Speciation and determination of trace lead : methodology development and analysis of natural waters using Chelex-100 and GFAAS

The development of a method for speciation, preconcentration, and determination of lead was successfully conducted using Chelex-100 and graphite furnace atomic absorption spectroscopy (GFAAS). Sorption under static conditions and direct measurements in the bead slurry or after fast elution of lead from beads with HNO3 solution was used. The effects of buffers, sorption kinetics, filtration criteria, sample concentration, resin particle size and temperature program for the GFAAS were carefully studied. In this study, synthetic aqueous samples containing lead were preconcentrated under static conditions using the ion exchange resin, Chelex-100, and the resin was directly analyzed using GFAAS. The batch preconcentration process was optimized using 250 ml (0.3 ppb Pb) of synthetic lead sample with addition of 0.25g Chelex-100, buffered at pH=5.0 and sorption for 1.5 hours under magnetic stirring. The Chelex-100 resins were separated from the equilibrated solution by filtering under vacuum. Nitric acid (5 ml, 5% v/v) was then used to desorb lead from Chelex-100 resins. Three natural waters, lake, canal and river waters were studied, comparing the Chelex-100 with evaporation methods. The distribution of lead species among suspended solids, colloids or complexes, and ionic form was successfully differentiated with this combination method

AIREPAIR: A Repair Platform for Neural Networks

We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different repair techniques. We evaluate AIREPAIR with three state-of-the-art repair tools on popular deep-learning datasets and models. Our evaluation confirms the utility of AIREPAIR, by comparing and analyzing the results from different repair techniques. A demonstration is available at https://youtu.be/UkKw5neeWhw

Simple slopes are not as simple as you think

Simple slopes analysis is commonly used to evaluate moderator or interaction effects in multiple linear regression models. In usual practice, the moderator is treated as a fixed value when the standard error of simple slopes is estimated. The usual method used for choosing the conditional value of moderator (i.e., at one sample SD below, one SD above, and at the mean) makes the moderator a random variable and therefore renders the standard error suspect. In this study I examined whether the standard error used in post hoc probing for interaction effect is a biased estimator of the population variance when moderator is a random variable. I conducted Monte Carlo simulations to evaluate the variance of the simple slope under a variety of conditions corresponding to a 5 (sample size, N) x 5 (variance of focal predictor, x) x 5 (variance of moderator, z) x 4 (levels of r, the correlation between x and z) x 5(model fit, R2) x 4 (population slope for interaction, bxz) factorial design. I present circumstances under which usual practice yields an ”almost” unbiased estimator and conditions when the estimator is more severely biased and less so

Mitochondrial hypermetabolism precedes impaired autophagy and synaptic disorganization in App knock-in Alzheimer mouse models.

Accumulation of amyloid β-peptide (Aβ) is a driver of Alzheimer's disease (AD). Amyloid precursor protein (App) knock-in mouse models recapitulate AD-associated Aβ pathology, allowing elucidation of downstream effects of Aβ accumulation and their temporal appearance upon disease progression. Here we have investigated the sequential onset of AD-like pathologies in AppNL-F and AppNL-G-F knock-in mice by time-course transcriptome analysis of hippocampus, a region severely affected in AD. Strikingly, energy metabolism emerged as one of the most significantly altered pathways already at an early stage of pathology. Functional experiments in isolated mitochondria from hippocampus of both AppNL-F and AppNL-G-F mice confirmed an upregulation of oxidative phosphorylation driven by the activity of mitochondrial complexes I, IV and V, associated with higher susceptibility to oxidative damage and Ca2+-overload. Upon increasing pathologies, the brain shifts to a state of hypometabolism with reduced abundancy of mitochondria in presynaptic terminals. These late-stage mice also displayed enlarged presynaptic areas associated with abnormal accumulation of synaptic vesicles and autophagosomes, the latter ultimately leading to local autophagy impairment in the synapses. In summary, we report that Aβ-induced pathways in App knock-in mouse models recapitulate key pathologies observed in AD brain, and our data herein adds a comprehensive understanding of the pathologies including dysregulated metabolism and synapses and their timewise appearance to find new therapeutic approaches for AD

Accurately identifying hemagglutinin using sequence information and machine learning methods

IntroductionHemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.MethodsIn this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.Results and discussionThe model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA

Single-Cell Analysis of Blood-Brain Barrier Response to Pericyte Loss

Rationale: Pericytes are capillary mural cells playing a role in stabilizing newly formed blood vessels during development and tissue repair. Loss of pericytes has been described in several brain disorders, and genetically induced pericyte deficiency in the brain leads to increased macromolecular leakage across the blood-brain barrier (BBB). However, the molecular details of the endothelial response to pericyte deficiency remain elusive. Objective: To map the transcriptional changes in brain endothelial cells resulting from lack of pericyte contact at single-cell level, and to correlate them with regional heterogeneities in BBB function and vascular phenotype. Methods and Results: We reveal transcriptional, morphological and functional consequences of pericyte absence for brain endothelial cells using a combination of methodologies, including single-cell RNA sequencing, tracer analyses and immunofluorescent detection of protein expression in pericyte-deficient adult Pdgfbret/ret mice. We find that endothelial cells without pericyte contact retain a general BBB-specific gene expression profile, however, they acquire a venous-shifted molecular pattern and become transformed regarding the expression of numerous growth factors and regulatory proteins. Adult Pdgfbret/ret brains display ongoing angiogenic sprouting without concomitant cell proliferation providing unique insights into the endothelial tip cell transcriptome. We also reveal heterogeneous modes of pericyte-deficient BBB impairment, where hotspot leakage sites display arteriolar-shifted identity and pinpoint putative BBB regulators. By testing the causal involvement of some of these using reverse genetics, we uncover a reinforcing role for angiopoietin 2 at the BBB. Conclusions: By elucidating the complexity of endothelial response to pericyte deficiency at cellular resolution, our study provides insight into the importance of brain pericytes for endothelial arterio-venous zonation, angiogenic quiescence and a limited set of BBB functions. The BBB-reinforcing role of ANGPT2 is paradoxical given its wider role as TIE2 receptor antagonist and may suggest a unique and context-dependent function of ANGPT2 in the brain

Reassessing the atmospheric oxidation mechanism of toluene

Photochemical oxidation of aromatic hydrocarbons leads to tropospheric ozone and secondary organic aerosol (SOA) formation, with profound implications for air quality, human health, and climate. Toluene is the most abundant aromatic compound under urban environments, but its detailed chemical oxidation mechanism remains uncertain. From combined laboratory experiments and quantum chemical calculations, we show a toluene oxidation mechanism that is different from the one adopted in current atmospheric models. Our experimental work indicates a larger-than-expected branching ratio for cresols, but a negligible formation of ring-opening products (e.g., methylglyoxal). Quantum chemical calculations also demonstrate that cresols are much more stable than their corresponding peroxy radicals, and, for the most favorable OH (ortho) addition, the pathway of H extraction by O_2 to form the cresol proceeds with a smaller barrier than O_2 addition to form the peroxy radical. Our results reveal that phenolic (rather than peroxy radical) formation represents the dominant pathway for toluene oxidation, highlighting the necessity to reassess its role in ozone and SOA formation in the atmosphere