and accelerated modern AI methods. The complex thermochemistry of syngas-containing auxiliary components has been assessed using experimentally validated numerical models. The approach is utilized to create data for the successful construction and training of AI-based machine-learning models. Multi-regression studies shed light on the interactions between process variables to improve and prepare SOC-ready fuel which is essential for successful operation. A recently developed ML model is effectively employed to forecast and optimize the reforming procedures with different fuel constellations including oxygen-containing syngas compositions. Consequently, the results contribute to better knowledge and qualitative benefits of the preparation of high-quality, pure syngas, improved fuel utilisation furthering sustainable research and r-SOC operation in a safe, consistent manner. Hence, the availability of valuable information early in the process is achieved. Moreover, the approach reduced prohibitive experiments contributing to the sustainable use of resources.

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection.

A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

Multiple-input multiple-output active vibration control of a composite sandwich beam by fractional order positive position feedback

Positive Position Feedback (PPF) is an active vibration control algorithm widely employed for the vibration mitigation of thin-walled structures equipped with active flexural patches. Fractional order PPF (FOPPF) is a recent development of PPF that has been shown to achieve substantially better spillover characteristics, thanks to the inclusion of fractional calculus. However, so far, FOPPF literature has focused almost exclusively on single-input–single-output (SISO) applications, with little consideration of multiple-input-multiple-output (MIMO) capabilities and no experimental work on the subject. To fill this gap, a MIMO FOPPF control architecture was tested numerically and experimentally on a cantilever composite sandwich beam of rectangular section. The feedback algorithm involved two piezoelectric actuators and two piezoelectric sensors in a collocated configuration for the control of the lowest four resonances of the beam, excited by a random external excitation. The participation matrices, necessary for the simulation of the response of the electromechanical system, were obtained with a recently developed experimental method. The fractional orders of derivation, together with other controller parameters such as gain, resonant frequency and damping ratio, were chosen according to an optimization algorithm aimed at the vibration amplitude reduction of the first four resonances of the controlled system. As a result, the performance of the vibration control resulted superior to the equivalent PPF with integer exponents, both in simulations and experiments.

The SSS revolution in fungal diagnostics: speed, simplicity and sensitivity.

Fungal disease has historically presented a diagnostic challenge due to its often non-specific clinical presentations, relative infrequency and reliance on insensitive and time-intensive fungal culture. We present the recent developments in fungal diagnostics in the fields of serological and molecular diagnosis for the most clinically relevant pathogens; developments that have the potential to revolutionize fungal diagnosis through improvements in speed, simplicity and sensitivity. We have drawn on a body of evidence including recent studies and reviews demonstrating the effectiveness of antigen and antibody detection and polymerase chain reaction (PCR) in patients with and without concurrent human immunodeficiency virus infection. This includes recently developed fungal lateral flow assays, which have a low cost and operator skill requirement that give them great applicability to low-resource settings. Antigen detection for Cryptococcus, Histoplasma and Aspergillus spp. are much more sensitive than culture. PCR for Candida spp., Aspergillus spp., Mucorales and Pneumocystis jirovecii is more sensitive than culture and usually faster. Effort must be made to utilize recent developments in fungal diagnostics in clinical settings outside of specialist centres and integrate their use into standard medical practice. Given the clinical similarities of the conditions and frequent co-infection, further study is required into the use of serological and molecular fungal tests, particularly in patients being treated for tuberculosis. Further study is needed to clarify the utility of these tests in low-resource settings confounded by a high prevalence of tuberculosis. The diagnostic utility of these tests may require revision of laboratory work flows, care pathways and clinical and lab coordination, especially for any facility caring for the immunosuppressed, critically ill or those with chronic chest conditions, in whom fungal disease is common and underappreciated.

Recent advances and challenges in the use of CRISPR/Cas9 genome editing for understanding neuronal cell biology.

The ability to accurately map and manipulate the dynamic subcellular distribution of proteins is key for a mechanistic understanding of neuronal functioning. Current fluorescence microscopy techniques provide access to subcellular protein organization at increasing resolution but are often restricted by the availability of methods that reliably label endogenous proteins. Excitingly, recent development in CRISPR/Cas9 genome editing now allows researchers to specifically tag and visualize endogenous proteins, overcoming limitations associated with current labeling strategies. This article will discuss the progress that has been made in the last years that has led to the development of CRISPR/Cas9 genome editing tools for the reliable mapping of endogenous proteins in neurons. Furthermore, recently developed tools enable the duplex labeling of two proteins simultaneously and acute manipulation of protein distribution. Future implementations of this generation of genome editing technologies will undoubtedly drive progress in molecular and cellular neurobiology.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Enhancers are emerging as critical regulatory regions in regulating gene expression across diverse tissues and cell types. Perturbation of enhancer-driven transcriptional regulatory circuits has been observed in cancer and other human complex diseases. Systematic analyses of the landscape of core enhancer- or super-enhancer-driven regulatory circuits (CRCs) will provide novel insights into the regulation of gene expression. In this study, Feng et al. have identified the CRCs in large cell/tissue samples.1Feng C. Song C. Jiang Y. Zhao J. Zhang J. Wang Y. Yin M. Zhu J. Ai B. Wang Q. et al.Landscape and significance of human super enhancer-driven core transcription regulatory circuitry.Mol. Ther. Nucleic Acids. 2023; https://doi.org/10.1016/j.omtn.2023.03.014Abstract Full Text Full Text PDF Scopus (0) Google Scholar The authors analyzed the sequence conservation, CRC activity, and genome binding affinity for common transcription factors (TFs), moderate TFs, and specific TFs, which exhibited distinct biological features. Moreover, network analysis highlighted the potential as prognostic biomarkers of the CRC module in cancer. A user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed for aiding analysis of CRCs. The transcriptional activity of a gene is usually regulated by the crosstalk among enhancers and various cis-regulatory elements bound by TFs and RNA binding proteins. Recent developments of high-throughput sequencing technologies have generated valuable multiple-omics data resources (Figure 1A), which accelerated the studies of CRCs in diverse tissues and complex diseases contexts. TRANSFAC is widely used for identification of TF-gene binding and ChIPBase has been developed with >10,000 curated ChIP-seq datasets. In addition, enhancers and super-enhancers are usually identified based on the H3K27ac and H3K4me1 signals. The Encyclopedia of DNA Elements Project and Roadmap Epigenomics Project provide rich histone modification datasets across diverse tissues and cell types. Moreover, the development of biological methods (e.g., Hi-C, 3C, and ChIA-PET) to investigate the 3D chromatin organization is pivotal for understanding the enhancer-gene interactions.2Salviato E. Djordjilović V. Hariprakash J.M. Tagliaferri I. Pal K. Ferrari F. Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory interactions.Nucleic Acids Res. 2021; 49: e97Crossref PubMed Scopus (1) Google Scholar However, these experimental data are generally considered to be tissue- or cell-type specific. Increasing omics data allows genome-wide identification of the CRCs constituted by enhancers, TFs, and genes, which would be a valuable resource in biomedical research. The increases of high-throughput sequencing data have provided foundational resources for modeling the CRCs across diverse tissues and complex diseases. Experimental determination of the CRCs is time-consuming and laborious. Thus, numerous computational methods have attempted to map the CRCs by efforts to integrate global information regarding these resources (Figure 1A). CRC mapper was developed to construct the core regulatory circuitry models across human cell types.3Saint-André V. Federation A.J. Lin C.Y. Abraham B.J. Reddy J. Lee T.I. Bradner J.E. Young R.A. Models of human core transcriptional regulatory circuitries.Genome Res. 2016; 26: 385-396Crossref PubMed Google Scholar The python package Coltron (https://pypi.python.org/pypi/coltron) has also been developed to identify CRCs by integrating H3K27ac ChIP-seq and ATAC-seq data. FIMO is widely used for scanning TF motifs in gene sequences. Moreover, SCENIC is a recently developed computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.4Aibar S. González-Blas C.B. Moerman T. Huynh-Thu V.A. Imrichova H. Hulselmans G. Rambow F. Marine J.C. Geurts P. Aerts J. et al.SCENIC: single-cell regulatory network inference and clustering.Nat. Methods. 2017; 14: 1083-1086Crossref PubMed Scopus (1493) Google Scholar These computational methods for constructing core circuitry models should prove valuable tools for further investigating CRCs in healthy and diseased contexts. In this study, Feng et al. identified the CRCs and core TFs by integrating the results of CRC mapper and Coltron,1Feng C. Song C. Jiang Y. Zhao J. Zhang J. Wang Y. Yin M. Zhu J. Ai B. Wang Q. et al.Landscape and significance of human super enhancer-driven core transcription regulatory circuitry.Mol. Ther. Nucleic Acids. 2023; https://doi.org/10.1016/j.omtn.2023.03.014Abstract Full Text Full Text PDF Scopus (0) Google Scholar generating a comprehensive landscape of CRCs driven by super-enhancers across diverse tissues and cell types. However, the landscape of CRCs across diverse cancer types is still largely unknown. The CRCs are under intricate regulation in physiological conditions but could go awry upon genome instability caused by genetic and epigenetic alterations. A detailed understanding of the molecular mechanisms of CRC perturbations in cancer still remains an important question. In the past decade, thousands of genomic mutations have been identified by high-throughput sequencing technologies in a variety of cancer patients. A large number of genomic mutations and structural alterations were observed in enhancers, TFs, and target genes (Figure 1B). Moreover, copy number variations may impact the activities of a variety of oncogenic or tumor-suppressive pathways by perturbing the CRCs in cancer. For example, Xiao et al. have performed an integrative analysis of the enhancer-centered regulatory circuit perturbations in gliomas and revealed the potential CRC biomarkers.5Xiao J. Jin X. Zhang C. Zou H. Chang Z. Han N. Li X. Zhang Y. Li Y. Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma.Theranostics. 2021; 11: 3060-3073Crossref PubMed Google Scholar DNA methylation is the most widely studied epigenetic regulation (Figure 1B), which could also drive the tissue-specific enhancer basal transcription and target gene expression in human cancers.6Pan X. Li X. Sun J. Xiong Z. Hu H. Ning S. Zhi H. Enhancer methylation dynamics drive core transcriptional regulatory circuitry in pan-cancer.Oncogene. 2022; 41: 3474-3484Crossref PubMed Scopus (1) Google Scholar Gene expressions are regulated by the complex model of CRCs, represented as “nodes” and “edges” in the regulatory network model, respectively. Genetic and epigenetic alterations not only perturbed the nodes but also exhibited edge-specific alterations (edgetic). The edgetic alterations of CRCs might improve our understanding of gene expression regulation in cancer. The gene regulatory network has been observed to be perturbed by genetic and epigenetic variations,7Li Y. McGrail D.J. Xu J. Mills G.B. Sahni N. Yi S. Gene regulatory network perturbation by genetic and epigenetic variation.Trends Biochem. Sci. 2018; 43: 576-592Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar,8Li Y. Zhang Y. Li X. Yi S. Xu J. Gain-of-Function mutations: an emerging advantage for cancer biology.Trends Biochem. Sci. 2019; 44: 659-674Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar such as genomic mutations and DNA methylation. Genomic mutations and DNA methylation can regulate gene expression by gain or loss of the binding of transcription factor(s) to DNA (Figure 1B). Such efforts have already been initiated in cancer for understanding the edgetic perturbations of CRCs. The global landscape of DNA methylation-mediated transcriptional dysregulation across 22 human cancer types has been portrayed recently,9Wang Z. Yin J. Zhou W. Bai J. Xie Y. Xu K. Zheng X. Xiao J. Zhou L. Qi X. et al.Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types.Nucleic Acids Res. 2020; 48: 2287-2302Crossref PubMed Scopus (20) Google Scholar and a network module that consisted of methylation-sensitive TFs with prognostic potential was identified. Moreover, regulatory network and pathway perturbations analyses help prioritizing cancer driver mutations and genes.10Li Y. Burgman B. Khatri I.S. Pentaparthi S.R. Su Z. McGrail D.J. Li Y. Wu E. Eckhardt S.G. Sahni N. Yi S.S. e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.Nucleic Acids Res. 2021; 49: e2Crossref PubMed Scopus (5) Google Scholar Feng et al. also identified the core TFs in tissue-specific CRC networks as potential biomarkers, which have regulatory potential for cancer immunotherapy.1Feng C. Song C. Jiang Y. Zhao J. Zhang J. Wang Y. Yin M. Zhu J. Ai B. Wang Q. et al.Landscape and significance of human super enhancer-driven core transcription regulatory circuitry.Mol. Ther. Nucleic Acids. 2023; https://doi.org/10.1016/j.omtn.2023.03.014Abstract Full Text Full Text PDF Scopus (0) Google Scholar Functional characterization of the node and edgetic effects of genetic and epigenetic variants on CRCs will undoubtedly facilitate our understanding of the molecular mechanism of cancer, which will help identifying biomarkers for clinical diagnosis and therapy. In conclusion, the findings of Feng et al. provided a valuable resource for investigating the CRCs across diverse tissues and cell types. Comprehensive analysis of the enhancer-TF-gene regulatory circuits across diverse tissue/cell types and cancers may thus help better understanding of cancer-associated genetic and epigenetic alterations, leading to potential CRC-directed therapeutic interventions in cancer. This work was supported by the National Natural Science Foundation of China (32170676, 32060152, 31970646), Marshal Initiative Funding of Hainan Medical University (JBGS202103), Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical University, and Heilongjiang Touyan Innovation Team Program. The authors declare no financial interest.

Recent Developments of the International Future Lab of Hydrogen Economy: Using Artificial Intelligence (AI) Based Machine Learning (ML) in r-SOC Development

Current r-SOC research places special emphasis on the integrated approach of traditional numerical methods like CFD and accelerated AI methods. Using experimentally validated numerical models, the complex thermochemistry of auxiliary components containing syngas was evaluated. This method is used to generate data for the successful development and training of AI-based machine-learning models. Analyses shed light on the interactions between process variables in order to improve and prepare SOC-ready fuel, which is crucial for operation success. A recently developed ML model is utilised effectively to forecast and optimise reforming processes with various fuel constellations, including syngas compositions containing oxygen. Consequently, the results contribute to a greater understanding and qualitative benefits of preparing high-quality, pure syngas, improved fuel utilisation advancing sustainable research, and safe, consistent r-SOC operation. Consequently, early availability of valuable information is achieved. In addition, the strategy reduced prohibitive experiments, which contributed to the sustainable utilisation of resources.

Plasma Biomarkers of Alzheimer's Disease: A Review of Available Assays, Recent Developments, and Implications for Clinical Practice.

Recently, low-sensitive plasma assays have been replaced by new ultra-sensitive assays such as single molecule enzyme-linked immunosorbent assay (Simoa), the Mesoscale Discovery (MSD) platform, and immunoprecipitation-mass spectrometry (IP-MS) with higher accuracy in the determination of plasma biomarkers of Alzheimer's disease (AD). Despite the significant variability, many studies have established in-house cut-off values for the most promising available biomarkers. We first reviewed the most used laboratory methods and assays to measure plasma AD biomarkers. Next, we review studies focused on the diagnostic performance of these biomarkers to identify AD cases, predict cognitive decline in pre-clinical AD cases, and differentiate AD cases from other dementia. We summarized data from studies published until January 2023. A combination of plasma Aβ42/40 ratio, age, and APOE status showed the best accuracy in diagnosing brain amyloidosis with a liquid chromatography-mass spectrometry (LC-MS) assay. Plasma p-tau217 has shown the best accuracy in distinguishing Aβ-PET+ from Aβ-PET-even in cognitively unimpaired individuals. We also summarized the different cut-off values for each biomarker when available. Recently developed assays for plasma biomarkers have undeniable importance in AD research, with improved analytical and diagnostic performance. Some biomarkers have been extensively used in clinical trials and are now clinically available. Nonetheless, several challenges remain to their widespread use in clinical practice.

Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies

Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical properties of these methods in the context of control and reinforcement learning. This article surveys some of the recent developments on policy optimization, a gradient-based iterative approach for feedback control synthesis that has been popularized by successes of reinforcement learning. We take an interdisciplinary perspective in our exposition that connects control theory, reinforcement learning, and large-scale optimization. We review a number of recently developed theoretical results on the optimization landscape, global convergence, and sample complexityof gradient-based methods for various continuous control problems, such as the linear quadratic regulator (LQR), [Formula: see text] control, risk-sensitive control, linear quadratic Gaussian (LQG) control, and output feedback synthesis. In conjunction with these optimization results, we also discuss how direct policy optimization handles stability and robustness concerns in learning-based control, two main desiderata in control engineering. We conclude the survey by pointing out several challenges and opportunities at the intersection of learning and control.

Extreme precipitation return levels for multiple durations on a global scale

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.