Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review: New Trends in Electrode Systems for Sensitive Drug and Biomolecule Analysis

Abstract Drug and biomolecule analysis with high precision, fast response, not expensive, and user-friendly methods have been very important for developing technology and clinical applications. Electrochemical methods are highly capable for assaying the concentration of electroactive drug or biomolecule and supply excellent knowledge concerning its physical and chemical properties such as electron transfer rates, diffusion coefficients, electron transfer number, and oxidation potential. Electrochemical methods have been widely applied because of their accuracy, sensitivity, cheapness, and can applied on-site determinations of various substances. The progress on electronics has allowed developing reliable, more sensitive and less expensive instrumentations, which have significant contribution in the area of drug development, drug and biomolecule analysis. The developing new sensors for electrochemical analysis of these compounds have growing interest in recent years. Screen-printed based electrodes have a great interest in electrochemical analysis of various drugs and biomolecules due to their easy manufacturing procedure of the electrode allow the transfer of electrochemical laboratory experiments for disposable on-site analysis of some compounds. Paper based electrodes are also fabricated by new technology. They can be preferred due to their easy, cheap, portable, disposable, and offering high sensitivity properties for many application field such as environmental monitoring, food quality control, clinical diagnosis, drug, and biomolecules analysis. In this review, the recent electrochemical drug and biomolecule (DNA, RNA, µRNA, Biomarkers, etc.) studies will be presented that involve new trend disposable electrodes.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves people’s lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents’ experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contain valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.
Machine-learned analysis of the association of next-generation sequencing based genotypes with persistent pain after breast cancer surgery.

ABTRACT Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoing breast cancer surgery, assigned based on a three-year follow-up to either a persistent or non-persistent pain phenotype, next generation sequencing was performed for 77 genes selected for known functional involvement in persistent pain. Applying machine learning and item categorization techniques, 21 variants in 13 different genes were found to be relevant to the assignment of a patient to either the persistent pain or the non-persistent pain phenotype group. In descending order of importance for correct group assignment, the relevant genes comprised DRD1, FAAH, GCH1, GPR132, OPRM1, DRD3, RELN, GABRA5, NF1, COMT, TRPA1, ABHD6, and DRD4, of which one in the DRD4 gene was a novel discovery. Particularly relevant variants were found in the DRD1 and GPR132 genes, or in a cis-eCTL position of the OPRM1 gene. Supervised machine learning based classifiers, trained with 2/3 of the data, identified the correct pain phenotype group in the remaining 1/3 of the patients at accuracies and areas under the receiver operator characteristic curves of 65 - 72 %. When using conservative classical statistical approaches, none of the variants passed α-corrected testing. The present data analysis approach, using machine learning and training artificial intelligences, provided biologically plausible results and outperformed classical approaches to genotype phenotype association.
Fetal sex and perinatal outcomes

Abstract Objective: To evaluate the relationship of fetal sex with obstetric risk factors and perinatal outcomes. Material and methods: Retrospective study of deliveries during 2003–2009 at a tertiary hospital based on the analysis of obstetric and neonatal risk variables according to neonatal sex. Results: Of the 29,530 deliveries studied, the neonate was a boy in 15,038 (50.92%) and a girl in 14,492 (49.08%). The rates of gestational diabetes, prematurity, true umbilical cord knot and nuchal cord were higher for male newborns, whereas the rate of echographic diagnosis of intrauterine fetal growth retardation was higher for female newborns. Mothers of male fetuses had higher rates of cesarean delivery and instrumental vaginal delivery. The indication for ending labor due to the non-reassuring fetal heart rate was also more frequent among male newborns. This group had higher rates of acidemia, although there were no significant differences between sexes in other short-term neonatal outcomes. In all subgroups analyzed here, fetal weight was higher in male newborns. Conclusions: Male sex is an independent risk factor for unfavorable outcomes of pregnancy and delivery.
Holistic generational offsets: Fostering a primitive online abstraction for human vs. machine cognition

We propose a unified architecture for next generation cognitive, low cost, mobile internet. The end user platform is able to scale as per the application and network requirements. It takes computing out of the data center and into end user platform. Internet enables open standards, accessible computing and applications programmability on a commodity platform. The architecture is a super-set to present day infrastructure web computing. The Java virtual machine (JVM) derives from the stack architecture. Applications can be developed and deployed on a multitude of host platforms. O(1) ↔ O(N). Computing and the internet today are more accessible and available to the larger community. Machine learning has made extensive advances with the availability of modern computing. It is used widely in NLP, Computer Vision, Deep learning and AI. A prototype device for mobile could contain N compute and N MB of memory. Keywords— Mobile, AI, Cognitive, Server, Internet
Simultaneous Generation of Arbitrary Assembly of Polarization States with Geometrical-Scaling-Induced Phase Modulation

Manipulating the polarization of light on the microscale or nanoscale is essential for integrated photonics and quantum optical devices. Nowadays, the metasurface allows one to build on-chip devices that efficiently manipulate the polarization states. However, it remains challenging to generate different types of polarization states simultaneously, which is required to encode information for quantum computing and quantum cryptography applications. By introducing geometrical-scaling-induced (GSI) phase modulations, we demonstrate that an assembly of circularly polarized (CP) and linearly polarized (LP) states can be simultaneously generated by a single metasurface made of L-shaped resonators with different geometrical features. Upon illumination, each resonator diffracts the CP state with a certain GSI phase. The interaction of these diffractions leads to the desired output beams, where the polarization state and the propagation direction can be accurately tuned by selecting the geometrical shape, size, and spatial sequence of each resonator in the unit cell. As an example of potential applications, we show that an image can be encoded with different polarization profiles at different diffraction orders and decoded with a polarization analyzer. This approach resolves a challenging problem in integrated optics and is inspiring for on-chip quantum information processing.
GazeGAN: A Generative Adversarial Saliency Model based on Invariance Analysis of Human Gaze During Scene Free Viewing

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: this https URL.
Structural and Functional Characterization of a Ketosteroid Transcriptional Regulator of Mycobacterium tuberculosis*

Background: KstR2 regulates cholesterol catabolic genes in Mycobacterium tuberculosis. Results: Dimeric KstR2Mtb binds two molecules of HIP-CoA with high affinity. Each binding site spans the subunits and includes residues conserved in TetR family repressors (TFRs) that bind CoA thioesters. Conclusion: HIP-CoA binding to KstR2Mtb induces a conformation that abrogates DNA binding. Significance: The study identifies molecular determinants of cholesterol catabolism and CoA binding in TFRs. Catabolism of host cholesterol is critical to the virulence of Mycobacterium tuberculosis and is a potential target for novel therapeutics. KstR2, a TetR family repressor (TFR), regulates the expression of 15 genes encoding enzymes that catabolize the last half of the cholesterol molecule, represented by 3aα-H-4α(3′-propanoate)-7aβ-methylhexahydro-1,5-indane-dione (HIP). Binding of KstR2 to its operator sequences is relieved upon binding of HIP-CoA. A 1.6-Å resolution crystal structure of the KstR2Mtb·HIP-CoA complex reveals that the KstR2Mtb dimer accommodates two molecules of HIP-CoA. Each ligand binds in an elongated cleft spanning the dimerization interface such that the HIP and CoA moieties interact with different KstR2Mtb protomers. In isothermal titration calorimetry studies, the dimer bound 2 eq of HIP-CoA with high affinity (Kd = 80 ± 10 nm) but bound neither HIP nor CoASH. Substitution of Arg-162 or Trp-166, residues that interact, respectively, with the diphosphate and HIP moieties of HIP-CoA, dramatically decreased the affinity of KstR2Mtb for HIP-CoA but not for its operator sequence. The variant of R162M that decreased the affinity for HIP-CoA (ΔΔG = 13 kJ mol−1) is consistent with the loss of three hydrogen bonds as indicated in the structural data. A 24-bp operator sequence bound two dimers of KstR2. Structural comparisons with a ligand-free rhodococcal homologue and a DNA-bound homologue suggest that HIP-CoA induces conformational changes of the DNA-binding domains of the dimer that preclude their proper positioning in the major groove of DNA. The results provide insight into KstR2-mediated regulation of expression of steroid catabolic genes and the determinants of ligand binding in TFRs.
Deep learning incorporating biologically-inspired neural dynamics

Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs). Simultaneously, Spiking Neural Networks (SNNs) incorporating biologically-feasible spiking neurons have held great promise because of their rich temporal dynamics and high-power efficiency. However, the developments in SNNs were proceeding separately from those in ANNs, effectively limiting the adoption of deep learning research insights. Here we show an alternative perspective on the spiking neuron that casts it as a particular ANN construct called Spiking Neural Unit (SNU), and a soft SNU (sSNU) variant that generalizes its dynamics to a novel recurrent ANN unit. SNUs bridge the biologically-inspired SNNs with ANNs and provide a methodology for seamless inclusion of spiking neurons in deep learning architectures. Furthermore, SNU enables highly-efficient in-memory acceleration of SNNs trained with backpropagation through time, implemented with the hardware in-the-loop. We apply SNUs to tasks ranging from hand-written digit recognition, language modelling, to music prediction. We obtain accuracy comparable to, or better than, that of state-of-the-art ANNs, and we experimentally verify the efficacy of the in-memory-based SNN realization for the music-prediction task using 52,800 phase-change memory devices. The new generation of neural units introduced in this paper incorporate biologically-inspired neural dynamics in deep learning. In addition, they provide a systematic methodology for training neuromorphic computing hardware. Thus, they open a new avenue for a widespread adoption of SNNs in practical applications.
Geometric Pose Affordance: 3D Human Pose with Scene Constraints

Full 3D estimation of human pose from a single image remains a challenging task despite many recent advances. In this paper, we explore the hypothesis that strong prior information about scene geometry can be used to improve pose estimation accuracy. To tackle this question empirically, we have assembled a novel $\textbf{Geometric Pose Affordance}$ dataset, consisting of multi-view imagery of people interacting with a variety of rich 3D environments. We utilized a commercial motion capture system to collect gold-standard estimates of pose and construct accurate geometric 3D CAD models of the scene itself. To inject prior knowledge of scene constraints into existing frameworks for pose estimation from images, we introduce a novel, view-based representation of scene geometry, a $\textbf{multi-layer depth map}$, which employs multi-hit ray tracing to concisely encode multiple surface entry and exit points along each camera view ray direction. We propose two different mechanisms for integrating multi-layer depth information pose estimation: input as encoded ray features used in lifting 2D pose to full 3D, and secondly as a differentiable loss that encourages learned models to favor geometrically consistent pose estimates. We show experimentally that these techniques can improve the accuracy of 3D pose estimates, particularly in the presence of occlusion and complex scene geometry.
DynaMo: Dynamic Modularity-based Community Detection in Evolving Social Networks.

Community detection is one of the most popular research topics in the field of online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, nowadays, the real world networks (e.g., Facebook, LinkedIn and Twitter) are inherently evolving rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the evolving networks. We propose DynaMo, a novel dynamic modularity-based community detection algorithm, aiming to detect communities in evolving social networks. DynaMo is an adaptive and incremental algorithm, which is designed for maximizing the modularity gain while updating the community structure of evolving networks. In the experimental evaluation, a comprehensive comparison has been made among our algorithm, Louvain algorithm (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real world social networks. The experimental results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of effectiveness, and achieves (on average) 2 to 5 times faster than Louvain algorithm.
Uniform Bounds for Invariant Subspace Perturbations

For a fixed matrix A and perturbation E we develop purely deterministic bounds on how invariant subspaces of A and A+E can differ when measured by a suitable "row-wise" metric rather than via traditional norms such as two or Frobenius. Understanding perturbations of invariant subspaces with respect to such metrics is becoming increasingly important across a wide variety of applications and therefore necessitates new theoretical developments. Under minimal assumptions we develop new bounds on subspace perturbations under the two-to-infinity matrix norm and show in what settings these row-wise differences in the invariant subspaces can be significantly smaller than the two or Frobenius norm differences. We also demonstrate that the constitutive pieces of our bounds are necessary absent additional assumptions and therefore our results provide a natural starting point for further analysis of specific problems.
Overall Mental Health and Misuse of Erectile Dysfunction Medication Among Sexual Minority Men

Abstract Background: Research has noted that rates of recreational use of erectile dysfunction medication (EDM) are particularly high among men who have sex with other men. Though previous research has investigated recreational EDM use in relation to substance use, its association with other general mental health symptoms and diagnoses remains unexamined. Objectives: To address the paucity of research on EDM misuse, the current study examined prevalence rates of EDM misuse among sexual minority men and its association with last 12-month and 30-day mental health symptoms and diagnoses. Method: A secondary analysis of 7,658 sexual minority undergraduate and graduate male students recruited as part of the American College of Health Association’s National College Health Assessment was performed. Results: Results indicated that 2.2% of sexual minority men reported misuse of EDMs within the last 12 months. Last 12-month EDM misuse was significantly associated with a number of mental health symptoms and diagnoses in the last 12 months and last 30 days. Most notably, small to moderate effects were found for misuse of other prescribed medications, such as antidepressants, painkillers, and sedatives, and diagnosis of substance use within the last 12 months. Small effects were found for sedatives and steroid misuse in the last 30 days. Conclusion: Results suggest that worse mental health, particularly substance use, is associated with EDM misuse among sexual minority men. As such, further research in this area is needed.
Flaxseed oil supplementation manipulates correlations between serum individual mol % free fatty acid levels and insulin resistance in type 2 diabetics. Insulin resistance and percent remaining pancreatic β-cell function are unaffected

Abstract Objectives. Elevated total serum free fatty acids (FFAs) concentrations have been suggested, controversially, to enhance insulin resistance and decrease percent remaining β-cell function. However, concentrations of individual serum FFAs have never been published in terms of their relationship (correlation) to homeostatic model assessment-insulin resistance (HOMA-IR) and percent remaining β-cell function (HOMA-%β) in the type 2 diabetics (T2Ds). Alpha-linolenic acid consumption has a negative correlation with the insulin resistance, which in turn is negatively correlated with the remaining β-cell function. The primary objective was to test the hypothesis that there would be different relationship (correlation) between the blood serum individual free FFA mol % levels and HOMA-IR and/or HOMA-%β in T2D. The secondary objective was to test the hypothesis that flaxseed oil, previously being shown to be ineffective in the glycemic control in T2Ds, may alter these correlations in a statistically significant manner as well as HOMA-IR and/or HOMA-%β. Methods. Patients were recruited via a newspaper advertisement and two physicians have been employed. All the patients came to visit one and three months later for a second visit. At the second visit, the subjects were randomly assigned (double blind) to flaxseed or safflower oil treatment for three months, until the third visit. Results. Different statistically significant correlations or trends towards among some serum individual free FFA mol % levels and HOMA-IR and HOMA-%β, pre- and post-flaxseed and safflower oil supplementation were found. However, flaxseed oil had no impact on HOMA-IR or HOMA-%β despite statistically significant alterations in correlations compared to baseline HOMA-IR. Conclusions. The obtained data indicate that high doses of flaxseed oil have no statistically significant effect on HOMA-IR or HOMA-%β in T2Ds, probably due to the additive effects of negative and positive correlations.
Physical Models of Collective Cell Migration

Collective cell migration is a key driver of embryonic development, wound healing, and some types of cancer invasion. Here, we provide a physical perspective of the mechanisms underlying collective cell migration. We begin with a catalog of the cell–cell and cell–substrate interactions that govern cell migration, which we classify into positional and orientational interactions. We then review the physical models that have been developed to explain how these interactions give rise to collective cellular movement. These models span the subcellular to the supracellular scales, and they include lattice models, phase-field models, active network models, particle models, and continuum models. For each type of model, we discuss its formulation, its limitations, and the main emergent phenomena that it has successfully explained. These phenomena include flocking and fluid–solid transitions, as well as wetting, fingering, and mechanical waves in spreading epithelial monolayers. We close by outlining remaining challenges and future directions in the physics of collective cell migration.