Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence of type 1 diabetes in China, 2010-13: population based study

Abstract Objective To estimate the incidence of type 1 diabetes in all age groups in China during 2010-13. Design Population based, registry study using data from multiple independent sources. Setting National registration system in all 505 hospitals providing diabetes care, and communities of patients with diabetes in 13 areas across China, covering more than 133 million person years at risk, approximately 10% of the whole population. Participants 5018 people of all ages with newly diagnosed type 1 diabetes and resident in the study areas from 1 January 2010 to 31 December 2013. Main outcome measures Incidence of type 1 diabetes per 100 000 person years by age, sex, and study area. Type 1 diabetes was doctor diagnosed and further validated by onsite follow-up. Completeness of case ascertainment was assessed using the capture mark recapture method. Results 5018 cases of newly diagnosed type 1 diabetes were ascertained: 1239 participants were aged <15 years, 1799 were aged 15-29 years, and 1980 were aged ≥30 years. The proportion of new onset cases in participants aged ≥20 years was 65.3%. The estimated incidence of type 1 diabetes per 100 000 persons years for all ages in China was 1.01 (95% confidence interval 0.18 to 1.84). Incidence per 100 000 persons years by age group was 1.93 (0.83 to 3.03) for 0-14 years, 1.28 (0.45 to 2.11) for 15-29 years, and 0.69 (0.00 to 1.51) for ≥30 years, with a peak in age group 10-14 years. The incidence in under 15s was positively correlated with latitude (r=0.88, P<0.001), although this association was not observed in age groups 15-29 years or ≥30 years. Conclusion Most cases of new onset type 1 diabetes in China occurred among adults. The incidence of type 1 diabetes in Chinese children was among the lowest reported in the study.
Growth and calcification in the cephalopod Sepia officinalis under elevated seawater pCO2

Ocean acidification and associated changes in seawater carbonate chemistry negatively influence calcification processes and depress metabolism in many calcifying marine invertebrates. We present data on the cephalopod mollusc Sepia officinalis, an invertebrate that is capable of not only maintaining calcification, but also growth rates and metabolism when exposed to elevated partial pressures of carbon dioxide (pCO(2)). During a 6 wk period, juvenile S. officinalis maintained calcification under similar to 4000 and similar to 6000 ppm CO2, and grew at the same rate with the same gross growth efficiency as did control animals. They gained approximately 4%, body mass daily and increased the mass of their calcified cuttlebone by over 500 %. We conclude that active cephalopods possess a certain level of pre-adaptation to long-term increments in carbon dioxide levels. Our general understanding of the mechanistic processes that limit calcification must improve before we can begin to predict what effects future ocean acidification will have on calcifying marine invertebrates.
Multi-User Motion Matching Interaction for Interactive Television using Smartwatches

Motion matching input, following continuously moving targets by performing bodily movements, offers new interaction possibilities in multiple domains. Unlike optical motion matching input systems, our technique utilizes a smartwatch to record motion data from the users' wrists, providing robust input regardless of lighting conditions or momentary occlusions. We demonstrate an implementation of motion matching input using smartwatches for interactive television, that allows multi-user input using bodily movements and offers new interaction possibilities by means of a second screen as extension on TV display.
Empirical research on distribution characteristics of online news comment

"No comments, no news. " Readers' online comments have become an important part of online news. Over the last few years, researches and applications on online news comments have been carried out. However, there are a few of researches about the distribution characteristics of online news comments. In this paper, first, we collect about one hundred thousand online news with their comments from six news websites in a given time period, and then study the online news comments'spatial distribution among online news and temporal distribution during their appearance. The result shows that the distribution of online news comments is greatly uneven. A small amount of news possess the vast majority of comments, and most of the news only have a few of online comments or even have no following comments. In addition, it shows the growth rate of online comments drops rapidly during a news conference. The findings can provide theoretical support to collection of online news comments, analysis of hot issues and the concern-specific behaviors characteristics of general public.
Live demonstration: Real-time event-driven object recognition on SpiNNaker

This live demonstration shows real-time visual object recognition based on a spiking neural network adaptation of the HMAX model running on a purely event-based computational hardware platform. Visual input to the system is provided by an ATIS spiking silicon retina sensor. A SpiNNaker board processes the event-encoded visual information from the scene. Using a Leaky Integrate-and-Fire (LIF) neuron model implemented on SpiNNaker, an event-driven, multi-layer network is created that performs real-time orientation extraction and recombination. In this demonstration, the network will be tuned to recognize complex objects such as printed characters.
Inhibition of Transient Receptor Potential Melastatin 3 ion channels by G-protein βγ subunits

Transient receptor potential melastatin 3 (TRPM3) channels are activated by heat, and chemical ligands such as pregnenolone sulphate (PregS) and CIM0216. Here, we show that activation of receptors coupled to heterotrimeric Gi/o proteins inhibits TRPM3 channels. This inhibition was alleviated by co-expression of proteins that bind the βγ subunits of heterotrimeric G-proteins (Gβγ). Co-expression of Gβγ, but not constitutively active Gαi or Gαo, inhibited TRPM3 currents. TRPM3 co-immunoprecipitated with Gβ, and purified Gβγ proteins applied to excised inside-out patches inhibited TRPM3 currents, indicating a direct effect. Baclofen and somatostatin, agonists of Gi-coupled receptors, inhibited Ca2+ signals induced by PregS and CIM0216 in mouse dorsal root ganglion (DRG) neurons. The GABAB receptor agonist baclofen also inhibited inward currents induced by CIM0216 in DRG neurons, and nocifensive responses elicited by this TRPM3 agonist in mice. Our data uncover a novel signaling mechanism regulating TRPM3 channels. DOI: http://dx.doi. org/10.7554/eLife.26147. 001
Effect of Visualization Training on Uncertain Spatial Trajectory Predictions

Objective: The goal of this study was to explore the ways in which visualizations influence the prediction of uncertain spatial trajectories (e.g., the unknown path of a downed aircraft or future path of a hurricane) and participant overconfidence in such prediction. Background: Previous research indicated that spatial predictions of uncertain trajectories are challenging and are often associated with overconfidence. Introducing a visualization aid during training may improve the understanding of uncertainty and reduce overconfidence. Method: Two experiments asked participants to predict the location of various trajectories at a future time. Mean and variance estimates were compared for participants who were provided with a visualization and those who were not. Results: In Experiment 1, participants exhibited less error in mean estimations when a linear visualization was present but performed worse than controls once the visualization was removed. Similar results were shown in Experiment 2, with a nonlinear visualization. However, in both experiments, participants who were provided with a visualization did not retain any advantage in their variance estimations once the visualization was removed. Conclusions: Visualizations may support spatial predictions under uncertainty, but they are associated with benefits and costs for the underlying knowledge being developed. Application: Visualizations have the potential to influence how people make spatial predictions in the presence of uncertainty. Properly designed and implemented visualizations may help mitigate the cognitive biases related to such predictions.
Long-range wireless technologies for IoT applications: A review

This review paper explains the available long-range wireless technologies for IoT applications. There are licensed and unlicensed spectrums available with a few advantages and disadvantages. The licensed spectrum has strong signal levels, low noise level, better throughput and better range performances. Initial costing of licensed spectrum is hard, and it is expensive to acquire. The unlicensed spectrum is free to obtain and does not cause harmful interference. All the technologies are discussed which will be useful to decide to develop any applications.
IEKF-based Visual-Inertial Odometry using Direct Photometric Feedback

This paper presents a visual-inertial odometry framework which tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter (IEKF). By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of non-corner shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it to other state-of-the-art visual-inertial frameworks. The experimental results show that robust localization with high accuracy can be achieved with this filterbased framework.
Mining Trending Hash Tags for Arabic Sentiment Analysis

People text millions of posts everyday on microblogging social networking especially Twitter which make microblogs a rich source for public opinions, customer’s comments and reviews. Companies and public sectors are looking for a way to measure the public response and feedback on particular service or product. Sentiment analysis is an encouraging technique capable to sense the public opinion in a fast and less cost tactic than traditional survey methods like questionnaires and interviews. Various sentiment methods were developed in many languages, such as English and Arabic with much more studies in the first one. Sometime, hash tags are misleading or may have a title that does not really reflects the subject. Tweets in trend hash tags may contain keyword or topics titles better represent the subject of the hash tag. This research aims at proposing an approach to explore Twitter Hash tag trends to retrieve tweets, group retrieved tweets to learn topics’ profiles, do sentiment analysis to test the subjectivity of tweets then develop a prediction model using deep learning to classify a new tweet to the appropriate topic profile. Arabic hash tags trends have been used to evaluate the proposed approach. The performance of the proposed approach (clustering topics within hashtag trend to learn topics profiles then do sentiment analysis) shows better accuracy than sentiment analysis without clustering the topics.
Evaluating Reliability/Survivability of Capacitated Wireless Networks

In telecommunication network design problems, survivability and reliability are often used to evaluate quality of service while usually ignoring link capacity. In this paper, a new metric that combines network reliability with network resilience is presented to measure reliability/survivability effectively for capacitated networks. Capacitated resilience is compared with well-known network reliability/survivability metrics (<inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula>-terminal reliability, all-terminal reliability, traffic efficiency, and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-connectivity), and its benefits and computational efficiency are discussed. An application is shown using heterogeneous wireless networks (HetNets). With the growing use of new telecommunication technologies such as 4G and wireless hotspots, HetNets are gaining more attention. The source of heterogeneity of a HetNet can either be the differences in nodes (such as transmission ranges, failure rates, and energy levels) or the differences in services offered in the network (such as GSM and WiFi).
AMPK/GSK3β/β‐catenin cascade‐triggered overexpression of CEMIP promotes migration and invasion in anoikis‐resistant prostate cancer cells by enhancing metabolic reprogramming

Prostate cancer (PCa) represents one of the most common solid neoplasms, and metastasis is the second leading cause of death in adult males. Anoikis is a programmed cell death that is induced upon cell detachment from the extracellular matrix (ECM), which behaves as a critical protective mechanism for anchorage‐independent cell growth and metastasis formation. However, in the absence of ECM attachment, shift of metabolic pattern and tolerance to anoikis facilitate the survival of aggressive cancer cells in the circulatory system as well as their metastasis to distant sites. Few molecular targets in PCa have thus far been reported to prevent anoikis resistance, metabolic reprogramming and metastasis simultaneously. In the present study, elevated migration, invasion, pyruvate production, lactate generation, ATP level, and impaired detachment‐induced apoptosis were found in anoikis‐resistant PCa cells, and genome microarray analysis demonstrated that the cell migration‐inducing protein (CEMIP) was a potential molecular target for the regulation of the aforementioned malignant behaviors. Additional investigation revealed that the AMPK/glycogen synthase kinase 3b (GSK3b)/b‐catenin cascade‐triggered CEMIP overexpression in anoikis‐resistant PCa cells might be implicated in local progression, metabolic shift, and cellular migration and invasion, whereas knockout of CEMIP by clustered regularly interspaced short palindromic repeats (CRISPR)/ CRISPR‐associated protein 9 in anoikis‐resistant PCa cells reversed the described bioeffects by reducing expressions of matrix metalloproteinase 2 (MMP2), VEGF, pyruvate dehydrogenase kinase isoform 4 (PDK4), and lactate dehydrogenase A. In addition, inhibition of glycolysis by CEMIP‐mediated PDK4 down‐regulation impaired the migration and invasion of anoikis‐resistant PCa cells by attenuating MMP2 and VEGF expressions. Our findings establish that AMPK/GSK3b/ b‐catenin cascade‐triggered CEMIP overexpression might promote migration and invasion in anoikis‐resistant PCa cells by enhancing PDK4‐associated metabolic reprogramming, which may provide a novel, promising therapeutic target for the treatment of advanced PCa. —Zhang, P., Song, Y. , Sun, Y. , Li, X. , Chen, L. , Yang, L. , Xing, Y. AMPK/GSK3β/β‐catenin cascade‐triggered overexpression of CEMIP promotes migration and invasion in anoikis‐resistant prostate cancer cells by enhancing metabolic reprogramming. FASEB J. 32, 3924–3935 (2018). www.fasebj. org
A Coarse-Grained Integral Equation Method for Multiscale Electromagnetic Analysis

Nowadays, increasing demands are placed on enhancements of the model fidelity in electromagnetic (EM) analysis. One major difficulty comes from the multiscale nature of the high-definition geometry, in which the spatial scales differ by orders of magnitude. It often leads to strongly nonuniform discretizations, and a large, dense, and ill-conditioned matrix equation to solve. The work investigates an adaptive coarse-graining domain decomposition method for the integral equation-based solution of large, complex EM problems. A parallel and multilevel skeletonization approach is employed to construct effective coarse-grid basis functions locally per subdomain. The benefits of the work include a well-preconditioned system, an effective matrix compression, and the reduced computational costs. The numerical results validate the hypothesis and demonstrate a considerable reduction in the computational complexity for multiscale problems of interest.
Security Solutions for the Controller Area Network: Bringing Authentication to In-Vehicle Networks

Vehicles cannot be secured while the in-vehicle network remains insecure. The growing number of attacks reported each year shows that in-vehicle buses are not isolated from the outside world. By exploiting their lack of security, adversaries can gain control over virtually any functionality inside the car. We discuss the most promising approaches for assuring security on the controller area network (CAN) bus after a decade of attacks and security proposals. Most of the proposals are based on cryptographic mechanisms, but some exploit the physical layer or even the physical characteristics of the controllers. The surveyed solutions prove a significant degree of maturity and sophistication, which suggests that the moment for adoption and standardization by the industry has come.
A Fully Parallel Architecture for Designing Frequency-Agile and Real-Time Reconfigurable FPGA-Based RF Digital Transmitters

Real-time frequency agility is one of the key limitations of current all-digital transmitters (ADTs). In this paper, to address this problem, an ADT based on direct-RF generation is proposed and implemented in a field-programmable gate array (FPGA). To achieve this, the pulse encoding is performed after the digital upconversion, which presents challenges in the pulse encoder implementation. However, the proposed design considerably improves the frequency agility when compared with current state-of-the-art approaches. Two different pulse encoders were designed in this paper, one is based on pulsewidth modulation (PWM) and the other on delta–sigma modulation ( $\mathrm {\Delta \Sigma }\text{M}$ ). These units are implemented with high parallelization to achieve the required equivalent sampling rates. The proposed design enables the implementation of all-digital RF transmitters in FPGA with a widely tunable carrier frequency. In this paper, a variation from 0.1 up to 6 GHz of carrier frequency, with a maximum frequency resolution of 1.5625 MHz, is demonstrated. Measurement results in terms of signal-to-noise ratio (SNR) and error-vector magnitude are presented and discussed. In the case of the PWM, maximum SNRs of 39 and 36 dB were obtained for a 5 and 10 MBd of symbol rates (SRs). In the case of $\mathrm {\Delta \Sigma }\text{M}$ , maximum SNRs of 38 and 35 dB were obtained for 20 and 40 MBd of the SR.
Data Curation with Deep Learning [Vision]: Towards Self Driving Data Curation

Past. Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest data management problems. Unfortunately, it is still the most time consuming and least enjoyable work of data scientists. So far, successful data curation stories are mainly ad-hoc solutions that are either domain-specific (for example, ETL rules) or task-specific (for example, entity resolution). Present. The power of current data curation solutions are not keeping up with the ever changing data ecosystem in terms of volume, velocity, variety and veracity, mainly due to the high human cost, instead of machine cost, needed for providing the ad-hoc solutions mentioned above. Meanwhile, deep learning is making strides in achieving remarkable successes in areas such as image recognition, natural language processing, and speech recognition. This is largely due to its ability to understanding features that are neither domain-specific nor task-specific. Future. Data curation solutions need to keep the pace with the fast-changing data ecosystem, where the main hope is to devise domain-agnostic and task-agnostic solutions. To this end, we start a new research project, called AutoDC, to unleash the potential of deep learning towards self-driving data curation. We will discuss how different deep learning concepts can be adapted and extended to solve various data curation problems. We showcase some low-hanging fruits about the early encounters between deep learning and data curation happening in AutoDC. We believe that the directions pointed out by this work will not only drive AutoDC towards democratizing data curation, but also serve as a cornerstone for researchers and practitioners to move to a new realm of data curation solutions.
An assessment of exercise tolerance in normobaric hypoxia of patients with diabetes mellitus type 1

Purpose. Physical activity is an integral part of the treatment of diabetes. The aim of the study was to assess aerobic capacity and cardiovascular-respiratory reactions to a single physical exercise with gradually increasing intensity in normobaric hypoxia in patients with Type 1 diabetes. Methods. The study was conducted on a sample of adults with Type 1 diabetes (GT1D, n = 13) and a randomly chosen healthy control (GK, n = 15). The study participants performed a progressive exercise test to exhaustion in normoxia (FiO 2 ~ 20.90%) and 7 days later in normobaric hypoxia (FiO 2 ~ 15.14%). At rest, during exercise, and after comple-tion of the test blood was drawn and physiological indicators were monitored. Results. Two-way ANOVA revealed a significant effect of hypoxia and physical exercise on blood glucose concentrations ( F = 6.1 p < 0.01). In GT1D , lower glucose levels were observed in normobaric hypoxia compared with baseline and post-exercise levels in normoxia ( p < 0.05). A tendency to increased maximal oxygen uptake and significantly higher minute pulmonary ventilation was observed in both groups in response to exercise and hypoxia. Conclusions. Physical activity and hypoxia may effectively control glucose homeostasis and increase cardiores-piratory adaptation to exercise in Type 1 diabetics.