Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptation and molecular evidence for convergence in decapod crustaceans from deep‐sea hydrothermal vent environments

Hydrothermal vents are unique deep‐sea environments exhibiting extreme temperature gradients and toxic concentrations of H2S that limit the growth of biological communities. Notably, some decapod crustaceans are the dominant organisms inhabiting these environments, and share similar phenotypic and physiological traits, such as white body coloration and chemosynthetic capacity. However, a lack of genomic information has precluded an understanding of these convergent phenotypes. Here, comparative transcriptomic analyses were performed in 14 decapod species, including four deep‐sea hydrothermal vent species and 10 shallow‐water relatives. Phylogenetic analysis suggested that the four deep‐sea species stemmed from different ancestors despite being geographically close, and therefore their similar traits were probably the product of convergent evolution rather than lineal inheritance. A total of 391 positively selected genes, 109 parallel substituted genes and 33 significantly expanded gene families were identified in the deep‐sea decapods. Among these, only the SNARE interactions in vesicular transport pathway was significantly enriched, with both positively selected genes and parallel substituted genes, suggesting that specific macromolecule transport might be a strong convergent evolution trait in deep‐sea decapods. Furthermore, many genes involved in protein synthesis, processing and energy metabolism were detected under convergent evolution, suggesting a role for adaptive evolution in association with a specific metabolic pathway in response to chemosynthetic nutrition patterns. Moreover, our study suggests that convergently evolved white body colour might have resulted from the contraction of the crustacyanin gene family and the low content of astaxanthin in the body of deep‐sea decapods. Therefore, this study provides valuable genetic evidence for convergent evolution in deep‐sea decapods.
Diagnosis Codes and Case Definitions for Neonatal Abstinence Syndrome

The need for a standardized NAS definition and surveillance methodology is well known; we identify weaknesses in the recently proposed CSTE NAS definition. BACKGROUND AND OBJECTIVES: The increase in neonatal abstinence syndrome (NAS) has underscored the need for NAS surveillance programs, but many rely on passive surveillance using unverified diagnosis codes. Few studies have evaluated the validity of these codes, and no study has assessed the recently proposed Council of State and Territorial Epidemiologists (CSTE) case definition. The Florida Birth Defects Registry investigated the accuracy of International Classification of Diseases, 10th Revision, Clinical Modification codes related to NAS (P96.1 and P04.49) and assessed the sensitivity of the CSTE case definition. METHODS: We identified a sample of infants born during 2016 coded with P96.1 and/or P04.49. Record review was completed for 128 cases coded with P96.1, 68 with P04.49, and 7 with both codes. Lacking consensus regarding a gold standard definition of NAS, we used clinical data to classify each case using the Florida and CSTE definitions. The code-specific accuracy was measured by using the positive predictive value (PPV). The clinical characteristics indicative of NAS were compared for case classification based on both definitions. RESULTS: By using the Florida definition, the overall PPV was 68% but varied by code: 95.3% for P96.1 and 13.2% for P04.49. The overall (47.8%) and code-specific PPVs were lower by using the CSTE definition. Comparison of clinical characteristics demonstrated that 60.7% of cases classified as no NAS by using the CSTE definition had robust clinical signs of NAS. In our sample, the CSTE case definition underestimated NAS prevalence. CONCLUSIONS: Only the P96.1 International Classification of Diseases, 10th Revision, Clinical Modification code displayed high accuracy. Discordance in NAS case definitions and surveillance methodologies may result in erroneous comparisons and conclusions that negatively impact NAS-related surveillance and research.
Estimates of the Association of Dementia With US Mortality Levels Using Linked Survey and Mortality Records

Key Points Question Is the association of dementia with national mortality levels underestimated by death certificate data? Findings In this nationally representative cohort study of 7342 older adults, the estimated percentage of deaths attributable to dementia was 2.7 times that calculated from vital statistics data on the underlying cause of death. Meaning The findings of this study suggest that dementia may represent a more important factor in US mortality than indicated by routine mortality statistics, highlighting the need to expand population-based interventions focused on dementia prevention and care.
Role of plant compounds in the modulation of the conjugative transfer of pRet42a

One of the most studied mechanisms involved in bacterial evolution and diversification is conjugative transfer (CT) of plasmids. Plasmids able to transfer by CT often encode beneficial traits for bacterial survival under specific environmental conditions. Rhizobium etli CFN42 is a Gram-negative bacterium of agricultural relevance due to its symbiotic association with Phaseolus vulgaris through the formation of Nitrogen-fixing nodules. The genome of R. etli CFN42 consists of one chromosome and six large plasmids. Among these, pRet42a has been identified as a conjugative plasmid. The expression of the transfer genes is regulated by a quorum sensing (QS) system that includes a traI gene, which encodes an acyl-homoserine lactone (AHL) synthase and two transcriptional regulators (TraR and CinR). Recently, we have shown that pRet42a can perform CT on the root surface and inside nodules. The aim of this work was to determine the role of plant-related compounds in the CT of pRet42a. We found that bean root exudates or root and nodule extracts induce the CT of pRet42a in the plant rhizosphere. One possibility is that these compounds are used as nutrients, allowing the bacteria to increase their growth rate and reach the population density leading to the activation of the QS system in a shorter time. We tested if P. vulgaris compounds could substitute the bacterial AHL synthesized by TraI, to activate the conjugation machinery. The results showed that the transfer of pRet42a in the presence of the plant is dependent on the bacterial QS system, which cannot be substituted by plant compounds. Additionally, individual compounds of the plant exudates were evaluated; among these, some increased and others decreased the CT. With these results, we suggest that the plant could participate at different levels to modulate the CT, and that some compounds could be activating genes in the conjugation machinery.
FACTORS AFFECTING OXYGEN CONSUMPTION IN THE MARINE PULMONATE AMPHIBOLA CRENATA (GMELIN, 1791)

The rate of oxygen consumption by Amphibola crenata is unaffected by the salinity of the external medium in the range 0â€”1 25% sea water. There is no sig nificant difference between the rates of oxygen consumption in air and water, oxygen consumption varying with the 0.45 power of body weight. During exposure to anaerobic conditions the snails showed an increase in the post-anaerobic:pre anaerobic respiratory rate, this ratio reaching a maximum after 6 h anoxia over the range 0â€”1 25% sea water. Specimens of Amphibola crenata exposed to declining oxygen tensions at full salinity show almost no ability to regulate their rate of oxygen consumption. The ratio K1/K2, (see text) plotted against the weight specific oxygen consumption yields the following equation: K1/K2 = 527.7 QO21610.As salinity decreases, the ratio K1/K2 also decreases, indicating an increase of oxygen independence with decreasing salinities. As salinity decreases, the zone of critical pressure decreases, i.e. the snails become more oxygen independent; the maximum decrease in the zone of critical pressure coming in animals exposed to 0 and 25% sea water. When exposed to both salinity and anoxic stress, the animals reach their maximum degree of oxygen independence.
A low-overhead asynchronous consensus framework for distributed bundle adjustment

Generally, the distributed bundle adjustment (DBA) method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer. However, the performance considerably degrades owing to the overhead introduced by the additional block partitioning step and synchronous waiting. Therefore, we propose a low-overhead consensus framework. A partial barrier based asynchronous method is proposed to early achieve consensus with respect to the faster worker nodes to avoid waiting for the slower ones. A scene summarization procedure is designed and integrated into the block partitioning step to ensure that clustering can be performed on the small summarized scene. Experiments conducted on public datasets show that our method can improve the worker node utilization rate and reduce the block partitioning time. Also, sample applications are demonstrated using our large-scale culture heritage datasets.
Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in Iran

Abstract The purpose of this study was to develop models to predict the severity of pedestrian accidents on rural roads of Guilan, Iran. Therefore, the probability of occurrence of any type of accidents was measured using the accident data from March 2014 to March 2019. Eleven independent variables affecting the severity of pedestrian accidents as well as statistical analysis such as the frequency analysis, Friedman test and factor analysis, and modeling including multiple logistic regression and artificial neural networks using multi-layer perceptron (MLP) and radius basis function (RBF) have been used. Results of modeling and analysis of pedestrian accidents in different methods showed each of the methods depending on their function investigated the severity of accidents with different point of view and had different results. As a result, putting the output results together, the best measures can be suggested to increase the safety of pedestrians on the rural roads of Guilan.
Response of plant and ratoon plantain crops to K20 fertilization in the rain forest zone of Cross River State.

Nutrient potassium is critical in plantain (Musa species cv. AAB) nutrition. It plays a key role in the vegetative growth and bunch development. Application of 300 kg muriate of potash (K20) per hectare was beneficial in the plant crop, while a higher rate (400 kg K20/ha) enhanced growth and favorable yield components (number of hands and fingers/bunch, finger mass, girth and length). Plants fertilized with 400 kg K20/ha were tallest (3.5m), had largest pseudostem girth (56-58cm) with highest (11-12) number of functional leaves at flowering and attained flowering stage after 9 mouths of vegetative growth. Bunch mass, bunch yield/ha and yield components were significantly (P = 0.05) highest at 300 kg k20/ha in plant crop and at 400 kg K20/ha in ratoon crop. Heaviest bunches were 8.8 and 9.8 kg/plant in plant and ratoon crops, respectively, giving the respective corresponding bunch yields of 14.66 and 16.33 tonnes/ha. Application of 400 kg K20/ha appeared adequate for sustainable production of false horn plantain in the high rainforest zone of Cross River State.
Reinforcement-Learning-Based Dynamic Opinion Maximization Framework in Signed Social Networks

Dynamic opinion maximization (DOM) is a significant optimization issue, whose target is to select some nodes in the network and prorogate the opinions of network nodes, and produce the optimum node opinions. Until now, the node opinions of related researches are unchanged and seldom focus on social relationships. In the real scenario, the dynamic process of network nodes over time and user preference have existed. Therefore, this article proposes the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based DOM (QDOM) framework in signed social networks to solve the OM problem, which is made up of two phases: 1) the activated dynamic opinion model and 2) the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based seeding process. We propose the activated dynamic opinion model based on stateless <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning theory to derive the opinion propagation process. Moreover, we design the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based seeding algorithm to obtain the seed nodes. The experimental results on the four signed social network data sets demonstrate that the proposed framework outperforms the state-of-the-art approaches on positive opinions, the ratio of positive opinions, and activated nodes.
Disrupting HIV‐1 capsid formation causes cGAS sensing of viral DNA

Detection of viral DNA by cyclic GMP‐AMP synthase (cGAS) is a first line of defence leading to the production of type I interferon (IFN). As HIV‐1 replication is not a strong inducer of IFN, we hypothesised that an intact capsid physically cloaks viral DNA from cGAS. To test this, we generated defective viral particles by treatment with HIV‐1 protease inhibitors or by genetic manipulation of gag. These viruses had defective Gag cleavage, reduced infectivity and diminished capacity to saturate TRIM5α. Importantly, unlike wild‐type HIV‐1, infection with cleavage defective HIV‐1 triggered an IFN response in THP‐1 cells that was dependent on viral DNA and cGAS. An IFN response was also observed in primary human macrophages infected with cleavage defective viruses. Infection in the presence of the capsid destabilising small molecule PF‐74 also induced a cGAS‐dependent IFN response. These data demonstrate a protective role for capsid and suggest that antiviral activity of capsid‐ and protease‐targeting antivirals may benefit from enhanced innate and adaptive immunity in vivo.
Optimization of the genotyping‐by‐sequencing SNP calling for diversity analysis in cape gooseberry (Physalis peruviana L.) and related taxa

A robust Genotyping-By-Sequencing (GBS) pipeline platform was examined to provide accurate discovery of Single Nucleotide Polymorphisms (SNPs) in a cape gooseberry (Physalis peruviana L.) and related taxa germplasm collection. A total of 176 accessions representing, wild, weedy, and commercial cultivars as well as related taxa from the Colombian germplasm bank and other world repositories were screened using GBS. The pipeline parameters mnLCov of 0.5 and a mnScov of 0.7, tomato and potato genomes, and cape gooseberry transcriptome for read alignments, were selected to better assess diversity and population structure in cape gooseberry and related taxa. A total of 7,425 SNPs, derived from P. peruviana common tags (unique 64 bp sequences shared between selected species), were used. Within P. peruviana, five subpopulations with a high genetic diversity and allele fixation (HE: 0.35 to 0.36 and FIS: -0.11 to -0.01, respectively) were detected. Conversely, low genetic differentiation (FST: 0.01 to 0.05) was also observed, indicating a high gene flow among subpopulations. These results contribute to the establishment of adequate conservation and breeding strategies for Cape gooseberry and closely related Physalis species.
Electrocardiogram based cardiovascular disease detection with Ensemble Learning Classifier

Cardiovascular diseases are the main causes of death in today's world. This has driven the need for new technology to be employed to help detect any heart diseases faster and more conveniently. The goal of this study is to develop a novel Electrocardiogram based cardiovascular disease detection method using an Ensemble classifier. A dataset of the various heart diseases, including normal heartbeat, was collected for this study. Many popular machine learning algorithms like K Nearest Neighbor, Logistic Regression, and Support Vector Machine were first executed for the collected dataset. To improve the performance, a new Ensemble classifier has been developed, which gives better performance than the other machine learning algorithms listed before. Then its performance was compared with the other machine learning algorithms. It is seen that the Ensemble classifier can provide an overall accuracy of 93% and outperform all the other ML algorithms. A comparison of the F1-Score and Accuracy of these algorithms for the cases of normal, abnormal heartbeat and myocardial infarction is given in the form of a graph. A comparison table of the accuracy of the existing algorithms is also presented.
A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications

Internet of Things is emerging as an incredible future technology to improve the existing lifestyle from the research community, industry, and the public sector. The main intention of IoT is to create an efficient, interactive, and autonomous infrastructure for a safer and healthier world. Moreover, it grows faster day-to-day with the support of many other technologies, i.e. Cloud computing, Blockchain, Wireless Body Sensor Networks, Nanotechnology, and Artificial Intelligence for smart applications, including healthcare, environment, automotive industries, transportation, agriculture, etc. Nevertheless, managing big data is one of the challenging tasks due to the increased number of devices leading to various serious issues like security, privacy, accuracy, latency, scheduling, etc. Further, specific infrastructures with remarkable techniques are required to analyze the bulk of raw data to progress the quality of life and allow timely intervention through various capabilities, i.e., data capture, unique identification, actuation, communication, data mining, etc. In past literature, numerous reviews/surveys are presented that explore the technologies mentioned above as standalone and application specific. However, this paper aims to integrate all the mentioned technologies and deliver a clear vision to future researchers (newcomers) as a kick-start article to boost up and understand the status of the existing research through a comprehensive review of the Internet of Things and its evolution in wireless telecommunications from a general perspective. The most significant challenges and issues are highlighted to research further in these evolving domains.
Monopolar versus bipolar transurethral resection of bladder tumors

Abstract Background: To compare the efficacy and safety of bipolar and monopolar transurethral resection of bladder tumors (TURBT) in non-muscle invasive bladder cancer (NMIBC) treatment. Methods: This protocol established in this study has been reported following the preferred reporting items for systematic review and meta-analysis protocols. Web of Science, PubMed, EMBASE, and the Cochrane Library were searched for all randomized controlled trials comparing bipolar TURBT and monopolar TURBT in NMIBC treatment until 31st of June 2020. We will use a combination of Medical Subject Heading and free-text terms with various synonyms to search based on the eligibility criteria. Two investigators independently reviewed the included studies and extracted relevant data. The odds ratio and 95% confidence intervals of were used as effect estimate. I-square (I2) test, substantial heterogeneity, sensitivity analysis, and publication bias assessment will be performed accordingly. Stata 15.0 and Review Manger 5.3 are used for meta-analysis and systematic review. Results: The results will be published in a peer-reviewed journal. Conclusion: The results of this review will be widely disseminated through peer-reviewed publications and conference presentations. This evidence may also provide helpful evidence of the efficacy and safety of bipolar and monopolar transurethral resection of TURBT in NMIBC treatment. PROSPERO registration number: CRD42020151997
Healthcare professionals' experiences of using mindfulness training in a cardiology department - a qualitative study.

OBJECTIVE AND AIM Person-centred communication and healthcare professionals' ability to be attentively present in their encounter with patients are essential aspects of patients' experiences of well-being, ability to cope with illness-related challenges and feelings of being recognised. However, the ability to be attentive in relational encounters can be challenging for healthcare staff for many reasons, such as time constraints and a high work pace. Research suggests that mindfulness training could increase staff attentiveness and compassion, but only few qualitative studies have explored the subject. The aim of the current study was to explore doctors' and nurses' individual experiences of how attending an 8-week Mindfulness-Based Stress Reduction course (MBSR) influenced their clinical practice and encounters with colleagues and patients in a cardiology department. METHOD Qualitative interviews were held with six doctors and nurses who had completed the 8-week MBSR course. Interpretative phenomenological analysis was applied to explore and understand the meaning of the participants' accounts. FINDINGS The MBSR course appeared to have changed the healthcare professionals' thoughts and actions, especially regarding their ability to stay focused on the task at hand, to prioritise and to stay calm in an unpredictable and busy work environment. This was facilitated by using concrete techniques learned during the course, such as breathing and taking small breaks to clear their heads and help them be attentive in relation to themselves, colleagues and patients. Furthermore, they described an increased acceptance of their own limitations, better understanding of their colleagues and greater awareness of the unique patient. CONCLUSION These findings suggest that changing healthcare professionals' actions, mindset, awareness and understanding of others may result in a more compassionate work environment and more person-centred care.
Internet of Things Intrusion Detection System Based on D-GRU

Aiming at the shortcomings of slow training speed and insufficient accuracy of algorithms in IoT intrusion detection, an IoT intrusion detection system based on D-GRU neural network is proposed. The system uses window functions and D-GRU modules to extract the timing features in the network traffic and classify potential intrusions. The experiment was simulated on the intrusion detection dataset UNSW-NB15. In the binary-class and multi-class classification, the accuracy rates were 97.23% and 94.21%, respectively, and the parameters, training time, and prediction time were optimized in our scheme. The experimental results show that the IoT intrusion detection system based on the D-GRU algorithm maintains high accuracy while reducing the parameters, improving the performance of the system, and is more conducive to being deployed in resource-constrained edge nodes of the IoT.