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024 MPa). An artificial neural network was trained to predict contact pressure at each critical spot with orthosis type, gender, height, weight, discomfort scores and pressure measurements as input variables. The neural networks show satisfactory prediction accuracy with R2 values over 0.81 of regression between network outputs and measurements. This new method predicts a set of pressure limits at critical locations under the orthosis that the clinicians can use to make orthosis adjustment decisions.Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to improve the robustness and achieve end-to-end registration. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset containing 555 cases, and encouraging performances have been achieved. Compared to the commonly utilized registration methods, including VoxelMorph, SyN, and LT-Net, the proposed method achieves better registration performance with a Dice score of 0.8397±0.0756 in identifying stroke lesions. With regards to the registration speed, our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU. Moreover, we prove that our method can still perform well on more challenging tasks with lacking scanning information data, showing the high robustness for the clinical application.Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality medical data with a notable domain shift. Given that specific annotated imaging modalities may not be accessible nor complete. Our proposed solution is based on the cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists and bridge the domain gap in radiological images. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups. Built upon adversarial training, we propose a learnable self-attentive spatial normalization of the deep convolutional generator network's intermediate activations. Unlike previous attention-based image-to-image translation approaches, which are either domain-specific or require distortion of the source domain's structures, we unearth the importance of the auxiliary semantic information to handle the geometric changes and preserve anatomical structures during image translation. We achieve superior results for cross-modality segmentation between unpaired MRI and CT data for multi-modality whole heart and multi-modal brain tumor MRI (T1/T2) datasets compared to the state-of-the-art methods. We also observe encouraging results in cross-modality conversion for paired MRI and CT images on a brain dataset. Furthermore, a detailed analysis of the cross-modality image translation, thorough ablation studies confirm our proposed method's efficacy.We present an efficient foveal framework for object detection. A scale normalized image pyramid (SNIP) is generated which for each scale, like human vision, only attends to objects within a fixed size range. Such a restriction of objects' size during training affords better learning of object-sensitive filters, and therefore better accuracy. However, using image pyramid increases the computational cost. Hence, we propose an efficient spatial sub-sampling scheme which only operates on fixed-size sub-regions likely to contain objects (as object locations are known during training). The resulting approach, referred to as Scale Normalized Image Pyramid with Efficient Resampling or SNIPER, yields up to 3 times speed-up during training. Unfortunately, as object locations are unknown during inference, the entire image pyramid still needs processing. To this end, we adopt a coarse-to-fine approach, and predict the locations and extent of object-like regions which will be processed in successive scales of the image pyramid. Intuitively, it's akin to our active human-vision that first skims over the field-of-view to spot interesting regions for further processing and only recognizes objects at the right resolution. The resulting algorithm is referred to as AutoFocus and results in a 2.5-5 times speed-up during inference when used with SNIP.Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations.Impaired cerebrovascular function is an early biomarker for cerebral amyloid angiopathy (CAA), a neurovascular disease characterized by amyloid-β accumulation in the cerebral vasculature, leading to stroke and dementia. The transgenic Swedish Dutch Iowa (Tg-SwDI) mouse model develops cerebral microvascular amyloid-β deposits, but whether this leads to similar functional impairments is incompletely understood. We assessed cerebrovascular function longitudinally in Tg-SwDI mice with arterial spin labeling (ASL)-magnetic resonance imaging (MRI) and laser Doppler flowmetry (LDF) over the course of amyloid-β deposition. Unexpectedly, Tg-SwDI mice showed similar baseline perfusion and cerebrovascular reactivity estimates as age-matched wild-type control mice, irrespective of modality (ASL or LDF) or anesthesia (isoflurane or urethane and α-chloralose). Hemodynamic changes were, however, observed as an effect of age and anesthesia. Our findings contradict earlier results obtained in the same model and question to what extent microvascular amyloidosis as seen in Tg-SwDI mice is representative of cerebrovascular dysfunction observed in CAA patients.The global prevalence for diabetes mellitus nearly doubled from 4.7% in 1980 to 8.5% in 2014. Sirtuin 1 (SIRT1) is an NAD+-dependent deacetylase that is expressed in a variety of tissues. It modifies proteins that participate in DNA repair, stress, and inflammatory response. The aim of the study was to investigate the relationship between SIRT1 rs7069102 polymorphism and diabetic nephropathy (DN) in patients with T2DM. In our retrospective association study, we included 724 Slovene (Caucasian) patients who have had T2DM for at least 10 years. We classified the participants into two groups, the first group was comprised of 301 patients with DN, and the second (control) group was comprised of 423 patients without DN. We analyzed the rs7069102 polymorphism using StepOne Real-Time PCR System and TaqMan SNP Genotyping Assay. We found a statistically significant difference in the distribution of rs7069102 genotypes and alleles between the two groups. We used logistic regression analysis and adjusted for systolic pressure, arterial hypertension (AH), duration of AH, triglycerides, the value of HbA1c, carotid disease, diabetic foot, and DR. Furthermore, we discovered that patients with the CC genotype are significantly more likely to develop DN according to both the codominant (OR = 1.94; 95% CI = 1.09 - 3.45; p = 0.02) and recessive (OR = 2.39; 95% CI = 1.12 - 5.08; p = 0.02) models of inheritance. We found a significant association between the SIRT1 rs7069102 polymorphism and DN in T2DM. We speculate that SIRT1 rs7069102 might be an interesting marker of DN.This is the first report of molecular and epidemiology findings from Bosnia and Herzegovina related to ongoing SARS-CoV-2 epidemic. Whole Genome Sequence of four samples from COVID-19 outbreaks was done in two laboratories in Bosnia and Herzegovina (Veterinary Faculty Sarajevo and Alea Genetic Center). All four BiH sequences cluster mainly with European ones (Italy, Austria, France, Sweden, Cyprus, England). The constructed phylogenetic tree indicates possible multiple independent introduction events. The data presented contributes to a better understanding of COVID-19 in the current reemergence of the disease.There is a lack of predictive models to determine the prognosis of elderly patients diagnosed with stage I small-cell lung cancer (SCLC). The purpose of this study was to establish a useful nomogram to predict cancer-specific survival (CSS) in this patient population. Based on the Surveillance, Epidemiology, and End Results registry database, patients aged ≥ 65 years with pathological American Joint Committee on Cancer (AJCC) stage I SCLC from 2004 to 2014 were identified. The CSS was evaluated by the Kaplan-Meier method. Patients were randomly split into training and validation sets. In the training cohort, univariate analysis and multivariate analysis using the Cox regression identified risk factors that affected CSS. The results were utilized to construct a nomogram for the prediction of the 1-, 3-, and 5-year CSS rates of elderly patients with stage I SCLC. The effectiveness of the nomogram was validated internally and externally by the bootstrap method. The clinical practicability and accuracy of the nomogram were evaluated by the concordance index (C-index), calibration curve, receiver operating characteristic curve, and decision curve analysis. In total, we extracted 1,623 elderly patients with stage I SCLC. The median CSS was 34 months, and the 5-year CSS was 41%. Multivariate analysis revealed that histologic type, tumor size, age, and AJCC stage were significant predictors of CSS. A nomogram was constructed according to the results of multivariate Cox analysis. The C-indices of the nomogram for training and validation sets were 0.68 and 0.62, indicating that the nomogram demonstrated a favorable level of discrimination. The calibration curves exhibited satisfactory agreement between the actual observation and nomogram prediction. click here The net benefit of the nomogram was better than the AJCC TNM staging. We constructed a practical nomogram to predict the CSS of elderly patients with stage I SCLC. The predictive tool is helpful for patients counseling and treatment decision-making.
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