Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictability of the 2017 North Atlantic hurricane season

The 2017 North Atlantic hurricane season was one of the most active in recent history. The season recorded a total of 17 named storms, of which 10 became hurricanes and 6 became major hurricanes, and an Accumulated Cyclone Energy (ACE) index of 226. The number of major hurricanes was joint second highest since 1970, and the ACE index was third highest. We show that the Met Office Atlantic seasonal tropical cyclone forecast, initialised on May 15, 2017, predicted an increased risk of above‐average activity. The forecast also captured aspects of the spatial distribution of the tropical cyclones, such as the high frequency of tracks which crossed the northeast Caribbean in September 2017, at more than 3 months lead time. We show that the high levels of seasonal activity, particularly in terms of major hurricanes and ACE index, were likely a result of a combination of low wind shear in the western Atlantic, above‐average tropical Atlantic sea surface temperatures (SSTs) and a transition to cooler SSTs in the tropical Pacific due to a developing La Niña event, all of which appear to be predictable at seasonal lead times. This study provides evidence for seasonal predictability of the intense 2017 Atlantic hurricane season, including advanced warning of an increased risk of tropical cyclones transiting the northeast Caribbean.
Delicate Hybrid Laponite–Cyclic Poly(ethylene glycol) Nanoparticles as a Potential Drug Delivery System

The objective of the study was to explore the feasibility of a new drug delivery system using laponite (LAP) and cyclic poly(ethylene glycol) (cPEG). Variously shaped and flexible hybrid nanocrystals were made by both the covalent and physical attachment of chemically homogeneous cyclized PEG to laponite nanodisc plates. The size of the resulting, nearly spherical particles ranged from 1 to 1.5 µm, while PEGylation with linear methoxy poly (ethylene glycol) (mPEG) resulted in fragile sheets of different shapes and sizes. When infused with 10% doxorubicin (DOX), a drug commonly used in the treatment of various cancers, the LAP-cPEG/DOX formulation was transparent and maintained liquid-like homogeneity without delamination, and the drug loading efficiency of the LAP-cPEG nano system was found to be higher than that of the laponite-poly(ethylene glycol) LAP-mPEG system. Furthermore, the LAP-cPEG/DOX formulation showed relative stability in phosphate-buffered saline (PBS) with only 15% of the drug released. However, in the presence of human plasma, about 90% of the drug was released continuously over a period of 24 h for the LAP-cPEG/DOX, while the LAP-mPEG/DOX formulation released 90% of DOX in a 6 h burst. The results of the cell viability assay indicated that the LAP-cPEG/DOX formulation could effectively inhibit the proliferation of A549 lung carcinoma epithelial cells. With the DOX concentration in the range of 1–2 µM in the LAP-cPEG/DOX formulation, enhanced drug effects in both A549 lung carcinoma epithelial cells and primary lung epithelial cells were observed compared to LAP-mPEG/DOX. The unique properties and effects of cPEG nanoparticles provide a potentially better drug delivery system and generate interest for further targeting studies and applications.
Metabolomic Characterization of Phoradendron brachystachyum Mistletoe and In-Silico and In-Vitro Investigation of Its Therapeutic Potential in Metabolic Disorders

Plants of the Phoradendron genus have been traditionally used for their lipid- and glucose-lowering effects. However, the compounds responsible for these effects and the overall chemical profile of these plants have not been thoroughly investigated. We aimed to characterize the metabolome of leaves, stems, and aerial parts of the Phoradendron brachystachyum plant. We used mass spectrometry and colorimetric screening techniques (with various solvents) to identify and characterize the metabolites present. We also evaluated the antioxidant (FRAP, ORAC, TEAC, and DPPH assays) and inhibitory effects on pancreatic lipase and α-glucosidase enzymes of hydrophilic extracts. Furthermore, we compared the molecular fingerprints between the identified metabolites and FDA-approved drugs to gain insights into the metabolites that might be responsible for the observed effects on enzymes. Our findings revealed the presence of 59 putative metabolites, primarily flavonoids. However, we also hint at the presence of peptide and carbohydrate derivatives. The leaf extracts demonstrated the most promising metrics across all assays, exhibiting strong antioxidant and enzyme inhibitory effects as well as high levels of phenolic compounds, flavonoids, and tannins. Fingerprint analysis suggested potential peptide and carbohydrate metabolites as pancreatic lipase and α-glucosidase inhibitors. Overall, our study provides evidence on specific metabolites in Phoradendron brachystachyum that could be responsible for the therapeutic effects noted in obese and type 2 diabetes subjects.
A 47 GHz to 70 GHz Frequency Doubler Exploiting 2nd-Harmonic Feedback with 10.1 dBm Psat and ntotal of 22% in 65 nm CMOS

A wideband millimeter wave (mm-wave) frequency doubler (FDB) architecture is proposed, where a feedback network is employed to increase the 2nd-harmonic signal at the output. Unlike traditional approaches where the 2nd-harmonic is nulled at the input, common-mode/differential-mode signals are exploited to create frequency-dependent networks at fo and 2 fo. The stand-alone FDB in 65-nm CMOS achieves a saturated output power (Psat) of > 10.1 dBm. A wideband power amplifier (PA) follows the FDB to achieve a total Psatof 15 dBm with a maximum DC-to-RF efficiency (ntotal) of 24.5%, demonstrating the applicability of the FDB for mm-wave radar.
Small Molecules Incorporating Privileged Amidine Moiety as Potential Hits Combating Antibiotic-Resistant Bacteria

The continuing need for the discovery of potent antibacterial agents against antibiotic-resistant pathogens is the driving force for many researchers to design and develop such agents. Herein, we report the design, synthesis, and biological evaluation of amidine derivatives as new antibacterial agents. Compound 13d was the most active in this study against a wide range of antibiotic-resistant, and susceptible, Gram-positive, and Gram-negative bacterial strains. Time–kill assay experiments indicated that compound 13d was an effective bactericidal compound against the tested organisms at the log-phase of bacterial growth. Docking simulations were performed to assess in silico its mode of action regarding UPPS, KARI, and DNA as potential bacterial targets. Results unveiled the importance of structural features of compound 13d in its biological activity including central thiophene ring equipped with left and right pyrrolo[2,3-b]pyridine and phenyl moieties and two terminal amidines cyclized into 4,5-dihydro-1H-imidazol-2-yl functionalities. Collectively, compound 13d represents a possible hit for future development of potent antibacterial agents.
A 42.7Gb/s Optical Receiver with Digital CDR in 28nm CMOS

This paper presents a broadband optical receiver that employs multiple bandwidth extension techniques in the analog front-end (AFE) and has efficient digital clock and data recovery (CDR). Total AFE bandwidth is extended by 5.5X with continuous-time linear-equalizer (CTLE) peaking, series inductances between each AFE stage, and active inductors in the CTLE output and variable gain amplifier (VGA) stages. The resolution of a digitally controlled oscillator (DCO) is optimized at 9-bit to balance quantization and random noise-induced jitter from the CDR. Fabricated in 28nm CMOS, the 42.7Gb/s optical receiver achieves an optical modulation amplitude (OMA) sensitivity of -3.6dBm at a bit error rate (BER)-12, 10MHz CDR bandwidth, and 3.4pJ/bit energy efficiency.
An Efficient Data-Driven Conditional Joint Wind Power Scenario Generation for Day-Ahead Power System Operations Planning

Advanced stochastic programming-based operations planning (OP) requires wind power forecasts in the form of scenarios. The quality of the decisions made under uncertainty is sensitive to the quality of the infeed scenarios. Consequently, there is a need for accurate and efficient methods of scenario generation (SG). This article proposes a conditional joint wind power SG method for multiple wind farms. The proposed framework models the dependence among wind farms using vine copula and presents a novel analytical conditional sampling algorithm (CSA). Unlike the existing work, this CSA is inherently accurate, avoids the tedious manual conditional sample selection, and enables control over the number of conditional scenarios generated. Also, a uniform design-based variance reduction is introduced and integrated into the proposed CSA, which benefits the downstream OP applications with improved convergence and accuracy of the solutions. A detailed two-stage scenario evaluation procedure is carried out on a real-world dataset: univariate and multivariate quality metrics-based statistical evaluation in the first stage and an application-based evaluation in the second stage. Results suggest that the proposed SG method significantly improves the overall quality of the forecasted wind power scenarios and provides a better cost-risk balance in the application compared to the benchmarks.
Wernicke Encephalopathy in a Pediatric Patient with Avoidant Restrictive Food Intake Disorder: A Rare Presentation of Thiamine Deficiency

Patient: Female, 12-year-old Final Diagnosis: Avoidant restrictive food intake disorder • thiamine deficiency in a preterm infant • Wernicke’s encephalopath Symptoms: Nausea • vomiting • abdominal pain • double vision • blurred vision • nystagmus and an abnormal gait Clinical Procedure: — Specialty: Gastroenterology and Hepatology Objective: Rare disease Background: Wernicke encephalopathy is traditionally associated with chronic alcoholism, nutritional imbalance, prolonged intravenous feeding, hyperemesis, anorexia nervosa, and malabsorption syndromes. We report a case of Wernicke’s encephalopathy in a 12-year-old girl with avoidant restrictive food intake disorder. Case Report: The patient had lost 45.4 kg of body weight due to self-imposed changes to her diet, before presenting with decreased oral intake for 2–3 weeks, intermittent nausea, crampy epigastric pain, and post-prandial emesis. Her weight on admission was 78.2 kg. She received intravenous fluids of dextrose 5% with normal saline while she initially attempted to eat, but the post-prandial emesis persisted. She developed a fear of vomiting, which led to even more severe food intake restriction. After a week, she began to report double vision and blurred peripheral vision, with physical findings of nystagmus and an ataxic gait. She was empirically started on thiamine after negative neurology workup, with improvement of her gait, blurry vision, and nystagmus. Thiamine deficiency was later confirmed. Conclusions: In patients with large amounts of weight loss presenting with neurological symptoms, Wernicke’s encephalopathy must be considered in the differential diagnosis. Avoidant restrictive food intake disorder is rarely reported to cause Wernicke’s encephalopathy. To the best of our knowledge, this is the first pediatric case demonstrating that Wernicke encephalopathy can occur in this type of eating disorder and not just in anorexia nervosa.
SINGULAR LIMITING SOLUTIONS TO 4-DIMENSIONAL ELLIPTIC PROBLEMS INVOLVING EXPONENTIALLY DOMINATED NONLINEARITY AND NONLINEAR TERMS

Let 2 R 4 be a bounded open regular set, x1;x2;:::;xm 2 , ; > 0 and Q be a non linear operator (which will be dened later). We prove that the problem 2 u + Q (u) = 4 e u has a positive weak solution in with u = u = 0 on @, which is singular at each xi as the parameters and tends to 0.
A Comparative Teaching of Fractional Calculus and Integer Calculus

The main purpose of this paper is to give a teaching comparison of fractional calculus and integer calculus. Another purpose of this paper is to help students to understand fractional calculus and integer calculus are highly unified. Starting from the integral order derivative of the elementary power function, the definition of Riemann-Liouville fractional derivative is given. With the theory of integral calculus such as integral mean value theory, Green formula and classical inequality, the fractional differential equations are transformed into integers order differential equation, the differences and relations between two kinds of calculus are expounded. Therefore, it is concluded that fractional derivative and integral derivative can be exchanged under certain conditions. Keywords—Fractional calculus; Integral order calculus; Riemann-Liouville derivative; Riemann-Liouville integral; Comparative teaching
A 0.32pJ/b 90Gbps PAM4 Optical Receiver Front-End with Automatic Gain Control in 12nm CMOS FinFET

This work presents a 0.32pJ/b90Gbps PAM4 optical receiver (RX) front-end with a 13.4pA/$\sqrt{}$Hz noise density. The proposed design employs a new transimpedance amplifier (TIA) and a single-ended-to-differential (S2D) block providing a large gain-bandwidth product with less power and area overhead compared to the conventional designs. The proposed RX is implemented in 12nm CMOS FinFET process and co-packaged with a commercial photodiode (PD), offering the best power efficiency, input-referred noise, and figure-of-merit (FOM) amongst the previous state-of-the-art designs [1] –[5].
Review of Active Circuit and Passive Circuit Techniques to Improve the Performance of Highly Efficient Energy Harvesting Systems

In piezoelectric energy harvesting systems, energy harvesting circuits are the interface between piezoelectric devices and electrical loads. The conventional view of this interface is based on the concept of impedance matching. In fact, in the power supply circuit can also apply as an electrical boundary conditions, such as voltage and charge, to piezoelectric devices for each energy conversion cycle. The major drawback of piezoelectric power harvesting have low-power relationships in systems within (in the range of μW to mW), then system also have significantly reduced any potential losses in circuits that make up the EH system, whereas other condition into careful selection of circuits and components can enhanced the energy harvesting performance and electricity consumption. In the study of energy harvesting systems, it is an energy harvesting system approach that using active and passive electronic circuit to control voltage and or charge on piezoelectric devices as proposed and review to mechanical inputs for optimized energy conversion. Several factors in the practical limitation of active and passive energy consumption, due to device limitations and the power efficiency of electronic circuits, will be introduced and have played an important role into to enhance optimum and increase efficiency of energy harvesting system.
Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions.
Deep neural networks for source separation and noise-robust speech recognition

This thesis addresses the problem of multichannel audio source separation by exploiting deep neural networks (DNNs). We build upon the classical expectation-maximization (EM) based source separation framework employing a multichannel Gaussian model, in which the sources are characterized by their power spectral densities and their source spatial covariance matrices. We explore and optimize the use of DNNs for estimating these spectral and spatial parameters. Employing the estimated source parameters, we then derive a time-varying multichannel Wiener filter for the separation of each source. We extensively study the impact of various design choices for the spectral and spatial DNNs. We consider different cost functions, time-frequency representations, architectures, and training data sizes. Those cost functions notably include a newly proposed task-oriented signal-to-distortion ratio cost function for spectral DNNs. Furthermore, we present a weighted spatial parameter estimation formula, which generalizes the corresponding exact EM formulation. On a singing-voice separation task, our systems perform remarkably close to the current state-of-the-art method and provide up to 2 dB improvement of the source-to-interference ratio. On a speech enhancement task, our systems outperforms the state-of-the-art GEV-BAN beamformer by 14%, 7%, and 1% relative word error rate improvement on 6-channel, 4-channel, and 2-channel data, respectively
PM2.5 Level Forecasting using Transformer-Based Model

Air pollution is a significant public well-being concern in Indian cities, and forecasting PM 2.5 levels is important for effective air quality control. In this paper, we present a novel technique for PM2.5 level forecasting using transformer-based models, specifically the encoder part of the Transformer with modifications employed for the prediction of the concentration levels of PM2.5 particles using the previous years of data available to us. The proposed model makes use of historical PM 2.5 data to make predictions for multiple cities in India. Our experimental results indicate that the presented model surpasses previously used time series forecasting methods, achieving average error of 7.69% on the test set. Additionally, the given model can comprehend the complex temporal dependencies in the data and handle missing values effectively. These results demonstrate the potential of transformer-based models for PM2.5 level forecasting in Indian cities and have important implications for air quality management and public health policy.
Gabor frames on locally compact abelian groups and related topics

This thesis advances the theory of both separable and non-separable, discrete, semicontinuous and continuous Gabor systems. In particular, the well established structure theory for separable lattice Gabor frames is extended and generalized significantly to Gabor systems with time-frequency shifts along closed subgroups in the time-frequency plane. This includes density results, the Walnut representation, the Wexler-Raz biorthogonality relations, the Bessel duality and the duality principle between Gabor frames and Gabor Riesz bases.
MEDIDAS DE AJUSTE A UNA NUEVA REALIDAD DE VIDA TOMADAS POR LOS CONSUMIDORES COLOMBIANOS Y MEXICANOS ANTE LA LLEGADA DEL COVID-19

Sin duda alguna, toda nueva situación genera en los sujetos desajustes a nivel emocional, racional, conductual, fisiológico entre otros, conllevando en ocasiones a crisis en sus vidas, por ende, el ser humano, siempre tiende a buscar el equilibrio encaminado a eliminar la sensación de incertidumbre. Ante la llegada del Covid-19 la humanidad en general se vio avocada a implementar medidas de contingencia en busca de recobrar el equilibrio perdido. Con base en lo anterior, esta investigación persigue determinar cuáles fueron esas medidas de ajustes adoptadas por los consumidores mexicanos y colombianos como ajuste a una nueva realidad de vida, considerando que este hecho puede servir como apoyo a los profesionales del marketing en su planeación estrategia, y ser una puerta para nuevas investigaciones, dado que el trabajo fue realizado de forma temprana, a tan solo dos meses de haber sido oficializado el contagio y tomando medidas gubernamentales contingentes al respecto como el confinamiento.
The Emerging Role of Innate Lymphoid Cells (ILCs) and Alarmins in Celiac Disease: An Update on Pathophysiological Insights, Potential Use as Disease Biomarkers, and Therapeutic Implications

Celiac disease (CD) is an intestinal disease that develops in genetically predisposed individuals and is triggered by the ingestion of gluten. CD was considered a Th1-disease. Today, the role of Th17, IL-21, and IL-17A lymphocytes is well known. Inflammation is regulated by the activity of gluten-specific CD4+ T lymphocytes that produce pro-inflammatory cytokines, including IFN-γ, TNF-α, and IL-21, perpetuating the Th1 response. These cytokines determine an inflammatory state of the small intestine, with consequent epithelial infiltration of lymphocytes and an alteration of the architecture of the duodenal mucosa. B cells produce antibodies against tissue transglutaminase and against deamidated gliadin. Although the role of the adaptive immune response is currently known, the evidence about the role of innate immunity cells is still poorly understood. Epithelial damage determines the release of damage-associated molecular patterns (DAMPs), also known as alarmins. Together with the intestinal epithelial cells and the type 1 innate lymphoid cells (ILC1s), alarmins like TSLP, IL-33, and HMGB1 could have a fundamental role in the genesis and maintenance of inflammation. Our study aims to evaluate the evidence in the literature about the role of ILCs and alarmins in celiac disease, evaluating the possible future diagnostic and therapeutic implications.
Adaptation of Droplet Digital PCR-Based HIV Transcription Profiling to Digital PCR and Association of HIV Transcription and Total or Intact HIV DNA

In most people living with HIV (PLWH) on effective antiretroviral therapy (ART), cell-associated viral transcripts are readily detectable in CD4+ T cells despite the absence of viremia. Quantification of HIV RNA species provides insights into the transcriptional activity of proviruses that persist in cells and tissues throughout the body during ART (‘HIV reservoir’). One such technique for HIV RNA quantitation, ‘HIV transcription profiling’, developed in the Yukl laboratory, measures a series of HIV RNA species using droplet digital PCR. To take advantage of advances in digital (d)PCR, we adapted the ‘HIV transcription profiling’ technique to Qiagen’s dPCR platform (QIAcuity) and compared its performance to droplet digital (dd)PCR (Bio-Rad QX200 system). Using RNA standards, the two technologies were tested in parallel and assessed for multiple parameters including sensitivity, specificity, linearity, and intra- and inter-assay variability. The newly validated dPCR assays were then applied to samples from PLWH to determine HIV transcriptional activity relative to HIV reservoir size. We report that HIV transcriptional profiling was readily adapted to dPCR and assays performed similarly to ddPCR, with no differences in assay characteristics. We applied these assays in a cohort of 23 PLWH and found that HIV reservoir size, based on genetically intact proviral DNA, does not predict HIV transcriptional activity. In contrast, levels of total DNA correlated with levels of most HIV transcripts (initiated, proximally and distally elongated, unspliced, and completed, but not multiply spliced), suggesting that a considerable proportion of HIV transcripts likely originate from defective proviruses. These findings may have implications for measuring and assessing curative strategies and clinical trial outcomes.