Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in growth, anaemia, and iron deficiency among children aged 6–23 months in two districts in Nepal that were part of the post‐pilot scale‐up of an integrated infant and young child feeding and micronutrient powder intervention

Abstract There is limited research on integrated infant and young child feeding (IYCF) and micronutrient powders (MNPs) programmes operating at scale, despite widespread implementation. This study uses cross‐sectional baseline (n = 2,542) and endline (n = 2,578) surveys representative of children 6–23 months in two districts in Nepal that were part of a post‐pilot scale‐up of a IYCF–MNP programme. Multivariable log‐binomial regression models were used to estimate prevalence ratios (PRs) for stunting (length‐for‐age z‐score <−2), wasting (weight‐for‐length z‐score <−2), underweight (weight‐for‐age z‐score <−2), anaemia (altitude‐adjusted haemoglobin <110 μg/L), moderate or severe anaemia (altitude‐adjusted haemoglobin <100 g/L), iron deficiency (inflammation‐adjusted ferritin <12 μg/L), and iron deficiency anaemia (iron deficiency + anaemia [IDA]) at endline versus baseline and also to compare children in the endline survey based on frequency of mothers' interactions with female community health volunteers (FCHVs; >1× per month or monthly vs. <1× per month) and MNP coverage (1 or ≥2 distributions vs. none among children 12–23 months). Endline children were significantly less likely to be stunted than baseline children in both districts (multivariable‐adjusted PR [95% CI]: 0.77 [0.69, 0.85], P < 0.001 and 0.82 [0.75, 0.91], P < 0.001 in Kapilvastu and Achham, respectively); however, only Achham had significantly lower prevalences of underweight, moderate/severe anaemia, iron deficiency, and IDA at endline. At endline, 53.5% and 71.4% of children had tried MNP in Kapilvastu and Achham districts, respectively, consuming an average of 24 sachets from the last distribution. Frequent maternal–FCHV interactions were associated with a reduced risk of stunting and underweight at endline, whereas repeat MNP coverage was associated with reduced risk of anaemia and IDA. Future research using experimental designs should verify the potential of integrated IYCF–MNP programmes to improve children's nutritional status.
High Speed Whole Body Dynamic Motion Experiment with Real Time Master-Slave Humanoid Robot System

In this paper, we propose novel methods suitable for online real time whole body master-slave control with real life-sized humanoid robot. We conducted some dynamic whole body master-slave experiment with life-sized humanoid robot, and we achieved speedier and flexible master-slave operation compared to conventional study. Conventionally, master-slave operations with humanoid robots were available with only the upper body of the humanoid robot, and the COM movement was limited to be static. In our previous study, we introduced LIP model based restrictions to ensure the balance stability. In this study, we extend the safety restrictions by introducing foot landing delay prediction and trajectory smoothing method suitable for real robot. We conducted master-slave tennis swing experiment and high kick motion experiment with life-sized humanoid robot “JAXON”, and we evaluated the effectiveness of our proposed methods and system.
Predictors of Sleep Apnea in the Canadian Population

Older age, obesity, hypertension, snoring, and excessive daytime sleepiness have been associated with sleep apnea. The objective of this study was to determine the prevalence (crude and adjusted), as well as the risk factors, of sleep apnea in the adult Canadian population. Data from the 2009 Sleep Apnea Rapid Response (SARR) questionnaire were used to identify the risk factors, and all sleep-related questions in the SARR questionnaire were used. The outcome variable of interest was health professional-diagnosed sleep apnea. Covariates of interest were demographic variables, population characteristics, respiratory and cardiovascular diseases, and enabling resources. The multiple logistic regression model adjusted for the clustering effect was used to analyze the data. Sleep apnea was diagnosed in 858,913 adults (3.4% of the population), and more men (65.4%) than women (34.6%) were diagnosed with sleep apnea. Multivariable logistic regression analysis indicated that age (45 and older), loud snoring, sudden awakening with gasping/choking (rare/sometimes and once or more a week), and nodding off/falling asleep in driving in the past 12 months were significantly associated with diagnosed sleep apnea. Predictive probability demonstrated that in overweight and obese persons, ≥15 minutes of daily exercise significantly decreased the risk of diagnosed sleep apnea. The conclusion of this study is that in the Canadian population, sleep apnea is associated with older age, loud snoring, and sleeping problems. The protective effect of exercise warrants further investigation.
Fluorimetric detection of reserpine in mouse serum through online post-column electrochemical derivatization

A novel method combining high-performance liquid chromatography with online post-column electrochemical derivatization and fluorescence detection was established for the detection of reserpine in mouse serum. Reserpine separation was conducted using a C18 column with 5 mM H3PO4 and acetonitrile (55/45, v/v) as eluent. Reserpine was then electro-oxidized into a strongly fluorescent compound using an electrolytic cell device. Detection parameters, such as potential and fluorescence wavelength, were optimized. The linearity of the proposed method ranged from 0.01 to 5.0 mg l−1 with a correlation coefficient of 0.9997. The limit of qualification (S/N = 10) and limit of detection (S/N = 3) were 9.7 and 2.9 µg l−1, respectively. Resperine recoveries from spiked blank and drug-treated mouse serum samples ranged from 92.0 to 115%.
The Key Factors of Successful Internet Crowdfunding Projects - An Empirical Study Based on Different Platforms

Crowdfunding, as a new conception in fundraising, provides access to get fund in the background of Internet. This paper has conducted a research on the key factors to highly successful crowdfunding based on two biggest crowdfunding platforms in China - JingDong crowdfunding platform and TaoBao crowdfunding platform. By using Logistic regression model, we find that different platform designs and different mechanisms have effect on the project success. Meanwhile, quality signals that the founders show and their own experience do have a positive association with high success of projects. Other factors that are visible directly on crowdfunding website are also been discussed.
DeepInsight: Multi-Task Multi-Scale Deep Learning for Mental Disorder Diagnosis

We propose a novel deep learning approach, called DeepInsight, to quick diagnosis of autism spectrum disorder (ASD) and major depressive disorder (MDD). Our approach is motivated by recent advances in artiﬁcial intelligence (AI) for healthcare. In particular, researchers have found distinct differences between facial characteristics of children with ASD and those of typically developing children. Based upon these ﬁndings, we choose to explore deep learning in extracting discriminative facial features. However, for ASD diagnosis, the labelled data are far from sufﬁcient for training a deep learning model. Therefore, by considering the two typical mental disorders (i.e. ASD and MDD) together, we develop a multi-task deep learning model to augment the labelled data for each diagnosis task. Moreover, we also induce multi-scale combination into the proposed model to learn more discriminative facial features. Experimental results demonstrate the effectiveness and efﬁciency of our approach to mental disorder diagnosis.
Identification, functional characterization, and crystal structure determination of bacterial levoglucosan dehydrogenase

Levoglucosan is the 1,6-anhydrosugar of d-glucose formed by pyrolysis of glucans and is found in the environment and industrial waste. Two types of microbial levoglucosan metabolic pathways are known. Although the eukaryotic pathway involving levoglucosan kinase has been well-studied, the bacterial pathway involving levoglucosan dehydrogenase (LGDH) has not been well-investigated. Here, we identified and cloned the lgdh gene from the bacterium Pseudarthrobacter phenanthrenivorans and characterized the recombinant protein. The enzyme exhibited high substrate specificity toward levoglucosan and NAD+ for the oxidative reaction and was confirmed to be LGDH. LGDH also showed weak activities (∼4%) toward l-sorbose and 1,5-anhydro-d-glucitol. The reverse (reductive) reaction using 3-keto-levoglucosan and NADH exhibited significantly lower Km and higher kcat values than those of the forward reaction. The crystal structures of LGDH in the apo and complex forms with NADH, NADH + levoglucosan, and NADH + l-sorbose revealed that LGDH has a typical fold of Gfo/Idh/MocA family proteins, similar to those of scyllo-inositol dehydrogenase, aldose–aldose oxidoreductase, 1,5-anhydro-d-fructose reductase, and glucose–fructose oxidoreductase. The crystal structures also disclosed that the active site of LGDH is distinct from those of these enzymes. The LGDH active site extensively recognized the levoglucosan molecule with six hydrogen bonds, and the C3 atom of levoglucosan was closely located to the C4 atom of NADH nicotinamide. Our study is the first molecular characterization of LGDH, providing evidence for C3-specific oxidation and representing a starting point for future biotechnological use of LGDH and levoglucosan-metabolizing bacteria.
A Real-World Evaluation of Energy Budget Estimation Algorithms for Autonomous Long Range IoT Nodes

In order to enable IoT nodes to efficiently use their energy harvesting capabilities, algorithms are used to determine a reasonable energy budget and allocate it to the node tasks, enabling energy neutral operation. However, most of these algorithms have been implemented and evaluated in simulation frameworks. In this paper, we evaluate the implementation of these algorithms to manage the energy of real-world LoRaWAN IoT nodes. We measure and compare the performance of the different energy budget estimation methods on a commercial LoRaWAN IoT platform. Results show that in this use-case, the choice of algorithm impacts the system Quality of Service by less than 15 %. This enables much simpler energy budget estimation methods to be used.
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior

Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow.To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.
The tryptophan derivative 6-formylindolo[3,2-b]carbazole, FICZ, a dynamic mediator of endogenous aryl hydrocarbon receptor signaling, balances cell growth and differentiation

Abstract The aryl hydrocarbon receptor (AHR) is not essential to survival, but does act as a key regulator of many normal physiological events. The role of this receptor in toxicological processes has been studied extensively, primarily employing the high-affinity ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, regulation of physiological responses by endogenous AHR ligands remains to be elucidated. Here, we review developments in this field, with a focus on 6-formylindolo[3,2-b]carbazole (FICZ), the endogenous ligand with the highest affinity to the receptor reported to date. The binding of FICZ to different isoforms of the AHR seems to be evolutionarily well conserved and there is a feedback loop that controls AHR activity through metabolic degradation of FICZ via the highly inducible cytochrome P450 1A1. Several investigations provide strong evidence that FICZ plays a critical role in normal physiological processes and can ameliorate immune diseases with remarkable efficiency. Low levels of FICZ are pro-inflammatory, providing resistance to pathogenic bacteria, stimulating the anti-tumor functions, and promoting the differentiation of cancer cells by repressing genes in cancer stem cells. In contrast, at high concentrations FICZ behaves in a manner similar to TCDD, exhibiting toxicity toward fish and bird embryos, immune suppression, and activation of cancer progression. The findings are indicative of a dual role for endogenously activated AHR in barrier tissues, aiding clearance of infections and suppressing immunity to terminate a vicious cycle that might otherwise lead to disease. There is not much support for the AHR ligand-specific immune responses proposed, the differences between FICZ and TCDD in this context appear to be explained by the rapid metabolism of FICZ.
Apolipoprotein E Epsilon 4 Genotype, Mild Traumatic Brain Injury, and the Development of Chronic Traumatic Encephalopathy

The annual incidence of mild traumatic brain injury (MTBI) is 3.8 million in the USA with 10–15% experiencing persistent morbidity beyond one year. Chronic traumatic encephalopathy (CTE), a neurodegenerative disease characterized by accumulation of hyperphosphorylated tau, can occur with repetitive MTBI. Risk factors for CTE are challenging to identify because injury mechanisms of MTBI are heterogeneous, clinical manifestations and management vary, and CTE is a postmortem diagnosis, making prospective studies difficult. There is growing interest in the genetic influence on head trauma and development of CTE. Apolipoprotein epsilon 4 (APOE-ε4) associates with many neurologic diseases, and consensus on the ε4 allele as a risk factor is lacking. This review investigates the influence of APOE-ε4 on MTBI and CTE. A comprehensive PubMed literature search (1966 to 12 June 2018) identified 24 unique reports on the topic (19 MTBI studies: 8 athletic, 5 military, 6 population-based; 5 CTE studies: 4 athletic and military, 1 leucotomy group). APOE-ε4 genotype is found to associate with outcomes in 4/8 athletic reports, 3/5 military reports, and 5/6 population-based reports following MTBI. Evidence on the association between APOE-ε4 and CTE from case series is equivocal. Refining modalities to aid CTE diagnosis in larger samples is needed in MTBI.
Impact of Fairness Concern on Retailer-Dominated Supply Chain

On the basis of a two-echelon supply chain model, this study researches the impact of fairness concern on a supply chain with a dominant retailer and two manufacturers. Through mathematical analysis and numerical study, this work examines the effects of fairness concern coefficient on pricing, profitability, and utility of the supply chain members. Results show that when only the dominant retailer has fairness concerns, the utility of that retailer increases, while the retailer's profit declines slightly, and the profits of manufacturers decrease with the fairness concern coefficient of the retailer. In the case only the manufacturers have fairness concerns, when the manufacturers has more fairness concerns, then the utility of the manufacturer with the cost advantage increases and that of the manufacturer in the disadvantaged position decreases. The retailer benefits substantially with the fairness concerns of the manufacturers. In the case each member of the supply chain has fairness concerns, if members have additional fairness concerns, the profit of the dominant retailer will slightly decrease, whereas utility will increase. However, the profit and utility of the cost-disadvantaged manufacturer may significantly be reduced.
Mgn: Multi-Glimpse Network for Action Recognition

Current state-of-the-art action recognition approaches rely on optical flow to extract the local motion information and ignore the importance of global description of the videos. In this paper, we present a novel architecture, named Multi-Glimpse Network (MGN), to boost the performance of action recognition by combining the local and global information of the videos. Specifically, MGN makes predictions through two important modules, Local Glimpse and Global Glimpse. Local Glimpse extracts the local spatiotemporal features of different periods using temporal sampling method. Global Glimpse aggregates the extracted local features to develop global description of the videos. These two modules are complementary and indispensable. Our MGN achieves competitive results on four video action benchmarks of UCF10l, HMDB51, ODAR and Penn.
Tomography of Adaptive Multi-Agent Networks Under Limited Observation

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information from agent i to update its inference. The solution of this inference task is challenging for at least two reasons. First, only the output of the learning algorithm is available to the external observer and not the raw data. Second, only observations from a fraction of the network agents is available, with the total number of agents itself being also unknown. This work establishes, under reasonable conditions, that consistent tomography is possible, namely, that it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases. We characterize the decaying behavior of the error with the network size, and provide a set of numerical experiments to illustrate the results.
Spherical Visual Gyroscope for Autonomous Robots Using the Mixture of Photometric Potentials

In this paper, we present a new direct omnidirectional visual gyroscope for mobile robotic platforms. The gyroscope estimates the 3D orientation of a camera-robot by comparing the current spherical image with that acquired at a reference pose. By transforming pixel intensities into a Mixture of Photometric Potentials, we introduce a novel image-similarity measure which can be seamlessly integrated into a classical nonlinear least-squares optimization scheme, offering an extended convergence domain. Our method provides accurate and robust attitude estimates, and it is easy-to-use since it involves a single tuning parameter, the width of the photometric potentials (Gaussian functions, in this work) controlling the power of attraction of each pixel. The visual gyroscope has been successfully tested on spherical image sequences generated by a twin-fisheye camera mounted on the end-effector of a robot arm and on a fixed-wing UAV.
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.
A Deep Learning Based No-Reference Image Quality Assessment Model for Single-Image Super-Resolution

Single-image super-resolution (SISR) is a very important and classic problem of the computer vision community. Although a lot of SISR methods have been proposed, few studies have been conducted to address the quality assessment of SISR methods. In this paper, we proposed a deep learning based no-reference image quality assessment (NR-IQA) model for SISR. We took small patches from images to form our training set and labeled them with different scores. With the aid of well-designed architecture and training strategy, our method achieved a performance leap than state-of-the-art methods. Experimental results proved the generalizability and the effectiveness of the proposed model.
Semantic Embedding for Sketch-Based 3D Shape Retrieval

The main challenge for sketch-based 3D shape retrieval lies with the large domain gap between 2D sketch and 3D shape. Most existing works attempt to overcome the domain gap by learning a joint feature embedding space to align the two domains. In this work we argue that the large domain gap cannot be effectively bridged in a shared feature space. Instead, we propose to align them in their common class label space. To this end, a novel deep cross-domain semantic embedding model is proposed. Extensive experiments are carried out on two large benchmarking datasets, SHREC’13 and SHREC’14. The results show that the proposed model drastically improves over the state-of-the-art alternatives.