Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicological implications of a commercial formulation of deltamethrin (Decis®) in developing chick embryo

ABSTRACT A deltamethrin containing insecticide formulation (Decis®) was evaluated for its toxic potential in developing chick embryos. For the present study, three water emulsified concentrations of Decis® (12.5 mg L−1, 25 mg L−1, and 50 mg L−1) were used. Fertilized eggs of Gallus domesticus were immersed in these three concentrations of the insecticide for 60 min at 37°C on day 0 of incubation and kept for incubation till embryonic day 7. Recovered embryos were evaluated for teratogenic and biochemical changes. The results revealed that administration of Decis® at its lower concentrations (12.5 mg L−1 and 25 mg L−1) did not show any significant teratological changes but the significant number of abnormal survivors was observed at 50 mg L−1 of dose concentration when compared with vehicle-treated control. Among biochemical changes, total glycogen and RNA contents of embryos was significantly decreased at 25 mg L−1 and 50 mg L−1 of Decis® concentrations. Similarly, significant alteration (p ≤ .05) was observed in alanine transaminase activity at 50 mg L−1 concentration of Decis®. Thus, the present study concluded that the no-effect-level for developmental toxicity for Decis® is below the concentration of 25 mg L−1 under standard laboratory conditions.
Sex differences in the perception of itch and quality of life in patients with chronic pruritus in the United States

Using cross-sectional survey data from 335 patients with chronic itch, we analyzed differences in itch intensity, characteristics, and quality of life between males and females. The intensity of chronic pruritus was significantly greater in females compared with males (7.70 vs. 6.95; P<0.05) and females more often described their pruritus as dreadful, unbearable, hurting, and oppressive (P<0.05). In addition, females achieved a greater overall quality of life mean score compared with males (3.65 vs. 3.19; P<0.05), indicating a more negative impact on quality of life. Overall, our study shows that significant differences exist between females and males with chronic pruritus, and clinicians should be aware of these distinctions.
DRCS-SR: Deep Robust Compressed Sensing for Single Image Super-Resolution

Compressed sensing (CS) represents an efficient framework to simultaneously acquire and compress images/signals while reducing acquisition time and memory requirements to process or transmit them. Specifically, CS is able to recover an image from a random measurements. Recently, deep neural networks (DNNs) are exploited not only to acquire and compress but also for recovering signals/images from a highly incomplete set of measurements. Super-resolution (SR) algorithms attempt to generate a single high resolution (HR) image from one or more low resolution (LR) images of the same scene. Despite the success of the existing SR networks to recover HR images with better visual quality, there are still some challenges that need to be addressed. Specifically, for many practical applications, the original images may be affected by various transformation effects including rotation, scaling, and translation. Moreover, in real-time transmissions, image compression is carried out first, followed by acquisition time reduction. To address this problem, we propose a novel robust deep CS framework that is able to mitigate the geometric transformation and recover HR images. Specifically, the proposed framework is able to perform two tasks. First, it is able to compress the transformed image with the help of an optimized generated measurement matrix. Second, the proposed framework is able not only to recover the original image from the compressed version but also to mitigate the transformation effects. The simulation results reported in this article show that the proposed framework is able to achieve high level of robustness against different geometric transformations in terms of peak signal-to-noise-ratio (PSNR) and similar structure index measurements (SSIM). For the convenience of dissemination, we make our source codes available at GitHuba.
A review of the management of uremic pruritus: current perspectives and future directions

Uremic pruritus (UP) is a common and distressing symptom experienced by up to half of all patients with end-stage renal disease (ESRD) receiving dialysis. It is associated with multiple health-related quality of life impairments and has been independently associated with mortality. Despite the prevalence and associated impact on quality of life, UP remains a difficult symptom to treat because of the relative lack of existing high quality evidence on which to base recommendations and the sheer volume of poorly studied therapeutic options. This review outlines the existing data of available treatment options including topical therapy, systemic therapy, and phototherapy as well as explore emerging data on therapies that are targeting novel pruritus pathways including the cannabinoid and opioid pathways. Overall, neuromodulators, in particular gabapentin, appear to have the most robust data in the treatment of UP. In individuals who cannot tolerate oral systemic therapy or in those with refractory generalized UP, ultraviolet phototherapy, specifically broad-band UVB, has shown significant promise. However, access is often a limiting factor. Lastly, the emergence of new therapies targeting a peripheral acting κ-opioid agonist, difelikefalin, has demonstrated effect in both early phase 2 and 3 clinical trials.
Cyathostominae distribution in experimentally infected ponies

Summary Nine ponies were experimentally infected with third stage cyathostome larvae. These animals were examined post-mortem for small strongyle infections from October 2011 to January 2012. Seventeen species of the following Cyathostominae genera were identified: Coronocyclus, Cyathostomum, Cylicocyclus, Cylicostephanus, Gyalocephalus and Petrovinema. Of the 17 species of Cyathostominae recovered, Cyathostomum catinatum, Cyathostomum alveatum and Cylicocyclus nassatus were the most prevalent (100 %). From the entire population of Cyathostominae, 65.6 % were located in the caecum and only 34.4 % in the colon. Only 1,736 (24,6 %) females and 5,329 (75,4 %) males were observed. There was a significant difference between the number of males and females in the genus Cyathostomum, Cylicocyclus and Gyalocephalus.
An Adaptive Cruise Control Method Based on Improved Variable Time Headway Strategy and Particle Swarm Optimization Algorithm

During the car following process, in order to improve ACC ability to coordinate various goals and adaptability in a complex and variable driving environment, the variable time headway spacing strategy and multi-objective adaptive cruise control algorithm are studied. Aiming at the deceleration adaptability, an improved variable time headway strategy is proposed, which adjusts the value of acceleration weight according to the deceleration duration and deceleration changes to increase the relative distance between the two vehicles in deceleration conditions. In order to improve the multi-objective coordination ability, the longitudinal kinematics model between the two vehicles is established, the objective function and constraints considering multiple factors are designed, and the relaxation factor vector is introduced to soften the boundary of different hard constraints to solve the problem with no feasible solution. Based on model predictive control theory, the objective function is converted into a multi-constrained quadratic programming problem in the rolling optimization process, and an improved particle swarm algorithm is used to solve the function. Through numerical simulation, the results show that the improved ACC algorithm can effectively improve the safety of the vehicle during deceleration, as well as the fuel economy, comfort and tracking ability under cycling conditions. The effectiveness of the algorithm is verified by the joint simulation of MATLAB/Simulink and CarSim.
The Relative Thermo-stabilities of the Actomyosin-ATPase from the Dorsal Muscles of Various Fish Species

Actomyosins from the dorsal muscles of various fish species were prepared and the thermo-stability of the respective Ca2+-ATPase activities was studied. 1) The rate of inactivation of ATPase at 35° varied greatly among actomyosins from different fish species. 2) Since the inactivation of actomyosin ATPase at 25, 30, 35 and 40°C respectively, was a first order process, the rate constants of inactivation at the respective temperatures were calculated. 3) The rates of inactivation of actomyosin ATPase on incubation for definite periods were measured with respect to temperature. The temperature at which approximately fifty percent loss of ATPase activity occurred within 30 minutes varied greatly among actomyosins from various fish species. 4) From the results of the thermal inactivation studies, an order of relative thermo-stability is clearly evident, tilapia actomyosin being the most stable and eel, carp (=herabuna), yellowtail, rainbow trout (=kokanee salmon), rockfish, greenling, Atka mackerel, and Alaska pollack, the least stable among the fish species studied. With regard to the thermo-stability of actomyosin ATPase, it is suggested that the actomyosins of different fish species have become adapted to the respective body temperatures of these species.
Polycythemia vera and essential thrombocythemia: 2021 update on diagnosis, risk‐stratification and management

Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms (MPN) respectively characterized by clonal erythrocytosis and thrombocytosis; other disease features include leukocytosis, splenomegaly, thrombosis, bleeding, microcirculatory symptoms, pruritus and risk of leukemic or fibrotic transformation.
Contending memory in heterogeneous SoCs: Evolution in NVIDIA Tegra embedded platforms

Modern embedded platforms are known to be constrained by size, weight and power (SWaP) requirements. In such contexts, achieving the desired performance-per-watt target calls for increasing the number of processors rather than ramping up their voltage and frequency. Hence, generation after generation, modern heterogeneous System on Chips (SoC) present a higher number of cores within their CPU complexes as well as a wider variety of accelerators that leverages massively parallel compute architectures. Previous literature demonstrated that while increasing parallelism is theoretically optimal for improving on average performance, shared memory hierarchies (i.e. caches and system DRAM) act as a bottleneck by exposing the platform processors to severe contention on memory accesses, hence dramatically impacting performance and timing predictability. In this work we characterize how subsequent generations of embedded platforms from the NVIDIA Tegra family balanced the increasing parallelism of each platform's processors with the consequent higher potential on memory interference. We also present an open-source software for generating test scenarios aimed at measuring memory contention in highly heterogeneous SoCs.
Detection of Freezing of Gait in People with Parkinson’s Disease using Smartphones

Freezing of Gait (FOG) is one of the most troublesome motor symptoms associated with Parkinson's disease (PD), characterised by brief episodes of inability to step. It involves increased risk of falls and reduced quality of life, and correlates with motor fluctuations and progression of the disease. Hence, the knowledge of FOG event frequency, duration, daily distribution and response to drug therapy is fundamental for a reliable patient's assessment. In this study, we propose a FOG detection algorithm that takes as input inertial data from a single waistmounted smartphone, and provides information about presence and duration of FOG episodes. Data acquisition was carried on 38 PD patients and 21 elderly subjects executing a standard 6-minute walking test. More than 3.5 hours of acceleration data have been collected. A combination of Support Vector Machine and k-Nearest Neighbour classifiers has been designed. Sensitivity of 95.4%, specificity of 98.8%, precision of 92.8% and accuracy of 98.3% in the 10-fold cross validation, and a detection rate of 84% in Leave-one-Subject-Out validation were obrained. These results, along with a good time resolution in the FOG duration identification and very efficient processing times, make the algorithm a promising tool for reliable FOG assessment during activities of daily living.
The greater wax moth Galleria mellonella: biology and use in immune studies

ABSTRACT The greater wax moth Galleria mellonella is an invertebrate that is increasingly being used in scientific research. Its ease of reproduction, numerous offspring, short development cycle, and finally, its known genome and immune-related transcriptome provide a convenient research model for investigation of insect immunity at biochemical and molecular levels. Galleria immunity, consisting of only innate mechanisms, shows adaptive plasticity, which has recently become the subject of intensive scientific research. This insect serves as a mini host in studies of the pathogenicity of microorganisms and in vivo tests of the effectiveness of single virulence factors as well as new antimicrobial compounds. Certainly, the Galleria mellonella species deserves our attention and appreciation for its contribution to the development of research on innate immune mechanisms. In this review article, we describe the biology of the greater wax moth, summarise the main advantages of using it as a model organism and present some of the main techniques facilitating work with this insect
Nutritional status and eating habits of people who use drugs and/or are undergoing treatment for recovery: a narrative review

Abstract A comprehensive overview is presented of the nutritional issues faced by people who use drugs or are undergoing treatment for recovery. Chronic substance use affects a person’s nutritional status and body composition through decreased intake, nutrient absorption, and dysregulation of hormones that alter the mechanisms of satiety and food intake. Anthropometrics alone is not the best indicator of nutritional status, because this population has hidden deficiencies and disturbed metabolic parameters. Socioeconomic factors (eg, higher education, higher income, presence of a partner, living at home) positively affect nutritional status. Scarce available data on users undergoing treatment indicate improvement in anthropometric and metabolic parameters but with micronutrient intake remaining suboptimal. Weight gain is noted especially among women who use drugs and potentially increases their risk of relapse. Finally, specific amino acids and omega-3 fatty acids are promising in decreasing relapse and improving mental health during treatment; however, additional high-quality studies are needed. Nutrition intervention for people who use drugs or are undergoing treatment for recovery is underused; comprehensive programs addressing this population’s unique needs are necessary. Future research will identify which components are needed.
Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery

Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by > 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 % improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 %. This resulted in a 97.4 % overall accuracy score and a 96.2 % F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.
Single nucleotide polymorphism haplotypes of adiponectin gene are associated with type 2 diabetes in Han population

Objective To investigate the association of single nucleotide polymorphism (SNP) haplotypes in adiponectin gene with type 2 diabetes mellitus (T2DM). Methods The study included 202 patients with T2DM and 143 nondiabetic(NDM) healthy subjects with no family history of diabetes.Using polymerase chain reaction-based restriction fragment-length polymorphism (PCR-RFLP),we genotyped the SNP-11391,SNP-11377,SNP-4522,SNP+45 and SNP+331 in adiponectin gene,and constructed their haplotypes.Then,we assessed the association of the five SNPs with T2DM.Results (1)The genotypic and allelic frequencies of SNP-11391,SNP-11377,SNP-4522,SNP+45 and SNP +331 had no significant difference between T2DM and NDM groups (all P0.05). (2)The frequencies of haplotype SNP-11377C-SNP-4522T had difference between T2DM and NDM groups(P=0.02). Conclusions The haplotype SNP-11377C-SNP -4522T of adiponectin gene is associated with increased T2DM risk (Or=1.60,95%CI:1.09～2.35).
Averaging Methods for a Multimode Fiber Interferometer: Experimental and Interpretation

The averaged characteristics of the multimode fiber interferometer signals have been investigated, with the average being performed over ensemble, “long realization”, frequency, cross-section, and combination of these methods as well. The signals were produced at two wavelengths in few- and multi-mode regimes in the graded-index fiber. A practical example of the cross-section average is discussed in more details. The results would be useful for the multimode fiber interferometer utilization as a real-time high-accuracy sensor.
Quantitative Analysis of Somatostatin and Dopamine Receptors Gene Expression Levels in Non-functioning Pituitary Tumors and Association with Clinical and Molecular Aggressiveness Features

The primary treatment for non-functioning pituitary tumors (NFPTs) is surgery, but it is often unsuccessful. Previous studies have reported that NFPTs express receptors for somatostatin (SST1-5) and dopamine (DRDs) providing a rationale for the use of dopamine agonists and somatostatin analogues. Here, we systematically assessed SST1-5 and DRDs expression by real-time quantitative PCR (RT-qPCR) in a large group of patients with NFPTs (n = 113) and analyzed their potential association with clinical and molecular aggressiveness features. SST1-5 expression was also evaluated by immunohistochemistry. SST3 was the predominant SST subtype detected, followed by SST2, SST5, and SST1. DRD2 was the dominant DRD subtype, followed by DRD4, DRD5, and DRD1. A substantial proportion of NFPTs displayed marked expression of SST2 and SST5. No major association between SSTs and DRDs expression and clinical and molecular aggressiveness features was observed in NFPTs.
A general model for individual host selection

We formalise some ideas on host choice by individual insects, into a model which allows an animal to change diet during a lifetime. We assume that factors affecting acceptance thresholds through insect condition are not host-specific. Major predictions are that: individual insects may change diets, but should never become monophagous on plants which have previously been rejected; current eggload may affect diet width in ovipositing females; conditioning of host selection behaviour may be quite complex, including cross-conditioning to different hosts; genetic variance for host choice may be maintained in populations; genetic correlations between use of different novel hosts, and between host choice and fecundity, should be significant and positive. Evidence for these and corollary predictions appears to corroborate the model. In addition, we offer explanations for two previously unexplained phenomena: asymmetrical cross-conditioning; and genetic variance for use of hosts which have not been previously experienced. Such genetic variance allows host shifts to evolve rapidly. Finally, we argue that these ideas and results indicate that chemical similarity between hosts is sufficient but not necessary for host shifts to occur.
Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

Fog Computing has emerged as an extension to cloud computing by providing an efficient infrastructure to support IoT. Fog computing acting as a mediator provides local processing of the end-users’ requests and reduced delays in communication between the end-users and the cloud via fog devices. Therefore, the authenticity of incoming network traffic on the fog devices is of immense importance. These devices are vulnerable to malicious attacks. All kinds of information, especially financial and health information travel through these devices. Attackers target these devices by sending malicious data packets. It is imperative to detect these intrusions to provide secure and reliable service to the user. So, an effective Intrusion Detection System (IDS) is essential for the secure functioning of fog without compromising efficiency. In this paper, we propose a method (Auto-IF) for intrusion detection based on deep learning approach using Autoencoder (AE) and Isolation Forest (IF) for the fog environment. This approach targets only binary classification of the incoming packets as fog devices are more concerned about differentiating attack from normal packets in real-time. We validate the proposed method on the benchmark NSL-KDD dataset. Our technique of intrusion detection achieves a high accuracy rate of 95.4% as compared to many other state-of-art intrusion detection methods.
Principles and applications of typing methods for commensal Neisseria

This review describes the design as well as advantages and disadvantages for current phenotypic (antibiogram and serotyping) and molecular techniques (plasmid analysis, arbitrarily primed PCR, 16S rRNA, pulse-field gel electrophoresis and multilocus sequence typing) for typing of commensal Neisseria spp. compared with pathogenic Neisseria species. Identical methods for typing Neisseria species should not be used in all situations, that is, for microepidemiological, macroepidemiological or evolutionary questions.