Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevated histone H3 acetylation and loss of the Sp1–HDAC1 complex de-repress the GM2-synthase gene in renal cell carcinoma

GM2-synthase produces sialic acid–containing glycosphingolipids called gangliosides, and its mRNA overexpression and the gangliosides it generates are linked to tumor progression, migration, and suppression of tumor-specific host immune responses. However, the mechanism underlying GM2-synthase de-repression in renal cell carcinoma (RCC) is poorly understood. Here, we demonstrate that higher GM2-synthase mRNA expression levels in various cancer cells and in human RCC tumors correlate with higher histone acetylation levels (H3K9, H3K14, or both) at region +38/+187 relative to the transcription start site (TSS) of the GM2-synthase gene than in normal kidney epithelial (NKE) cells or healthy adjacent tissues. An increase in GM2-synthase mRNA expression in cells treated with a histone deacetylase (HDAC) inhibitor was accompanied by increased histone acetylation levels at this promoter region. DNA methylation around the TSS was absent in both RCC cell lines and NKE cells. Of note, both the transcription factor Sp1 and corepressor HDAC1 associated with the +38/+187 region when the GM2-synthase gene was repressed in NKE and tumor-adjacent tissues, indicating plausible site-specific repressive roles of HDAC1 and Sp1 in GM2-synthase mRNA expression. Site-directed mutagenesis of the Sp1-binding site within the +38/+187 region relieved repressed luciferase activity of this region by limiting HDAC1 recruitment. Moreover, Sp1 or HDAC1 knock down increased GM2-synthase transcription, and butyrate-mediated activation of GM2-synthase mRNA expression in SK-RC-45 cells was accompanied by Sp1 and HDAC1 loss from the +38/+187 region. Taken together, we have identified an epigenetic mechanism for the de-repression of the GM2-synthase gene in RCC.
An intermittent hypercaloric diet alters gut microbiota, prefrontal cortical gene expression and social behaviours in rats

Excessive consumption of high fat and high sugar (HFHS) diets are known to alter reward processing and aspects of behaviour, and change microbiota profiles. Studies in gnotobiotic mice also provide evidence that gut microorganisms influence social behaviour. To further investigate these interactions, the impact of intermittent access to a HFHS diet on social behaviour, gene expression and microbiota composition was examined. Rats were permitted intermittent daily access (2h / day) to a palatable HFHS diet for 28 days across the adolescent period. Social interaction, social memory and novel object recognition were assessed during this period. Following testing, RT-PCR was conducted on hippocampal and prefrontal cortex (PFC) samples. 16S ribosomal RNA amplicon sequencing was used for identification and relative quantification of bacterial taxa. Reduced social interaction behaviours, and impaired social memory and novel object recognition were observed in HFHS diet rats. Reduced levels of monoamine oxidase A (Maoa), catechol-O-methyltransferase (Comt) and brain derived neurotrophic factor (Bdnf) mRNA were observed in the PFC of HFHS diet rats. The relative abundance of a number of specific taxa differed significantly between the two diet groups, in particular, Lachnospiraceae and Ruminoccoceae bacteria, which also predicted social behaviours, novel object recognition performance and Maoa expression. This is the first study to show that limited daily access to HFHS diet alters social behaviour and cognition in rats. Furthermore, behavioural changes are associated with alterations to cortical gene expression of enzymes involved in monoamine synthesis and neuroplasticity, and microbiota profiles predicted diet-induced changes to behaviour and gene expression.
Better Bounds for Online Line Chasing

We study online competitive algorithms for the \emph{line chasing problem} in Euclidean spaces $\reals^d$, where the input consists of an initial point $P_0$ and a sequence of lines $X_1,X_2,. ,X_m$, revealed one at a time. At each step $t$, when the line $X_t$ is revealed, the algorithm must determine a point $P_t\in X_t$. An online algorithm is called $c$-competitive if for any input sequence the path $P_0, P_1,. ,P_m$ it computes has length at most $c$ times the optimum path. The line chasing problem is a variant of a more general convex body chasing problem, where the sets $X_t$ are arbitrary convex sets. To date, the best competitive ratio for the line chasing problem was $28.1$, even in the plane. We significantly improve this bound, by providing a~$3$-competitive algorithm for any dimension $d$. We also improve the lower bound on the competitive ratio, from $1.412$ to $1.5358$.
Sex differences in quality indicator attainment for myocardial infarction: a nationwide cohort study

Aim To investigate sex differences in acute myocardial infarction (AMI) guideline-indicated care as defined by the European Society of Cardiology (ESC) Acute Cardiovascular Care Association (ACCA) quality indicators. Methods Nationwide cohort study comprising 691 290 AMI hospitalisations in England and Wales (n=233 hospitals) from the Myocardial Ischaemia National Audit Project between 1 January 2003 and 30 June 2013. Results There were 34.5% (n=238 489) women (median age 76.7 (IQR 66.3–84.0) years; 33.9% (n=80 884) ST-elevation myocardial infarction (STEMI)) and 65.5% (n=452 801) men (median age 67.1 (IQR 56.9–77.2) years; 42.5% (n=192 229) STEMI). Women less frequently received 13 of the 16 quality indicators compared with men, including timely reperfusion therapy for STEMI (76.8% vs 78.9%; p<0.001), timely coronary angiography for non-STEMI (24.2% vs 36.7%; p<0.001), dual antiplatelet therapy (75.4% vs 78.7%) and secondary prevention therapies (87.2% vs 89.6% for statins, 82.5% vs 85.6% for ACE inhibitor/angiotensin receptor blockers and 62.6% vs 67.6% for beta-blockers; all p<0.001). Median 30-day Global Registry of Acute Coronary Events risk score adjusted mortality was higher for women than men (median: 5.2% (IQR 1.8%–13.1%) vs 2.3% (IQR 0.8%–7.1%), p<0.001). An estimated 8243 (95% CI 8111 to 8375) deaths among women could have been prevented over the study period if their quality indicator attainment had been equal to that attained by men. Conclusion According to the ESC ACCA AMI quality indicators, women in England and Wales less frequently received guideline-indicated care and had significantly higher mortality than men. Greater attention to the delivery of recommended AMI treatments for women has the potential to reduce the sex-AMI mortality gap.
An Off-policy Policy Gradient Theorem Using Emphatic Weightings

Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradient. In off-policy learning, however, where the behaviour policy is not necessarily attempting to learn and follow the optimal policy for the given task, the existence of such a theorem has been elusive. In this work, we solve this open problem by providing the first off-policy policy gradient theorem. The key to the derivation is the use of $emphatic$ $weightings$. We develop a new actor-critic algorithm$\unicode{x2014}$called Actor Critic with Emphatic weightings (ACE)$\unicode{x2014}$that approximates the simplified gradients provided by the theorem. We demonstrate in a simple counterexample that previous off-policy policy gradient methods$\unicode{x2014}$particularly OffPAC and DPG$\unicode{x2014}$converge to the wrong solution whereas ACE finds the optimal solution.
Study of Almost Blank Subframe Configurations for Traffic offload in HetNets

Nowadays, the demand for data usage on mobile phones is increasing dramatically. In addition, smartphones with powerful multimedia capabilities have become popular. All these factors are enhancing the amount of data traffic in mobile communication systems. As a result, the users could experience increasing interference to their desired data transmission. In this paper, the techniques for enhanced Inter-Cell Interference Coordination (eICIC) in Heterogeneous Networks (HetNets) such as 4G and 5G mobile systems are studied. The Cell Range Expansion (CRE) and Almost Blank Subframe (ABS), which are the core techniques under eICIC are focused in this work. Simulation model adapted based on Vienna LTE downlink system simulation is used to verify the system performance under the implementation of CRE and ABS mechanisms. System performance in terms of cell association user fairness, SINR differences, average throughput and throughput CDF (Cumulative Distribution Function) is fully investigated while varying the major CRE and ABS configurations i.e. CRE bias value and ABS ratio. The simulation results for the system under different traffic load situations are also studied.
Generalized Range Moves

We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs). Since this is not a tractable problem in general, a commonly used heuristic is to minimize over subsets of labels and variables in an iterative procedure. Such methods include {\alpha}-expansion, {\alpha}{\beta}-swap, and range-moves. In each iteration, a small subset of variables are active in the optimization, which diminishes their effectiveness, and increases the required number of iterations. In this paper, we present a method in which optimization can be carried out over all labels, and most, or all variables at once. Experiments show substantial improvement with respect to previous move-making algorithms.
Visual-Texual Emotion Analysis With Deep Coupled Video and Danmu Neural Networks

User emotion analysis toward videos is to automatically recognize the general emotional status of viewers from the multimedia content embedded in the online video stream. Existing works fall into two categories: 1) visual-based methods, which focus on visual content and extract a specific set of features of videos. However, it is generally hard to learn a mapping function from low-level video pixels to high-level emotion space due to great intra-class variance. 2) textual-based methods, which focus on the investigation of user-generated comments associated with videos. The learned word representations by traditional linguistic approaches typically lack emotion information and the global comments usually reflect viewers’ high-level understandings rather than instantaneous emotions. To address these limitations, in this paper, we propose to jointly utilize video content and user-generated texts simultaneously for emotion analysis. In particular, we introduce exploiting a new type of user-generated texts, i.e., “danmu,” which are real-time comments floating on the video and contain rich information to convey viewers’ emotional opinions. To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions. Afterward, we propose a novel visual-textual emotion analysis model with Deep Coupled Video and Danmu Neural networks (DCVDN), in which visual and textual features are synchronously extracted and fused to form a comprehensive representation by deep-canonically-correlated-autoencoder-based multi-view learning. Through extensive experiments on a self-crawled real-world video-danmu dataset, we prove that DCVDN significantly outperforms the state-of-the-art baselines.
Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal

The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual weighted lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes the previous work and provides a better approximation to the rank minimization problem for original matrix as well as the l0-norm minimization problem for sparse data. Moreover, an approximate closed-form solution is introduced to solve the lp-norm minimization, which has more stability in the nonconvex optimization and provides a more accurate estimation for the low-rank and sparse matrix recovery problem. We then apply the DWLP model to remove salt-and-pepper noise by exploiting the image nonlocal self-similarity. Both qualitative and quantitative experiments demonstrate that the proposed method outperforms other state-of-the-art methods. In terms of PSNR evaluation, our DWLP achieves about 7.188dB, 5.078dB, 3.854dB, 2.536dB and 0.158dB improvements over the current WSNM-RPCA under 10\% to 50\% salt-and-pepper noise with an interval 10\% respectively.
Financial gain‐ and loss‐related BOLD signals in the human ventral tegmental area and substantia nigra pars compacta

Neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) play central roles in reward‐related behaviours. Nonhuman animal studies suggest that these neurons also process aversive events. However, our understanding of how the human VTA and SNC responds to such events is limited and has been hindered by the technical challenge of using functional magnetic resonance imaging (fMRI) to investigate a small structure where the signal is particularly vulnerable to physiological noise. Here we show, using methods optimized specifically for the midbrain (including high‐resolution imaging, a novel registration protocol, and physiological noise modelling), a BOLD (blood‐oxygen‐level dependent) signal to both financial gain and loss in the VTA and SNC, along with a response to nil outcomes that are better or worse than expected in the VTA. Taken together, these findings suggest that the human VTA and SNC are involved in the processing of both appetitive and aversive financial outcomes in humans.
Entwicklung und Anwendung einer Methodik zur Synthese zukünftiger Verbraucherlastgänge

Fur Planung und Auslegung zukunftiger Kraftwerke sowie zur Betrachtung der Notwendigkeit von Stromspeichern werden moglichst genaue Lastgangprognosen benotigt. Hierzu wurde ein Lastgangmodell entwickelt, welches fur verschiedene Regionen zukunftige Stromlastgange generieren kann. Lastgange aus verschiedenen Jahren oder Klimaregionen wurden durch Modellierung und Synthetisierung an ein Referenzjahr und -klima angepasst, um eine in sich konsistente Datenbasis zu erhalten. Der zukunftig zu erwartende Einsatz schaltbarer Lasten wurde im Rahmen eines Dynamisierungsmodells berucksichtigt.
The relationship between social support and anxiety amongst children living with HIV in rural northern Namibia

This study examined how HIV-related stigma and social support are related to anxiety among children living with HIV in rural northern Namibia. This is a cross-sectional exploratory study with a sample of 132 caregiver–child dyads. Our study found that higher levels of social support was correlated with lower levels of anxiety among children living with HIV, but higher levels of HIV-related stigma was not correlated with higher levels of anxiety. These findings point to the need for the development of more culturally and age specific interventions that enhance social support among children living with HIV, especially those in high-prevalence and socio-economically deprived settings.
Autonomous Indoor Robot Navigation via Siamese Deep Convolutional Neural Network

The vast majority of indoor navigation algorithms either rely on manual scene augmentation and labelling or exploit multi-sensor fusion techniques in achieving simultaneous localization and mapping (SLAM), leading to high computational costs, hardware complexities and robustness deficiencies. This paper proposes an efficient and robust deep learning-based indoor navigation framework for robots. Firstly, we put forward an end-to-end trainable siamese deep convolutional neural network (DCNN) which decomposes navigation into orientation and localization in one branch, while achieving semantic scene mapping in another. In mitigating the computational costs associated with DCNNs, the proposed model design shares a significant amount of convolutional operations between the two branches, streamlining the model and optimizing for efficiency in terms of memory and inference latency. Secondly, a transfer learning regime is explored in demonstrating how such siamese DCNNs can be efficiently trained for high convergence rates without extensive manual dataset labelling. The resulting siamese framework combines semantic scene understanding with orientation estimation towards predicting collision-free and optimal navigation paths. Experimental results demonstrate that the proposed framework achieves accurate and efficient navigation and outperforms existing "navigation-by-classification" variants.
Enhancing the Robustness of Prior Network in Out-of-Distribution Detection

With the recent surge of interests in deep neural networks, more real-world applications start to adopt it in practice. However, deep neural networks are known to have limited control over its prediction under unseen images. Such weakness can potentially threaten society and cause annoying consequences in real-world scenarios. In order to resolve such issue, a popular task called out-of-distribution detection was proposed, which aims at separating out-of-distribution images from in-distribution images. In this paper, we propose a perturbed prior network architecture, which can efficiently separate model-level uncertainty from data-level uncertainty via prior entropy. To further enhance the robustness of proposed entropy-based uncertainty measure, we propose a concentration perturbation algorithm, which adaptively adds noise to concentration parameters so that the in- and out-of-distribution images are better separable. Our method can directly rely on the pre-trained deep neural network without re-training it, and also requires no knowledge about the network architecture and out-of-distribution examples. Such simplicity makes our method more suitable for real-world AI applications. Through comprehensive experiments, our methods demonstrate its superiority by achieving state-of-the-art results on many datasets.
Mediators of increased self-harm and suicidal ideation in sexual minority youth: a longitudinal study

Abstract Background Sexual minority youth have elevated suicidal ideation and self-harm compared with heterosexual young people; however, evidence for mediating mechanisms is predominantly cross-sectional. Using a longitudinal design, we investigated self-esteem and depressive symptoms as mediators of increased rates of suicidal ideation or self-harm (SISH) among sexual minority youth, and the roles of childhood gender nonconformity (CGN) and sex as moderators of these relationships. Method In total, 4274 youth from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort reported sexual orientation at age 15 years, and past-year SISH at age 20 years. Self-esteem and depressive symptoms were assessed at ages 17 and 18 years, respectively. CGN was measured at 30–57 months. Covariates included sociodemographic variables and earlier measures of mediator and outcome variables. Mediation pathways were assessed using structural equation modelling. Results Sexual minority youth (almost 12% of the sample) were three times more likely than heterosexual youth to report past-year SISH (95% confidence interval 2.43–3.64) at 20 years. Two mediation pathways were identified: a single mediator pathway involving self-esteem and a multiple-mediated pathway involving self-esteem and depressive symptoms. Although CGN was associated with past-year SISH, it did not moderate any mediation pathways and there was no evidence for moderation by sex. Conclusions Lower self-esteem and increased depressive symptoms partly explain the increased risk for later suicidal ideation and self-harm in sexual minority youth. Preventive strategies could include self-esteem-enhancing or protecting interventions, especially in female sexual minority youth, and treatment of depression.
The magnitude of neuromuscular fatigue is not intensity dependent when cycling above critical power but relates to aerobic and anaerobic capacities

What is the central question of this study? Is the magnitude of neuromuscular fatigue dependent upon exercise intensity above critical power (CP) when W′ (the curvature constant of the power–duration relationship) is depleted? What is the main finding and its importance? The magnitude of neuromuscular fatigue is the same after two bouts of supra‐CP cycling (3 versus 12 min) when controlling for W′ depletion but is larger for individuals of greater anaerobic capacity after the shorter bout and smaller for individuals of greater aerobic capacity after the longer exercise bout. These findings provide new insight into the mechanisms underpinning exercise above CP.
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the stateaction space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.1
Improving POI Recommendation via Dynamic Tensor Completion

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.