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We introduce a new type of influence function, the asymptotic expected sensitivity function, which is often equivalent to but mathematically more tractable than the traditional one based on the Gâteaux derivative. To illustrate, we study the robustness of some important measures of association, including Spearman’s rank correlation and Kendall’s concordance measure, and the recently developed Chatterjee’s correlation.

Through the objective and fair evaluation of the comprehensive ability of accounting talents cultivated in colleges and universities, on the one hand, it is beneficial to provide direction for cultivating accounting applied talents in colleges and universities, and on the other hand, it can also provide reference for enterprises to select talents. Firstly, this study constructs an evaluation index system for the comprehensive ability of accounting applied talents and selects 15 indicators from three aspects of knowledge level, ability level, and moral level. Secondly, the group fuzzy analytic hierarchy process is used to determine the index weight. Finally, a fuzzy comprehensive evaluation model based on the cloud model is constructed and tested in practice. This study hopes to make an objective and fair evaluation for accounting talents by quantitatively evaluating the comprehensive ability of accounting applied talents.

The fast-developing computer network not only brings convenience to people but also brings security problems to people due to the appearance of various abnormal flows. However, various current detection systems for abnormal network flows have more or less flaws, such as the most common intrusion detection system (IDS). Due to the lack of self-learning capabilities of market-oriented IDS, developers and maintenance personnel have to update the virus database of the system in real time to make the system work normally. With the emergence of machine learning and data mining in recent years, new ideas and methods have emerged in the detection of abnormal network flows. In this paper, the random forest algorithm is introduced into the detection of abnormal samples, and the concept of abnormal point scale is proposed to measure the abnormal degree of the sample based on the similarity of the samples, and the abnormal samples are screened out according to this scale. Simulation experiments show that compared with the other two distance-based abnormal sample detection techniques, the random forest-based abnormal sample detection has greater advantages than the other two methods in terms of improving the accuracy of the model and reducing the computing time.

Background The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR. Methods In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001–2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. Results Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate “U-shape”. Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950–0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011–1.026 for all-cause mortality and HR 1.028, 95% CI 1.014–1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged < 65 years. Conclusions In conjunction with the results of the Boruta algorithm, METS-IR demonstrated a more significant association with all-cause and cardiovascular mortality in the U.S. population compared to the other three alternative IR indexes (TyG index, TG/HDL-C and HOMA-IR), particularly evident in individuals under 65 years old.

Measuring and testing association between categorical variables is one of the long-standing problems in multivariate statistics. In this paper, I define a broad class of association measures for categorical variables based on weighted Minkowski distance. The proposed framework subsumes some important measures including Cramér’s V, distance covariance, total variation distance and a slightly modified mean variance index. In addition, I establish the strong consistency of the defined measures for testing independence in two-way contingency tables, and derive the scaled forms of unweighted measures.

Native protein complexes have garnered interest as targets for structural dissemination. Cryogenic electron microscopy (cryo-EM) with its ability to image protein mixtures is the most promising tool to enable structural proteomics. Additionally, image processing has evolved and can deal with conformational and compositional heterogeneity. Integrative approaches, namely mass spectrometry in conjunction with cryo-EM, have made it possible to characterize and identify complex mixtures. However, this comes at a cost of generating models and interpreting mass spectra. Here we present a modified approach that builds on publicly available software. By generating maps around 4 Å and unsupervised model building we were able to identify the most abundant proteins in our sample. This sample consisted of co-purified membrane proteins in nanodiscs. We found a novel structure and unexpected nanodisc assemblies. Our maps imply a direct interaction between membrane proteins and membrane scaffolding proteins.

Sentiment analysis is an essential component of natural language processing, which focuses on extracting subjective insights, like emotions and opinions from text. In this research, an innovative framework has been introduced for Twitter sentiment analysis that integrates Echo State Networks (ESN), Improved Student Psychology Based Optimization (ISPBO), and BERT embeddings. The proposed ESN-ISPBO-BERT model was evaluated on the SemEval-2016-1 and SemEval-2016-2 datasets and compared against other models, including SVM-Glove, CNN-BERT, LSTM, CNN, KNN, SVM, BERT, and GRU. The outcomes indicate that the suggested model exceeds all baseline models, and has achieved outstanding performance. On SemEval-2016-2, it achieves 98.82% accuracy, 98.79% precision, 98.96% recall, and 98.87% F1-score, while on SemEval-2016-1, it achieves 98.76% accuracy, 98.81% precision, 98.92% recall, as well as 98.86% F1-score. Moreover, the suggested model achieved the values of 98.51%, 98.42%, 98.87%, and 98.64% in terms of accuracy, precision, recall, and F1-score on Stanford Sentiment Treebank (SST-2). These outcomes indicate the efficiency of combining reservoir calculating, innovative optimization techniques, and contextual embeddings for the aim of sentiment analysis. The proposed model is a strong solution for the evaluation of Twitter data, with possible applications in brand monitoring, analyzing customer feedback, and tracking sentiment in real-time. This investigation represents the importance of hybrid strategies in tackling the issues of sentiment analysis on social media.

While N6-methyladenosine (m6A), the most abundant internal modification in eukaryotic mRNA, is linked to cell differentiation and tissue development, the biological significance of m6A modification in mammalian glial development remains unknown. Here, we identify a novel m6A reader, Prrc2a (Proline rich coiled-coil 2 A), which controls oligodendrocyte specification and myelination. Nestin-Cre-mediated knockout of Prrc2a induces significant hypomyelination, decreased lifespan, as well as locomotive and cognitive defects in a mouse model. Further analyses reveal that Prrc2a is involved in oligodendrocyte progenitor cells (OPCs) proliferation and oligodendrocyte fate determination. Accordingly, oligodendroglial-lineage specific deletion of Prrc2a causes a similar phenotype of Nestin-Cre-mediated deletion. Combining transcriptome-wide RNA-seq, m6A-RIP-seq and Prrc2a RIP-seq analysis, we find that Olig2 is a critical downstream target gene of Prrc2a in oligodendrocyte development. Furthermore, Prrc2a stabilizes Olig2 mRNA through binding to a consensus GGACU motif in the Olig2 CDS (coding sequence) in an m6A-dependent manner. Interestingly, we also find that the m6A demethylase, Fto, erases the m6A modification of Olig2 mRNA and promotes its degradation. Together, our results indicate that Prrc2a plays an important role in oligodendrocyte specification through functioning as a novel m6A reader. These findings suggest a new avenue for the development of therapeutic strategies for hypomyelination-related neurological diseases.

A flexible mechanoluminophore device that is capable of converting mechanical energy into visualizable patterns through light-emission holds great promise in many applications, such as human-machine interfaces, Internet of Things, wearables, etc. However, the development has been very nascent, and more importantly, existing mechanoluminophore materials or devices emit light that cannot be discernible under ambient light, in particular with slight applied force or deformation. Here we report the development of a low-cost flexible organic mechanoluminophore device, which is constructed based on the multi-layered integration of a high-efficiency, high-contrast top-emitting organic light-emitting device and a piezoelectric generator on a thin polymer substrate. The device is rationalized based on a high-performance top-emitting organic light-emitting device design and maximized piezoelectric generator output through a bending stress optimization and have demonstrated that it is discernible under an ambient illumination as high as 3000 lux. A flexible multifunctional anti-counterfeiting device is further developed by integrating patterned electro-responsive and photo-responsive organic emitters onto the flexible organic mechanoluminophore device, capable of converting mechanical, electrical, and/or optical inputs into light emission and patterned displays. It has been challenging to achieve discernible mechanoluminophore devices under ambient light. Here, authors integrate top-emitting organic light-emitting device and piezoelectric generator on thin polymer substrate for the realization of flexible devices under an ambient illuminance of 3000 lux.

Data science and informatics tools have been proliferating recently within the computational materials science and catalysis fields. This proliferation has spurned the creation of various frameworks for automated materials screening, discovery, and design. Underpinning these frameworks are surrogate models with uncertainty estimates on their predictions. These uncertainty estimates are instrumental for determining which materials to screen next, but the computational catalysis field does not yet have a standard procedure for judging the quality of such uncertainty estimates. Here we present a suite of figures and performance metrics derived from the machine learning community that can be used to judge the quality of such uncertainty estimates. This suite probes the accuracy, calibration, and sharpness of a model quantitatively. We then show a case study where we judge various methods for predicting density-functional-theory-calculated adsorption energies. Of the methods studied here, we find that the best performer is a model where a convolutional neural network is used to supply features to a Gaussian process regressor, which then makes predictions of adsorption energies along with corresponding uncertainty estimates.