Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining the relationship between blood lead level and stunting, wasting and underweight- A cross-sectional study of children under 2 years-of-age in a Bangladeshi slum

Elevated blood lead level (BLL) is known to cause cardiac, immune, and cognitive damage but had not been thoroughly studied in relation to stunting among children under two years of age. We primarily aimed to assess the relationship between elevated BLL, the accumulation of concerned amount of the metal lead in blood and stunting and secondarily—wasting and underweight amongst Bangladeshi children less than two years of age. For this cross-sectional study, BLL measurements, anthropometric data, and socioeconomic indicator information were collected and analyzed for 729 children under two years of age upon enrollment in the MAL-ED study conducted in a Bangladeshi slum area. Univariate, bivariate and multivariate analyses were carried out to observe the proportion and mean and contribution of elevated BLL and other relevant variables in explaining the occurrence of stunting. Of the enrolled subjects, 39.0% were stunted [length-for-age z score (LAZ<-2)], 50.3% were male, and 86.6% had an elevated BLL (≥5μg/dL). Mean BLL of stunted children was 8.47 ± 3·37 μg/dL and 8.10 ± 3·80 μg/dL for non-stunted children. Proportion of children with elevated BLL was not significantly different between the stunted and non-stunted groups (p>0.05). When adjusted for other variables, elevated BLL was found to be a significant predictor of stunting and underweight (p<0.05) but not wasting (p>0.05). Elevated BLL (p<0·01), child’s gender and weight (p<0·001), maternal body mass index (BMI) (p<0.05) and severe household food insecurity (p<0·05) were all significantly associated with stunting in the multivariate model. Increased odds of stunting was also observed for increased BLL. The findings suggest that chronic lead poisoning is significantly associated with high level of stunting among child slum dwellers in Bangladesh. These findings strengthen the argument for improved lead reduction efforts in Bangladesh, where lead poisoning and stunting are both highly prevalent.
The Influence of Self-Referential Processing on Attentional Orienting in Frontoparietal Networks

Self-referential processing refers to the processing of information relevant to oneself and plays an important role in cognition. Behavioral studies have shown that directional cue stimuli have a qualitatively different function during attentional orienting after presentation of the cue associated with the self. However, it is necessary to determine how neural activity is influenced by self-referential processing during attentional orienting. The present study involved establishing an association between non-predictive arrow cues and the “self” during a training task and then investigating the influence of self-referential processing on neural activity during attentional orienting. Enhanced neural activity was observed in cortical midline structures (CMS) during the use of self- vs. neutral-arrow cues, which suggests that the arrow associated with the “self” triggered self-referential processing during attentional orienting due to the experiences of the participant in the training task. Comparison of obtained under the incongruent and congruent conditions revealed a qualitative difference in neural activities between the self- and neutral-arrow cues associated with attentional orienting. Furthermore, when the neutral-arrow cue was treated as a baseline condition, neural activity was reduced in the frontoparietal attention networks by self-referential processing under the incongruent condition, but it was enhanced under the congruent condition. Thus, the stimulus modulated subsequent attentional neural processes after being associated with the self as a cue, which indicates that this process may be triggered by self-reference to automatically and effectively capture information. Our findings extend those of previous behavioral studies of neural activity, suggesting that directional cues were qualitatively influenced by self-referential processing, and showed different functions during attentional orienting. Moreover, the present study provides important evidence of how self-referential processing affects attentional orienting in the frontoparietal network. Highlights - Enhanced activity was observed in CMS due to self-referential processing. - The influence of self-referential processing differed in the frontoparietal network. - Activity was enhanced by self-referential processing under the congruent condition. - Activity was reduced by self-referential processing under the incongruent condition.
Effect of ASLR on Memory Duplicate Ratio in Cache-based Virtual Machine Live Migration

【Cache based live migration method utilizes a cache, which is accessible to both side (remote and local), to reduce the virtual machine migration time, by transferring only irredundant data. However, address space layout randomization (ASLR) is proved to reduce the memory duplicate ratio between targeted migration memory and the migration cache. In this pager, we analyzed the behavior of ASLR to find out how it changes the physical memory contents of virtual machines. We found that among six virtual memory regions, only the modification to stack influences the page-level memory duplicate ratio. Experiments showed that: (1) the ASLR does not shift the heap region in sub-page level; (2) the stack reduces the duplicate page size among VMs which performed input replay around 40MB, when ASLR was enabled; (3) the size of memory pages, which can be reconstructed from the fresh booted up state, also reduces by about 60MB by ASLR. With those observations, when applying cache-based migration method, we can omit the stack region. While for other five regions, even a coarse page-level redundancy data detecting method can figure out most of the duplicate memory contents. 】
Flux: Revisiting Near Blocks for Proof-of-Work Blockchains

—The term near or weak blocks describes Bitcoin blocks whose PoW does not meet the required target difﬁculty to be considered valid under the regular consensus rules of the protocol. Near blocks are generally associated with protocol improvement proposals striving towards shorter transaction conﬁrmation times. Existing proposals assume miners will act rationally based solely on intrinsic incentives arising from the adoption of these changes, such as earlier detection of blockchain forks. In this paper we present Flux , a protocol extension for proof-of-work blockchains that leverages on near blocks, a new block reward distribution mechanism, and an improved branch selection policy to incentivize honest participation of miners. Our protocol reduces mining variance, improves the responsiveness of the underlying blockchain in terms of transaction processing, and can be deployed without conﬂicting modiﬁcations to the underlying base protocol as a velvet fork. We perform an initial analysis of selﬁsh mining which suggests Flux not only provides security guarantees similar to pure Nakamoto consensus, but potentially renders selﬁsh mining strategies less proﬁtable.
McEliece-type Cryptosystems over Quasi-cyclic Codes

In this thesis, we study algebraic coding theory based McEliece-type cryptosystems over quasi-cyclic codes. The main goal of this thesis is to construct a cryptosystem that resists quantum Fourier sampling making it quantum secure. We propose a new variant of Niederreiter cryptosystem over rate $\frac{m-1}{m}$ quasi-cyclic codes which is secure against quantum Fourier sampling due to indistinguishability of the hidden subgroup. The proof of indistinguishability is achieved due to two constraints over automorphism group; small size and large minimal degree. Apart from this cryptosystem, we also present a class of $\frac{1}{m}$ quasi-cyclic codes, with small size and large minimal degree of the automorphism group.
Role of Calcium-Sensing Receptor in Mechanotransducer-Channel-Mediated Ca2+ Influx in Hair Cells of Zebrafish Larvae

The calcium-sensing receptor (CaSR) is an extracellular Ca2+ sensor that plays a critical role in maintaining Ca2+ homeostasis in several organs, including the parathyroid gland and kidneys. In this study, through in situ hybridization, the expression of CaSR mRNA was found in the neuromasts of zebrafish larvae. Immunohistochemistry further demonstrated that the CaSR protein was present in neuromast hair cell stereocilia and basolateral membranes. Based on the expression and subcellular localization of the CaSR in hair cells, we hypothesized that the CaSR is expressed in zebrafish lateral-line hair cells to regulate mechanotransducer (MET)-channel-mediated Ca2+ entry. Using the scanning ion-selective electrode technique, MET-channel-mediated Ca2+ influx at the stereocilia of hair cells was measured in intact larvae. Ca2+ influx was suppressed after larvae were pretreated with a CaSR activator (R-568) or high-Ca2+ (HCa) medium. Gene knockdown by using morpholino oligonucleotides decreased CaSR expression in hair cells and eliminated the effects of R-568 and HCa on Ca2+ influx. In addition, we found that treatment with R-568 attenuated neomycin-induced hair cell death. This study is the first to demonstrate that the CaSR is involved in mechanotransduction in zebrafish hair cells.
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection

This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on “Irony detection in English tweets”. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.
Semantic Pleonasm Detection

Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.
The Fructose-Specific Phosphotransferase System of Klebsiella pneumoniae Is Regulated by Global Regulator CRP and Linked to Virulence and Growth

Klebsiella pneumoniae is an opportunistic pathogen, and its hypervirulent variants cause serious invasive community-acquired infections. A genomic view of K. pneumoniae NTUH-2044 for the carbohydrate phosphotransferase system (PTS) found a putative fructose PTS, namely, the Frw PTS gene cluster. ABSTRACT Klebsiella pneumoniae is an opportunistic pathogen, and its hypervirulent variants cause serious invasive community-acquired infections. A genomic view of K. pneumoniae NTUH-2044 for the carbohydrate phosphotransferase system (PTS) found a putative fructose PTS, namely, the Frw PTS gene cluster. The deletion mutant and the complemented mutant of frwC (KP1_1992), which encodes the putative fructose-specific enzyme IIC, were constructed, and the phenotypes were characterized. This transmembrane PTS protein is responsible for fructose utilization. frwC deletion can enhance biofilm formation and capsular polysaccharide (CPS) biosynthesis but decreases the growth rate and lethality in mice. frwC expression was repressed in the cyclic AMP receptor protein (CRP) mutant. Electrophoretic mobility shift assay showed that CRP can directly bind to the promoter of frwC. These results indicated that frwC expression is controlled by CRP directly and that such regulation contributes to bacterial growth, CPS synthesis, and the virulence of the Δcrp strain. The findings help elucidate fructose metabolism and the CRP regulatory mechanism in K. pneumoniae.
Foraging behaviour of Apis mellifera adansonii Latreille (Hymenoptera : Apidae) on Daniellia oliveri, Delonix regia, Hymenocardia acida and Terminalia mantaly flowers in Ngaoundéré (Cameroon)

To determine the apicultural value of Daniellia oliveri, Delonix regia, Hymenocardia acida and Terminalia mantaly , the foraging activity of the honey bee, Apis mellifera adansonii was studied on flowers in the area of Ngaoundere, from December 2001 to May 2002 and from December 2002 to May 2003. At Dang, flowers of each plant species were prospected at least four days per month, between 07.00 and 18.00 h, for recording of the nectar and/or pollen foraging behaviour of A. m. adansonii workers. Results showed that A. m. adansonii harvested nectar and pollen of each plant species. The greatest number of workers foraging simultaneously on a plant varied from 230 (H. acida) to 3500 (De. Regia). A. m. adansonii workers that visited flowers of a given plant species once generally remained with this nectar and/or pollen sources throughout the observation period. Thus Da. oliveri, De. regia, H. acida and T. mantaly could be cultivated and protected to increase honey production. Each of the four plant species could enable beekeepers to increase their pollen production as a hive product. On each plant species, A. m. adansonii workers carried pollen from one flower to another and can thus be considered as pollinators. © 2010 International Formulae Group. All rights reserved. Keywords: Nectar, pollen, bee plant, apicultural value, honey, pollination, Dang.
Using a simulation approach to optimize time-domain diffuse correlation spectroscopy measurement on human head

Abstract. Time-domain diffuse correlation spectroscopy (TD-DCS) has been recently proposed to improve detection of deep blood flow dynamics in a biological tissue, such as human brain. To obtain a high sensitive measurement, several experimental parameters such as the source–detector (SD) distance, gate opening time, and width need to be considered and optimized. We use a simulation approach to optimize these parameters based on Monte Carlo computations using a realistic human head model. Two cortical regions are investigated including the frontal and temporal lobes. SD distance ranging from 0 to 45 mm, gate opening time from 400 to 1000 ps, and gate width from 50 to 3000 ps are considered. The goal is to find out the optimal combinations of these parameters by which the higher contrast measurement on the cortical dynamics can be achieved. The simulations show that with an acceptable input power of light, the combinations of SD distance ranging from 0 to 15 mm, gate opening time at 700 to 800 ps, and gate width of 800 ps are optimal for achieving higher contrast measurement on the cortical dynamics. The simulation approach and results are helpful for the optimization of TD-DCS experimental design focused on brain functional detection.
Análise do tempo de alinhamento em sistemas de navegação Inercial solidários

Neste artigo e apresentada uma metodologia de auto-alinhamento usualmente empregada em sistemas de navegacao inercial solidarios (SINS) e sua correspondente analise de erros. E demonstrado que, com excecao do alinhamento em azimute, esta metodologia e influenciada basicamente pelas incertezas atreladas as leituras dos acelerometros. Uma analise estatistica utilizando o desvio padrao de Allan e realizada e permite se estimar analiticamente o tempo necessario para se obter uma determinada classe de precisao no alinhamento em azimute. Este resultado e comparado com resultados experimentais obtidos a partir de um SINS de alta precisao, demonstrando que as incertezas provenientes do giroscopio apontado para leste constituem o fator preponderante na determinacao do tempo de alinhamento em azimute, que por sua vez, determina o tempo e a precisao final do alinhamento em um SINS.
A SPECK Based 3G Authentication Algorithm for Download onto IoT Security Modules

—3G/4G mobile standards beneﬁt from an authentication algorithm called MILENAGE that supports mutual authentication, protection against replay attack and generates session keys to protect conﬁdentiality and integrity. Its usefulness attracts interest beyond the original usage, such as in securing diverse wireless and wired bearers used in Machine-to-Machine and Internet of Things (IoT) systems. The long-term reliance on one algorithm is a risk, and recently an alternative algorithm, called TUAK, was standardised as a safe-guard, should the Advanced Encryption Standard (AES) core of MILENAGE ever be found vulnerable. Previous performance evaluation of TUAK on Subscriber Identity Modules (SIM), found that it needed to be implemented in low level native code to satisfy system timing requirements; indeed this is usually the case for MILENAGE. However, deployed security modules, anticipated for the Internet of Things (IoT), generally provide access at an application layer, abstracted from the underlying hardware. Application layer implementation of TUAK was shown to be too slow to comply with standardised requirements and so an alternative faster algorithm was sought that could be downloaded and run in a compliant manner from the application level. The National Institute of Standards and Technology (NIST) has standardised a lightweight block-cipher called SPECK, and this paper describes work to create a SPECK alternative to MILENAGE and compares its performance with earlier results from TUAK.
RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy

High-throughput sequencing (HTS) has the potential to decipher the diversity of T cell repertoires and their dynamics during immune responses. Applied to T cell subsets such as T effector and T regulatory cells, it should help identify novel biomarkers of diseases. However, given the extreme diversity of TCR repertoires, understanding how the sequencing conditions, including cell numbers, biological and technical sampling and sequencing depth, impact the experimental outcome is critical to proper use of these data. Here, we assessed the representativeness and robustness of TCR repertoire diversity assessment according to experimental conditions. By comparative analyses of experimental datasets and computer simulations, we found that (i) for small samples, the number of clonotypes recovered is often higher than the number of cells per sample, even after removing the singletons; (ii) high-sequencing depth for small samples alters the clonotype distributions, which can be corrected by filtering the datasets using Shannon entropy as a threshold; and (iii) a single sequencing run at high depth does not ensure a good coverage of the clonotype richness in highly polyclonal populations, which can be better covered using multiple sequencing. Altogether, our results warrant better understanding and awareness of the limitation of TCR diversity analyses by HTS and justify the development of novel computational tools for improved modeling of the highly complex nature of TCR repertoires.
Design and implementation of monitoring data storage and processing scheme based on distributed computing

The monitoring system in cloud computing environment will generate a large number of monitoring data in real time, how to achieve efficient storage and processing of monitoring data in the big data environment is particularly important. To solve this problem, a scheme of monitoring data storage and processing based on Hadoop is proposed. The scheme adopts the HBase database to store the timing monitoring data, and enhance the field method of wide table storage model improved HBase database to improve storage efficiency monitoring data; the traffic data, using MapReduce distributed computing to improve the processing efficiency. The experimental results show that the scheme is scientific and effective, and it can improve the processing speed of the monitoring system under the massive monitoring data, and solve the bottleneck problem of the monitoring data in the cloud computing environment.
Screening for selective anticancer activity of plants from Grazalema Natural Park, Spain

Abstract Since plants are an important source of anticancer drugs, we have carried out a random screening for selective anticancer activity of 57 extracts from 45 plants collected in Grazalema Natural Park, an area in the South of Spain of high plant diversity and endemism. Using lung cancer cells (A549) and lung non-malignant cells (MRC-5), we found that several extracts were more cytotoxic and selective against the cancer cells than the standard anticancer agent cisplatin. Five active extracts were further tested in cancer and normal cell lines from other tissues, including three skin cell lines with increasing degree of malignancy. An extract from the leaves of Daphne laureola L. (Thymelaeaceae) showed a striking potency and selectivity on lung cancer cells and leukemia cells; the IC50 values against these cancer cells were approximately 10,000-fold lower than against the normal cells. Daphnane-type diterpene orthoesters may be responsible for this highly selective anticancer activity.
Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification.
An Efficient Parallelization Approach for Large-Scale Sparse Non-Negative Matrix Factorization Using Kullback-Leibler Divergence on Multi-GPU

Matrix factorization (MF), as one of the most accurate and scalable approaches in dimension reduction techniques, has become popular in the collaborative filtering (CF) recommender systems, social network and graph communities. Currently, Kullback-Lerbler Non-negative Matrix Factorization (KL-NMF) is one of the most famous approaches for MF, due to its representative non-negativity feature of the CF model. However, it is non-trivial to obtain high performance KL-NMF on $\emph{Graphic Processing Units}$ (GPU) for large-scale problems, due to the redundant large-scale intermediate data, frequent matrices manipulation, and access of sparse and irregular entries characteristic of KL-NMF. In this work, we propose single-thread-based KL-NMF, which depends on the involved feature tuples multiplication and summation, and then, we present $L_{2}$ norm regularized single-thread-based KL-NMF. On that basis, a novel CUDA parallelization KL-NMF approach (CuKL-NMF) is presented for GPU computing. Furthermore, to process large-scale CF data sets and make advantages of GPU computation power, we propose multi-GPU CuKL-NMF (MCuKL-NMF). Compared with state-of-the-art parallel algorithms, e.g., CUMF, CCD++, MCuKL-NMF obtains the highest performance.