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Megacolon is one of the main late complications of Chagas disease, affecting approximately 10% of symptomatic patients. However, studies are needed to understand the mechanisms involved in the progression of this condition. During infection by Trypanosoma cruzi (T. cruzi), an inflammatory profile sets in that is involved in neural death, and this destruction is known to be essential for megacolon progression. One of the proteins related to the maintenance of intestinal neurons is the type 2 bone morphogenetic protein (BMP2). Intestinal BMP2 homeostasis is directly involved in the maintenance of organ function. Thus, the aim of this study was to correlate the production of intestinal BMP2 with immunopathological changes in C57Bl/6 mice infected with the T. cruzi Y strain in the acute and chronic phases. The mice were infected with 1000 blood trypomastigote forms. After euthanasia, the colon was collected, divided into two fragments, and a half was used for histological analysis and the other half for BMP2, IFNγ, TNF-α, and IL-10 quantification. The infection induced increased intestinal IFNγ and BMP2 production during the acute phase as well as an increase in the inflammatory infiltrate. In contrast, a decreased number of neurons in the myenteric plexus were observed during this phase. Collagen deposition increased gradually throughout the infection, as demonstrated in the chronic phase. Additionally, a BMP2 increase during the acute phase was positively correlated with intestinal IFNγ. In the same analyzed period, BMP2 and IFNγ showed negative correlations with the number of neurons in the myenteric plexus. As the first report of BMP2 alteration after infection by T. cruzi, we suggest that this imbalance is not only related to neuronal damage but may also represent a new route for maintaining the intestinal proinflammatory profile during the acute phase.Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. [https://www.selleckchem.com/products/anacetrapib-mk-0859.html this website] It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.This study aimed to investigate the influence of the task type on the relative electromyography (EMG) activity of biceps femoris long head (BFlh) to semitendinosus (ST) muscles, and of proximal to distal regions during isometric leg-curl (LC) and hip-extension (HE). Twenty male volunteers performed isometric LC with the knee flexed to 30° (LC30) and 90° (LC90), as well as isometric HE with the knee extended (HE0) and flexed to 90° (HE90), at 40% and 100% maximal voluntary contraction (MVIC). Hip position was neutral in all conditions. EMG activity was recorded from the proximal and distal region of the BFlh and ST muscles. BFlh/ST was calculated from the raw root-mean-square (RMS) amplitudes. The RMS of 40% MVIC was normalized using MVIC data and the proximal/distal (P/D) ratio of normalized EMG (NEMG) was calculated. The BFlh/ST ratio was higher in HE0 than in LC90 during MVIC and 40% MVIC (p less then 0.05), and was higher in HE90 than in LC90 (p less then 0.05) during 40% MVIC at the proximal region, whereas no difference was observed between HE0 and LC30. There was no inter-task difference in BFlh/ST ratio in the distal region. Furthermore, the P/D ratio was higher in LC90 than in LC30 and HE0 (p less then 0.05) in BFlh and ST muscles, and was higher in HE90 than in LC30 and HE0 (p less then 0.05) in BFlh during 40% MVIC. However, there was no difference in P/D ratio between LC30 and LC90, and HE0 and HE90. This showed that there was no task-dependent difference in the EMG activity of the BFlh muscle relative to the ST muscle between prone hip extension and prone knee flexion when the knee joint was set at an equivalent angle. Similarly, there was no task-dependent difference in the NEMG of the proximal region relative to the distal region in BFlh and ST muscles during 40% MVIC.