Miracle-dz/UniFAR-FTUs huggingface dataset:
Dextran-Coated Iron Oxide Nanoparticles Loaded with Curcumin for Antimicrobial Therapies

The current trend in antimicrobial-agent development focuses on the use of natural compounds that limit the toxicity of conventional drugs and provide a potential solution to the antimicrobial resistance crisis. Curcumin represents a natural bioactive compound with well-known antimicrobial, anticancer, and antioxidant properties. However, its hydrophobicity considerably limits the possibility of body administration. Therefore, dextran-coated iron oxide nanoparticles can be used as efficient drug-delivery supports that could overcome this limitation. The iron oxide nanoparticles were synthesized through the microwave-assisted hydrothermal method by varying the treatment parameters (pressure and reaction time). The nanoparticles were subsequently coated with dextran and used for the loading of curcumin (in various concentrations). The drug-delivery systems were characterized through X-ray diffraction (XRD) coupled with Rietveld refinement, transmission electron microscopy (TEM), high-resolution TEM (HR-TEM), selected area electron diffraction (SAED), dynamic light scattering (DLS) and zeta potential, thermogravimetry and differential scanning calorimetry (TG-DSC), vibrating sample magnetometry (VSM), and UV-Vis spectrophotometry, as well as regarding their antimicrobial efficiency and biocompatibility using the appropriate assays. The results demonstrate a promising antimicrobial efficiency, as well as an increased possibility of controlling the properties of the resulted nanosystems. Thus, the present study represents an important step forward toward the development of highly efficient antimicrobial drug-delivery systems.
B Cells in the Adult Predominantly Products of Affinity-Matured Phosphorylcholine Antibodies Are Naturally Occurring Human

Phosphorylcholine (PC) is a classic T-independent Ag that is exposed on apoptotic cells, oxidized phospholipids, and bacterial polysaccharides. Experimental as well as epidemiological studies have over the past decade implicated Abs against PC (anti-PC) as anti-inﬂammatory and a strong protective factor in cardiovascular disease. Although clinically important, little is known about the development of anti-PC in humans. This study was conceived to dissect the human anti-PC repertoire and generate human mAbs. We designed a PC-speciﬁc probe to identify, isolate, and characterize PC-reactive B cells from 10 healthy individuals. The donors had all mounted somatically mutated Abs toward PC using a broad variety of Ig genes. PC-reactive B cells were primarily found in the IgM + memory subset, although signiﬁcant numbers also were detected among naive, IgG + , and CD27 + CD43 + B cells. Abs from these subsets were clonally related, suggesting a common origin. mAbs derived from the same donors exhibited equivalent or higher afﬁnity for PC than the well-characterized murine T-15 clone. These results provide novel insights into the cellular and molecular ontogeny of atheroprotective PC Abs, thereby offering new opportunities for Ab-based therapeutic interventions. The Journal of Immunology , 2014, 192: 000–000.
That Protects against Autoimmunity Anti-CD79 Antibody Induces B Cell Anergy

B cells play a major role in the pathogenesis of many autoimmune disorders, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, and type I diabetes mellitus, as indicated by the efﬁcacy of B cell–targeted therapies in these diseases. Therapeutic effects of the most commonly used B cell–targeted therapy, anti-CD20 mAb, are contingent upon long-term depletion of peripheral B cells. In this article, we describe an alternative approach involving the targeting of CD79, the transducer subunit of the B cell AgR. Unlike anti-CD20 mAbs, the protective effects of CD79-targeted mAbs do not require cell depletion; rather, they act by inducing an anergic-like state. Thus, we describe a novel B cell–targeted approach predicated on the induction of B cell anergy. The Journal of Immunology , 2014, 192: 1641–1650.
Multi-omics Mendelian randomisation using expression, protein and splicing quantitative trait loci to identify novel drug targets associated with risk of gliomagenesis

Genetically predicted changes in molecular traits are associated with genetic liability to glioma risk. We sought to identify and provide evidence for the prioritisation of these traits using a combined two-sample Mendelian randomisation and colocalisation pipeline. We used genetic variants from genome-wide association studies (GWAS) to compare the effects of gene expression, protein abundance and splicing variation on genetic liability to glioma risk. We performed analysis of quantitative trait loci (QTL) (expression QTL [MetaBrain, n=4,119,012]; protein QTL [BrainQTL, n=786,632] and splicing QTL [GTEx, n=166,721]) derived from 15 different brain tissues and summary-level data from eight glioma GWAS (12,496 cases and 18,190 controls). We found robust evidence linking QTL for 25 genes in 15 loci with risk of glioma following relevant sensitivity analysis. We identified four novel genes putatively implicated in glioma risk: BTN3A2 (6p22.2), FAIM (3q22.3), GALNT6 (12q13.13) and GMPPB (3p21.31). Identification of these novel genes may improve understanding in pathogenic mechanisms underlying gliomagenesis and should be followed up in further studies, which may inform novel prevention studies following future clinical trials.
Risk factors associated with Ahmed valve implant dysfunction in patients with neovascular glaucoma during the first postoperative year

Background. The current surgical management of Neovascular Glaucoma (NGV) is with the placement of the Ahmed Valve. However, up to 70% fail and the factors associated with this failure are unknown. Recognizing the factors associated with dysfunction will help to identify patients prone to failure in a timely manner to establish timely management and improve visual and functional prognosis. Objective. To determine the risk factors associated with Ahmed valve dysfunction in patients with NVG during the first year after surgery. Population. Patients diagnosed with VNG who have undergone Ahmed valve implantation in the Glaucoma - APEC service, during the 2013 to 2019 period. Methods. ambispective cohort. Patients with NVG defined as: Presence of neovessels in the iris or angle of the anterior chamber were included, those who had a history of implantation of a drainage device, patients with a diagnosis of glaucoma in the terminal phase, worse visual acuity of perception were excluded. of light. The research unit was eyes subjected to Ahmed valve implantation. The demographic characteristics of the patients were evaluated, as well as the characteristics of the eyes. The primary outcome was and the secondary outcomes were adverse events, visual acuity and final intraocular pressure An unadjusted Cox proportional hazards model was used to determine risk factors as Hazzard ratios (HR). Those significant factors were included in a Cox proportional hazards model to adjust for the main confounding variables. Results. 174 eyes were included. Baseline intraocular pressure (IOP) was 46.03({+/-}11.8) mmHG. Baseline VA in LogMAR was 1.52(0.89- 2.3). Risk factors were: Age<50 years with HR 1.54(95% CI 1.04- 2.30); HB1Ac>8% HR 1.71 (95% CI 1.12- 2.60) and presence hypertensive phase HR 3.13 (95% CI 1.57-6.23). The multivariate model was adjusted for the following variables, baseline IOP less than 40mmHg HR 1.60 (95% CI 1.04- 2.47); HB1Ac>8% HR 1.80 (95% CI 1.16- 2.78); FP7 valve type HR 1.75 (95% CI 1.04- 2.94) and presence of hypertensive phase HR 3.24 (95% CI 1.60- 6.59). Conclusion. Basal IOP less than 40mmHg, HB1Ac>8%, type of FP7 valve, surgery performed by a resident, lack of photocoagulation after implantation, and the presence of a hypertensive phase are the independent risk factors for implant valvuar dysfunction.
Smoking trajectories over the first year of the pandemic in UK middle-aged adults: evidence from the UKHLS COVID-19 study

Background. The COVID-19 pandemic has altered the conditions leading people to smoke. Multiple studies have examined changes in population levels of smoking at the start of the pandemic. However, conclusions remain mixed due to the high proportion of studies with poor methods and short follow-up periods. Methods. This study used longitudinal data from the UKHLS COVID-19 study to derive smoking trajectories among 4,130 UK adults aged 35-64 across four time points over the first year of the pandemic (2018-19, April 2020, September 2020, and January 2021). Random-effects models were used to examine subject-specific changes across time points. Results. Between the pre-pandemic estimate and January 2021, there was a significant decline in smoking from 14.8% to 13.1% (PR = 0.89, 95%CI 0.83-0.95). The number of cigarettes smoked per day among smokers increased in April 2020 (B = 0.5, 95%CI 0.0, 1.0) and September 2020 (B = 1.0, 95%CI 0.4, 1.5), but declined back to pre-pandemic levels in January 2021 (B = 0.3, 95%CI -0.3, 0.8). These changes did not vary by sex, ethnicity, relationship status, education, occupation, or household income. Conclusion. Among UK adults aged 35-64, there has been a slight decrease in smoking which was maintained up to January 2021. Whereas there was an increase in cigarette consumption among smokers at the start of the pandemic, this was no longer observable in January 2021. The findings support the argument that the first year of the pandemic is unlikely to have had a negative effect on the trajectory of most middle-aged adult smokers.
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that deep learning methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.
Laboratory Diagnostic of Acute Kidney Injury and Its Progression: Risk of Underdiagnosis in Female and Elderly Patients

Objective: Acute kidney injury (AKI) is a common disease, with high morbidity and mortality rates. In this study, we investigate the potential influence of sex and age on laboratory diagnostic and outcome. It is known that serum creatinine (SCr) has limitations as a laboratory diagnostic parameter for AKI due to its dependence on muscle mass, which may lead to incorrect or delayed diagnosis for certain patient groups, such as women and the elderly. Methods: Overall, 7592 cases with AKI, hospitalized at the University of Leipzig Medical Center (ULMC) between 1st January 2017 and 31st December 2019, were retrospectively analyzed. Diagnosis and staging of AKI was performed according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, based on the level and dynamics of SCr. The impact of sex and age was analyzed by the recalculation of a female to male and an old to young SCr using the CKD-EPI equation. Results: The incidence proportion of AKI in our study cohort was 12.0%, with progressive AKI occurring in 19.2% of these cases (n = 1458). Male cases with AKI were overrepresented (59.6%), with a significantly higher first (+3.5 ml/min) and last eGFR (+2.7 ml/min) (p < 0.001). The highest incidence proportion of AKI was found in the [61-81) age group in female (49.5%) and male (52.7%) cases. Males with progressive AKI were overrepresented (p = 0.04). By defining and staging AKI on the basis of relative and absolute changes in SCr level, it is more difficult for patients with low muscle mass and thus a lower baseline SCr to be diagnosed by an absolute SCr increase. AKIN1 and AKIN3 can be diagnosed by a relative or absolute change in SCr. In females, both stages were less frequently detected by an absolute criterion alone (AKIN1 female 20.2%, male 29.5%, p < 0.001; AKIN3 female 13.4%, male 15.2%, p < 0.001). A recalculated SCr for females (to males) and males (to young males) displayed the expected increase in AKI occurrence and severity with age and in general in females. Conclusion: Our study illustrates how SCr as the sole parameter for diagnosis and staging of AKI bears the risk of underdiagnosis of patient groups with low muscle mass, such as women and the elderly. A sex- and age-adapted approach might offer advantages.
THE SYNERGY OF FISCAL AND MONETARY POLICY FOR REAL SECTOR

O n e of t h e causes of low gr owth and t he r eal sector's contrib u tion is econ o m ic polic y , b oth fiscal and mon e tary that i s less supportive of t he e c onomic actors i n the real s e c t or. From t he fiscal s i d e, t h e budget magn i t ud es can be s e en to t he real se c tor ( e .g. Agricult u r e) that is less than 5 pe r c e n t. T he funds ar e s ti l l skimpy that a sse s sed it i s difficu l t to develop t h e agricult u r a l secto r . F rom t h e monetary sid e , t he interest r ate is judged s till too h i gh and cause e conom i c a c tors i n t h e real s ector i s quite diff i cult to get ca p it a l acce s s. Th i s st u dy aims to f i nd synerg i es b e tween fis c al an d monetary p o licy to support the r e al sector. T his r e search was u s ing Ana l yti c a l Hie rar c h y P ro c ess (AHP) t hr ough the distr i bu ti o n of questionnair e s to respond e n t s of SME i n Sura ba y a . T h e r e sul t s sh o w e d t hat interest ra t e an d capital a c c ess to financial i n s t itutions are f actors c onsi d e r e d by SME i n obt a ining credit (m on e tary polic y ). On t h e other hand, raw ma te r i a ls subs idi e s an d fuel are a l so a m ajor conc e rn f o r t hem to increase pr oduct i o n ou tput (fiscal po l i cy). Key words : Fi s cal Po l icy, Monetary Policy, Real Secto r , S M E, AHP.
The $\psi^{(\ast)}p$ scattering length based on near-threshold charmoniums photoproduction

Under the framework of Vector Meson Dominance model, the value of scattering length can be expressed as a function of the ratio between total cross section σ ( W ) and R ( W ), where R ( W ) is the ratio between ﬁnal momentum | p 3 | and initial momentum | p 1 | and positively correlated with the center-of-mass energy. Based on the theoretical study of charmoniums photoproduction within two gluon exchange model and e ﬀ ective pomeron model, we research the scattering lengths of vector mesons and proton interaction in this work. Results show that the scattering length (cid:12)(cid:12)(cid:12) α J /ψ p (cid:12)(cid:12)(cid:12) obtained from the two models are close and basically in agreement with the theoretical prediction of Strakovsky and co-workers. Additionally, we ﬁrst calculate the scattering length of ψ (2 S )-proton interaction in two gluon exchange model and e ﬀ ective pomeron model as 1 . 31 ± 0 . 92 am (1 am = 10 − 3 fm) and 3 . 24 ± 0 . 63 am, respectively. This is a little bit di ﬀ erent from the two models and requires precise measurements from subsequent experiments. In short, our results will provide a theoretical reference for future studies on characterizing the vector meson-proton scattering length.
Feedback of Non-Local d xy Nematicity on the Magnetic Anisotropy in FeSe

We analyze theoretically the magnetic anisotropy in the nematic phase of FeSe by computing the spin and the orbital susceptibilities from the microscopic multiorbital model. In particular, we take into account both the xz/yz and the recently proposed non-local xy nematic ordering and show that the latter one could play a crucial role in reproducing the experimentally-measured temperature dependence of the magnetic anisotropy. This provides a direct fingerprint of the different nematic scenarios on the magnetic properties of FeSe.
On Lasso and Slope drift estimators for Lévy-driven Ornstein–Uhlenbeck processes

We investigate the problem of estimating the drift parameter of a high-dimensional L\'evy-driven Ornstein--Uhlenbeck process under sparsity constraints. It is shown that both Lasso and Slope estimators achieve the minimax optimal rate of convergence (up to numerical constants), for tuning parameters chosen independently of the confidence level, which improves the previously obtained results for standard Ornstein--Uhlenbeck processes. The results are nonasymptotic and hold both in probability and conditional expectation with respect to an event resembling the restricted eigenvalue condition.
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed costs in both full information and bandit feedback settings. In contrast to previous works on online unconstrained submodular minimization, we focus on a class of nonsubmodular functions with special structure, and prove regret guarantees for several variants of the online and approximate online bandit gradient descent algorithms in static and delayed scenarios. We derive bounds for the agent’s regret in the full information and bandit feedback setting, even if the delay between choosing a decision and receiving the incurred cost is unbounded. Key to our approach is the notion of ( α, β ) -regret and the extension of the generic convex relaxation model from El Halabi & Jegelka (2020), the analysis of which is of independent interest. We conduct and showcase several simulation studies to demonstrate the efﬁcacy of our algorithms.
Long-Term Benefits of Network Boosters for Renewables Integration and Corrective Grid Security

The preventative strategies for N − 1 network security dominant in European networks mean that network capacity is kept free in case a line fails. If instead fast corrective actions are used to overcome network overloading when single lines fail, this has the potential to free up network capacity that is otherwise underused in preventive N − 1 security strategies. In this paper, we investigate the impact on renewable integration of a corrective network security strategy, whereby storage or other ﬂexibility assets are used to correct overloading shortly after line outages. In this way, we ﬁnd signiﬁcant cost savings for the integration of renewable energy of up to 2.4 billion euros per year in a aggregated 50-bus model of the German power system utilizing these ﬂexibility assets, so-called network boosters (NB). This oﬀers a role for storage beyond energy arbitrage or ancillary services like frequency control. While previous literature has focused on the potential savings of NB in the short-term operation, we focus on the long-term beneﬁts in systems with high shares of renewable energy sources, where the capacities and dispatch of generation and NB are optimised. We demonstrate the beneﬁts of NB for various shares of renewable energy, NB and ﬂexibility costs, as well as diﬀerent allowed levels of temporary overloading the lines in both (i) a sequential model, where long-run generation investments are optimised separately from the NB capacities, and (ii) a simultaneous model, where generation is co-optimised with NB investment so that mixed preventive-corrective approaches are possible.
Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordstr\"om (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(\phi)=e^{-b\phi^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of system parameters during dynamic scalarization. For parameter regions where spontaneous scalarization occurs, we find that the evolution of the scalar field at the horizon is dominated by the fundamental unstable mode from linear analysis at early times. At late times, the nonlinear evolution can be viewed as the perturbation of scalarized black holes.
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths

Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less memory and computational resources. However, our theoretical understanding of when and how first-order methods such as gradient descent (GD) converge on \textit{nonlinear} implicit networks is limited. Although this type of problem has been studied in standard feed-forward networks, the case of implicit models is still intriguing because implicit networks have \textit{infinitely} many layers. The corresponding equilibrium equation probably admits no or multiple solutions during training. This paper studies the convergence of both gradient flow (GF) and gradient descent for nonlinear ReLU activated implicit networks. To deal with the well-posedness problem, we introduce a fixed scalar to scale the weight matrix of the implicit layer and show that there exists a small enough scaling constant, keeping the equilibrium equation well-posed throughout training. As a result, we prove that both GF and GD converge to a global minimum at a linear rate if the width $m$ of the implicit network is \textit{linear} in the sample size $N$, i.e., $m=\Omega(N)$.
Robust Representation via Dynamic Feature Aggregation

Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse, resulting in a large space for the generation of adversarial samples. In this study, we propose a method, denoted as Dynamic Feature Aggregation, to compress the embedding space with a novel regularization. Particularly, the convex combination between two samples are regarded as the pivot for aggregation. In the embedding space, the selected samples are guided to be similar to the representation of the pivot. On the other side, to mitigate the trivial solution of such regularization, the last fully-connected layer of the model is replaced by an orthogonal classifier, in which the embedding codes for different classes are processed orthogonally and separately. With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks. An averaging accuracy of 56.91% is achieved by our method on CIFAR-10 against various attack methods, which significantly surpasses a solid baseline (Mixup) by a margin of 37.31%. More surprisingly, empirical results show that, the proposed method can also achieve the state-of-the-art performance for out-of-distribution (OOD) detection, due to the learned compact feature space. An F1 score of 0.937 is achieved by the proposed method, when adopting CIFAR-10 as in-distribution (ID) dataset and LSUN as OOD dataset. Code is available at https://github.com/HaozheLiu-ST/DynamicFeatureAggregation.