Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association of a Frailty Index and Incident Delirium in Older Hospitalized Patients: An Observational Cohort Study

Introduction/Background Frailty identifies patients that have vulnerability to stress. Acute illness and hospitalization are stressors that may result in delirium and further accelerate the negative consequences of frailty. Purpose The purpose of this study was to determine whether frailty, identified at hospital admission and as measured by a frailty index, is associated with incident delirium. Methods A retrospective, observational, cohort study was done at a Veterans hospital between January 2013 and March 2014. English-speaking patients over 55 years were eligible. Exclusion criteria included inability to complete baseline assessments due to pre-existing cognitive impairment, emergent surgery; and/or admission from a nursing home, pre-existing delirium, and those with psychiatric disease or substance use disorder. Main Outcomes and Measures Frailty index (FI) variables included cognitive screening, physical function and comorbidities. The FI was calculated as a proportion of possible deficits (range 0 to 1; higher scores indicate increased frailty). Incident delirium was measured daily by an expert clinician interview. Results A total of 247 patients were admitted and 218 met inclusion/exclusion criteria, with a mean age of 71.54 years (SD = 9.53 years) and were predominantly white (92.7%) and male (91.7%). Participants were grouped using FI ranges as non-frail (FI <0.25, n=56 (26%)), pre-frail (FI =0.25–0.35, n=86 (39%)), and frail (FI >0.35, n=76 (35%)). Pre-frailty and frailty were associated with incident delirium (non-frail: 3.6% vs pre-frail: 20.9% vs frail: 29.3%, p=0.001) and total delirium days (mean day =non-frail 0.04 vs pre-frail 0.35 vs frail 0.57, p=0.003). After adjustment for sociodemographic factors, pre-frail (adjusted OR=5.64, 95% CI: 1.23, 25.99) and frail status (adjusted OR=6.80, 95% CI: 1.38, 33.45) were independently associated with delirium. Conclusion This study demonstrates that a frailty index is independently associated with incident delirium and suggests that admission assessments for frailty may identify patients at high risk of developing delirium.
Impact of complex many-body correlations on electron capture in thermally excited nuclei around 
Ni78

We link complex many-body correlations, which play a decisive role in the structural properties of atomic nuclei, to the electron capture occurring during star evolution. The recently developed finite-temperature response theory, taking into account the coupling between single-nucleon and collective degrees of freedom, is applied to spin-isospin transitions, which dominate the electron capture rates. Calculations are performed for $^{78}\mathrm{Ni}$ and for the surrounding even-even nuclei associated with a high-sensitivity region of the nuclear chart in the context of core-collapse supernova simulations. The obtained electron capture rates are compared to those of a simpler thermal quasiparticle random phase approximation (TQRPA), which is standardly used in such computations. The comparison indicates that correlations beyond TQRPA lead to significantly higher electron capture rates under the typical thermodynamical conditions.
Data-Driven Koopman Controller Synthesis Based on the Extended $\mathcal{H}_{2}$ Norm Characterization

This letter presents a new data-driven controller synthesis based on the Koopman operator and the extended $\mathcal{H}_{2}$ norm characterization of discrete-time linear systems. We model dynamical systems as polytope sets which are derived from multiple data-driven linear models obtained by the finite approximation of the Koopman operator and then used to design robust feedback controllers combined with the $\mathcal{H}_{2}$ norm characterization. The use of the $\mathcal{H}_{2}$ norm characterization is aimed to deal with the model uncertainty that arises due to the nature of the data-driven setting of the problem. The effectiveness of the proposed controller synthesis is investigated through numerical simulations.
Some remarks on X(6900)

The analysis of the LHCb data on $X(6900)$ found in the di-$\jpsi$ system is performed using a momentum-dependent Flatt\`{e}-like parameterization. The use of the pole counting rule and spectral density function sum rule give consistent conclusions that both confining states and molecular states are possible, or it is unable to distinguish the nature of $X(6900)$, if only the di-$\jpsi$ experimental data with current statistics are available. We suggest that experiments measure other channels, such as $\jpsi \psi(3770)$, $\jpsi \psi_2(3823)$, $\jpsi \psi_3(3842)$, and $\chi_{c0} \chi_{c1}$, to better understand the nature of $X(6900)$, and $\jpsi \psi(4160)$, $\chi_{c0}\chi_{c1}(3872)$, $etc.$, to confirm the $X(7200)$ state.
Learning to Localize in New Environments from Synthetic Training Data

Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene. This can either be very expensive or simply impossible for large, unknown environments, for example in search-and-rescue scenarios. Although there are learning-based approaches that operate scene-agnostically, the generalization capability of these methods is still outperformed by classical approaches. In this paper, we present an approach that can generalize to new scenes by applying specific changes to the model architecture, including an extended regression part, the use of hierarchical correlation layers, and the exploitation of scale and uncertainty information. Our approach outperforms the 5-point algorithm using SIFT features on equally big images and additionally surpasses all previous learning-based approaches that were trained on different data. It is also superior to most of the approaches that were specifically trained on the respective scenes. We also evaluate our approach in a scenario with only very few reference images, showing that under such more realistic conditions our learning-based approach considerably exceeds both existing learning-based and classical methods.
Comparison between ARIMA and Deep Learning Models for Temperature Forecasting

Weather forecasting benefits us in various ways from farmers in cultivation and harvesting their crops to airlines to schedule their flights. Weather forecasting is a challenging task due to the chaotic nature of the atmosphere. Therefore lot of research attention has drawn to obtain the benefits and to overcome the challenges of weather forecasting. This paper compares ARIMA (Auto Regressive Integrated Moving Average) model and deep learning models to forecast temperature. The deep learning model consists of one dimensional convolutional layers to extract spatial features and LSTM layers to extract temporal features. Both of these models are applied to hourly temperature data set from Szeged, Hungry. According to the experimental results deep learning model was able to perform better than the traditional ARIMA methodology.
FIRST Winged Radio Galaxies with X and Z Symmetry

X-shaped radio galaxies (XRGs) are a subclass of radio sources that exhibit a pair of secondary low surface brightness radio lobes oriented at an angle to the primary high surface brightness lobes. Sometimes, the secondary low brightened lobes emerge from the edges of the primary high brightened lobes and form a Z-symmetric morphology. We present a systematical search result for XRGs and Z-shaped radio galaxies (ZRGs) from the VLA Faint Images of the Radio Sky at Twenty-Centimeters (VLA FIRST) Survey at 1.4 GHz. Our search yields a total of 296 number of radio sources, out of which 161 are XRGs and 135 are ZRGs. We have also made optical identification of these sources from the different available literature. J1124 + 4325 and J1319 + 0502 are the farthest known XRG and ZRG, respectively. We have estimated spectral index and radio luminosity of these radio sources and made a comparative study with previously detected XRGs and ZRGs. The average value of luminosities for XRGs is higher than that of ZRGs. With the help of a large sample size of the newly discovered XRGs and ZRGs, various statistical properties of these sources are studied. Out of 161 XRGs presented in the current paper, 70% (113) are FR II radio galaxies and 13% (20) are FR I radio galaxies. For 28 XRGs, the morphology is complex and could not be classified. For XRGs, the statistical studies are done on the angle between the major axis and minor axis and the relative size of the major and minor axes. For the ZRGs a statistical study is done on the angular size.
Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments

In autonomous navigation, sensors suffer from massive occlusion in cluttered environments, leaving a significant amount of space unknown. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance. Therefore, aggressiveness and safety cannot be satisfied at the same time. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space. Specifically, the proposed method utilizes contextual information of environments and prior knowledge to predict obstacle distributions in the occluded space. Our self-supervised learning method use unlabeled and no-ground-truth data and augments the data by simulating navigation trajectories. Our Occupancy Prediction Network is faster than current SOTA scene completion models and is successfully applied to unseen test environments without any refinement. Results show that our predictor leverages the performance of a kinodynamic planner by improving security with no reduction of speed in cluttered environments.
Noninvasive Molecular Imaging of the Enhanced Permeability and Retention Effect by 64Cu-Liposomes: In vivo Correlations with 68Ga-RGD, Fluid Pressure, Diffusivity and 18F-FDG

Background The accumulation of liposome encapsulated chemotherapy in solid cancers is dependent on the presence of the enhanced permeability and retention (EPR) effect. Positron emission tomography (PET) imaging with a liposome encapsulated radioisotope, such as liposome encapsulated Cu-64 (64Cu-liposome) may help to identify tumors with high liposome accumulation, and thereby stratify patients based on expected benefit from liposomal chemotherapy. However, intravenous administration of liposomes without a cytotoxic content is complicated by the accelerated blood clearance (ABC) phenomenon for succeeding therapeutic liposome dosing. Alternative markers for assessing the tumor’s EPR level are therefore warranted. Materials and Methods To increase our understanding of EPR variations and to ultimately identify an alternative marker for the EPR effect, we investigated the correlation between 64Cu-liposome PET/CT (EPR effect) and 68Ga-RGD PET/CT (neoangiogenesis), 18F-FDG PET/CT (glycolysis), diffusion-weighted MRI (diffusivity) and interstitial fluid pressure in two experimental cancer models (CT26 and COLO 205). Results 64Cu-liposome and 68Ga-RGD SUVmax displayed a significant moderate correlation, however, none of the other parameters evaluated displayed significant correlations. These results indicate that differences in neoangiogenesis may explain some EPR variability, however, as correlations were only moderate and not observed for SUVmean, 68Ga-RGD is probably insufficient to serve as a stand-alone surrogate marker for quantifying the EPR effect and stratifying patients.
Active Cousinia thomsonii Extracts Modulate Expression of Crucial Proinflammatory Mediators/Cytokines and NFκB Cascade in Lipopolysaccharide-Induced Albino Wistar Rat Model

Introduction Chronic inflammation is implicated in a multitude of diseases, including arthritis, neurodegeneration, autoimmune myositis, type 2 diabetes, rheumatic disorders, spondylitis, and cancer. Therefore, strategies to explore potent anti-inflammatory regimens are pivotal from a human-health perspective. Medicinal plants represent a vast unexplored treasure trove of therapeutically active constituents with diverse pharmacological activities, including anti-inflammatory properties. Herein, we evaluated Cousinia thomsonii, an edible medicinal herb, for its anti-inflammatory/immunomodulatory properties. Methods Soxhlet extraction was used to obtain different solvent extracts (hexane, ethyl acetate, ethanol, methanol, and aqueous extract) in increasing order of polarity. In vitro anti-inflammatory assays were performed to investigate the effects of extracts on protein denaturation, proteinase activity, nitric oxide surge, and erythrocyte-membrane stabilization. The most effective extracts, ie, ethyl acetate (CTEA) and ethanol (CTE) extracts (150–200 g) were selected for further in vivo analysis using albino Wistar rats. Wistar rats received varying concentrations of CTEA and CTE (25, 50, and 100 mg/kg) for 3 weeks, followed by a single subplantar injection of lipopolysaccharide. Dexamethasone served as positive control. Blood was obtained from the retro-orbital plexus and serum separated for estimation of proinflammatory cytokines (IL6, IL1β, IFNγ and TNFα). Western blotting was performed to study expression patterns of crucial proteins implicated in the NFκB pathway, ie, NFκB p65, NFκB1 p50, and NFκB2 p52. Histopathological examination was done and gas chromatography–mass spectrometry (GC-MS) carried out to reveal the identity of compounds responsible for ameliorating effects of C. thomsonii. Results Among five tested extracts, CTEA and CTE showed marked inhibition of protein denaturation, proteinase activity, nitric oxide surge and erythrocyte-membrane hemolysis at 600 μg/mL (P<0.001). Both these extracts showed no toxic effects up to a dose of 2,500 mg/kg. Extracts exhibited concentration-dependent reductions in expression of IL6, IL1β, IFNγ, TNFα, NFκB-p65, NFκB1, and NFκB2 (P<0.05). Healing effects of extracts were evident from histopathological investigation. GC-MS analysis revealed the presence of important anti-inflammatory compounds, notably stigmast-5-en-3-ol, oleate, dotriacontane, ascorbic acid, n-hexadecanoic acid, and α-tocopherol, in C. thomsonii. Conclusion C. thomsonii possesses significant anti-inflammatory/immunomodulatory potential by virtue of modifying levels of proinflammatory cytokines/markers and NFκB proteins.
ENHANCEMENT OF DISSOLUTION RATE OF GLIMEPIRIDE USING NEWER CARRIERS

Solid dispersions (SDs) of Glimepiride in five super-disintegrants, namely, sodium starch glycollate (SSG), microcrystalline cellulose (MCC), crospovidone (CP), pre-gelatinized starch (PGS), croscarmellose sodium (CCS) and with water soluble carriers polyvinyl pyrrolidone (PVP) and polyethylene glycol (PEG) were prepared and investigated by invitro method with an objective of enhancing the dissolution rate of Glimepiride. SDs of Glimepiride in super-disintegrants gave a marked enhancement in its dissolution rate and dissolution efficiency. The order of increasing dissolution rate observed with various super-disintegrants were CCS > PGS > SSG > CP > MCC. CCS alone provided 26.34 fold increase whereas water soluble carriers 4.51 and 4.28 fold increase in the dissolution rate with PVP and PEG, respectively. SD in super-disintegrants could be used as an effective and efficient technique for enhancing the dissolution rate of Glimepiride, which is a poorly soluble drug.
Blockade of Orexin Receptors in the Posterior Paraventricular Nucleus of the Thalamus Prevents Stress-Induced Reinstatement of Reward-Seeking Behavior in Rats With a History of Ethanol Dependence

Neural systems involved in processing natural rewards and drugs of abuse overlap and exposure to drugs of abuse induce neuroadaptations that can cause compulsive-like behavior. For example, the recruitment of the orexin (Orx) system by drugs of abuse has been proposed to induce neuroadaptations that in turn alter its function, reflected by maladaptive, compulsive, and addictive behavior. Orexin neurons project to the paraventricular nucleus of the thalamus (PVT)—particularly the posterior part (pPVT), a structure that plays a key role in stress regulation. This study investigated whether Orx transmission in the pPVT plays a role in stress-induced reinstatement of reward-seeking behavior toward ethanol (EtOH) and a highly palatable food reward [sweetened condensed milk (SCM)] in rats and whether this role changes with EtOH dependence. After being trained to orally self-administer EtOH or SCM, the rats were made dependent (EtOHD and SCMD) by chronic intermittent EtOH vapor exposure. The control nondependent groups (EtOHND and SCMND) were exposed to air. Following extinction, the rats were tested for stress-induced reinstatement of EtOH- and SCM-seeking behavior. Stress reinstated EtOH- and SCM-seeking behavior in all groups (EtOHD/ND and SCMD/ND). Administration of the dual Orx receptor (OrxR) antagonist TCS1102 (15 μg) in the pPVT prevented stress-induced reinstatement only in dependent rats (EtOHD and SCMD). In parallel, the qPCR analysis showed that Orx mRNA expression in the hypothalamus and OrxR1/R2 mRNA expression in the pPVT were increased at the time of testing in the EtOHD and SCMD groups. These results are the first to implicate Orx transmission in the pPVT in the stress-induced reinstatement of reward-seeking behavior in EtOH dependent rats and indicate the maladaptive recruitment of Orx transmission in the pPVT by EtOH dependence.
Subtle Variations of the Electronic Structure and Mechanical Properties of High Entropy Alloys With 50% Carbon Composites

Binary and ternary transition metal carbides are stable ceramic crystals with outstanding mechanical properties. In recent years, multicomponent single-phase high entropy alloys enjoyed explosive growth due to many of their outstanding physical properties with its large and flexible composition space. Hence the composite between them can be advantageous in forming a new class of ceramic materials with combined superiority in their properties for many applications. On the basis of a systematic large-scale ab initio simulations using density functional theory that are specifically designed for their compositional variations, subtle differences in their structures, electronic and mechanical properties are revealed and discussed in detail. Fifteen supercell models with 512 atoms with equal composition of C and high entropy alloys occupying the sub-lattices of the rock-slat structure were constructed. These models are fully optimized, and their properties carefully characterized, compared and contrasted. By applying the novel concept of total bond order density and its partial components, the partial bond order density, we revealed many subtle variations in their properties that have not been known before. This large database can play an important and valuable role in the design and synthesis of high entropy ceramic carbides.
Fusion Lasso and Its Applications to Cancer Subtype and Stage Prediction

Effectively integrating and mining multi-view, high-dimensional omics data is instrumental to precision medicine. Numerous methods have been proposed for addressing this problem. However, they more or less neglect the challenges (e.g., interpretability, stability and consistency) pertaining to this integration process, whereby suffering from unstable or inconsistent variable selection and prediction accuracy deterioration. In this paper, we introduce a novel Fusion Lasso (FL) framework in which variable selection and data integration are formulated as a weighted constrained optimization problem. Specifically, four regularization constraints, i.e., sparsity, fusion penalty, instability and inconsistency, are simultaneously taken into account in the fusion model using multi-view data, while sparse features are revealed from data of each individual view through the ℓ1-norm minimization. We use the ADMM and Accelerated ADMM (AADMM) schemes to solve this optimization problem, leading to a scalable model convergence with solid theoretical guarantee. By applying FL to fve multi-omics cancer datasets collected by The Cancer Genome Atlas (TCGA), we demonstrate that FL outperforms popular variable selection and data integration approaches, such as Elastic Net, Precision Lasso, B-RAIL and MDBN, in cancer subtype and/or stage prediction. The proposed method is useful and can be further adopted to systems biology and other advanced clinical research areas where multi-view data integration is a necessity.
Research on suppressing shading from 3D scanned data using a scanned reference model

When a physical object is scanned with an optical 3D scanner in a normal environment, shading is caused by external lighting that effect on the 3D scanned color data. This shading is not easy to separate from original surface color of the model and can make abnormal rendering result when rendered with additional artificial lighting. Therefore, various kind of automatic or manual processing is required to suppressing shading in general. However, the processing requires information of original scanned physical object and lighting environment which is not simple to sense and record. In our research, using white spherical plaster as a reference model, scan in lighting environment as similar as possible then make a model of external lighting environment using the normal vector and brightness information of the reference model to calibrate the color change due to shading. We have scanned experimental data and be applied processing to show feasibility of our method. We found that sphere or hemisphere reference model is effective reference model for process of suppressing shading. It is recommendable to end user application practice and can be included as component of standard or recommendation.
The Association Between Mycoplasma pneumoniae and Chlamydia pneumoniae, a Life-Threatening Condition in Small Children—A Case Report and a Review of the Literature

Mycoplasma pneumoniae (MP) and Chlamydia pneumoniae (CP) are two atypical pathogens that may result in mild, moderate or severe acute respiratory infections. We report the case of a 2 years and 9-month-old male child admitted with prolonged fever, dry cough, and shortness of breath for which he underwent symptomatic treatment. The laboratory tests showed leukocytosis with neutrophilia, anemia, and elevated inflammatory biomarkers and the thoracic radiography revealed pleural effusion raising the suspicion of inferior right pneumonia. Although we the initial evolution was favorable being treated with 3rd class cephalosporin and Oxacillin, on the 8th day of admission the fever and the acute phase reactants levels increased as well as the quantity of the pleural effusion, requiring surgical drainage. We ruled out lung tuberculosis, but we identified positive IgM for both MP and CP. Based on these findings we changed the antibiotic therapy on Levofloxacin for 10 days with favorable evolution. MP and CP are two atypical pathogen that are difficult to be diagnosed due to their slow-growing pattern. Despite their self-limiting feature, the association between them might carry a vital risk in small children, especially in the lack of a proper and timely diagnosis.
SYSTEM ARCHITECTURE FOR SUPPORTING MULTIPLE LIVE ACTORS IN WEB3D VIRTUAL CONFERENCE: Extended Abstract

For live actors to perform as 3D models with seamless graphics computing, we need to overcome the huge challenges of developing a methodology to construct a cleaned and smooth model in real time and a system architecture design that supports the streaming of multi-model live actors. By applying the technique of semantic image segmentation to generate the entire human body, a 3D model needs to be built up with a segmented image as a texture. Moreover, to enable a live actor to virtually interact with others, multi-actor models must be produced for telecommuting systems. Therefore, by combining the concepts above, a system with multiple live actors can be created to conduct live group conferences in virtual reality that provide an immersive experience.