Miracle-dz/UniFAR-FTUs huggingface dataset:
Imaging and Quantifying the Morphology and Nanoelectrical Properties of Quantum Dot Nanoparticles Interacting with DNA

Rapid detection and imaging the interactions between quantum dots (QDs) and DNA are of interest for research on applications as well as for determining their potential cytotoxicity. This paper introduces Kelvin force microscopy (KFM), an electric mode of atomic force microscopy, as a technique to examine the binding of QDs with DNA in vitro and in vivo. KFM provides information about the nanoelectrical properties of QDs and DNA that is complementary to the topography and phase images that conventional AFM provides. With this unique function, KFM demonstrated its ability in determining the morphological and electrical changes in DNA after exposure to QDs as well as to distinguishing individual QDs from DNA matrices. Our results indicated that the nonspecific binding with QDs led to transformation of polymeric DNA into pearl-like spheres. In vivo experiments showed that QDs could permeate into E. coli cells and bind with genomic DNA. To the best of our knowledge, this is the first successful demonstration o.
The design and construction of a novel pipe flow apparatus for exploring polymer drag reduction

Turbulent flows are inherently less efficient than their laminar counterparts, and this additional dissipation results in the waste of a substantial amount of energy in any turbulent fluid system. It has long been known that the addition of a small amount of high molecular weight polymer to a turbulent flow can greatly increase flow efficiency improvements of 70 % or more are not uncommon. While the mechanism behind this so-called polymer drag reduction also known as the Toms Effect is not yet well understood, it has been asserted that the redistribution of energy in the turbulent flow structure via molecular stretching and transport is essential to the increased flow efficiency. This implies that the relevant dimensionless parameter is the ratio of the polymer time scale the relaxation time to the relevant flow time scale the diffusion time. This ratio is known as the Weissenberg Number, and the role it plays in polymer drag reduction has not been explored experimentally in a systematic way. It has been known for some time that the slimes produced by fishes are effective drag reducing agents per unit weight; the slime of the Pacific Hagfish (Eptatretus stoutb), in particular, contains both long, flexible fibers and high molecular weight mucin polymer chains. It has been demonstrated both numerically and experimentally that small quantities of fibers can be used to achieve a drag reducing effect similar that of polymers, although less dramatic, while being less susceptible to the degradation and subsequent loss of drag reducing effectiveness that is characteristic of polymers in turbulent flow. It has been tentatively shown that polymers and fibers behave synergistically when combined in turbulent flow to achieve higher levels of drag reduction with less susceptibility to degradation than polymers alone. It is therefore suspected that the slime of the hagfish would be a remarkably effective drag reducing agent, in addition to being non-toxic and biodegradable.
Landscape Genetics of Hume's Pheasant Syrmaticus humiae: Rivers Act as Potential Genetic Barriers

Abstract Landscape features, such as rivers, can act as geographical barriers to dispersal and gene flow and thus influence the population structure of some species. In this study, tissue samples were collected from 73 Hume's Pheasant Syrmaticus humiae, from six localities in Guangxi and Guizhou provinces, China, to examine the influence of rivers in landscape structure on genetic diversity and structure based on 12 microsatellite loci. Results indicated a high genetic diversity in Hume's Pheasant. Individuals from populations in Tianlin, Longlin and Xilin counties (TXL) (three geographically proximate populations) tended to form a genetic cluster, distinct from three other geographically proximate populations 100 km to the west in Pojie town (PJ), Luodian county (LD) and Leye county (LX), which showed more mixing and were less genetically distinct. Using simulated Markov-switching VAR (MSVAR), we found that the median population sizes of the posterior distributions were approximately 3,715 individuals for N0, and approximately 100,000 for N1, indicating that Hume's Pheasant experienced a significant genetic bottleneck 4,800 years ago, possibly due to human activity. Hume's Pheasant shows female-biased dispersal. The results of STRUCTURE and GENELAND indicate that Nanpan River, Hongshui River and national road G324 act as potential genetic barriers for Hume's Pheasant in Guangxi and Guizhou provinces. In addition, genetic distinctiveness has persisted despite population declines of the Hume's Pheasant due to the bottleneck approximately 5,000 years ago and population declines in the last 100 years.
Online Healthcare Practices and Associated Stakeholders: Review of Literature for Future Research Agenda

The rise of online platforms for health information exchange is changing the traditional healthcare delivery practices. With Internet technologies, online healthcare practices are continuously growing through their diversified delivery functionalities with better and faster reachability. Participants use such forums for various purposes, for instance, to gather information, seek medical assistance, connect with other users, and purchase healthcare products or services. The need for a consolidated study, considering all key aspects of virtual healthcare, is crucial. We aim to examine the existing literature to study stakeholders of online healthcare and their behaviour dynamics, available functionalities and service delivery approaches during different phases. We have identified five key stakeholders who are vital for online healthcare deliveries and explored their contributions across life-cycle stages of online healthcare community (OHC) in India. The PRISMA reporting approach was used to select suitable literature for this study. About 78 articles were identified, which were shortlisted to 67 articles, keeping in mind the objectives of this study. Key findings from all these articles are segregated according to identified themes and systematically presented to enlarge the visibilities of associated functionalities. This study is one of the initial studies on India’s OHC that has defined associated stakeholders, functionalities and association dynamics during different life-cycle phases. People are proactively emerging towards OHC to acquire knowledge, share experience and decide their healthcare drives. Being operated virtually, information technology (IT) is the driving factor of OHC, and we have addressed the diversified IT aspects applicable in an OHC set-up. We also have accumulated all key attributes of OHC and produced a theoretical framework through this study, which academic researchers can use to understand the present approaches better and derive futuristic studies. Similarly, service providers can use the findings to evaluate their services, identify the present loopholes in the operational approaches and effectively manage the deliverables to meet end-user needs.
A Hybrid Neural Network based on Particle Swarm Optimization for Predicting the Diabetes

In recent years, more and more studies have applied hybrid models in order to improve the performance of traditional neural networks. By combining particle swarm optimization and neural network, this research proposes a new hybrid neural prediction algorithm named as PSONN. The algorithm was applied to Pima Indians Diabetes Database and compared with eight other algorithms including Logistic regression, Ridge regression, Lasso regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Random Forest, Gradient Boosting Machine, and Adam (a neural network algorithm). The findings indicated that the proposed algorithm had higher accuracy and stability, but it took more time to execute. It is suggested that, future research could apply parallelization technology for reducing execution time.
The Ethical Gravity Thesis: Marrian Levels and the Persistence of Bias in Automated Decision-making Systems

Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more general ones. We defend this thesis by adapting Marr's famous 1982 framework for understanding information-processing systems. We show how this framework allows one to situate ethical problems at the appropriate level of abstraction, which in turn can be used to target appropriate interventions.
Towards an extensible ontology for streaming sensor data for clinical trials

The use of wearable sensors for clinical trials can lead to better data collection and a better patient experience during trials, and can further allow more patients to participate in trials by allowing more remote monitoring and fewer site visits. However, extracting maximum value from the data collected via streaming sensors presents some specific technical challenges, including processing the data in real time, and storing the sensor data in a representation that facilitates the use of biomarker algorithms that can be used and reused with different similar sensors, at different scales, and across different clinical trials. Here we present our initial work on SORBET, a Sensor Ontology for Reusable Biometric Expressions and Transformations. Our design strategy is presented, along with the initial design and examples. While this ontology has been created for the Medidata Sensor Cloud product, it is our hope that others working in this space will join us in extending and hardening this ontology, as we expand it to incorporate more sensors and more needs for clinical trials research.
Simulation of Preemptive Scheduling of the Independent Tasks Using Timed Automata

To consider the effects of preemptive scheduling occurred in the concurrent threads of the independent tasks with priority values, we propose an alternative to simulate the preemptive scheduling using timed automata tool. Firstly, each independent task with priority value is expected to be represented as an ordinary timing diagram with its priority value. The timing diagram focuses on the state changings within and among lifeline along a linear timeline, of a particular independent task. Then, we propose a set of translating rules to map the timing diagrams and their associated priority values into the corresponding timed automata with the extra dormant states as to serve the task preemptions. Our resulting timed automata are sufficient and ready to be simulated using UPPAAL real-time simulation tool, yielding the model-in-loop simulation as to verify the logic hazards that might be found in the preemptive scheduling. The possibility of the violation of the hard time constraint and deadlock is checked.
Class-Based Analysis of Russell's Four-Quadrant Emotion Prediction in Virtual Reality using Multi-Layer Feedforward ANNs

The following research describes the potential of classifying a four-class emotion using a wearable EEG headset and using VR to induce emotional responses from the users. Various researchers have conducted emotion recognition using medical-grade EEG devices supported with a 2D monitor screen to induce emotional responses. This method of approach could cause additional artifacts due to the lack of concentration focusing within the border of the monitor screen of the intended stimulation thus reducing the classification accuracies. The large and complex EEG machine used by medical professions are sensitive equipment must be operated by trained professions thus making it difficult to seek permit to access such device. Hence, using a wearable EEG headset which is small and portable was considered for the brainwave signal samplings. this favors the researchers for use in conducting experiments for a human recognition system. The wearable EEG headset collects the brainwave signals at TP9, TP10, AF7, and AF8 electrode placements sampled at 256Hz with the five-bands (Delta, Theta, Alpha, Beta, Gamma). Additionally, the wearable EEG headset combines with the virtual reality (VR) headset to induce emotional responses presented to the users using the prepared VR video stimulus. The VR video was presented using the Arousal Valence Space (AVS) model with each of the respective quadrant having four videos presented in 80-seconds with a 10-second rest interval during transitions totaling up to 360-seconds from beginning to end. The collected samples are classified using Feedforward Artificial Neural Network (FANN) with 10-fold cross-validation and the model was trained using 90% of the total dataset with 10% used for validation purposes. The highest average classification result obtained from FANN was at 41.04%. While the classification performance was low, the confusion matrix presented a different view of the four-classes performed using different trained epoch values. Observations of trained epoch (2000, 3000, and 5000) showed each of the emotion classes happy, scared, bored, and calm, achieved classification accuracy of 75.15%, 75.12%, 75.02%, and 74.24% respectively.
Gazelle: transcript abundance query against large-scale RNA-seq experiments

The exponential growth of high throughput sequencing data has been witnessed in almost every sequencing data repository. To date, most of the exploratory analysis on these large datasets requires heavy lifting data processing pipelines that are both resource and labor intensive. Very recently, various algorithms have been developed to enable arbitrary sequence query over large collections of sequencing data. These algorithms were designed to support presence/absence query, i.e., screening for RNA-seq samples containing a given transcript sequence. Their utility is rather limited as they cannot retrieve abundance information of query sequence. Such abundance information is indeed critical in real applications in order to understand how the variation of transcript expression associates with different biological conditions or disease subtypes. In this paper, we present Gazelle, a sequence query engine that enables fast and quantified query against large-scale RNA-seq experiments. Gazelle exploits the advantages of two different types of hashing algorithms and seamlessly combines them into one integrated structure to support highly efficient and accurate sequence queries with abundance. We evaluated the performance of Gazelle on three datasets to benchmark its efficiency, accuracy as well as its utility in real-life applications. Our result shows that Gazelle achieves near-perfect k-mer query, supports on-demand sequence query against moderately large sequence database, and renders highly consistent abundance estimation with RT-qPCR as well as traditional transcript quantification method such as Kallisto.
Predicting Movie Box-Office Success and The Main Determinants of Movie Box Office Sales in Malaysia using Machine Learning Approach

A movie's box office is the revenue generated by the movie via ticket sales. Predicting the success of a movie in the box office is never easy. There are many factors that could potentially affect movie box office, such as its reviews and ratings, star power, genre, seasonality, and et cetera. This study aims to explore the most vital factors that influence the Malaysian box office, and to build an accurate predictive model that is tailored to this market using knowledge discovery in databases (KDD) methodology. Movie data will be obtained from FINAS [1], Box Office Mojo [2] and IMDb [3], so that it can be cleaned and processed. The cleaned dataset will be used to build support vector machine (SVM), neural networks (NN) and multilayer perceptron (MLP) models. This paper analyses the efficiency of the three models to predict the box-office success of movies, while analysing the influence of variables. At the end of the study, the most suitable model will be selected. The analysis shows that the most important factors that influence movie box office are movie budget and movie review scores for both local and international movies. In addition, the multilayer perceptron (MLP) model with its accuracy of 0.7529 is the best fit model to predict Malaysia box office for local movies. On the other hand, for predicting the Malaysia box office performance of international movies, neural network (NN) is the best fit model with an accuracy of 0.6171.
Recent strategies to minimise fouling in electrochemical detection systems

Abstract Electrode fouling is a phenomenon that can severely affect the analytical characteristics of a technique or a sensor, such as sensitivity, detection limit, reproducibility, and overall reliability. Electrode fouling generally involves the passivation of an electrode surface by a fouling agent that forms an increasingly impermeable layer on the electrode, inhibiting the direct contact of an analyte of interest with the electrode surface for electron transfer. Some potential fouling agents include proteins, phenols, amino acids, neurotransmitters, and other biological molecules. Various antifouling strategies have been reported to reduce or eliminate electrode fouling. Most antifouling strategies exploit a protective layer or barrier on an electrode substrate to prevent the fouling agent from reaching the electrode surface. Although such strategies can be quite effective, they are inappropriate for systems in which the analyte itself is also the fouling agent. In such cases, other strategies must be used, including electrode surface modification and electrochemical activation. In this review, recent strategies to minimise and efforts to overcome electrode fouling across a diverse range of analytes and fouling agents will be presented.
IN VITRO CHARACTERIZATION OF BACTERIAL ENDOPHYTES FROM TOMATO ( SOLANUM LYCOPERSICUM L . ) FOR PHYTOBENEFICIAL TRAITS

Bacterial endophytes, with plant beneficial traits, are being routinely isolated and investigated to address environmental impacts and low nutrient use efficiency of chemical fertilizers. The current study was framed with a hypothesis that local cultivar of tomato (Solanum lycopersicum L. cv. Nagina) harbors multi-trait endophytes with plant-beneficial attributes. From a collection of purified root endophytes, only two isolates viz. NgE3 and NgE4 with desired PGP traits were identified as Bacillus subtilis and Paenibacillus sp. , respectively on the basis of 16S rRNA gene sequence analysis. Both the strains exhibited substantial nitrogenase activity, tricalcium phosphate-solubilizing ability and were positive for 1-aminocyclopropnae-1-carboxylate (ACC) deaminase synthesis. A considerable quantity of Indole-3-acetic acid was measured only in the culture supernatant of Paenibacillus strain NgE4, while Nacyl homoserine lactones (AHLs) production was found positive in Bacillus subtilis NgE3. Both the strains also revealed considerable antifungal activity (> 70%) against at least 3 out 5 fungal pathogens tested. The root colonization potential of Bacillus and Paenibacillus strains was confirmed by localizing the strains in the tomato root cortical cells as endophytes through transmission electron microscopy. Keeping in view very limited research reported on bacterial endophytes in Pakistan, both phytobeneficial strains are potential resources as bio-inoculants for the tomato to design greenhouse and field
A non-functional copy of the salmonid sex determining gene (sdY) is responsible for the “apparent” XY females in Chinook salmon, Oncorhynchus tshawytscha

Many salmonids have a male heterogametic (XX/XY) sex determination system, and they are supposed to have a conserved master sex determining gene (sdY), that interacts at the protein level with Foxl2 leading to the blockage of the synergistic induction of Foxl2 and Nr5a1 of the cyp19a1a promoter. However, this hypothesis of a conserved master sex determining role of sdY in salmonids is still challenged by a few exceptions, one of them being the presence of some naturally occurring “apparent” XY Chinook salmon females. Here we show that XY Chinook salmon females have a sdY gene (sdY-N183), which has one missense mutation leading to a substitution of a conserved isoleucine to an asparagine (SdY I183N). In contrast, Chinook salmon males have both a non-mutated sdY-I183 gene and the missense mutation sdY-N183 gene. The 3D model of SdY-N183 predicts that the I183N hydrophobic to hydrophilic amino acid change leads to a local modification of the β-sandwich structure of SdY. Using in vitro cell transfection assays we found that SdY-N183, like SdY-I183, is preferentially localized in the cytoplasm. However, compared to SdY-I183, SdY-N183 is more prone to degradation, its nuclear translocation by Foxl2 is reduced and SdY-N183 is unable to significantly repress the synergistic Foxl2/Nr5a1 induction of the cyp19a1a promoter. Altogether our results suggest that the sdY-N183 gene of XY Chinook females is a non-functional gene and that SdY-N183 is no longer able to promote testicular differentiation by impairing the synthesis of estrogens in the early differentiating gonads of wild Chinook salmon XY females.
DynaMorph: learning morphodynamic states of human cells with live imaging and sc-RNAseq

Morphological states of human cells are widely imaged and analyzed to diagnose diseases and to discover biological mechanisms. Morphodynamics of cells capture their functions more fully than their morphology. Discovery of morphodynamic states of human cells is challenging, because genetic labeling or manual annotation may not be feasible. We propose a computational framework, DynaMorph, that combines quantitative label-free imaging and deep learning for automated discovery of morphodynamic states. As a case study, we apply DynaMorph to study the morphodynamic states of live primary human microglia, which are mobile immune cells of the brain that exhibit complex functional states. DynaMorph identifies two distinct morphodynamic states of microglia under perturbation by cytokines and glioblastoma supernatant. We find that microglia actively transition between the two states. Moreover, single-cell RNA-sequencing of the perturbed microglia shows that the morphodynamic states correspond to distinct transcriptomic clusters of the cells, revealing how perturbations alter gene expression and phenotype. DynaMorph can broadly enable automated discovery of functional states of cellular systems.