Miracle-dz/UniFAR-FTUs huggingface dataset:
A meta-analysis of the relationship between polycystic ovary syndrome and sleep disturbances risk

Objective: A meta-analysis is used to explore the relationship between polycystic ovary syndrome (PCOS) and the risk of Sleep disturbances. Method: Cochrane Library, PubMed, Embase, and Web of Science databases are searched by computer from their establishment to 1 May 2022. Review Manager 5.4 software is used for the meta-analysis. Results: A total of nine articles are included, with 1,107 subjects. The results show that PCOS is positively associated with the risk of Sleep disturbances. Comparing with the “PCOS group” (experimental group) with the “NON-PCOS group” (control group), the incidence of Sleep disturbances is higher (OR = 11.24, 95% CI: 2.00–63.10, Z = 2.75, p = 0.006); the Pittsburgh Sleep Quality Index (PSQI) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 0.78, 95% CI: 0.32–1.25, Z = 3.30, p = 0.001); the Epworth Sleepiness Scale (ESS) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 2.49, 95% CI: 0.80–4.18, Z = 2.88, p = 0.004); Apnea hypopnea index (AHIs) in the PCOS group are higher than those in the NON-PCOS group (MD = 2.68, 95% CI: 1.07–4.28, Z = 3.27, p = 0.001); the sleep efficiency of the PCOS group is lower than that of the NON-PCOS group (MD = -5.16, 95% CI: 9.39–-0.93, Z = 2.39, p = 0.02); the sleep onset latency of the PCOS group is higher than that of the NON-PCOS group (MD = 2.45, 95% CI: 1.40–3.50, Z = 4.57, p < 0.001); and the Rapid Eyes Movement (REM) sleep in the PCOS group is higher than that in the NON-PCOS group (MD = 17.19, 95% CI: 11.62–55.76, Z = 6.05, p < 0.001). The studies included in each analysis have publication biases of different sizes. After subgroup analysis and sensitivity analysis, the heterogeneity of each study in the meta-analysis is reduced, the bias is reduced accordingly, and the stability of the results can be maintained. Conclusion: PCOS is positively associated with the risk of Sleep disturbances. In order to reduce such risk, attention should be paid to the role of PCOS management, and PCOS prevention and treatment should be actively carried out.
Ethylene emitted by viral pathogen-infected pepper (Capsicum annuum L.) plants is a volatile chemical cue that attracts aphid vectors

Plant viruses are obligate intracellular pathogens, and most depend on insect vectors for transmission between plants. Viral infection causes various physiological and metabolic changes in host traits, which subsequently influence the behavior and fitness of the insect vectors. Cucumber mosaic virus (CMV), one of the most widespread pathogens in pepper (Capsicum annuum L.), is transmitted by aphid vectors in a non-persistent manner. Here, we examined whether CMV infection in pepper affects the behavior of aphid vectors (Myzus persicae and Aphis glycines) in pepper. Aphid preference test revealed that significantly more aphids were attracted to CMV-infected pepper plants than to healthy plants. Comparative transcriptome analysis revealed a significant activation of the ethylene biosynthesis pathway in CMV-infected pepper plants. Indeed, gas chromatography analysis demonstrated that ethylene emission was significantly increased by CMV infection in pepper plants. Elevated ethylene emission in ethephon-treated healthy pepper increased their attractiveness to aphids. In contrast, aphid preference decreased after chemical inhibition of ethylene biosynthesis in CMV-infected pepper plants. Our results suggest that the ethylene emitted by CMV infection is a volatile cue that regulates the attractiveness of pepper plants to M. persicae and A. glycines.
Nonlinear optomechanics with gain and loss: amplifying higher-order sideband and group delay

We study the nonlinear optomechanically induced transparency (OMIT) with gain and loss. We find that (i) for a single active cavity, significant enhancement can be achieved for the higher-order sidebands, including the transmission rate and the group delay; (ii) for active–passive-coupled cavities, hundreds of microsecond of optical delay or advance are attainable for the nonlinear sideband pulses in the parity-time-symmetric regime. The active higher-order OMIT effects, as firstly revealed here, open up the way to make a low-power optomechaical amplifier, which can amplify both the strength and group delay of not only the probe light but also its higher-order sidebands.
Modeling Future for Neural Machine Translation by Fusing Target Information

Sequence-to-sequence Neural Machine Trans-001 lation (NMT) models have achieved excellent 002 performance. However, the NMT decoder 003 only makes predictions based on the source 004 and the target historical context, ignores the 005 target future information completely, leading 006 to a problem that NMT does not consider po-007 tential future information when making de-008 cisions. To alleviate this problem, we pro-009 pose a simple and effective Fu ture-fused NMT 010 model called F U NMT, which introduces a re-011 verse decoder to explicitly model the target 012 future information, then adopts an agreement 013 mechanism to enable the forward decoder to 014 learn this future information. Empirical stud-015 ies on multiple benchmarks show that our pro-016 posed model signiﬁcantly improves translation 017 quality. 018
RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools either do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, or lack flexibility in expressing various types of user preferences. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. RankASco leverages a series of visual interfaces, enabling broad user groups to a) select attributes of interest, b) express preferences on attribute scorings based on different mental models, and c) analyze and refine item ranking results. ,
Performance Analysis of DWT-Based Epileptic Seizure Detection

: Epilepsy is a central nervous system disorder that is well defined by the startling and atypical behavior of seizures and causes loss of consciousness. The Electroencephalogram (EEG) signal is particularly good for assessing neurogenic activity and is often used in central nervous system interfaces in the human brain, and neuron disease diagnosis. Among the three modules, discrete wavelet transforms(DWT), feature extraction (FE), and the classification of the support vector machine(SVM), the FE unit matches the relevant neurobiological areas of the EEG data using the 9/7th DWT and extracts the required features. Feature extraction classifies the extracted EEG information into four basic sub-bands namely, alpha, beta, gamma, and theta. Feature Extraction utilizes signal analysis methods and computer technologies to extract information from electroencephalography signals. The proposed design consists of medically converted EEG data, DWT, wavelet decomposition, higher-order statistics, a feature extraction (FE) module, and an SVM module unit. Among the various machine-learning techniques, the support vector machine differentiates between healthy and unhealthy illnesses such as epileptic seizures, SVM is deployed due to its high reliability & adaptation to the presence of arbitrary nonlinear decision limits. The posited designed system provides higher accuracy and reliability compared to the conventional methods designed before in time.
Influence of molecular conformations on the electronic structure of organic charge transfer salts

We report ab-initio calculations for the electronic structure of organic charge transfer salts $\kappa$-(ET)$_2$Cu[N(CN)$_2$]Br, $\kappa$-(ET)$_2$Cu[N(CN)$_2$]I, $\kappa^{\prime \prime}$-(ET)$_2$Cu[N(CN)$_2$]Cl and $\kappa$-(ET)$_2$Cu$_2$(CN)$_3$. These materials show an ordering of the relative orientation of terminal ethylene groups in the BEDT-TTF molecules at finite temperature and our calculations correctly predict the experimentally observed ground state molecular conformations (eclipsed or staggered). Further, it was recently demonstrated that the ethylene endgroup relative orientations can be used to reversibly tune $\kappa$-(ET)$_2$Cu[N(CN)$_2$]Br through a metal-insulator transition. Using a tight-binding analysis, we show that the molecular conformations of ethylene endgroups are intimately connected to the electronic structure and significantly influence hopping and Hubbard repulsion parameters. Our results place $\kappa$-(ET)$_2$Cu[N(CN)$_2$]Br in eclipsed and staggered configurations on opposite sides of the metal-insulator transition.
A goodness-of-fit test for a receiver operating characteristic curve from continuous diagnostic test data

The receiver operating characteristic (ROC) curve is a useful way to display the performance of a medical diagnostic test for detecting whether or not a patient is diseased or healthy. The diagnostic data consist of independent random samples on continuous measurement scales from diseased and healthy populations. We propose assessing the goodness-of-fit of a model by comparing a model-based estimate with a nonparametric estimate of the area under the curve (AUC). We focus on two parametric models, so-called Bi-Normal and Bi-Weibull models, and briefly on associated semiparametric transformation models. We also consider the null hypothesis that a parametric model is valid after an unspecified monotone transformation of the measurement scales. High power of the test implies sensitivity of the AUC to model assumptions; low power implies robustness of the estimate. The test is exemplified with a data set on the diagnosis of pancreatic cancer. A simulation study of the statistical power of the test is included.
Can postoperative pain be predicted? New parameter: analgesia Can postoperative pain be predicted? New parameter: analgesia nociception index nociception index

Background/aim: The Analgesia Nociception Index (ANI) is a new method of identifying nociception-analgesia balance. In this study, we investigate the correlation between the ANI and numeric rating scale (NRS) values immediately before and after extubation. The NRS values were recorded in the postanesthesia care unit, in a group of patients who underwent laparoscopic cholecystectomy, with the aim of evaluating the potential use of ANI values in the prediction of postoperative pain levels. Materials and methods: The and values, heartbeat rate arterial and oxygen saturation (SpO ) values were recorded into three groups on the initial in the postanesthesia Results: Statistically significant increases were noted in HR, SAP, and DAP after extubation, while there was no significant change in ANI values. A weak correlation was identified between the ANI and NRS values of all patient groups. Conclusion: We failed to identify a correlation between ANI and NRS values before and after extubation. Previous studies suggested that the ANI provides more valuable information in anesthetized patients, whereas our findings show that it is ineffective in the prediction of potential postoperative pain.
Spectral classification of Pleiades brown dwarf candidates

We report on the results of the spectroscopy of 10 objects previously classified as brown dwarf candidates via RIJHK colors by Eisenbeiss et al. (2009), who performed deep imaging observations on a ∼0.4 sq.deg. field at the edge of the Pleiades. We describe and judge on classification techniques in the region of M-type stars. To classify and characterise the objects, visual and near infrared spectra have been obtained with VLT FORS and ISAAC. The spectral classification was performed using the shape of the spectra as well as spectral indices that are sensitive to the spectral type and luminosity class of M-type stars and late M-type brown dwarfs. Furthermore a spectrophotometric distance was calculated and compared the distance of the Pleiades to investigate the membership probability. As a second argument we analyzed the proper motion. The brown dwarf candidates were found not to be brown dwarfs, but late-K to mid-M-type dwarf stars. Based on the obtained distance and tabulated proper motions we conclude that all objects are background dwarf stars (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
ANALYTICAL HIERARCHY PROCESS (AHP) APPROACH ON CONSUMER PREFERENCE IN HOTEL SELECTION DURING COVID-19 PANDEMIC (CASE STUDY:

: The hospitality industry is one of the most significant contributions to the Indonesian economy. In Manado, the impact of the Covid 19 pandemic resulted in the tourism industry, especially the hotel business being very down. The purpose of this study was to determine the most preferred hotel among the three hotels (Novotel, Swiss Bell, and Four Points). And what criteria most influenced respondents to stay at the three hotels using the Analytical Hierarchy Process method. This type of research approach is a quantitative method. The population in this study is people in Manado who have stayed and have experience with Novotel, Swiss Bell, and Four Points. Hotel criteria mean factors of the hotel are Price, Place, Customer Satisfaction, Advertisement, and Peer Influence. Based on the result and discussion, two conclusions can be formulated, Place is the criterion that influences the most in selecting a hotel. Four Points is the most chosen hotel in Manado, Four Points became the most preferred hotel because it excels in four criteria; place, advertising, customer satisfaction, and peer influence. And in the second position is Novotel, and Swiss Bell is in the last position. paling signifikan dalam perekonomian Indonesia. Di Manado, dampak pandemi Covid 19 mengakibatkan industri pariwisata khususnya bisnis perhotelan sangat terpuruk. Tujuan dari penelitian ini adalah untuk menentukan hotel yang paling disukai diantara ketiga hotel (Novotel, Swiss Bell dan Four Points). Dan kriteria apa yang paling mempengaruhi responden untuk menginap di ketiga hotel tersebut dengan menggunakan metode Analytical Hierarchy Process. Jenis pendekatan penelitian ini adalah metode kuantitatif. Populasi dalam penelitian ini adalah masyarakat di Manado yang pernah menginap dan memiliki pengalaman dengan Novotel, Swiss Bell dan Four Points. Preferensi Konsumen diukur dari tingkat kepuasan menginap di hotel. Kriteria hotel yang dimaksud dengan faktor hotel adalah Harga, Tempat, Kepuasan Pelanggan, Iklan, Pengaruh Sebaya. Berdasarkan hasil dan pembahasan dapat dirumuskan dua kesimpulan, Tempat merupakan kriteria yang paling berpengaruh dalam pemilihan hotel. Four Points menjadi hotel yang paling banyak dipilih di Manado, Four Points menjadi hotel yang paling disukai karena unggul dalam empat kriteria; tempat, iklan, kepuasan pelanggan, dan pengaruh teman sebaya. Dan di posisi kedua ada Novotel, dan Swiss Bell di posisi terakhir.
Learning Emotion-Aware Contextual Representations for Emotion Cause Analysis

Emotion Cause Analysis has been a key topic 001 in natural language processing. Previous 002 works focus on Emotion Cause Extraction 003 (ECE), a clause-level classiﬁcation task aimed 004 at extracting causes of certain given emotion 005 in text. The task has been expanded to Emo-006 tion Cause Pair Extraction (ECPE) that focus 007 on extracting both emotions and correspond-008 ing causes in the context. Most existing meth-009 ods for the ECPE task implement a joint model 010 that performs extracting and matching of emo-011 tion and cause clauses simultaneously. How-012 ever, we argue that different input features are 013 needed for the two subtasks, thus sharing con-014 textual representations may be suboptimal. In 015 this work, we propose a pipelined approach 016 that builds on two independent pre-trained en-017 coders, in which the emotion extraction model 018 only provide input features for the cause ex-019 traction model. Based on a series of careful 020 experiments, we validate that our model can 021 create distinct contextual representations ac-022 cording to speciﬁc emotional texts, and thus 023 achieve state-of-the-art performance in both 024 ECE and ECPE tasks, with the absolute F1 im-025 provements of 1.5% and 4.72% over best pre-026 vious works respectively. Besides, we apply a 027 set of simple clause selection rules to extract 028 multiple pairs in the document, strengthening 029 the applicability of our model in real world sce-030 narios. 031
Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems

Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-Computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context.
Suzaku study of the centrally brightened supernova remnant G272.2−3.2

In this paper, we present the results from a Suzaku observation of the Galactic supernova remnant G272.2−3.2. The spectra of G272.2−3.2 are well fitted by a single-temperature variable abundance non-equilibrium ionization (VNEI) model with an electron temperature kTe∼ 0.77 keV, an ionization time-scale τ∼ 6.5 × 1010 cm−3 s and an absorbing column density NH∼ 1.1 × 1022 cm−2. We have detected enhanced abundances of Si, S, Ca, Fe and Ni in the centre region, indicating that the X-ray emission has an ejecta origin. We estimate that the electron density is ne∼ 0.48f−1/2 cm−3, the age is ∼4300f1/2 yr and the X-ray total mass is Mx= 475f1/2 M⊙ by taking the distance to be d= 10 kpc. To understand the origin of the centrally peaked X-ray emission of the remnant, we have studied the radial variations of the electron temperature and surface brightness. The relative abundances in the centre region suggest that G272.2−3.2 is the result of a Type Ia supernova explosion.
Long-Tail Classiﬁcation for Distinctive Image Captioning: A Simple yet Effective Remedy for Side Effects of Reinforcement Learning

Distinctiveness is a desirable feature of image 001 captions. Captions should cover the charac-002 teristic details of input images. However, re-003 cent high-performing captioning models that 004 are trained with reinforcement learning (RL) 005 tend to generate overly generic captions de-006 spite their high performance in various other 007 criteria. Interestingly, it has also been reported 008 that their outputs are composed of a limited 009 number of common words and rarely contain 010 tail-class words, i.e. , low-frequency words in 011 the training corpus. Vocabulary size is closely 012 related to distinctiveness as it is difﬁcult for 013 a model to describe details beyond its vocabu-014 lary. Based on this insight, we hypothesize that 015 the limited vocabulary of RL models is the ma-016 jor factor limiting their distinctiveness. We re-017 cast distinctive image captioning as a simpler 018 task of long-tail classiﬁcation to increase the 019 vocabulary and then propose lightweight ﬁne-020 tuning methods to encourage tail-class word 021 generation. The experimental results demon-022 strate that our methods signiﬁcantly enhance 023 the distinctiveness of existing RL models as 024 well as their vocabulary size, without sacri-025 ﬁcing quality. Our methods also outperform 026 previous distinctiveness-aware methods with a 027 small computational cost of minor modiﬁca-028 tions to pre-trained RL models. 1 029
Automatic estimation of human’s psychophysiological state by speech

The survey of biometric methods and existing systems of identification and verification of individual is outlined in this paper. There are two types of analyzed data in biometric systems: static and dynamic. At that the accuracy of last ones strongly depends on psychophysiological state of a man. Therefore it is proposed to use the methods of speech processing for estimation of the current state of a speaker. As the instruments for speech processing were used the modules of parameterization of speech signal and semantic syntactic analysis developed in Speech Informatics Group of SPIIRAS. Besides the proposed model has a capability for combining with other types of biometric information.
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2 2 DD C T Method

of the target gene relative to some reference group The two most commonly used methods to analyze data from such as an untreated control or a sample at time zero real-time, quantitative PCR experiments are absolute quantifica-in a time-course study. tion and relative quantification. Absolute quantification deter-Absolute quantification should be performed in situ- mines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal ations where it is necessary to determine the absolute of the target transcript in a treatment group to that of another transcript copy number. Absolute quantification has sample such as an untreated control. The 2 2 DD C T method is a combined with real-time PCR and numerous re- convenient way to analyze the relative changes in gene expression appeared in the literature including from real-time quantitative PCR experiments. The purpose of this two articles in this issue 11). In some situations, report is to present the derivation, assumptions, and applications it may be unnecessary to determine the absolute tran- of the 2 2 DD C T method. In addition, we present the derivation and script copy number and reporting the relative change applications of two variations of the 2 2 DD C T method that may be useful in the analysis of real-time, quantitative PCR data.
Analyzing Green View Index and Green View Index best path using Google Street View and deep learning

As an important part of urban landscape research, analyzing and studying street-level greenery can increase the understanding of a city's greenery, contributing to better urban living environment planning and design. Planning the best path of urban greenery is a means to effectively maximize the use of urban greenery, which plays a positive role in the physical and mental health of urban residents and the path planning of visitors. In this paper, we used Google Street View (GSV) to obtain street view images of Osaka City. The semantic segmentation model is adopted to segment the street view images and analyze the Green View Index (GVI) of Osaka City. Based on the GVI, we take advantage of the adjacency matrix and Floyd-Warshall Algorithm to calculate Green View Index best path, solving the limitations of ArcGIS software. Our analysis not only allows the calculation of specific routes for the GVI best paths but also realizes the visualization and integration of neighborhood urban greenery. By summarizing all the data, we can conduct an intuitive feeling and objective analysis of the street-level greenery in the research area. Based on this, such as urban residents and visitors can maximize the available natural resources for a better life. The dataset and code are available at https://github.com/Jackieam