Miracle-dz/UniFAR-FTUs huggingface dataset:
A mutation of SCN1B associated with GEFS+ causes functional and maturation defects of the voltage‐dependent sodium channel

Voltage‐dependent sodium channels are responsible of the rising phase of the action potential in excitable cells. These integral membrane proteins are composed of a pore‐forming α‐subunit, and one or more auxiliary β subunits. Mutation p.Asp25Asn (D25N; c.73G > A) of the β1 subunit, coded by the gene SCN1B, has been reported in a patient with generalized epilepsy with febrile seizure plus type 1 (GEFS+). In human embryonic kidney 293 (HEK) cells, the heterologous coexpression of D25N‐β1 subunit with Nav1.2, Nav1.4, and Nav1.5 α subunits, representative of brain, skeletal muscle, and heart voltage gated sodium channels, determines a reduced sodium channel functional expression and a negative shift of the activation and inactivation steady state curves. The D25N mutation of the β1 subunit causes a maturation (glycosylation) defect of the protein, leading to a reduced targeting to the plasma membrane. Also the β1‐dependent gating properties of the sodium channels are abolished by the mutation, suggesting that D25N is no more able to interact with the α subunit. Our work underscores the role played by the β1 subunit, highlighting how a defective interaction between the sodium channel constituents could lead to a disabling pathological condition, and opens the possibility to design a mutation‐specific GEFS+ treatment based on protein maturation.
Scalable Rule Learning via Learning Representation

We study the problem of learning first-order rules from large Knowledge Graphs (KGs). With recent advancement in information extraction, vast data repositories in the KG format have been obtained such as Freebase and YAGO. However, traditional techniques for rule learning are not scalable for KGs. This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. Experimental results show that our system outperforms some state-of-the-art systems. Specifically, for massive KGs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in KGs such as AMIE+. We also used the RLvLR-mined rules in an inference module to carry out the link prediction task. In this task, RLvLR outperformed Neural LP, a state-of-the-art link prediction system, in both runtime and accuracy.
SPLIT: Smart Protocol Loading for the IoT

The Internet of Things (IoT) permeates our everyday life, e.g., in the area of health monitoring, wearables, industry, and home automation. It comprises devices that provide only limited resources, operate in challenging network conditions, and are often battery-powered. To embed these devices into the Internet, they are envisioned to operate standard protocols. Yet, these protocols occupy the majority of limited program memory resources. Thus, devices can neither add application logic nor apply security updates or adopt optimizations for efficiency. This problem will further exacerbate in the future as the further ongoing permeation of smart devices in our environment demands for more and more functionality. To overcome limited functionality due to resource constraints, we show that not all functionality is required in parallel, and thus can be SPLIT in a feasible manner. This enables on-demand loading of functionality outsourced as (multiple) modules to the significantly lesser constrained flash storage of devices. We exemplify efficient modularization of DTLS and show that SPLIT enables operation of large protocol stacks while it incurs reasonable, tunable performance trade-offs. Our use case specific results show an initial runtime overhead of 23.34 % and 4.9 % for subsequent protocol executions.
Hemodynamic Sensing of 3-D Fingertip Force by Using Nonpulsatile and Pulsatile Signals in the Proximal Part

This study proposed a novel sensing method of 3-D contact force at a fingertip by using a photoplethysmogram (PPG) device on the proximal part of a finger. The proposed system detects nonpulsatile and pulsatile components of PPG signals from both sides of the proximal part, extracts 16 feature values related to the contact force, and estimates the 3-D force by using a multiple linear regression model. In the validation experiments, the participants wore a PPG device at the proximal parts of their index fingers and applied a contact force at the fingertips for the 11 types of touch actions. The results indicated that satisfactory agreements are observed between the system outputs and the reference forces by the calibrated force sensor. Moreover, the results revealed that the most effective number of feature values corresponded to six for the higher reproducible sensing. Although the development of the effective calibration method is expected to increase robustness, we realized that the proposed method can potentially be used for a 3-D input user interface.
New Stability Criterion for Continuous-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delay

In this technical paper, a new Lyapunov–Krasovskii functional (LKF) is designed to study the stability of continuous-time Takagi–Sugeno fuzzy systems with time-varying delay. The integrand of the LKF depends on integral variable and time <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> which can help to reduce the number of linear matrix inequalities (LMIs). Then, a new stability criterion is derived by analyzing the sign of the time derivatives of membership functions. Compared with the existing results, larger delay bounds can be obtained by applying the new criterion. In the end, two examples show the effectiveness of the conclusions.
Animation Plans for Before-and-After Satellite Images

Before-and-after image pairs show how entities in a given region have evolved over a specific period of time. Satellite images are a major source of such data, that capture how natural phenomena or human activity impact a geographical area. These images are used both for data analysis and to illustrate the resulting findings to diverse audiences. The simple techniques used to display them, including juxtaposing, swapping and monolithic blending, often fail to convey the underlying phenomenon in a meaningful manner. We introduce Baia, a framework to create advanced animated transitions, called animation plans, between before-and-after images. Baia relies on a pixel-based transition model that gives authors much expressive power, while keeping animations for common types of changes easy to create thanks to predefined animation primitives. We describe our model, the associated animation editor, and report on two user studies. In the first study, advanced transitions enabled by Baia were compared to monolithic blending, and perceived as more realistic and better at focusing viewer's attention on a region of interest than the latter. The second study aimed at gathering feedback about the usability of Baia's animation editor.
Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation

Objective: Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Methods: Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component. Results: The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Conclusion: Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. Significance : The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.
Antiscarring effect of intraoperative bevacizumab in experimental glaucoma filtration surgery.

PURPOSE To determine the effects of bevacizumab and mitomycin C alone and in combination on intraocular pressure and the scarring process after modified glaucoma filtration surgery in rabbits. METHODS The rabbits underwent modified glaucoma filtration surgery and were allocated into three groups to receive intraoperative treatment with subconjunctival bevacizumab (group A), mitomycin C and subconjunctival bevacizumab (group B), or mitomycin C (group C). Intraocular pressure was measured immediately preoperatively and on postoperative days 8, 14, 17, 21, 26, and 30. The scarring process was assessed 30 days after surgery by tissue section using hematoxylin and eosin, Masson's trichrome, and picrosirius. Expression of vascular endothelial growth factor (VEGF) was assessed by immunohistochemical analyses. All analyses were performed by a masked observer. RESULTS Animals in group A had higher intraocular pressure than those in groups B and C (p<0.01). Intraocular pressure did not differ significantly between groups B and C. The amount of fibrosis was similar with all stains used: group A had the highest level of fibrosis compared with groups B and C (p>0.05). There was less VEGF expression in group A than in groups B and C (p<0.01). Groups B and C did not differ in VEGF expression. CONCLUSION Mean intraocular pressure and fibrosis were lower in animals receiving bevacizumab in combination with mitomycin C but did not differ from values in animals receiving mitomycin C alone. Inhibition of VEGF was greater when bevacizumab was used alone than when bevacizumab was combined with mitomycin C.
An Algorithm for Heart Rate Extraction From Acoustic Recordings at the Neck

Heart rate is an important physiological parameter to assess the cardiac condition of an individual and is traditionally determined by attaching multiple electrodes on the chest of a subject to record the electrical activity of the heart. The installation and handling complexities of such systems does not prove feasible for a user to undergo a long-term monitoring in the home settings. A small-sized, battery-operated wearable monitoring device is placed on the suprasternal notch at neck to record acoustic signals containing information about breathing and cardiac sounds. The heart sounds obtained are heavily corrupted by the respiratory cycles and other external artifacts. This paper presents a novel algorithm for reliably extracting the heart rate from such acoustic recordings, keeping in mind the constraints posed by the wearable technology. The methodology constructs the Hilbert energy envelope of the signal by calculating its instantaneous characteristics to segment and classify a cardiac cycle into S1 and S2 sounds using their timing characteristics. The algorithm is tested on a dataset consisting of 13 subjects with an approximate data length of 75 h and achieves an accuracy of 94.34%, an RMS error of 3.96 bpm and a correlation coefficient of 0.93 with reference to a commercial device in use.
Endovascular treatment for symptomatic intracranial artery stenosis: protocol for a systematic review and network meta-analysis

Introduction Atherosclerotic intracranial artery stenosis (ICAS) is one of most common causes of stroke, which is the second-leading cause of death worldwide. Medical, surgical and endovascular therapy are three major treatments for ICAS. Currently, medical therapy is considered as the standard of care for most patients with ICAS, while extracranial to intracranial bypass is only used in rare situations. Balloon angioplasty alone, balloon-mounted stent and self-expanding stent, collectively called endovascular treatment, have shown promising potentials in treating specific subgroups of patients with symptomatic ICAS; however, their comparative safety and efficacy is still unclear. Therefore, a systematic review with network meta-analysis is needed to establish a hierarchy of these endovascular treatments. Methods and analysis The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols was followed to establish this protocol. The search will be limited to studies published from 1 January 2000 to the formal search date. Major databases including Cochrane Library, MEDLINE, EMBASE, Chinese Biomedical Literature Database, conference proceedings and grey literature database will be searched for clinical studies comparing at least two interventions for patients with symptomatic ICAS. Primary outcomes include short-term and long-term mortality or stroke rate. Random effects pairwise and network meta-analyses of included studies will be performed on STATA (V.14, StataCorp, 2015). The surface under the cumulative ranking curve and mean rank will be calculated in order to establish a hierarchy of the endovascular treatments. Evaluation of the risk of bias, heterogeneity, consistency, transitivity and quality of evidence will follow the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. Ethics and dissemination Ethics approval is not needed as systematic review is based on published studies. Study findings will be presented at international conferences and published on a peer-reviewed journal. PROSPERO registration number CRD42018084055; Pre-results.
Towards Personalized Image Captioning via Multimodal Memory Networks

We address personalized image captioning, which generates a descriptive sentence for a user's image, accounting for prior knowledge such as her active vocabulary or writing style in her previous documents. As applications of personalized image captioning, we solve two post automation tasks in social networks: hashtag prediction and post generation. The hashtag prediction predicts a list of hashtags for an image, while the post generation creates a natural text consisting of normal words, emojis, and even hashtags. We propose a novel personalized captioning model named Context Sequence Memory Network (CSMN). Its unique updates over existing memory networks include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. For evaluation, we collect a new dataset InstaPIC-1.1M, comprising 1.1M Instagram posts from 6.3 K users. We further use the benchmark YFCC100M dataset [1] to validate the generality of our approach. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show that the three novel features of the CSMN help enhance the performance of personalized image captioning over state-of-the-art captioning models.
Cross-View Discriminative Feature Learning for Person Re-Identification

The viewpoint variability across a network of non-overlapping cameras is a challenging problem affecting person re-identification performance. In this paper, we investigate how to mitigate the cross-view ambiguity by learning highly discriminative deep features under the supervision of a novel loss function. The proposed objective is made up of two terms, the steering meta center term and the enhancing centers dispersion term, that steer the training process to mining effective intra-class and inter-class relationships in the feature domain of the identities. The effect of our loss supervision is to generate a more expanded feature space of compact classes where the overall level of the inter-identities’ interference is reduced. Compared with the existing metric learning techniques, this approach has the advantage of achieving a better optimization because it jointly learns the embedding and the metric contextually. Our technique, by dismissing side-sources of performance gain, proves to enhance the CNN invariance to viewpoint without incurring increased training complexity (like in Siamese or triplet networks) and outperforms many related state-of-the-art techniques on Market-1501 and CUHK03.
Bayes-TDG: effective test data generation using Bayesian belief network: toward failure-detection effectiveness and maximum coverage

This study presents a novel test data generation method called Bayes-TDG. It is based on principles of Bayesian networks and provides the possibility of making inference from probabilistic data in the model to increase the prime path-coverage ratio for a given programme under test (PUT). In this regard, a new programme structure-based probabilistic network, TDG-NET, is proposed that is capable of modelling the conditional dependencies among the programme basic blocks (BBs) on one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure-detection effectiveness, the authors propose a path selection strategy that works based on the predicted outcome of generated test cases. So, they mitigate the need for a human oracle, and the generated test suite could be directly used in fault localisation. Several experiments are conducted to evaluate the performance of Bayes-TDG. The results reveal that the method is promising and the generated test suite could be quite effective.
A Vibrotactile Alarm System for Pleasant Awakening

There has been a vast development of personal informatics devices combining sleep monitoring with alarm systems, in order to find an optimal time to awaken a sleeping person in a pleasant way. Most of these systems implement auditory feedback as the alarm signal, which is not always pleasant and may disturb other sleepers in the same space. In this paper, we present an adaptive alarm system that detects sleeping cycles and triggers the alarm signal during shallow sleep, in order to minimize sleep inertia. Since tactile sensation is associated with positive valence, vibrotactile stimulation is investigated as a silent alarm to enhance pleasant awakening. Three modulation techniques to render the tactile stimuli for pleasant awakening are considered, namely simultaneous, continuous, and successive stimulation. Two experimental studied are conducted. Experiment 1 studied exogenous attention towards tactile stimulation in a multimodal scenario (involving visual and haptic interactions) with fully awake individuals. Results from the attention task and the subjective valence rating suggest that the vibrotactile stimulation should be based on the continuous modulation, since this not only is very perceivable but also associated with positive attention. Experiment 2 evaluated the user experience with tactile stimulation patterns during sleep. Results confirmed the findings of experiment 1. Continuous modulation was rated highest for pleasant yet arousing sleep-awake transition.
Investigation of Piezoelectricity and Curie Temperature of Pb(Mg1/3, Nb2/3)O3–PbTiO3 Epitaxial Thin Film on Si Prepared by Sputter Deposition With Fast Cooling

This paper reports on an abnormally high Curie temperature (<inline-formula> <tex-math notation="LaTeX">$T_{c}$ </tex-math></inline-formula>) of a Pb(Mg<sub>1/3</sub>, Nb<sub>2/3</sub>)O<sub>3</sub>–PbTiO<sub>3</sub> (PMN-PT) epitaxial thin film on Si prepared by sputter deposition with fast cooling. This deposition method was previously applied to Pb(Mn, Nb)O<sub>3</sub>–Pb(Zr, Ti)O<sub>3</sub>, and a c-axis-oriented epitaxial film with high <inline-formula> <tex-math notation="LaTeX">$T_{c}$ </tex-math></inline-formula> was obtained. Using the same method, a crack-free 2-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick PMN-PT thin film was epitaxially grown on a Si substrate covered with buffer layers. The piezoelectricity, <inline-formula> <tex-math notation="LaTeX">$\vert \text{e}_{31,f}\vert $ </tex-math></inline-formula>, was as large as 18~19 C/m<sup>2</sup> under an electric field ranging from 25 to 75 kV/cm. The temperature characteristics of the dielectric constant and crystalline structure were significantly different from those of a bulk single crystal of PMN-PT, and suggested <inline-formula> <tex-math notation="LaTeX">$T_{c}$ </tex-math></inline-formula> higher than 500 °C. The enhanced <inline-formula> <tex-math notation="LaTeX">$T_{c}$ </tex-math></inline-formula> was possibly caused by thermally induced compressive strain received from the Si substrate. This approach can be an effective method for breaking the well-known tradeoff relationship between piezoelectricity and <inline-formula> <tex-math notation="LaTeX">$T_{c}$ </tex-math></inline-formula> of a piezoelectric thin film. 0
Recent developments in vaccines and biological therapies against Japanese encephalitis virus

ABSTRACT Introduction: Japanese encephalitis is the most common vaccine-preventable encephalitis in the Asia-Pacific region. Areas covered: We provide an overview on Japanese encephalitis virus and associated disease, review the results of studies on the immunogenicity and efficacy of the licensed vaccines, and describe the new vaccines that are under development. We also discuss data on candidate anti-Japanese encephalitis drugs that have shown promising results in experimental models. Expert opinion: The global burden of the Japanese encephalitis and associated mortality is still high, thus emphasizing the need to achieve the highest vaccination coverage in endemic areas. Clinical trials exhaustively demonstrated the safety and efficacy of current Japanese encephalitis vaccines. In addition, several new vaccine candidates, characterized by high immunogenicity and broad cross-protection, have been developed and evaluated in experimental models, warranting further clinical testing. No licensed anti-Japanese encephalitis drugs are available, notwithstanding intense research efforts. Some candidate antiviral agents that inhibit viral entry and replication have been identified, including compounds with broad-spectrum antiviral activity. Further research is needed to refine candidate compounds into drugs suitable for clinical evaluation, characterized by low toxicity, ability to penetrate the blood–brain barrier, activity during the late phase of infection, and high genetic barrier to resistance.