Browse Articles

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P , however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.

Impact of interprovincial pairing assistance policies on sustainable agricultural development in Xinjiang of China

Sample observed effects: enumeration, randomization and generalization

Abstract We study generalization of intervention effects across several simulated and real-world samples. We start by formulating the concept of the ‘background’ of a sample effect observation. We then formulate conditions for effect generalization based on a sample’s set of (observed and unobserved) backgrounds. This reveals two limits for effect generalization: (1) when effects of a variable are observed under all their enumerable backgrounds, or, (2) when backgrounds have become sufficiently randomized. We use the resulting combinatorial framework to re-examine open issues in current causal effect estimators: out-of-sample validity, concurrent estimation of multiple effects, bias-variance tradeoffs, statistical power, and connections to current predictive and explaining techniques. Methodologically, these definitions also allow us to replace the parametric estimation problems that followed the ‘counterfactual’ definition of causal effects by combinatorial enumeration and randomization problems in non-experimental samples. We use the resulting non-parametric framework to demonstrate (External Validity, Unconfoundness and Precision) tradeoffs in the performance of popular supervised, explaining, and causal-effect estimators.

Enhanced diplopia detection and binocular single vision assessment through virtual reality: A comprehensive study

An investigation of the mechanical properties and adsorption potentials of Fe2O3@SiO2-L-cysteine-cellulose system

The mitochondrial genome of the steppe carpenter moth (Paracossulus thrips Hübner, 1818): Structural analysis and phylogenetic implications

Abstract Paracossulus thrips (Lepidoptera: Cossoidea) is a rare micromoth species native to the Eurasian steppe that occurs in fragmented populations across its distribution area. In Europe, it persisted only in a few isolated populations, which warranted protection by the EU’s Habitats Directive. We assembled the first complete mitochondrial genomes of two individuals of P. thrips using shotgun whole-genome sequencing data. The assembled mitogenomes were complete and circular; they contained 13 protein-coding genes, 22 tRNA genes, and two rRNA genes. The A + T-rich control region (CR) was identified between the 12 S rRNA and tRNA-Met (CAU) regions. We performed phylogenetic tree reconstruction focusing on the Cossoidea superfamily within the Lepidoptera order by incorporating the new mitochondrial genome assemblies presented in this study. Using available mitogenomes of the superfamily, the mitochondrial phylogeny placed P. thrips within the Cossinae subfamily as a sister to the only other species with an assembled mitogenome. These assemblies may provide valuable genetic resources for further large-scale phylogenetic studies of the Cossoidea superfamily, a poorly studied group of the Lepidoptera order. This work could also support the long-term conservation management of this unique species by providing resources for conservation genetic research.

Author Correction: Hybridization of synergistic swarm and differential evolution with graph convolutional network for distributed denial of service detection and mitigation in IoT environment

Local-structure insight into the improved superconducting properties of Pb-substituted La(O, F)BiS2: a photoelectron holography study

Abstract Pb-substituted La(O, F)BiS 2 (Pb-LaOFBiS 2 ) exhibits improved superconducting properties and a resistivity anomaly around 100 K that is attributed to a structural transition. We have performed temperature( T )-dependent photoelectron holography (PEH) to study dopant incorporation sites and the local structure change across the anomaly. The PEH study of Pb-LaOFBiS 2 provided evidence for the dominant incorporation sites of Pb and F: Pb atoms are incorporated into the Bi sites and F atoms are incorporated into the O site. No remarkable difference in the local structures around Pb and Bi atoms was observed. Across the temperature of the resistivity anomaly ( T * ), photoelectron holograms of Bi 4f changed. Comparisons of holograms with those of non-substituted LaOFBiS 2 sample, as well as simulated holograms, suggested that (1), above T * , the tetragonal structure of Pb-LaOFBiS 2 is different from the tetragonal structure of LaOFBiS 2 and (2), below T * , the tetragonal structure still remains in Pb-LaOFBiS 2 . We discuss a possible origin of the difference in the structure above T * and the implication of the result below T * , which are necessary ingredients to understand the physical properties of Pb-LaOFBiS 2 .

Geographic inequalities and factors associated with unfavorable outcomes in diabetes-tuberculosis and diabetes-covid comorbidities in Brazil

Assessment of health risks of university professors through exposure to BTEX compounds from white board markers

Effect of lockdown during COVID-19 pandemic on physical endurance among healthy male university students in Wuhan: a retrospective cohort study

An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods

Study of the hydrogeochemical processes and its impact on the quality of groundwater in the area between Abu Simbil and Tushka, Western Desert, Egypt

Abstract The study area is located in a hyper arid area in the Western Egyptian Desert, which represents a massive agricultural project where irrigation water is extracted from the Nubian sandstone aquifer. This study focuses on the hydrogeochemical processes and its impact on the quality of the groundwater aquifer. Based on the geomorphology, the study area includes five geomorphological units, Wadi Kurkur bediplain, Aswan High Dam Lake, the Nile Valley, the West Dungul plain, and basement outcrops. According to the geology, the study area is distinguished by sedimentary cover ranging in age from Upper Jurassic to Quaternary. Regarding the hydrogeology of the study area, the Nubian sandstone aquifer is the main aquifer in the study area, which it is represented by 24 groundwater samples plus one sample from the stem of the Lake Nasser. All samples were collected, analyzed, and interpreted. Groundwater salinity classification shows that all the groundwater samples are fresh water types, except one sample considered brackish water type. The pH values of the selected groundwater samples vary from 5.8 in the center to 7.6 in the northern side, with a median value of 7.3. The relationship between salinity content and the other ions shows a strong correlation between the values of Ca 2+ , Cl − , and SO 4 2− with the TDS values and a moderate correlation between Na + values with TDS values. Silicate and carbonate weathering are the main hydrochemical processes affecting the groundwater. From the saturation indices (SI) results, it is indicated that gypsum, anhydrite and manganite dissolve with negative SI values, whereas iron minerals are supersaturated with positive values. The hypothetical salts indicate a recharge from the lake Nasser in addition to leaching and dissolution of terrestrial salts. From the mixing Model, it is indicated that the Nubian sandstone aquifer has paleowater contribution percent ranging from 81 to 92% and Lake Nasser water contribution percent ranging from 7 to 18%. The nitrate concentrations are below the maximum allowable limits of the WHO; however, high concentrations of heavy metals were recorded in the groundwater samples at various extents.

Monkeys increase scratching when encountering unexpected good fortune

Dynamic change of estrogen and progesterone metabolites in human urine during pregnancy

Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand

Abstract This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire survey on the demand for medical and daily care services of 1291 elderly was conducted using multi-stage stratified whole cluster random sampling. SPSS21.0 statistical analysis software was used to describe the basic data of the elderly statistically, and univariate analysis was used to screen variables for model construction and binary logistic regression analysis. The acquired dataset has class imbalance, and to handle this issue, Synthetic Minority Over Sampling Technique with TomekLink (SMOTE-TomekLink) was adopted to resample the dataset for class-balancing. To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. The performance of each model was measured, and the performance of the prediction model was obtained using the following performance metrics: accuracy (ACC), recall (R), precision (P), F1-score, and area under the receiver operating characteristic (AUC). The prediction models for the medical and daily care services demand of the elderly were developed and validated using 12 and 13 key features, respectively. The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. For the medical service demand prediction model, LightGBM achieved an AUC of 0.910 and F1-score of 0.841. In the daily care services demand prediction model, LightGBM demonstrated an AUC of 0.906 and an F1-score of 0.819. In the LightGBM model, the analysis of feature importance indicates that the number of chronic diseases, education level, and financial sources emerge as the most significant predictors for the demand of healthcare services, encompassing both medical and daily care services. Based on questionnaire information combined with feature selection, unbalanced data processing and machine learning methods, this study constructed a machine learning model for predicting the demand for medical and daily care services for the elderly, and analyzed the influencing factors of the demand for medical and daily care services for the elderly, providing a reference for the construction and verification of future prediction models for the demand for medical and daily care services for the elderly.

Risk factors and protective strategies for hypotony following preserflo microshunt implantation

Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state

Vaginal microbiota transplantation alleviates vaginal atrophy in ovariectomized mice

Radiative impact of record-breaking wildfires from integrated ground-based data

Abstract The radiative effects of wildfires have been traditionally estimated by models using radiative transfer calculations. Assessment of model-predicted radiative effects commonly involves information on observation-based aerosol optical properties. However, lack or incompleteness of this information for dense plumes generated by intense wildfires reduces substantially the applicability of this assessment. Here we introduce a novel method that provides additional observational constraints for such assessments using widely available ground-based measurements of shortwave and spectrally resolved irradiances and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges. We apply our method to quantify the radiative impact of the record-breaking wildfires that occurred in the Western US in September 2020. For our quantification we use integrated ground-based data collected at the Atmospheric Measurements Laboratory in Richland, Washington, USA with a location frequently downwind of wildfires in the Western US. We demonstrate that remarkably dense plumes generated by these wildfires strongly reduced the solar surface irradiance (up to 70% or 450 Wm-2 for total shortwave flux) and almost completely masked the sun from view due to extremely large AOD (above 10 at 500 nm wavelength). We also demonstrate that the plume-induced radiative impact is comparable in magnitude with those produced by a violent volcano eruption occurred in the Western US in 1980 and continental cumuli.