Non-overlapping Sets Research Articles

A novel algorithmic multi-attribute decision-making framework for solar panel selection using modified aggregations of cubic intuitionistic fuzzy hypersoft set

To address the shortcomings of the cubic intuitionistic fuzzy sets (CIFSs) for the entitlement of multi-argument approximate function, the cubic intuitionistic fuzzy hypersoft set (Ω-set) is an emerging study area. This type of setting associates the sub-parametric tuples with the collection of CIFSs. Categorizing the evaluation of parameters into their corresponding sub-parametric values based on non-overlapping sets has significance in decision making and optimization related situations. Some operations of Ω-set are proposed in this study, along with certain practical features. We provide the complement, P-order, and R-order subsets, P-union (∪P), R-union (∪R), P-intersection (∩P) and R-intersection (∩R) of Ω-sets. The internal cubic intuitionistic fuzzy hypersoft set (ΩI-set) and the external cubic intuitionistic fuzzy hypersoft set (ΩE-set) are also proposed in this paper, which will aid researchers in applying this new theory to other areas of study. We show a few examples in this context and look into some more aspects of ∪P, ∪R, ∩P and ∩R of ΩI-sets and ΩE-sets. Arguments for a few significant theorems about ΩI-sets and ΩE-sets are also presented. Lastly, an algorithm is presented that assists decision-makers in evaluating appropriate solar panels to establish solar plants. The proposed algorithm uses the idea of ∪P and ∪R for two Ω-sets constructed based on expert opinions of decision makers.

Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400)

IntroductionSquamous cell cancer (SqCC) is a lung cancer subtype with few targeted therapy options. Molecular characterization, that is, by next-generation sequencing (NGS), is needed to identify potential targets. Lung Cancer Master Protocol Southwest Oncology Group S1400 enrolled patients with previously treated stage IV or recurrent SqCC to assess NGS biomarkers for therapeutic sub-studies. MethodsTumors underwent NGS using Foundation Medicine’s FoundationOne research platform, which sequenced the exons and/or introns of 313 cancer-related genes. Mutually exclusive gene set analysis and Selected Events Linked by Evolutionary Conditions across Human Tumors were performed to identify mutually exclusive and co-occurring gene alterations. Comparisons were performed with data on 495 lung SqCC downloaded from The Cancer Genome Atlas. Cox proportional hazards models were used to assess associations between genetic variants and survival. ResultsNGS data are reported for 1672 patients enrolled on S1400 between 2014 and 2019. Mutually exclusive gene set analysis identified two non-overlapping sets of mutually exclusive alterations with a false discovery rate of less than 15%: NFE2L2, KEAP1, and PARP4; and CDKN2A and RB1. PARP4, a relatively uncharacterized gene, showed three frequent mutations suggesting functional significance: 3116T>C (I1039T), 3176A>G (Q1059R), and 3509C>T (T1170I). When taken together, NFE2L2 and KEAP1 alterations were associated with poorer survival. ConclusionsAs the largest dataset to date of lung SqCC profiled on a clinical trial, the S1400 NGS dataset establishes a rich resource for biomarker discovery. Mutual exclusivity of PARP4 and NFE2L2 or KEAP1 alterations suggests that PARP4 may have an uncharacterized role in a key pathway known to impact oxidative stress response and treatment resistance.

Deep Fuzzy Min-Max Neural Network: Analysis and Design.

Fuzzy min-max neural network (FMNN) is one kind of three-layer models based on hyperboxes that are constructed in a sequential way. Such a sequential mechanism inevitably leads to the input order and overlap region problem. In this study, we propose a deep FMNN (DFMNN) based on initialization and optimization operation to overcome these limitations. Initialization operation that can solve the input order problem is to design hyperboxes in a simultaneous way, and side parameters have been proposed to control the size of hyperboxes. Optimization operation that can eliminate overlap region problem is realized by means of deep layers, where the number of layers is immediately determined when the overlap among hyperboxes is eliminated. In the optimization process, each layer consists of three sections, namely, the partition section, combination section, and union section. The partition section aims to divide the hyperboxes into a nonoverlapping hyperbox set and an overlapping hyperbox set. The combination section eliminates the overlap problem of overlapping hyperbox set. The union section obtains the optimized hyperbox set in the current layer. DFMNN is evaluated based on a series of benchmark datasets. A comparative analysis illustrates that the proposed DFMNN model outperforms several models previously reported in the literature.

Effects of electroconvulsive therapy on cortical thickness in depression: a systematic review.

Electroconvulsive therapy (ECT) is one of the most studied and validated available treatments for severe or treatment-resistant depression. However, little is known about the neural mechanisms underlying ECT. This systematic review aims to critically review all structural magnetic resonance imaging studies investigating longitudinal cortical thickness (CT) changes after ECT in patients with unipolar or bipolar depression. We performed a search on PubMed, Medline, and Embase to identify all available studies published before April 20, 2023. A total of 10 studies were included. The investigations showed widespread increases in CT after ECT in depressed patients, involving mainly the temporal, insular, and frontal regions. In five studies, CT increases in a non-overlapping set of brain areas correlated with the clinical efficacy of ECT. The small sample size, heterogeneity in terms of populations, comorbidities, and ECT protocols, and the lack of a control group in some investigations limit the generalisability of the results. Our findings support the idea that ECT can increase CT in patients with unipolar and bipolar depression. It remains unclear whether these changes are related to the clinical response. Future larger studies with longer follow-up are warranted to thoroughly address the potential role of CT as a biomarker of clinical response after ECT.

The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis.

Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain. We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis. We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8). Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.

Opposing chemosensory functions of closely related gustatory receptors

In the fruit fly Drosophila melanogaster, gustatory sensory neurons express taste receptors that are tuned to distinct groups of chemicals, thereby activating neural ensembles that elicit either feeding or avoidance behavior. Members of a family of ligand -gated receptor channels, the Gustatory receptors (Grs), play a central role in these behaviors. In general, closely related, evolutionarily conserved Gr proteins are co-expressed in the same type of taste neurons, tuned to chemically related compounds, and therefore triggering the same behavioral response. Here, we report that members of the Gr28 subfamily are expressed in largely non-overlapping sets of taste neurons in Drosophila larvae, detect chemicals of different valence, and trigger opposing feeding behaviors. We determined the intrinsic properties of Gr28 neurons by expressing the mammalian Vanilloid Receptor 1 (VR1), which is activated by capsaicin, a chemical to which wild-type Drosophila larvae do not respond. When VR1 is expressed in Gr28a neurons, larvae become attracted to capsaicin, consistent with reports showing that Gr28a itself encodes a receptor for nutritious RNA. In contrast, expression of VR1 in two pairs of Gr28b.c neurons triggers avoidance to capsaicin. Moreover, neuronal inactivation experiments show that the Gr28b.c neurons are necessary for avoidance of several bitter compounds. Lastly, behavioral experiments of Gr28 deficient larvae and live Ca2+ imaging studies of Gr28b.c neurons revealed that denatonium benzoate, a synthetic bitter compound that shares structural similarities with natural bitter chemicals, is a ligand for a receptor complex containing a Gr28b.c or Gr28b.a subunit. Thus, the Gr28 proteins, which have been evolutionarily conserved over 260 million years in insects, represent the first taste receptor subfamily in which specific members mediate behavior with opposite valence.

Opposing chemosensory functions of closely related gustatory receptors.

In the fruit fly Drosophila melanogaster, gustatory sensory neurons express taste receptors that are tuned to distinct groups of chemicals, thereby activating neural ensembles that elicit either feeding or avoidance behavior. Members of a family of ligand -gated receptor channels, the Gustatory receptors (Grs), play a central role in these behaviors. In general, closely related, evolutionarily conserved Gr proteins are co-expressed in the same type of taste neurons, tuned to chemically related compounds, and therefore triggering the same behavioral response. Here, we report that members of the Gr28 subfamily are expressed in largely non-overlapping sets of taste neurons in Drosophila larvae, detect chemicals of different valence, and trigger opposing feeding behaviors. We determined the intrinsic properties of Gr28 neurons by expressing the mammalian Vanilloid Receptor 1 (VR1), which is activated by capsaicin, a chemical to which wild-type Drosophila larvae do not respond. When VR1 is expressed in Gr28a neurons, larvae become attracted to capsaicin, consistent with reports showing that Gr28a itself encodes a receptor for nutritious RNA. In contrast, expression of VR1 in two pairs of Gr28b.c neurons triggers avoidance to capsaicin. Moreover, neuronal inactivation experiments show that the Gr28b.c neurons are necessary for avoidance of several bitter compounds. Lastly, behavioral experiments of Gr28 deficient larvae and live Ca2+ imaging studies of Gr28b.c neurons revealed that denatonium benzoate, a synthetic bitter compound that shares structural similarities with natural bitter chemicals, is a ligand for a receptor complex containing a Gr28b.c or Gr28b.a subunit. Thus, the Gr28 proteins, which have been evolutionarily conserved over 260 million years in insects, represent the first taste receptor subfamily in which specific members mediate behavior with opposite valence.

Protein language models can capture protein quaternary state

BackgroundDetermining a protein’s quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models, such as ESM-2, that apply computational natural-language models to proteins successfully capture secondary structure, protein cell localization and other characteristics, from a single sequence. Here we hypothesize that information about the protein quaternary state may be contained within protein sequences as well, allowing us to benefit from these novel approaches in the context of quaternary state prediction.ResultsWe generated ESM-2 embeddings for a large dataset of proteins with quaternary state labels from the curated QSbio dataset. We trained a model for quaternary state classification and assessed it on a non-overlapping set of distinct folds (ECOD family level). Our model, named QUEEN (QUaternary state prediction using dEEp learNing), performs worse than approaches that include information from solved crystal structures. However, it successfully learned to distinguish multimers from monomers, and predicts the specific quaternary state with moderate success, better than simple sequence similarity-based annotation transfer. Our results demonstrate that complex, quaternary state related information is included in such embeddings.ConclusionsQUEEN is the first to investigate the power of embeddings for the prediction of the quaternary state of proteins. As such, it lays out strengths as well as limitations of a sequence-based protein language model approach, compared to structure-based approaches. Since it does not require any structural information and is fast, we anticipate that it will be of wide use both for in-depth investigation of specific systems, as well as for studies of large sets of protein sequences. A simple colab implementation is available at: https://colab.research.google.com/github/Furman-Lab/QUEEN/blob/main/QUEEN_prediction_notebook.ipynb.

Cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.

The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach.

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

Multiresolution categorical regression for interpretable cell-type annotation.

In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method for fitting the multinomial logistic regression model in high dimensions that addresses this problem in a unified and data-driven way. Our method allows practitioners to identify which predictors distinguish between coarse categories but not fine categories, which predictors distinguish between fine categories, and which predictors are irrelevant. For model fitting, we propose a scalable algorithm that can be applied when the coarse categories are defined by either overlapping or nonoverlapping sets of fine categories. Statistical properties of our method reveal that it can take advantage of this multiresolution structure in a way existing estimators cannot. We use our method to model cell-type probabilities as a function of a cell's gene expression profile (i.e., cell-type annotation). Our fitted model provides novel biological insights which may be useful for future automated and manual cell-type annotationmethodology.

Functional dissection and assembly of a small, newly evolved, W chromosome-specific genomic region of the African clawed frog Xenopus laevis.

Genetic triggers for sex determination are frequently co-inherited with other linked genes that may also influence one or more sex-specific phenotypes. To better understand how sex-limited regions evolve and function, we studied a small W chromosome-specific region of the frog Xenopus laevis that contains only three genes (dm-w, scan-w, ccdc69-w) and that drives female differentiation. Using gene editing, we found that the sex-determining function of this region requires dm-w but that scan-w and ccdc69-w are not essential for viability, female development, or fertility. Analysis of mesonephros+gonad transcriptomes during sexual differentiation illustrates masculinization of the dm-w knockout transcriptome, and identifies mostly non-overlapping sets of differentially expressed genes in separate knockout lines for each of these three W-specific gene compared to wildtype sisters. Capture sequencing of almost all Xenopus species and PCR surveys indicate that the female-determining function of dm-w is present in only a subset of species that carry this gene. These findings map out a dynamic evolutionary history of a newly evolved W chromosome-specific genomic region, whose components have distinctive functions that frequently degraded during Xenopus diversification, and evidence the evolutionary consequences of recombination suppression.

Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank.

Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear. In this study, we focused on N = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets. The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69. High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Environmental stress responses and adaptive evolution in the alpine ground beetle Nebria vandykei

Abstract The oscillating glacial–interglacial climate has had well-characterized effects on alpine species, driving rapid distributional and demographic shifts that have led to lineage diversification. It is unclear whether adaptive evolution has occurred during these rapid demographic changes, because strong genetic drift can overcome the force of selection. Here, using the alpine ground beetle Nebria vandykei, we test for evidence of adaptive evolution. Initially, we explore the genetic pathways induced during environmental stress responses through RNA sequencing, showing that cold, heat and desiccation stress activate a largely non-overlapping set of molecular pathways. Using additional transcriptome sequencing, we estimate the evolutionary relationship of N. vandykei to related species in the subgenus Catonebria and several outgroups. Phylogenetic analyses suggest that a history of admixture or very rapid diversification underlies the evolution of N. vandykei. Finally, using tests for selection polarized by high- and low-elevation relatives, we demonstrate selection acting on stress response pathways and on pathways known to function in tolerance to cold and hypoxic environments. These results support the role of environmental adaptation in alpine species despite rapid demographic change, while demonstrating that admixture might play a key role in facilitating adaptive diversification of alpine species.

Effect of Storage Time on the Physical, Chemical, and Rheological Properties of Blueberry Jam: Experimental Measurements and Artificial Neural Network Simulation.

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel’s local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Cell surface nucleocapsid protein expression: A betacoronavirus immunomodulatory strategy.

We recently reported that SARS-CoV-2 nucleocapsid (N) protein is abundantly expressed on the surface of both infected and neighboring uninfected cells, where it enables activation of Fc receptor-bearing immune cells with anti-N antibodies (Abs) and inhibits leukocyte chemotaxis by binding chemokines (CHKs). Here, we extend these findings to N from the common cold human coronavirus (HCoV)-OC43, which is also robustly expressed on the surface of infected and noninfected cells by binding heparan sulfate/heparin (HS/H). HCoV-OC43 N binds with high affinity to the same set of 11 human CHKs as SARS-CoV-2 N, but also to a nonoverlapping set of six cytokines. As with SARS-CoV-2 N, HCoV-OC43 N inhibits CXCL12β-mediated leukocyte migration in chemotaxis assays, as do all highly pathogenic and common cold HCoV N proteins. Together, our findings indicate that cell surface HCoV N plays important evolutionarily conserved roles in manipulating host innate immunity and as a target for adaptive immunity.

Essential roles of RNA cap-proximal ribose methylation in mammalian embryonic development and fertility

Eukaryotic RNA pol II transcripts are capped at the 5' end by the methylated guanosine (m7G) moiety. In higher eukaryotes, CMTR1 and CMTR2 catalyze cap-proximal ribose methylations on the first (cap1) and second (cap2) nucleotides, respectively. These modifications mark RNAs as "self," blocking the activation of the innate immune response pathway. Here, we show that loss of mouse Cmtr1 or Cmtr2 leads to embryonic lethality, with non-overlapping sets of transcripts being misregulated, but without activation of the interferon pathway. In contrast, Cmtr1 mutant adult mouse livers exhibit chronic activation of the interferon pathway, with multiple interferon-stimulated genes being expressed. Conditional deletion of Cmtr1 in the germline leads to infertility, while global translation is unaffected in the Cmtr1 mutant mouse liver and human cells. Thus, mammalian cap1 and cap2 modifications have essential roles in gene regulation beyond their role in helping cellular transcripts to evade the innate immune system.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study.

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI.

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists.