Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined genetic and epigenetic alterations of the TERT promoter affect clinical and biological behavior of bladder cancer

In urothelial bladder cancer (UBC), risk stratification remains an important unmet need. Limitless self‐renewal, governed by TERT expression and telomerase activation, is crucial for cancer progression. Thus, telomerase activation through the interplay of mutations (TERTpMut) and epigenetic alterations in the TERT promoter may provide further insight into UBC behavior. Here, we investigated the combined effect of TERTpMut and the TERT Hypermethylated Oncological Region (THOR) status on telomerase activation and patient outcome in a UBC international cohort (n = 237). We verified that TERTpMut were frequent (76.8%) and present in all stages and grades of UBC. Hypermethylation of THOR was associated with higher TERT expression and higher‐risk disease in nonmuscle invasive bladder cancers (NMIBC). TERTpMut alone predicted disease recurrence (HR: 3.18, 95%CI 1.84 to 5.51, p < 0.0001) but not progression in NMIBC. Combined THORhigh/TERTpMut increased the risk of disease recurrence (HR 5.12, p < 0.0001) and progression (HR 3.92, p = 0.025). Increased THOR hypermethylation doubled the risk of stage progression of both TERTpwt and TERTpMut NMIBC. These results highlight that both mechanisms are common and coexist in bladder cancer and while TERTpMut is an early event in bladder carcinogenesis THOR hypermethylation is a dynamic process that contributes to disease progression. While the absence of alterations comprises an extremely indolent phenotype, the combined genetic and epigenetic alterations of TERT bring additional prognostic value in NMIBC and provide a novel insight into telomere biology in cancer.
Stability for Sampled-Data Systems with Delay and Applications for Networked Control Systems

Stability1 of networked control systems with nonlinear plant is a challenge problem and this problem is investigated with approximate discrete-time design approach in this paper. The stability conditions for nonlinear sampled-data systems are extended to the time-delay situations, which can guarantee stability of networked control systems when delay and data packet dropout may take place. For these systems, the controller can be designed based on Euler approximation, while the stability can be guaranteed with fast sampling and suitable hardware and software configuration, which control the delay time and data packet dropout.
TED-KISS: A Known-Item Speech Video Search Benchmark

Known-item search is an everyday natural scenario that we search for a specific thing (maybe a song) while only remembering some details about it. Existing benchmarks generally focus on brief user requests which specify some metadata like the title, or the time. However, in most cases, the users can hardly recall such information accurately. In order to embrace the research of known-item search, we present a new publicly available known-item speech video search benchmark, namely TED-KISS, which takes TED talks as an example. The video collection is constructed with up-to-date nearly 80,000 TED and TEDx talks on Youtube. These talks cover various topics, and their titles, speakers, descriptions, full-text subtitles, as well as original links are extracted as metadata, which makes the researches on text-based retrieval and multimedia retrieval feasible. Unlike other benchmarks concerning visual contents in segments, the user requests in TED-KISS are generated through a more natural process, partly through original related topics posted on Reddit and Baidu Tieba, and partly through manual imitative requests annotated by volunteers in a scenario simulation. In addition, we analyze the characteristics of our benchmark through evaluations of several existing text-based IR and Neural-IR models, which also can be served as baselines for this task.
Jaguar: Low Latency Mobile Augmented Reality with Flexible Tracking

In this paper, we present the design, implementation and evaluation of Jaguar, a mobile Augmented Reality (AR) system that features accurate, low-latency, and large-scale object recognition and flexible, robust, and context-aware tracking. Jaguar pushes the limit of mobile AR's end-to-end latency by leveraging hardware acceleration with GPUs on edge cloud. Another distinctive aspect of Jaguar is that it seamlessly integrates marker-less object tracking offered by the recently released AR development tools (e.g., ARCore and ARKit) into its design. Indeed, some approaches used in Jaguar have been studied before in a standalone manner, e.g., it is known that cloud offloading can significantly decrease the computational latency of AR. However, the question of whether the combination of marker-less tracking, cloud offloading and GPU acceleration would satisfy the desired end-to-end latency of mobile AR (i.e., the interval of camera frames) has not been eloquently addressed yet. We demonstrate via a prototype implementation of our proposed holistic solution that Jaguar reduces the end-to-end latency to ~33 ms. It also achieves accurate six degrees of freedom tracking and 97% recognition accuracy for a dataset with 10,000 images.
An Aptamer-Based Capacitive Sensing Platform for Specific Detection of Lung Carcinoma Cells in the Microfluidic Chip

Improvement of methods for reliable and early diagnosis of the cellular diseases is necessary. A biological selectivity probe, such as an aptamer, is one of the candidate recognition layers that can be used to detect important biomolecules. Lung cancer is currently a typical cause of cancer-related deaths. In this work, an electrical sensing platform is built based on amine-terminated aptamer modified-gold electrodes for the specific, label-free detection of a human lung carcinoma cell line (A549). The microdevice, that includes a coplanar electrodes configuration and a simple microfluidic channel on a glass substrate, is fabricated using standard photolithography and cast molding techniques. A procedure of self-assembly onto the gold surface is proposed. Optical microscope observations and electrical impedance spectroscopy measurements confirm that the fabricated microchip can specifically and effectively identify A549 cells. In the experiments, the capacitance element that is dominant in the change of the impedance is calculated at the appropriate frequency for evaluation of the sensitivity of the biosensor. Therefore, a simple, inexpensive, biocompatible, and selective biosensor that has the potential to detect early-stage lung cancer would be developed.
Antiviral resistance markers in influenza virus sequences in Mexico, 2000–2017

Background Influenza causes high rates of morbidity and mortality. Genetic variability of influenza viruses generates resistance to antivirals, which are of two types, since they act on two different viral targets: adamantanes, which block the M2 ion channel, and the neuraminidase (NA) inhibitors. Methods In Mexico, the available studies on the antiviral resistance of circulating influenza strains are scarce, so this work undertook an analysis of the Mexican sequences reported in public gene banks to perform a systematic analysis of the antiviral resistance markers on both M2 and NA. In all, 284 M2 sequences and 423 NA sequences were retrieved from three genetic databases (sequences from 2000 to 2017 were considered). Results The resistance markers to M2 blockers were present in 100% of H1N1 pdm2009, 83.6% of H3N2, and 5.8% of seasonal H1N1 sequences. Two resistance markers conferring resistance to NA inhibitors were present in seasonal H1N1 sequences, H275Y (50.0%) and N70S (33.3%). None of these viruses had both resistance markers, which are associated with oseltamivir resistance. The more frequent resistance marker in H1N1 pdm2009 NA sequences was H275Y, present in 3.6%, while S247N was present in 0.30%. Only one of the resistance-associated markers (Q136K) in NA (1.5%) was present in the analyzed H3N2 sequences, while sequences of influenza B virus did not present resistance markers to NA inhibitors. Some influenza A H1N1 pdm2009 sequences (1.8%) presented resistance markers to both M2 and NA. Conclusion Based on the present analysis, 7.1% of the all serotypes of influenza virus A sequences analyzed in Mexico from 2000 to 2017 have mutations conferring resistance to NA inhibitors. Because of this, and the limited availability of influenza drugs, it is necessary to increase the epidemiological surveillance, including molecular analysis, which will provide data such as the presence of changes associated with antiviral resistance.
Multi-View Group Anomaly Detection

Multi-view anomaly detection is a challenging issue due to diverse data generation mechanisms and inconsistent cluster structures of different views. Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal, but their collective behavior as a group is abnormal. In this paper, we formalize this group anomaly detection issue, and propose a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD). By representing the multi-view data with different latent group and topic structures, MGAD first discovers the distribution of groups or topics in each view, then detects group anomalies effectively. In order to solve the proposed model, we conduct the collapsed Gibbs sampling algorithm for model inference. We evaluate our model on both synthetic and real-world datasets with different anomaly settings. The experimental results demonstrate the effectiveness of the proposed approach on detecting multi-view group anomalies.
Video-to-Video Translation with Global Temporal Consistency

Although image-to-image translation has been widely studied, the video-to-video translation is rarely mentioned. In this paper, we propose an unified video-to-video translation framework to accom- plish different tasks, like video super-resolution, video colouriza- tion, and video segmentation, etc. A consequent question within video-to-video translation lies in the flickering appearance along with the varying frames. To overcome this issue, a usual method is to incorporate the temporal loss between adjacent frames in the optimization, which is a kind of local frame-wise temporal con- sistency. We instead present a residual error based mechanism to ensure the video-level consistency of the same location in different frames (called (lobal temporal consistency). The global and local consistency are simultaneously integrated into our video-to-video framework to achieve more stable videos. Our method is based on the GAN framework, where we present a two-channel discrimina- tor. One channel is to encode the video RGB space, and another is to encode the residual error of the video as a whole to meet the global consistency. Extensive experiments conducted on different video- to-video translation tasks verify the effectiveness and flexibleness of the proposed method.
Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions

While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models.
Towards a Better Gold Standard: Denoising and Modelling Continuous Emotion Annotations Based on Feature Agglomeration and Outlier Regularisation

Emotions are often perceived by humans through a series of multimodal cues, such as verbal expressions, facial expressions and gestures. In order to recognise emotions automatically, reliable emotional labels are required to learn a mapping from human expressions to corresponding emotions. Dimensional emotion models have become popular and have been widely applied for annotating emotions continuously in the time domain. However, the statistical relationship between emotional dimensions is rarely studied. This paper provides a solution to automatic emotion recognition for the Audio/Visual Emotion Challenge (AVEC) 2018. The objective is to find a robust way to detect emotions using more reliable emotion annotations in the valence and arousal dimensions. The two main contributions of this paper are: 1) the proposal of a new approach capable of generating more dependable emotional ratings for both arousal and valence from multiple annotators by extracting consistent annotation features; 2) the exploration of the valence and arousal distribution using outlier detection methods, which shows a specific oblique elliptic shape. With the learned distribution, we are able to detect the prediction outliers based on their local density deviations and correct them towards the learned distribution. The proposed method performance is evaluated on the RECOLA database containing audio, video and physiological recordings. Our results show that a moving average filter is sufficient to remove the incidental errors in annotations. The unsupervised dimensionality reduction approaches could be used to determine a gold standard annotations from multiple annotations. Compared with the baseline model of AVEC 2018, our approach improved the arousal and valence prediction of concordance correlation coefficient significantly to respectively 0.821 and 0.589.
JIM: Joint Influence Modeling for Collective Search Behavior

Previous work has shown that popular trending events are important external factors which pose significant influence on user search behavior and also provided a way to computationally model this influence. However, their problem formulation was based on the strong assumption that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would pose a joint influence on the user search behavior rather than posing influence independently. In this paper, we study this novel problem of Modeling the Joint Influences posed by multiple correlated events on user search behavior. We propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the inter-dependency among multiple events in terms of their influence upon user search behavior. We evaluate the proposed Joint Influence Model using two months query-log data from https://search.yahoo. com/. Experimental results show that the model can indeed capture the temporal dynamics of the joint influence over time and also achieves superior performance over different baseline methods when applied to solve various interesting prediction problems as well as real-word application scenarios, e.g., query auto-completion.
Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes that are excluded from training classes. ZSL suffers from 1) Zero-shot bias (Z-Bias) --- model is biased towards seen classes because unseen data is inaccessible for training; 2) Zero-shot variance (Z-Variance) --- associating different images to same semantic embedding yields large associating error. To reduce Z-Bias, we propose a pseudo transfer mechanism, where we first synthesize the distribution of unseen data using semantic embeddings, then we minimize the mismatch between the seen distribution and the synthesized unseen distribution. To reduce Z-Variance, we implicitly corrupted one semantic embedding multiple times to generate image-wise semantic vectors, with which our model learn robust classifiers. Lastly, we integrate our Z-Bias and Z-variance reduction techniques with a linear ZSL model to show its usefulness. Our proposed model successfully overcomes the Z-bias and Z-variance problems. Extensive experiments on five benchmark datasets including ImageNet-1K demonstrate that our model outperforms the state-of-the-art methods with fast training.
Median nerve stimulation induces analgesia via orexin-initiated endocannabinoid disinhibition in the periaqueductal gray

Significance Pain remains an unmet medical need due to the ineffectiveness or significant side effects of current pain therapies. Peripheral neurostimulation has been used to relieve pain for decades, but its mechanism(s) remain unsettled. Here, we established an animal model of median nerve stimulation by electrically stimulating the PC6 (Neiguan) acupoint of mice (MNS-PC6). We found MNS-PC6 can release an endogenous neuropeptide (orexin) from the hypothalamus to inhibit pain responses in mice through an endocannabinoid (an endogenous lipid functioning like chemicals from cannabis) that reduces the inhibitory (GABAergic) control in a midbrain pain-control region (the periaqueductal gray). Importantly, MNS-PC6–induced pain relief is endogenous opioid independent. Thus, MNS-PC6 may provide an alternative strategy for pain management in opioid-tolerant patients. Adequate pain management remains an unmet medical need. We previously revealed an opioid-independent analgesic mechanism mediated by orexin 1 receptor (OX1R)-initiated 2-arachidonoylglycerol (2-AG) signaling in the ventrolateral periaqueductal gray (vlPAG). Here, we found that low-frequency median nerve stimulation (MNS) through acupuncture needles at the PC6 (Neiguan) acupoint (MNS-PC6) induced an antinociceptive effect that engaged this mechanism. In mice, MNS-PC6 reduced acute thermal nociceptive responses and neuropathy-induced mechanical allodynia, increased the number of c-Fos–immunoreactive hypothalamic orexin neurons, and led to higher orexin A and lower GABA levels in the vlPAG. Such responses were not seen in mice with PC6 needle insertion only or electrical stimulation of the lateral deltoid, a nonmedian nerve-innervated location. Directly stimulating the surgically exposed median nerve also increased vlPAG orexin A levels. MNS-PC6–induced antinociception (MNS-PC6-IA) was prevented by proximal block of the median nerve with lidocaine as well as by systemic or intravlPAG injection of an antagonist of OX1Rs or cannabinoid 1 receptors (CB1Rs) but not by opioid receptor antagonists. Systemic blockade of OX1Rs or CB1Rs also restored vlPAG GABA levels after MNS-PC6. A cannabinoid (2-AG)-dependent mechanism was also implicated by the observations that MNS-PC6-IA was prevented by intravlPAG inhibition of 2-AG synthesis and was attenuated in Cnr1−/− mice. These findings suggest that PC6-targeting low-frequency MNS activates hypothalamic orexin neurons, releasing orexins to induce analgesia through a CB1R-dependent cascade mediated by OX1R-initiated 2-AG retrograde disinhibition in the vlPAG. The opioid-independent characteristic of MNS-PC6–induced analgesia may provide a strategy for pain management in opioid-tolerant patients.
Meta-Learning Multi-task Communication

In this paper, we describe a general framework: Parameters Read-Write Networks (PRaWNs) to systematically analyze current neural models for multi-task learning, in which we find that existing models expect to disentangle features into different spaces while features learned in practice are still entangled in shared space, leaving potential hazards for other training or unseen tasks. We propose to alleviate this problem by incorporating an inductive bias into the process of multi-task learning, that each task can keep informed of not only the knowledge stored in other tasks but the way how other tasks maintain their knowledge. In practice, we achieve above inductive bias by allowing different tasks to communicate by passing both hidden variables and gradients explicitly. Experimentally, we evaluate proposed methods on three groups of tasks and two types of settings (\textsc{in-task} and \textsc{out-of-task}). Quantitative and qualitative results show their effectiveness.
Factors contributing to hospitalization costs for patients with COPD in China: a retrospective analysis of medical record data

Purpose Hospitalization brings considerable economic pressure on COPD patients in China. A clear understanding of hospitalization costs for patients with COPD is warranted to improve treatment strategies and to control costs. Currently, investigation on factors contributing to hospitalization costs for patients with COPD in China is limited. This study aimed to measure the hospitalization costs of COPD and to determine the contributing factors. Patients and methods Medical record data from the First Affiliated Hospital of Guangzhou Medical University from January 2016 to December 2016 were used for a retrospective analysis. Patients who were hospitalized with a diagnosis of COPD were included. Patient characteristics, medical treatment, and hospitalization costs were analyzed by descriptive statistics and multivariable regression. Results Among the 1,943 patients included in this study, 87.85% patients were male; the mean (SD) age was 71.15 (9.79) years; 94.49% patients had comorbidities; and 82.30% patients had health insurance. Regarding medical treatment, the mean (SD) length of stay was 9.38 (7.65) days; 11.12% patients underwent surgery; 87.91% used antibiotics; and 4.53% underwent emergency treatment. For hospitalization costs, the mean (SD) of the total costs per COPD patient per admission was 24,372.75 (44,173.87) CNY (3,669.33 [6,650.38] USD), in which Western medicine fee was the biggest contributor (45.53%) followed by diagnosis fee (27.00%) and comprehensive medical fee (12.04%). Regression found that reimbursement (−0.032; 95% CI −0.046 to 0.007), length of stay (0.738; 95% CI 0.832–0.892), comorbidity (0.044; 95% CI 0.029–0.093), surgery (0.145; 95% CI 0.120–0.170), antibiotic use (0.086; 95% CI 0.060–0.107), and emergency treatment (0.121; 95% CI 0.147–0.219) were significantly (P<0.01) associated with total hospitalization costs. Conclusion To control hospitalization costs for COPD patients in China, the significance of comorbidity, length of stay, antibiotic use, surgery, and emergency treatment suggests the importance of controlling the COPD progression and following clinical guidelines for inpatients. Interventions such as examination of pulmonary function for early detection, quality control of medical treatment, and patient education warrant further investigation.