Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing respondent driven sampling to find undiagnosed HIV-infected people who inject drugs.

OBJECTIVE We evaluated whether identification of undiagnosed HIV-infected people who inject drugs (PWID) via respondent-driven sampling (RDS) can be enhanced through a precision RDS (pRDS) approach. DESIGN/METHODS First, using prior RDS data from PWID in India, we built a prediction algorithm for recruiting undiagnosed HIV-infected PWID. pRDS was tested in Morinda, Punjab where participants were randomly assigned to standard or pRDS. In the standard RDS approach, all participants received 2 recruitment coupons. For pRDS, the algorithm determined an individual's probability of recruiting an undiagnosed PWID, and individuals received either 2 (low probability) or 5 (high probability) coupons. Efficiency in identifying undiagnosed HIV-infected PWID for the RDS approaches was evaluated in two ways: the number needed to recruit (NNR) and identification rate/week. RESULTS Predictors of recruiting undiagnosed PWID included HIV/HCV infection, network size, syringe services utilization, and injection environment. 1631 PWID were recruited in Morinda. From the standard RDS approach, 615 were recruited, including 39 undiagnosed; from pRDS, 1012 were recruited, including 77 undiagnosed. In pRDS, those with higher predicted probability were more likely to recruit others with HIV/HCV co-infection, undiagnosed and viremic HIV, and who utilized services. pRDS had a significantly higher identification rate of undiagnosed PWID (1.5/week) compared to the standard (0.8/week). The NNR for pRDS (13.1) was not significantly lower than the standard approach (15.8). CONCLUSIONS pRDS identified twice as many undiagnosed and viremic PWID significantly faster than the standard approach. Leveraging RDS or similar network-based strategies should be considered alongside other strategies to ensure meeting UNAIDS targets.
Down‐regulation of dual‐specificity phosphatase 6, a negative regulator of oncogenic ERK signaling, by ACA‐28 induces apoptosis in NIH/3T3 cells overexpressing HER2/ErbB2

Dual‐specificity phosphatase 6 (DUSP6) is a key negative feedback regulator of the member of the RAS‐ERK MAPK signaling pathway that is associated with cellular proliferation and differentiation. Deterioration of DUSP6 expression could therefore result in deregulated growth activity. We have previously discovered ACA‐28, a novel anticancer compound with a unique property to stimulate ERK phosphorylation and induce apoptosis in ERK‐active melanoma cells. However, the mechanism of cancer cell‐specific‐apoptosis by ACA‐28 remains obscure. Here, we investigated the involvement of DUSP6 in the mechanisms of the ACA‐28‐mediated apoptosis by using the NIH/3T3 cells overexpressing HER2/ErbB2 (A4‐15 cells), as A4‐15 exhibited higher ERK phosphorylation and are more susceptible to ACA‐28 than NIH/3T3. We showed that A4‐15 exhibited high DUSP6 protein levels, which require ERK activation. Notably, the silencing of the DUDSP6 gene by siRNA inhibited proliferation and induced apoptosis in A4‐15, but not in NIH/3T3, indicating that A4‐15 requires high DUSP6 expression for growth. Importantly, ACA‐28 preferentially down‐regulated the DUSP6 protein and proliferation in A4‐15 via the proteasome, while it stimulated ERK phosphorylation. Collectively, the up‐regulation of DUSP6 may exert a growth‐promoting role in cancer cells overexpressing HER2. DUSP6 down‐regulation in ERK‐active cancer cells might have the potential as a novel cancer measure.
Rates and Predictors of Undergoing Different Hepatocellular Carcinoma Screening Tests in Patients With Cirrhosis.

INTRODUCTION We aimed to assess rates and predictors of hepatocellular carcinoma (HCC) screening among patients with cirrhosis. METHODS We reviewed electronic health records of 11,361 patients with cirrhosis from 11 U.S. Veterans Health Administration facilities for receipt of HCC screening in the 6 months preceding October 1, 2019. RESULTS Nearly half of the cohort (46%) received HCC screening over a 6-month period. Screening rates and modalities (ultrasound, computed tomography, magnetic resonance imaging, serum alpha fetoprotein) varied by facility. Screening was associated with race/ethnicity, body mass index ≥ 25, cirrhosis etiology, thrombocytopenia, Fibrosis-4 ≥ 3.25, and lower Model for End-Stage Liver Disease-Sodium. DISCUSSION HCC screening rates varied by facility. Higher risk patients were more likely to receive screening.
Update on headache and brain tumors

Headache is one of the leading symptoms often associated with brain tumours. Secondary headaches attributed to intracranial neoplasias have been included in subchapter 7.4 of the third edition of the International Classification of Headache Disorders (ICHD-3). According to ICHD-3, the headache may be attributed to a brain tumour if it has developed in close temporal relation with the development of the neoplasia, has significantly worsened in parallel with the worsening of the tumour, and/or has significantly improved following the successful treatment of the neoplasia. Brain tumour headache was traditionally thought to display some specific clinical characteristics, including worsening in the morning and/or when lying down, being aggravated by Valsalva-like manoeuvres and accompanied by nausea and/or vomiting; however, the studies performed after the advent of modern neurodiagnostic techniques have pointed out that the “classic” brain tumour headache is uncommon, particularly at the time of clinical presentation. Therefore, it becomes critical to seek some specific factors associated with the presence of an intracranial mass (the so-called “red flags”) that can guide the physician to establish an accurate diagnosis.
Hydrosilylation : a comprehensive review on recent advances

Introduction Part I: Hydrosilylation of Carbon - Carbon Multiple Bonds in Synthesis of Molecular Organosilicon Compounds 1. Hydrosilylation of Alkenes and Their Derivatives 2. Hydrosilylation of Akynes and Their Derivatives 3. Hydrosilylation of Carbon - Carbon Multiple Bonds in Organic Synthesis 4. Asymmetric Hydrosilylation of Prochiral Alkenes and Their Derivatives Part II: Hydrosilylation of Carbon - Carbon Multiple Bonds in Polymer Chemistry and Materials Science 5. Functionalisation and Cross-Linking of Organosilicon Polymers 6. Hydrosilylation Polymerisation 7. Functionalised (poly)silsesquioxanes and Silicon-Containing Dendrimers 8. Organosilicon - Organic Hybrid Polymers and Materials Part III: Chemo- and Enantio-Selective Hydrosilylation of Unsaturated Carbon-Heteroatom Bonds 9. Hydrosilylation of Unsaturated Carbon-Heteroatom Bonds 10. Asymmetric Hydrosilylation of Unsaturated Carbon-Heteroatom Bonds
Robust Active Learning Strategies for Model Variability

Active learning methods are useful when a limited budget for data labelling is available. However, the most widely used methods – uncertainty sampling – may suffer from problems derived from an excessive dependence on the model learned during data acquisition. This results in datasets which are not optimal when they are used to train models very different from those used during data creation. In this paper, we link this to the tendency of uncertainty sampling to select outliers and show that other methods that favour selection of representative sampling are more robust to changes in models. We validate this experimentally on four NLP datasets.
Activated O2 dissociation and formation of oxide islands on the Be(0001) surface: Another atomistic model for metal oxidation

By simulating the dissociation of O2 molecules on the Be(0001) surface using the first-principles molecular dynamics approach, we propose a new atomistic model for the surface oxidation of sp metals. In our model, only the dissociation of the first oxygen molecule needs to overcome an energy barrier, while the subsequent oxygen molecules dissociate barrierlessly around the adsorption area. Consequently, oxide islands form on the metal surface, and grow up in a lateral way. We also discover that the firstly dissociated oxygen atoms are not so mobile on the Be(0001) surface, as on the Al(111) surface. Our atomistic model enlarges the knowledge on metal surface oxidations by perfectly explaining the initial stage during the surface oxidation of Be, and might be applicable to some other sp metal surfaces.
Accessing Raman spectral variability in human stratum corneum for quantitative in vitro depth profiling

The opportunity of label-free and non-destructive detection of substances inside human skin by confocal Raman microscopy represents a novel approach for investigating drug penetration and permeation. Several studies already introduced confocal Raman microscopy for depth profiling in skin; however, the reported results show high deviations. Thus, analysis of the spectral variability of human skin itself is a necessary prerequisite for systematic quantitative investigations of drug penetration and permeation by confocal Raman microscopy. In our work, we acquired Raman depth profiles from excised human skin samples after abdominal plastic surgery and investigated the absolute intensity fluctuation of four major skin derived Raman peaks. The results prove the expected high variability in spectral intensity, but we could not detect dissimilarities between different skin donors. A detailed analysis of the major endogenous skin components revealed differences in spatial distribution which consequently affects their individual applicability as reference peaks for relative depth profiling. Furthermore, we discovered an increase in signal variability in deeper stratum corneum layers, which has to be considered in future substance depth profiling investigations. In addition, we discovered an exponential decay of the Raman signal for all major skin components accounting for signal attenuation inside biological tissue. Based on this mathematical description, quantitative follow-up of substances in human skin can be realized. All in all, the results of this study elucidate the necessity of substantial understanding of endogenous spectral characteristics inside human skin as essential prerequisite for rational depth profiling of substances in human skin. Copyright © 2013 John Wiley & Sons, Ltd.
Compactification of Extensive Game Structures and Backward Dominance Procedure.

We study the relationship between invariant transformations on extensive game structures and backward dominance procedure (BD), a generalization of the classical backward induction introduced in Perea (2014). We show that behavioral equivalence with unambiguous orderings of information sets, a critical property that guarantees BD's applicability, can be characterized by the classical Coalescing and a modified Interchange/Simultanizing in Battigalli et al. (2020). We also give conditions on transformations that improve BD's efficiency. In addition, we discuss the relationship between transformations and Bonanno (2014)'s generalized backward induction.
Reducing Textural Bias Improves Robustness of Deep Segmentation CNNs

Despite current advances in deep learning, domain shift remains a common problem in medical imaging settings. Recent findings on natural images suggest that deep neural models can show a textural bias when carrying out image classification tasks, which goes against the common understanding of convolutional neural networks (CNNs) recognising objects through increasingly complex representations of shape. This study draws inspiration from recent findings on natural images and aims to investigate ways in which addressing the textural bias phenomenon could be used to bring up the robustness and transferability of deep segmentation models when applied to three-dimensional (3D) medical data. To achieve this, publicly available MRI scans from the Developing Human Connectome Project are used to investigate ways in which simulating textural noise can help train robust models in a complex segmentation task. Our findings illustrate how applying specific types of textural filters prior to training the models can increase their ability to segment scans corrupted by previously unseen noise.
Field-wise Learning for Multi-field Categorical Data

We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a field. The existing methods try to learn a universal model that fits all data, which is challenging and inevitably results in learning a complex model. In contrast, we propose a field-wise learning method leveraging the natural structure of data to learn simple yet efficient one-to-one field-focused models with appropriate constraints. In doing this, the models can be fitted to each category and thus can better capture the underlying differences in data. We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters. The model is also interpretable in a field-wise manner. As the dimensionality of multi-field categorical data can be very high, the models applied to such data are mostly over-parameterized. Our theoretical analysis can potentially explain the effect of over-parametrization on the generalization of our model. It also supports the variance constraints in the learning objective. The experiment results on two large-scale datasets show the superior performance of our model, the trend of the generalization error bound, and the interpretability of learning outcomes. Our code is available at https://github.com/lzb5600/Field-wise-Learning.
Functional Linear Regression with Mixed Predictors

We consider a general functional linear regression model, allowing for both functional and high-dimensional vector covariates. Furthermore, the proposed model can accommodate discretized observations of functional variables and different reproducing kernel Hilbert spaces (RKHS) for the functional regression coefficients. Based on this general setting, we propose a penalized least squares approach in RKHS, where the penalties enforce both smoothness and sparsity on the functional estimators. We also show that the excess prediction risk of our estimators is minimax optimal under this general model setting. Our analysis reveals an interesting phase transition phenomenon and the optimal excess risk is determined jointly by the sparsity and the smoothness of the functional regression coefficients. We devise a novel optimization algorithm, simultaneously handling the smoothness and sparsity penalization.
The Concept of Soluble Residual Product Formation in the Modelling of Activated Sludge

The soluble effluent COD of a well operated activated sludge plant is likely to be composed almost entirely of organic matter generated by microbial activity. There is evidence to show that a significant portion of this soluble organic matter is non-degradable and may be due to similar microbial mechanisms. A model for the formation of these soluble residual products, (SRP), is propose'd, relating the SRP formation to the hydrolysis of non-viable cellular materials in the reactor. The set of equations describing the model are successfully calibrated and verified for a set of representative experimental data derived from Sequencing Batch Reactors. The model is found to explain and predict COD accumulation at the end of each cycle of these activated sludge systems.
Ambient Moisture Effects and Closed Vessel Studies of Gun Propellant Combustion

Abstract : This report covers several topics in the field of solid gun propellant combustion. They include: (1) mass spectrometry to define experimentally the gas properties of gun propellant combustion and correlate these experimental results with theoretical predictions; (2) the use of closed combustion bomb pressurization rates to extract solid propellant linear burning rate values is discussed, and correlations with strand burner data are considered; (3) the effects of moisture adsorption on solid propellant combustion rates and energy are experimentally defined; and (4) a reference section of solid propellant combustion product specific heat ratio values is provided.
Sheaf-theoretic framework for optimal network control.

In this paper, we use tools from sheaf theory to model and analyze optimal network control problems and their associated discrete relaxations. We consider a general problem setting in which pieces of equipment and their causal relations are represented as a directed network, and the state of this equipment evolves over time according to known dynamics and the presence or absence of control actions. First, we provide a brief introduction to key concepts in the theory of sheaves on partial orders. This foundation is used to construct a series of sheaves that build upon each other to model the problem of optimal control, culminating in a result that proves that solving our optimal control problem is equivalent to finding an assignment to a sheaf that has minimum consistency radius and restricts to a global section on a particular subsheaf. The framework thus built is applied to the specific case where a model is discretized to one in which the state and control variables are Boolean in nature, and we provide a general bound for the error incurred by such a discretization process. We conclude by presenting an application of these theoretical tools that demonstrates that this bound is improved when the system dynamics are affine.
Small instantons and the strong 
CP
 problem in composite Higgs models

We show that QCD instantons can generate large effects at small length scales in the ultraviolet in standard composite Higgs models that utilise partial compositeness. This has important implications for possible solutions of the strong CP problem in these models. First we show that in the simplest known UV completions of composite Higgs models, if an axion is also present, it can have a mass much larger than the usual QCD axion. Even more remarkable is the case where there are no axions, but the strong CP problem can be solved by generating the up quark mass entirely from the contribution of instantons thus reviving the massless up-quark solution for these models. In both cases no additional field content is required apart from what is required to realise partial compositeness.
Fluorination of Carbon Nanostructures and Their Comparative Investigation by XPS and XAES Spectroscopy

Abstract X‐ray photoelectron (XPS) and Auger electron (XAES) spectroscopies have been used to investigate fluorine interaction with surfaces of nanocarbons. MWCNT, SWCNT and Fiber were fluorinated in F2 and HOPG in ClF3 atmosphere at room temperature. The performed measurements imply that the surface of the samples is inert to the environmental before and after fluorination. The XPS spectra show that MWCNT and Fiber after fluorination have similar composition, which includes CF, CF2 and CF3 states, but MWCNT includes CF4 state as well. During defluorination of the fluorinated HOPG sample with initial C‐F bonds the formation of CF2 and CF3 states has been observed. XAES spectra show the absence of C‐F bonding in the outer layers of fluorinated HOPG, MWCNT and Fiber, while C‐F bonding exists in SWCNT.
Assessing and Accelerating Coverage in Deep Reinforcement Learning

Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space. However, particularly in high dimensions, relying on randomness may lead to gaps in coverage of the trained DRL neural network model, which in turn may lead to drastic and often fatal real-world situations. To the best of the author's knowledge, the assessment of coverage for DRL is lacking in current research literature. Therefore, in this paper, a novel measure, Approximate Pseudo-Coverage (APC), is proposed for assessing the coverage in DRL applications. We propose to calculate APC by projecting the high dimensional state space on to a lower dimensional manifold and quantifying the occupied space. Furthermore, we utilize an exploration-exploitation strategy for coverage maximization using Rapidly-Exploring Random Tree (RRT). The efficacy of the assessment and the acceleration of coverage is demonstrated on standard tasks such as Cartpole, highway-env.
Secrecy Performance of Body-Centric Communications over Alternate Rician Shadowed Fading Channels

In this paper, we investigate the physical layer security over the Alternate Rician Shadowed fading channel, which is a novel channel model for body-centric wireless links and land mobile satellite. We derive exact closed-form expressions for the average secrecy capacity (ASC), secrecy outage probability (SOP), and probability of non-zero secrecy capacity (PNZ) for two cases: (i) m is a positive real number and (ii) m is a positive integer number, where m describes the level of fluctuation of the line-of-sight component. In the first case, SOP is derived in terms of the Meijer's G-function, while ASC and PNZ are derived in terms of the multivariate Fox's H-function. In the second case, ASC is derived in terms of the Meijer's G-function, while SOP and PNZ are derived in terms of elementary functions. In addition, we derive the asymptotic ASC, SOP and PNZ expressions which all match well the exact ones at high values of signal-to-noise ratio, respectively. The capacity slope of asymptotic ASC and the secrecy diversity order of asymptotic SOP have been derived for providing more physical insights. Finally, the accuracy of our derived expressions is validated by Monte-Carlo simulations.
MEG Source Localization via Deep Learning

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
Towards a Unified Framework for Emotion Analysis

We present EmoCoder, a modular encoder-decoder architecture that generalizes emotion analysis over different tasks (sentence-level, word-level, label-to-label mapping), domains (natural languages and their registers), and label formats (e.g., polarity classes, basic emotions, and affective dimensions). Experiments on 14 datasets indicate that EmoCoder learns an interpretable language-independent representation of emotions, allows seamless absorption of state-of-the-art models, and maintains strong prediction quality, even when tested on unseen combinations of domains and label formats.