Miracle-dz/UniFAR-FTUs huggingface dataset:
One-pot Fluorination and Organocatalytic Robinson Annulation for Asymmetric Synthesis of Mono- and Difluorinated Cyclohexenones

A one-pot fluorination and organocatalytic Robinson annulation sequence has been developed for asymmetric synthesis of 6-fluoroyclohex-2-en-1-ones and 4,6-difluorocyclohex-2-en-1-ones. The reactions promoted by cinchona alkaloid amine afforded products bearing two or three stereocenters in good to excellent yields with up to 99% ee and 20:1 dr.
Limb Skin Temperature as a Tool to Predict Orthostatic Instability

Orthostatic instability is one of the main consequences of weightlessness or gravity challenge and plays as well a crucial role in public health, being one of the most frequent disease of aging. Therefore, the assessment of effective countermeasures, or even the possibility to predict, and thus prevent orthostatic instability is of great importance. Heat stress affects orthostatic stability and may lead to impaired consciousness and decrease in cerebral perfusion, specifically during the exposure to G-forces. Conversely, peripheral cooling can prevent orthostatic intolerance – even in normothermic healthy subjects. Indicators of peripheral vasodilation, as elevated skin surface temperatures, may mirror blood decentralization and an increased risk of orthostatic instability. Therefore, the aim of this study was to quantify orthostatic instability risk, by assessing in 20 fighter jet pilot candidates’ cutaneous limb temperatures, with respect to the occurrence of G-force-induced almost loss of consciousness (ALOC), before and during exposure to a push-pull maneuver, i.e., head-down tilt, combined with lower body negative pressure. Peripheral skin temperatures from the upper and lower (both proximal and distal) extremities and core body temperature via heat-flux approach (i.e., the Double Sensor), were continuously measured before and during the maneuver. The 55% of subjects that suffered an ALOC during the procedure had higher upper arm and thigh temperatures at baseline compared to the 45% that remained stable. No difference in baseline core body temperature and distal limbs (both upper and lower) skin temperatures were found between the two groups. Therefore, peripheral skin temperature data could be considered a predicting factor for ALOC, prior to rapid onset acceleration. Moreover, these findings could also find applications in patient care settings such as in intensive care units.
Intraspinal calcinosis mimicking intervertebral disc extrusion: A clinical and surgical case report

Background: Subcutaneous calcinosis is a well-recognized manifestation of systemic sclerosis that usually involves multiple pressure points and may also be found in the paraspinal or intraspinal regions. In this case, intraspinal calcinosis uniquely led to a severe neurological deficit. Case Description: A patient with severe systemic sclerosis/calcinosis exhibited left greater than right lower extremity radiculopathy attributed to intraspinal left-sided L4-L5 calcinosis. On examination, the patient exhibited bilateral positive Lasegue signs, distal lower extremity weakness (left greater than right), and bilaterally decreased Achilles responses. When the magnetic resonance imaging (MRI) revealed a significant intracanalicular mass on the left side at the L4-L5 level, the patient underwent a left-sided L4-L5 decompressive laminectomy. The MRI scan 5 years later revealed no recurrence of the calcinosis, and the patient had no residual neurological deficit. Conclusions: Spinal calcinosis rarely involves the lumbar spinal canal. Here, a patient with a large left-sided L4-L5 focus of intraspinal calcinosis, mimicking a disc herniation, required a laminectomy to resect the lesion. Lumbar calcinosis should be radiologically evaluated utilizing using X-ray, MRI, and computed tomography studies to adequately document the pathology. Patients, when symptomatic, may require surgical decompression and excision of these lesions.
GanDouLing combined with Penicillamine improves cerebrovascular injury via PERK/eIF2α/CHOP endoplasmic reticulum stress pathway in the mouse model of Wilson’s disease

We aim to investigate the function and mechanism of GanDouLing combinated with Penicillamine on cerebrovascular injury in Wilson’s disease (WD). ELISA was performed to analyze the expression of vascular injury factors. Pathological changes of cerebral vessels were observed by HE stain. Immunohistochemistry assays were performed to analyze the expression of ICAM-1, VCAM-1, and GRP78. Western blotting was measured to analyze the expression of caspase-3, caspase-12, PERK, eIF2α, and CHOP. Apoptosis was detected with TUNEL assay. The expression of vascular injury factors and ICAM-1, VCAM-1 was significantly increased by WD and markedly decreased in GanDouLing-Penicillamine group. The expression of caspase-3, caspase-12, PERK, eIF2α, and CHOP were obviously expressed in Wilson group, GanDouLing-Penicillamine suppressed apoptosis and endoplasmic reticulum (ER) stress. Our findings suggested that GanDouLing-Penicillamine improved cerebrovascular injury through PERK/eIF2α/CHOP ER stress pathway in the mouse model of WD.
Automated Machine Learning Service Composition

Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper presents \tool, an algorithm for automated service composition applied to the area of machine learning. Empirically, we show that \tool is competitive and sometimes beats algorithms that solve the same task but not benefit of the advantages of a service model. Thereby, we present a real-world example that demonstrates the utility of automated service composition in contrast to non-service oriented solutions in the same area.
Matrix Difference in Pose-Graph Optimization

Pose-Graph optimization is a crucial component of many modern SLAM systems. Most prominent state of the art systems address this problem by iterative non-linear least squares. Both number of iterations and convergence basin of these approaches depend on the error functions used to describe the problem. The smoother and more convex the error function with respect to perturbations of the state variables, the better the least-squares solver will perform. In this paper we propose an alternative error function obtained by removing some non-linearities from the standard used one - i.e. the geodesic error function. Comparative experiments conducted on common benchmarking datasets confirm that our function is more robust to noise that affects the rotational component of the pose measurements and, thus, exhibits a larger convergence basin than the geodesic. Furthermore, its implementation is relatively easy compared to the geodesic distance. This property leads to rather simple derivatives and nice numerical properties of the Jacobians resulting from the effective computation of the quadratic approximation used by Gauss-Newton algorithm.
Post marketing study of hemodynamic and hematological noninvasive readings in a blood bank

Objectives: This validation test was conducted in the Fujisan Blood Bank, Fortaleza, Brazil and evaluated the noninvasive TensorTip MTX (MTX, Cnoga Medical Ltd.) readings of hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate compared to reference lab device readings. Generally, these parameters are measured from venous or capillary blood samples run on a laboratory analyzer or handheld invasive testing devices. Needle sticks are inconvenient to blood donors with relatively high exposure risks. To our vision, noninvasive determination would be of benefit to blood contributors and medical professional teams; it would be fast and painless. Methods: A total of 334 subjects were included in the Fujisan blood bank validation (65% male, 35% female). Hemoglobin, hematocrit, and red blood cells, as well as blood pressure and heart rate, were measured noninvasively using the MTX device and were compared to venous blood samples run on two laboratory hematology analyzers (Horiba ABX Micros60 and Siemens blood count analyzer), to digital sphygmomanometer (OMRON BP786) and to manual auscultation. The noninvasive measurement with the appropriate virtual arm cuff setting was performed simultaneously with the blood sample extraction of the reference devices measurement. Results: There was no statistically significant difference (p > 0.05, paired, two-tailed t-test) between the average daily hemoglobin, hematocrit, and red blood cells measurements provided by the MTX device and the laboratory hematology analyzer. In addition, there was no significant difference between the daily blood pressure and heart rate results provided by the MTX device and the digital and manual sphygmomanometers. The error calculated between the MTX and the reference device was found to be sufficiently accurate according to the relevant standards. Conclusion: The MTX accuracy of noninvasive hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate measurements satisfies the industrial standards; therefore, the device enables more accurate, efficient, and effective patient care.
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.
Optimal multi-source forecasting of seasonal influenza

Forecasting the emergence and spread of influenza viruses is an important public health challenge. Timely and accurate estimates of influenza prevalence, particularly of severe cases requiring hospitalization, can improve control measures to reduce transmission and mortality. Here, we extend a previously published machine learning method for influenza forecasting to integrate multiple diverse data sources, including traditional surveillance data, electronic health records, internet search traffic, and social media activity. Our hierarchical framework uses multi-linear regression to combine forecasts from multiple data sources and greedy optimization with forward selection to sequentially choose the most predictive combinations of data sources. We show that the systematic integration of complementary data sources can substantially improve forecast accuracy over single data sources. When forecasting the Center for Disease Control and Prevention (CDC) influenza-like-illness reports (ILINet) from week 48 through week 20, the optimal combination of predictors includes public health surveillance data and commercially available electronic medical records, but neither search engine nor social media data.
Identification of new-onset atrial fibrillation after cardiac surgery in Vietnam. A feasibility study of a novel screening strategy in a limited-resource setting: study protocol

Introduction Atrial fibrillation (AF) developing after cardiac surgery is the most common postoperative complication with an incidence up to 50%. The presence of postoperative AF is associated with significant morbidity, mortality and economic burden. However, in Vietnam, data on AF postcardiac surgery are limited, in part due to a shortage of screening equipment. This project aims to identify the incidence, risk factors and postoperative complications of new-onset postoperative AF after cardiac surgery, and the feasibility of introducing a novel screening strategy using the combination of two portable devices to detect AF. Methods and analysis This is a feasibility study examining patients who are (1) ≥18 years old; (2) undergoing coronary artery bypass graft and/or valve surgery and (3) in normal sinus rhythm prior to their operation. Patients with congenital heart disease, a prior history of AF or those who require a pacemaker after surgery will be excluded. All patients will be followed up for the duration of their hospitalisation. The screening strategy will include monitoring the continuous ECG tracing in the intensive care unit, and if AF is suspected, a 30 s lead-1 ECG will be recorded using the smartphone-based AliveCor Kardia Mobile. On the postoperative wards, blood pressure will be measured three times daily using a modified blood pressure device (Microlife BP200 Afib): and if AF is suspected a 30 s ECG will be recorded using the AliveCor Kardia Mobile. A 12-lead ECG may be ordered subsequently if clinically indicated. The primary outcome is the incidence of postoperative AF. Secondary outcomes include establishing the risk factors and complications associated with postoperative AF; and the barriers and facilitators of the screening strategy. Ethics and dissemination Ethics approval was granted by Scientific Board of Cardiovascular Centre, E Hospital on 28 September, 2017. Study results will be disseminated through local and international conferences and peer-reviewed publications.
Flower Strips in Wheat Intercropping System: Effect on Pollinator Abundance and Diversity in Belgium

The decline of pollinators in agricultural areas has been observed for some decades, this being partly due to landscape simplification in intensive agrosystems. Diversifying agricultural landscapes by sowing flower strips within fields could reduce these adverse effects on biodiversity. In this context, the study presented here aimed at assessing and comparing the abundance and diversity of bees (Hymenoptera: Anthophila) and hoverflies (Diptera: Syrphidae) found and visiting flowers in three types of flower strips in Belgium: (i) a mixture of 11 wild flowers, (ii) a monofloral strip of Dimorphoteca pluvialis (Asteraceae) and (iii) a monofloral strip of Camelina sativa (Brassicaceae), where the last two are considered to be intercrops since they are valuable on the market, all sown within a field of winter wheat (Triticum aestivum L.). Pollinators were captured with pan traps and by netting in standardised transects from May to July 2017. One-thousand one-hundred and eighty-four individuals belonging to 43 bee species and 18 hoverfly species were collected. Significant differences in hoverfly diversity were found between the different flower strips. The multifloral treatment supported a greater diversity of syrphid species. Various pollinator species visited the different flowers composing the mixture and also D. pluvialis. The pollinator community proved to be predominantly generalist, with the exception of an oligolectic species in Belgium, Andrena nitidiuscula. Moreover, the three tested flower strips were effective in attracting hoverflies, among them natural enemies of insect pests. This study opens new perspectives in the design of intercropping systems with flower strips towards the design of sustainable agro-ecosystems. Improving economic profitability of sowing flower strips could encourage farmers to diversify their agricultural systems and foster conservation biology strategies.
Handwriting Styles: Benchmarks and Evaluation Metrics

Extracting styles of handwriting is a challenging problem, since the style themselves are not well defined. It is a key component to develop systems with more personalized experiences for humans. In this paper, we propose baseline benchmarks, in order to set anchors to estimate the relative quality of different handwriting style methods. This will be done using deep learning techniques, which have shown remarkable results in different machine learning tasks, learning classification, regression, and most relevant to our work, generating temporal sequences. We discuss the challenges associated with evaluating our methods, which is related to evaluation of generative models in general. We then propose evaluation metrics, which we find relevant to this problem, and we discuss how we evaluate the performance metrics. In this study, we use IRON-OFF dataset [1]. To the best of our knowledge, no existing benchmarks or evaluation metrics for this task exit yet, and this dataset has not been used before in the context of handwriting synthesis.
Rapalog combined with CCR4 antagonist improves anticancer vaccines efficacy

mTOR pathway inhibitors such as rapalogs represent a promising tool to induce functional memory CD8 T cells. In our study, we investigated the combination of temsirolimus with anticancer vaccines. Using various designs of cancer vaccines (short and long peptides or the B subunit of Shiga toxin as an antigen delivery vector) and tumor models (melanoma, lung and colon cancer), we showed that the administration of temsirolimus efficiently decreased tumor growth and enhanced tumor‐specific CD8 T‐cell responses induced by vaccination. Furthermore, tumor‐specific CD8 T cells induced by the bi‐therapy (vaccine + temsirolimus) exhibit phenotypic characteristics of central memory (CD127+CD62L+) CD8 T cells compared to vaccination alone. We demonstrated that regulatory CD4 T cells (Tregs) expansion in vivo limits the efficacy of the bi‐therapy by altering the antitumor CD8 T‐cell responses. Finally, the use of a small molecule CCR4 antagonist to prevent Tregs induction considerably improved the efficacy of the bi‐therapy by enhancing CD8 T cells‐mediated antitumor immunity. Taken together, our study highlights the potential interest of combining cancer vaccines with drugs that promote memory CD8 T cells and inhibit Tregs.
Patch Correspondences for Interpreting Pixel-level CNNs

We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e.g., image synthesis and segmentation). It does so by reconstructing both a CNN's input and output image by copy-pasting corresponding patches from the training set with similar feature embeddings. To do so efficiently, it makes of a patch-match-based algorithm that exploits the fact that the patch representations learned by a CNN for pixel level tasks vary smoothly. Finally, we show that CompNN can be used to establish semantic correspondences between two images and control properties of the output image by modifying the images contained in the training set. We present qualitative and quantitative experiments for semantic segmentation and image-to-image translation that demonstrate that CompNN is a good tool for interpreting the embeddings learned by pixel-level CNNs.
A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle

Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning phenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and the smaller scope of solutions led to the poor results. To address this issue, this paper proposes a novel version of TLBO that is augmented with error correction strategy and Cauchy distribution (ECTLBO) in which Cauchy distribution is utilized to expand the searching space and error correction to avoid detours to achieve more accurate solutions. The experimental results verify that the ECTLBO algorithm has overall better performance than various versions of TLBO and is very competitive with respect to other nine original intelligence optimization algorithms. Finally, the ECTLBO algorithm is also applied to path planning of unmanned aerial vehicle (UAV), and the promising results show the applicability of the ECTLBO algorithm for problem-solving.
Query Log Compression for Workload Analytics

Analyzing database access logs is a key part of performance tuning, intrusion detection, benchmark development, and many other database administration tasks. Unfortunately, it is common for production databases to deal with millions or more queries each day, so these logs must be summarized before they can be used. Designing an appropriate summary encoding requires trading off between conciseness and information content. For example: simple workload sampling may miss rare, but high impact queries. In this paper, we present L OG R, a lossy log compression scheme suitable for use in many automated log analytics tools, as well as for human inspection. We formalize and analyze the space/fidelity trade-off in the context of a broader family of "pattern" and "pattern mixture" log encodings to which L OG R belongs. We show through a series of experiments that L OG R compressed encodings can be created efficiently, come with provable information-theoretic bounds on their accuracy, and outperform state-of-art log summarization strategies.
Bilinear Recovery Using Adaptive Vector-AMP

We consider the problem of jointly recovering the vector <inline-formula><tex-math notation="LaTeX">$\boldsymbol{b}$</tex-math></inline-formula> and the matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol{C}$</tex-math></inline-formula> from noisy measurements <inline-formula><tex-math notation="LaTeX">$\boldsymbol{Y} = \boldsymbol{A}(\boldsymbol{b})\boldsymbol{C} + \boldsymbol{W}$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$\boldsymbol{A}(\cdot)$</tex-math></inline-formula> is a known affine linear function of <inline-formula><tex-math notation="LaTeX">$\boldsymbol{b}$</tex-math></inline-formula> (i.e., <inline-formula><tex-math notation="LaTeX">$\boldsymbol{A}(\boldsymbol{b})=\boldsymbol{A}_0+\sum _{i=1}^Q b_i \boldsymbol{A}_i$</tex-math></inline-formula> with known matrices <inline-formula><tex-math notation="LaTeX">$\boldsymbol{A}_i$</tex-math></inline-formula>). This problem has applications in matrix completion, robust PCA, dictionary learning, self-calibration, blind deconvolution, joint-channel/symbol estimation, compressive sensing with matrix uncertainty, and many other tasks. To solve this bilinear recovery problem, we propose the Bilinear Adaptive Vector Approximate Message Passing (VAMP) algorithm. We demonstrate numerically that the proposed approach is competitive with other state-of-the-art approaches to bilinear recovery, including lifted VAMP and Bilinear Generalized Approximate Message Passing.