Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing case complexity: is clinician selected dose of vocational rehabilitation related to questionnaire results?

Abstract Background: To establish an optimum dosage interdisciplinary vocational rehabilitation, it is important to be able to reliably and validly assess case complexity. Assessment of case complexity is currently clinician based because no validated means to assess case complexity is presently available. Indices assumed to associate with case complexity can contribute to the choice of dosage. The objective of this study was to explore the extent in which results of questionnaires were associated with the choice of treatment dosage in vocational rehabilitation. Methods: Design: cross-sectional study of observational data. The study population consisted of workers on part-time or full-time sick leave due to chronic multifactorial problems. Thousand eighty-nine patients who were referred to a privately owned organization with outpatient vocational rehabilitation centers in the Netherlands between July 2016 and March 2017 were allocated to one of the three programs based on case complexity as determined by clinicians based on clinical interview and questionnaires. Results: Questionnaires accounted for 13% of the variance in the total group, 13% in patients with chronic musculoskeletal pain (n = 662) and 29% in patients with chronic fatigue (n = 235). Conclusion: The results of the questionnaires contribute little in the assessment of case complexity and dose recommendation. Implications for Rehabilitation Assessment of case complexity of patients with chronic multifactorial complaints and disability is complex. The results of this study suggest that case complexity and choice of treatment dose is slightly explained by questionnaire results. It is largely determined on heuristics developed by knowledge and experience of clinicians. No reliable and validated means to assess case complexity is presently available in the field of rehabilitation and optimum treatment dose cannot be determined transparently. Routinely collected clinical data of baseline characteristics, process measures and results are a valuable source that can be used to answer research questions.
Guiding Ketogenic Diet with Breath Acetone Sensors

Ketogenic diet (KD; high fat, low carb) is a standard treatment for obesity, neurological diseases (e.g., refractory epilepsy) and a promising method for athletes to improve their endurance performance. Therein, the level of ketosis must be regulated tightly to ensure an effective therapy. Here, we introduce a compact and inexpensive breath sensor to monitor ketosis online and non-invasively. The sensor consists of Si-doped WO3 nanoparticles that detect breath acetone selectively with non-linear response characteristics in the relevant range of 1 to 66 ppm, as identified by mass spectrometry. When tested on eleven subjects (five women and six men) undergoing a 36-h KD based on the Johns Hopkins protocol, this sensor clearly recognizes the onset and progression of ketosis. This is in good agreement to capillary blood β-hydroxybutyrate (BOHB) measurements. Despite similar dieting conditions, strong inter-subject differences in ketosis dynamics were observed and correctly identified by the sensor. These even included breath acetone patterns that could be linked to low tolerance to that diet. As a result, this portable breath sensor represents an easily applicable and reliable technology to monitor KD, possibly during medical treatment of epilepsy and weight loss.
Regulation of DNA Double-Strand Break Repair by Non-Coding RNAs

DNA double-strand breaks (DSBs) are deleterious lesions that are generated in response to ionizing radiation or replication fork collapse that can lead to genomic instability and cancer. Eukaryotes have evolved two major pathways, namely homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DSBs. Whereas the roles of protein-DNA interactions in HR and NHEJ have been fairly well defined, the functions of small and long non-coding RNAs and RNA-DNA hybrids in the DNA damage response is just beginning to be elucidated. This review summarizes recent discoveries on the identification of non-coding RNAs and RNA-mediated regulation of DSB repair.
Impact of Maternal Antibody on the Immunogenicity of Inactivated Polio Vaccine in Infants Immunized With Bivalent Oral Polio Vaccine: Implications for the Polio Eradication Endgame

Abstract Background Quantifying interference of maternal antibodies with immune responses to varying dose schedules of inactivated polio vaccine (IPV) is important for the polio endgame as IPV replaces oral polio vaccine (OPV). Methods Type 2 poliovirus humoral and intestinal responses were analyzed using pre-IPV type 2 seropositivity as proxy for maternal antibodies from 2 trials in Latin America. Infants received 1 or 2 doses of IPV in sequential IPV–bivalent oral polio vaccine (bOPV) or mixed bOPV-IPV schedules. Results Among infants vaccinated with bOPV at age 6, 10, and 14 weeks of age and IPV at 14 weeks, those with type 2 pre-IPV seropositivity had lower seroprotection rates than seronegative infants at 4 weeks (92.7% vs 83.8%; difference, 8.9% [95% confidence interval, 0.6%–19.9%]; n = 260) and 22 weeks (82.7% vs 60.4%; difference, 22.3 [12.8%–32.4%]; n = 481) post-IPV. A second IPV at age 36 weeks resulted in 100% seroprotection in both groups. Among infants vaccinated with 1 IPV at age 8 weeks followed by 2 doses of bOPV, pre-IPV type 2-seropositive infants had lower seroprotection at age 28 weeks than those who were seronegative (93.0% vs 73.9%; difference, 19.6% [95% confidence interval, 7.3%–29.4%]; n = 168). A second dose of IPV at 16 weeks achieved >97% seroprotection at age 24 or 28 weeks, regardless of pre-IPV status. Poliovirus shedding after challenge with monovalent OPV, serotype 2, was higher in pre-IPV seropositive infants given sequential IPV-bOPV. No differences were observed in the mixed bOPV-IPV schedule. Conclusions The presence of maternal antibody is associated with lower type 2 post-IPV seroprotection rates among infants who receive a single dose of IPV. This impact persists until late in infancy and is overcome by a second IPV dose.
Piecemeal Graph Exploration by a Mobile Robot

We study how a mobile robot can learn an unknown environment in a piecemeal manner. The robot's goal is to learn a complete map of its environment, while satisfying the constraint that it must return every so often to its starting position (for refueling, say). The environment is modeled as an arbitrary, undirected graph, which is initially unknown to the robot. We assume that the robot can distinguish vertices and edges that it has already explored. We present a surprisingly efficient algorithm for piecemeal learning an unknown undirected graph G=(V, E) in which the robot explores every vertex and edge in the graph by traversing at most O(E+V1+o(1)) edges. This nearly linear algorithm improves on the best previous algorithm, in which the robot traverses at most O(E+V2) edges. We also give an application of piecemeal learning to the problem of searching a graph for a “treasure. ”
Effects of High-Definition and Conventional Transcranial Direct-Current Stimulation on Motor Learning in Children

Background: Transcranial direct current stimulation (tDCS) can improve motor learning in children. High-definition approaches (HD-tDCS) have not been examined in children. Objectives/Hypothesis: We hypothesized that primary motor cortex HD-tDCS would enhance motor learning but be inferior to tDCS in children. Methods: Twenty-four children were recruited for a randomized, sham-controlled, double-blinded interventional trial (NCT03193580, clinicaltrials.gov/ct2/show/NCT03193580) to receive (1) right hemisphere (contralateral) primary motor cortex (M1) 1 mA anodal conventional 1 × 1 tDCS (tDCS), (2) right M1 1 mA anodal 4 × 1 HD-tDCS (HD-tDCS), or (3) sham. Over five consecutive days, participants trained their left hand using the Purdue Pegboard Test (PPTL). The Jebsen–Taylor Test, Serial Reaction Time Task, and right hand and bimanual PPT were also tested at baseline, post-training, and 6-week retention time (RT). Results: Both the tDCS and HD-tDCS groups demonstrated enhanced motor learning compared to sham with effects maintained at 6 weeks. Effect sizes were moderate-to-large for tDCS and HD-tDCS groups at the end of day 4 (Cohen’s d tDCS = 0.960, HD-tDCS = 0.766) and day 5 (tDCS = 0.655, HD-tDCS = 0.851). Enhanced motor learning effects were also seen in the untrained hand. HD-tDCS was well tolerated and safe with no adverse effects. Conclusion: HD-tDCS and tDCS can enhance motor learning in children. Further exploration is indicated to advance rehabilitation therapies for children with motor disabilities such as cerebral palsy. Clinical Trial Registration: clinicaltrials.gov, identifier NCT03193580.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification

Effective detection of arrhythmia is an important task in the remote monitoring of electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the clinicians' experience, but the results suffer from the probability of human error due to the fatigue. To solve this problem, an ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs). First, we compare the accuracy and robustness between one-dimensional ECG signal input method and two-dimensional image input method in AIexNet network. Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AIexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification. The performance evaluated on the MIT-BIH arrhythmia database demonstrates that the proposed method can achieve the accuracy of 98% and maintain high accuracy within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs initialized with AIexNet weights performs better than one-dimensional signal method without a large-scale dataset.
Deep Learning for the Gaussian Wiretap Channel

End-to-end learning of communication systems with neural networks and particularly autoencoders is an emerging research direction which gained popularity in the last year. In this approach, neural networks learn to simultaneously optimize encoding and decoding functions to establish reliable message transmission. In this paper, this line of thinking is extended to communication scenarios in which an eavesdropper must further be kept ignorant about the communication. The secrecy of the transmission is achieved by utilizing a modified secure loss function based on cross-entropy which can be implemented with state-of-the-art machine-learning libraries. This secure loss function approach is applied in a Gaussian wiretap channel setup, for which it is shown that the neural network learns a trade-off between reliable communication and information secrecy by clustering learned constellations. As a result, an eavesdropper with higher noise cannot distinguish between the symbols anymore.
IoT Cloud Platforms: an Application Development Perspective

With the growing number of Internet of Things (IoT) devices, the data generated through these devices is also growing. By 2025, it has been predicted that the number of IoT devices will exceed the number of human beings on earth. Thus, the data generated through these IoT devices will be gigantic. This gives upsurge in storage. One of the most promising solutions is to store data on cloud. Market is over helming with the number of IoT cloud platforms. In-spite of availability of huge variety of IoT cloud platforms, very little attempt in classifying or comparing it for the applications to be developed is found across the literature databases. This paper categorizes IoT platforms into four categories namely: publicly traded, open source, developer friendly and end to end connectivity. Some of the popular platforms in each category are identified and compared based on the given general IoT architecture. This study can be useful for newbies and application developers in IoT to select the most appropriate platform according to requirement for building applications.
Core influence mechanism on vertex-cover problem through leaf-removal-core breaking

Leaf-removal process has been widely researched and applied in many mathematical and physical fields to help understand the complex systems. A lot of problems including the minimum vertex-cover are deeply related to this process and the leaf-removal cores. In this paper, based on the structural features of the leaf-removal cores, a method named core influence is proposed to break the graphs into no-leaf-removal-core ones, which takes advantages of identifying some significant nodes by localized and greedy strategy. By decomposing the minimum vertex-cover problem into the leaf-removal core breaking process and maximal matching of the remaining graphs, it is proved that any minimum vertex-cover of the whole graph can be located into these two processes and the best boundary is achieved at the transition point. Compared with other node importance indices, the core influence method could break down the leaf-removal cores much faster and get the no-core graphs by removing fewer nodes from the graphs. Also, the vertex-cover numbers resulted from this method are lower than those of existing node importance measurements, and compared with the exact minimum vertex-cover numbers, this method performs appropriate accuracy and stability at different scales. This research provides a new localized greedy strategy to break the hard leaf-removal cores efficiently and heuristic methods could be constructed to promote the comprehension of the intrinsic hardness of NP problems.
Profile of the VERSANT HCV genotype 2.0 assay

ABSTRACT Introduction: Hepatitis C virus (HCV) is divided into 7 genotypes and 67 subtypes. HCV genotype studies reflect the viral transmission patterns as well as human migration routes. In a clinical setting, HCV genotype is a baseline predictor for the sustained virological response (SVR) in chronic hepatitis C patients treated with peginterferon or some direct acting antivirals (DAAs). The Versant HCV genotype 2.0 assay has been globally used for HCV genotyping over a decade. Areas covered: The assay is based on reverse hybridization principle. It is evolved from its former versions, and the accuracy and successful genotyping/subtyping rate are substantially improved. It shows an accuracy of 99–100% for genotypes 1–6. It can also reliably identify subtypes 1a and 1b. However, the assay does not allow a high resolution for many other subtypes. Reasons for indeterminate or inaccurate genotyping/subtyping results are discussed. Expert commentary: Genotyping helps to find the most efficacious and cost-effective treatment regimen. The rapid development of anti-HCV treatment regimens, however, is greatly simplifying laboratory tests. In the near future, the need for HCV genotyping and frequent serial on-treatment HCV RNA tests will decrease along with the wide use of the more potent and pan-genotypic DAA regimens.
Membrane Potential Correlates of Network Decorrelation and Improved SNR by Cholinergic Activation in the Somatosensory Cortex

The nucleus basalis (NB) projects cholinergic axons to the cortex, where they play a major role in arousal, attention, and learning. Cholinergic inputs shift cortical dynamics from synchronous to asynchronous and improve the signal-to-noise ratio (SNR) of sensory responses. However, the underlying mechanisms of these changes remain unclear. Using simultaneous extracellular and whole-cell patch recordings in layer 4 of the mouse barrel cortex, we show that electrical or optogenetic activation of the cholinergic system has a differential effect on ongoing and sensory evoked activities. Cholinergic activation profoundly reduced the large spontaneous fluctuations in membrane potential and decorrelated ongoing activity. However, NB stimulation had no effect on the response to whisker stimulation or on signal correlations. These effects of cholinergic activation provide a unified explanation for the increased SNR of sensory response and for the reduction in noise correlations and explain the shift into the desynchronized cortical state, which are the hallmarks of arousal and attention. SIGNIFICANCE STATEMENT Attention increases the signal-to-noise ratio (SNR) of cortical sensory response, which may reflect either reduction in background firing rate or increased sensory response. Extracellular recordings showed that attention also reduces the correlation in network activity. These effects are partially mediated by cholinergic axons from the nucleus basalis projecting to the entire cortex. To reveal the cellular and synaptic correlates of these cholinergic effects, we performed simultaneous intracellular and LFP recordings in the somatosensory cortex. Global or local cholinergic activation increased the SNR of sensory response mainly by reducing the rate and amplitude of background synaptic activity and also reduced network correlations. Therefore, coding of sensory information is enhanced by the cholinergic system mainly due to a reduction in spontaneous activity.
Towards compilation of an imperative language for FPGAs

Field-Programmable Gate Arrays (FPGA's) have been around since the early 1980s and have now achieved relatively wide-spread use. For example, FPGAs are routinely used for high-performance computing, financial applications, seismic modelling, DNA sequence alignment, software defined networking and, occasionally, are even found in smartphones. And yet, despite their success, there still remains something of a gap between programming languages and circuit designs for an FPGA. We consider the compilation of an imperative programming language, Whiley, to VHDL for use on an FPGA. A key challenge lies in splitting an arbitrary function into a series of pipeline stages, as necessary to expose as much task parallelism as possible. To do this, we introduce a language construct which gives the programmer control over how the pipeline is constructed.
Learn to Steer through Deep Reinforcement Learning

It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-end deep reinforcement learning algorithm in this paper to improve the performance of autonomous steering in complex environments. By embedding a branching noisy dueling architecture, the proposed model is capable of deriving steering commands directly from raw depth images with high efficiency. Specifically, our learning-based approach extracts the feature representation from depth inputs through convolutional neural networks and maps it to both linear and angular velocity commands simultaneously through different streams of the network. Moreover, the training framework is also meticulously designed to improve the learning efficiency and effectiveness. It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images. The proposed method is evaluated and compared with a series of baseline methods in various virtual environments. Experimental results demonstrate the superiority of the proposed model in terms of average reward, learning efficiency, success rate as well as computational time. Moreover, a variety of real-world experiments are also conducted which reveal the high adaptability of our model to both static and dynamic obstacle-cluttered environments.
Non‐oral Continuous Drug Delivery Techniques in Parkinson's Disease: For Whom, When, and How?

Continuous dopaminergic stimulation (CDS) has become one of the main concepts in present Parkinson's disease (PD) research. This is based on the assumption that CDS, or rather near CDS, is the normal striatal setting in a healthy individual. In PD, the degeneration of dopaminergic neurons leads to a reduced capacity to buffer dopamine, which could increase the vulnerability to a pulsatile administration of drugs. The term continuous drug delivery (CDD) describes the process of delivering drugs continuously with the aim of achieving CDS. There are three principal techniques for non‐oral CDD: continuous subcutaneous apomorphine infusion CSAi), levodopa‐carbidopa intestinal gel infusion (LCIGi), and transdermal rotigotine therapy. CDD has repeatedly been shown effective in the day‐to‐day treatment of PD patients. Although this review does not replace local guidelines regarding the use of the included non‐oral CDD‐based therapies, we have compiled the current base of evidence or consensus view with the intention of facilitating both the selection and the use in a clinical setting. The indications for CSAi and LCIGi are very similar and are centered around motor complications in advanced PD, whereas rotigotine has been proven effective both as a monotherapy in early PD and as an add‐on to levodopa in advanced PD. Deep‐brain stimulation is a relevant option for many of the patients with advanced PD, and we therefore also discuss its use in relation to the CDD‐based techniques. Blinded and controlled trials have shown that non‐oral CDD is an effective approach for the treatment of PD.
PEGylated carboxyhemoglobin bovine (SANGUINATE) ameliorates myocardial infarction in a rat model

Abstract Artificial oxygen (O2) carriers were reported to be protective in ischemia/reperfusion (I/R) in various organs including the heart. In the current study, 20 rats underwent ligation (MI) of the left anterior descending artery, were treated with 10 mL/kg of PEGylated carboxyhemoglobin bovine (SANGUINATE, S+, n = 10) or saline (S−, n = 10) 10 minutes after MI and daily thereafter for 3 days, and were followed by weekly echocardiography for 4 weeks, when they had left ventricular pressure volume relationship (PVR) analyses followed by necropsy. Echocardiography showed an increase in end‐systolic dimension rather than end‐diastolic dimension, preserved fractional shortening (36 vs. 26%, P < .01), and milder mitral regurgitation in S+ compared with S‐ rats. PVR revealed a milder increase in end‐systolic volume, larger stroke volume (101 vs. 74 μL, P < .005) and cardiac output (33.4 vs. 23.8 mL/min, P = .004) in S+ rats in actual determination and under a wide range of standardized loading conditions 4 weeks after MI. Excised heart showed significantly limited area of MI (8.9 vs. 13.3%, P = .028). The results suggest that SANGUINATE in short‐term repeated doses may accelerate weight recovery, preserving the myocardium, mitral competence, and cardiac function after MI. The mechanism of action and optimal treatment for MI remain to be studied.