Multiple Weight Research Articles

Analysis of Influential Elements and Performance Evaluation of the Development of New Business Subjects in the Perspective of Rural Revitalization

Abstract This study deeply explores the development influencing factors and performance evaluation of new agricultural management subjects under the perspective of rural revitalization, provides data support through empirical analysis, and provides guidance for promoting rural economic development and optimizing agrarian management system. A performance evaluation model containing four dimensions of technology, finance, market and society is constructed using the hierarchical analysis method (AHP), multiplicative exponential weights (MEW) and entropy value method. The study results show that among the technical indicators, the member learning ability scores the highest (0.06575), while among the social indicators, the social responsibility and radiation ability scores are the same (both 0.087). Among the financial indicators, operational capacity scored the highest (0.07025) and customer acquisition rate scored the highest (0.07575) among the market indicators. The comprehensive evaluation shows that the technical indicators have the most significant impact on the development performance of new agricultural business entities. This study provides a quantitative analysis of the development of new farm business subjects, which is effective for promoting the rural revitalization strategy.

Identification and characterization of calcium binding protein, spermatid-associated 1 (CABS1)# in selected human tissues and fluids.

Calcium binding protein, spermatid associated 1 (CABS1) is a protein most widely studied in spermatogenesis. However, mRNA for CABS1 has been found in numerous tissues, albeit with little information about the protein. Previously, we identified CABS1 mRNA and protein in human salivary glands and provided evidence that in humans CABS1 contains a heptapeptide near its carboxyl terminus that has anti-inflammatory activities. Moreover, levels of an immunoreactive form of CABS1 were elevated in psychological stress. To more fully characterize human CABS1 we developed additional polyclonal and monoclonal antibodies to different sections of the protein and used these antibodies to characterize CABS1 in an overexpression cell lysate, human salivary glands, saliva, serum and testes using western blot, immunohistochemistry and bioinformatics approaches exploiting the Gene Expression Omnibus (GEO) database. CABS1 appears to have multiple molecular weight forms, consistent with its recognition as a structurally disordered protein, a protein with structural plasticity. Interestingly, in human testes, its cellular distribution differs from that in rodents and pigs, and includes Leydig cells, primary spermatogonia, Sertoli cells and developing spermatocytes and spermatids, Geodata suggests that CABS1 is much more widely distributed than previously recognized, including in the urogenital, gastrointestinal and respiratory tracts, as well as in the nervous system, immune system and other tissues. Much remains to be learned about this intriguing protein.

Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction models

In the recent years, a majority of Americans have moved to investing in stocks through algorithmic trading. Not only does this underline the public’s interest in the stock market, but it also shows the important role that artificial intelligence plays when it comes to predicting and trading stocks. These prediction models can benefit investors by improving their investment decisions and potentially increasing financial gains. In this research, we aimed to explore the impact of incorporating historical stock price data types, such as the opening prices, closing prices and highest prices, on the accuracy of stock price prediction models. The primary objective was to optimize the performance of our models. To carry out this research, we developed three supervised learning artificial intelligence models using Python: linear regression, neural network, and multiplicative weight update. Our models predicted the stock prices for Microsoft, Amazon, Google, Tesla, and Apple. First, the models used the opening prices for the past three days to predict the opening price on the fourth day. However, to enhance each model’s performance, we evaluated whether adding extra information, such as closing and highest prices, would be beneficial. We hypothesized that incorporating the opening, closing, and highest prices would yield the highest accuracy as it would provide the models with the most information and help them better predict patterns in the stock market. The results supported our hypothesis as the models’ average percent errors significantly decreased when they were given all three of these data types.

Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.

The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves.

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The "a priori" misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.

PLUG: A City-Friendly Navigation Model for Electric Vehicles with Power Load Balancing upon the Grid

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model.

Diversified AI Techniques for Augmenting Brain Tumor Diagnosis

Brain tumors affect thousands of people worldwide each year and can be extremely fatal if not diagnosed early. They are challenging to diagnose due to their complexity and the overlapping features of different tumor types. This research explores the application of AI technology to expedite the diagnosis of brain tumors. The proposed AI-based approaches involve using deep learning algorithms to analyze medical imaging data, specifically MRI scans. The goal was to build a robust and accurate model that could overcome distribution shifts. Some of the models used include classical machine learning models and a convolutional neural network. The results demonstrate that AI-based approaches can significantly improve the accuracy and expedite the process of brain tumor diagnosis.
 The performances of the models were evaluated by using cross-validation and measuring accuracy, using a completely different dataset of MRI scans, to assess how the models performed when dealing with distribution shifts. The logistic regression model achieved a testing accuracy of 78.56%. The multi-layer perceptron (MLP) model achieved a testing accuracy of 74.89%. The multiplicative weight update method combined two models (MLP and logistic regression) with dynamically adjusted weights and achieved a testing accuracy of 83.83%. An approach where multiple aggregating models were used collaboratively achieved a testing accuracy of 86.46%. The best performing model, a convolutional neural network (CNN), yielded a testing accuracy of 98.20%.

Impact of silanization on the characterization of Corchorus olitorius particle‐based composites for sustainable applications

AbstractResearchers are actively working on developing a long‐term substitute for harmful manmade fibres. Because of their unique qualities that fit diverse applications, natural fibres/fillers are proposed as sustainable reinforcements. Integrating natural fibres, such as Corchorus olitorius particles, into composites provides an environmentally conscious choice for a variety of purposes. This research aims to investigate the influence of silanization, a surface modification technique, on the various characterizations and characteristics of Corchorus olitorius particle‐based composites. The characterizations included morphological, mechanical and thermal investigations to assess the influence of silanization on the characteristics of the composite. The contact layup process is applied to create composite samples by reinforcing multiple weight proportions of treated particles (2.5%, 5%, 7.5%, 10% and 12.5%) in epoxy resins. The findings demonstrate that silanization elevates the interfacial connectivity between Corchorus olitorius particles and the resins, which produces upgraded mechanical and thermal attributes. The composite containing 5 wt% particles generated the most significant tensile and flexural strength, with gains of about 45% and 31%, respectively, compared to the epoxy composite. Because of the higher interfacial connections of the molecules, the viscoelastic features of the composites indicated that the 7.5 wt% particle‐based composite possesses the greatest storage modulus (2.96 GPa), loss modulus (0.4 GPa) and glass transition temperature (90 °C). This study emphasizes the significance of surface alteration procedures such as silanization for boosting the efficiency and long‐term viability of composites containing Corchorus olitorius particles. © 2023 Society of Industrial Chemistry.

Comparative effectiveness ofbiological disease-modifying antirheumatic drugs and Janus kinase inhibitor monotherapy in rheumatoid arthritis.

The objective of this study was to examine the effectiveness and drug tolerability of biological DMARD (bDMARD) and Janus kinase inhibitor (JAKi) monotherapy in patients with RA in a multicentre cohort study. Patients with RA for whom bDMARD/JAKi monotherapy without conventional synthetic DMARDs has been initiated were included. Monotherapy regimens were categorized as IL-6 receptor inhibitors (IL-6Ris), cytotoxic T-lymphocyte-associated protein 4 immunoglobulin (CTLA4Ig), JAKis, or TNF inhibitors (TNFis). Multiple propensity score-based inverse probability weighting (IPW) was used to reduce selection bias. Linear mixed-effect models with IPW were used to examine changes in the DAS in 28 joints using ESR (DAS28)-ESR at 24 weeks, and drug retention was compared between monotherapy groups using IPW Cox proportional hazards models. A total of 849 treatment courses were included, involving 635 patients (IL-6Ris, 218; CTLA4Ig, 183; JAKis, 92; TNFis, 356). The change in DAS28-ESR at week 24 as the primary outcome was -0.93 (95% CI: -1.20 to -0.66) lower in the IL-6Ri group than in the TNFi group, while those of the CTLA4Ig and JAKi groups were similar to that of the TNFi group [-0.20 (-0.48 to 0.08), -0.25 (-0.67 to 0.16), respectively]. IL-6Ri use was associated with significantly lower overall drug discontinuation than that for TNFi use [hazard ratio = 0.55 (0.39-0.78), P = 0.001]. Similar retention rates were identified for the CTLA4Ig and JAKi groups to that of the TNFi group. In the analysis with IPW to reduce selection bias, IL-6Ri monotherapy was superior to TNFi monotherapy in terms of effectiveness and drug retention. No significant differences were identified between CTLA4Ig, JAKi and TNFi monotherapy.

Urban-rural differences in associations among perceived stress, resilience and self-care in Chinese older adults with multiple chronic conditions.

Social determinants of health (SDH) are mainly comprised of structural and intermediary domains. Emerging evidence suggests that the burden of multiple chronic conditions (MCCs) in older adults is exacerbated by structural determinants (e.g. low income and low education). However, less attention was paid to the intermediary determinants (i.e. material circumstances, psychosocial factors and behavioural factors) of MCCs. To investigate the associations among perceived stress, resilience and self-care in Chinese older adults with MCCs by comparing urban and rural groups. A convenience sample (125 and 115 participants from urban and rural settings, respectively) of Chinese older adults with MCCs was enrolled between January and April 2022. Hierarchical multiple regression analyses and propensity score weights were used to determine the associations among perceived stress, resilience and self-care. Hypothesis 1 regarding the negative associations between perceived stress and self-care was fully supported in the rural group. However, for the urban group, the negative association was only supported for the relationship between MCCs-related perceived stress and self-care maintenance. Hypothesis 2 was fully supported regarding the positive associations between resilience and the three components of self-care in both groups, although the relationship between resilience and self-care monitoring was marginally significant in the urban group. Hypothesis 3 regarding the moderating effect of resilience was only supported in the relationship between general perceived stress and self-care monitoring in the rural group. After adding the propensity score weights, the moderating effect was no longer statistically significant in the rural group. The urban-rural disparities in the Chinese context might largely be attributed to the complex interactions of the structural determinants and intermediary determinants. Findings can inform the development of culturally tailored interventions to promote self-care and reduce urban-rural disparities for Chinese older adults with MCCs. With the increasing number of older adults in China who are living with multiple chronic conditions and the call for effective interventions to improve their health outcomes, current findings can inform the development and implementation of nurse-led culturally tailored interventions to promote self-care and reduce urban-rural disparities for Chinese older adults with MCCs.

Causal association of physical activity with low back pain, intervertebral disc degeneration and sciatica: a two-sample mendelian randomization analysis study.

Objective: Previous studies are insufficient to confirm a causal association between physical activity (PA) and low back pain (LBP), intervertebral disc degeneration (IDD), and sciatica. The present study used a two-sample Mendelian randomization (MR) analysis method to demonstrate whether or not there was a causal connection. Methods: First, four PA phenotypes were selected [accelerometer-based PA (average acceleration), accelerometer-based PA (acceleration fraction >425mg), self-reported moderate-to-vigorous PA, and self-reported vigorous PA], setting thresholds for single nucleotide polymorphisms (SNPs) significantly concerned with PA p < 5 × 10-8, linkage disequilibrium (LD) r 2 < 0.01, genetic distance >5,000kb, and F-value >10. SNPs associated with the outcome and confounding factors were then excluded using the PhenoScanncer database. Finally, after coordinating the genetic instruments from genome-wide association studies (GWAS) effect alleles for exposure and outcomes, multiplicative random effects inverse variance weighting (IVW), MR-Egger, weighted median method (WMM), and weighted mode method were used to assess exposure-outcome causality and perform sensitivity analysis on the estimated results. Results: The current study's IVW findings revealed proof of a causal connection between PA and LBP. While there was a positive causal tie between accelerometer-based PA (acceleration fraction >425mg) and LBP [OR: 1.818, 95% CI:1.129-2.926, p = 0.012], there was a negative causal link between accelerometer-based PA (average acceleration) and LBP [OR: 0.945, 95% CI: 0.909-0.984, p = 0.005]. However causal relationship between PA and IDD or sciatica was not found. Conclusion: Increasing average PA but needing to avoid high-intensity PA may be an effective means of preventing low back pain. Although PA is not directly causally related to disc degeneration and sciatica, it can act through indirect pathways.

The association between thyroid and breast cancers: a bidirectional mendelian randomization study.

Thyroid and breast cancers are the two most frequent types of endocrine-related tumors among women worldwide, and their incidence is still on the rise. Observational studies have shown a relationship between thyroid and breast cancers. Nevertheless, many confounders predispose the results to interference. Accordingly, we performed a two-sample Mendelian randomization (MR) study to investigate the causal association between thyroid and breast cancers. We acquired breast cancer data from the UK Biobank (13,879 breast cancer cases and 198,523 controls) and the Breast Cancer Association Consortium (BCAC; 122,977 breast cancer cases and 105,974 controls), and thyroid cancer data from FinnGen Biobank (989 thyroid cancer and 217,803 controls). Then, the multiplicative random effects inverse variance weighting (IVW), weight median (WM), and MR Egger methods were executed for MR analysis. Overall, IVW showed a causal effect of breast cancer on thyroid cancer using the BCAC dataset (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.036-1.322; P = 0.011), and this relationship was also supported by the UK Biobank dataset (OR = 23.899; 95% CI = 2.331-245.003; P = 0.007), which showed that breast cancer patients were more likely to be diagnosed with thyroid cancer. On the whole, the reverse MR analysis did not show a causal effect of breast cancer on thyroid cancer. However, IVW showed a causal effect of thyroid cancer on estrogen receptor -negative breast cancer using the BCAC dataset (OR = 1.019; 95% CI = 1.001-1.038; P = 0.043), which suggested that people with thyroid cancer were more likely to develop breast cancer. Breast cancer represents a possible risk factor for thyroid cancer and thyroid cancer also represents a possible risk factor for ER-negative breast cancer. Future studies using powerful genetic tools to determine the causal relationship between breast and thyroid cancers are required.

A weight Monte Carlo estimation of fluctuations in branching processes

Abstract It is well known that shortened modeling of particle trajectories with the use multiplicative statistical weights, as a rule, increases the efficiency of the program (in terms of accuracy/time ratio). This trick is often used in non-branching schemes simulating transfer processes without multiplication (for example, the transfer of X-ray radiation), in which it is sufficient to confine ourselves to studying only the average values of the field characteristics. With an increase in energy, however, multiplication processes begin to play a significant role (the production of electron-photon pairs by gamma quanta with energies above 1.022 MeV, etc.), when the resulting trajectory is not just a broken curve in the phase space, but a branched tree. This technique is also applicable to this process, but only if the study of statistical fluctuations and correlations is not the purpose of the calculation. The present review contains the basic concepts of the Monte Carlo method as applied to the theory of particle transport, demonstration of the weighting method in non-branching processes, and ends with a discussion of unbiased estimates of the second moment and covariance of additive functionals.

Parametric study of the effects of clump weights on the performance of a novel wind-wave hybrid system

This paper presents a novel catenary mooring system incorporating multiple clump weights (CWs) designed for a SPIC-Wavestar wind-wave energy hybrid system. To enhance the performance of the hybrid system, a parametric analysis was performed on mooring CWs. The motions of the SPIC platform, along with the power absorbed by the Wavestar wave energy converters (WECs), specifically in response to the operational regular wave, were simulated. The hydrodynamic analysis program AQWA was used for the analysis. The results indicated that the surge and pitch motions of the platform comprised low-frequency and wave-frequency components, whereas the heave motion only comprised a wave-frequency component. However, the low-frequency responses of the surge and pitch motions were substantially smaller than their wave-frequency counterparts. In addition, the properties of the CW exerted notable influence on both platform motion and the overall power absorption by the WEC array. The variation trends with respect to the CW weight, location, and number were complex. Overall, the results of this study provide valuable insights into the mooring design of ocean energy hybrid systems.

The effects of different endometrial preparation regimens on pregnancy outcomes in frozen-thawed embryo transfer cycles: a prospective randomized controlled study

Objective An increasing number of research have emerged to compare the pregnancy outcomes between the natural cycle and the hormone replacement therapy (HRT) cycle in preparing the endometrium for frozen-thawed embryo transfer (FET), but the results are controversial. This prospective randomized controlled study was hence designed to obtain more solid evidence. Materials and methods In this study, patients with regular menstrual cycle length (21–35 days) who underwent FET between January 2010 to December 2017 were recruited for this study. Upon further filtering with the selection criteria of patients being, a total of 405 patients were recruited and randomized. Finally, analysis was performed on 384 patients: 178 belonged to the natural cycle group whereas the remaining 206 were in the HRT group. The primary outcome was live birth rate, while the secondary outcomes were implantation rate, clinical pregnancy rate, early miscarriage rate, late miscarriage rate, multiple birth rate and low birth weight rate. Results The live birth rate (37.6% vs 30.1%, p = 0.119) of natural cycle group were higher than those of the hormone replacement therapy group, although the difference was not significant. The secondary outcomes were not found to differ significantly between the two groups. Nonetheless, the endometrium was found to be thicker in the natural cycle group (10.75 mm) than the HRT group (9.00 mm) (p < 0.001). Conclusion No significant differences were observed between the pregnancy outcomes of the natural cycle group and the HRT group which comprised of patients with regular menstrual cycle length.

A group consensus model with prospect theory under probabilistic linguistic term sets

The challenges encountered in the realm of multi-attribute group decision-making (MAGDM) involving probabilistic linguistic term sets (PLTSs) have garnered substantial attention. Within the PLTS context, this study introduces a consensus reaching process (CRP) that iteratively refines the weights assigned to decision-makers (DMs) by leveraging the principles of prospect theory (PT). The primary goal of this iterative weight adjustment process is to enhance the overall decision-making procedure when dealing with PLTSs. To circumvent any data loss during transformation and compute the prospect values of PLTSs directly, a novel transformation formula is developed. Acknowledging the distinct cognitive levels among different DMs, the integration of multiple weights into the consensus process is characterized by its dynamic and iterative nature. Concerning the measurement of consensus, this study employs a method based on the gap between prospect values, which enhances objectivity while overcoming the limitations associated with the distance formula of PLTSs. Furthermore, the feedback mechanism incorporated into the modification process incorporates dynamic adjustment parameters that are tailored to different evaluation values, thereby preventing excessive adjustments that are either too low or too high. By utilizing the newly proposed prospect value function, this research aggregates the group evaluation value and identifies the optimal alternative. In conclusion, this paper concludes with a comparative analysis involving various counterparts, shedding light on the feasibility and validity of the proposed model.

Association of per- and polyfluoroalkyl substance exposure with metabolic syndrome and its components in adults and adolescents

Per- and polyfluoroalkyl substances (PFAS) are widespread contaminants, but few studies have explored the relationship between PFAS and levels of metabolic syndrome (MetS) in the population. The available evidence of an association is also conflicting. We selected adults and adolescents with complete PFAS data from the National Health and Nutrition Examination Survey conducted between 2003 and 2018. We analyzed the association between PFAS and MetS using multivariate logistic regression models and evaluated potential nonlinear relationships with restricted cubic spline models. Additionally, we employed weighted quantile sum (WQS) regressions to uncover the multiple exposure effects and relative weights of each PFAS. Finally, we conducted a series of sensitivity analyses to test the robustness of our findings. In this population-based study, we analyzed data from a total of 4,973 adults, aged 20–85 years, and 1,381 adolescents, aged 12–19 years. Using fully adjusted multivariate logistic regression models, we found that serum levels of perfluorodecanoate (PFDA) [0.65 (0.50, 0.85)] and total PFAS [0.92 (0.85, 0.99)] were negatively associated with the prevalence of MetS in adults. Similarly, in adolescents, we observed negative correlations between the prevalence of MetS and levels of PFDA [0.55 (0.38, 0.80)], perfluorooctanoic acid (PFOA) [0.62 (0.39, 1.00)], perfluorooctane sulfonic acid (PFOS) [0.59 (0.36, 0.96)], and total PFAS [0.61 (0.37, 0.99)]. Additionally, our study identified statistically significant negative associations between serum levels of PFAS and certain components of MetS, primarily elevated fasting glucose and lower high-density lipoprotein cholesterol. Our study found that PFAS was associated with a lower prevalence of MetS in both adults and adolescents, offering new insights into the relationship between PFAS and metabolic health. Interestingly, however, we observed conflicting findings across the components of MetS. Specifically, we observed that PFAS had a negative correlation with some metrics and a positive correlation with others. These conflicting results point to a complex interplay between PFAS and various metrics of metabolic health.

Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks.

Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.

A Deep-Learning Phase Picker with Calibrated Bayesian-Derived Uncertainties for Earthquakes in the Yellowstone Volcanic Region

ABSTRACT Traditional seismic phase pickers perform poorly during periods of elevated seismicity due to inherent weakness when detecting overlapping earthquake waveforms. This weakness results in incomplete seismic catalogs, particularly deficient in earthquakes that are close in space and time. Supervised deep-learning (DL) pickers allow for improved detection performance and better handle the overlapping waveforms. Here, we present a DL phase-picking procedure specifically trained on Yellowstone seismicity and designed to fit within the University of Utah Seismograph Stations (UUSS) real-time system. We modify and combine existing DL models to label the seismic phases in continuous data and produce better phase arrival times. We use transfer learning to achieve consistency with UUSS analysts while maintaining robust models. To improve the performance during periods of enhanced seismicity, we develop a data augmentation strategy to synthesize waveforms with two nearly coincident P arrivals. We also incorporate a model uncertainty quantification method, Multiple Stochastic Weight Averaging-Gaussian (MultiSWAG), for arrival-time estimates and compare it to dropout—a more standard approach. We use an efficient, model-agnostic method of empirically calibrating the uncertainties to produce meaningful 90% credible intervals. The credible intervals are used downstream in association, location, and quality assessment. For an in-depth evaluation of our automated method, we apply it to continuous data recorded from 25 March to 3 April 2014, on 20 three-component stations and 14 vertical-component stations. This 10-day period contains an Mw 4.8 event, the largest earthquake in the Yellowstone region since 1980. A seismic analyst manually examined more than 1000 located events, including ∼855 previously unidentified, and concluded that only two were incorrect. Finally, we present an analyst-created, high-resolution arrival-time data set, including 651 new arrival times, for one hour of data from station WY.YNR for robust evaluation of missed detections before association. Our method identified 60% of the analyst P picks and 81% of the S picks.

N-Acetylcysteine Displaces Glutathionyl-Moieties from Hg2+ and MeHg+ to Form More Hydrophobic Complexes at Near-Physiological Conditions.

The anthropogenic release of Hg is associated with an increased human exposure risk. Since Hg2+ and MeHg+ have a high affinity for thiols, their interaction with L-glutathione (GSH) within mammalian cells is fundamentally involved in their toxicological chemistry and excretion. To gain insight into the interaction of these mercurials with multiple small molecular weight thiols, we have investigated their competitive interactions with GSH and N-acetylcysteine (NAC) at near-physiological conditions, using a liquid chromatographic approach. This approach involved the injection of each mercurial onto a reversed-phase (RP)-HPLC column (37 °C) using a PBS buffer mobile phase containing 5.0 mM GSH to simulate cytosolic conditions with Hg being detected in the column effluent by an inductively coupled plasma atomic emission spectrometer (ICP-AES). When the 5.0 mM GSH mobile phase was amended with up to 10 mM NAC, gradually increasing retention times of both mercurials were observed. To explain this behavior, the experiment with 5.0 mM NAC and 5.0 mM GSH was replicated using 50 mM Tris buffer (pH 7.4), and the Hg-containing fractions were analyzed by electrospray ionization mass spectrometry. The results revealed the presence of Hg(GS)(NAC) and Hg(NAC)2 for Hg2+ and MeHg(GS) and MeHg(NAC) for MeHg+, which suggests that the coordination/displacement of GS-moieties from each mercurial by the more hydrophobic NAC can explain their retention behavior. Since the biotransformations of both mercurials were observed at near-physiological conditions, they are of toxicological relevance as they provide a biomolecular explanation for some results that were obtained when animals were administered with each mercurial and NAC.