Chomeredith0639

Rhodnius pallescens is the principal vector of Chagas disease in Panama. Recently a dark chromatic morph has been discovered in the highlands of Veraguas Province. Limited genetic studies have been conducted with regards to the population structure and dispersal potential of Triatominae vectors, particularly in R. pallescens. Next generation sequencing methods such as RADseq and complete mitochondrial DNA (mtDNA) genome sequencing have great potential for examining vector biology across space and time. Here we utilize a RADseq method (3RAD), along with complete mtDNA sequencing, to examine the population structure of the two chromatic morpho types of R. pallescens in Panama. We sequenced 105 R. pallescens samples from five localities in Panama. We generated a 2216 SNP dataset and 6 complete mtDNA genomes. RADseq showed significant differentiation among the five localities (FCT = 0.695; P = .004), but most of this was between localities with the dark vs. light chromatic morphs (Veraguas vs. Panama Oeste). The mtDNA genomes showed a 97-98% similarity between dark and light chromatic morphs across all genes and a 502 bp insert in light morphs. Thus, both the RADseq and mtDNA data showed highly differentiated clades with essentially no gene flow between the dark and light chromatic morphs from Veraguas and central Panama respectively. We discuss the growing evidence showing clear distinctions between these two morpho types with the possibility that these are separate species, an area of research that requires further investigation. Finally, we discuss the cost-effectiveness of 3RAD which is a third of the cost compared to other RADseq methods used recently in Chagas disease vector research.Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.A plethora of dissolution tests exists for oral dosage forms, with variations in selection of the dissolution medium, the hydrodynamics and the dissolution equipment. This work aimed at determining the influence of media composition, the type of dissolution test and the method for entering the data into a PBPK model on the ability to simulate the in vivo plasma profile of an immediate release formulation. Using two rDCS IIa substances, glibenclamide and dipyridamole, housed in immediate-release formulations as model dosage forms, dissolution tests were performed in USP apparatus II with the biorelevant media FaSSGF, FaSSIF V1, V2 and V3 using both single-stage and two-stage test designs. The results were then integrated into the PBPK software SimcypⓇ either as the observed release profile (dissolution rate model, DRM) or using a semi-mechanistic model (diffusion layer model, DLM) and compared with in vivo plasma profiles. The selection of the FaSSIF version did not appear to have any relevant influence on the dissolution of the weakly basic dipyridamole, while the weakly acidic glibenclamide was sensitive to the difference in pH between FaSSIF V1, V2 and FaSSIF V3. Since both compounds have pKa values close to the pH of biorelevant media representing conditions in the small intestine, these results may be specific to compounds with similar ionization behavior. Single-stage and two-stage testing led to equivalent simulations for glibenclamide. Only results from the single-stage test in FaSSGF led to a close simulation of the pharmacokinetic profile of dipyridamole when data were inputted using the DRM, while simulations from two-stage testing were most similar to the observed pharmacokinetic profile when DLM with selection of a dynamic pH profile in the small intestine was selected as the data input method. These results emphasize the importance of data input to the simulation results.Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are widely used detection technology in screening, diagnosis, and image-guided therapy for both clinical and research. However, CT imposes ionizing radiation to patients during acquisition. Compared to CT, MRI is much safer and does not involve any radiations, but it is more expensive and has prolonged acquisition time. Therefore, it is necessary to estimate one modal image from another given modal image of the same subject for the case of radiotherapy planning. Considering that there is currently no bidirectional prediction model between MRI and CT images, we propose a bidirectional prediction by using multi-generative multi-adversarial nets (BPGAN) for the prediction of any modal from another modal image in paired and unpaired fashion. In BPGAN, two nonlinear maps are learned by projecting same pathological features from one domain to another with cycle consistency strategy. Technologically, pathological prior information is introduced to constrain the feature generation to attack the potential risk of pathological variance, and edge retention metric is adopted to preserve geometrically distortion and anatomical structure. Algorithmically, spectral normalization is designed to control the performance of discriminator and to make predictor learn better and faster, and the localization is proposed to impose regularizer on predictor to reduce generalization error. Experimental results show that BPGAN generates better predictions than recently state-of-the-art methods. Specifically, BPGAN achieves average increment of MAE 33.2% and 37.4%, and SSIM 24.5% and 44.6% on two baseline datasets than comparisons.Learning from label proportions (LLP), where the training data is in form of bags, and only the proportions of classes in each bag are available, has attracted wide interest in machine learning community. In general, most LLP algorithms adopt random sampling to obtain the proportional information of different categories, which correspondingly obtains some labeled samples in each bag. However, LLP training process always fails to leverage these labeled samples, which may contain essential data distribution information. To address this issue, in this paper, we propose end-to-end LLP solver based on convolutional neural networks (ConvNets), called LLP with labeled samples (LLP-LS). First, we reshape the cross entropy loss in ConvNets, so that it can combine the proportional information and labeled samples in each bag. Second, in order to comply with the training data in a bag manner, ADAM based on batch is employed to train LLP-LS. Hence, the batch size in training process is in accordance with the bag size. read more Compared with up-to-date methods on multi-class problem, our algorithm can obtain the state-of-the-art on several image datasets.Due to their unprecedented capacity to learn patterns from raw data, deep neural networks have become the de facto modeling choice to address complex machine learning tasks. However, recent works have emphasized the vulnerability of deep neural networks when being fed with intelligently manipulated adversarial data instances tailored to confuse the model. In order to overcome this issue, a major effort has been made to find methods capable of making deep learning models robust against adversarial inputs. This work presents a new perspective for improving the robustness of deep neural networks in image classification. In computer vision scenarios, adversarial images are crafted by manipulating legitimate inputs so that the target classifier is eventually fooled, but the manipulation is not visually distinguishable by an external observer. The reason for the imperceptibility of the attack is that the human visual system fails to detect minor variations in color space, but excels at detecting anomalies in geometric shapes. We capitalize on this fact by extracting color gradient features from input images at multiple sensitivity levels to detect possible manipulations. We resort to a deep neural classifier to predict the category of unseen images, whereas a discrimination model analyzes the extracted color gradient features with time series techniques to determine the legitimacy of input images. The performance of our method is assessed over experiments comprising state-of-the-art techniques for crafting adversarial attacks. Results corroborate the increased robustness of the classifier when using our discrimination module, yielding drastically reduced success rates of adversarial attacks that operate on the whole image rather than on localized regions or around the existing shapes of the image. Future research is outlined towards improving the detection accuracy of the proposed method for more general attack strategies.The acaricides cyflumetofen, cyenopyrafen, and pyflubumide act as inhibitors of the mitochondrial electron transport system at complex II (succinate dehydrogenase; SDH), a new mode of action in arthropods. The development and mechanisms of low-level resistance against cyenopyrafen and cyflumetofen have been previously reported in Tetranychus urticae. In the present study, we investigated high levels of resistance against three SDH inhibitors in T. urticae field populations and clarify the genetic basis of resistance using quantitative trait locus (QTL) analysis. First, we constructed a microsatellite linkage map comprising 64 markers assembled into three linkage groups (LGs) with total length of 683.8 cM and average marker spacing of 11.03 cM. We then used the linkage map to perform QTL mapping, and identified significant QTLs contributing to resistance to cyflumetofen (one QTL on LG1), cyenopyrafen (one QTL on LG3), and pyflubumide (two QTLs on LG1 and LG3). The QTL peaks on LG1 for cyflumetofen and pyflubumide overlapped and included the SdhB locus. For cyenopyrafen resistance, the QTLs on LG3 included the SdhC locus. For cyflumetofen resistance, we found an I260T mutation in SdhB. For pyflubumide and cyenopyrafen resistance, we detected I260V and S56L substitutions in SdhB and SdhC, respectively, by direct sequencing. Both I260 in SdhB and S56 in SdhC were present in highly conserved regions of the ubiquinone binding site formed at the interface among SdhB, SdhC, and SdhD. Mutations at these positions have been implicated in resistance against fungicides that act as Sdh inhibitors in various pathogens. Therefore, we consider these mutations to be target-site resistance mutations for these acaricidal SDH inhibitors.Background Inhibitory control refers to a central cognitive capacity involved in the interruption and correction of actions. Dysfunctions in these cognitive control processes have been identified as major maintaining mechanisms in a range of mental disorders such as ADHD, binge eating disorder, obesity, and addiction. Improving inhibitory control by transcranial direct current stimulation (tDCS) could ameliorate symptoms in a broad range of mental disorders. Objective The primary aim of this pre-registered meta-analysis was to investigate whether inhibitory control can be improved by tDCS in healthy and clinical samples. Additionally, several moderator variables were investigated. Methods A comprehensive literature search was performed on PubMed/MEDLINE database, Web of Science, and Scopus. To achieve a homogenous sample, only studies that assessed inhibitory control in the go-/no-go (GNG) or stop-signal task (SST) were included, yielding a total of 75 effect sizes from 45 studies. Results Results of the meta-analysis indicate a small but significant overall effect of tDCS on inhibitory control (g = 0.