Miracle-dz/UniFAR-FTUs huggingface dataset:
Who should provide care for patients receiving palliative chemotherapy? A qualitative study among Dutch general practitioners and oncologists

Abstract Introduction: While close collaboration between general practitioners (GPs) and hospital specialists is considered important, the sharing of care responsibilities between GPs and oncologists during palliative chemotherapy has not been clearly defined. Objective: Evaluate the opinions of GPs and oncologists about who should provide different aspects of care for patients receiving palliative chemotherapy. Design: We conducted semi-structured interviews using six hypothetical scenarios with purposively sampled GPs (n = 12) and oncologists (n = 10) in the Netherlands. Each represented an example of a clinical problem requiring different aspects of care: problems likely, or not, related to cancer or chemotherapy, need for decision support, and end-of-life care. Results: GPs and oncologists agreed that GPs should provide end-of-life care and that they should be involved in decisions about palliative chemotherapy; however, for the other scenarios most participants considered themselves the most appropriate provider of care. Themes that emerged regarding who would provide the best care for the patients in the different scenarios were expertise, continuity of care, accessibility of care, doctor–patient relationship, and communication. Most participants mentioned improved communication between the GP and oncologist as being essential for a better coordination and quality of care. Conclusion: GPs and oncologists have different opinions about who should ideally provide different aspects of care during palliative chemotherapy. Findings raise awareness of the differences in reasoning and approaches and in current communication deficits between the two groups of health professionals. These findings could be used to improve coordination and collaboration and, ultimately, better patient care as results demonstrated that both disciplines can add value to the care for patients with advanced cancer. Key points This study identified contrasting opinions of GPs and oncologists about who should provide different aspects of care for patients receiving palliative chemotherapy. Important themes that emerged were expertise, continuity of care, doctor-patient relations, accessibility of care, and communication. Although frequently using the same arguments, GPs and oncologists often considered themselves to be the most appropriate providers of palliative care.
The Association Between Resilience and Mental Health in the Somatically Ill.

BACKGROUND Resilience refers to an individual's positive adaptation to the experience of adversity. The maintenance of mental health is commonly considered a sign of successful coping with adverse conditions. The goal of the present meta-analysis was to investigate the association between resilience and mental health in patients with a somatic illness or health problem. METHODS Studies were included if they reported measures of association between resilience, as assessed using a version of Wagnild and Young's Resilience Scale, and self-reported mental health. A systematic literature search was conducted in the Medline, Web of Science, PsycInfo, PubPsych, and ProQuest databases and in the dissertation catalogue of the German National Library. In addition, a manual search was carried out. The study was registered with PROSPERO (registration number: CRD42017054822). RESULTS 55 studies involving a total of 15 003 patients were included in the meta-analysis. Assuming a random-effects model, the weighted mean Pearson correlation between resilience and mental health was r = 0.43 (95% confidence interval [0.39; 0.48], p<0.001). This association was robust, although the heterogeneity among individual effect sizes was substantial (I2 = 89.6%). Correlations tended to be weaker in unpublished studies than in published ones. CONCLUSION Despite substantial heterogeneity across studies, the findings suggest a strong association between resilience and mental health in the somatically ill. In clinical practice, a lack of resilience as a resource for successful coping might indicate a need for psychosocial support during treatment for somatic illness.
Carbon Quantum Dots Encapsulated Molecularly Imprinted Fluorescence Quenching Particles for Sensitive Detection of Zearalenone in Corn Sample

An eco-friendly and efficient one-step approach for the synthesis of carbon quantum dots (CDs) that encapsulated molecularly imprinted fluorescence quenching particles (MIFQP) and their application for the determination of zearalenone (ZEA) in a cereal sample are described in this study. CDs with high luminescence were first synthesized, and then encapsulated in the silica-based matrix through a non-hydrolytic sol-gel process. The resulting ZEA-imprinted particles exhibited not only an excellent specific molecular recognition of ZEA, but also good photostability and obvious template binding-induced fluorescence quenching. Under the optimized conditions, the fluorescence intensity of MIFQP was inversely proportional to the concentration of ZEA. By validation, the detection range of these fluorescence quenching materials for ZEA was between 0.02 and 1.0 mg L−1, and the detection limit was 0.02 mg L−1 (S/N = 3). Finally, the MIFQP sensor was successfully applied for ZEA determination in corn with recoveries from 78% to 105% and the relative standard deviation (RSD %) was lower than 20%, which suggests its potential in actual applications.
Learning to Doodle with Stroke Demonstrations and Deep Q-Networks

Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to draw real and more complex doodles without ground truth actions; thus, it is trained with Qlearning. Our experiments confirm that (1) doodling can be learned without direct stepby- step action supervision and (2) pretraining with stroke demonstration via supervised learning is important to improve performance. We further show that Doodle-SDQ is effective at producing plausible drawings in different media types, including sketch and watercolor.
Recent Advances in Mobile Grid and Cloud Computing

Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering areas. These systems include powerful computing resources connected through high-speed networks. Due to recent advances in mobile computing and networking technologies, it has become feasible to integrate various mobile devices such as robots, aerial vehicles, sensors, and smartphones with grid and cloud computing systems. This integration enables design and development of next generation of applications through sharing of resources in mobile environments and also introduces several challenges due to dynamic and unpredictable network. This paper discusses applications, research challenges involved in design and development of mobile grid and cloud computing systems, and recent advances in the field.
Overview of CAIL2018: Legal Judgment Prediction Competition

In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to predict the judgment results according to the given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for the contestants, i.e., predicting relevant law articles, charges and prison terms given the fact descriptions. CAIL2018 has attracted several hundreds participants (601 teams, 1, 144 contestants from 269 organizations). In this paper, we provide a detailed overview of the task definition, related works, outstanding methods and competition results in CAIL2018.
Self-contained development environments

Operating systems are traditionally implemented in low- level, performance-oriented programming languages. These languages typically rely on minimal runtime support and provide unfettered access to the underlying hardware. Tra- dition has benefits: developers control the resources that the operating system manages and few performance bottle- necks cannot be overcome with clever feats of programming. On the other hand, this makes operating systems harder to understand and maintain. Furthermore, those languages have few built-in barriers against bugs. This paper is an ex- periment in side-stepping operating systems, and pushing functionality into the runtime of high-level programming languages. The question we try to answer is how much sup- port is needed to run an application written in, say, Smalltalk or Python on bare metal, that is, with no underlying oper- ating system. We present a framework named NopSys that allows this, and we validate it with the implementation of CogNos a Smalltalk virtual machine running on bare x86 hardware. Experimental results suggest that this approach is promising.
An Instance Transfer based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

Recently, neural networks have shown promising results for named entity recognition (NER), which needs a number of labeled data to for model training. When meeting a new domain (target domain) for NER, there is no or a few labeled data, which makes domain NER much more difficult. As NER has been researched for a long time, some similar domain already has well labelled data (source domain). Therefore, in this paper, we focus on domain NER by studying how to utilize the labelled data from such similar source domain for the new target domain. We design a kernel function based instance transfer strategy by getting similar labelled sentences from a source domain. Moreover, we propose an enhanced recurrent neural network (ERNN) by adding an additional layer that combines the source domain labelled data into traditional RNN structure. Comprehensive experiments are conducted on two datasets. The comparison results among HMM, CRF and RNN show that RNN performs bette than others. When there is no labelled data in domain target, compared to directly using the source domain labelled data without selecting transferred instances, our enhanced RNN approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure.
Behavioral Modeling in Weight Loss Interventions

Designing systems with human agents is difficult because it often requires models that characterize agents' responses to changes in the system's states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals' weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution towards treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches which cannot.
Proteomic Characterization of Two Medically Important Malaysian Snake Venoms, Calloselasma rhodostoma (Malayan Pit Viper) and Ophiophagus hannah (King Cobra)

Calloselasma rhodostoma (CR) and Ophiophagus hannah (OH) are two medically important snakes found in Malaysia. While some studies have described the biological properties of these venoms, feeding and environmental conditions also influence the concentration and distribution of snake venom toxins, resulting in variations in venom composition. Therefore, a combined proteomic approach using shotgun and gel filtration chromatography, analyzed by tandem mass spectrometry, was used to examine the composition of venoms from these Malaysian snakes. The analysis revealed 114 proteins (15 toxin families) and 176 proteins (20 toxin families) in Malaysian Calloselasma rhodostoma and Ophiophagus hannah species, respectively. Flavin monoamine oxidase, phospholipase A2, phosphodiesterase, snake venom metalloproteinase, and serine protease toxin families were identified in both venoms. Aminopeptidase, glutaminyl-peptide cyclotransferase along with ankyrin repeats were identified for the first time in CR venom, and insulin, c-type lectins/snaclecs, hepatocyte growth factor, and macrophage colony-stimulating factor together with tumor necrosis factor were identified in OH venom for the first time. Our combined proteomic approach has identified a comprehensive arsenal of toxins in CR and OH venoms. These data may be utilized for improved antivenom production, understanding pathological effects of envenoming, and the discovery of biologically active peptides with medical and/or biotechnological value.
People logistics in smart cities

I M A G E B Y T O S T P H O T O C I T I E S I N C H I N A are growing rapidly in terms of both size and complexity. Governments have been searching for new technologies to make cities more efficient, and smart mobility has been the top priority in all solutions. The past few years have seen a paradigm shift for smart mobility in China, that is, data-centric companies, mostly Internet companies, are taking a leading role in such initiatives instead of governments and academic researchers. For example, Alibaba, Baidu, Tencent, Ctrip, and Didi, among others, are spearheading the smart mobility initiatives. The driving force is twofold: these companies have accumulated a huge volume of data and invested a great deal of resources in the AI arena. Their main focus involves AI systems able to predict city traffic conditions with full spatiotemporal coverage and optimize transportation systems accordingly. The blossoming of smart mobility initiatives is due to the fact the potential of big city traffic data has not been fully mined. Researchers are still pursuing a better way to break the data silos while also preserving privacy. Fortunately, we have seen significant efforts from both industries and governments in China to promote data sharing for innovations. Here, we will focus on the smart mobility scenarios that are representative for cities in China. We will elaborate on two aspects: in-city and intercity transport. For the in-city scenario, we take Alibaba’s City Brain program as an example to introduce how the big city data can be used to optimize the traffic signal scheduling and accelerate access for life-saving emergency vehicles (EV). For the intercity scenario, we focus on the weekend/holiday crowdedness unique to China, and introduce how big tourism data can be leveraged to divert tourists to more suitable attractions to avoid traffic congestion.
Text-based Sentiment Analysis and Music Emotion Recognition

Sentiment polarity of tweets, blog posts or product reviews has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. Deep learning techniques are becoming top performers on analyzing such texts. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First, deep neural networks need to be fed with data sets that are big in size as well as properly labeled. Second, there are various uncertainties regarding the use of word embedding vectors: should they be generated from the same data set that is used to train the model or it is better to source them from big and popular collections? Third, to simplify model creation it is convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song data sets are created utilizing social tags. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted, trying word embeddings of various parameters. Regarding the third problem, a series of experiments involving convolution and max-pooling neural layers were conducted. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews.
Effect of purified anthocyanins or anthocyanin-rich extracts on C-reactive protein levels: a systematic review and meta-analysis of randomised clinical trials

Abstract As the results of clinical trials are inconsistent, we conducted this research to assess the effect of purified anthocyanins or anthocyanin-rich extract supplementation on C-reactive protein (CRP) levels. We searched several databases to identify and extract data on characteristics, methods and outcomes of the eligible randomised controlled trials (RCT). A random-effects model, weighted mean difference (WMD) and 95 % CI were applied for data analysis. To investigate the effects of the study quality score or design on our results, we performed the same analysis by excluding the studies of Karlsen et al., with the lowest quality score, and Hassellund et al., with a cross-over design. Meta-analysis showed that anthocyanins had no significant impact on CRP levels (WMD=0·018; 95 % CI –0·44, 0·47; P=0·94). Although the effect of anthocyanins was independent of supplementation duration (slope: 0·01; 95 % CI –0·002, 0·03; P=0·08), their effect depended on the dose of anthocyanins (slope: 0·001; 95 % CI 0·0007, 0·002; P<0·001). However, no significant relationship was found between the anthocyanin dosage and CRP levels after excluding the studies of Karlsen et al. and Hassellund et al. Finally, anthocyanins had no effect on CRP levels regarding healthy participants, patients and types of anthocyanins. Although changes in CRP concentrations had no association with trial duration, a significant relationship was found between anthocyanin dosage and CRP level. No significant result was observed between the anthocyanin dosage and CRP levels after excluding the mentioned studies. Further well-designed RCT are needed to validate these findings.
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings these challenges to the fore. Human pilots can fly a previously unseen track after a handful of practice runs. In contrast, state-of-the-art autonomous navigation algorithms require either a precise metric map of the environment or a large amount of training data collected in the track of interest. To bridge this gap, we propose an approach that can fly a new track in a previously unseen environment without a precise map or expensive data collection. Our approach represents the global track layout with coarse gate locations, which can be easily estimated from a single demonstration flight. At test time, a convolutional network predicts the poses of the closest gates along with their uncertainty. These predictions are incorporated by an extended Kalman filter to maintain optimal maximum-a-posteriori estimates of gate locations. This allows the framework to cope with misleading high-variance estimates that could stem from poor observability or lack of visible gates. Given the estimated gate poses, we use model predictive control to quickly and accurately navigate through the track. We conduct extensive experiments in the physical world, demonstrating agile and robust flight through complex and diverse previously-unseen race tracks. The presented approach was used to win the IROS 2018 Autonomous Drone Race Competition, outracing the second-placing team by a factor of two.
Influence of interactional structure on patient’s participation during interprofessional discharge planning meetings in rehabilitation centers

ABSTRACT Interprofessional practice has become increasingly important. In addition, patients are expected to participate more actively in health-care decisions. While comprehensive discharge planning has been shown to be effective, it is unclear how interactional structure influences patients’ participation during discharge planning meetings. The aims of this qualitative study were to examine the interactional structure of interprofessional meetings in two rehabilitation clinics and to identify patients’ types of communicative involvement (patient participation) during discharge planning meetings. Using an ethnomethodological approach and Conversation Analysis, 121 interprofessional meetings were video-recorded (19 hours of recordings). Twenty-five patients (30– 87 years) with neurological or musculoskeletal disorders and their teams were included. The findings revealed two types of meetings aimed at either (a) exchanging information with team members and patient (“information exchange meeting”) or (b) negotiating care plans with patients and the team. “Negotiation meetings” were often led by allied health professionals or nurses and were characterized by active patient participation. Those meetings offered patients an opportunity to give additional information rather than only ask questions. The discussion includes reflections on how interactional analysis can help understand the social organization of meetings and how patient participation can be enhanced in this context and concludes with practice implications.
Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome–Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure–activity data sets in drug discovery to predict ligand–protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.