Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in health care outcomes between postdischarge COPD patients treated with inhaled corticosteroid/long-acting β 2 -agonist via dry-powder inhalers and pressurized metered-dose inhalers

Purpose: The aim of this study was to examine real-world differences in health care resource use (HRU) and costs among COPD patients in the USA treated with a dry powder inhaler (DPI) or pressurized metered-dose inhaler (pMDI) following a COPD-related hospitalization. Methods: This retrospective analysis used the Truven MarketScan ® databases. Eligibility criteria included 1) age $ 40 years, 2) COPD diagnosis, 3) inpatient admission with a diagnosis of COPD exacerbation, 4) inhaled corticosteroid (ICS)/long-acting β 2 -agonist (LABA) prescription within 10 days of hospital discharge (index date), and 5) continuous enrollment for 12 months preindex and 90 days postindex. Outcomes included pre- and postindex HRU and costs. DPI and pMDI groups were compared on postindex outcomes via multivariate models controlling for demographic and baseline characteristics. Results: The sample included 1,960 DPI and 1,086 pMDI ICS/LABA patients. During the preindex period, pMDI patients were significantly more likely to be prescribed a short-acting β -agonist, experienced more COPD exacerbation-related hospital days, and had a greater number of pulmonologist visits compared to DPI patients ( P , 0.05), all suggestive of greater disease severity. However, multivariate models revealed that pMDI patients incurred 10% lower all-cause postindex costs (predicted mean costs [2016 US dollars]: $2,673 vs $2,956) and 19% lower COPD-related costs (predicted mean costs: $138 vs $169; P , 0.05). Additionally, pMDI patients were 28% less likely to experience a COPD exacerbation-related hospital readmission within 60 days postdischarge compared to the DPI patients (OR: 0.72, 95% CI: 0.52–0.99, P , 0.05). Conclusion: Despite greater COPD-related HRU and costs preceding index hospitalization, US patients using a pMDI after hospital discharge incurred significantly lower all-cause and COPD-related health care costs compared with those using a DPI, in addition to a decreased likelihood of a COPD exacerbation-related hospital readmission. Results suggest that inhaler device type may influence COPD outcomes and that COPD patients may derive greater clinical benefit from treatment delivered via pMDI vs DPI.
Functional diversity between HSP70 paralogs due to variable interactions with specific co-chaperones.

Heat shock protein 70 (HSP70) chaperones play a central role in protein quality control and are crucial for many cellular processes, including protein folding, degradation, and disaggregation. Human HSP70s compose a family of thirteen members that carry out their functions with the aid of even larger families of co-chaperones. A delicate interplay between HSP70s and co-chaperone recruitment is thought to determine substrate fate. Yet, it has been generally assumed that all Hsp70
Métallogenèse de l'uranium dans la région de Litsa (Péninsule de Kola, Russie)

The Litsa district is one of the most interesting areas of the Kola Peninsula with respect to uranium metallogenesis. Presenting all the features of uraniferous province, it endured a protracted geological history with several successive uranium enrichments. There are over 30 uranium occurrences and manifestations, totally estimated at 102,000 tons in Speculative Resources (IAEA nomenclature), with an average grade of Uranium 0.01%. They occur at different stratigraphic levels of the geological succession in the district: Archean, Paleoproterozoic, Mesoproterozoic and Paleozoic. Uranium content in the geological formations gradually increases during the geological evolution of the Litsa district from Archean to Paleozoic. The most abundant and probably prospective for the economic-grade uranium mineralization is Paleozoic pitchblende vein type, located around the Litsa-Araguba granitoid complex, where favorable protoliths, structures and epigenetic processes are encountered
A Radial Transformation-Optics Mapping for Flat Ultra-Wide-Angle Dual-Polarized Stacked GRIN MTM Luneburg Lens Antenna

A transformation-optics mapping that radially compresses a sphere into a cylindrical slab with an arbitrary ratio of D/t is proposed to design a flat and thin dual-polarized Luneburg lens antenna (LLA) for ultrawide-angle operation at microwave bands. Also, the feasibility of the simple approximations to the transformed material properties is demonstrated in the design procedure of the presented LLA. Compared with the benchmark, the proposed mapping can achieve a moderate index distribution, wider scanning range, lighter weight, as well as an easy implementation. For example, a slab consisting of 18-layered Gaussian-distributed gradient index metamaterials is utilized to realize an X-band dual-polarized LLA with an aperture of <inline-formula> <tex-math notation="LaTeX">$94.3\times94.3$ </tex-math></inline-formula> mm<sup>2</sup>, a thickness of 14.9 mm, and a focal length of 44 mm. Three nonresonant unit cells of patch, circular, and square holes are used for the upper, middle, and lower index regions, respectively. Moreover, a prototype of half of the designed LLA combined with a PEC sheet is implemented for a conformal application. The experiments reveal that our design can achieve a ~20% bandwidth for both <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert < -10$ </tex-math></inline-formula> dB and <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{21}\vert < -17$ </tex-math></inline-formula> dB, a scanning angle of ±54° with a scanning loss of 0.7/2.2 dB, a peak gain of ~13/13.2 dBi, and a cross-polarization level of less than −15/−16 dB for the transverse electric and transverse magnetic polarizations at 10 GHz, respectively. Hence, the proposed mapping is a good method to design a light wide-scan dual-polarized LLA for far-field imaging applications and 5G wireless communications.
Methodology for the design of a multi-functional device with switchable absorption and polarization conversion modes by graphene and metallic metasurfaces

A methodology to design a multi-functional device (MFD) with switchable absorption and polarization conversion (PC) modes is proposed in this research. The methodology combines graphene absorbing metasurface (GAM) and polarization conversion metasurface (PCM) in one component. The key point is that the absorbing metasurface (AM) should be made completely by conductivity tunable material, such as graphene. The PCM can be constructed by fine metal. A prototype is designed to verify the designing methodology by stacking a layer of gold pattern PCM and two layers of GAM with fishnet shape patterns. In the PC mode, the chemical potentials of the two GAMs are µc1 = µc2 = 0 eV, then, the graphene layers act as thin dielectric layers, which can be treated as transparency. Thus, the PCM plays a leading role in the performances of the whole structure. Therefore, a linear polarized incidence is rotated by 90° in the 2.10-3.13 THz (39.5% at 2.61 THz) with polarization conversion ratio (PCR) larger than 90%. In the absorption mode with µc1 = 0.5 eV and µc2 = 0.8 eV, the GAMs have strong surface plasmon-polaritons (SPPs), and they concentrate most of the incident power, which makes the GAMs dominate roles of the whole structure. Therefore, the incident power is dissipated in the GAMs, and the absorption rate is larger than 90% in the 1.30-3.01 THz (79.5% at 2.15 THz). The absorption band for absorption rate larger than 70% is 1.19-3.90 THz (106.7% at 2.54 THz). The design methodology is useful for the designing of components with switchable absorption and PC modes.
BBA-Molecular and Cell Biology of Lipids

Cholesteryl ester transfer protein (CETP) inhibitors are a new class of therapeutics for dyslipidemia that simultaneously improve two major cardiovascular disease (CVD) risk factors: elevated low-density lipoprotein (LDL) cholesterol and decreased high-density lipoprotein (HDL) cholesterol. However, the detailed molecular mechanisms underlying their efficacy are poorly understood, as are any potential mechanistic differences among the drugs in this class. Herein, we used electron microscopy (EM) to investigate the effects of three of these agents (Torcetrapib, Dalcetrapib and Anacetrapib) on CETP structure, CETP-lipoprotein complex formation and CETP-mediated cholesteryl ester (CE) transfer. We found that although none of these inhibitors altered the structure of CETP or the conformation of CETP-lipoprotein binary complexes, all inhibitors, especially Torcetrapib and Anacetrapib, increased the binding ratios of the binary complexes (e.g., HDL-CETP and LDLCETP) and decreased the binding ratios of the HDL-CETP-LDL ternary complexes. The findings of more binary complexes and fewer ternary complexes reflect a new mechanism of inhibition: one distal end of CETP bound to the first lipoprotein would trigger a conformational change at the other distal end, thus resulting in a decreased binding ratio to the second lipoprotein and a degraded CE transfer rate among lipoproteins. Thus, we suggest a new inhibitor design that should decrease the formation of both binary and ternary complexes. Decreased concentrations of the binary complex may prevent the inhibitor was induced into cell by the tight binding of binary complexes during lipoprotein metabolism in the treatment of CVD.
Intrusion Detection System Employing Neural Network MLP and Detection Trees Using Different Techniques

: by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives. For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset attacks.
interventional tools to improve medication adherence: review of literature

: Medication adherence and persistence is recognized as a worldwide public health problem, particularly important in the management of chronic diseases. Nonadherence to medical plans affects every level of the population, but particularly older adults due to the high number of coexisting diseases they are affected by and the consequent polypharmacy. Chronic disease management requires a continuous psychological adaptation and behavioral reorganization. In literature, many interventions to improve medication adherence have been described for different clinical conditions, however, most interventions seem to fail in their aims. Moreover, most interventions associated with adherence improvements are not associated with improvements in other outcomes. Indeed, in the last decades, the degree of nonadherence remained unchanged. In this work, we review the most frequent interventions employed to increase the degree of medication adherence, the measured outcomes, and the improvements achieved, as well as the main limitations of the available studies on adherence, with a particular focus on older persons.
The challenge of Mothers Learning About Second-hand Smoke (MLASS): a quasi-experimental, mixed methods feasibility study

Exposure to second-hand smoke (SHS) in the antenatal and postnatal period is associated with a detrimental health impact to the foetus and new-born baby and is recognised as a preventable public health challenge. The aim of the MLASS study was to test the feasibility of delivering and evaluating the effectiveness of a Smoke Free Homes (SFH) health education intervention in the antenatal and postnatal period to reduce foetal and new-born babies exposure to SHS.
StyleGAN as a Utility-Preserving Face De-identification Method

Several face de-identiﬁcation methods have been proposed to preserve users’ privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, e.g., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN [20], have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identiﬁed faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identiﬁed face that carries the utilities of the target face. We examined this de-identiﬁcation method with respect to preserving utility and privacy, by implementing several face detection, veriﬁcation, and identiﬁcation attacks. Through extensive experiments and also comparing with two state-of-the-art face de-identiﬁcation methods, we show that StyleGAN preserves the quality and utility of the faces much better than the other approaches and also by choosing the style mixing levels correctly, it can preserve the privacy of the faces much better than other methods.
A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality (Extended Abstract)

We developed a formal framework for CDCL (conflict-driven clause learning) in Isabelle/HOL. Through a chain of refinements, an abstract CDCL calculus is connected to a SAT solver expressed in a functional programming language, with total correctness guarantees. The framework offers a convenient way to prove metatheorems and experiment with variants. Compared with earlier SAT solver verifications, the main novelties are the inclusion of rules for forget, restart, and incremental solving and the application of refinement.
Thirty Years Later: Some Reflections on Ontological Analysis in Conceptual Modeling

The idea that ontological theories could be used to provide a theoretical basis for evaluating conceptual-modeling grammars and scripts was initiated some thirty years ago. The authors provide some brief history about the early research that discussed how ontological theories might be used to study conceptual-modeling grammars and scripts. They then give an overview of the progress that has been made using various elements of this early research. Next, they outline some of the major critiques that have been made of conceptual-modeling work that uses ontological foundations. Finally, the authors suggest some areas that they believe hold substantial promise in using ontology to better understand and predict conceptual-modeling phenomena.
Thermal stability of soil organic matter pools and their turnover times calculated by 13 C under elevated CO 2 and two levels of N fertilisation

Thermal stability of soil organic matter pools and their turnover times calculated by 13C under elevated CO2 and two levels of N fertilisation Maxim Dorodnikov abc; Andreas Fangmeier a; Anette Giesemann d; Hans-Joachim Weigel d; Karl Stahr e; Yakov Kuzyakov b a Institute of Landscape and Plant Ecology (320), University of Hohenheim, Stuttgart, Germany b Department of Agroecosystem Research, University of Bayreuth, Bayreuth, Germany c Institute of Physico-Chemical and Biological Problems in Soil Science, RAS, Puschino, Russia d Institute of Agroecology, FAL-AOE, Braunschweig, Germany e Institute of Soil Science and Land Evaluation (310), University of Hohenheim, Stuttgart, Germany
Synthesizing Verdant Landscape Using Volumetric Textures

: Volumetric textures are able to represent complex repetitive data such as foliage, fur and forests by storing one sample of geometry in a volumetric texel to be mapped onto a surface. This volume consists in samples of densities and reﬂectances stored in voxels. The texel can be preﬁltered similarly to the mip-mapping algorithm, giving an efﬁcient rendering in ray-tracing with low aliasing, using a single ray per pixel. Our generalpurposeis to extendthe volumetric texture methodin orderto provide a convenient and efﬁcient tool for modeling, animating and rendering highly complex scenes in ray-tracing. In this paper, we show how to convert usual 3D models into texels, and how to render texels mapped onto any mesh type. We illustrate our method with verdant landscapessuch as forests and lawns.
The unique epigenetic features of Pack-MULEs and their impact on chromosomal base composition and expression spectrum

Abstract Acquisition and rearrangement of host genes by transposable elements (TEs) is an important mechanism to increase gene diversity as exemplified by the ∼3000 Pack-Mutator-like TEs in the rice genome which have acquired gene sequences (Pack-MULEs), yet remain enigmatic. To identify signatures of functioning Pack-MULEs and Pack-MULE evolution, we generated transcriptome, translatome, and epigenome datasets and compared Pack-MULEs to genes and other TE families. Approximately 40% of Pack-MULEs were transcribed with 9% having translation evidence, clearly distinguishing them from other TEs. Pack-MULEs exhibited a unique expression profile associated with specificity in reproductive tissues that may be associated with seed traits. Expressed Pack-MULEs resemble regular protein-coding genes as exhibited by a low level of DNA methylation, association with active histone marks and DNase I hypersensitive sites, and an absence of repressive histone marks, suggesting that a substantial fraction of Pack-MULEs are potentially functional in vivo. Interestingly, the expression capacity of Pack-MULEs is independent of the local genomic environment, and the insertion and expression of Pack-MULEs may have altered the local chromosomal expression pattern as well as counteracted the impact of recombination on chromosomal base composition, which has profound consequences on the evolution of chromosome structure.
Industry-wide assessment and characterisation of problem fermentations

Sluggish and stuck fermentations are major problems in the international wine industry, including South Africa. Several empirical studies to identify the causes of sluggish and stuck fermentations have been conducted in the past, and data suggest that numerous factors can have a significant influence on the successful completion of fermentations. Due to the multiple factors which can lead to slow or incomplete fermentations, previous studies on problem fermentations were mostly limited to investigating one or two well-controlled factors at a time. In this study, we attempt to overcome this limitation by applying chemometrics and multivariate data analysis to investigate the factors affecting the likelihood of stuck fermentations in industrial wine fermentations. For this purpose collection of problem fermentation samples from commercial cellars took place in the 2005 and 2006 vintages and a multivariate approach was implemented. Fourier transform infrared (FT-IR) spectroscopy and additional measurements including microbial analysis, SO2 determinations, free assimilable nitrogen (FAN) determinations and volatile flavour components determined by gas chromatography were incorporated in this multi-instrument and multivariate data analysis approach. Principle component analysis (PCA) was used to identify the main sources of variation in the spectra of the 379 must/wine samples. Reducing sugar, alcohol content, cultivar and stage of fermentation represent the main sources of variation amongst the samples. Multivariate partial least squares (PLS) regression models were established to discriminate between problem and control fermentations. The data suggest that FT-IR spectroscopy represents a high throughput and cost-effective technique to monitor fermentation. It was also shown that multivariate data analysis could be used to effectively discriminate problem fermentations from control fermentations and this feature shows potential for prediction purposes in order to identify potential problem fermentations and to take preventative measures. Chapter 3. Research Results
Heterogeneous molecular processes among the causes of how sequence similarity scores can fail to recapitulate phylogeny

Abstract Sequence similarity tools like Basic Local Alignment Search Tool (BLAST) are essential components of many functional genetic, genomic, phylogenetic and bioinformatic studies. Many modern analysis pipelines use significant sequence similarity scores (p- or E-values) and the ranked order of BLAST matches to test a wide range of hypotheses concerning homology, orthology, the timing of de novo gene birth/death and gene family expansion/contraction. Despite significant contrary findings, many of these tests still implicitly assume that stronger or higher-ranked E-value scores imply closer phylogenetic relationships between sequences. Here, we demonstrate that even though a general relationship does exist between the phylogenetic distance of two sequences and their E-value, significant and misleading errors occur in both the completeness and the order of results under realistic evolutionary scenarios. These results provide additional details to past evidence showing that studies should avoid drawing direct inferences of evolutionary relatedness from measures of sequence similarity alone, and should instead, where possible, use more rigorous phylogeny-based methods.
A Machine Learning Approach to Revenue Generation within the A Machine Learning Approach to Revenue Generation within the Professional Hair Care Industry Professional Hair Care Industry

– The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue growth. The present challenge facing HairCo is the lack of models that learn from aggregated data centered on the buying behavior, demographic, and other publicly available data of end consumers tied to historical sales data of their customers, i.e., salons and stylists. The proposed clustering and regression models achieved notably improved results using the aggregated data in comparison to models solely using internal company-provided data. Recommendations on which features are most important from both models that improve customer profiling and predicting sales were presented. With these results, HairCo can increase its revenue and expand its market share.