Miracle-dz/UniFAR-FTUs huggingface dataset:
Thymidylate kinase is critical for DNA repair via ATM‐dependent Tip60 complex formation

Cellular supply of deoxythymidine triphosphate (dTTP) is crucial for DNA replication and repair. Thymidylate kinase (TMPK) catalyzes the conversion of thymidine monophosphate to thymidine diphosphate, which is an essential step for dTTP synthesis. Despite their major cellular localization in cytosol, TMPK and ribonucleotide reductase (RNR) are detected at DNA damage sites for local dNDP formation. Because deoxyuridine diphosphate is synthesized by RNR, the simultaneous recruitment of TMPK and RNR to DNA damage sites is critical for preventing deoxyuridine triphosphate–mediated toxic repair. This study investigates the mechanism responsible for the recruitment of TMPK to DNA damage sites. Our data demonstrate the requirement of ataxia telangiectasia mutated (ATM) kinase activity for TMPK recruitment to DNA lesion sites. Moreover, we find that TMPK is able to form the complex with histone acetyltransferase Tip60 and RNR. Inhibition of ATM kinase reduces the complex formation and TMPK phosphorylation. Our analysis further shows the presence of TMPK phosphorylation at serine 88, which is an ATM kinase consensus site. A phosphorylation‐defective mutation at this site suppresses TMPK recruitment to DNA damage sites and the complex formation with Tip60. Finally, we provide evidence that this site is critical for the function of TMPK in DNA repair but not for catalytic activity. Together, these findings suggest that Tip60‐ATM signaling has a functional contribution to the recruitment of TMPK to DNA damage sites, thereby increasing local dTTP synthesis for DNA repair. —Hu, C. ‐M. , Tsao, N. , Wang, Y. ‐T. , Chen, Y. ‐J., Chang, Z. ‐F. Thymidylate kinase is critical for DNA repair via ATM‐dependent Tip60 complex formation. FASEB J. 33, 2017–2025 (2019). www.fasebj. org
Gestational Diabetes Alters the Metabolomic Profile in 2nd Trimester Amniotic Fluid in a Sex-Specific Manner

Maternal diabetes and obesity induce marked abnormalities in glucose homeostasis and insulin secretion in the fetus, and are linked to obesity, diabetes, and metabolic disease in the offspring, with specific metabolic characterization based on offspring sex. Gestational diabetes (GDM) has profound effects on the intrauterine milieu, which may reflect and/or modulate the function of the maternal–fetal unit. In order to characterize metabolic factors that affect offspring development, we profiled the metabolome of second trimester amniotic fluid (AF) from women who were subsequently diagnosed with gestational diabetes (GDM) using a targeted metabolomics approach, profiling 459 known biochemicals through gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) assays. Using a nested case-control study design, we identified 69 total biochemicals altered by GDM exposure, while sex-specific analysis identified 44 and 58 metabolites in male and female offspring, respectively. The most significant changes were in glucose, amino acid, glutathione, fatty acid, sphingolipid, and bile acid metabolism with specific changes identified based on the offspring sex. Targeted isotope dilution LC/MS confirmatory assays measured significant changes in docosahexaenoic acid and arachidonic acid. We conclude that the sex-specific alterations in GDM maternal–fetal metabolism may begin to explain the sex-specific metabolic outcomes seen in offspring exposed to GDM in utero.
Resolution and the binary encoding of combinatorial principles

Res(s) is an extension of Resolution working on s-DNFs. We prove tight nΩ(k) lower bounds for the size of refutations of the binary version of the k-Clique Principle in Res(o(log log n)). Our result improves that of Lauria, Pudlák et al. [27] who proved the lower bound for Res(1), i.e. Resolution. The exact complexity of the (unary) k-Clique Principle in Resolution is unknown. To prove the lower bound we do not use any form of the Switching Lemma [35], instead we apply a recursive argument specific for binary encodings. Since for the k-Clique and other principles lower bounds in Resolution for the unary version follow from lower bounds in Res(log n) for their binary version we start a systematic study of the complexity of proofs in Resolution-based systems for families of contradictions given in the binary encoding. We go on to consider the binary version of the weak Pigeonhole Principle Bin-PHPmn for m > n. Using the the same recursive approach we prove the new result that for any δ > 0, Bin-PHPmn requires proofs of size 2n1--δ in Res(s) for s = o(log1/2 n). Our lower bound is almost optimal since for m ≥ [EQUATION] there are quasipolynomial size proofs of Bin-PHPmn in Res(log n). Finally we propose a general theory in which to compare the complexity of refuting the binary and unary versions of large classes of combinatorial principles, namely those expressible as first order formulae in Π2-form and with no finite model.
AAG-Stega: Automatic Audio Generation-based Steganography

Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. Audio is one of the most common means of information transmission in our daily life. Thus it's of great practical significance to using audio as a carrier of information hiding. At present, almost all audio-based information hiding methods are based on carrier modification mode. However, this mode is equivalent to adding noise to the original signal, resulting in a difference in the statistical feature distribution of the carrier before and after steganography, which impairs the concealment of the entire system. In this paper, we propose an automatic audio generation-based steganography(AAG-Stega), which can automatically generate high-quality audio covers on the basis of the secret bits stream that needs to be embedded. In the automatic audio generation process, we reasonably encode the conditional probability distribution space of each sampling point and select the corresponding signal output according to the bitstream to realize the secret information embedding. We designed several experiments to test the proposed model from the perspectives of information imperceptibility and information hidden capacity. The experimental results show that the proposed model can guarantee high hidden capacity and concealment at the same time.
Multi-Objective Deep CNN for Outdoor Auto-Navigation

Target-guided navigation establishes the foundation for efficiently addressing vision-based multi-agent coordination for robotics. This work proposes a multi-objective deep convolution network which consists of two parallel branches built atop a shared feature extractor. The proposed network is capable of concurrently constructing semantic maps while achieving efficient visual detection of a designated guider robot or landmark towards outdoor navigation. In order to achieve the low latency requirements of the navigation controller, the structure and parameters of the network have been meticulously designed to boost run-time performance. The model is trained and tested on an altered version of the Cityscape outdoor dataset. We further finetune using a collected dataset in order to improve generalization performance on unseen outdoor scenes. Experimental results on an outdoor navigation robot equipped with an RGBD camera and GPU mini PC verifies the feasibility of the model.
A Robotic Auto-Focus System based on Deep Reinforcement Learning

Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an end-to-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method - discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based control problems. Separate phases of training in virtual and real environments are applied to obtain an effective model. Virtual experiments, which are carried out after the virtual training phase, indicates that our method could achieve 100% accuracy on a certain view with different focus range. Further training on real robots could eliminate the deviation between the simulator and real scenario, leading to reliable performances in real applications.
The Healing Effect of Human Milk Fat Globule-EGF Factor 8 Protein (MFG-E8) in A Rat Model of Parkinson’s Disease

We searched for drugs that alleviate the reduction of dopaminergic neurons caused by the administration of lipopolysaccharide (LPS) to the substantia nigra of the rat brain. Human milk fat globule-EGF factor 8 protein (MFG-E8) is similar to MFG-E8-S, a short isoform, of the mouse MFG-E8. However, the function of MFG-E8-S was not clear. Rats with LPS-induced Parkinson’s disease were prepared and the effects of human MFG-E8 were examined. MFG-E8 improved the significant reduction in mesencephalic dopamine neurons induced by the administration of LPS. LPS was administered to human induced pluripotent stem cell (iPSC)-derived dopaminergic neurons to induce inflammation and the effect of MFG-E8 was examined. MFG-E8 showed no toxicity toward neurons. We reanalyzed the results using public microarray data. MFG-E8 mRNA was found to be expressed in all parts of the body, particularly by adipose-derived stem cells (ADSCs). Furthermore, we investigated the culture supernatant of ADSCs using the liquid chromatography-tandem mass spectrometry (LC–MS/MS) analysis method and successfully identified the peptide of the MFG-E8 F5/8 type C domain. The results suggested that MFG-E8-S may have a preventive effect against Parkinson’s disease.
Online social networking addiction and depression: The results from a large-scale prospective cohort study in Chinese adolescents

Background and aims The aim of this study is to estimate the longitudinal associations between online social networking addiction (OSNA) and depression, whether OSNA predicts development of depression, and reversely, whether depression predicts development of OSNA. Methods A total of 5,365 students from nine secondary schools in Guangzhou, Southern China were surveyed at baseline in March 2014, and followed up 9 months later. Level of OSNA and depression were measured using the validated OSNA scale and CES-D, respectively. Multilevel logistic regression models were applied to estimate the longitudinal associations between OSNA and depression. Results Adolescents who were depressed but free of OSNA at baseline had 1.48 times more likely to develop OSNA at follow-up compared with those non-depressed at baseline [adjusted OR (AOR): 1.48, 95% confidence interval (CI): 1.14–1.93]. In addition, compared with those who were not depressed during the follow-up period, adolescents who were persistently depressed or emerging depressed during the follow-up period had increased risk of developing OSNA at follow-up (AOR: 3.45, 95% CI: 2.51–4.75 for persistent depression; AOR: 4.47, 95% CI: 3.33–5.99 for emerging depression). Reversely, among those without depression at baseline, adolescents who were classified as persistent OSNA or emerging OSNA had higher risk of developing depression compared with those who were no OSNA (AOR: 1.65, 95% CI: 1.01–2.69 for persistent OSNA; AOR: 4.29; 95% CI: 3.17–5.81 for emerging OSNA). Conclusion The findings indicate a bidirectional association between OSNA and depression, meaning that addictive online social networking use is accompanied by increased level of depressive symptoms.
Multimodal feedback for active robot-object interaction

In this work, we present a multimodal system for active robot-object interaction using laser-based SLAM, RGBD images, and contact sensors. In the object manipulation task, the robot adjusts its initial pose with respect to obstacles and target objects through RGBD data so it can perform object grasping in different configuration spaces while avoiding collisions, and updates the information related to the last steps of the manipulation process using the contact sensors in its hand. We perform a series of experiment to evaluate the performance of the proposed system following the the RoboCup2018 international competition regulations. We compare our approach with a number of baselines, namely a no-feedback method and visual-only and tactile-only feedback methods, where our proposed visual-and-tactile feedback method performs best.
Multidisciplinary groups learning to develop mobile applications from the challenge based learning methodology

For years traditional learning approaches have been applied in schools and universities, where teachers are considered the knowledge center, leaving students as secondary actors of learning processes. In order to investigate new learning methodologies a set of new approaches have been tried. Challenge Based Learning (CBL) is one of these proposals, created with the essential principles of a 21st century learning environment in mind. Since 2014, the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) have applied such methodology in the context whose multidisciplinary students from different courses learn to develop mobile apps, and work together looking for solutions that can impact lives. This paper presents how CBL was applied for two student groups, mentioning learned lessons and changes performed in the learning process from these lessons.
Synthesis of Bis(1,2,3-Triazole) Functionalized Quinoline-2,4-Diones †

Derivatives of 3-(1H-1,2,3-triazol-1-yl)quinoline-2,4(1H,3H)-dione unsubstituted on quinolone nitrogen atom, which are available by the previously described four step synthesis starting from aniline, were exploited as intermediates in obtaining the title compounds. The procedure involves the introduction of propargyl group onto the quinolone nitrogen atom of mentioned intermediates by the reaction of them with propargyl bromide in N,N-dimethylformamide (DMF) in presence of a potassium carbonate and the subsequent formation of a second triazole ring by copper catalyzed cyclisation reaction with azido compounds. The products were characterized by 1H, 13C and 15N NMR spectroscopy. The corresponding resonances were assigned on the basis of the standard 1D and gradient selected 2D NMR experiments (1H–1H gs-COSY, 1H–13C gs-HSQC, 1H–13C gs-HMBC) with 1H–15N gs-HMBC as a practical tool to determine 15N NMR chemical shifts at the natural abundance level of 15N isotope.
Association Between Meconium Acetaminophen and Childhood Neurocognitive Development in GESTE, a Canadian Cohort Study

Acetaminophen is the only over-the-counter pain reliever that is not contraindicated during pregnancy, but recent studies have questioned whether acetaminophen is safe for the fetus, particularly the developing brain. This prospective birth cohort study probed the previously observed association between in utero exposure to acetaminophen and neurodevelopment by using concentrations of acetaminophen measured in meconium, which more objectively captures exposure of the fetus than maternal report. Exposure, measured by liquid chromatography coupled with tandem mass spectrometry, was categorized into nondetection, low detection, and high detection levels. At age 6-8 years, children completed a set of subtests from the Wechsler Intelligence Scale for Children, 4th edition. Additionally, this study examined potential effect modification by child sex on the association between acetaminophen exposure and neurodevelopment. In fully adjusted models, in utero exposure to acetaminophen was not statistically significantly associated with decreased scores on any of the examined subtests in all children combined (n = 118). The effect of in utero acetaminophen exposure on the Coding subtest was marginally significantly different among boys and girls, with girls performing significantly better on the task with higher levels of acetaminophen compared with girls with undetectable levels of exposure (βgirls, low = 2.83 [0.97, 4.70], βgirls, high = 1.95 [-0.03, 3.93], βboys, low = .02 [-1.78, 1.81], βboys, high = -.39 [-2.09, 1.31], pinteraction = .06). Effect modification by child sex was not observed on other subtests. These results do not support prior reports of adverse neurodevelopmental effects of in utero exposure to acetaminophen.
Traveling through Space and Time, or: Making Historical Travelogues Accessible

Investigating perceptions of Otherness is the overarching goal of the Travelogues project. It studies a corpus comprising of thousands of recently digitized travelogues dating back to the 16th century held by the Austrian National Library. Driven by an interdisciplinary team of historians and data scientists, it aims at making knowledge that is now hidden in a huge text corpus accessible to researchers. In the current, initial project phase, we explore how statistical methods, such as word embeddings, can be used to assess the structure and semantics of large text corpora in order to make those resources accessible. We developed an initial methodology that combines visual and statistical cues for identifying possible starting points for a more fine-grained text corpus exploration. Ultimately, this data-driven approach is expected to result in new and possibly unexpected insights stemming from resources that were previously de-facto inaccessible.
Project STEP: Implementing the Veterans Health Administration’s Stepped Care Model of Pain Management

Objective The "stepped care model of pain management" (SCM-PM) prioritizes the role of primary care providers in optimizing pharmacological management and timely and equitable access to patient-centered, evidence-based nonpharmacological approaches, when indicated. Over the past several years, the Veterans Health Administration (VHA) has supported implementation of SCM-PM, but few data exist regarding changes in pain care resulting from implementation. We examined trends in prescribing and referral practices of primary care providers with hypotheses of decreased opioid prescribing, increased nonopioid prescribing, and increased referrals to specialty care for nonpharmacological services. Design An initiative was designed to foster implementation and systematic evaluation of the SCM-PM over a five-year period at the VA Connecticut Healthcare System (VACHS) while fostering collaborative, partnered initiatives to promote organizational improvements in the delivery of pain care. Subjects Participants were veterans receiving care at VACHS with at least one pain intensity rating ≥4/10 over the course of the study period (7/2008-6/2013). Methods We used electronic health record data to examine changes in indicators of pain care including pharmacy and health care utilization data. Results We observed hypothesized changes in long-term opioid and nonopioid analgesic prescribing and increased utilization of nonpharmacological treatments such as physical therapy, occupational therapy, and clinical health psychology. Conclusions Through a multifaceted comprehensive implementation approach, primary care providers demonstrated increases in guideline-concordant pain care practices. Findings suggest that engagement of interdisciplinary teams and partnerships to promote organizational improvements is a useful strategy to increase the use of integrated, multimodal pain care for veterans, consistent with VHA's SCM-PM.
Modelling User's Theory of AI's Mind in Interactive Intelligent Systems

Many interactive intelligent systems, such as recommendation and information retrieval systems, treat users as a passive data source. Yet, users form mental models of systems and instead of passively providing feedback to the queries of the system, they will strategically plan their actions within the constraints of the mental model to steer the system and achieve their goals faster. We propose to explicitly account for the user's theory of the AI's mind in the user model: the intelligent system has a model of the user having a model of the intelligent system. We study a case where the system is a contextual bandit and the user model is a Markov decision process that plans based on a simpler model of the bandit. Inference in the model can be reduced to probabilistic inverse reinforcement learning, with the nested bandit model defining the transition dynamics, and is implemented using probabilistic programming. Our results show that improved performance is achieved if users can form accurate mental models that the system can capture, implying predictability of the interactive intelligent system is important not only for the user experience but also for the design of the system's statistical models.
Breath hold effect on cardiovascular brain pulsations – A multimodal magnetic resonance encephalography study

Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem – hypothalamus – thalamus – amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.
Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation