High-Dimensional Dependency Structure Learning for Physical Processes

In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Such processes are often synthetically characterized using PDEs (partial differential equations) and are observed in a variety of natural phenomena, including geoscience data capturing atmospheric and hydrological phenomena. Classical structure learning approaches such as the PC algorithm and variants are challenging to apply due to their high computational and sample requirements. Modern approaches, often based on sparse regression and variants, do come with finite sample guarantees, but are usually highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$ for sparsity inducing constraint or regularization. In this paper, we present ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning, which estimates an edge specific parameter $\lambda_{ij}$ in the first step, and uses these parameters to learn the structure in the second step. Both steps of our algorithm use (inexact) ADMM to solve suitable linear programs, and all iterations can be done in closed form in an efficient block parallel manner. We compare ACLIME-ADMM with baselines on both synthetic data simulated by partial differential equations (PDEs) that model advection-diffusion processes, and real data (50 years) of daily global geopotential heights to study information flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and competitive, usually better than the baselines especially on difficult problems. On real data, ACLIME-ADMM recovers the underlying structure of global atmospheric circulation, including switches in wind directions at the equator and tropics entirely from the data.Comment: 21 pages, 8 figures, International Conference on Data Mining 201

Associations between smoking status and infertility: a cross-sectional analysis among USA women aged 18-45 years

BackgroundAlthough many studies have proven the harmful effects of smoking on human health, the associations between smoking status and infertility are limited in large epidemiologic studies. We aimed to investigate the associations between smoking status and infertility among child-bearing women in the United States of America (USA).MethodsA total of 3,665 female participants (aged 18-45) from the National Health and Nutrition Examination Survey (NHANES) (2013-2018) were included in this analysis. All data were survey-weighted, and corresponding logistic regression models were performed to investigate the associations between smoking status and infertility.ResultsIn a fully adjusted model, the risk of infertility was found to be increased by 41.8% among current smokers compared to never smokers (95% CI: 1.044-1.926, P=0.025). In the subgroup analysis, the odds ratios (95% CI) of the risk of infertility for current smokers were 2.352 (1.018-5.435) in the unadjusted model for Mexican American, 3.675 (1.531-8.820) in the unadjusted model but 2.162 (0.946-4.942) in fully adjusted model for people aged 25-31, 2.201 (1.097-4.418) in the unadjusted model but 0.837 (0.435-1.612) in fully adjusted model for people aged 32-38.ConclusionCurrent smokers was associated with a higher risk of infertility. The underlying mechanism of these correlations still needs more research. Our findings indicated that quitting smoking may serve as a simple index to reduce the risk of infertility

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

Effects of environmental factors on vertical distribution of the eukaryotic plankton community in early summer in Danjiangkou Reservoir, China

IntroductionEukaryotic plankton plays crucial roles in ecosystem processes, impacting aquatic ecosystem stability. This study focuses on Danjiangkou Reservoir, a canyon lake in central China, that acts as the water source of the Mid-route of the South-to-North Water Diversion Project.MethodsIn this study, high-throughput 18S rDNA gene sequencing was employed to investigate eukaryotic plankton community at four water depths (0.5 m, 5 m, 10 m, and 20 m). The environmental factors including pH, water temperature (WT), nitrate nitrogen (NO3−-N), ammonia nitrogen (NH4+-N), total nitrogen (TN), conductivity (Cond), and dissolved oxygen (DO) in reservoir areas were measured, and their correlations with abundance and diversity of eukaryotic plankton were analyzed.ResultsThe results showed the presence of 122 genera of eukaryotic plankton from 38 phyla. Eukaryotic plankton communities were mainly composed of Eurytemora, Thermocyclops, Sinocalanus, Mesocyclops, and Cryptomonas. In particular, significant differences in the diversity of eukaryotic plankton communities were found in vertical distribution. The diversity and abundance of eukaryotic plankton communities in 7 sampling sites decreased with the increase of depth from 0.5 to 10 m, while the diversity and abundance of plankton communities increased at 20 m. RDA analysis indicated that pH, depth, WT, NH4+-N, DO, Cond, and NO3−-N could influence the vertical distribution of the eukaryotic plankton community in the Danjiangkou Reservoir. Among these eukaryotic plankton, Eurytemora, Thermocyclops, and Volvox were negatively correlated with pH and WT and positively correlated with depth.DiscussionThis study revealed a novel perspective on the distribution of the eukaryotic plankton community in Danjiangkou Reservoir, particularly in terms of vertical variation, which will be helpful to comprehensively understand ecological processes and to further ensure the water quality safety in this canyon-style reservoir

Pretreatment technology of lignocellulose

Lignocellulose is the most abundant renewable biomass resource in nature. Pretreatment of lignocellulose can improve the accessibility of cellulase to cellulose raw materials, reduce the ineffective adsorption of cellulase, reduce the crystallinity and obtain higher reducing sugar. In this paper, several practical pretreatment technologies of lignocellulose are summarized, and the methods, principles, advantages and disadvantages of each pretreatment technology are summarized, and then the development prospect of lignocellulose pretreatment methods is prospected

Being a morning man has causal effects on the cerebral cortex: a Mendelian randomization study

IntroductionNumerous studies have suggested a connection between circadian rhythm and neurological disorders with cognitive and consciousness impairments in humans, yet little evidence stands for a causal relationship between circadian rhythm and the brain cortex.MethodsThe top 10,000 morningness-related single-nucleotide polymorphisms of the Genome-wide association study (GWAS) summary statistics were used to filter the instrumental variables. GWAS summary statistics from the ENIGMA Consortium were used to assess the causal relationship between morningness and variates like cortical thickness (TH) or surficial area (SA) on the brain cortex. The inverse-variance weighted (IVW) and weighted median (WM) were used as the major estimates whereas MR-Egger, MR Pleiotropy RESidual Sum and Outlier, leave-one-out analysis, and funnel-plot were used for heterogeneity and pleiotropy detecting.ResultsRegionally, morningness decreased SA of the rostral middle frontal gyrus with genomic control (IVW: β = −24.916 mm, 95% CI: −47.342 mm to −2.490 mm, p = 0.029. WM: β = −33.208 mm, 95% CI: −61.933 mm to −4.483 mm, p = 0.023. MR Egger: β < 0) and without genomic control (IVW: β = −24.581 mm, 95% CI: −47.552 mm to −1.609 mm, p = 0.036. WM: β = −32.310 mm, 95% CI: −60.717 mm to −3.902 mm, p = 0.026. MR Egger: β < 0) on a nominal significance, with no heterogeneity or no outliers.Conclusions and implicationsCircadian rhythm causally affects the rostral middle frontal gyrus; this sheds new light on the potential use of MRI in disease diagnosis, revealing the significance of circadian rhythm on the progression of disease, and might also suggest a fresh therapeutic approach for disorders related to the rostral middle frontal gyrus-related

A new method for the characterization of microcracks based on seepage characteristics

Microcracks are the main seepage channels and reservoir space for oil and gas in dense sandstone reservoirs, and the degree of development dominates the reservoir’s high and stable production capacity. A new method has been devised to address the lack of quantitative identification and characterization methods for microcrack networks. The method is based on core stress sensitivity, permeability anisotropy, and two-phase seepage rule testing. By improving upon the traditional black oil model, this method can accurately calculate the impact that microcracks of varying degrees of development have on the capacity of tight oil reservoirs. The study shows that 1) the higher the degree of microcrack development, the stronger the reservoir stress sensitivity and the greater the permeability anisotropy. As the degree of microcrack development increases, the irreducible water saturation decreases, the residual oil saturation gradually increases, and the oil–water two-phase co-infiltration zone becomes more extensive and smaller. The degree of microcrack development in tight reservoirs can be characterized based on the seepage characteristic parameters; 2) a microcrack characterization method and classification criteria have been established. It is based on stress sensitivity coefficients, permeability anisotropy parameters, and phase seepage characteristics in cores with different microcrack development degrees. For the first time, the method enables a macroscopic-level description of microcrack seepage; 3) numerical calculations show that the degree of microcrack development significantly affects the reservoir’s oil production and water production. The higher the degree of microcrack development, the higher the reservoir’s initial oil production and cumulative oil production. However, when the degree of microcrack development is too high, the microcracks are connected, thus exhibiting the nature of large fractures. This strengthens the bypassing communication effect and causes the microscopic inhomogeneity to strengthen, the oil production decreases rapidly, and water production increases quickly at the later stage. This research result enriches the reservoir microcrack characterization and evaluation system, which has essential theoretical guidance and practical significance for the rational and effective development of tight oil and tight sandstone gas

Optimizing the face paradigm of BCI system by modified mismatch negative paradigm

Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400) in addition to P300 potentials. Recently, it has been proved that the performance of traditional P300-based BCIs could be improved through a modification of the mismatch pattern. In this paper, a mismatch inverted face pattern (MIF-pattern) was presented to improve the performance of the inverted face pattern (IF-pattern), one of the state of the art patterns used in visual-based BCI systems. Ten subjects attended in this experiment. The result showed that the mismatch inverted face pattern could evoke significantly larger vertex positive potentials (p < 0.05) and N400s (p < 0.05) compared to the inverted face pattern. The classification accuracy (mean accuracy is 99.58%) and ITRs (mean bit rate is 27.88 bit/min) of the mismatch inverted face pattern was significantly higher than that of the inverted face pattern (p < 0.05)

The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients

ObjectiveDiagnosis classification and risk stratification are crucial in the prognosis prediction and treatment selection of acute myeloid leukemia (AML). Here, we used a database of 536 AML patients to compare the 4th and 5th WHO classifications and the 2017 and 2022 versions of ELN guidance.MethodsAML patients were classified according to the 4th and 5th WHO classifications, as well as the 2017 and 2022 versions of the European LeukemiaNet (ELN) guidance. Kaplan–Meier curves with log-rank tests were used for survival analysis.ResultsThe biggest change was that 25 (5.2%), 8 (1.6%), and 1 (0.2%) patients in the AML, not otherwise specified (NOS) group according to the 4th WHO classification, were re-classified into the AML-MR (myelodysplasia-related), KMT2A rearrangement, and NUP98 rearrangement subgroups based on the 5th WHO classification. Referring to the ELN guidance, 16 patients in the favorable group, six patients in the adverse group, and 13 patients in the intermediate group based on the 2017 ELN guidance were re-classified to the intermediate and adverse groups based on the 2022 ELN guidance. Regrettably, the Kaplan–Meier curves showed that the survival of intermediate and adverse groups could not be distinguished well according to either the 2017 or 2022 ELN guidance. To this end, we constructed a risk model for Chinese AML patients, in which the clinical information (age and gender), gene mutations (NPM1, RUNX1, SH2B3, and TP53), and fusions (CBFB::MYH11 and RUNX1::RUNX1T1) were included, and our model could help divide the patients into favorable, intermediate, and adverse groups.ConclusionThese results affirmed the clinical value of both WHO and ELN, but a more suitable prognosis model should be established in Chinese cohorts, such as the models we proposed