Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

OBJECTIVE Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available, but there is a lack of evidence to decide which treatment works best for whom. This study aimed to identify subgroups of patients who respond differentially to cognitive-behavioral therapy (CBT) or person-centered counseling for depression (CfD). METHOD This was a retrospective analysis of archival routine practice data for 1,435 patients who received either CBT (N = 1,104) or CfD (N = 331) in primary care. The main outcome was posttreatment reliable and clinically significant improvement (RCSI) in the PHQ-9 depression measure. A targeted prescription algorithm was developed in a training sample (N = 1,085) using a supervised machine learning approach (elastic net with optimal scaling). The clinical utility of the algorithm was examined in a statistically independent test sample (N = 350) using chi-square analysis and odds ratios. RESULTS Cases in the test sample that received their model-indicated "optimal" treatment had a significantly higher RCSI rate (62.5%) compared to those who received the "suboptimal" treatment (41.7%); χ2(df = 1) = 4.79, p = . 03, OR = 2.33 (95% CI [1.09, 5.02]). CONCLUSION Targeted prescription has the potential to make best use of currently available evidence-based treatments, improving outcomes for patients at no additional cost to psychological services. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Tritrophic metabolism of plant chemical defenses and its effects on herbivore and predator performance

Insect herbivores are frequently reported to metabolize plant defense compounds, but the physiological and ecological consequences are not fully understood. It has rarely been studied whether such metabolism is genuinely beneficial to the insect, and whether there are any effects on higher trophic levels. Here, we manipulated the detoxification of plant defenses in the herbivorous pest diamondback moth (Plutella xylostella) to evaluate changes in fitness, and additionally examined the effects on a predatory lacewing (Chrysoperla carnea). Silencing glucosinolate sulfatase genes resulted in the systemic accumulation of toxic isothiocyanates in P. xylostella larvae, impairing larval development and adult reproduction. The predatory lacewing C. carnea, however, efficiently degraded ingested isothiocyanates via a general conjugation pathway, with no negative effects on survival, reproduction, or even prey preference. These results illustrate how plant defenses and their detoxification strongly influence herbivore fitness but might only subtly affect a third trophic level.
A simple cut-off for waist-to-height ratio (0·5) can act as an indicator for cardiometabolic risk: recent data from adults in the Health Survey for England

Abstract The National Institute for Health and Care Excellence (NICE) has acknowledged the value of waist-to-height ratio (WHtR) as an indicator for ‘early health risk’. We used recent UK data to explore whether classification based on WHtR identifies more adults at cardiometabolic risk than the ‘matrix’ based on BMI and waist circumference, currently used for screening. Data from the Health Survey for England (4112 adults aged 18+ years) were used to identify cardiometabolic risk, indicated by raised glycated Hb, dyslipidaemia and hypertension. HbA1c, total/HDL-cholesterol and systolic blood pressure (BP) were more strongly associated with WHtR than the ‘matrix’. In logistic regression models for HbA1c ≥ 48 mmol/mol, total/HDL-cholesterol > 4 and hypertension (BP > 140/90 mmHg or on medication), WHtR had a higher predictive value than the ‘matrix’. AUC was significantly greater for WHtR than the ‘matrix’ for raised HbA1c and hypertension. Of adults with raised HbA1c, 15 % would be judged as ‘no increased risk’ using the ‘matrix’ in contrast to 3 % using WHtR < 0·5. For hypertension, comparative values were 23 and 9 %, and for total/HDL-cholesterol > 4, 26 and 13 %. Nearly one-third of the ‘no increased risk’ group in the ‘matrix’ had WHtR ≥ 0·5 and hence could be underdiagnosed for cardiometabolic risk. WHtR has the potential to be a better indicator of cardiometabolic risks associated with central obesity than the current NICE ‘matrix’. The cut-off WHtR 0·5 in early screening translates to a simple message, ‘your waist should be less than half your height’, that allows individuals to be aware of their health risks.
Phenolic profiling of different Jasminum species cultivated in Egypt and their antioxidant activity

Abstract This study focused on the profiling of phenolic constituents in 80% methanolic extracts of the leaves of some Jasminum species cultivated in Egypt and their antioxidant activities. Phenolic profiling was performed by total phenolic contents, total flavonoid contents and HPLC-DAD for selected species in comparison to authentic standards. DPPH assay was used to estimate the antioxidant activities of Jasminum azoricum L. , Jasminum humile L. , Jasminum multiflorum (Burm.f.) Andrew, Jasminum officinale L. , Jasminum sambac (Ait) L. “Arabian Nights cultivar” and Jasminum sambac (Ait) L. “Grand Duke of Tuscany cultivar”. Jasminum multiflorum showed the highest antioxidant activity among selected species with IC50 of 34.8 µg/ml. J. multiflorum showed high concentrations of hydroxytyrosol, protocatechuic acid, hydroxybenzoic acid, kaempferol-3-O- neohesperidoside, and quercetin-3-O-glucoside with concentrations of 977.1 µg/g, 2224.7 µg/g, 714.8 µg/g, 1738.8 µg/g, and 4356.1 µg/g, respectively. Graphical Abstract
Long-term deep remission during maintenance therapy with biological agents in inflammatory bowel diseases

Abstract Background and aims: A multicentre, retrospective, non-interventional, patient chart review study was conducted to investigate deep (DR) and histological remission rates during maintenance therapy with biological agents in inflammatory bowel disease (IBD). Methods: We reviewed clinical, endoscopic, and histological findings, and laboratory markers such as C-reactive protein (CRP) and faecal calprotectin (FC) on average of nine years after the initiation of anti-TNF-therapy. DR was defined as no clinical symptoms (The physicians’ global assessment scores; PGA = 0) with endoscopic remission (the Simple Endoscopic Score for Crohn’s Disease [SES-CD] ≤ 2 or Mayo endoscopic subscore ≤1). Histological activity was defined as normal if only architectural alterations without cellularity changes occurred. Results: Of 117 IBD patients on maintenance therapy, 72 (62%; CD n = 55 [56%], UC n = 17 [85%]) patients were in DR. Of patients in DR, 76% were also in histological remission. 77% of patients remained on initiated biological treatment. UC patients achieved DR significantly more often than CD patients (p = .016). Both median CRP and FC levels were significantly lower in patients with DR. Conclusion: Reassuringly, almost two thirds of the IBD patients on maintenance therapy with biological agents maintained DR in the long-term, and more than two thirds of patients in DR achieved also histological remission. CD patients in DR had fewer surgical operations due to CD than patients not achieving DR.
Nonmodal Tollmien-Schlichting waves.

The instability of flows via two-dimensional perturbations is analyzed theoretically and numerically in a nonmodal framework. In particular, it is shown that the growth mechanism of nonmodal Tollmien-Schlichting waves naturally reduces to the eigenvalue problem for the energy bound formulated by [Davis and Von Kerczek (1973)]. This eigenvalue equation thus allows for a broader interpretation. It provides the discrete growth rates for the base flow in question. In contrast to traditional Orr-Sommerfeld modal analysis, the eigenvalue equation defines an orthogonal set of eigenfunctions allowing to decompose the perturbations into base perturbations with discrete growth rates and frequencies. As a result of this decomposition, it can be shown that the evolution of two-dimensional perturbations is governed by two mechanisms: A first one, responsible for extracting and returning energy from and to the base flow, in addition to viscous dissipation and, a second one, responsible for dispersing energy among the different base perturbations constituting the perturbation. We investigate the relation between base flow profile and growth and provide a mathematical proof concerning Rayleigh's inflection point theorem, that if the base flow possesses inflection points, the modal eigenfunctions of Rayleigh's stability equation display no growth in general. On the other hand, the present analysis provides some insight why some base flow profiles are more susceptible to instability than others. As a general result, we show that the stability of a flow is not only determined by the growth rates of the base perturbations, but it is also closely related to its ability to disperse energy away from the base perturbations with positive growth rates to the ones with negative growth rates.
Discovery of Millisecond Pulsars in the Globular Cluster Omega Centauri

The globular cluster Omega Centauri is the most massive and luminous cluster in the Galaxy. The γ-ray source FL8Y J1326.7–4729 is coincident with the core of the cluster, leading to speculation that hitherto unknown radio pulsars or annihilating dark matter may be present in the cluster core. Here we report on the discovery of five millisecond pulsars (MSPs) in Omega Centauri following observations with the Parkes radio telescope. Four of these pulsars are isolated with spin periods of 4.1, 4.2, 4.6, and 6.8 ms. The fifth has a spin period of 4.8 ms and is in an eclipsing binary system with an orbital period of 2.1 hr. Deep radio continuum images of the cluster center with the Australian Telescope Compact Array reveal a small population of compact radio sources, making it likely that other pulsars await discovery. We consider it highly likely that the MSPs are the source of the γ-ray emission. The long-term timing of these pulsars opens up opportunities to explore the dynamics and interstellar medium of the cluster.
Learning to Dress 3D People in Generative Clothing

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is. tue.mpg. de.
Structure of numerical algorithms and advanced mechanics

Most elementary numerical schemes found useful for solving classical trajectory problems are {\it canonical transformations}. This fact should be make more widely known among teachers of computational physics and Hamiltonian mechanics. From the perspective of advanced mechanics, there are no bewildering number of seemingly arbitrary elementary schemes based on Taylor's expansion. There are only {\it two} canonical first and second order algorithms, on the basis of which one can comprehend the structures of higher order symplectic and non-symplectic schemes. This work shows that, from the most elementary first-order methods to the most advanced fourth-order algorithms, all can be derived from canonical transformations and Poisson brackets of advanced mechanics.
Gamma-Ray Emission of 60Fe and 26Al Radioactivity in Our Galaxy

The isotopes 60Fe and 26Al originate from massive stars and their supernovae, reflecting ongoing nucleosynthesis in the Galaxy. We studied the gamma-ray emission from these isotopes at characteristic energies 1173, 1332, and 1809 keV with over 15 yr of SPI data, finding a line flux in 60Fe combined lines of and the Al line flux of above the background and continuum emission for the whole sky. Based on the exponential disk grid maps, we characterize the emission extent of 26Al to find scale parameters and kpc; however, the 60Fe lines are too weak to spatially constrain the emission. Based on a point-source model test across the Galactic plane, the 60Fe emission would not be consistent with a single strong point source in the Galactic center or somewhere else, providing a hint of a diffuse nature. We carried out comparisons of emission morphology maps using different candidate source tracers for both 26Al and 60Fe emissions and suggest that the 60Fe emission is more likely to be concentrated toward the Galactic plane. We determine the 60Fe/26Al γ-ray flux ratio at 18.4% ± 4.2% when using a parameterized spatial morphology model. Across the range of plausible morphologies, it appears possible that 26Al and 60Fe are distributed differently in the Galaxy. Using the best-fitting maps for each of the elements, we constrain flux ratios in the range 0.2–0.4. We discuss the implications for massive star models and their nucleosynthesis.
A nonparametric Bayesian approach to simultaneous subject and cell heterogeneity discovery for single cell RNA-seq data

The advent of the single cell sequencing era opens new avenues for the personalized treatment. The first but important step is to discover the subject heterogeneity at the single cell resolution. In this article, we address the two-level-clustering problem of simultaneous subject subgroup discovery (subject level) and cell type detection (cell level) based on the scRNA-seq data from multiple subjects. However, the current statistical approaches either cluster cells without considering the subject heterogeneity or group subjects not using the single-cell information. To overcome the challenges and fill the gap between cell clustering and subject grouping, we develop a solid nonparametric Bayesian model SCSC (Subject and Cell clustering for Single-Cell expression data) to achieve subject and cell grouping at the same time. SCSC does not need to prespecify the subject subgroup number or the cell type number, automatically induces subject subgroup structures and matches cell types across subjects, and directly models the scRNA-seq raw count data by deliberately considering the data's dropouts, library sizes, and over-dispersion. A computationally efficient blocked Gibbs sampler is proposed for the posterior inference. The simulation and the application to a multi-subject iPSC scRNA-seq dataset validate the function of SCSC to discover subject and cell heterogeneity.
A literature survey of matrix methods for data science

Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines. With this survey we want to illustrate that numerical linear algebra has played and is playing a crucial role in enabling and improving data science computations with many new developments being fueled by the availability of data and computing resources. We highlight the role of various different factorizations and the power of changing the representation of the data as well as discussing topics such as randomized algorithms, functions of matrices, and high‐dimensional problems. We briefly touch upon the role of techniques from numerical linear algebra used within deep learning.
Nanoscale Microscopy Images Colourisation Using Neural Networks

Microscopy images are powerful tools and widely used in the majority of research areas, such as biology, chemistry, physics and materials fields by various microscopies (Scanning Electron Microscope (SEM), Atomic Force Microscope (AFM) and the optical microscope, et al.). However, most of the microscopy images are colourless due to the unique imaging mechanism. Though investigating on some popular solutions proposed recently about colourizing microscopy images, we notice the process of those methods are usually tedious, complicated, and time-consuming. In this paper, inspired by the achievement of machine learning algorithms on different science fields, we introduce two artificial neural networks for grey microscopy image colourization: An end-to-end convolutional neural network (CNN) with a pre-trained model for feature extraction and a pixel-to-pixel Neural Style Transfer convolutional neural network (NST-CNN) which can colourize grey microscopy images with semantic information learned from a user-provided colour image at inference time. Our results show that our algorithm not only could able to colour the microscopy images under complex circumstances precisely but also make the colour naturally according to a massive number of nature images training with proper hue and saturation.
Results from UPLOAD-DOWNLOAD: A phase-interferometric axion dark matter search

First experimental results from a room-temperature table-top phase-sensitive axion haloscope experiment are presented. A dual-mode configuration exploits the axion-photon coupling, which serves as a source of frequency modulation in microwave cavity resonances, offering a new pathway to axion detection with certain advantages over the traditional haloscope method. A tunable copper cavity supports orthogonally polarized microwave modes, enabling simultaneous sensitivity to axions with masses corresponding to the sum and difference of the microwave frequencies. The results place axion exclusion limits between 11.6 19.4 neV, excluding a minimal coupling strength above ∼ 5×10−7 1/GeV, and simultaneously place limits between 74.45 74.46 μeV at ∼ 2×10−3 1/GeV, after a measurement period of two hours. We show that, in some frequency ranges, a cryogenic implementation of this technique, ambitious but realizable, may reach axion model limits.
LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database for NIR-VIS Face Recognition

Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS database LAMP-HQ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2Pixel, CycleGAN, and ADFL. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ.
Monotone 3-Sat-(2,2) is NP-complete

We show that Monotone 3-Sat remains NP-complete if (i) each clause contains exactly three distinct variables, (ii) each clause is unique, i.e., there are no duplicates of the same clause, and (iii), amongst the clauses, each variable appears unnegated exactly twice and negated exactly twice. Darmann and Döcker [6] recently showed that this variant of Monotone 3-Sat is either trivial or NP-complete. In the first part of the paper, we construct an unsatisfiable instance which answers one of their open questions (Challenge 1) and places the problem in the latter category. Then, we adapt gadgets used in the construction to (1) sketch two reductions that establish NP-completeness in a more direct way, and (2), to show that ∀∃ 3-SAT remains Π 2 -complete for quantified Boolean formulas with the following properties: (a) each clause is monotone (i.e., no clause contains an unnegated and a negated variable) and contains exactly three distinct variables, (b) each universal variable appears exactly once unnegated and exactly once negated, (c) each existential variable appears exactly twice unnegated and exactly twice negated, and (d) the number of universal and existential variables is equal. Furthermore, we show that the variant where (b) is replaced with (b’) each universal variable appears exactly twice unnegated and exactly twice negated, and where (a), (c) and (d) are unchanged, is Π 2 -complete as well. Thereby, we improve upon two recent results by Döcker et al. [8] that establish Π 2 -completeness of these variants in the non-monotone setting. We also discuss a special case of Monotone 3-Sat-(2, 2) that corresponds to a variant of Not-All-Equal Sat, and we show that all such instances are satisfiable.
Performance of Regression Models as a Function of Experiment Noise

Background: A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset. Results: We address this challenge by deriving an expected upper bound for the coefficient of determination (R2) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data. Conclusions: The new method for estimating upper bounds for model performance on test data should aid researchers in developing ML regression models that reach their maximum potential. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.