Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient algorithms for inverting evolution

Evolution can be mathematically modelled by a stochastic process that operates on the DNA of species. Such models are based on the established theory that the DNA sequences, or genomes, of all extant species have been derived from the genome of the common ancestor of all species by a process of random mutation and natural selection. A stochastic model of evolution can be used to construct phylogenies, or evolutionary trees, for a set of species. Maximum Likelihood Estimation (MLE) methods seek the evolutionary tree which is most likely to have produced the DNA under consideration. While these methods are intellectually satisfying, they have not been widely accepted because of their computational intractability. In this paper, we address the intractability of MLE methods as follows: We introduce a metric on stochastic process models of evolution. We show that this metric is meaningful by proving that in order for any algorithm to distinguish between two stochastic models that are close according to this metric, it needs to be given many observations. We complement this result with a simple and efficient algorithm for inverting the stochastic process of evolution, that is, for building a tree from observations on two-state characters. (We will use the same techniques in a subsequent paper to solve the problem for multistate characters, and hence for building a tree from DNA sequence data.) The tree we build is provably close, in our metric, to the tree generating the data and gets closer as more observations become available. Though there have been many heuristics suggested for the problem of finding good approximations to the most likely tree, our algorithm is the first one with a guaranteed convergence rate, and further, this rate is within a polynomial of the lower-bound rate we establish. Ours is also the first polynomial-time algorithm that is proven to converge at all to the correct tree.
Influences of Extremely Low Frequency Electromagnetic Fields on Germination and Early Growth of Mung Beans

This study aims to assess extremely low frequency electromagnetic field (ELF-EMF), which is similar to Schumann resonance (SR) frequency. Schumann resonance frequency which is part of the geomagnetic field frequency, affects to mung beans to increase germination rate and early growth. The aim of the study is to evaluate the effects of SR ELF-EMF on mung beans germination rate and growth rate. SR ELF-EMF pretreatment increases the germination rate since the applied SR ELF-EMF promotes level of Ca2+ and K+contents. Three groups of mung beans were exposed by SR ELF-EMF of 0.3 mT at swapped frequency $\mathbf{7.83}\ \mathbf{Hz} \pm \mathbf{0.1} \mathbf{Hz}$. The experiment results show that germination rates of experimental groups with SR ELF-EMF exposing have 8% to 23.3% higher than control groups. Moreover, the length of stem of the experimental groups have the maximum 14% longer than control groups after 72 hours growth.
Using EEG to Decode Subjective Levels of Emotional Arousal During an Immersive VR Roller Coaster Ride

Emotional arousal is a key component of a user's experience in immersive virtual reality (VR). Subjective and highly dynamic in nature, emotional arousal involves the whole body and particularly the brain. However, it has been difficult to relate subjective emotional arousal to an objective, neurophysiological marker-especially in naturalistic settings. We tested the association between continuously changing states of emotional arousal and oscillatory power in the brain during a VR roller coaster experience. We used novel spatial filtering approaches to predict self-reported emotional arousal from the electroencephalogram (EEG) signal of 38 participants. Periods of high vs. low emotional arousal could be classified with accuracies significantly above chance level. Our results are consistent with prior findings regarding emotional arousal in less naturalistic settings. We demonstrate a new approach to decode states of subjective emotional arousal from continuous EEG data in an immersive VR experience.
A Comparative Study of Nutritional Status, Knowledge Attitude and Practices (KAP) and Dietary Intake between International and Chinese Students in Nanjing, China

University students tend to have poor dietary practices, which ultimately affect their nutritional status. International students are becoming more prevalent in China. The current study aimed to compare the nutritional status, knowledge attitude and practices (KAP) and dietary intake between international and Chinese students in China. A comparative study was conducted in undergraduate students of Nanjing Medical University aged 17–31 years including 308 international and 393 Chinese students. Data was collected by self-administered questionnaire. Body composition was detected by bioelectrical impedance analysis (BIA). Student t-test and chi square test were used for comparison. Linear regressions were used for association of nutritional status with determinants. The prevalence of overweight and obesity in international student was higher than Chinese students. International male and female students were having significantly (p < 0.05) high BMI and percent body fats than Chinese male and female students. Nutritional KAP scores of Chinese students was significantly (p < 0.05) higher than international students. As for diet consumption, daily milk consumption was high in international students while daily egg and weekly fish and meat consumption were found more in Chinese students. Fast foods and carbonated drinks weekly consumption was significantly (p < 0.001) high in international students. After adjusted for age, gender, education, sleeping duration and physical exercise, the inverse association of nutritional KAP with BMI remained significant. Our data indicate that international students had more percent body fats, less nutritional KAP scores and unhealthy dietary habits than Chinese students. Proper nutrition education and guidance for improving good habits and nutritional status is suggested for international students.
Movie Dataset Analysis Using Hadoop-Hive

In today's world there is a huge growth in data. This data is generated from variety of sources like social media, industry, transaction records, cell phone, GPS signals etc. It is difficult and challenging to store such a huge amount data in traditional data warehouse. Big Data is the dataset with 3 V's that are Volume, Variety and Velocity and difficult to store and process using traditional database management systems. Big Data Analytics is the way of processing the large amount of data. Hadoop is a popular open source software which is very useful in analyzing the larger data. Hadoop provides several tools for this purpose like Hive, Pig, Hbase, Cassandra etc. In this paper, we have used Hadoop framework. For the analysis of movie dataset Hive tool is used with Hadoop framework. We have got significant improvement in processing time for analyzing dataset compared to traditional system
Salivary opiorphin levels in anorexia nervosa: A case–control study

Abstract Objectives: Opiorphin is a physiological inhibitor of peptidases inactivating endogenous opioids displaying strong analgesic properties without undesirable side effects, antidepressant properties or hormonal dependency. It might therefore play an important role in patients with painful diseases related to neuro-hormonal dysregulation of the nervous system, affecting saliva secretion and composition such as anorexia nervosa (AN). The main objective aim of this study was to compare the level of opiorphin in saliva of patients with AN to matched subjects free of eating disorders. Methods: A case–control clinical trial was conducted in 68 AN patients and 43 healthy matched control subjects. Depression symptoms were assessed with the self-scored questionnaire Beck Depression Inventory (BDI) and salivary samples were taken during the acute stage of AN (BMI <15 kg/m2) for measuring opiorphin. Opiorphin levels were measured with a quantitative assay using a commercial immunoenzymatic Elisa kit (cat no. EH1927, Wuchan, Hubei, China). Results: No statistically significant difference was found in salivary opiorphin levels between the AN and control groups, (P = 0.499, Mann–Whitney U-test). Positive correlations to duration of the disease, BDI and bodyweight in AN patients were evidenced. Conclusions: Measurement of salivary opiorphin levels cannot be used as a marker of AN but may allow new perspectives in monitoring AN in its early stages.
Ferrocene-Containing Impiridone (ONC201) Hybrids: Synthesis, DFT Modelling, In Vitro Evaluation, and Structure–Activity Relationships

Inspired by the well-established clinical evidence about the interplay between apoptotic TRAIL (tumour necrosis factor-related apoptosis-inducing ligand) mechanism and reactive oxygen species (ROS)-mediated oxidative stress, a set of novel ONC201 hybrids containing the impiridone core and one or two differently positioned ferrocenylalkyl groups were synthesised in our present work. These two types of residues have been implicated in the aforementioned mechanisms associated with cytotoxic activity. A straightforward, primary amine-based synthetic approach was used allowing the introduction of a variety of N-substituents into the two opposite regions of the heterocyclic skeleton. Reference model compounds with benzyl and halogenated benzyl groups were also synthesised and tested. The in vitro assays of the novel impiridones on five malignant cell lines disclosed characteristic structure-activity relationship (SAR) featuring significant substituent-dependent activity and cell-selectivity. A possible contribution of ROS-mechanism to the cytotoxicity of the novel metallocenes was suggested by density functional theory (DFT)studies on simplified models. Accordingly, unlike the mono-ferrocenylalkyl-substituted products, the compounds containing two ferrocenylalkyl substituents in the opposite regions of the impiridone core display a much more pronounced long-term cytotoxic effect against A-2058 cell line than do the organic impiridones including ONC201 and ONC212. Furthermore, the prepared bis-metallocene derivatives also present substantial activity against COLO-205- and EBC-1 cell lines.
Inhibitory Effect of Alisma canaliculatum Ethanolic Extract on NF-κB-Dependent CXCR3 and CXCL10 Expression in TNFα-Exposed MDA-MB-231 Breast Cancer Cells

CXC motif chemokine ligand 10 (CXCL10) and its receptor CXC motif chemokine receptor 3 (CXCR3), play important roles in the motility of breast cancer cells. Alisma canaliculatum is a herb that has been used as a traditional medicine for thousands of years in Korea and China. Whether A. canaliculatum inhibits the motility of metastatic breast cancer cells is not clear yet. In this study, we show that A. canaliculatum ethanolic extract (ACE) prevented tumor necrosis factor-alpha (TNFα)-induced migration of MDA-MB-231 cells. ACE significantly attenuated TNFα-induced upregulation of CXCL10 and CXCR3 expression at the gene promoter level. Mechanistically, ACE inhibits TNFα-induced phosphorylation of inhibitor of κB (IκB) kinase (IKK), IκB and p65/RelA, leading to the suppression of nuclear translocation of p65/RelA nuclear factor kappa-B (NF-κB). Also, ACE inhibited NF-κB-dependent CXCR3 and CXCL10 promoter activities. These results suggest that ACE abrogates TNFα-induced migration of MDA-MB-231 breast cancer cells through down-regulation of IKK-NF-κB-dependent CXCR3 and CXCL10 expression. Our results suggest that ACE has potential as a herbal supplement for the inhibition of breast cancer metastasis.
BeeKeeper: A Blockchain-Based IoT System With Secure Storage and Homomorphic Computation

Currently, Internet of Things (IoT) and blockchain technologies are experiencing exponential growth in academia and industry. Generally, IoT is a centralized system whose security and performance mainly rely on centralized servers. Therefore, users have to trust the centralized servers; in addition, it is difficult to coordinate external computing resources to improve the performance of IoT. Fortunately, the blockchain may provide this decentralization, high credibility and high security. Consequently, blockchain-based IoT may become a reasonable choice for the design of a decentralized IoT system. In this paper, we propose a novel blockchain-based threshold IoT service system: BeeKeeper. In the BeeKeeper system, servers can process a user’s data by performing homomorphic computations on the data without learning anything from them. Furthermore, any node can become a leader’s server if the node and the leader desire so. In this way, BeeKeeper’s performance can continually increase by attracting external computing resources to join in it. Moreover, malicious nodes can be scrutinized. In addition, BeeKeeper is fault tolerant since a user’s BeeKeeper protocol may work smoothly as long as a threshold number of its servers are active and honest. Finally, we deploy BeeKeeper on the Ethereum blockchain and give the corresponding performance evaluation. In our experiments, servers can generate their response with about 107 ms. Moreover, the performance of BeeKeeper mainly depends on the blockchain platform. For instance, the response time is about 22.5 s since the block interval of Ethereum blockchain is about 15 s. In fact, if we use some other blockchain with short block interval, the response time may be obviously short.
Handling Process Overruns and Underruns on Multiprocessors in a Fault-Tolerant Real-Time Embedded System

The failure of safety-critical hard real-time embedded systems, can have catastrophic consequences. In such systems, a fault tolerant design is often necessary to enable the system to continue to provide a specified service, possibly at a reduced level of performance, rather than failing completely, in spite of system errors. One approach for achieving fault tolerance in real-time embedded systems, is to provide two versions of programs for each real-time task: a primary and an alternate. If an error in the execution of the primary of a task is detected, or if the successful completion of the primary cannot be guaranteed, then the alternate will be activated, while the primary will be aborted. This paper presents a method which provides a higher level of system dependency and reliability by effectively handling underruns and overruns in a fault tolerant real-time embedded system which uses a primary and an alternate for each real-time task to achieve fault tolerance. A main advantage of this method is that it significantly increases the chances that either the primary or the alternate of each process will be able to successfully complete its computation before its deadline despite overrunning, which significantly increases system robustness and reliability, while at the same time any additional processor capacity created at run-time due to primary or alternate underruns can be efficiently utilized, which increases system resource and processor utilization, while also satisfying additional complex constraints defined on the primaries and alternates such as precedence and exclusion relations.
Analyzing Software Engineering Experiments: Everything You Always Wanted to Know but Were Afraid to Ask

Experimentation is a key issue in science and engineering. But it is one of software engineering's stumbling blocks. Quite a lot of experiments are run nowadays, but it is a risky business. Software engineering has some special features, leading to some experimentation issues being conceived of differently than in other disciplines. The aim of this technical briefing is to help participants to avoid common pitfalls when analyzing the results of software engineering experiments. The technical briefing is not intended as a data analysis course, because there is already plenty of literature on this subject. It reviews several key issues that we have identified in published software engineering experiments, and addresses them based on the knowledge acquired after 19 years running experiments.
Collagen Partition in Polymeric Aqueous Two-Phase Systems for Tissue Engineering

Aqueous two-phase systems (ATPS) of polyethylene glycol (PEG) and dextran (DEX) are commonly used to partition proteins. Protein partition in ATPS is a complex phenomenon and depends on factors including molecular weight of polymers, and electrochemical and ionic properties of the phases. We studied the effect of polymer molecular weight on the partition of a natural protein, collagen, in several ATPS formulations made with non-ionic polymers polyethylene glycol (PEG) and dextran (DEX). We found that partitioning of collagen to an aqueous phase significantly increases when the molecular weight of the corresponding phase polymer decreases. Additionally, a large difference between the molecular weight of the phase-forming polymers was necessary to cause a significant uneven collagen distribution between the aqueous phases. We then employed one of the systems to create a three-dimensional breast cancer microtissue by entrapping a spheroid of breast cancer cells within the partitioned collagen. This convenient technique to generate 3D microtissues offers a convenient and promising approach for tissue engineering applications.
A Comparative Study of Wavelet and CSP Features Classified Using LDA, SVM and ANN in EEG Based Motor Imagery

Brain-computer interface (BCI) can interchange messages and orders between the user's brain and the computer. The motor imagery (MI) is presented by specific signal features that reflect the user's intention to be extracted and interpreted as commands. This paper focuses on the classification of two types of MI tasks (Right Hand and Foot). We deployed various feature extraction techniques for EEG data using wavelet transform and common spatial pattern. For the wavelet features, statistical values, energy, entropy and band power were used to form the desired feature vectors. Before extracting wavelet coefficients, we performed two scenarios, with and without surface laplacian filter around the channels C3, C4 and Cz. Three types of classifiers were employed for classification, linear discriminant analysis (LDA), support vector machines (SVM) and artificial neural network (ANN). The aim of this work is to compare between them and to recommend the suitable combination for synchronous two-class motor-imagery-based brain-computer interface experiments. The data were recorded from five subjects, provided by BCI-Competition III. The results show that SVM is more suitable with the features than those extracted from wavelet coefficients and combination of entropy-energy-band power, and LDA is more suitable with common spatial pattern. Overall, the results from CSP-LDA are better than those obtained from WT-SVM with the average classification accuracy of 84.79% and 82.64%, respectively.
The Static Balancing of Single-Loop Reconfigurable Mechanisms

Reconfigurable mechanisms are a relatively recent contribution to the field of mechanism design and authors have only just been producing real-life applications of these mechanisms. This paper builds on the application of two categories of reconfigurable mechanisms by elaborating on a generalised static balancing theory suggested in a previous review paper [1]. These categories are single loop mechanisms with one degree of freedom per mode, and single loop mechanisms with multiple degrees of freedom per mode. In this paper it is shown that all configurations of the single DoF mechanisms can be balanced using a single spring. The multiple DoF mechanisms can be balanced by balancing each link with respect to the previous link. These balancing strategies are mathematically shown to be valid. The balancing strategy is then applied to a reconfigurable rehabilitation mechanism for patients with a lower limb paralysis by Tseng et al. [2] of which a simulation and a prototype is made to validate the theory. From the simulation and prototype follows that the single spring balancing of multiple configurations of a single DoF mechanism is possible.
Normative posterior pole asymmetry analysis data in healthy Caucasian population

Purpose: The aim of this study is to evaluate the variability of central retinal thickness asymmetry in healthy Caucasian adults with the posterior pole asymmetry analysis to serve as a reference. Methods: In total, 404 eyes of 202 subjects who aged between 18 and 80 years, who had no ocular pathology, were included in this cross-sectional observational study. Retinal thickness maps with posterior pole asymmetry analysis mode were taken with the optical coherence tomography (SPECTRALIS SD-OCT; Heidelberg Engineering). Superior and inferior hemifields were divided into five zones resembling to the strategy in Glaucoma Hemifield Test. Mean retinal thickness in each of the five zones was compared with the thickness of the corresponding zone in each eye (paired-samples t-test), and differences in retinal thickness (DRT1–5) and ganglion cell layer thickness between reciprocal locations were measured. Differences in retinal thickness values of two eyes of each subject were also compared (independent-samples t-test). Results: The intra-eye asymmetry was statistically significant in zones 4 and 5. The highest mean intraocular differences in retinal thickness were 5.8 µm (zone 5) in all eyes, 5.8 µm (zone 5) in the right eyes, and 5.9 µm (zones 4 and 5) in the left eyes. The only statistically significant interocular local differences in retinal thickness asymmetries were found in zone 3. The intraocular asymmetry in retinal thickness was found to be the lowest in zone 1. The differences of ganglion cell layer thickness (GCLTs) were not statistically significant. Conclusion: There were statistically significant physiological inter-eye asymmetry in zone 3 and intra-eye asymmetries in zones 4 and 5. These measurements must be considered during screening for glaucoma with posterior pole asymmetry analysis in the Caucasian population.
Simultaneous Sparsity and Parameter Tying for Deep Learning Using Ordered Weighted ℓ1 Regularization

A deep neural network (DNN) usually contains millions of parameters, making both storage and computation extremely expensive. Although this high capacity allows DNNs to learn sophisticated mappings, it also makes them prone to over-fitting. To tackle this issue, we adopt a recently proposed sparsity-inducing regularizer called OWL (ordered weighted ℓ1, which has proven effective in sparse linear regression with strongly correlated covariates. Unlike the conventional sparsity-inducing regularizers, OWL simultaneously eliminates unimportant variables by setting their weights to zero, while also explicitly identifying correlated groups of variables by tying the corresponding weights to a common value. We evaluate the OWL regularizer on several deep learning benchmarks, showing that it can dramatically compress the network with slight or even no loss on generalization accuracy.
Combining Tranmission Line Protection with Voltage Stability Monitoring

This paper introduces a Relay-based Local Index (RLI) of voltage emergency for voltage instability detection and considers ways to avoid undesirable line disconnections by distance protection during long-term voltage instability. The RLI stems from the previously presented New LIVES Index (NLI), which is calculated by processing voltage/current phasor measurements from Phasor Measurement Units (PMUs) at transmission substations on the boundaries of a weak system area. Distance relays of transmission lines can similarly calculate the RLI in a similar time scale. The RLI should not provide false alarm during the occurrence of a remote short-circuit fault, whereas it should provide an early alarm before any of the critical transmission lines forming a corridor is tripped by any distance element. Comparison between the alarms provided by NLI and RLI is performed using simulation studies of the Nordic Test System where a case of unwanted remote Zone-3 tripping is identified.