Unknown Factors Research Articles

Role of TOMM34 on NF-κB activation-related hyperinflammation in severely ill patients with COVID-19 and influenza

Highly pathogenic respiratory RNA viruses such as SARS-CoV-2 and its associated syndrome COVID-19 pose a tremendous threat to the global public health. Innate immune responses to SARS-CoV-2 depend mainly upon the NF-κB-mediated inflammation. Identifying unknown host factors driving the NF-κB activation and inflammation is crucial for the development of immune intervention strategies. Published single-cell RNA sequencing (scRNA-seq) data was used to analyze the differential transcriptome profiles of bronchoalveolar lavage (BAL) cells between healthy individuals (n=27) and patients with severe COVID-19 (n=21), as well as the differential transcriptome profiles of peripheral blood mononuclear cells (PBMCs) between healthy individuals (n=22) and severely ill patients with COVID-19 (n=45) or influenza (n=16). Loss-of-function and gain-of-function assays were performed in diverse viruses-infected cells and male mice models to identify the role of TOMM34 in antiviral innate immunity. TOMM34, together with a list of genes encoding pro-inflammatory cytokines and antiviral immune proteins, was transcriptionally upregulated in circulating monocytes, lung epithelium and innate immune cells from individuals with severe COVID-19 or influenza. Deficiency of TOMM34/Tomm34 significantly impaired the type I interferon responses and NF-κB-mediated inflammation in various human/murine cell lines, murine bone marrow-derived macrophages (BMDMs) and invivo. Mechanistically, TOMM34 recruits TRAF6 to facilitate the K63-linked polyubiquitination of NEMO upon viral infection, thus promoting the downstream NF-κB activation. In this study, viral induction of TOMM34 is positively correlated with the hyperinflammation in severely ill patients with COVID-19 and influenza. Our findings also highlight the physiological role of TOMM34 in the innate antiviral signallings. A full list of funding sources can be found in the acknowledgements section.

Quantifying microbial DNA in metagenomes improves microbial trait estimation

Abstract Shotgun metagenomics is a powerful tool for studying the genomic traits of microbial community members, such as genome size, gene content, etc. While such traits can be used to better understand the ecology and evolution of microbial communities, the accuracy of their estimations can be critically influenced by both known and unknown factors. One factor that can bias trait estimations is the proportion of eukaryotic and viral DNA in a metagenome, as some bioinformatic tools assume that all DNA reads in a metagenome are bacterial or archaeal. Here, we add to a recent debate about the influence of eukaryotic DNA in the estimation of average genome size from a global soil sample dataset using a new bioinformatic tool. Contrary to what was assumed, our reanalysis of this dataset revealed that soil samples can contain a substantial proportion of non-microbial DNA, which severely inflated the original estimates of average genome size. Correcting for this bias significantly improves the statistical support for the negative relationship between average bacterial genome size and soil pH. These results highlight that metagenomes can contain large quantities of non-microbial DNA, and that new methods that correct for this can improve microbial trait estimation.

Genomics of Terpene Biosynthesis in Dictyoceratid Sponges (Porifera) - What Do We (Not) Know?

Sponges are recognized as promising sources for novel bioactive metabolites. Among them are terpenoid metabolites that constitute key biochemical defense mechanisms in several sponge taxa. Despite their significance, the genetic basis for terpenoid biosynthesis in sponges remains poorly understood. Dictyoceratida comprise demosponges well-known for their bioactive terpenoids. In this study, we explored the currently available genomic data for insights into the metabolic pathways of dictyoceratid terpenoids. We first identified prenyltransferase (PT) and terpene cyclase (TC) enzymes essential for the terpenoid biosynthetic processes in the terrestrial realm by analyzing available transcriptomic and genomic data of Dictyoceratida sponges and 10 other sponge species. All Dictyoceratida sponges displayed various PTs involved in either sesqui- or diterpene, steroid and carotenoid production. Additionally, it was possible to identify a potential candidate for a dictyoceratid sesterterpene PT. However, analogs of common terrestrial TCs were absent, suggesting the existence of a distinct or convergently evolved sponge-specific TC. Our study aims to contribute to the foundational understanding of terpene biosynthesis in sponges, unveiling the currently evident genetic components for terpenoid production in species not previously studied. Simultaneously, it aims to identify the known and unknown factors, as a starting point for biochemical and genetic investigations in sponge terpenoid production.

406. EXPLORING THE OPTIMAL TIMING OF MINIMALLY INVASIVE SURGERY FOR ESOPHAGEAL SQUAMOUS CELL CARCINOMA AFTER NEOADJUVANT CHEMORADIOTHERAPY

Abstract Background Neoadjuvant chemoradiotherapy has gradually become a first-line treatment for esophageal squamous cell carcinoma patients. After the completion of neoadjuvant chemoradiotherapy, patients usually undergo minimally invasive surgery to ensure complete removal of the tumor and lymphatic tissue. However, the optimal interval between neoadjuvant chemoradiotherapy and minimally invasive surgery is still an unknown factor, and whether it affects postoperative disease-free survival (DFS) and overall survival (OS) of patients remains unknown. Therefore, the main purpose of this study is to explore the specific impact of the interval between neoadjuvant chemoradiotherapy and minimally invasive surgery on patients, and to find the optimal interval time. Methods Patients who underwent neoadjuvant chemoradiotherapy and minimally invasive surgery from two institutions between October 2012 and September 2022 were included in this study. The time interval is defined as the days between the last time a patient receives neoadjuvant chemoradiotherapy and the execution of surgery. Determine the optimal interval truncation point using the surv_cutpoint function in the survminer package. The main research result is the OS and DFS of different interval groups. A multivariate Cox regression model was established to determine the main factors affecting Patient's OS. Construct a nomogram based on selected factors to meet clinical needs. Results 252 patients were included in the study. The average interval between patients undergoing neoadjuvant chemoradiotherapy and minimally invasive surgery is 59 days. The earliest interval for patients to undergo surgery is 28 days. Determine the optimal interval of 46 days based on the survminer package and divide the patients into two groups. The five-year OS rates for the short interval group and the long interval group were 82.6% and 61.0% (P = 0.023). The five-year DFS rates for the two groups were 67.6% and 48.2% (P=0.068). Multivariate Cox regression shows that the time interval, patient's sex, tumor differentiation, ypT, and ypN are the main factors affecting overall survival. Conclusions The interval between neoadjuvant chemoradiotherapy and minimally invasive surgery has a certain impact on patient survival, and it is recommended to undergo surgery between 28 and 46 days to improve patient prognosis and survival.

Falls among Pediatric Patients Receiving Home Care.

Children with medical complexity (CMC) often require home healthcare services to manage chronic health conditions. Evaluation of home safety is recommended when children transition from hospital to home care, though despite best efforts, safety events, such as falls, still occur. Understanding the prevalence and causal factors of falls in CMC is critical for the development of fall prevention interventions and protocols. This study aims to describe demographics and reasons for falls reported in CMC receiving home healthcare services. A retrospective analysis was performed using data from an incident reporting database from January 2019 to March 2023. Participants included CMC who received home healthcare services from a single institution and had at least one documented fall. A total of 43 falls were experienced by 31 unique participants. The participants were predominantly male (58.1%), White (71.0%), and non-Hispanic/Latino (96.8%), with a median age of 10 years at the time of the fall. Primary diagnoses of CMC with falls included neurological disorders (41.9%), congenital chromosomal abnormalities (25.8%), and oncological conditions (16.1%). The most common reasons for falls were loss of balance (32.6%), unknown factors (19.6%), and trip/slips (17.4%). Half of falls were deemed to be potentially preventable. This study provides valuable insight into falls among CMC receiving home healthcare services and emphasizes the multifactorial nature of fall risks in this population. Understanding demographic characteristics, diagnoses, and causal factors of falls is critical in the development of proactive fall prevention strategies. Responding proactively to mitigate fall risks is an important step in enhancing the safety and quality of life for CMC. Future collaborative research efforts are warranted to validate findings and evaluate potentially successful fall prevention interventions.

Notes on Measuring Concentration Ratio Distribution for a Solar Tower Using Moonlight

The authors' original concept of indirect solar flux mapping of a heliostat field measures CRD using a compact stationary array of moonlight illuminometers on the receiver aperture and a reference moonlight illuminometer on the dual-axis moon tracker. Two sets of moonlight concentration experiments (CCD camera + white target; concentrated lunar beam passing across the linear array of illuminators) were carried out on 63 heliostats at Badaling Solar Tower Power Plant in Beijing on a full moon night in 2018. How the sun and moon shapes differ for CRD as light sources on a solar dish is investigated. Solar/lunar CRDs are similar. Another lunar flux mapping model is also presented, allowing different moon shapes to estimate the solar CRD and contribute improvements in extrapolating measured lunar CRDs to a solar tower working with the real Sun by repeated smooth-filtering. In this paper, some unknown factors, effects, and improvements are carefully considered to enhance the CRD measurement accuracy of a solar tower power plant by using moonlight concentration.

Patient resuscitated after cardiopulmonary arrest exhibits abnormally increased phenytoin metabolic rate due to unknown factors: a case report

BackgroundFosphenytoin (FOS) is a prodrug of phenytoin (PHT) with a metabolism that exhibits Michaelis–Menten-type kinetics. Genetic polymorphisms of the metabolic enzymes of PHT make it challenging to predict its plasma concentrations. High plasma PHT concentrations are typically problematic, and several causes have been elucidated. In contrast, cases of patients with low PHT plasma concentrations that did not increase despite the administration of appropriate PHT doses have been reported, and the causes may include changes in plasma protein-binding rates, genetic mutations, and concomitant use of drugs that induce liver enzymes; however, even these factors do not explain the low PHT plasma concentrations in some cases.Case presentationWe encountered the case of a patient with plasma PHT concentrations that were continuously < 0.7 µg/mL after daily use of FOS for seizures that occurred after cardiopulmonary arrest. We analyzed the protein-unbound fraction, urinary metabolites, and related genes to investigate the cause. False negatives due to the measurement method, errors in dosage and administration method, and increased excretion of PHT were excluded. Hepatic metabolic activity of PHT increased to 4.6–6.1 times the normal level. The S/R ratio of 5-(p-hydroxyphenyl)-5-phenylhydantoin-glucuronide, a major PHT metabolite, was normal at 15.2, suggesting increased activities of CYP2C9 and CYP2C19. Furthermore, the protein-unbound fraction of PHT was 5.2–6.9%, CYP2C19*17 was wild type, and there was no concomitant drug use to induce both enzymes.ConclusionsThe low PHT plasma concentration in this patient was found to be caused by increased hepatic metabolic activity that could not be explained by known factors. Careful monitoring is necessary to consider the possibility of increased hepatic metabolic activity in similar cases.

Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing

Uncommon genetic illnesses pose significant challenges for detection due to their low occurrence and intricate genetic makeup. Traditional targeted genetic testing methods are often limited, missing rare or unidentified genetic variations. This project proposes a novel strategy that leverages Whole-Genome Sequencing (WGS) data and Random Forest (RF) analysis to overcome these limitations. WGS provides a comprehensive view of an individual's genetic profile, capturing a wide array of genetic variations that targeted approaches might overlook. By employing the RF method, which excels at handling complex datasets and detecting non-linear interactions, this project aims to uncover intricate links between rare genetic diseases and gene variations. The Swedish Genome Reference dataset will serve as the foundation for this research. RF analysis will be applied to this extensive dataset to identify patterns and connections that might reveal new genetic markers and previously unknown risk factors for these illnesses. This approach allows for exploring vast genetic datasets to detect structures and associations, providing deeper insights into the genetic underpinnings of rare diseases. Combining WGS with RF analysis offers a powerful tool for discovering genetic indicators and risk factors contributing to rare genetic disorders, achieving an accuracy rate of 97 %. This innovative approach can significantly enhance understanding, diagnosis, and treatment of these conditions. By highlighting the value of advanced computational techniques and comprehensive WGS databases, the project aims to pave the way for more personalized and specialized medical treatments, ultimately improving patient outcomes for those affected by rare genetic diseases.

Fate of heavy metals in ecosystems of dam reservoirs: Transport, distribution and significance of the origin of organic matter

In this article, a multivariate analysis of the parameters determining the transport and fate of selected heavy metals in the water - bottom sediment interface was carried out. The studies were carried out in the summer season of 2019 at Nielisz Reservoir (southeastern Poland, Lublin Voivodeship).Finally, a previously unknown factor related to the quality of organic matter was identified. Autochthonous organic matter was shown to promote the accumulation of the studied heavy metals. To date, the significance of the origin of organic matter in the context of the transport and fate of heavy metals in retention reservoirs has rarely been reported in the scientific literature. More than that, this factor was not considered an important component in the process of heavy metal deposition in bottom sediments. However, it turns out that not only the quantity of organic matter, but also its quality plays an important role in the circulation of heavy metals in retention reservoir ecosystems. It was found that autochthonous organic matter promotes the accumulation of the studied heavy metals. It can be assumed that, in a sense, it plays the role of a catenary ("hub") controlling the fate of heavy metals in the water-sediment system. It has also been conjectured that, in a sense, OMS may reflect the potential for heavy metal assimilation by aquatic vascular plants (mainly of the C3 group). Plants with a photosynthetic pathway similar to the C3 group generally have a much lower enrichment in the 13C isotope (δ13C from −38‰ to −22‰). In our case, the lowest δ13C-TOCS value was −24.05‰, and the average for the whole reservoir was −21.53‰. In addition, it was observed that quantitative changes in the isotopic composition of total organic carbon δ13C-TOCS, corresponded with changes in the content of the heavy metals studied in entrapped sediments.

KREH2 helicase represses ND7 mRNA editing in procyclic-stage Trypanosoma brucei by opposite modulation of canonical and 'moonlighting' gRNA utilization creating a proposed mRNA structure.

Unknown factors regulate mitochondrial U-insertion/deletion (U-indel) RNA editing in procyclic-form (PCF) and bloodstream-form (BSF) T. brucei. This editing, directed by anti-sense gRNAs, creates canonical protein-encoding mRNAs and may developmentally control respiration. Canonical editing by gRNAs that specify protein-encoding mRNA sequences occurs amid massive non-canonical editing of unclear sources and biological significance. We found PCF-specific repression at a major early checkpoint in mRNA ND7, involving helicase KREH2-dependent opposite modulation of canonical and non-canonical 'terminator' gRNA utilization. Terminator-programmed editing derails canonical editing and installs proposed repressive structure in 30% of the ND7 transcriptome. BSF-to-PCF differentiation in vitro recreated this negative control. Remarkably, KREH2-RNAi knockdown relieved repression and increased editing progression by reverting canonical/terminator gRNA utilization. ND7 transcripts lacking early terminator-directed editing in PCF exhibited similar negative editing control along the mRNA sequence, suggesting global modulation of gRNA utilization fidelity. The terminator is a 'moonlighting' gRNA also associated with mRNA COX3 canonical editing, so the gRNA transcriptome seems multifunctional. Thus, KREH2 is the first identified repressor in developmental editing control. This and our prior work support a model whereby KREH2 activates or represses editing in a stage and substrate-specific manner. KREH2's novel dual role tunes mitochondrial gene expression in either direction during development.

Genetic Basis of Hypertrophic Cardiomyopathy in Cats.

Hypertrophic cardiomyopathy (HCM) is a common cardiovascular condition in cats, affecting yth males and females of all ages. Some breeds, such as Ragdolls and Maine Coons, can develop HCM at a young age. The disease has a wide range of progression and severity, characterized by various pathological changes in the heart, including arteritis, fibrous tissue deposition, and myocardial cell hypertrophy. Left ventricular hypertrophy, which can restrict blood flow, is a common feature of HCM. The disease may persist into old age and eventually lead to heart failure and increased diastolic pressure. The basis of HCM in cats is thought to be genetic, although the exact mechanisms are not fully understood. Mutations in sarcomeric proteins, in particular myosin-binding protein C (MYBPC3), have been identified in cats with HCM. Two specific mutations, MYBPC3 [R818W] and MYBPC3 [A31P], have been classified as 'pathogenic'. Other variants in genes such as MYBPC3, TNNT2, ALMS1, and MYH7 are also associated with HCM. However, there are cases where cats without known genetic mutations still develop HCM, suggesting the presence of unknown genetic factors contributing to the disease. This work aims to summarise the new knowledge of HCM in cats and the alterations in cardiac tissue as a result of genetic variants.

Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea

The supply of livestock products depends on many internal and external factors. Omitting any one factor can make it difficult to describe the market patterns. So, forecasting livestock indexes such as prices and supplies is challenging due to the effect of unknown factors. This paper proposes a Stacking Forest Ensemble method (SFE-NET) to forecast pork supply by considering both internal and external factors, thereby contributing to sustainable pork production. We first analyze the internal factors to explore features related to pork supply. External factors such as weather conditions, gas prices, and disease information are also collected from different sources. The combined dataset is from 2016 to 2022. Our SFE-NET method utilizes Random Forest, Gradient Boosting, and XGBoost as members and a neural network as the meta-method. We conducted seven experiments for daily, weekly, and monthly pork supply using different sets of factors, such as internal, internal and external, and selected. The results showed the following findings: (a) The proposed method achieved Coefficient of Determination scores between 84% and 91% in short and long periods, (b) the external factors increased the performance of forecasting methods by about 2% to 12%, and (c) the proposed stacking ensemble method outperformed other comparative methods by 1% to 18%. These improvements in forecasting accuracy can help promote more sustainable pork production by enhancing market stability and resilience.

An enhanced Eco1 retron editor enables precision genome engineering in human cells from a single-copy integrated lentivirus.

Retrons are a retroelement class found in diverse prokaryotes that can be adapted to augment CRISPR-Cas9 genome engineering technology to efficiently rewrite short stretches of genetic information in bacteria and yeast; however, efficiency in human cells has been limited by unknown factors. We identified non-coding RNA (ncRNA) instability and impaired Cas9 activity as major contributors to poor retron editor efficiency. We re-engineered the Eco1 ncRNA to incorporate an exoribonuclease-resistant RNA pseudoknot from the Zika virus 3' UTR and devised an RNA processing strategy using Csy4 ribonuclease to liberate the sgRNA and ncRNA. These modifications yielded a ncRNA with 5'- and 3'-end protection and an sgRNA with minimal 5' extension. This strategy increased steady-state ncRNA levels and rescued Cas9 activity leading to enhanced efficiency of the Eco1 retron editor in human cells. The enhanced Eco1 retron editor enabled the insertion of missense mutations in human cells from a single integrated lentivirus, thereby ensuring genotype-phenotype linkage over multiple cell divisions. This work reveals a previously unappreciated role for ncRNA stability in retron editor efficiency in human cells. Here we present an enhanced Eco1 retron editor that enables efficient introduction of missense mutations in human cells from a single heritable genome copy.

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing.

Reconstructing the past of observed fluids has been known as an ill-posed problem due to both numerical and physical challenges, especially when observations are distorted by inevitable noise, resolution limits, or unknown factors. When employing traditional differencing schemes to reconstruct the past, the computation often becomes highly unstable or diverges within a few backward time steps from the distorted and noisy observation. Although several techniques have been recently developed for inverse problems, such as adjoint solvers and supervised learning, they are also unrobust against errors in observation when there is time-reversed simulation. Here we present that by using physics-informed neural computing, robust time-reversed fluid simulation is possible. By seeking a solution that closely satisfies the given physics and observations while allowing for errors, it reconstructs the most probable past from noisy observations. Our work showcases time rewinding in extreme fluid scenarios such as shock, instability, blast, and magnetohydrodynamic vortex. Potentially, this can be applied to trace back the interstellar evolution and determining the origin of fusion plasma instabilities.

Mathematical Structure of RelB Dynamics in the NF-κB Non-Canonical Pathway

This study analyzed the non-canonical NF-κB pathway, which controls functions distinct from those of the canonical pathway. Although oscillations of NF-κB have been observed in the non-canonical pathway, a detailed mechanism explaining the observed behavior remains elusive, owing to the different behaviors observed across cell types. This study demonstrated that oscillations cannot be produced by the experimentally observed pathway alone, thereby suggesting the existence of an unknown reaction pathway. Assuming this pathway, it became evident that the oscillatory structure of the non-canonical pathway was caused by stable periodic orbits. In addition, we demonstrated that altering the expression levels of specific proteins reproduced various behaviors. By fitting 14 parameters, excluding those measured in previous studies, this study successfully reproduce nuclear retention (saturation), oscillation, and singular events that had been experimentally confirmed. The analysis also provided a comprehensive understanding of the dynamics of the RelB protein and suggested a potential inhibitory role for the unknown factor. These findings indicate that the unknown factor may be an isoform of IκB, contributing to the regulation of NF-κB signaling. Based on these models, we gained invaluable understanding of biological systems, paving the way for the development of new strategies to manipulate specific biological processes.

Appraisal of awareness about single use plastic bags (supbs) among consumers and shopkeepers of Lanka market, Varanasi, Uttar Pradesh, India

Abstract The awareness level of humans varies depending upon various known and unknown factors. Environmental awareness of an individual is the reflection of his/her thoughts, feelings and sensitivities towards the environment and its associated problems like degradation. It is the most important parameter for the measurement of implementation of government’s policies designed for the safeguard of environment and other related issues. The Indian Government decision for the complete ban on the Single-Use Plastic Bags (SUPBs) has been implemented in the whole country since 01 July 2022. Single-use plastic bags pose serious concerns for the environment and the economy of the country. The consumption as well as the disposal of plastic-related products is increasing day by day in India. Around 3.5 million tonnes of plastic waste are generated every year (CPCB 2020). The present paper is about the appraisal of the awareness level of consumers and shopkeepers about the use of plastic bags. In the present study, the primary survey is conducted to understand the awareness of consumers and shopkeepers regarding Single-Use Plastic Bags (SUPBs) in Lanka market, Varanasi, Uttar Pradesh. The study tries to relate the level of literacy, economic status, hesitation to carry own bags and demand for the plastic bags with the environment awareness level. Study reflects, there is a strong relationship between uses of plastic bags with economic, social, cultural, political status of the people. It also reflects the lack of enforcement of policies about the Single-Use Plastic Bags (SUPBs).

GMM estimation for high-dimensional panel data models

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel data models. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate both the empirical relevance and the applicability of the proposed framework and corresponding estimation and testing methods.

Activation of the helper NRC4 immune receptor forms a hexameric resistosome

Innate immune responses to microbial pathogens are regulated by intracellular receptors known as nucleotide-binding leucine-rich repeat receptors (NLRs) in both the plant and animal kingdoms. Across plant innate immune systems, "helper" NLRs (hNLRs) work in coordination with "sensor" NLRs (sNLRs) to modulate disease resistance signaling pathways. Activation mechanisms of hNLRs based on structures are unknown. Our research reveals that the hNLR, known as NLR required for cell death 4 (NRC4), assembles into a hexameric resistosome upon activation by the sNLR Bs2 and the pathogenic effector AvrBs2. This conformational change triggers immune responses by facilitating the influx of calcium ions (Ca2+) into the cytosol. The activation mimic alleles of NRC2, NRC3, or NRC4 alone did not induce Ca2+ influx and cell death in animal cells, suggesting that unknown plant-specific factors regulate NRCs' activation in plants. These findings significantly advance our understanding of the regulatory mechanisms governing plant immune responses.

Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM

The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD–GAT–BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

Use of genetic correlations to examine selection bias.

Observational studies are rarely representative of their target population because there are known and unknown factors that affect an individual's choice to participate (the selection mechanism). Selection can cause bias in a given analysis if the outcome is related to selection (conditional on the other variables in the model). Detecting and adjusting for selection bias in practice typically requires access to data on nonselected individuals. Here, we propose methods to detect selection bias in genetic studies by comparing correlations among genetic variants in the selected sample to those expected under no selection. We examine the use of four hypothesis tests to identify induced associations between genetic variants in the selected sample. We evaluate these approaches in Monte Carlo simulations. Finally, we use these approaches in an applied example using data from the UK Biobank (UKBB). The proposed tests suggested an association between alcohol consumption and selection into UKBB. Hence, UKBB analyses with alcohol consumption as the exposure or outcome may be biased by this selection.