Miracle-dz/UniFAR-FTUs huggingface dataset:
Meteorological Factors and the Transmissibility of Hand, Foot, and Mouth Disease in Xiamen City, China

Background: As an emerging infectious disease, the prevention and control of hand, foot, and mouth disease (HFMD) poses a significant challenge to the development of public health in China. In this study, we aimed to explore the mechanism of the seasonal transmission characteristics of HFMD and to reveal the correlation and potential path between key meteorological factors and the transmissibility of HFMD. Methods: Combined with daily meteorological data such as average temperature, average relative humidity, average wind velocity, amount of precipitation, average air pressure, evaporation capacity, and sunshine duration, a database of HFMD incidence and meteorological factors was established. Spearman rank correlation was used to calculate the correlation between the various meteorological factors and the incidence of HFMD. The effective reproduction number (Reff) of HFMD was used as an intermediate variable to further quantify the dynamic relationship between the average temperature and Reff. Results: A total of 43,659 cases of HFMD were reported in Xiamen from 2014 to 2018. There was a significantly positive correlation between the average temperature and the incidence of HFMD (r = 0.596, p < 0.001), and a significantly negative correlation between the average air pressure and the incidence of HFMD (r = −0.511, p < 0.001). There was no correlation between the average wind velocity (r = 0.045, p > 0.05) or amount of precipitation (r = 0.043, p > 0.05) and incidence. There was a temperature threshold for HFMD's transmissibility. Owing to the seasonal transmission characteristics of HFMD in Xiamen, the temperature threshold of HFMD's transmissibility was 13.4–18.4°C and 14.5–29.3°C in spring and summer and in autumn and winter, respectively. Conclusions: HFMD's transmissibility may be affected by the average temperature; the temperature threshold range of transmissibility in autumn and winter is slightly wider than that in spring and summer. Based on our findings, we suggest that the relevant epidemic prevention departments should pay close attention to temperature changes in Xiamen to formulate timely prevention strategies before the arrival of the high-risk period.
The Associations Between Physical Performance and Anthropometric Characteristics in Obese and Non-obese Schoolchild Handball Players

This study investigated the relationship of body fat and fitness measures in schoolchild handball players. Twenty-eight young male handball players from handball first youth league volunteered for the present investigation (age: 10.9 ± 0.72 years; body mass: 54.8 ± 22.9 kg; height: 1.48 ± 0.10 m; body fat: 27.6 ± 9.23%). Measures included the Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1), jumping ability [squat and counter-movement jumps (SJ, CMJ)], and sprint tests (10 m, 15 m). Anthropometry was assessed by body mass, body mass index (BMI), and fat percentage (%BF). The power of the upper limb was measured as the total distance thrown overhead using a 2 kg medicine ball. Intrarater reliability for all parameters showed a coefficient of variation (CV) below 10% and an intraclass correlation coefficient (ICC) above 0.75. All ICC were excellent (ICC ≥ 0.96). Reliability as shown by the CV differed between 1.0 (sprint 15 m) and 5.6 (sprint 10 m). With the exception of medicine ball throw, we found significant differences between non-obese and obese in all performance parameters. The differences ranged from ηp2 = 0.47 (sprint 10 m) to ηp2 = 0.09 (medicine ball throw). The two-step-linear regression analysis using the predictors body height and body weight (step 1) and body fat (step 2) showed a marked increase of explained variance by adding body fat. The largest r2 changes were calculated for sprint 10 m (0.54), CMJ (0.49), and sprint 15 m (0.42). The lowest influence of the predictors was observed for medicine ball throw (step 1: r2 = 0.03, step 2: r2 = 0.07). With the exception of sprint parameters (β-coefficient sprint 10 m: -0.74; β-coefficient sprint 20: -0.66), a decrease of %BF leads to a higher performance in all parameters. %BF in youth handball players should be an important concern for practitioners working in this team sport in contrast to the frequently used BMI. It seems sensible and appropriate to engage very young children in physical activities such as team handball in order to improve their physical fitness. Decrease in% body fat could be considered both as a training and nutritional target to enhance and optimize sport performance-related outcomes.
New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification

Last two decades, neural network and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network system means the combination of fuzzy logic and neural network concepts, which includes the advantages of fuzzy logic and neural network. This FNN is applied in many scientific and engineering areas. Wherever there is an uncertainty associated with data fuzzy logic place a vital rule. The fuzzy set can represent and handles uncertainty information effectively. The main objective o f the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either neural network structure, activation function or learning algorithms. In computer vision and intelligent system, Convlutional Neural Network has more popular architectures and their performance is excellent in many applications. In this paper fuzzy based CNN image classification methods are analysed, also interval type-2 fuzzy based CNN is proposed. From the experiment it is identified that the proposed method performance is well.
Feature Selection with IG-R for Improving Performance of Intrusion Detection System

As the popularity of the internet computer continued to grow and become an indispensable in human life, the security of computer network has become an important issue in computer security field. The Intrusion Detection System (IDS) is a system used in computer security for network security. The feature selection stage of IDS is considered to be the most critical stage in IDS. This stage is very costly both in efforts and time. However, many machine learning approaches have been presented to improve this stage in order to improve the performance of an IDS. However, these approaches did not give desirable results with respect to the detection accuracy in the IDS. A novel technique is proposed in this paper combining the Information Gain and Ranker (IG+R) method as the feature selection strategy with Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classifiers. The performance of these IG+R-NB, IG+R-SVM, and IG+R-KNN was evaluated on NSLKDD dataset. The experimental results of our proposed method gave high accuracy and low false alarm rate. The results obtained was compared and benchmarked with existing works. The results of this paper outperformed the existing approaches in terms of the detection accuracy.
Amplitude of Low-Frequency Oscillations in Major Depressive Disorder With Childhood Trauma

Major Depressive Disorder (MDD) with childhood trauma is one of the functional subtypes of depression. Frequency-dependent changes in the amplitude of low-frequency fluctuations (ALFF) have been reported in MDD patients. However, there are few studies on ALFF about MDD with childhood trauma. Resting-state functional magnetic resonance imaging was used to measure the ALFF in 69 MDD patients with childhood trauma (28.7 ± 9.6 years) and 30 healthy subjects (28.12 ± 4.41 years). Two frequency bands (slow-5: 0.010–0.027 Hz; slow-4: 0.027–0.073 Hz) were analyzed. Compared with controls, the MDD with childhood trauma had decreased ALFF in left S1 (Primary somatosensory cortex), and increased ALFF in left insula. More importantly, significant group × frequency interactions were found in right dorsal anterior cingulate cortex (dACC). Our finding may provide insights into the pathophysiology of MDD with childhood trauma.
DeCban: Prediction of circRNA-RBP Interaction Sites by Using Double Embeddings and Cross-Branch Attention Networks

Circular RNAs (circRNAs), as a rising star in the RNA world, play important roles in various biological processes. Understanding the interactions between circRNAs and RNA binding proteins (RBPs) can help reveal the functions of circRNAs. For the past decade, the emergence of high-throughput experimental data, like CLIP-Seq, has made the computational identification of RNA-protein interactions (RPIs) possible based on machine learning methods. However, as the underlying mechanisms of RPIs have not been fully understood yet and the information sources of circRNAs are limited, the computational tools for predicting circRNA-RBP interactions have been very few. In this study, we propose a deep learning method to identify circRNA-RBP interactions, called DeCban, which is featured by hybrid double embeddings for representing RNA sequences and a cross-branch attention neural network for classification. To capture more information from RNA sequences, the double embeddings include pre-trained embedding vectors for both RNA segments and their converted amino acids. Meanwhile, the cross-branch attention network aims to address the learning of very long sequences by integrating features of different scales and focusing on important information. The experimental results on 37 benchmark datasets show that both double embeddings and the cross-branch attention model contribute to the improvement of performance. DeCban outperforms the mainstream deep learning-based methods on not only prediction accuracy but also computational efficiency. The data sets and source code of this study are freely available at: https://github.com/AaronYll/DECban.
Anti-Foc RT4 Activity of a Newly Isolated Streptomyces sp. 5–10 From a Medicinal Plant (Curculigo capitulata)

Fusarium wilt of banana caused by Fusarium oxysporum f. sp. cubense (Foc) is a disastrous soil-borne fungal disease. Foc tropical race 4 (Foc TR4) can infect almost all banana cultivars. Until now, there is a shortage of safety and effective control methods and commercial banana cultivars with a resistance against Foc TR4. Biocontrol using environmentally friendly microbes is a promising strategy for the management of Foc TR4. Here, a strain 5–10, newly isolated from a medicinal plant (Curculigo capitulata), exhibited a high antifungal activity against Foc TR4. Combing the morphological characteristics and molecular identification, strain 5–10 was classified as a Streptomyces genus. The sequenced genome revealed that more than 39 gene clusters were involved in the biosynthesis of secondary metabolites. Some multidrug resistance gene clusters were also identified such as mdtD, vatB, and vgaE. To improve the anti-Foc TR4 activity of the strain 5–10 extracts, an optimization method of fermentation broth was established. Antifungal activity increased by 72.13% under the fermentation system containing 2.86 g/L of NaCl and 11.57% of inoculation amount. After being treated with the strain 5–10 extracts, the Foc TR4 hyphae shrinked, deformed, and ruptured. The membrane integrity and cell ultrastructure incurred irreversible damage. Streptomyces sp. 5–10 extracts play a fungicidal role in Foc TR4. Hence, Streptomyces sp. 5–10 will be a potential biocontrol agent to manage fungal diseases by exploring the microbial fertilizer.
Research on the Role of Combined Chemotherapy and Radiotherapy in Patients With N+ Non-Metastatic Metaplastic Breast Carcinoma: A Competing Risk Analysis Model Based on the SEER database, 2000 to 2015

Purpose Due to the rarity of metaplastic breast carcinoma (MpBC), no randomized trials have investigated the role of combined chemotherapy and radiotherapy (CCRP) in this condition. We aimed to explore and identify the effectiveness of CCRP in patients with regional lymph node metastasis (N+) non-metastatic MpBC. Materials and Methods Data were obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program database. We assessed the effects of CCRP on overall survival (OS), breast cancer-specific survival (BCSS), and breast cancer-specific death (BCSD) using Kaplan-Meier analysis, competing risk model analysis, and competing risk regression mode analysis. Results A total of 707 women and 361 death cases were included in the unmatched cohort, of which 76.45% (276/361) were BCSD, and 23.55% (85/361) were non-breast cancer-specific deaths (non-BCSD). Both the ChemT and CCRP groups had better OS (ChemT group: HR: 0.59, 95% CI: 0.45–0.78, P<0.001; CCRP group: HR: 0.31, 95% CI: 0.23–0.41, P<0.001) and BCSS (ChemT group: HR: 0.63, 95% CI: 0.45–0.87, P<0.001; CCRP group: HR: 0.32, 95%CI: 0.22–0.46, P<0.001) than the non-therapy group. Subjects in the CCRP group tended to have significantly lower cumulative BCSD (Gray’s test, P=0.001) and non-BCSD (Gray’s test, P<0.001) than the non-therapy group or ChemT group. In competing risk regression model analysis, subjects in the CCRP group had a better prognosis in BCSD (HR: 0.710, 95% CI: 0.508–0.993, P=0.045) rather than the ChemT group (HR: 1.081, 95% CI: 0.761–1.535, P=0.660) than the non-therapy group. Conclusion Our study demonstrated that CCRP could significantly decrease the risk of death for both BCSD and non-BCSD and provided a valid therapeutic strategy for patients with N+ non-metastatic MpBC.
Integrating Network Pharmacology and Experimental Validation to Investigate the Effects and Mechanism of Astragalus Flavonoids Against Hepatic Fibrosis

Hepatic fibrosis (HF) represents the excessive wound healing where an excess amount of connective tissues is formed within the liver, finally resulting in cirrhosis or even hepatocellular carcinoma (HCC). Therefore, it is significant to discover the efficient agents and components to treat HF, thus restraining the further progression of hepatopathy. Astragalus membranaceus (Fisch.) Bunge [also called Astragali Radix (AR)] is a famous herb in traditional Chinese medicine (TCM), which possesses a variety of biological activities and exerts good therapeutic effects in the treatment of HF. Flavonoids account for the major active ingredients related to the AR pharmacological effects. Total AR flavonoids have been proved to exert inhibitory effects on hepatic fibrosis. This study aimed to further undertake network pharmacology analysis coupled with experimental validation and molecular docking to investigate the effects and mechanism of multiple flavonoid components from AR against liver fibrosis. The results of the network pharmacology analysis showed that the flavonoids from AR exerted their pharmacological effects against liver fibrosis by modulating multiple targets and pathways. The experimental validation data showed that the flavonoids from AR were able to suppress transforming growth factor beta 1 (TGF-β1)-mediated activation of hepatic stellate cells (HSCs) and reduce extracellular matrix deposition in HSC-T6 cells via regulating the nuclear factor kappa B (NF-κB) signal transduction pathway. The results of the molecular docking study further showed that the flavonoids had a strong binding affinity for IκB kinase (IKKβ) after docking into the crystal structure. The above results indicated that, flavonoids possibly exerted the anti-inflammatory effect on treating HF by mediating inflammatory signaling pathways. The potential mechanism of these flavonoids against liver fibrosis may be related to suppression of the NF-κB pathway through effective inhibition of IKKβ. This study not only provides a scientific basis for clarifying the effects and mechanism of AR flavonoids against liver fibrosis but also suggests a novel promising therapeutic strategy for the treatment of liver fibrosis.
Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification

Extreme Multi-label Text Classiﬁcation (XMTC) refers to supervised learning of a classiﬁer which can predict a small subset of relevant labels for a document from an extremely large set. Even though deep learning algorithms have surpassed linear and kernel methods for most natural language processing tasks over the last decade; recent works show that state-of-the-art deep learning methods can only barely manage to work as well as a linear classiﬁer for the XMTC task. The goal of this work is twofold : (i) to investigate the reasons for the comparable performance of these two strands of methods for XMTC, and (ii) to document this observation explicitly, as the efﬁcacy of linear classiﬁers in this regime, has been ignored in many relevant recent works.
Towards Adversarial Attack Resistant Deep Neural Networks

Recent publications have shown that neural network based classiﬁers are vulnerable to adversarial inputs that are virtually indistinguishable from normal data, constructed explicitly for the purpose of forcing misclassiﬁcation. In this paper, we present several defenses to counter these threats. First, we observe that most adversarial attacks succeed by mounting gradient ascent on the conﬁdence returned by the model, which allows adversary to gain understanding of the classiﬁcation boundary. Our defenses are based on denying access to the precise classiﬁcation boundary. Our ﬁrst defense adds a controlled random noise to the output conﬁdence levels, which prevents an adversary from converging in their numerical approximation attack. Our next defense is based on the observation that by varying the order of the training, often we arrive at models which oﬀer the same classiﬁcation accuracy, yet they are diﬀerent numerically. An ensemble of such models allows us to randomly switch between these equivalent models during query which further blurs the classiﬁcation boundary. We demonstrate our defense via an adversarial input generator which defeats previously published defenses but cannot breach the proposed defenses do to their non-static nature.
Detecting End-Point (EP) Man-In-The-Middle (MITM) Attack based on ARP Analysis: A Machine Learning Approach

End-Point (EP) Man-In-The-Middle (MITM) attack is a well-known threat in computer security. This attack targets the flow of information between endpoints. An attacker is able to eavesdrop on the communication between two targets and can either perform active or passive monitoring; this affects the confidentiality and integrity of the data flow. Several techniques have been developed by researchers to address this kind of attack. With the current emergence of machine learning (ML) models, we explore the possibility of applying ML in EP MITM detection. Our detection technique is based on Address Resolution Protocol (ARP) analysis. The technique combines signal processing and machine learning in detecting EP MITM attack. We evaluated the accuracy of the proposed technique using linear-based ML classification models. The technique proved itself to be efficient by producing a detection accuracy of 99.72%.
Oxidation of Methanol on 2nd and 3rd Row Group VIII Transition Metals (Pt, Ir, Os, Pd, Rh, and Ru): Application to Direct Methanol Fuel Cells

Using first principles quantum mechanics [nonlocal density functional theory (B3LYP)], we calculated the 13 most likely intermediate species for methanol oxidation on clusters of all 2nd and 3rd row Group VIII transition metals for all three likely binding sites (top, bridge, and cap). This comprehensive set of binding energies and structures allows a detailed analysis of possible reaction mechanisms and how they change for different metals. This illustrates the role in which modern quantum chemical methods can be used to provide data for combinatorial strategies for discovering and designing new catalysts. We find that methanol dehydrogenation is most facile on Pt, with the hydrogens preferentially stripped off the carbon end. However, water dehydrogenation is most facile on Ru. These results support the bifunctional mechanism for methanol oxidation on Pt−Ru alloys in direct methanol fuel cells (DMFCs). We find that pure Os is capable of performing both functionalities without cocatalyst. We suggest that.
ENGINEERED BIOFILTRATION FOR ULTRAFILTRATION FOULING CONTROL AND DBP PRECURSOR REMOVAL

Recently, treatment plants have adopted biofiltration to compliment conventional treatment and ozonation. Previous literature has focused on passive applications of biofiltration. In this study, several pilot-scale biofiltration trains were designed in parallel to conventional treatment to investigate the impact of nutrient addition (nitrogen and phosphorus), use of hydrogen peroxide, aluminum sulphate (alum), and different filtration media (anthracite vs. granular activated carbon (GAC)) on biofiltration performance. Parameters measured included organic removal, reduction of DBP precursor, improvements in filter runtimes and ultrafiltration (UF) fouling control. Nutrient addition did not improve biofiltration performance. Supplementing hydrogen peroxide (<1 mg/L) decreased headloss, DBP formation potentials while adversely affecting UF fouling. In-line alum addition (<0.5 mg/L) improved biofilter’s ability to control fouling and DBP precursor without adversely impacting headloss. GAC provided superior performance when compared to anthracite. Conventional treatment provided higher DOC, and DBP precursor removal, as well as better UF fouling control compared to biofiltration.
Measurement of DNA denaturation on B‐NCD coated diamond micro‐cantilevers

We report the detection of DNA denaturation using diamond micro‐cantilevers operating in dynamic mode in liquid environment. Intrinsic diamond cantilevers were fabricated using a bottom up approach which consists of a selective growth of diamond patterns in micro‐machined silicon moulds. They were then coated with a thin layer of boron doped diamond to allow electrochemical immobilization of DNA on their surface. DNA was attached onto the measuring cantilever using a four‐step electrochemical process involving diazonium salt grafting on diamond surface. The DNA immobilization was validated by cyclic voltammetry and fluorescence imaging. DNA denaturation was detected by comparing the resonance frequency shift of the measuring diamond cantilever against that of a reference cantilever when adding the denaturing agent in the solution. A frequency shift was measured and was attributed to surface stress modification upon denaturation.
Compositional Attributes of the Calyces of Roselle (Hibiscus sabdariffa L.)

In this study, chemical and mineral composition of the calyces of green, red and dark red roselle were evaluated. There was no significant difference in crude protein of green (17.9%) and red (17.4%) coloured roselle calyces. Crude fibre (11.2%), ascorbic acid (86.5mg/100g) and sodium (9.5mg/100g) contents of green coloured calyx were significantly higher than other calyx samples. However, dark red calyx was significantly higher in ash (6.8%) and potassium (2320mg/100g) contents than the other two calyces. Also, there was no significant differences in calcium, magnesium and zinc contents of red and dark red coloured roselle calyces at P Roselle calyces appeared to be cheap source of vegetable protein, fat and minerals therefore its consumption should be encouraged. The Journal of Food Technology in Africa Volume 6 No.4, 2001, pp. 133-134 KEY WORDS: Roselle calyces, chemical analysis.