Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Are I: Time Inconsistency and Intrapersonal Conflict and Reconciliation

Time inconsistency is prevalent in dynamic choice problems: a plan of actions to be taken in the future that is optimal for an agent today may not be optimal for the same agent in the future. If the agent is aware of this intrapersonal conflict but unable to commit herself in the future to following the optimal plan today, the rational strategy for her today is to reconcile with her future selves, namely to correctly anticipate her actions in the future and then act today accordingly. Such a strategy is named intra-personal equilibrium and has been studied since as early as in the 1950s. A rigorous treatment in continuous-time settings, however, had not been available until a decade ago. Since then, the study on intra-personal equilibrium for time-inconsistent problems in continuous time has grown rapidly. In this chapter, we review the classical results and some recent development in this literature.
Charged black hole in 4D Einstein-Gauss-Bonnet gravity: particle motion, plasma effect on weak gravitational lensing and centre-of-mass energy

We study the motion of charged and spinning particles and photons in the 4D charged Einstein-Gauss-Bonnet (EGB) black hole vicinity. We determine the radius of the innermost stable circular orbit (ISCO) for test particles. We show that the combined effect of the Gauss-Bonnet (GB) coupling parameter and black hole charge decreases the ISCO and the radius of the photon sphere. Further, we study the gravitational deflection angle and show that the impact of GB term and black hole charge on it is quite noticeable. We also consider the effect of plasma and find the analytical form of the deflection angle in the case of a uniform and non-uniform plasma. Interestingly we find that the deflection angle becomes larger when uniform plasma is considered in comparison to the case of non-uniform plasma. We also study the center of mass energy (E C.M.) obtained by collision process for non-spinning particles and show that the impact of GB parameter and black hole charge leads to high energy collision. In addition, we also study the E C.M. for the case of spinning particles and show that if the two spinning particles collide near the horizon of 4D charged EGB BH, the E C.M. becomes infinitely high which is in disparity with the non-spinning particles counterpart where E C.M. never grows infinitely. To achieve this, an important role is played by the spinning particle known as the near-critical particle (i.e. a particle with fine-tuned parameters). In order to achieve the unbounded E C.M. from the collision of two spinning particles, the energy per unit mass must be less than unity for a near-critical particle, which means such a particle starts from some intermediate position r > rh and not from infinity.
Non-Gaussianity and the induced gravitational wave background

Scalar metric fluctuations generically source a spectrum of gravitational waves at second order in perturbation theory, poising gravitational wave experiments as potentially powerful probes of the small-scale curvature power spectrum. We perform a detailed study of the imprint of primordial non-Gaussianity on these induced gravitational waves, emphasizing the role of both the disconnected and connected components of the primoridal trispectrum. Specializing to local-type non-Gaussianity, we numerically compute all contributions and present results for a variety of enhanced primordial curvature power spectra.
COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach

The COVID-19 pandemic has had devastating effects on the well-being of the global population. The pandemic has been so prominent partly due to the high infection rate of the virus and its variants. In response, one of the most effective ways to stop infection is rapid diagnosis. The main-stream screening method, reverse transcription-polymerase chain reaction (RT-PCR), is time-consuming, laborious and in short supply. Chest radiography is an alternative screening method for the COVID-19 and computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed;however, one of the challenges in training the CAD models is the limited number of training data, especially at the onset of the pandemic. This becomes outstanding precisely when the quick and cheap type of diagnosis is critically needed for flattening the infection curve. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights, taking advantage of the abundance of samples from known illnesses such as pneumonia to improve the detection performance on COVID-19.
Preparation and characterization of dialdehyde starch urea (DASU) and, it`s sorption potential for Co(ii), Pb(ii) and Zn(ii) ions from aqueous solution

Dialdehyde starch urea (DASU) was prepared by the reaction of dialdehyde starch (DAS) from periodate oxidized cassava starch with urea, which was then used to adsorb Co(II), Pb(II) and Zn(II) ions from aqueous solution. Starch modified starches and starch complexes were characterized by Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) to determine the structure and morphology respectively. The percentages of aldehyde contents of the three kinds of DAS, designated as DAS1, DAS2 and DAS3 were 45.2%, 61.2% and 86.94% respectively. The results indicated that the higher the concentration of periodates, the higher the percentage of aldehyde content in DAS. DAS1 with the content of the aldehyde groups of 45.2% had an average molecular weight of 85,853, while the average molecular weight of DAS2 with the content of the aldehyde groups of 61.2% was 15,466. When the content of aldehyde groups increased to 86.94%, the average molecular weight of DAS3 was fund to decrease to 13,182. The Degree of substitution (DS) of urea was 0.67and DS significantly increase with the increase in percentages of nitrogen content in the polymer adsorbent. Adsorption capacity for Co(II), Pb(II) and Zn(II) ions were found to be 16.01mg/g, 21.73mg/g and 7.18mg/g respectively. Keywords : Starch Polymer, Dialdehyde starch, Dialdehyde Starch Urea, Chelating resin
Simplicity of the Lyapunov spectrum for classes of Anosov flows

Abstract For $k \geq 2$ , we prove that in a $C^{1}$ -open and $C^{k}$ -dense set of some classes of $C^{k}$ -Anosov flows, all Lyapunov exponents have multiplicity one with respect to appropriate measures. The classes are geodesic flows with equilibrium states of Holder-continuous potentials, volume-preserving flows, and all fiber-bunched Anosov flows with equilibrium states of Holder-continuous potentials.
RIANN—A Robust Neural Network Outperforms Attitude Estimation Filters

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a ready-to-use, neural network-based, parameter-free, real-time-capable inertial attitude estimator, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We gather six publicly available datasets of which we exploit two datasets for the method development and the training, and we use four datasets for evaluation of the trained estimator in three different test scenarios with varying practical relevance. Results show that RIANN outperforms state-of-the-art attitude estimation filters in the sense that it generalizes much better across a variety of motions and conditions in different applications, with different sensor hardware and different sampling frequencies. This is true even if the filters are tuned on each individual test dataset, whereas RIANN was trained on completely separate data and has never seen any of these test datasets. RIANN can be applied directly without adaptations or training and is therefore expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.
Impact of Representation Learning in Linear Bandits

We study how representation learning can improve the efficiency of bandit problems. We study the setting where we play $T$ linear bandits with dimension $d$ concurrently, and these $T$ bandit tasks share a common $k (\ll d)$ dimensional linear representation. For the finite-action setting, we present a new algorithm which achieves $\widetilde{O}(T\sqrt{kN} + \sqrt{dkNT})$ regret, where $N$ is the number of rounds we play for each bandit. When $T$ is sufficiently large, our algorithm significantly outperforms the naive algorithm (playing $T$ bandits independently) that achieves $\widetilde{O}(T\sqrt{d N})$ regret. We also provide an $\Omega(T\sqrt{kN} + \sqrt{dkNT})$ regret lower bound, showing that our algorithm is minimax-optimal up to poly-logarithmic factors. Furthermore, we extend our algorithm to the infinite-action setting and obtain a corresponding regret bound which demonstrates the benefit of representation learning in certain regimes. We also present experiments on synthetic and real-world data to illustrate our theoretical findings and demonstrate the effectiveness of our proposed algorithms.
The Effect of the Approach to Gas Disk Gravitational Instability on the Rapid Formation of Gas Giant Planets. II. Quadrupled Spatial Resolution

Observations support the hypothesis that gas disk gravitational instability might explain the formation of massive or wide-orbit gas giant exoplanets. The situation with regard to Jupiter-mass exoplanets orbiting within ∼20 au is more uncertain. Theoretical models yield divergent assessments often attributed to the numerical handling of the gas thermodynamics. Boss used the β cooling approximation to calculate three-dimensional hydrodynamical models of the evolution of disks with initial masses of 0.091 M ⊙ extending from 4 to 20 au around 1 M ⊙ protostars. The models considered a wide range (1–100) of β cooling parameters and started from an initial minimum Toomre stability parameter of Q i = 2.7 (gravitationally stable). The disks cooled down from initial outer disk temperatures of 180 K to as low as 40 K as a result of the β cooling, leading to fragmentation into dense clumps, which were then replaced by virtual protoplanets (VPs) and evolved for up to ∼500 yr. The present models test the viability of replacing dense clumps with VPs by quadrupling the spatial resolution of the grid once dense clumps form, sidestepping in most cases VP insertion. After at least ∼200 yr of evolution, the new results compare favorably with those of Boss: similar numbers of VPs and dense clumps form by the same time for the two approaches. The results imply that VP insertion can greatly speed disk instability calculations without sacrificing accuracy.
dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

Hospital profiling provides a quantitative comparison of health care providers for their quality of care regarding certain clinical outcomes. To implement hospital profiling, the generalized linear mixed model (GLMM) is usually used to fit clinical or administrative claims data, adjusting for the effects of covariates. For better generalizability, data across multiple hospitals, databases or networks are desired. However, due to the privacy regulation and the computation complexity of GLMM, a convenient distributed algorithm for hospital profiling is needed. In this paper, we develop a novel distributed Penalized Quasi Likelihood algorithm (dPQL) to fit GLMM, when only aggregated data, rather than the individual patient data are available across hospitals. The dPQL algorithm is based on a newly-developed distributed linear mixed model (DLMM) algorithm. This proposed dPQL algorithm is lossless, i.e. it obtains identical results as if the individual patient data are pooled from all hospitals. We demonstrate the usage of the dPQL algorithms by ranking 929 hospitals for COVID-19 mortality or referral to hospice in Asch, et al. 2020.
Efflux at the Blood-Brain Barrier Reduces the Cerebral Exposure to Ochratoxin A, Ochratoxin α, Citrinin and Dihydrocitrinone

Recent studies have implied that environmental toxins, such as mycotoxins, are risk factors for neurodegenerative diseases. To act directly as neurotoxins, mycotoxins need to penetrate or affect the integrity of the blood-brain barrier, which protects the mammalian brain from potentially harmful substances. As common food and feed contaminants of fungal origin, the interest in the potential neurotoxicity of ochratoxin A, citrinin and their metabolites has recently increased. Primary porcine brain capillary endothelial cells were used to investigate cytotoxic or barrier-weakening effects of ochratoxin A, ochratoxin α, citrinin and dihydrocitrinone. The transfer and transport properties of the mycotoxins across the barrier formed by porcine brain capillary endothelial cell monolayers were analysed using HPLC-MS/MS. High levels of Ochratoxin A caused cytotoxic and barrier-weakening effects, whereas ochratoxin α, citrinin and dihydrocitrinone showed no adverse effects up to 10 µM. Likely due to efflux transporter proteins, the transfer to the brain compartment was much slower than expected from their high lipophilicity. Due to their slow transfer across the blood-brain barrier, cerebral exposure of ochratoxin A, ochratoxin α, citrinin and dihydrocitrinone is low and neurotoxicity is likely to play a subordinate role in their toxicity at common physiological concentrations.
Metabolic Profiling of VOCs Emitted by Bacteria Isolated from Pressure Ulcers and Treated with Different Concentrations of Bio-AgNPs

Considering the advent of antibiotic resistance, the study of bacterial metabolic behavior stimulated by novel antimicrobial agents becomes a relevant tool to elucidate involved adaptive pathways. Profiling of volatile metabolites was performed to monitor alterations of bacterial metabolism induced by biosynthesized silver nanoparticles (bio-AgNPs). Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae and Proteus mirabilis were isolated from pressure ulcers, and their cultures were prepared in the presence/absence of bio-AgNPs at 12.5, 25 and 50 µg mL−1. Headspace solid phase microextraction associated to gas chromatography–mass spectrometry was the employed analytical platform. At the lower concentration level, the agent promoted positive modulation of products of fermentation routes and bioactive volatiles, indicating an attempt of bacteria to adapt to an ongoing suppression of cellular respiration. Augmented response of aldehydes and other possible products of lipid oxidative cleavage was noticed for increasing levels of bio-AgNPs. The greatest concentration of agent caused a reduction of 44 to 80% in the variety of compounds found in the control samples. Pathway analysis indicated overall inhibition of amino acids and fatty acids routes. The present assessment may provide a deeper understanding of molecular mechanisms of bio-AgNPs and how the metabolic response of bacteria is untangled.
Nucleoside Analogue Reverse Transcriptase Inhibitors Improve Clinical Outcome in Transcriptional Active Human Parvovirus B19-Positive Patients

Human parvovirus B19 (B19V) is the predominant cardiotropic virus associated with dilated inflammatory cardiomyopathy (DCMi). Transcriptionally active cardiotropic B19V infection is clinically relevant and triggers adverse long-term mortality. During the study; we evaluated whether antiviral treatment with the nucleoside analogue telbivudine (LTD) is effective in suppressing transcriptional active B19V in endomyocardial biopsies (EMBs) of B19V positive patients and improving clinical outcomes. Seventeen B19V-positive patients (13 male; mean age 45.7 ± 13.9 years; mean left ventricular ejection fraction (LVEF) 37.7 ± 13.5%) with positive B19V DNA and transcriptional activity (B19V mRNA) in EMBs were treated with 600 mg/d LTD over a period of six months. Patients underwent EMBs before and after termination of the LTD treatment. B19V RNA copy numbers remained unchanged in 3/17 patients (non-responder) and declined or disappeared completely in the remaining 14/17 patients (responder) (p ≤ 0.0001). Notably; LVEF improvement was more significant in patients who reduced or lost B19V RNA (responder; p = 0.02) in contrast to non-responders (p = 0.7). In parallel; responder patients displayed statistically significant improvement in quality of life (QoL) questionnaires (p = 0.03) and dyspnea on exertion (p = 0.0006), reflecting an improvement in New York Heart Association (NYHA) Classification (p = 0.001). Our findings demonstrated for the first time that suppression of B19V transcriptional activity by LTD treatment improved hemodynamic and clinical outcome significantly. Thus; the present study substantiates the clinical relevance of detecting B19V transcriptional activity of the myocardium.
Antioxidants and Bioactive Compounds in Licorice Root Extract Potentially Contribute to Improving Growth, Bulb Quality and Yield of Onion (Allium cepa)

The increasing culinary use of onion (Alium cepa) raises pressure on the current production rate, demanding sustainable approaches for increasing its productivity worldwide. Here, we aimed to investigate the beneficial effects of licorice (Glycyrrhiza glabra) root extract (LRE) in improving growth, yield, nutritional status, and antioxidant properties of two high-yielding onion cultivars, Shandaweel and Giza 20, growing under field conditions in two consecutive years. Our results revealed that pretreatments of both onion cultivars with LRE exhibited improved growth indices (plant height and number of leaves) and yield-related features (bulb length, bulb diameter, and bulb weight) in comparison with the corresponding LRE-devoid control plants. Pretreatments with LRE also improved the nutritional and antioxidant properties of bulbs of both cultivars, which was linked to improved mineral (e.g., K+ and Ca2+) acquisition, and heightened activities of enzymatic antioxidants (e.g., superoxide dismutase, catalase, ascorbate peroxidase, glutathione peroxidase, and glutathione S-transferase) and increased levels of non-enzymatic antioxidants (e.g., ascorbic acid, reduced glutathione, phenolics, and flavonoids). LRE also elevated the contents of proline, total free amino acids, total soluble carbohydrates, and water-soluble proteins in both onion bulbs. In general, both cultivars displayed positive responses to LRE pretreatments; however, the Shandaweel cultivar performed better than the Giza 20 cultivar in terms of yield and, to some extent, bulb quality. Collectively, our findings suggest that the application of LRE as biostimulant might be an effective strategy to enhance bulb quality and ultimately the productivity of onion cultivars under field conditions.
Comparative analysis of profitability and resource use efficiency between Penaeus monodon and Litopenaeus vannamei in India

The study aimed to highlight the profitability and production function analysis of Penaeus monodon and Litopenaeus vannamei in intensified shrimp farms in Gujarat (India). Two hundred and twenty (220) shrimp farm households were used to identify (principal component and cluster analyses) 8 clusters of management practices that reflected various scales of production intensity ranging from 0–2999 kg/ha/crop to 9000kg/ha/crop and above for both the species. The Cobb-Douglas production function, which relates production output to several independent input variables, was used to determine productivity. The budgeting analysis for both the species showed that more intensively managed farms performed more than the less intensive farm. Empirical results show feed as most significant input for Penaeus monodon and Litopenaeus vannamei seed and labor that affected production. Average net returns/ha/year for Penaeus monodon was $16313.13 and for Litopenaeus vannamei $41640.99. Aquaculture exhibited decreasing returns to scale for both the species and estimates on resource use efficiency revealed that in Penaeus monodon the resources were economically utilized and in case of Litopenaeus vannamei the output was likely to increase if more of seed and less of labor would have been used. The major constraint for the shrimp farmers was diseases which can be mitigated by optimum stocking densities and proper feed management.
Adipose triglyceride lipase protects renal cell endocytosis in a Drosophila dietary model of chronic kidney disease

Obesity-related renal lipotoxicity and chronic kidney disease (CKD) are prevalent pathologies with complex aetiologies. One hallmark of renal lipotoxicity is the ectopic accumulation of lipid droplets in kidney podocytes and in proximal tubule cells. Renal lipid droplets are observed in human CKD patients and in high-fat diet (HFD) rodent models, but their precise role remains unclear. Here, we establish a HFD model in Drosophila that recapitulates renal lipid droplets and several other aspects of mammalian CKD. Cell type–specific genetic manipulations show that lipid can overflow from adipose tissue and is taken up by renal cells called nephrocytes. A HFD drives nephrocyte lipid uptake via the multiligand receptor Cubilin (Cubn), leading to the ectopic accumulation of lipid droplets. These nephrocyte lipid droplets correlate with endoplasmic reticulum (ER) and mitochondrial deficits, as well as with impaired macromolecular endocytosis, a key conserved function of renal cells. Nephrocyte knockdown of diglyceride acyltransferase 1 (DGAT1), overexpression of adipose triglyceride lipase (ATGL), and epistasis tests together reveal that fatty acid flux through the lipid droplet triglyceride compartment protects the ER, mitochondria, and endocytosis of renal cells. Strikingly, boosting nephrocyte expression of the lipid droplet resident enzyme ATGL is sufficient to rescue HFD-induced defects in renal endocytosis. Moreover, endocytic rescue requires a conserved mitochondrial regulator, peroxisome proliferator-activated receptor-gamma coactivator 1α (PGC1α). This study demonstrates that lipid droplet lipolysis counteracts the harmful effects of a HFD via a mitochondrial pathway that protects renal endocytosis. It also provides a genetic strategy for determining whether lipid droplets in different biological contexts function primarily to release beneficial or to sequester toxic lipids.
Delivery of Cinnamic Aldehyde Antioxidant Response Activating nanoParticles (ARAPas) for Vascular Applications

Selective delivery of nuclear factor erythroid 2-related factor 2 (Nrf2) activators to the injured vasculature at the time of vascular surgical intervention has the potential to attenuate oxidative stress and decrease vascular smooth muscle cell (VSMC) hyperproliferation and migration towards the inner vessel wall. To this end, we developed a nanoformulation of cinnamic aldehyde (CA), termed Antioxidant Response Activating nanoParticles (ARAPas), that can be readily loaded into macrophages ex vivo. The CA-ARAPas-macrophage system was used to study the effects of CA on VSMC in culture. CA was encapsulated into a pluronic micelle that was readily loaded into both murine and human macrophages. CA-ARAPas inhibits VSMC proliferation and migration, and activates Nrf2. Macrophage-mediated transfer of CA-ARAPas to VSMC is evident after 12 h, and Nrf2 activation is apparent after 24 h. This is the first report, to the best of our knowledge, of CA encapsulation in pluronic micelles for macrophage-mediated delivery studies. The results of this study highlight the feasibility of CA encapsulation and subsequent macrophage uptake for delivery of cargo into other pertinent cells, such as VSMC.