Representing Input Transformations by Low-Dimensional Parameter Subspaces

Deep models lack robustness to simple input transformations such as rotation, scaling, and translation, unless they feature a particular invariant architecture or undergo specific training, e.g., learning the desired robustness from data augmentations. Alternatively, input transformations can be treated as a domain shift problem, and solved by post-deployment model adaptation. Although a large number of methods deal with transformed inputs, the fundamental relation between input transformations and optimal model weights is unknown. In this paper, we put forward the configuration subspace hypothesis that model weights optimal for parameterized continuous transformations can reside in low-dimensional linear subspaces. We introduce subspace-configurable networks to learn these subspaces and observe their structure and surprisingly low dimensionality on all tested transformations, datasets and architectures from computer vision and audio signal processing domains. Our findings enable efficient model reconfiguration, especially when limited storage and computing resources are at stake

NCACO-score: An effective main-chain dependent scoring function for structure modeling

<p>Abstract</p> <p>Background</p> <p>Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.</p> <p>Results</p> <p>Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.</p> <p>Conclusions</p> <p>Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.</p

Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.Comment: 11 pages, accepted by IEEE Robotics and Automation Letter

Operator radiation dose during trans-hepatic arterial chemoembolization: different patients’ positions via transradial or transfemoral access

PURPOSEThis study aimed to compare the radiation dose received by the operator among different patients’ positions via transradial access (TRA) or transfemoral access (TFA) during transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC).METHODSA total of 120 patients with HCC undergoing TACE for the first time between January and November 2019 were randomized into 4 groups with 30 patients in each group. In group A, patients were placed in the foot-first position with the left upper arm abducted, and TACE was performed via the left radial artery. In group B, patients were placed in the conventional headfirst position with the left hand placed at the left groin, and TACE was performed via the left radial artery. In group C, patients were placed in the conventional head-first position, and TACE was performed via the right radial artery. In group D, patients were placed in the conventional head-first position, and TACE was performed via the right femoral artery. Before each procedure, thermoluminescent dosimeters were taped at 7 different body parts of the operator and the radiation dose was measured and collected after the procedure. The normalized radiation dose was also calculated. Procedural parameters included radiation dose, fluoroscopy time (FT), dose–area product (DAP), and air kerma (AK) were recorded. Patients’ demographics, tumor baseline characteristics, radiation dose, and procedural parameters were compared between groups.RESULTSNo significant differences were found in patients’ demographics, tumor baseline characteristics, as well as in total FT, DAP, and AK. However, significant differences were found in the total radiation dose received by the operator and the doses on the pelvic cavity and the right wrist (P < .05). In group C, the radiation doses received on the pelvic cavity, the right wrist, and the total radiation doses were relatively higher. Significant differences were also found in the normalized radiation doses received by the operator on the thyroid, chest, left wrist, right wrist, and pelvic cavity, and the total normalized doses (all P < .05). Similarly, the radiation doses received by the operator at the aforementioned parts in group C were higher, while those in group A were lower.CONCLUSIONNo statistically significant differences were observed in the FT, DAP, and AK in TACE via TRA when patients were placed in different positions. However, TACE via the left TRA, with patients in the feet-first position, reduced the radiation dose received by the operator, thereby reducing the radiation risk

Photodynamic therapy with light-emitting diode arrays producing different light fields induces apoptosis and necrosis in gastrointestinal cancer

IntroductionLight-emitting diodes (LEDs) have become a new light source for photodynamic therapy (PDT) because of their excellent optical properties, small size, and low cost. LED arrays have so far been designed to meet the need for accurate illumination of irregular lesions. However, LED arrays determine not only the shape of the illuminated spot but also the light field, which has a significant impact on the efficacy of PDT.MethodsWe designed three types of LED arrays producing different light fields, namely an intensive LED array for a uniform light field, a sparse LED array for a non-uniform light field, and a point LED array for a Gaussian-like light field, and investigated the effect and mechanism of these light fields on PDT for gastrointestinal cancer both in vitro and in vivo.ResultsWe found that intensive LED-PDT induced earlier and more serious cell death, including apoptosis and necrosis, than sparse LED-PDT and point LED-PDT. Among the three LED arrays, the intensive LED array induced cells to produce more differential proteins (DEPs), mainly related to mitochondria, ribosomes, and nucleic acids. DEPs in cells subjected to sparse LED- and point LED-PDT were mainly involved in extracellular activities. For MGC-803 tumor-bearing mice, intensive LED-PDT and point LED-PDT had better tumor ablation effect than sparse LED-PDT. Notably, recurrence was observed on day 7 after sparse LED-PDT. VCAM-1 and ICAM-1 were highly expressed in sparse LEDs-PDT treated tumor tissues and were associated tumor angiogenesis, which in turn lead to poor tumor suppression.ConclusionsTherefore, the type of LED array significantly affected the performance of PDT for gastrointestinal cancer. Uniform light field with low power densities work better than non-uniform and Gaussian-like light fields

CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont.

Common variable immunodeficiency (CVID), the most common symptomatic primary antibody deficiency, is accompanied in some patients by a duodenal inflammation and malabsorption syndrome known as CVID enteropathy (E-CVID).The goal of this study was to investigate the immunological abnormalities in CVID patients that lead to enteropathy as well as the contribution of intestinal microbiota to this process.We found that, in contrast to noE-CVID patients (without enteropathy), E-CVID patients have exceedingly low levels of IgA in duodenal tissues. In addition, using transkingdom network analysis of the duodenal microbiome, we identified Acinetobacter baumannii as a candidate pathobiont in E-CVID. Finally, we found that E-CVID patients exhibit a pronounced activation of immune genes and down-regulation of epithelial lipid metabolism genes. We conclude that in the virtual absence of mucosal IgA, pathobionts such as A. baumannii, may induce inflammation that re-directs intestinal molecular pathways from lipid metabolism to immune processes responsible for enteropathy

Unexpected links reflect the noise in networks

Gene regulatory networks are commonly used for modeling biological processes and revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challenging task, especially considering the large number of players (e.g. genes) involved and the small number of biological replicates available for analysis. Herein, we propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between correlation and causality, and allows for the identification and to removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and the commonly used false discovery rate (FDR) technique. Furthermore, simulation analysis demonstrates that with large networks our new method provides a more accurate estimate of network error than FDR

Cardiac biophysical detailed synergetic modality rendering and visible correlation

The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders