Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrophysiological and Morphological Characterization of Contact Chemosensilla in Adults and Larvae of the Butterfly, Atrophaneura alcinous

Simple Summary In butterflies, oviposition and feeding are induced by the recognition of host plant compounds with contact sensory organs called chemosensilla. Oviposition behavior of the butterfly Atrophaneura alcinous involves detection of two plant compounds—an alkaloid and a monosaccharide—using tarsal contact chemosensilla of the foreleg. Feeding behavior of the adults and larvae involves detection of sucrose, sodium chloride, and other chemicals. We examined the distribution and electrophysiological response of contact chemosensilla in A. alcinous adults and larvae. In adults, tarsal contact chemosensilla were classified into long- and short-type sensilla. Long-type sensilla were distributed widely much more in females than in males, whereas short-type sensilla were found at the edge of the tarsi in a similar manner in both sexes. Long- and short-type sensilla showed different responses to methanol extracts of host plant compounds. Our findings suggest that the adult butterfly discriminates between host plant compounds using long-type sensilla during oviposition and recognizes the taste using short-type sensilla. In the larval mouthparts, contact chemosensilla were present on the maxillary galea and epipharynx. Electrophysiological responses of chemosensilla suggest that larvae can discriminate between host plant compounds. Abstract Distribution and electrophysiological responses of contact chemosensilla were examined in the Aristolochiaceae-feeding butterfly Atrophaneura alcinous. In adult butterflies, tarsal contact chemosensilla of the foreleg were classified into two groups based on length: long- and short-type sensilla. Long-type sensilla were distributed much more widely in females than in males, whereas short-type sensilla were found at the edge of the tarsi in a similar manner in both sexes. Taste responses of the long- and short-type sensilla to methanol extracts of Aristolochia debilis and Citrus spp. were recorded. Aristolochia debilis extracts evoked spikes with different amplitudes, whereas Citrus spp. extracts evoked spikes with a single amplitude in the long-type sensilla. Short-type sensilla did not respond to either extract. Moreover, we recorded responses to different concentrations of sucrose and NaCl. Results suggest that adult butterflies can discriminate the taste of host plant components from other chemicals using long-type sensilla during oviposition and may recognize diets containing sugar and salts during feeding using short-type sensilla. In the larval mouthparts, there were lateral and medial styloconic sensilla on the maxillary galea and epipharyngeal sensillum on the epipharynx. Electrophysiological responses of these sensilla suggest that larvae can discriminate between host plant compounds.
Methylation of RUNX3 Promoter 2 in the Whole Blood of Children with Ulcerative Colitis

Ulcerative colitis (UC) results from a complex interplay between the environment, gut microbiota, host genetics, and immunity. Runt-related transcription factor 3 (RUNX3) regulates Th1/Th2 balance and, thus, the synthesis of cytokines and inflammation. We aimed to analyze the dependence of RUNX3 promoter 2 (P2) methylation level on: age, sex, body mass index (BMI), C-reactive protein (CRP), serum albumin, disease duration, Pediatric Ulcerative Colitis Activity Index (PUCAI), the Paris classification, and exposure to medications. This multicenter, cross-sectional study recruited hospitalized children with UC. Methylation of RUNX3 P2 was measured with methylation-sensitive restriction enzymes in the whole blood DNA. Sixty-four children were enrolled, with a mean age of 14.5 ± 2.8 years. Half of them were female (51.6%), and the average BMI Z-score was −0.44 ± 1.14. The mean methylation of RUNX3 P2 was 54.1 ± 13.3%. The methylation level of RUNX3 P2 did not correlate with age, sex, nutritional status, CRP, albumin, PUCAI, or the extent of colitis (Paris E1–E4). RUNX3 P2 methylation did not differ between patients recruited within two and a half months of diagnosis and children who had UC for at least a year. Current or past exposure to biologics, immunosuppressants, or steroids was not associated with RUNX3 P2 methylation. Methylation of RUNX3 promoter 2 in whole blood DNA does not seem to be associated with the characteristics of UC in children.
Break and bend verbs in Sesotho

The break and bend verbs in Sesotho are investigated with a view to account for the unique properties of these verbs. The study gives a full account of the predicate argument structure of break and bend verbs. In Sesotho we have three types of predicates, namely, one-place, two-place and three-place predicates. The study also focuses on the number of arguments these verbs may assign. Some of these verbs are intransitive and therefore they assign one argument, which is an external argument. Other verbs are transitive and as such they assign more than one argument (i.e. external and internal arguments). These verbs are investigated as to whether they would select certain arguments to appear with them and it was found that when they do, they also select semantic features which these arguments must have in order to appear with such verbs. These verbs are also examined with the view to account for their difference. It was found that certain bend verbs show the same syntactic properties as the break verbs, except those bend verbs name reverse actions. Another observation was that not all break and bend verbs have zero-related nominaIs. When they do, the nominals describe the result of the action named by the verb. The study investigate these verbs as to whether they would allow cognate objects, verbal alternations and derived verbs (in the case of bend verbs), and it was found that some of them would allow such lexical items. Stellenbosch University http://scholar.sun.ac.za
Risk factors of anaphylaxis in Korea: Identifying drug-induced anaphylaxis culprits using big data

Drug-induced anaphylaxis is a fatal medical condition whose incidence has been increasing continuously. Due to differences between genetic backgrounds and health care systems, different populations may be prone to various causative drugs. Using the Health Insurance Service and Assessment Service database, we investigated culprit drugs for drug-induced anaphylaxis and common medication risk factors in the Korean general population. We collected medical prescription histories within 3 days prior to anaphylaxis between January 2011 and December 2019 from the HIRA database. Designed as a case-crossover study, the attributable visits (case visits) were matched to medical visits (control visits) with the drug sets for each visit. We collected a list of medication risk factors for anaphylaxis and calculated the risk ratio of each agent using the chi-square test and conditional logistic regression analysis. A total of 159,473 individuals were listed in the database with a diagnosis of anaphylaxis in the HIRA from 2011 to 2019. After evaluating the suitability of control visits for matching with a case visit, 8168 subjects and 767 drugs were analyzed. The chi-square analysis identified 31 drugs as potential risk factors for drug-induced anaphylaxis in Korea. After applying a conditional logistic regression analysis for each agent, 5 drugs were found to be the common medication risk factors for drug-induced anaphylaxis: cefaclor, iopromide, iohexol, iomeprol, and tolperisone. We found 5 medication risk factors that showed the highest risk of drug-induced anaphylaxis and their degree of risk using an objective methodology in the Korean general population.
Molecular and cellular mechanisms in diabetic heart failure: Potential therapeutic targets

Diabetes Mellitus (DM) is a worldwide health issue that can lead to a variety of complications. DM is a serious metabolic disorder that causes long-term microvascular and macro-vascular complications, as well as the failure of various organ systems. Diabetes-related cardiovascular diseases (CVD) including heart failure cause significant morbidity and mortality worldwide. Concurrent hypertensive heart disease and/or coronary artery disease have been thought to be the causes of diabetic heart failure in DM patients. However, heart failure is extremely common in DM patients even in the absence of other risk factors such as coronary artery disease and hypertension. The occurrence of diabetes-induced heart failure has recently received a lot of attention. Understanding how diabetes increases the risk of heart failure and how it mediates major cellular and molecular alteration will aid in the development of therapeutics to prevent these changes. Hence, this review aimed to summarize the current knowledge and most recent findings in cellular and molecular mechanisms of diabetes-induced heart failure.
Genetic background strongly influences the transition to chronic kidney disease of adriamycin nephropathy in mice

Animal models of podocytopathy and chronic kidney diseases (CKD) help elucidate these pathologies. Adriamycin (ADR)-induced nephropathy is a common rodent model of podocytopathy. BALB/c mice are sensitive to ADR, whereas C57BL/6 (B6) mice, the most commonly used strain, are resistant to ADR. Therefore, mouse strains with the B6 genetic background cannot be used as an ADR nephropathy model. We previously generated DNA-dependent protein kinase catalytic subunit (Prkdc) mutant B6 mice (B6-PrkdcR2140C) carrying the R2140C mutation that causes ADR nephropathy. However, whether ADR nephropathy in the novel strain progresses to CKD after ADR administration has not been evaluated. Therefore, we examined whether the B6-PrkdcR2140C mice develop CKD after ADR administration. We also evaluated whether differences existed in the genetic background in ADR nephropathy by comparing the B6-PrkdcR2140C mice with BALB/c mice. Our findings demonstrated that B6-PrkdcR2140C progresses to CKD and is resistant to nephropathy compared with the BALB/c mice. The B6-PrkdcR2140C and BALB/c mice differed in the expression of genes related to inflammatory mediators, and further analysis is required to identify factors that contribute to resistance to nephropathy.
THE EFFECT OF PSYCHOLOGICAL EMPOWERMENT ON PSYCHOLOGICAL CAPITAL AND INNOVATIVE WORK BEHAVIOR

The purpose of this study is to analyze the effect of psychological capital in mediating the effect of psychological empowerment on innovative work behavior. This research was conducted at the marketing of PT. BPR Lestari Bali with a sample of 168 marketing employees. Determination of the sample using probability sampling with saturated sampling technique. Data were analyzed by SEM (Structural Equation Modeling) with Partial Least Square (PLS) approach. The results showed that psychological capital partially mediated the effect of psychological empowerment on innovative work behavior. These results illustrate that to improve innovative work behavior in marketing employees, it is necessary to provide psychological empowerment to employees, where later these employees feel the fit between their environment, organizational values and work environment so as to increase psychological capital for these employees. The conclusion in this study is that psychological capital partially mediates the effect of psychological empowerment on the innovative work behavior of marketing PT. BPR Lestari Bali. It can be suggested that the company increase the sense of togetherness and cooperation in the team, provide appropriate incentives and provide opportunities for all employees to develop themselves.
Wigner transform for continuous and for discrete finite-dimensional Hilbert spaces

We study the Wigner Transform (WT) in the case of an infinite-dimensional continuous Hilbert space and a discrete Hilbert space of finite prime-number dimensionality $N$. In the discrete case we define a family of WT's as a function of a phase parameter. It is only for a specific value of the parameter that all the properties we have examined, including the matrix elements of the phase-space operator $P$ and the relation between Wigner's function and Kirkwood's distribution, have a parallel in both types of Hilbert space. This close parallel was found constructing the two WT's in terms of a complete set of operators having similar properties, in the sense that the position and momentum operators appear "disentangled" in the two situations. We give a geometric interpretation of the properties involving the phase-space operator in the discrete case.
Optimal long-run money growth rate in a cash-in-advance economy with labor-market frictions

We revisit the Friedman rule in a labor search model and extend Heer (2003), Cooley and Quadrini (2004), and Wang and Xie (2013) to one that allows for endogenous growth. We show that, even without a liquidity effect or a CIA constraint on firms’ wage payment, our model offers a different channel for moderate money growth to increase welfare. Intuitively, in a one-sector endogenous growth economy, the technology is of constant returns with respect to capital. When the labor market is frictional, a moderate increase in money growth induces an expansion in vacancy and employment. Labor and capital are complements in production. With an increase in employment, when the technology is neoclassical, the decreasing return in capital leads to a lower marginal product of labor. However, in an endogenous growth framework wherein the technology exhibits socially constant returns in capital, the marginal product of labor is constant. Due to a constant marginal product of labor, modest inflation raises employment, enlarges economic growth, and increases welfare. Moreover, the optimal long-run inflation rate departs from the Friedman rule, even when the Hosios rule holds. Finally, we find that our model with sustainable growth fits the data better than that without sustainable growth.
A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is the criterion for the filter’s optimality. By dynamically adjusting the noise covariance matrices, the DSTMCKF ensures that the correntropy distribution is accurate in the presence of non-Gaussian noise (NGN), thus improving its ability to handle the NGN. In simulation and real environment positioning experiments, the DSTMCKF is used to compare with the MCKF, variable kernel width–maximum correntropy Kalman filter (VKW-MCKF) and robust minimum error entropy Kalman filter (R-MEEKF). Among the four filters, the DSTMCKF has the highest accuracy, and the error of the DSTMCKF is reduced by 34.5, 42.9 and 40.0%, respectively, compared with the MCKF, VKW-MCKF and R-MEEKF in the real-world environment positioning experiment. The application of the DSTMCKF in WSNs positioning systems improves the stability of the control systems because of the rising positioning accuracy, which makes WSNs positioning systems more widely used in scenarios requiring high stability, such as automatic parking.
Attributes of Personal Electronic Records

The purpose of this article is to identify the key attributes of personal electronic records in order to develop systems that may enable people to manage them in the home. As more personal information becomes electronic, this is increasingly necessary. Personal electronic records were identified and categorised using interviews and virtual guided tours. Three main attributes were identified: primary user-subjective categories; attributes which identify the circumstances that give rise to the records; and attributes which describe the legal validity of each record. In addition to providing an improved understanding of personal electronic records in the home, these attributes are developed into a set of potential metadata fields.
A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model

Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the selection of the most influential major features for each cryptocurrency using the volatility features of cryptocurrency, derived from the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models, along with the closing price of the cryptocurrency. In addition, we sought to predict the log-return price of cryptocurrencies by implementing various types of time-series model. Based on the selected major features, the log-return price of cryptocurrency was predicted through the autoregressive integrated moving average (ARIMA) time-series prediction model and the artificial neural network-based time-series prediction model. As a result of log-return price prediction, the neural-network-based time-series prediction models showed superior predictive power compared to the traditional time-series prediction model.
Self‐supervised domain adaptation for cross‐domain fault diagnosis

Unsupervised domain adaptation‐based fault diagnosis methods have been extensively studied due to their powerful knowledge transferability under different working conditions. Despite their encouraging performance, most of them cannot sufficiently account for the temporal dimension of the vibration signal, resulting in incomplete feature information used in the domain alignment procedure. To alleviate the limitation, we present a self‐supervised domain adaptation fault diagnosis network (SDAFDN), which considers two temporal dependencies to improve the transferability of the learned representations. Specifically, we first design a down‐sampling and interaction network that considers the temporal dependency among subsequences with low temporal resolution in feature space. Then, we combine domain adversarial learning with feature mapping to achieve domain alignment. Finally, we introduced a self‐supervised learning module, which considers the temporal dependency between the past and future temporal segments via classification tasks. Extensive experiments on public Paderborn University and PHM data sets demonstrate the superiority of the proposed SDAFDN and the effectiveness of considering temporal dependencies in domain alignment.
Measures of polynomial growth and classical convolution inequalities

We study $L^p(\mu) \to L^q(\nu)$ mapping properties of the convolution operator $ T_{\lambda}f(x)=\lambda*(f\mu)(x)$ and of the corresponding maximal operator $ {\mathcal T}_{\lambda}f(x)=\sup_{t>0} |\lambda_t*(f\mu)(x)|$, where $\lambda$ is a tempered distribution, and $\mu$ and $\nu$ are compactly supported measures satisfying the polynomial growth bounds $\mu(B(x,r)) \leq Cr^{s_{\mu}}$ and $\nu(B(x,r)) \leq Cr^{s_{\nu}}$. As a result, we prove variants of the classical $L^p$-improving (Littman; Strichartz) and maximal (Stein) inequalities in a setting where the Plancherel formula is not available. Connections with the David-Semmes conjecture are also discussed.
A Jackknife Approach to Error-Reduction in Nonlinear Regression Estimation

The problems involving the use of jackknife methods in estimating the parameters of non linear regression models have been identified in this paper. We developed new algorithms for the estimation of nonlinear regression parameters. For estimating these parameters, computer programs were written in R for the implementation of these algorithms. We adopted the Gauss-Newton method based on Taylor’s series to approximate the nonlinear regression model with the linear term, and subsequently employ least square method iteratively. In the estimation of the nonlinear regression parameters, the results obtained from numerical problems using the Jackknife based algorithm developed yielded a reduced error sum of squares than the analytic result. As the number of d observations deleted in each resampling stage increases, the error sum of squares reduces minimally. This reveals the appropriateness of the new algorithms for the estimation of nonlinear regression parameters and in the reduction of the error terms in nonlinear regression estimation.
Investigating the Neural Bases of Risky Decision Making Using Multi-Voxel Pattern Analysis

Choices between smaller certain reward and larger riskier reward are referred to as risky decision making. Numerous functional magnetic resonance imaging (fMRI) studies have investigated the neural substrates of risky decision making via conventional univariate analytical approaches, revealing dissociable activation of decisions involving certain rewards and risky rewards. However, it is still unclear how the patterns of brain activity predict the choice that the individual will make. With the help of multi-voxel pattern analyses, which is more sensitive for evaluating information encoded in spatially distributed patterns, we showed that fMRI activity patterns represent viable signatures of certain and risky choice and individual differences. Notably, the regions involved in representation of value and risk and cognitive control play prominent roles in differentiating certain and risky choices as well as individuals with distinct risk preference. These results deepen our understanding of the neural correlates of risky decision making as well as emphasize the important roles of regions involved in representation of value and risk cognitive control in predicting risky decision making and individual differences.
Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Bearing fault is a process of gradual development and deepening. In the early stage of the fault, if it can be found out in time and taken reasonable prevention and elimination measures, we can avoid serious losses and safety accidents. Therefore, the feature extraction and analysis of early weak fault has important practical significance. In this paper, an improved multiscale permutation entropy (IMPE) method was proposed to overcome the shortcomings in the coarse-grained process. In order to solve the problem that only considering a single coarse-grained sequence under a certain scale may lead to the loss of feature information, this paper proposed to calculate the time series with equal overlapping segments, that was to consider all coarse-grained sequences under the same scale to reflect the feature information of fault signals more comprehensively. In order to solve the problem that feature extraction is not refined enough when using the first-order moment mean value calculation in traditional MPE calculation, a calculation method based on the skewness of the third-order moment was proposed. The calculation method is more sensitive to the complexity and fluctuation of signals and can better describe the feature details and extract the fault features effectively. IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.