Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering Li/Na selectivity in 12-Crown-4–functionalized polymer membranes

Significance Lithium is a key ingredient in batteries, which are integral components of next-generation automobiles, airplanes, grid energy storage, and electronic devices. Unfortunately, lithium extraction from natural sources is laborious, slow, and costly, motivating the search for more efficient isolation techniques. While polymeric membranes could reduce the cost of lithium recovery, current membrane materials lack sufficient lithium-ion selectivity. To address this challenge, we introduce a class of polymeric membranes that incorporate ion binding sites, which significantly increases the transport selectivity of LiCl over NaCl. These studies provide guidelines and practical considerations for incorporating specific interactants into polymers that mediate selective ion transport. Lithium is widely used in contemporary energy applications, but its isolation from natural reserves is plagued by time-consuming and costly processes. While polymer membranes could, in principle, circumvent these challenges by efficiently extracting lithium from aqueous solutions, they usually exhibit poor ion-specific selectivity. Toward this end, we have incorporated host–guest interactions into a tunable polynorbornene network by copolymerizing 1) 12-crown-4 ligands to impart ion selectivity, 2) poly(ethylene oxide) side chains to control water content, and 3) a crosslinker to form robust solids at room temperature. Single salt transport measurements indicate these materials exhibit unprecedented reverse permeability selectivity (∼2.3) for LiCl over NaCl—the highest documented to date for a dense, water-swollen polymer. As demonstrated by molecular dynamics simulations, this behavior originates from the ability of 12-crown-4 to bind Na+ ions more strongly than Li+ in an aqueous environment, which reduces Na+ mobility (relative to Li+) and offsets the increase in Na+ solubility due to binding with crown ethers. Under mixed salt conditions, 12-crown-4 functionalized membranes showed identical solubility selectivity relative to single salt conditions; however, the permeability and diffusivity selectivity of LiCl over NaCl decreased, presumably due to flux coupling. These results reveal insights for designing advanced membranes with solute-specific selectivity by utilizing host–guest interactions.
Prognostic ability of five clinical risk scores in follicular lymphoma: A single‐center evaluation

With the intention of identifying follicular lymphoma (FL) patients at higher risk of progression, early relapse (POD24), histological transformation (HT) or death, multiple risk scores (RS) have been proposed. However, it has not yet been established whether any of them globally outperforms the others. We evaluated the clinical utility and statistical performance of the five most widely used clinical scores (IPI, ILI, FLIPI, FLIPI2, PRIMA‐PI) in a single‐center series of 414 grade 1‐3A FL patients diagnosed in the rituximab era. Overall concordance (proportion of patients allocated to the same risk category by all five RS) was 24%. FLIPI and FLIPI2 were predictive of time to first treatment. All five scores were predictive of response, POD24, progression‐free, and OS, while only FLIPI predicted HT. IPI identified a small subset (7%) of truly high‐risk patients (10‐year OS of 16%). In subgroup analyses, we showed that ILI is useful in the prognostication of limited‐disease patients, and PRIMA‐PI is an age‐independent score that can identify a high‐risk subset of older patients. Performance metrics were slightly better for IPI in terms of calibration (Harrell's c‐index 0.73), without major differences among RS regarding other parameters. Although the incorporation of molecular and imaging data will continue to refine the stratification of FL patients, FLIPI remains the most powerful clinical prognostic index in the rituximab era, predicting the greatest number of endpoints.
Beta-agonist Exposure Preferentially Impacts Lung Macrophage Cyclic AMP Related Gene Expression in Asthma and Asthma COPD Overlap Syndrome.

Bronchoalveolar lavage (BAL) samples from Severe Asthma Research Program (SARP) patients display suppression of a module of genes involved in cAMP-signaling pathways (BALcAMP) correlating with severity, therapy, and macrophage constituency. We sought to establish if gene expression changes were specific to macrophages and compared gene expression trends from multiple sources. Datasets included Single-cell RNA sequencing (scRNA-seq) from lung specimens including a fatal exacerbation of severe Asthma COPD Overlap Syndrome (ACOS) after intense therapy and controls without lung disease, Bulk RNA sequencing from cultured macrophage (THP1) cells after acute or prolonged beta-agonist exposure, SARP datasets, and data from the Immune Modulators of Severe Asthma (IMSA) cohort). THP monocytes suppressed BALcAMP network gene expression after prolonged relative to acute beta agonist exposure, corroborating SARP observations. scRNA-seq from healthy and diseased lung tissue revealed 13 cell populations enriched for macrophages. In severe ACOS, BALcAMP gene network expression scores were decreased in many cell populations, most significantly for macrophage populations (p < 3.9e-111). NK cell and type II alveolar epithelial cells displayed less robust network suppression (p < 9.2e-8). Alveolar macrophages displayed the most numerous individual genes affected and the highest amplitude of modulation. Key BALcAMP genes demonstrate significantly decreased expression in severe asthmatics in the IMSA cohort. We conclude that suppression of the BALcAMP gene module identified from SARP BAL samples is validated in the IMSA patient cohort with physiologic parallels observed in a monocytic cell line and in a severe ACOS patient sample with effects preferentially localizing to macrophages.
Statins, Enzyme CoQ10 Supplement Use, and Cognitive Functioning

Objective: The current study assessed the effects of statin and CoQ10 supplement use on changes in cognitive functioning in the Wisconsin Registry for Alzheimer’s Prevention study. Methods: 1,573 subjects were administered medical histories, the Mini-Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT), Wechsler Memory Scale, Logical Memory subtest, and the Trail Making Test, Parts A (TMT-A) and B (TMT-B) 3-4 times over 5-10 years. Results: Linear mixed models did not yield significant effects for statin or CoQ10 supplement use on changes in mental status, learning and memory, psychomotor speed, and cognitive flexibility. Conclusions: Statin and/or CoQ10 supplement use was not associated with neuropsychological test performance in the Wisconsin Registry for Alzheimer’s Prevention study.
Metabolic syndrome in antipsychotic-naïve patients with first-episode psychosis: a systematic review and meta-analysis

Abstract Background It is unclear what the prevalence of metabolic syndrome (MetS) in drug-naïve first-episode of psychosis (FEP) is, as previous meta-analyses were conducted in minimally exposed or drug-naïve FEP patients with psychotic disorder at any stage of the disease; thus, a meta-analysis examining MetS in naïve FEP compared with the general population is needed. Methods Studies on individuals with FEP defined as drug-naïve (0 days exposure to antipsychotics) were included to conduct a systematic review. A meta-analysis of proportions for the prevalence of MetS in antipsychotic-naïve patients was performed. Prevalence estimates and 95% CI were calculated using a random-effect model. Subgroup analyses and meta-regressions to identify sources and the amount of heterogeneity were also conducted. Results The search yielded 4143 articles. After the removal of duplicates, 2473 abstracts and titles were screened. At the full-text stage, 112 were screened, 18 articles were included in a systematic review and 13 articles in the main statistical analysis. The prevalence of MetS in naïve (0 days) FEP is 13.2% (95% CI 8.7–19.0). Ethnicity accounted for 3% of the heterogeneity between studies, and diagnostic criteria used for MetS accounted for 7%. When compared with controls matched by sex and age, the odds ratio is 2.52 (95% CI 1.29–5.07; p = 0.007). Conclusions Our findings of increased rates of MetS in naïve FEP patients suggest that we are underestimating cardiovascular risk in this population, especially in those of non-Caucasian origin. Our findings support that altered metabolic parameters in FEPs are not exclusively due to antipsychotic treatments.
Novel Antifungal Activity of Q-Griffithsin, a Broad-Spectrum Antiviral Lectin

Fungal infections contribute to morbidity and mortality annually, and the number of organisms that are nonresponsive to the current available drug regimens are on the rise. There is a need to develop new agents to counter these infections and to add to the limited arsenal available to treat fungal infections. ABSTRACT There is a rising global incidence of Candida strains with high levels of resistance to fluconazole and other antifungal drugs, hence the need for novel antifungal treatment strategies. Here, we describe the first evidence of antifungal activity of Q-Griffithsin (Q-GRFT), a recombinant oxidation-resistant variant of Griffithsin, a marine red algal lectin with broad-spectrum antiviral activity. We demonstrated that Q-GRFT binds to α-mannan in the Candida albicans cell wall. We also observed that Q-GRFT binding disrupted cell wall integrity and induced reactive oxidative species (ROS) formation, resulting in cell death. Furthermore, we showed that Q-GRFT inhibited the growth of other Candida species C. glabrata, C. parapsilosis, and C. krusei and had modest activity against some strains of multi- and pandrug-resistant C. auris. We found that Q-GRFT induced differential expression of numerous genes involved in response to cell stress, including those responsible for neutralizing ROS production and cell cycle regulation. In conclusion, this novel antifungal activity suggests that Q-GRFT is potentially an ideal drug candidate and represents an alternative strategy for the prevention and treatment of candidiasis. IMPORTANCE Fungal infections contribute to morbidity and mortality annually, and the number of organisms that are nonresponsive to the current available drug regimens are on the rise. There is a need to develop new agents to counter these infections and to add to the limited arsenal available to treat fungal infections. Our study has identified Q-GRFT, a broad-spectrum antiviral protein that harbors growth-inhibitory activity against several Candida strains, as a potential candidate for the prevention and treatment of fungal infections.
Vimentin Regulates Chemokine Expression and NOD2 Activation in Brain Endothelium during Group B Streptococcal Infection

Streptococcus agalactiae (group B Streptococcus, or GBS) is an opportunistic pathogen capable of causing invasive disease in susceptible individuals, including the newborn. Currently, GBS is the leading cause of meningitis in the neonatal period. ABSTRACT Streptococcus agalactiae (group B Streptococcus, or GBS) is an opportunistic pathogen capable of causing invasive disease in susceptible individuals, including the newborn. Currently, GBS is the leading cause of meningitis in the neonatal period. We have recently shown that GBS interacts directly with host type III intermediate filament vimentin to gain access to the central nervous system. This results in characteristic meningeal inflammation and disease progression; however, the specific role of vimentin in the inflammatory process is unknown. Here, we investigate the contribution of vimentin to the pathogenesis of GBS meningitis. We show that a CRISPR-targeted deletion of vimentin in human cerebral microvascular endothelial cells (hCMEC) reduced GBS induction of neutrophil attractants interleukin-8 (IL-8) and CXCL-1 as well as NF-κB activation. We further show that inhibition of vimentin localization also prevented similar chemokine activation by GBS. One known chemokine regulator is the nucleotide-binding oligomerization domain containing protein 2 (NOD2), which is known to interact directly with vimentin. Thus, we hypothesized that NOD2 would also promote GBS chemokine induction. We show that GBS infection induced NOD2 transcription in hCMEC comparably to the muramyl dipeptide (MDP) NOD2 agonist, and the chemokine induction was reduced in the presence of a NOD2 inhibitor. Using a mouse model of GBS meningitis, we also observed increased NOD2 transcript and NOD2 activation in brain tissue of infected mice. Lastly, we show that NOD2-mediated IL-8 and CXCL1 induction required vimentin, further indicating the importance of vimentin in mediating inflammatory responses in brain endothelium.
The food and nutrient intake of 5- to 12-year-old Australian children during school hours: a secondary analysis of the 2011–2012 National Nutrition and Physical Activity Survey

Abstract Objective: School food intake of Australian children is not comprehensively described in literature, with limited temporal, nationally representative data. Greater understanding of intake at school can inform school-based nutrition promotion. This study aimed to describe the dietary intake of primary-aged children during school hours and its contribution to daily intake. Design: This secondary analysis used nationally representative, cross-sectional data from the 2011 to 2012 National Nutrition and Physical Activity Survey. Dietary intake was assessed using validated 24-h dietary recalls on school days. Descriptive statistics were undertaken to determine energy, nutrients, food groups and food products consumed during school hours, as well as their contributions to total daily intake. Associations between school food intake and socio-demographic characteristics were explored. Setting: Australia. Participants: Seven hundred and ninety-five children aged 5–12 years. Results: Children consumed 37 % of their daily energy and 31–43 % of select nutrient intake during school hours, with discretionary choices contributing 44 % of school energy intake. Most children consumed less than one serve of vegetables, meat and alternatives or milk and alternatives during school hours. Commonly consumed products were discretionary choices (34 %, including biscuits, processed meat), bread (17 %) and fruit (12 %). There were limited associations with socio-economic position variables, apart from child age. Conclusions: Children’s diets were not aligned with national recommendations, with school food characterised by high intake of discretionary choices. These findings are consistent with previous Australian evidence and support transformation of the Australian school food system to better align school food consumption with recommendations.
From nuclear to unnuclear physics

We provide a brief commentary on recent work by Hammer and Son on the scaling behavior of nuclear reactions involving the emission of several loosely bound neutrons. In this work they discover a regime, termed unnuclear physics, in which these reactions are governed by an approximate conformal symmetry of the nuclear force. Remarkably, the scaling exponents that govern nuclear reactions can be related to the energies of ultracold atomic drops confined in harmonic potentials. We also comment on the importance and the limitations of this approximate symmetry in the physics of neutron stars.
A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors

ABSTRACT The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer’s disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning algorithm for building QSAR models to predict the activity of inhibitors for AChE and BuChE enzymes. More precisely, it uses a transfer function to convert the continuous search space of PSO to binary. Furthermore, it utilizes the concepts of catfish effect and chaotic map to improve exploration ability in searching for an optimum subset of descriptors for QSAR model constructions. Then, through a statistical method, it employs a machine learning algorithm to evaluate the fitness value of each candidate subset of features. Different combinations of four transfer functions with four machine learning algorithms, including K-nearest neighbour, multiple linear regression, support vector machine, and regression tree, were used to build several variants of the proposed algorithm. QSAR models constructed by each version were verified by internal and external validations. The best variants were selected based on a method called sum of ranking differences.
Development of the pupillary light reflex from 9 to 24 months: association with common autism spectrum disorder (ASD) genetic liability and 3-year ASD diagnosis.

BACKGROUND Although autism spectrum disorder (ASD) is heritable, the mechanisms through which genes contribute to symptom emergence remain unclear. Investigating candidate intermediate phenotypes such as the pupillary light reflex (PLR) prospectively from early in development could bridge genotype and behavioural phenotype. METHODS Using eye tracking, we longitudinally measured the PLR at 9, 14 and 24 months in a sample of infants (N = 264) enriched for a family history of ASD; 27 infants received an ASD diagnosis at 3 years. We examined the 9- to 24-month developmental trajectories of PLR constriction latency (onset; ms) and amplitude (%) and explored their relation to categorical 3-year ASD outcome, polygenic liability for ASD and dimensional 3-year social affect (SA) and repetitive/restrictive behaviour (RRB) traits. Polygenic scores for ASD (PGSASD ) were calculated for 190 infants. RESULTS While infants showed a decrease in latency between 9 and 14 months, higher PGSASD was associated with a smaller decrease in latency in the first year (β = -.16, 95% CI = -0.31, -0.002); infants with later ASD showed a significantly steeper decrease in latency (a putative 'catch-up') between 14 and 24 months relative to those with other outcomes (typical: β = .54, 95% CI = 0.08, 0.99; other: β = .53, 95% CI = 0.02, 1.04). Latency development did not associate with later dimensional variation in ASD-related traits. In contrast, change in amplitude was not related to categorical ASD or genetics, but decreasing 9- to 14-month amplitude was associated with higher SA (β = .08, 95% CI = 0.01, 0.14) and RRB (β = .05, 95% CI = 0.004, 0.11) traits. CONCLUSIONS These findings corroborate PLR development as possible intermediate phenotypes being linked to both genetic liability and phenotypic outcomes. Future work should incorporate alternative measures (e.g. functionally informed structural and genetic measures) to test whether distinct neural mechanisms underpin PLR alterations.
The first 8 months of COVID-19 pandemic in three West African countries: leveraging lessons learned from responses to the 2014–2016 Ebola virus disease outbreak

Abstract Experience gained from responding to major outbreaks may have influenced the early coronavirus disease-2019 (COVID-19) pandemic response in several countries across Africa. We retrospectively assessed whether Guinea, Liberia and Sierra Leone, the three West African countries at the epicentre of the 2014–2016 Ebola virus disease outbreak, leveraged the lessons learned in responding to COVID-19 following the World Health Organization's (WHO) declaration of a public health emergency of international concern (PHEIC). We found relatively lower incidence rates across the three countries compared to many parts of the globe. Time to case reporting and laboratory confirmation also varied, with Guinea and Liberia reporting significant delays compared to Sierra Leone. Most of the selected readiness measures were instituted before confirmation of the first case and response measures were initiated rapidly after the outbreak confirmation. We conclude that the rapid readiness and response measures instituted by the three countries can be attributed to their lessons learned from the devastating Ebola outbreak, although persistent health systems weaknesses and the unique nature of COVID-19 continue to challenge control efforts.
Adsorption of 4-Nitrophenol (PNP) Using Pilli Nut Shell Active Carbon

An economically feasible technology for the removal of pollutants from wastewater is adsorption. Active carbon was prepared by single stage method via chemical impregnation of Pili Nut-Shell with orthophosphoric acid and activation at a temperature of 450 o C using precursor-acid ratio of 1:4 for a period of 2 h. The effects of initial concentration, contact time, adsorbent dose and pH on the uptake of 4-nitrophenol (PNP) using Pili nut-based active carbon (PAC) were determined. The PAC maximum adsorption capacity value (190.39 mg/g) was obtained at an initial concentration of 1000 mg/L and was found to be greater than that of a commercial granular active carbon, CGAC (166.97 mg/g) at an initial concentration of 800 mg/L. The BET surface area and total pore volume of PAC (960 m 2 /g, 0.422 cm 3 /g) respectively were also greater than that of CGAC (426.3 m 2 /g, 0.208 cm 3 /g). The pore size distribution of the PAC (2.842 nm) classifies it to be within range of super-microporous and as such could be used for toxic gas removal as well as small liquid molecules. The Langmuir isotherm best described the adsorption of PNP onto PAC while Pseudo second order kinetics fitted best. The adsorption process was exothermic and spontaneous. Thus, active carbon produced from Pili Nut-Shell can be used to adsorb PNP.
Priming PCA with EigenGame

We introduce primed-PCA (pPCA), an extension of the recently proposed EigenGame algorithm for computing principal components in a large-scale setup. Our algorithm ﬁrst runs EigenGame to get an approximation of the principal components, and then applies an exact PCA in the subspace they span. Since this subspace is of small dimension in any practical use of EigenGame, this second step is extremely cheap computationally. Nonetheless, it improves accuracy signiﬁcantly for a given computational budget across datasets. In this setup, the purpose of EigenGame is to narrow down the search space, and prepare the data for the second step, an exact calculation. We show formally that pPCA improves upon EigenGame under very mild conditions, and we provide experimental validation on both synthetic and real large-scale datasets showing that it systematically translates to improved performance. In our experiments we achieve improvements in convergence speed by factors of 5-25 on the datasets of the original EigenGame paper.
On the Fundamental Trade-offs in Learning Invariant Representations

Many applications of representation learning, such as privacy-preservation, algorithmic fairness and domain adaptation, desire explicit control over semantic information being discarded. This goal is often formulated as satisfying two potentially competing objectives: maximizing utility for predicting a target attribute while simultaneously being independent or invariant with respect to a known semantic attribute. In this paper, we identify and determine two fundamental trade-offs between utility and semantic dependence induced by the statistical dependencies between the data and its corresponding target and semantic attributes. We derive closed-form solutions for the global optima of the underlying optimization problems under mild assumptions, which in turn yields closed formulae for the exact trade-offs. We also derive empirical estimates of the trade-offs and show their convergence to the corresponding population counterparts. Finally, we numerically quantify the trade-offs on representative problems and compare to the solutions achieved by baseline representation learning algorithms.