Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on the micro-structure of bone with phase contrast CT imaging based on synchrotron radiation

In this paper, the micro-structure of bone was studied with phase contrast CT imaging based on synchrotron radiation. Firstly, the projection images of mouse hind limb tissue were obtained using the phase-contrast microscope CT imaging method based on synchrotron radiation. Then, the phase information were extracted from the projection images using different algorithms of the PITRE software. The slice and 3D images were reconstructed by Amira software. Finally, the effect of various algorithms on phase information extraction was studied by the profile of recovering projection using phase contour, the clarity and the contrast of slice, the bone volume fraction in 3D images. At the same time, the micro-structure of hind limb bone of mouse can be observed from the slice and 3D image reconstructed using the projection of the sample. There are some differences in slice and 3D images because the various algorithm can extract different phase information. Through comparing the information in the slice and the bone volume fraction (BV) in 3D images with different algorithms, the finally results show the restore phase information of the projection is the most ideal for the PAD-PA algorithm.
Dasatinib induces severe hemorrhagic colitis in a patient with accelerated phase of chronic myelogenous leukemia

Copyright © 2018 The Korean Association of Internal Medicine This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. pISSN 1226-3303 eISSN 2005-6648 http://www.kjim. org Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea Dasatinib induces severe hemorrhagic colitis in a patient with accelerated phase of chronic myelogenous leukemia
Comparative studies magnetic actuation for drug delivery: A challenge and constraint

Magnetic actuation micro-robot has developed a promising technology in clinical medicine application. The wireless control or powered-self micro-robot makes the existing invention therapy flexible, accuracy, short cover time. In this work, the major challenges and constraints that must be addressed and reviewed and some of the different magnetic platform are presented and compared. The reviewed magnetic micro-robot highlights the ability and classification of wireless magnetic actuation. Here, the actuation method including offboard and on-board approach is discussed, then provide the design constraints and existing problems for the magnetic actuation micro-robot. Finally, we provide a useful reference information for the design of the medical micro-robot.
Epithelial splicing regulatory protein 1 and 2 paralogues correlate with splice signatures and favorable outcome in human colorectal cancer

ESRPs are master splice regulators implicated in alternative mRNA splicing programs important for epithelial-mesenchymal transition (EMT) and tumor progression. ESRP1 was identified in some tumors as good or worse predictor of outcome, but in colorectal cancer (CRC) the prognostic value of ESRPs and relation with mesenchymal splice variants is not clear. Here, we studied 68 CRC cases, compared tissue expression of ESRPs with clinical data and with EMT gene splice patterns of conditional CRC cells with deficient ESRP1 expression. Around 72% of patients showed global decreased transcript expression of both ESRPs in tumor as compared to matched non-neoplastic colorectal epithelium. Reduction of ESRP1 in tumor cells was evaluated by immunohistochemistry, associated with microsatellite stability and switch to mesenchymal splice signatures of FGFRs, CD44, ENAH and CTNND1(p120-catenin). Expression of ESRPs was significantly associated with favorable overall survival (log-rank test, P=0.0186 and 0.0408), better than prognostic stratification by tumor staging; and for ESRP1 confirmed with second TCGA cohort (log-rank test, P=0.0435). Prognostic value is independent of the pathological stage and microsatellite instability (ESRP1: HR=0.36, 95%CI 0.15–0.91, P=0.032; ESRP2: HR=0.23, 95%CI 0.08–0.65, P=0.006). Our study supports the role of ESRP1 as tumor suppressor and strongly suggests that ESRPs are candidate markers for early detection, diagnosis, and prognosis of CRC.
Malate Synthase and β-Methylmalyl Coenzyme A Lyase Reactions in the Methylaspartate Cycle in Haloarcula hispanica

ABSTRACT Haloarchaea are extremely halophilic heterotrophic microorganisms belonging to the class Halobacteria (Euryarchaeota). Almost half of the haloarchaea possesses the genes coding for enzymes of the methylaspartate cycle, a recently discovered anaplerotic acetate assimilation pathway. In this cycle, the enzymes of the tricarboxylic acid cycle together with the dedicated enzymes of the methylaspartate cycle convert two acetyl coenzyme A (acetyl-CoA) molecules to malate. The methylaspartate cycle involves two reactions catalyzed by homologous enzymes belonging to the CitE-like enzyme superfamily, malyl-CoA lyase/thioesterase (haloarchaeal malate synthase [hMS]; Hah_2476 in Haloarcula hispanica) and β-methylmalyl-CoA lyase (haloarchaeal β-methylmalyl-CoA lyase [hMCL]; Hah_1341). Although both enzymes catalyze the same reactions, hMS was previously proposed to preferentially catalyze the formation of malate from acetyl-CoA and glyoxylate (malate synthase activity) and hMCL was proposed to primarily cleave β-methylmalyl-CoA to propionyl-CoA and glyoxylate. Here we studied the physiological functions of these enzymes during acetate assimilation in H. hispanica by using biochemical assays of the wild type and deletion mutants. Our results reveal that the main physiological function of hMS is malyl-CoA (not malate) formation and that hMCL catalyzes a β-methylmalyl-CoA lyase reaction in vivo. The malyl-CoA thioesterase activities of both enzymes appear to be not essential for growth on acetate. Interestingly, despite the different physiological functions of hMS and hMCL, structural comparisons predict that these two proteins have virtually identical active sites, thus highlighting the need for experimental validation of their catalytic functions. Our results provide further proof of the operation of the methylaspartate cycle and indicate the existence of a distinct, yet-to-be-discovered malyl-CoA thioesterase in haloarchaea. IMPORTANCE Acetate is one of the most important substances in natural environments. The activated form of acetate, acetyl coenzyme A (acetyl-CoA), is the high-energy intermediate at the crossroads of central metabolism: its oxidation generates energy for the cell, and about a third of all biosynthetic fluxes start directly from acetyl-CoA. Many organic compounds enter the central carbon metabolism via this key molecule. To sustain growth on acetyl-CoA-generating compounds, a dedicated assimilation (anaplerotic) pathway is required. The presence of an anaplerotic pathway is a prerequisite for growth in many environments, being important for environmentally, industrially, and clinically important microorganisms. Here we studied specific reactions of a recently discovered acetate assimilation pathway, the methylaspartate cycle, functioning in extremely halophilic archaea.
Recurrent Predictive State Policy Networks

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in partially observable environments. Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward. The recursive filter leverages predictive state representations (PSRs) (Rosencrantz and Gordon, 2004; Sun et al., 2016) by modeling predictive state-- a prediction of the distribution of future observations conditioned on history and future actions. This representation gives rise to a rich class of statistically consistent algorithms (Hefny et al., 2018) to initialize the recursive filter. Predictive state serves as an equivalent representation of a belief state. Therefore, the policy component of the RPSP-network can be purely reactive, simplifying training while still allowing optimal behaviour. Moreover, we use the PSR interpretation during training as well, by incorporating prediction error in the loss function. The entire network (recursive filter and reactive policy) is still differentiable and can be trained using gradient based methods. We optimize our policy using a combination of policy gradient based on rewards (Williams, 1992) and gradient descent based on prediction error. We show the efficacy of RPSP-networks under partial observability on a set of robotic control tasks from OpenAI Gym. We empirically show that RPSP-networks perform well compared with memory-preserving networks such as GRUs, as well as finite memory models, being the overall best performing method.
Emergent Communication in a Multi-Modal, Multi-Step Referential Game

Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.
Exploring the inhibitory effect of membrane tension on cell polarization

Cell polarization toward an attractant is influenced by both physical and chemical factors. Most existing mathematical models are based on reaction-diffusion systems and only focus on the chemical process occurring during cell polarization. However, membrane tension has been shown to act as a long-range inhibitor of cell polarization. Here, we present a cell polarization model incorporating the interplay between Rac GTPase, filamentous actin (F-actin), and cell membrane tension. We further test the predictions of this model by performing single cell measurements of the spontaneous polarization of cancer stem cells (CSCs) and non-stem cancer cells (NSCCs), as the former have lower cell membrane tension. Based on both our model and the experimental results, cell polarization is more sensitive to stimuli under low membrane tension, and high membrane tension improves the robustness and stability of cell polarization such that polarization persists under random perturbations. Furthermore, our simulations are the first to recapitulate the experimental results described by Houk et al., revealing that aspiration (elevation of tension) and release (reduction of tension) result in a decrease in and recovery of the activity of Rac-GTP, respectively, and that the relaxation of tension induces new polarity of the cell body when a cell with the pseudopod-neck-body morphology is severed.
Recommendations for analysis of repeated‐measures designs: testing and correcting for sphericity and use of manova and mixed model analysis

A common experimental design in ophthalmic research is the repeated‐measures design in which at least one variable is a within‐subject factor. This design is vulnerable to lack of ‘sphericity’ which assumes that the variances of the differences among all possible pairs of within‐subject means are equal. Traditionally, this design has been analysed using a repeated‐measures analysis of variance (RM‐anova) but increasingly more complex methods such as multivariate anova (manova) and mixed model analysis (MMA) are being used. This article surveys current practice in the analysis of designs incorporating different factors in research articles published in three optometric journals, namely Ophthalmic and Physiological Optics (OPO), Optometry and Vision Science (OVS), and Clinical and Experimental Optometry (CXO), and provides advice to authors regarding the analysis of repeated‐measures designs.
Quality of life of patients after total laryngectomy: the struggle against stigmatization and social exclusion using speech synthesis

Abstract In this article, we deal with the problem of potential stigmatization and social exclusion of people that have voice/speech problems due to neck cancer, especially those who have lost their voice because of total laryngectomy, and we analyze how this potential stigmatization could be alleviated by personalized speech synthesis. We used a mixed-method approach, consisting of quality of life questionnaires and in-depth semi-structured interviews, in order to provide more generalizable as well as rich information. We found out that people with speech problems feel depressed because of their health status and anxiety during social interactions, as they think that other people perceive them as abnormal, or they directly experience symbolic violence. For overcoming these negative feelings, the crucial factors are family relationships and maintaining employment. In both factors, speech synthesis can help significantly. Implications for rehabilitation Personalized speech synthesis can help considerably to people with severe voice impairment in their (re)integration into society. Where possible and appropriate, personalized speech synthesis should be offered as an option to “traditional” voice substitutions.
Regulatory effect of microRNA‐34a on osteogenesis and angiogenesis in glucocorticoid‐induced osteonecrosis of the femoral head

Glucocorticoid‐induced osteonecrosis of the femoral head (GIOFH) is a common and devastating orthopedic disease, and its underlying mechanism remains unclear. The aim of this study was to determine the role of microRNA‐34a (mir‐34a) in GIOFH. C57 mouse mesenchymal stem cells (mMSCs) and human umbilical vein endothelial cells (HUVECs) were cultured with dexamethasone (Dex). A total of 48 adult rats were treated with glucocorticoids, and after the onset of GIOFH, each femoral head was removed. Mir‐34a mimics, an inhibitor and over‐expressing lentivirus were used in vitro and in vivo, respectively. Real‐time PCR, immunohistochemistry, ELISA, cell proliferation assays, osteoblastic differentiation, and endothelial activity assays were employed to evaluate the effect of mir‐34a on mMSCs, osteoblasts, and vascular endothelial cells in glucocorticoid‐treated mice. We found that Dex inhibited mMSC proliferation and osteoblastic differentiation, as well as the viability and activity of endothelial cells. Dex also caused osteonecrosis and decreased new vessel formation in vivo. Mir‐34a alleviated the inhibitory effects of Dex on mMSCs and osteoblasts, while facilitating its inhibitory effects on endothelial cells. Mir‐34a is an important regulator in osteogenesis and angiogenesis, and it might be useful as a therapeutic target for GIOFH. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:417–424, 2018.
Loss of Dnmt 1 catalytic activity reveals multiple roles for DNA methylation during pancreas development and regeneration

a Department of Biochemistry and Biophysics, Programs in Developmental Biology, Genetics, and Human Genetics, Diabetes Center, and Liver Center, University of California, San Francisco, MC 2711 Rock Hall Room 384, 1550 Fourth Street, San Francisco, CA 94158-2324, USA b Department of Embryology, Carnegie Institution for Science 3520 San Martin Drive, Baltimore MD 21218, USA c Department of Medicine, University of California, San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA d Department of Medicine, Division of Endocrinology, San Francisco General Hospital, 1001 Potrero Ave., San Francisco, CA 941110, USA
Dictionary learning for hash representation

For many computer vision applications, hash coding is getting more attention with the increasing growth of data. The key idea of hash learning is to learn a short binary code for a set of given data such that the query data within the same distributions has similar binary code. In this paper, a novel dictionary learning model is proposed to extract has features via projection dictionary. The new model is called DLHR, which denotes dictionary learning for hash representation. The projection dictionary of DLHR model is built upon training samples, while the corresponding hash features need to be learned by training samples and query samples simultaneously. When the dictionary of DLHR model is slim, the central technique is compressed approach while main idea is the same as kernel method when the dictionary is fat or redundant. In simulation process, we also find that the dictionary is appropriate when the column is about 5–100 times larger than the row and vice versa. In contrary to traditional hash algorithms, DLHR is competitive due to its robustness.
A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio

Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, the delta normalized burn ratio (dNBR) and its relativized form (RdNBR), is most suitable for quantifying burn severity; each has its critics. In this study, we propose and evaluate a new Landsat-based burn severity metric, the relativized burn ratio (RBR), that provides an alternative to dNBR and RdNBR. For 18 fires in the western US, we compared the performance of RBR to both dNBR and RdNBR by evaluating the agreement of these metrics with field-based burn severity measurements. Specifically, we evaluated (1) the correspondence between each metric and a continuous measure of burn severity (the composite burn index) and (2) the overall accuracy of each metric when classifying into discrete burn severity classes (i.e., unchanged, low, moderate, and high). Results indicate that RBR corresponds better to field-based measurements (average R2 among 18 fires = 0.786) than both dNBR (R2 = 0.761) and RdNBR (R2 = 0.766). Furthermore, the overall classification accuracy achieved with RBR (average among 18 fires = 70.5%) was higher than both dNBR (68.4%) and RdNBR (69.2%). Consequently, we recommend RBR as a robust alternative to both dNBR and RdNBR for measuring and classifying burn severity.
A General Framework: Combining Statistical Models and Network Structure for Deep Content Mining

In this paper, we propose a general framework for content mining, which combines statistical model and network structure to leverages the power of both statistical topic models and network method. This method is a novel view to both text oriented method and network oriented method. The proposed framework is general, it can be applied to any text collections with an associated network structure.
Threats and Limitations of Terrestrial Broadcast Attacks

The DVB standard does not mandate the use of authentication and integrity protection for transport streams. This allows malicious third parties to replace legitimate broadcasts by overpowering terrestrial transmissions. The rogue signal can then deliver a malicious broadcast stream to exploit security vulnerabilities on Smart TVs (STVs) in range. We implemented a proof-of-concept attack based on a malicious Hybrid Broadcast Broadband TV app, able to acquire permanent system-level access to an STV over the air, in less than 10 s. These attacks, however, are severely limited in range due to required co-channel protection ratios (CCPRs), which is in direct contradiction to previous publications. We present evidence for these limitations in form of laboratory experiments, extensive simulations, and field measurements. To this end, we developed an automated, low-cost method for CCPR determination, as well as a method for non-disruptive attack range measurements based on a gap filler and the resulting channel impulse response.
Advanced, hardware supported in-network processing for the internet of things

We present a novel wireless sensor network (WSN) architecture that relies heavily on the in-network processing of information. The information captured by the WSN nodes is being filtered and transformed by other nodes so that fewer pieces of information of higher semantic value reach the network sink and are fed to the applications. This type of IoT infrastructure assists in the efficient energy management within the network (and the elongation of network lifetime) and the reduction of the processing overhead incurred by the application. The application has to "confront" a small volume of data with the very specific focus that it requires. This in-network processing architecture usually involves information filtering/transformation nodes that rely on computationally demanding algorithms. Such nodes are based on accelerated, not conventional hardware that expedites all the advanced processing with a minimum energy overhead. This is imperative for the efficient operation of the WSN as it addresses two important needs: performing demanding tasks at low energy cost, filtering the information to support energy efficiency and render the network sustainable over time.
A Point-to-Ray Framework for Generating Smooth Parallel Parking Maneuvers

The problem of generating smooth parallel parking maneuver for autonomous car-like vehicles is considered, wherein the vehicle moves forward from the starting point to an intermediate point and then traces a path backward to the parking position. Any error in accurately reaching the intermediate point leads to maneuver replanning and hence puts extra computational burden on the planner. Addressing the issue, this letter formulates the problem in a point-to-ray framework connecting the parking position to all points in a neighborhood of the intermediate point. Exploiting its asymptotic convergence, four parameter logistic (4PL) curve based S-shaped path is proposed as a solution with a single maneuver connecting the intermediate point neighborhood to the parking position. Inherent to the 4PL curve, the maneuver presents a continuous steering profile with zero steering angle at the ends. Parking slot collision avoidance and steering limit of the vehicle are satisfied by deducing closed-form analytic conditions on the two design parameters. Together with the robustness to intermediate point variation, comparison with existing approaches highlights computational and design advantages. The proposed approach is validated using a double-tracked Ackermann steering vehicle model.
Ja n 20 03 Decoherence-Free Subspaces and Subsystems

Decoherence is the phenomenon of non-unitary dynamics that arises as a consequence of coupling between a system and its environment. It has important harmful implications for quantum information processing, and various solutions to the problem have been proposed. Here we provide a detailed a review of the theory of decoherence-free subspaces and subsystems, focusing on their usefulness for preservation of quantum information.