Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of novel-vector control target proteins of Aedes sp.: A Systems Network Biology Approach

Aedes is an important vector for various viruses that cause dengue, chikungunya and zika, which affect human health globally. Due to regular outbreaks of these diseases worldwide, there is a need to identify essential vector proteins that are critical for the survival of the vector, which may be targeted to control the spread of vector-borne disease (VBD). In silico computational methods involving comparative proteomics, analysis of orthologous proteins common amongst members of Aedes genus and protein-protein interaction (PPI) pathway were used to identify essential proteins that could act as novel therapeutic candidates. Twenty-three conserved proteins between A. aegypti and A. albopictus were identified from a BLASTP search with an e-value threshold of 0.005, and their PPI networks were constructed in the STRING database. The merged network was analyzed using various Cytoscape plugins viz. ClusterONE, Cytohubba and MCODE. Thirty-one hub proteins were identified from the system's network biology analysis, and detailed data and literature mining were carried out. Twelve novel vector-control target proteins of A. aegypti, having no human homologs, were determined in the present study that can effectively act as potential therapeutic candidates for drug design and vaccine development.
Mindfulness-Based Interventions for the Treatment of Aberrant Interoceptive Processing in Substance Use Disorders

Altered interoception, or the processing of bodily signals, has been argued to play a role in the development and maintenance of substance use disorders (SUD). Therefore, interoceptive interventions focusing on bodily awareness, such as mindfulness meditation, may improve treatment outcomes for individuals with SUD. Here we review: (1) subjective, behavioral and brain evidence for altered interoceptive processing in SUD, focusing on insular and anterior cingulate cortices (INS, ACC), key regions for interoceptive processing; (2) research highlighting links between mindfulness and brain function; and (3) extant brain research investigating mindfulness-based interventions in SUD. SUD tend to be characterized by heightened INS and ACC responses to drug cues but blunted interoceptive awareness and attenuated INS and ACC responses during tasks involving bodily attention and/or perturbations. In contrast, mindfulness interventions in healthy individuals are linked to enhanced INS and ACC responses and heightened interoceptive awareness. It is crucial for future research to identify: (1) whether mindfulness-based treatments are efficacious across substance classes; (2) what particular approaches and dosages show the largest effect sizes in enhancing INS and ACC function to non-drug stimuli and reducing responsivity to substance cues, thereby improving SUD treatment outcomes (reducing drug craving and relapse).
Review on foreign body in rumen and reticulum of ruminants

Cattle play significant contribution in Ethiopian economy as source of meat, milk, drought power, income and foreign exchange. Gastrointestinal foreign bodies are among the most common surgical emergency in veterinary medicine. Swallowed metallic object such as nails or piece of wires, fall directly into the reticulum or pass in to the rumen and are subsequently carried over the rumen reticular folds into the carnio-ventral part of the reticulum. Cattle are more likely to ingest foreign bodies than small ruminant since they don’t use their lips for prehension and are more likely to eat a chopped feed. The condition is more common during drought because animals are grazing closer to the ground. The object can penetrate the diaphragm and enter the thoracic cavity causing pleuritis. Metal detectors were used at one time to aid in the diagnosis of traumatic reticuloperitonitis. Ferrous metallic foreign bodies can be detected with metal detectors. Foreign bodies have great economic significance associated with reduced production and productivity of animals suffering from them.
Inferring the biogeography of New Zealand’s only endemic holoparasitic plant, the threatened Dactylanthus taylorii (Mystropetalaceae)

ABSTRACT Despite a rich history of botanical research, little is known about the distribution of genetic variation within New Zealand plant species. With a native vascular flora that consists of ∼80% endemics, nearly one-half of which are threatened, there is a dire need for a better understanding of intraspecific diversity for individual plant species and of biogeographic patterns for the wider New Zealand flora. To these ends, we have investigated population genetic structure in the endemic root holoparasite, Dactylanthus taylorii (pua o te Rēinga, Mystropetalaceae). A total of 235 individuals from 31 locations across the North Island of New Zealand (Te Ika-a-Māui, Aotearoa) were genotyped for a suite of nuclear microsatellite loci. Genotype data were analysed at the individual and population levels to assess genetic diversity within and among populations and to investigate the geographic distribution of genetic variation within the species. Population structure was assessed using Bayesian clustering, discriminant analysis of principal components, and a Neighbor-Net analysis. A hierarchical pattern of genetic diversity was revealed, with evidence for three broad geographic regional groupings (West, East, and Central Volcanic Plateau (CVP)). Upon further analysis, each of the three geographic groups further subdivide hierarchically into two or three genetic subgroups. Genetic diversity was found to be relatively high in this endangered species but varies considerably across populations. The distribution of genetic groups appears to have been influenced by past volcanic activity as the CVP group is largely coincident with a distinct tephra isopach from the 1.8 ka Taupō eruption and the West and East groups may have diverged due to recurring volcanic activity in the Central Volcanic Zone. The genetic structure resolved here provides important information for the management of D. taylorii specifically and for our understanding of New Zealand biogeography generally.
Established full-scale applications for energy recovery from water: anaerobic digestion

(CC BY-NC-ND 4.0), which permits copying and redistribution for noncommercial purposes with no derivatives, provided the original work is properly cited (https://creativecommons.org/licenses/by-nc-nd/4.0/). This does not affect the rights licensed or assigned from any third party in this book. The chapter is from the book Resource Recovery from Water: Principles and Application, Ilje Pikaar, Jeremy Guest, Ramon Ganigué, Paul Jensen, Korneel Rabaey, Thomas Seviour, John Trimmer, Olaf van der Kolk, Céline Vaneeckhaute, Willy Verstraete (Eds.) doi: 10.2166/9781780409566
Association between Hyperosmolar Hyperglycemic State and Venous Thromboembolism in Diabetes Patients: A Nationwide Analysis in Taiwan

Background: Previous studies in Western countries have shown that a hyperosmolar hyperglycemic state (HHS) is associated with an increased risk of venous thromboembolism (VTE); in these cases, prophylactic anticoagulant treatment is suggested. However, the association between HHS and VTE in Asian populations remains undetermined. Therefore, we aimed to evaluate whether HHS is associated with an increased risk of VTE in diabetic Taiwanese patients. Methods: This nationwide, population-based, retrospective cohort study was conducted using the Taiwan National Health Insurance Research Database. We enrolled a total of 4,723,607 admission records of patients with diabetes diagnosed with one or more of seven common diseases (pneumonia, urinary tract infection, sepsis, heart disease, stroke, malignancy, and respiratory tract disease) between 2001 and 2018 in Taiwan. The patients were divided into two groups based on the presence (n = 46,000) or absence (n = 4,677,607) of HHS. We estimated the adjusted odds ratio (aOR) for developing VTE within 90 days after the index hospitalization using multivariable logistic regression with generalized estimating equations accounting for repeated measures. Results: Overall, patients admitted with HHS had a similar risk of VTE compared with those admitted without HHS (408/46,000 vs. 39,345/4,677,607; aOR = 1.06, 95% CI: 0.97–1.17, p = 0.190). A similar non-significant association between HHS and VTE was found regardless of age and sex subgroups. Conclusions: There was no significant association between HHS and overall VTE risk in patients with diabetes in Taiwan. The results of our study may not support the use of prophylactic anticoagulant therapy in diabetic Taiwanese patients with HHS.
Building an agroecological model to understand the effects of agrochemical subsidies on farmer decisions

ABSTRACT We present a simple simulation model whose purpose is to explore qualitatively and heuristically the effects that agrochemical subsidies could have on the type of decisions farmers make about their plots (keeping other variables fixed). We use elements of Chayanovian thinking and ecological complexity as a basis, considering that this decision is made by families with heterogeneous preferences in a context with a dynamic that, on the one hand, is non-linear for ecological cycles, and, on the other hand, is chaotic in terms of the interaction with the market. The model describes the relationship between the ecological-productive potential or capacity of a plot and the total production, as well as the effect of management (agroecological or agrochemical) on the ecological capacity. We explore the effect of agrochemical subsidies on this decision and discuss the consequences of these public policy instruments on the lives of farming families. The model shows that by using agrochemicals, both the economy and the ecological balance of the plot enter an unstable pattern with harmful long-term consequences, as there could be a significant reduction in household savings. The same effect is observed on production, since the ecological capacity tends to decrease throughout the simulation.
SCREENING FOR BIOLOGICALS TO CONTROL RUMEX LUNARIA L. IN LANZAROTE ISLAND

"Rumex lunaria L. , a Polygonaceae shrub, has colonized large areas of Lanzarote, Canary Islands, from the 1980s to the present day, revealing an invasive behaviour. Recently, symptoms of disease like leaf spots and lack of turgidity in trunks have been observed. This fact has led to the investigation on fungal communities present in this invasive species in several areas of the National Park and other locations in Lanzarote Island. Our objectives were to detect and select fungal strains with phytopathogenic traits isolated from disease symptomatic plants using different screening techniques. To isolate the potential fungi causing disease symptoms, samples were processed using mycological classical techniques. Identification of fungi was based on morphological and molecular analysis using the accepted DNA barcode for fungi, the rDNA ITS region. Five rapid and cost-effective screening methods were carried out in the laboratory in a sequential order before scaling the study to the nursery plants assay. This sequence of methods allows us to test the potential pathogenic effects of a high number of strains in a relatively short period of time: method 1 -both sides of the midrib of a cut leaf were inoculated with droplets of a spores suspension; method 2 -a branch with leaves maintained in water was inoculated with droplets of a spores suspension; method 3 -the aerial part of seedlings was sprayed with spores suspension; method 4 and 5 -leaves of an adult plant were inoculated with droplets of a spores suspension. Several scales for symptoms were developed according to strains-specific effects on tissue like chlorosis, spots, necrosis. Size of leaves spots were recorded. Of 76 fungal isolated strains, 21 genera were identified, with Alternaria and Penicillium being the most frequent. The screening process led to the selection of Alternaria tenuissima (strain 739) as it showed a constant pathogenic ability along the screening sequence. Thus, it makes it a strong potential biological control agent for the invasive species R. lunaria in Lanzarote Island. This study also contributes to the knowledge on the fungal communities of R. lunaria. "
Accurate and Efficient Performance Prediction for Mobile IoV Networks Using GWO-GR Neural Network

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.
Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization

Wafer map inspection is essential for semiconductor manufacturing quality control and analysis. The deep convolutional neural network (DCNN) is the most effective algorithm in wafer defect pattern analysis. Traditional DCNNs rely heavily on high quality datasets for training. However, obtaining balanced and sufficient labeled data is difficult in practice. This paper reconsiders the causes of the imbalance and proposes a deep learning method that can learn robust knowledge from an imbalanced dataset using the attention mechanism and cosine normalization. We interpret the dataset imbalance as both a feature and a quantity distribution imbalance. To compensate for feature distribution imbalance, we add an improved convolutional attention module to the DCNN to enhance representation. In particular, a feature-map-specific direction mapping module is developed to amplify the positional information of defect clusters. For quantity distribution imbalance, the cosine normalization algorithm is proposed to replace the fully connected layer, and classifier fine-tuning is realized through a small amount of iterative training, which decreases the sensitivity to the quantitative distribution. The experimental results on real-world datasets demonstrate that the proposed method significantly improves the robustness of wafer map inspection and outperforms existing algorithms when trained on imbalanced datasets.
FORT: Right-proving and Attribute-blinding Self-sovereign Authentication

Nowadays, there are a plethora of services that are provided and paid for online, such as video streaming subscriptions, car-share, vehicle parking, purchasing tickets for events, etc. Online services usually issue tokens that are directly related to the identities of their users after they sign up to a platform; users need to authenticate themselves by using the same credentials each time they use the service. Likewise, when using in-person services, such as going to a concert, after paying for this service, the user usually receives a ticket, which proves that he/she has the right to use that service. In both scenarios, the main concerns surround the centralization of these systems and that they do not ensure customers’ privacy. The involved service providers are trusted third parties—authorities that offer services and handle private data about users. In this paper, we designed and implemented FORT, a decentralized system that allows customers to prove their rights to use specific services (either online or in-person) without revealing sensitive information. To achieve decentralization, we proposed a solution where all of the data are handled by a blockchain. We describe and uniquely identify users’ rights using non-fungible tokens (NFTs), and possession of these rights is demonstrated by using zero-knowledge proofs—cryptographic primitives that allow us to guarantee customers’ privacy. Furthermore, we provide benchmarks of FORT, which show that our protocol is efficient enough to be used in devices with low computing resources, such as smartphones or smartwatches, which are devices commonly used in our use case scenario.
A novel method for measuring sublingual temperature using conventional non-contact forehead thermometer

Background: Sublingual temperature measurement is a quick and accurate representation of oral temperature and corresponds closely with core temperature. Sub-lingual temperature measurement using non-contact infrared thermometers has not been studied for this purpose and if accurate they would be a reliable and convenient way of recording temperature of a patient very quickly. The aim of the study was to evaluate the utility of recording sublingual temperature using an infrared non-contact thermometer and establish its accuracy by comparing the readings with tympanic thermometer recordings. Methods: This cross-sectional study was carried out in 29 patients (328 paired recordings from sublingual and tympanic sites simultaneously). Subjects were requested to keep their mouth closed for five minutes before recording the temperature. Sublingual recordings were performed for each patient at different times of the day using an infrared thermometer. The infrared thermometer was quickly brought 1cm away from the sublingual part of the tongue and the recordings were then done immediately. Readings were compared with the corresponding tympanic temperature. Results: The non-contact sublingual temperature correlated very closely with tympanic temperature (r=0.86, p<0.001). The mean difference between the infrared sublingual and tympanic temperature was 0.21°C (standard deviation [SD]:0.48°C, 95% confidence interval [CI] of 0.16-0.27). The intra-class correlation co-efficient (ICC) between core and sublingual temperatures was 0.830 (95% CI: 0.794 to 0.861) p<0.001. The sensitivity of sublingual IR (infrared) temperature of 37.65°C was 90% and specificity was 89% for core temperature >38°C. Conclusions: This innovative modification of using the forehead infrared thermometer to measure the sublingual temperature offers an accurate, rapid and non-contact estimation of core temperature.
Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis

Driver identification has shown sustainable development in recent years in a wide variety of applications including but not limited to security, personalization, fleet management, insurance telematics, or ride-hailing. However, the current progress suffers from several challenges such as costly data collections and the need for a huge amount of data from each individual for both driver identification and impostor detection. Therefore, more novel and efficient solutions are required to mitigate the existing challenges. In this paper, we address driver identification and impostor detection tasks using driving behavior analysis of the drivers. We design a deep learning-based system architecture that analyzes windows of 30 seconds of driving data to capture the unique underlying characteristics of the individuals steering behavior based on which it further distinguishes the drivers. We also develop a novel strategy to tackle driver verification and impostor detection tasks based on the combination of the proposed system architecture and Siamese networks concepts. We map the steering behavior of the drivers into latent representations which can be later used to train a similarity function. The performance of the proposed systems is tested over a real-world dataset of 95 drivers. The evaluation results indicate that our system outperforms well-established benchmarks and baseline methodologies.
Adipokines as New Biomarkers of Immune Recovery: Apelin Receptor, RBP4 and ZAG Are Related to CD4+ T-Cell Reconstitution in PLHIV on Suppressive Antiretroviral Therapy

A significant proportion of people living with HIV (PLHIV) who successfully achieve virological suppression fail to recover CD4+ T-cell counts. Since adipose tissue has been discovered as a key immune organ, this study aimed to assess the role of adipokines in the HIV immunodiscordant response. This is a multicenter prospective study including 221 PLHIV starting the first antiretroviral therapy (ART) and classified according to baseline CD4+ T-cell counts/µL (controls > 200 cells/µL and cases ≤ 200 cells/µL). Immune failure recovery was considered when cases did not reach more than 250 CD4+ T cells/µL at 144 weeks (immunological nonresponders, INR). Circulating adipokine concentrations were longitudinally measured using enzyme-linked immunosorbent assays. At baseline, apelin receptor (APLNR) and zinc-alpha-2-glycoprotein (ZAG) concentrations were significantly lower in INRs than in immunological responders (p = 0.043 and p = 0.034), and they remained lower during all ART follow-up visits (p = 0.044 and p = 0.028 for APLNR, p = 0.038 and p = 0.010 for ZAG, at 48 and 144 weeks, respectively). ZAG levels positively correlated with retinol-binding protein 4 (RBP4) levels (p < 0.01), and low circulating RBP4 concentrations were related to a low CD4+ T-cell gain (p = 0.018 and p = 0.039 at 48 and 144 weeks, respectively). Multiple regression adjusted for clinical variables and adipokine concentrations confirmed both low APLNR and RBP4 as independent predictors for CD4+ T cells at 144 weeks (p < 0.001). In conclusion, low APLNR and RBP4 concentrations were associated with poor immune recovery in treated PLHIV and could be considered predictive biomarkers of a discordant immunological response.