Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Annual Fluctuations in Malaria Transmission Intensity and in the Use of Malaria Control Interventions in Five Sub-Saharan African Countries

Abstract. RTS,S/AS01E malaria vaccine safety, effectiveness, and impact will be assessed in pre- and post-vaccine introduction studies, comparing the occurrence of malaria cases and adverse events in vaccinated versus unvaccinated children. Because those comparisons may be confounded by potential year-to-year fluctuations in malaria transmission intensity and malaria control intervention usage, the latter should be carefully monitored to adequately adjust the analyses. This observational cross-sectional study is assessing Plasmodium falciparum parasite prevalence (PfPR) and malaria control intervention usage over nine annual surveys performed at peak parasite transmission. Plasmodium falciparum parasite prevalence was measured by microscopy and nucleic acid amplification test (quantitative PCR) in parallel in all participants, and defined as the proportion of infected participants among participants tested. Results of surveys 1 (S1) and 2 (S2), conducted in five sub-Saharan African countries, including some participating in the Malaria Vaccine Implementation Programme (MVIP), are reported herein; 4,208 and 4,199 children were, respectively, included in the analyses. Plasmodium falciparum parasite prevalence estimated using microscopy varied between study sites in both surveys, with the lowest prevalence in Senegalese sites and the highest in Burkina Faso. In sites located in the MVIP areas (Kintampo and Kombewa), PfPR in children aged 6 months to 4 years ranged from 24.8% to 27.3%, depending on the study site and the survey. Overall, 89.5% and 86.4% of children used a bednet in S1 and S2, of whom 68.7% and 77.9% used impregnated bednets. No major difference was observed between the two surveys in terms of PfPR or use of malaria control interventions.
Host-induced spermidine production in motile Pseudomonas aeruginosa triggers phagocytic uptake

Exploring the complexity of host–pathogen communication is vital to understand why microbes persist within a host, while others are cleared. Here, we employed a dual-sequencing approach to unravel conversational turn-taking of dynamic host–pathogen communications. We demonstrate that upon hitting a host cell, motile Pseudomonas aeruginosa induce a specific gene expression program. This results in the expression of spermidine on the surface, which specifically activates the PIP3-pathway to induce phagocytic uptake into primary or immortalized murine cells. Non-motile bacteria are more immunogenic due to a lower expression of arnT upon host-cell contact, but do not produce spermidine and are phagocytosed less. We demonstrate that not only the presence of pathogen inherent molecular patterns induces immune responses, but that bacterial motility is linked to a host-cell-induced expression of additional immune modulators. Our results emphasize on the value of integrating microbiological and immunological findings to unravel complex and dynamic host–pathogen interactions.
Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism

Cancer stem cells (CSCs) contribute to chemoresistance and tumor relapse. By using the distinct metabolic phenotype of CSC, we designed novel PET parameters for CSC metabolism and investigated their clinical values. Patients with breast cancer who underwent 18F-FDG PET/CT before neoadjuvant chemotherapy (NAC) were retrospectively included. We developed a method to measure CSC metabolism using standardized uptake value histogram data. The predictive value of novel CSC metabolic parameters for pathologic complete response (pCR) was assessed with multivariable logistic regression. The association between the CSC parameter and disease-free survival (DFS) was also determined. We identified 82 patients with HER2-positive/triple-negative subtypes and 38 patients with luminal tumors. After multivariable analysis, only metabolic tumor volume for CSC (MTVcsc) among metabolic parameters remained the independent predictor of pCR (OR, 0.12; p = 0.022). MTVcsc successfully predicted pathologic tumor response to NAC in HER2-positive/triple-negative subtypes (accuracy, 74%) but not in the luminal subtype (accuracy, 29%). MTVcsc was also predictive of DFS, with a 3-year DFS of 90% in the lower MTVcsc group (<1.75 cm3) versus 72% in the higher group (>1.75 cm3). A novel data-driven PET parameter for CSC metabolism provides early prediction of pCR after NAC and DFS in HER2-positive and triple-negative subtypes.
Population dynamics and reproductive aspects of the decorator crab Microphrys bicornutus (Crustacea: Brachyura: Majidae) in an extreme shallow Thalassia complex

The decorator crab, Microphrys bicornutus, is a common inhabitant of the turtle grass Thalassia testudinum. In order to establish the behaviour of a population of this species in an extreme shallow Thalassia complex (maximum depth less than 50 cm, and often exposed to the air), size distribution, population dynamics, reproductive effort and egg size were studied in Buchuaco—Venezuela. Monthly sampling was undertaken between June 1988 and December 1990. A total of 1403 specimens of M. bicornutus were captured. In each of the sampled years, size distribution was strongly skewed to the predominance of small size crabs. Females were significantly larger than males in all the three years. Small size and large size crabs showed periodic annual fluctuations in abundance, with peaks during the last months of the year for the first, and peaks between April and June for the latter. Medium size crabs showed no periodicity at all. Number of eggs per female ranged between 40 and 4305 (mean = 1067.4), and was positively correlated with female body size. Eggs increased their size gradually during their development, and differences in egg size between development stages were significant. The biological parameters found in this study are compared with other populations of this crab, found in Venezuela and Brazil, in different ecosystems. Results acquired in this work add new insights to the behaviour of this decorator crab, specifically on density variation and egg size (among others), living in an environment that is constantly exposed to air and to other physicochemical variations.
FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.
Iterative K-Closest Point Algorithms for Colored Point Cloud Registration

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.
Effect of Pre-Storage Application of Spermidine, Calcium Chloride and Hot Water on Chilling Injury of Cold Stored Pomegranate

Pomegranate (Punica granatum L. 'Malas Yazdi') fruit are very susceptible to chilling injury during cold storage. A series of experiments were conducted to test the effect of polyamine, calcium and hot water on chilling injury of pomegranate. Fruit were dipped in 2 and 4% calcium chloride solution alone or in combination with 1 and 2 mM spermidine and/or hot water (45°C) for 4 min. The treated fruit were air-dried at 24±1°C and stored at 2°C with 85±5% RH for 4.5 months. The results showed that all treatments had ameliorative effect on chilling injury and keeping quality after shelf life. Moreover, 2% calcium chloride in combination with 2 mM spermidine significantly reduced chilling injury, browning, water loss, electrolyte and K + leakage. Fruit dipped in cold and hot water had the highest TSS (16.63 and 16.46%, respectively) and the lowest total antioxidant activity (15.3 and 20.3%, respectively). Fruit treated with 2% calcium chloride in combination with 1 or 2 mM spermidine had the highest titratable acidity (1.71 and 1.67%, respectively). The results reported herein, showed that the effect of chemicals on chilling injury was more pronounced than that of heat treatment.
Impact evaluations of drug decriminalisation and legal regulation on drug use, health and social harms: a systematic review

Objectives To review the metrics and findings of studies evaluating effects of drug decriminalisation or legal regulation on drug availability, use or related health and social harms globally. Design Systematic review with narrative synthesis. Data sources We searched MEDLINE, Embase, PsycINFO, Web of Science and six additional databases for publications from 1 January 1970 through 4 October 2018. Inclusion criteria Peer-reviewed articles or published abstracts in any language with quantitative data on drug availability, use or related health and social harms collected before and after implementation of de jure drug decriminalisation or legal regulation. Data extraction and synthesis Two independent reviewers screened titles, abstracts and articles for inclusion. Extraction and quality appraisal (modified Downs and Black checklist) were performed by one reviewer and checked by a second, with discrepancies resolved by a third. We coded study-level outcome measures into metric groupings and categorised the estimated direction of association between the legal change and outcomes of interest. Results We screened 4860 titles and 221 full-texts and included 114 articles. Most (n=104, 91.2%) were from the USA, evaluated cannabis reform (n=109, 95.6%) and focussed on legal regulation (n=96, 84.2%). 224 study outcome measures were categorised into 32 metrics, most commonly prevalence (39.5% of studies), frequency (14.0%) or perceived harmfulness (10.5%) of use of the decriminalised or regulated drug; or use of tobacco, alcohol or other drugs (12.3%). Across all substance use metrics, legal reform was most often not associated with changes in use. Conclusions Studies evaluating drug decriminalisation and legal regulation are concentrated in the USA and on cannabis legalisation. Despite the range of outcomes potentially impacted by drug law reform, extant research is narrowly focussed, with a particular emphasis on the prevalence of use. Metrics in drug law reform evaluations require improved alignment with relevant health and social outcomes.
STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels

The Deep learning of optical flow has been an active area for its empirical success. For the difficulty of obtaining accurate dense correspondence labels, unsupervised learning of optical flow has drawn more and more attention, while the accuracy is still far from satisfaction. By holding the philosophy that better estimation models can be trained with better-approximated labels, which in turn can be obtained from better estimation models, we propose a self-taught learning framework to continually improve the accuracy using self-generated pseudo labels. The estimated optical flow is first filtered by bidirectional flow consistency validation and occlusion-aware dense labels are then generated by edge-aware interpolation from selected sparse matches. Moreover, by combining reconstruction loss with regression loss on the generated pseudo labels, the performance is further improved. The experimental results demonstrate that our models achieve state-of-the-art results among unsupervised methods on the public KITTI, MPI-Sintel and Flying Chairs datasets.
Structural basis of AMPA receptor inhibition by 4-BCCA.

BACKGROUND AND PURPOSE AMPA receptors, which shape excitatory postsynaptic currents and are directly involved in overactivation of synaptic function during seizures, represent a well-accepted target for antiepileptic drugs (AEDs). Trans-4-butylcyclohexane carboxylic acid (4-BCCA) has emerged as a new promising AED in multiple in vitro and in vivo seizure models but the mechanism of its action remained unknown. The purpose of this study is to characterize structure and dynamics of 4-BCCA interaction with AMPA receptors. EXPERIMENTAL APPROACH We studied the molecular mechanism of AMPA receptor inhibition by 4-BCCA using a combination of X-ray crystallography, mutagenesis, electrophysiology and molecular dynamics simulations. KEY RESULTS We identified 4-BCCA binding sites in the transmembrane domain (TMD) of AMPA receptor, at the lateral portals formed by transmembrane segments M1-M4. At this binding site, 4-BCCA is very dynamic, assumes multiple poses and can enter the ion channel pore. CONCLUSION AND IMPLICATIONS 4-BCCA represents a low-affinity inhibitor of AMPA receptors that acts at the TMD sites distinct from noncompetitive inhibitors, such as AED perampanel, and ion channel blockers. Future studies might examine a possibility of synergistic use of these inhibitors in treatment of epilepsy and a wide range of neurological disorders and gliomas.
When sexism is not a problem: The role of perceived intelligence in willingness to interact with someone who is sexist

ABSTRACT Two studies examined factors that would influence people’s preferences for interaction with a perpetrator of sexism. In Study 1 (n = 348), participants preferred to interact (being friends or developing a relationship) with an intelligent person regardless of whether or not that person was sexist. Study 2 (n = 614) replicated this finding and confirmed that where a perpetrator had a high level of intelligence, people were more willing to interact with them, regardless of the perpetrator’s sex and the perceived commission or non-commission of sexist behavior. Moreover, Study 2 provides evidence that participants’ hostile sexism beliefs are a significant covariate of a willingness to interact with unintelligent women. The results are discussed in terms of their implications for the understanding of person perception.
The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

Background Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends. Objective This study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time. Methods Using data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users’ data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results. Results On the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set. Conclusions Diabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.
Unique Pharmacological Properties of the Kappa Opioid Receptor Signaling Through Gαz as Shown with Bioluminescence Resonance Energy Transfer

Opioid receptors (ORs) convert extracellular messages to signaling events by coupling to the heterotrimeric G proteins, Gα•βγ. Classic pharmacological methods, such as [35S]GTPγS binding and inhibition of cyclic AMP production, allow for general opioid characterization, but they are subject to the varying endogenous Gα proteins in a given cell type. Bioluminescence resonance energy transfer (BRET) technology offers new insight by allowing the direct observation of Gα subunit–specific effects on opioid pharmacology. Using a Venus-tagged Gβγ and nanoluciferase-tagged truncated G protein receptor kinase 3, an increase in BRET signal correlated with OR activation mediated by a specific Gα protein. The magnitude of the BRET signal was normalized to the maximum response obtained with 10 µM 2-(3,4-dichlorophenyl)-N-methyl-N-[(1R,2R)-2-pyrrolidin-1-ylcyclohexyl]acetamide (U50,488) for the kappa OR (KOR). Opioids reached equilibrium with the KOR, and concentration-response curves were generated. Although the full agonists U50,488, salvinorin A, nalfurafine, and dynorphin peptides were equally efficacious regardless of the Gα subunit present, the concentration-response curves were leftward shifted when the KOR was signaling through Gαz compared with other Gαi/o subunits. In contrast, the Gα subunit distinctly affected both the efficacy and potency of partial kappa agonists, such as the benzomorphans, and the classic mu opioid antagonists, naloxone, naltrexone, and nalmefene. For example, (-)pentazocine had EC50 values of 7.3 and 110 nM and maximal stimulation values of 79% and 35% when the KOR signaled through Gαz and Gαi1, respectively. Together, these observations suggest KOR pharmacology varies based on the specific Gα subunit coupled to the KOR. SIGNIFICANCE STATEMENT Opioid receptors couple to various heterotrimeric Gαβγ proteins to convert extracellular cues to precise intracellular events. This paper focuses on how the various inhibitory Gα subunits influence the pharmacology of full and partial agonists at the kappa opioid receptor. Using a bioluminescent assay, the efficacy and potency of kappa opioids was determined. Opioid signaling was more potent through Gαz compared with other Gα proteins. These observations suggest that Gαz may impact opioid pharmacology and cellular physiology more than previously thought.