Understanding the dependence on the pulling speed of the unfolding pathway of proteins

The dependence of the unfolding pathway of proteins on the pulling speed is investigated. This is done by introducing a simple one-dimensional chain comprising $N$ units, with different characteristic bistable free energies. These units represent either each of the modules in a modular protein or each of the intermediate "unfoldons" in a protein domain, which can be either folded or unfolded. The system is pulled by applying a force to the last unit of the chain, and the units unravel following a preferred sequence. We show that the unfolding sequence strongly depends on the pulling velocity $v_{p}$. In the simplest situation, there appears a critical pulling speed $v_{c}$: for pulling speeds $v_{p}v_{c}$ it is the pulled unit that unfolds first. By means of a perturbative expansion, we find quite an accurate expression for this critical velocity.Comment: accepted for publication in JSTA

Deep semi-supervised segmentation with weight-averaged consistency targets

Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also devise a method to solve the problems that arise when using traditional data augmentation strategies for segmentation tasks on our new training scheme.Comment: 8 pages, 1 figure, accepted for DLMIA/MICCA

Reducing the Learning Domain by Using Image Processing to Diagnose COVID-19 from X-Ray Image

Over the last months, dozens of artificial intelligence (AI) solutions for COVID-19 diagnosis based on chest X-ray image analysis have been proposed. All of them with very impressive sensitivity and specificity results. However, its generalization and translation to the clinical practice are rather challenging due to the discrepancies between domain distributions when training and test data come from different sources. Consequently, applying a trained model on a new data set may have a problem with domain adaptation leading to performance degradation. This research aims to study the impact of image pre-processing on pre-trained deep learning models to reduce the learning domain. The dataset used in this research consists of 5,000 X-ray images obtained from different sources under two categories: negative and positive COVID-19 detection. We implemented transfer learning in 3 popular convolutional neural networks (CNNs), including VGG16, VGG19, and DenseNet169. We repeated the study following the same structure for original and pre-processed images. The pre-processing method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter application and image registration. After evaluating the models, the CNNs that have been trained with pre-processed images obtained an accuracy score up to 1.2% better than the unprocessed ones. Furthermore, we can observe that in the 3 CNN models, the repeated misclassified images represent 40.9% (207/506) of the original image dataset with the erroneous result. In pre-processed ones, this percentage is 48.9% (249/509). In conclusion, image processing techniques can help to reduce the learning domain for deep learning applications

A new approach to automatically evaluate problems that are solved using diagrams

Automatic correction of problems that are solved using diagrams through educational platforms is of great importance, especially in the field of engineering studies. In this paper, we present a new strategy to automatically assess diagrams. The proposed approach is described in detail as well as its application to assess entity/relationship diagrams used in the conceptual design of databases. The results indicate that the differences between manual and automatic assessment is less than 075 points over a total of ten which confirms the validity of the proposed approach. As a conclusion, the validity of the proposed method to assess entity/relationship diagrams reducing teacher correction time and unifying applied correction criteria is corroborated

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Síndrome hepatocutáneo : revisión bibliográfica y estudio retrospectivo de tres casos clínicos

El síndrome hepatocutáneo o dermatitis superficial necrolítica es una afección del perro que se manifiesta con lesiones costrosas y ulcerativas en la zona distal de las extremidades, en la cara y en los genitales externos. Estas lesiones cutáneas coinciden con lesiones hepáticas y con una disminución de los niveles de aminoácidos en sangre. En este trabajo se describen tres casos de perros afectados con este síndrome y se realiza un paralelismo con el eritema necrolítico migratorio descrito en medicina humana.The hepatocutaneous syndrome or superficial necrolytic dermatitis, is a dogs affection which is manifested with crusting and ulceration lesions in distal extremities on the face and the external genitals. These cutaneous lesions coincide together with hepatic lesions and decreased blood amino acid levels. In this article three cases of dogs with this syndrome are described. One comparison can be made with necrolytic migratory erythema described in human medecine

In vivo imaging of chronic active lesions in multiple sclerosis

New clinical activity in multiple sclerosis (MS) is often accompanied by acute inflammation which subsides. However, there is growing evidence that a substantial proportion of lesions remain active well beyond the acute phase. Chronic active lesions are most frequently found in progressive MS and are characterised by a border of inflammation associated with iron-enriched cells, leading to ongoing tissue injury. Identifying imaging markers for chronic active lesions in vivo are thus a major research goal. We reviewed the literature on imaging of chronic active lesion in MS, focussing on 'slowly expanding lesions' (SELs), detected by volumetric longitudinal magnetic resonance imaging (MRI) and 'rim-positive' lesions, identified by susceptibility iron-sensitive MRI. Both SELs and rim-positive lesions have been found to be prognostically relevant to future disability. Little is known about the co-occurrence of rims around SELs and their inter-relationship with other emerging techniques such as dynamic contrast enhancement (DCE) and positron emission tomography (PET)

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI

Background: We aimed to evaluate the implications for clinical trial design of the generalised boundary-shift integral (GBSI) for spinal cord atrophy measurement. / Methods: We included 220 primary-progressive multiple sclerosis patients from a phase 2 clinical trial, with baseline and week-48 3DT1-weighted MRI of the brain and spinal cord (1 × 1 × 1 mm3), acquired separately. We obtained segmentation-based cross-sectional spinal cord area (CSA) at C1-2 (from both brain and spinal cord MRI) and C2-5 levels (from spinal cord MRI) using DeepSeg, and, then, we computed corresponding GBSI. / Results: Depending on the spinal cord segment, we included 67.4–98.1% patients for CSA measurements, and 66.9–84.2% for GBSI. Spinal cord atrophy measurements obtained with GBSI had lower measurement variability, than corresponding CSA. Looking at the image noise floor, the lowest median standard deviation of the MRI signal within the cerebrospinal fluid surrounding the spinal cord was found on brain MRI at the C1-2 level. Spinal cord atrophy derived from brain MRI was related to the corresponding measures from dedicated spinal cord MRI, more strongly for GBSI than CSA. Spinal cord atrophy measurements using GBSI, but not CSA, were associated with upper and lower limb motor progression. / Discussion: Notwithstanding the reduced measurement variability, the clinical correlates, and the possibility of using brain acquisitions, spinal cord atrophy using GBSI should remain a secondary outcome measure in MS studies, until further advancements increase the quality of acquisition and reliability of processing

Naturbanization and urban : rural dynamics in Spain : case study of new rural landscapes in Andalusia and Catalonia

The early 20th century saw the beginning of a process of urbanizing rural space (Berry, 1976a; 1976b), described as counter-urbanization (Champion, 1989). The creation of Protected Natural Areas (PNAs) has defined some rural spaces, relatively far from large urban metropolitan areas, where the ecological and scenic value is a magnet for urbanization (Prados, 2005). Thus, PNAs make rural areas more attractive to new economic and leisure activities and can promote a more positive type of development that has been called naturbanization (Prados, 2009). We address this topic in six sections: (1) Introduction; (2) Conceptual framework of naturbanization; (3) Methodology to analyse the process of naturbanization; (4) Processes of naturbanization in Andalusia and in Catalonia; (5) Comparative analysis of two case studies, and (6) Conclusions and RecommendationsSe ha desarrollado un proceso de urbanización del espacio rural desde principios del siglo XX (Berry, 1976a; 1976b) descrito como la "counterurbanización" (Champion, 1989). La creación de los espacios naturales protegidos (ENP) ha delimitado unos espacios rurales, relativamente alejados de las grandes conurbaciones urbanas, donde la valoración ecológica y paisajística genera, en algunos casos, una atracción urbanizadora (Prados, 2005). De este modo, los ENP hacen más atractivo al espacio rural y promueven la naturbanización (Prados, 2009). En esta presentación trataremos el tema en seis apartados: (i) Introducción (ii) El marco conceptual de la naturbanización; (iii) La metodología para analizar la naturbanización; (iv) Los procesos de naturbanización en Andalucía y en Cataluña; (v) El análisis comparativo de los dos casos estudiados y, finalmente, (vi) presentamos conclusiones y propuestas de implementació