Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
The Effects of Affective State and Valence of Words on L2 Vocabulary Memory Retrieval

The effects of affective state on the cognitive process have enjoyed substantial empirical attention. The research aims to investigate the effects of mood conditions and valence of words on L2 vocabulary memory retrieval. Twenty undergraduates participated in the experiment and completed memory retrieval tasks after mood manipulation. To investigate whether mood conditions exerted influences on L2 vocabulary memory retrieval, 2 (mood: happy, sad) x 3 (valence of words: positive, neutral, negative) repeated measures were conducted. The results indicated that neither the interaction between mood conditions and accuracy rates nor mood conditions and valence propensity of L2 vocabulary memory retrieval were significant. An unanticipated finding was the significant interaction between retrieval accuracy rate and reaction time. On average, participants’ true responses accompany less reaction time and vice versa. This study provided evidence for the cognitive differences between L1 and L2 vocabulary memory retrieval.
Impaired Function of a Rare Mutation in the MMUT Gene Causes Methylmalonic Acidemia in a Chinese Patient

Methylmalonic acidemia (MMA) is an autosomal recessive metabolic disorder mainly caused by mutations in the methylmalonyl coenzyme A mutase (MCM) gene (MMUT) and leads to the reduced activity of MCM. In this study, a 3-year-old girl was diagnosed with carnitine deficiency secondary to methylmalonic acidemia by tandem mass spectrometry (MS/MS) and gas chromatography/mass spectrometry (GS/MS). Whole-exome sequencing (WES) was performed on the patient and identified two compound heterozygous mutations in MMUT: c.554C>T (p. S185F) and c.729–730insTT (p. D244Lfs∗39). Bioinformatics analysis predicted that the rare missense mutation of c.554C>T would be damaging. Moreover, this rare mutation resulted in the reduced levels of MMUT mRNA and MMUT protein. Collectively, our findings provide a greater understanding of the effects of MMUT variants and will facilitate the diagnosis and treatment of patients with MMA.
Efficacy of Traditional Chinese Medicine Injection in Preventing Oxaliplatin-Induced Peripheral Neurotoxicity: An Analysis of Evidence from 3598 Patients

Background Oxaliplatin is an effective chemotherapeutic agent for the treatment of malignant tumors. However, severe oxaliplatin-induced peripheral neurotoxicity (OIPN) has been well documented. Traditional Chinese medicine injections (TCMIs) have shown significant efficacy in preventing OIPN. However, it is difficult for clinicians to determine the differences in the efficacy of various TCMIs in preventing OIPN. The aim of this study was to compare the efficacy of various TCMIs in preventing OIPN through a network meta-analysis (NMA) to further inform clinical decision-making. Methods The Chinese Journal Full Text Database, Chinese Biomedical Literature Database, Wanfang Data Knowledge Service Platform, Chinese Science and Technology Journal Full Text Database, the Cochrane Library, Web of Science, PubMed, and Embase databases were searched for randomized controlled trials (RCTs) of TCMIs for OIPN prevention. The retrieval time was from the establishment of the database to April 12, 2021. NMA was performed using Stata 14.0 software after 2 evaluators independently screened the literature, extracted information, and evaluated the risk of bias of the included studies. Results A total of 45 eligible RCTs involving 3598 cancer patients and 13 TCMIs were included. The 13 TCMIs included Xiaoaiping injection (XAPI), compound kushen injection (CKSI), Aidi injection (ADI), Brucea javanica oil emulsion injection (BJOEI), Shenmai injection (SMI), Kangai injection (KAI), Astragalus injection (AI), elemene emulsion injection (EEI), Shenfu injection (SFI), Shenqi Fuzheng injection (SIFZI), Kanglaite injection (KLEI), Huachansu injection (HCSI), and lentinan injection (LI). NMA results showed that AI was superior to AD and SIFZI was superior to ADI in reducing the incidence of grade I neurotoxicity. SIFZI was superior to EEI and ADI, and BJOEI was superior to chemotherapy alone in reducing the incidence of grade II neurotoxicity. SMI was superior to LI and CKSI in reducing the incidence of grade III neurotoxicity. SIFZI was superior to LI, BJOEI, XAPI, EEI, SMI, chemotherapy alone, HCSI, KLEI, and ADI in reducing the total incidence of grade I–IV neurotoxicity. SFI was superior to ADI. Based on the SUCRA values, AI was the most likely intervention to reduce the incidence of grade I neurotoxicity, SIFZI was the most likely intervention to reduce the total incidence of grade II and I–IV neurotoxicity, and SMI was the most likely intervention to reduce the incidence of grade III and IV neurotoxicity. Conclusion TCMIs can prevent OIPN to some extent, among which SIFZI, SMI, and AI may be the most promising TCMIs. However, given the limitations of current studies, more well-designed, high-quality clinical trials will be needed in the future to validate the benefits of TCMIs.
Pencegahan Dini Kerusakan Mesin dengan Sistem Deteksi Suhu Radiator Mobil

Saat ini banyak kalangan yang memiliki kendaraan impian yang harganya terjangkau dengan kemampuan keuangan mereka. Namun banyak masyarakat yang tidak mengetahui cara mengantisipasi dan mencegah secara dini kerusakan dari mobil yang diakibatkan tidak berfungsinya sistem radiator pendingin mesin. Dibuat sistem yang memonitor suhu air radiator normal yang sesuai dengan suhu kerja yang disyaratkan pabrikan dan mengatasi suhu air overtemp yang mendadak yang tidak dapat diatasi sistem pendingin mobil. Sistem ini akan mengaktifkan control supaya mesin mati otomatis. Untuk mengindari kerusakan yang fatal sehingga usia dari komponen mesin mobil dapat bertahan lebih lama. Sistem ini inputnya sensor Negative Thermal Coeficient yang kontak langsung dengan air radiator. Metode yang digunakan yaitu fuzzy untuk monitoring, menggunakan device microcontroller arduino untuk komputasi. Outputnya melalui modul relay elektro mekanik menonaktifkan fuel injection dari mobil, sehingga mesin mobil mati otomatis. Saat suhu cut-off mencapai 100 ⁰C, mesin mobil mati secara otomatis.
Perceptions of Adults With Spinal Cord Injury or Disease Before and After Riding in an Autonomous Shuttle

Date Presented 04/02/2022 This study quantified changes in perceptions of adults with a spinal cord injury or disease (SCI/D) before and after an autonomous shuttle (AS) ride. Sixteen adults with an SCI/D and 16 age- and gender-matched controls completed surveys before and after a 15-minute AS ride. Perceived barriers to AS decreased after riding for both groups. Understanding perceptions regarding an AS may promote increased acceptance, adoption, and transportation equity for individuals with SCI/D. Primary Author and Speaker: Sherrilene Classen Contributing Authors: Justin Mason, Hannah Burns, Jordan Joseph, Emily Fox, Heidi E, Hannah Snyder, Lou Demark, Carolyn S. Hanson
Comparative Impact of Various Exercises on Circulating Irisin in Healthy Subjects: A Systematic Review and Network Meta-Analysis

Irisin is a myokine that is secreted from skeletal muscle during exercise and increases lipid metabolism, converting white adipose tissue to brown adipose tissue. Recent studies have shown conflicting results in relation to chronic and acute exercise and irisin. The aim of this study was to evaluate the effects of chronic and acute exercise training on circulating (plasma/serum) irisin level in healthy subjects. We conducted a search of Cochrane Library, PubMed, ISI, Scopus, Embase, and Pedro up to September 2021. A random effects network meta-analysis was performed to calculate the pooled estimate of standardized mean difference (SMD) for acute and chronic exercise effects on irisin level, using Hedge's g statistic. Of the 16 studies included, six were acute exercise studies (175 participants). The aerobic (Hedge's g = 0.23; 95% CI: -0.58, 1.03) and the anaerobic exercises (Hedge's g = 0.12; 95% CI: -0.45, 0.70) were associated with the increased level of irisin, compared to the control. In the ten chronic exercise studies (433 participants), the resistance training was superior to anaerobic and aerobic training (P score = 0.632). However, comparing acute and chronic exercise studies, acute training showed the most excellent potential as the best treatment to improve the irisin level (P score = 0.721). This network meta-analysis showed that acute aerobic exercise has a more effect on irisin levels than acute anaerobic exercise. Also, chronic resistance training has the greatest additive effect on irisin levels compared to chronic aerobic and anaerobic training.
Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.
Prototyping Prototyping Facilities: Developing and Bootstrapping Testbeds

The creation of prototypes is a convincing approach, demonstrating the feasibility of scientific ideas. Testbeds act as enablers for such prototypes, contributing the facilities to their construction. In this paper, we apply a prototype-driven approach to the development of the testbeds themselves. Thus, we select abstractions and APIs to modularize testbeds to allow a selective adaptation or substitution of specific components. To minimize costs, our approach aims to consolidate all components into a single system. Hence, we named it testbed on a single system (toast). The single-server approach demands the recreation of entire components in software such as networks or experiment nodes. Simultaneously, the softwarization of components enables flexible network topologies and scalability. At the same time, we try to keep the behavior and the performance as close to a hardware-based testbed as possible. Therefore, we rely heavily on hardware acceleration of IO using techniques such as single root IO virtualization (SR-IOV). A case study compares the accelerated IO of toast to a hardware-based testbed and a testbed without IO acceleration. We want to use toast as a training and teaching environment and a prototype facility for future research infrastructures.
Relaxed Collaborative Representation With Low-Rank and Sparse Matrix Decomposition for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection methods based on representation model have drawn more attention due to their simplicity and efficiency. The traditional collaborative representation (CR) model does not consider the differences between features, resulting in insufficient feature utilization. In addition, the dictionary construction of traditional CR model is from a dual window, where the dictionary is easy to be polluted by abnormal pixels. To address the abovementioned issues, this article proposes a novel relaxed collaborative representation relaxed collaborative representation (RCR) method with low-rank and sparse matrix decomposition. In the proposed approach, a relaxed regularization constraint term is imposed on the CR model, which takes into account the difference and similarity between features. In order to reduce the interference of redundant information in hyperspectral data on detection accuracy, the optimal clustering framework optimal clustering framework (OCF) is used for band selection. In terms of dictionary construction, the data with reduced dimensionality is preprocessed by low-rank and sparse matrix decomposition (LRaSMD) to realize the preliminary separation of anomalies and background. Then, the background component obtained by LRaSMD is partitioned into several categories by the improved k-means + + algorithm. Moreover, in order to enhance the contribution of useful information to the representation, according to the distance from the cluster center, logarithmic weighting is introduced to give larger weights to the atoms closer to the cluster center, so to select typical atoms to construct the dictionary. Experimental results reveal that the proposed method can attain better detection results than the existing advanced anomaly detection methods.
The complete mitochondrial genome of Melanoides tuberculata (Müller, 1774) in Guangdong, China

Abstract The Melanoides tuberculata is an invasive species, which is natively distributed in Africa and Southeast Asia. This study describes the first mitochondrial genome of the M. tuberculata based on the whole genome sequencing data. The complete sequence length of the mitogenome is 15,821 bp, including 37 genes (2 rRNA genes, 22 tRNA genes and 13 protein-coding genes). Phylogenetic analysis using the 13 species of Cerithioidea species showed that the M. tuberculata is closely related to P. dartevellei, forming the sister group to C. sinensis and C. obtuse.
Can the Agglomeration of New Energy Industries Improve Environmental Efficiency?—Evidence from China

Improving environmental efficiency is the key to solving the contradiction between economic development and environmental degradation. In the process of realizing the “dual carbon” goal, the development of the new energy industry is typically characterized by having low carbon and is an important way to improve environmental efficiency. In this study, based on panel data from 2009 to 2018, the agglomeration levels of the new energy industry and the levels of environmental efficiency were measured. Moreover, the linear and nonlinear relationships between the agglomeration of the new energy industry and environmental efficiency were investigated, and the mediation effect model was used to verify the mechanism of action from a two-dimensional perspective. The results showed that (1) the levels of the agglomeration of the new energy industry (NEA) and environmental efficiency (EE) have generally increased with obvious “regional heterogeneity” and “polar characteristics”; (2) NEA can promote EE, with a marginal effect of 0.0347, but a strong positive driving effect only exists in the eastern region at present, which is higher than the national average level; (3) there is a double threshold effect within the process and the driving trend is “weak negative → weak positive → strong positive”; and (4) the advancement of industrial structures and direct foreign investments are the critical intermedia. Accordingly, this paper puts forward corresponding policy recommendations.
The health belief model’s ability to predict COVID-19 preventive behavior: A systematic review

Objective: The health belief model specifies that individuals’ perceptions about particular behavior can predict the performance of respective behavior. So far, the model has been used to explain why people did not follow COVID-19 preventive behavior. Although we are using it, to our best knowledge, its predictive ability in COVID-19 preventive behavior is unexplored. So, this review aimed to assess the model’s predictive ability and identify the most frequently related construct. Method: A systematic review was conducted to examine the predictive ability of health belief model in COVID-19 preventive behavior using research done all over the world. Preferred reporting items for systematic review and meta-analysis guidelines were used. Comprehensive literature was searched using databases such as PubMed, Google scholar, and African Online Journal to retrieve related articles. Descriptive analyses such as the proportion of studies that better explained COVID-19 prevention behavior and the significance ratio of each construct of the model were made. Result: Overall, 1552 articles were retrieved using a search strategy and finally 32 articles fulfilling the inclusion criteria undergo the review. We found that in the majority (87.5%) of the studies health belief model has a good predictive ability of COVID-19-related behavior. Overall the explained variance for health belief model ranged from 6.5% to 90.1%. The perceived benefit was the most frequently significant predictor; highest significance ratio (96.7%) followed by self-efficacy, cues to action perceived barrier, susceptibility, and severity in decreasing order. Conclusion: Health belief model has a good predictive ability of COVID-19-related behavior in the majority of reviewed studies. The perceived benefit was the most frequently significant predictor of COVID-19-related behavior. Professionals who are in need can effectively use health belief model in planning and designing interventions to prevent and control the pandemic.
All Types Obesity and Physical Inactivity Associated with the Risk of Activity of Daily Living Limitations Among People with Asthma

Purpose To examine the association between all types of obesity, physical inactivity, and the risk of activity of daily living limitations in people with asthma. Patients and Methods In this cross-sectional study, data from 2555 people aged between 25 and 74 years were acquired from the National Survey of Midlife Development in the United States Refresher conducted between 2011 and 2014. Self-reported questions were used to specify the presence or absence of asthma and physical inactivity. All participants were categorized as having no asthma or asthma. Obesity was defined based on three distinctive indicators: body mass index, waist circumference, and waist-to-hip ratio. Results Logistic regression analysis showed that people with asthma who had all types of obesity alone or both all types of obesity and physical inactivity were significantly (P <0.0001) almost more than three times more likely to have limitations in the activity of daily living than those without this condition, even after adjusting for all covariates. Moreover, the odds of activity of daily living limitations were 1.69 times increased in asthma patients with physical inactivity alone, but this increase in risk was not significant (P =0.465). In addition, the odds of activity of daily living limitations were significantly (P <0.0001) more than twice independently in people with asthma aged between 60 and 74 years, female, undergraduate level of education, smoking, and having joint/bone underlying diseases. Conclusion The results demonstrated that the presence of all types of obesity is related to a higher risk of activity of daily living limitations in people with asthma than in those without asthma. Having both all types of obesity and physical inactivity are also linked to a greater risk of activity of daily living limitations in these patients.
Materials for Chemical Sensing: A Comprehensive Review on the Recent Advances and Outlook Using Ionic Liquids, Metal–Organic Frameworks (MOFs), and MOF-Based Composites

The ability to measure and monitor the concentration of specific chemical and/or gaseous species (i.e., “analytes”) is the main requirement in many fields, including industrial processes, medical applications, and workplace safety management. As a consequence, several kinds of sensors have been developed in the modern era according to some practical guidelines that regard the characteristics of the active (sensing) materials on which the sensor devices are based. These characteristics include the cost-effectiveness of the materials’ manufacturing, the sensitivity to analytes, the material stability, and the possibility of exploiting them for low-cost and portable devices. Consequently, many gas sensors employ well-defined transduction methods, the most popular being the oxidation (or reduction) of the analyte in an electrochemical reactor, optical techniques, and chemiresistive responses to gas adsorption. In recent years, many of the efforts devoted to improving these methods have been directed towards the use of certain classes of specific materials. In particular, ionic liquids have been employed as electrolytes of exceptional properties for the preparation of amperometric gas sensors, while metal–organic frameworks (MOFs) are used as highly porous and reactive materials which can be employed, in pure form or as a component of MOF-based functional composites, as active materials of chemiresistive or optical sensors. Here, we report on the most recent developments relative to the use of these classes of materials in chemical sensing. We discuss the main features of these materials and the reasons why they are considered interesting in the field of chemical sensors. Subsequently, we review some of the technological and scientific results published in the span of the last six years that we consider among the most interesting and useful ones for expanding the awareness on future trends in chemical sensing. Finally, we discuss the prospects for the use of these materials and the factors involved in their possible use for new generations of sensor devices.