Miracle-dz/UniFAR-FTUs huggingface dataset:
Non‐coding RNAs in cardiac hypertrophy

Heart failure is one of the largest contributors to disease burden and healthcare outflow in the Western world. Despite significant progress in the treatment of heart failure, disease prognosis remains very poor, with the only curative therapy still being heart transplantation. To counteract the current situation, efforts have been made to better understand the underlying molecular pathways in the progression of cardiac disease towards heart failure, and to link the disease to novel therapeutic targets such as non‐coding RNAs. The non‐coding part of the genome has gained prominence over the last couple of decades, opening a completely new research field and establishing different non‐coding RNAs species as fundamental regulators of cellular functions. Not surprisingly, their dysregulation is increasingly being linked to pathology, including to cardiac disease. Pre‐clinically, non‐coding RNAs have been shown to be of great value as therapeutic targets in pathological cardiac remodelling and also as diagnostic/prognostic biomarkers for heart failure. Therefore, it is to be expected that non‐coding RNA‐based therapeutic strategies will reach the bedside in the future and provide new and more efficient treatments for heart failure. Here, we review recent discoveries linking the function and molecular interactions of non‐coding RNAs with the pathophysiology of cardiac hypertrophy and heart failure.
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Perspective Shape from Shading: Ambiguity Analysis and Numerical Approximations Perspective Shape from Shading: Ambiguity Analysis and Numerical Approximations

In this paper we study a modern, perspective model for shape from shading and its numerical approximation. We show that a new form of the classic concave/convex ambiguity is still present, although the model has been shown to be well-posed under particular assumptions. This analytical result is confirmed by various numerical tests. Moreover, we present convergence results for two iterative approximation schemes recently introduced in the literature. The first one is based on a finite difference discretization, whereas the second one is based on a semi-Lagrangian discretization. The convergence results are obtained in the general framework of viscosity solutions of the underlying partial differential equation. We also show that it is possible to obtain even in complex scenes results of reasonable quality. To this end we solve the constituting equation on a previously-segmented input image, where we use state constraints boundary conditions at the segment borders.
Formal Traffic Characterization of LTI Event-Triggered Control Systems

Unnecessary communication and computation in the periodic execution of control tasks lead to overprovisioning in hardware design (or underexploitation in hardware utilization) in control applications, in particular, in networked control systems. To address these issues, researchers have proposed a new class of strategies, named event-driven strategies. Despite their beneficiary effects, matters such as task scheduling and appropriate dimensioning of communication components have become more complicated with respect to traditional periodic strategies. In this paper, we present a formal approach to derive an abstracted system that captures the sampling behavior of a family of event-triggered strategies for the case of LTI systems. This structure approximately simulates the sampling behavior of the aperiodic control system. Furthermore, the resulting abstract system is equivalent to a timed safety automaton. In the construction of the abstraction, the state space is confined to a finite number of convex regions, each of which represents a mode in the quotient system. An LMI-based technique is deployed to derive a sampling time interval associated with each region. Finally, reachability analysis is leveraged to find the transitions of the abstract system.
Editor’s Choice - Impact of initial hospital diagnosis on mortality for acute myocardial infarction: A national cohort study

Aims: Early and accurate diagnosis of acute myocardial infarction is central to successful treatment and improved outcomes. We aimed to investigate the impact of the initial hospital diagnosis on mortality for patients with acute myocardial infarction. Methods and results: Cohort study using data from the Myocardial Ischaemia National Audit Project of patients discharged with a final diagnosis of ST-elevation myocardial infarction (STEMI, n=221,635) and non-STEMI (NSTEMI, n=342,777) between 1 April 2004 and 31 March 2013 in all acute hospitals (n = 243) in England and Wales. Overall, 168,534 (29.9%) patients had an initial diagnosis which was not the same as their final diagnosis. After multivariable adjustment, for STEMI a change from an initial diagnosis of NSTEMI (time ratio 0.97, 95% confidence interval 0.92–1.01) and chest pain of uncertain cause (0.98, 0.89–1.07) was not associated with a significant reduction in time to death, whereas for other initial diagnoses the time to death was significantly reduced by 21% (0.78, 0.74–0.83). For NSTEMI, after multivariable adjustment, a change from an initial diagnosis of STEMI was associated with a reduction in time to death of 10% (time ratio 0.90, 95% confidence interval 0.83–0.97), but not for chest pain of uncertain cause (0.99, 0.96–1.02). Patients with NSTEMI who had other initial diagnoses had a significant 14% reduction in their time to death (time ratio 0.86, 95% confidence interval 0.84–0.88). STEMI and NSTEMI with other initial diagnoses had low rates of pre-hospital electrocardiograph (24.3% and 21.5%), aspirin on hospitalisation (61.6% and 48.5%), care by a cardiologist (60.0% and 51.5%), invasive coronary procedures (38.8 % and 29.2%), cardiac rehabilitation (68.9% and 62.6%) and guideline indicated medications at time of discharge from hospital. Had the 3.3% of patients with STEMI and 17.9% of NSTEMI who were admitted with other initial diagnoses received an initial diagnosis of STEMI and NSTEMI, then 33 and 218 deaths per year might have been prevented, respectively. Conclusion: Nearly one in three patients with acute myocardial infarction had other diagnoses at first medical contact, who less frequently received guideline indicated care and had significantly higher mortality rates. There is substantial potential, greater for NSTEMI than STEMI, to improve outcomes through earlier and more accurate diagnosis of acute myocardial infarction.
Supervised cognitive system: A new vision for cognitive engine design in wireless networks

Cognitive radio attracts researchers' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system parameters' adaptation using optimization and machine learning techniques. However, these techniques have strengths and weaknesses depending on the experienced network scenario that make one more appropriate than others. In this paper, we propose a novel design for the cognitive system called supervised cognitive system (SCS), which aims to perform radio parameters adaptation with the most appropriate CE learning technique for the encountered network scenario. To realize SCS, it is required to evaluate the performance of different CEs in different network scenarios and according to certain performance objectives. In addition, the ability to select the most appropriate CE learning technique for adaptation in the current network scenario is also a priority in our design. Therefore, SCS investigates the relationship between learning and performance improvement and it employs online learning to classify scenarios and select the most appropriate CE learning technique. The testbed implementation and evaluation results in terms of goodput, packet error rate, and spectral efficiency show that the proposed SCS achieves more than 50% in performance gain compared to the best standalone CE.
The Automatic Identification of Butterfly Species

The available butterfly data sets comprise a few limited species, and the images in the data sets are always standard patterns without the images of butterflies in their living environment. To overcome the aforementioned limitations in the butterfly data sets, we build a butterfly data set composed of all species of butterflies in China with 4270 standard pattern images of 1176 butterfly species, and 1425 images from living environment of 111 species. We propose to use the deep learning technique Faster-Rcnn to train an automatic butterfly identification system including butterfly position detection and species recognition. We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment. In order to construct the training subset for FasterRcnn, nine methods were adopted to amplifying the images in the training subset including the turning of up and down, and left and right, rotation with different angles, adding noises, blurring, and contrast ratio adjusting etc. Three prediction models were trained. The mAP (Mean Average prediction) criterion was used to evaluate the performance of the prediction model. The experimental results demonstrate that our Faster-Rcnn based butterfly automatic identification system performed well, and its worst mAP is up to 60%, and can simultaneously detect the positions of more than one butterflies in one images from living environment and recognize the species of those butterflies as well.
Plant Cation-Chloride Cotransporters (CCC): Evolutionary Origins and Functional Insights

Genomes of unicellular and multicellular green algae, mosses, grasses and dicots harbor genes encoding cation-chloride cotransporters (CCC). CCC proteins from the plant kingdom have been comparatively less well investigated than their animal counterparts, but proteins from both plants and animals have been shown to mediate ion fluxes, and are involved in regulation of osmotic processes. In this review, we show that CCC proteins from plants form two distinct phylogenetic clades (CCC1 and CCC2). Some lycophytes and bryophytes possess members from each clade, most land plants only have members of the CCC1 clade, and green algae possess only the CCC2 clade. It is currently unknown whether CCC1 and CCC2 proteins have similar or distinct functions, however they are both more closely related to animal KCC proteins compared to NKCCs. Existing heterologous expression systems that have been used to functionally characterize plant CCC proteins, namely yeast and Xenopus laevis oocytes, have limitations that are discussed. Studies from plants exposed to chemical inhibitors of animal CCC protein function are reviewed for their potential to discern CCC function in planta. Thus far, mutations in plant CCC genes have been evaluated only in two species of angiosperms, and such mutations cause a diverse array of phenotypes—seemingly more than could simply be explained by localized disruption of ion transport alone. We evaluate the putative roles of plant CCC proteins and suggest areas for future investigation.
Tackling modelling error in the application of electrical impedance tomography to the head

In the head application of Electrical Impedance Tomography (EIT), reconstruction of voltage measurements for a conductivity distribution image using an ordinary method, the absolute imaging approach, is impossible due to the traditional ignorance of modelling error. The modelling error comes from the inaccuracy of geometry and structure, which are unable to be known accurately in practice, and are usually large in head application of EIT. Difference imaging is an alternative approach which is able to reduce the size of this error, but it introduces other kinds of error. In this work, we demonstrate that in situations like head EIT, the nonlinear difference imaging approach can reconstruct difference conductivity effectively: the reduced modelling error and the new errors arising are able to be ignored, because they are much smaller than the original modelling error. The magnitude of conductivity change in the head-like situation is also investigated, and a selection scheme for the initial guess in the reconstruction process is also proposed.
Small artery mechanobiology: Roles of cellular and non‐cellular elements

Small arteries and their component cellular and non‐cellular elements are continually subjected to, and interact with, mechanical forces. Such interactions are key in both short‐ and long‐term adaptation of the structure and function of the microcirculation to its local environment and metabolic requirements. Following this brief introduction is a series of papers presented as a symposium (Small Artery Mechanobiology: Roles of Cellular and Non‐Cellular Elements) at the World Congress for Microcirculation, Kyoto 2015.
A smartphone-based design of wireless human-on-the-bike monitoring system

A wireless monitoring system for cyclists is considered. The system comprises three parts: heart rate data collection, bike speed control, and cloud computing. All three parts are integrated by a smartphone that exchanges information among the subsystems. In practice, the monitoring system can control the bike speed according to users' physical conditions in real time to enhance the users' experiences.
Adverse Pregnancy Outcomes among Adolescents in Northwest Russia: A Population Registry-Based Study

This study aimed to assess whether adolescents have an increased risk of adverse pregnancy outcomes (APO) compared to adult women. We used data on 43,327 births from the population-based Arkhangelsk County Birth Registry, Northwest Russia, for 2012–2014. The perinatal outcomes included stillbirth, preterm birth (<37 and <32 weeks), low and very low birthweight, 5 min Apgar score <7 and <4, perinatal infections, and the need for neonatal transfer to a higher-level hospital. Multivariable logistic regression was applied to assess the associations between age and APO. Altogether, 4.7% of deliveries occurred in adolescents. Both folic acid intake and multivitamin intake during pregnancy were more prevalent in adults. Adolescents were more likely to be underweight, to smoke, and to have infections of the kidney and the genital tract compared to adult women. Compared to adults, adolescents were at lower risk of low birthweight, a 5 min Apgar score <7, and need for neonatal transfer. Adolescents had no increased risk of other APO studied in the adjusted analysis, suggesting that a constellation of other factors, but not young age per se, is associated with APO in the study setting.
Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning

Demonstration trajectories collected from a supervisor in teleoperation are widely used for robot learning, and temporally segmenting the trajectories into shorter, less-variable segments can improve the efficiency and reliability of learning algorithms. Trajectory segmentation algorithms can be sensitive to noise, spurious motions, and temporal variation. We present a new unsupervised segmentation algorithm, transition state clustering (TSC), which leverages repeated demonstrations of a task by clustering segment endpoints across demonstrations. TSC complements any motion-based segmentation algorithm by identifying candidate transitions, clustering them by kinematic similarity, and then correlating the kinematic clusters with available sensory and temporal features. TSC uses a hierarchical Dirichlet process Gaussian mixture model to avoid selecting the number of segments a priori. We present simulated results to suggest that TSC significantly reduces the number of false-positive segments in dynamical systems observed with noise as compared with seven probabilistic and non-probabilistic segmentation algorithms. We additionally compare algorithms that use piecewise linear segment models, and find that TSC recovers segments of a generated piecewise linear trajectory with greater accuracy in the presence of process and observation noise. At the maximum noise level, TSC recovers the ground truth 49% more accurately than alternatives. Furthermore, TSC runs 100× faster than the next most accurate alternative autoregressive models, which require expensive Markov chain Monte Carlo (MCMC)-based inference. We also evaluated TSC on 67 recordings of surgical needle passing and suturing. We supplemented the kinematic recordings with manually annotated visual features that denote grasp and penetration conditions. On this dataset, TSC finds 83% of needle passing transitions and 73% of the suturing transitions annotated by human experts.
Indole-3-Acetic Acid Biosynthesis Pathways in the Plant-Beneficial Bacterium Arthrobacter pascens ZZ21

Arthrobacter pascens ZZ21 is a plant-beneficial, fluoranthene-degrading bacterial strain found in the rhizosphere. The production of the phytohormone indole-3-aectic acid (IAA) by ZZ21 is thought to contribute to its ability to promote plant growth and remediate fluoranthene-contaminated soil. Using genome-wide analysis combined with metabolomic and high-performance liquid chromatography-mass spectrometry (HPLC-MS) analyses, we characterized the potential IAA biosynthesis pathways in A. pascens ZZ21. IAA production increased 4.5-fold in the presence of 200 mg·L−1 tryptophan in the culture medium. The transcript levels of prr and aldH, genes which were predicted to encode aldehyde dehydrogenases, were significantly upregulated in response to exogenous tryptophan. Additionally, metabolomic analysis identified the intermediates indole-3-acetamide (IAM), indole-3-pyruvic acid (IPyA), and the enzymatic reduction product of the latter, indole-3-lactic acid (ILA), among the metabolites of ZZ21, and subsequently also IAM, ILA, and indole-3-ethanol (TOL), which is the enzymatic reduction product of indole-3-acetaldehyde, by HPLC-MS. These results suggest that the tryptophan-dependent IAM and IPyA pathways function in ZZ21.
A K-band 16-way combined high power amplifier in 0.18-um CMOS

A fully integrated K-band two stage 16-way power amplifier was designed and fabricated in 0.18-um CMOS technology. By using the direct shunt combining method, the matching networks and combining networks can be designed simultaneously, and this realizes the combining possibility of sixteen power unit cells. At the same time, some design skills and layout techniques are proposed to reduce the DC bias and layout complexity. To the best of the author's knowledge, this is the first demonstration of such large number devices combining power amplifier among the reported CMOS K-band PA.
Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber

The Internet of Things (IoT) is the next big possibility and challenge for the future information networks. It makes the interaction between people and things more active and provides the connection among different existing networks. Ubiquitous short‐range wireless access and cognitive radio are key technologies for the IoT's realization. This paper deals with some problems in an integrated system of wireless local area network (WLAN) and cognitive radio — cognitive WLAN over fiber (CWLANoF). CWLANoF is a cost‐effective and efficient architecture that combines radio over fiber and cognitive radio technologies to provide centralized radio resource management and equal spectrum access in infrastructure‐based IEEE 802.11 WLANs. In this paper, a reinforcement learning approach is applied to implement dynamic channel selection in CWLANoF. The cognitive access points select the best channels among the industrial, scientific, and medical band for data packet transmission, given that the objective is to minimize external interference and acquire better network‐wide performance. The reinforcement learning method avoids solving complex optimization problems while being able to explore the states of a CWLANoF system during normal operations. Simulation results reveal that the proposed strategy is effective in avoiding aggregated interference, reducing outage probability, and improving network throughput. Copyright © 2012 John Wiley & Sons, Ltd.
Robust 3D surface reconstruction from light fields

Light field data captures the intensity, as well as the direction of rays in 3D space, allowing to retrieve not only the 3D geometry information, but also the reflectance properties of the acquired scene. The main focus of this thesis is precise 3D geometry reconstruction from light fields, especially on scenes with specular objects. A new semi-global approach for 3D reconstruction from linear light fields is proposed. This method combines a modified version of the Progressive Probabilistic Hough Transform with local slope estimates to extract ori- entations, and consequently depth information, in epipolar plane images (EPIs). The resulting reconstructions achieve a higher accuracy than local methods, with a more precise localization of object boundaries, as well as preservation of fine details. In the second part of the thesis the proposed approach is extended to cir- cular light fields in order to determine the full 360° view of target objects. Additionally, circular light fields allow retrieving depth even from datasets acquired with telecentric lenses, a task which is not possible using a linearly moving camera. Experimental results on synthetic and real datasets demon- strate the quality and the robustness of the proposed algorithm, which pro- vides precise reconstructions even with highly specular objects. The quality of the final reconstruction opens up many possible application scenarios, such as precise 3D reconstruction for defect detection in industrial optical inspection, object scanning for heritage preservation, as well as depth segmentation for the movie industry.
Internalized stigma in people with severe mental illness in rural China

Background: It is unknown whether there are differences in self-stigma among persons with different types of severe mental illness (SMI) in rural communities. Aim: This study was to examine the differences of self-stigma and its correlates in persons with schizophrenia, major depressive disorder or bipolar disorder in a rural community in China. Methods: A total of 453 persons with schizophrenia, major depressive disorder or bipolar disorder in a rural community participated in the study. The Internalized Stigma of Mental Illness (ISMI) was used to measure self-stigma. The t-test and analyses of variance (ANOVA) were used to examine the differences in mean scores of ISMI and subscales among the three diagnoses. Logistic regression was used to explore the contributing factors to the level of self-stigma among the three groups. Results: Self-stigma was moderate and severe with 94.7% of the total sample. Persons with schizophrenia had significantly higher mean scores of total ISMI, alienation and discrimination experience than those with bipolar disorders. Lower family income was significantly associated with higher levels of self-stigma in persons with schizophrenia and major depressive disorder. Factors predicting the level of self-stigma among the three groups were various. Conclusion: Self-stigma is common and severe in persons with schizophrenia, major depressive disorder and bipolar disorder, especially those with lower income status in rural community in China. Persons with schizophrenia may have higher levels of self-stigma than those with bipolar disorder. Individual-level interventions should be developed to reduce self-stigma among persons with SMI in Chinese rural communities.