Anti-NMDAR antibodies, the blood-brain barrier, and anti-NMDAR encephalitis.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an antibody-related autoimmune encephalitis. It is characterized by the existence of antibodies against NMDAR, mainly against the GluN1 subunit, in cerebrospinal fluid (CSF). Recent research suggests that anti-NMDAR antibodies may reduce NMDAR levels in this disorder, compromising synaptic activity in the hippocampus. Although anti-NMDAR antibodies are used as diagnostic indicators, the origin of antibodies in the central nervous system (CNS) is unclear. The blood-brain barrier (BBB), which separates the brain from the peripheral circulatory system, is crucial for antibodies and immune cells to enter or exit the CNS. The findings of cytokines in this disorder support the involvement of the BBB. Here, we aim to review the function of NMDARs and the relationship between anti-NMDAR antibodies and anti-NMDAR encephalitis. We summarize the present knowledge of the composition of the BBB, especially by emphasizing the role of BBB components. Finally, we further provide a discussion on the impact of BBB dysfunction in anti-NMDAR encephalitis.

Cooperation between neurovascular dysfunction and Aß in Alzheimer's disease.

The amyloid-ß (Aß) hypothesis was once believed to represent the pathogenic process of Alzheimer's disease (AD). However, with the failure of clinical drug development and the increasing understanding of the disease, the Aß hypothesis has been challenged. Numerous recent investigations have demonstrated that the vascular system plays a significant role in the course of AD, with vascular damage occurring prior to the deposition of Aß and neurofibrillary tangles (NFTs). The question of how Aß relates to neurovascular function and which is the trigger for AD has recently come into sharp focus. In this review, we outline the various vascular dysfunctions associated with AD, including changes in vascular hemodynamics, vascular cell function, vascular coverage, and blood-brain barrier (BBB) permeability. We reviewed the most recent findings about the complicated Aß-neurovascular unit (NVU) interaction and highlighted its vital importance to understanding disease pathophysiology. Vascular defects may lead to Aß deposition, neurotoxicity, glial cell activation, and metabolic dysfunction; In contrast, Aß and oxidative stress can aggravate vascular damage, forming a vicious cycle loop.

Retracted: A new aquatic beetle (Adephaga: Coptoclavidae) from the Middle Jurassic Daohugou Biota.

Tigrivia baii gen. et sp. nov. (Coleoptera: Adephaga: Coptoclavidae) is described and named based on a fossil specimen from the Middle Jurassic Daohugou Biota, Nincheng County, Inner Mongolia of China. The fossil is very similar in morphology with the adult Coptoclava longipoda Ping 1928 (Laiyang Formation of Nanligezhuang Village, Laiyang City, Shandong Province, China, Lower Cretaceous, 121 ~ 120 Ma), but differs from C. longipoda by the adjacencies of two procoxae and two mesocoxae. T. baii gen. et sp. nov. differs from Daohugounectes primitinus Wang et al. 2010 by the absence of striae on the elytra and the absence of ventral eyes under the head. The new beetle species shows developed aquatic adaptions, such as specialized raptorial forelegs, and swimming middle and hind legs. Furthermore, it is speculated to lead a similar lifestyle as extant Dytiscoidae, according to the two eyes on the side of head and the absence of spiracles on ventrites. This new fossil expanded our knowledge of the diversity of the Middle Jurassic Coptoclavid beetles in Daohugou Biota and improved our understanding of their paleoecological significance.

The impairment of the hippocampal neuro-vascular unit precedes changes in spatial cognition in naturally aged rats.

Ageing is the major risk factor for the most neurodegenerative diseases, such as Alzheimer's disease (AD). Damage to neurovascular components (microvessels, glia, and neurons) occurs with ageing and is suspected to exacerbate or cause mild cognitive impairment (MCI), vascular dementia, and AD. However, whether vascular cells, glia, and neurons change synchronously or asynchronously during ageing is unclear, and the relationship between complex dynamic pathophysiological changes in the brain and cognitive ability needs to be further studied. We used male Sprague-Dawley (SD) rats of three different ages (2 months, 12 months, and 24 months) and explored changes in the neurovascular unit (cerebral vessels, microglia, astrocytes, and neurons) and spatial memory upon normal ageing by the Morris water maze (MWM) test and immunofluorescence staining. We found that the impairments of microvessels, glia, neurons, and spatial memory were age-dependent in the rat hippocampus. In middle-aged (12-month-old) rats, some neurovascular unit components have become abnormal: the density and length of microvessels, pyramidal neuron, and SST (Somatostatin) neuron number was decreased, the number of astrocytes was increased in the hippocampus. The diameter of microvessels and PV (Parvalbumin) neuron numbers were decreased, the microglial number was increased and spatial learning was deficit at 24 months of age. In conclusion, we found that the impairment of the hippocampal neuro-vascular unit precedes changes in spatial cognition in naturally aged rats.

A Clustering-Enhanced Memetic Algorithm for the Quadratic Minimum Spanning Tree Problem.

The quadratic minimum spanning tree problem (QMSTP) is a spanning tree optimization problem that considers the interaction cost between pairs of edges arising from a number of practical scenarios. This problem is NP-hard, and therefore there is not a known polynomial time approach to solve it. To find a close-to-optimal solution to the problem in a reasonable time, we present for the first time a clustering-enhanced memetic algorithm (CMA) that combines four components, i.e., (i) population initialization with clustering mechanism, (ii) a tabu-based nearby exploration phase to search nearby local optima in a restricted area, (iii) a three-parent combination operator to generate promising offspring solutions, and (iv) a mutation operator using Lévy distribution to prevent the population from premature. Computational experiments are carried on 36 benchmark instances from 3 standard sets, and the results show that the proposed algorithm is competitive with the state-of-the-art approaches. In particular, it reports improved upper bounds for the 25 most challenging instances with unproven optimal solutions, while matching the best-known results for all but 2 of the remaining instances. Additional analysis highlights the contribution of the clustering mechanism and combination operator to the performance of the algorithm.

Ferulic Acid Ameliorates Alzheimer's Disease-like Pathology and Repairs Cognitive Decline by Preventing Capillary Hypofunction in APP/PS1 Mice.

Brain capillaries are crucial for cognitive functions by supplying oxygen and other nutrients to and removing metabolic wastes from the brain. Recent studies have demonstrated that constriction of brain capillaries is triggered by beta-amyloid (Aß) oligomers via endothelin-1 (ET1)-mediated action on the ET1 receptor A (ETRA), potentially exacerbating Aß plaque deposition, the primary pathophysiology of Alzheimer's disease (AD). However, direct evidence is still lacking whether changes in brain capillaries are causally involved in the pathophysiology of AD. Using APP/PS1 mouse model of AD (AD mice) relative to age-matched negative littermates, we identified that reductions of density and diameter of hippocampal capillaries occurred from 4 to 7 months old while Aß plaque deposition and spatial memory deficit developed at 7 months old. Notably, the injection of ET1 into the hippocampus induced early Aß plaque deposition at 5 months old in AD mice. Conversely, treatment of ferulic acid against the ETRA to counteract the ET1-mediated vasoconstriction for 30 days prevented reductions of density and diameter of hippocampal capillaries as well as ameliorated Aß plaque deposition and spatial memory deficit at 7 months old in AD mice. Thus, these data suggest that reductions of density and diameter of hippocampal capillaries are crucial for initiating Aß plaque deposition and spatial memory deficit at the early stages, implicating the development of new therapies for halting or curing memory decline in AD.

Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe.

The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, Stipa bungeana, Artemisia capillaris, and Lespedeza davurica, data were observed from 165 quadrats during the peak plant growing season, and the product of plant height (PH) and canopy diameter (PC) were calculated for each species. AGB of population were predicted for the same species and other species through using 2/3 of the measured data, and the optimal predictive equation was linear in terms of determination coefficient. The other 1/3 of the data, which was measured from no grazing paddocks or rotational grazing paddocks, was substituted into the predictive equations for validation. Results showed that PC of one dominant species could be used to predict aboveground biomass (AGB) of the same species or other species well. The predicted and measured values were significantly correlative, and most of the predictive accuracy was above 80%, and not affected by the managements of grassland, grazing or no grazing. A combination of 3 to 6 representative species was used to predict AGB of the community, and the predictive equations with PC of six species as an independent variable were the most optimal because explaining 83.5% variation of AGB. The predictive methods cost 1/15, 1/9, and 1/51 of time, labor, and capital as much as the destructive sample method (quadrat sampling method), respectively, and thus improved the efficiency of field study and protecting the fragile study areas, especially the long-term study sites in grassland.

CDK4/6 regulate lysosome biogenesis through TFEB/TFE3.

Lysosomes are degradation and signaling organelles that adapt their biogenesis to meet many different cellular demands; however, it is unknown how lysosomes change their numbers for cell division. Here, we report that the cyclin-dependent kinases CDK4/6 regulate lysosome biogenesis during the cell cycle. Chemical or genetic inactivation of CDK4/6 increases lysosomal numbers by activating the lysosome and autophagy transcription factors TFEB and TFE3. CDK4/6 interact with and phosphorylate TFEB/TFE3 in the nucleus, thereby inactivating them by promoting their shuttling to the cytoplasm. During the cell cycle, lysosome numbers increase in S and G2/M phases when cyclin D turnover diminishes CDK4/6 activity. These findings not only uncover the molecular events that direct the nuclear export of TFEB/TFE3, but also suggest a mechanism that controls lysosome biogenesis in the cell cycle. CDK4/6 inhibitors promote autophagy and lysosome-dependent degradation, which has important implications for the therapy of cancer and lysosome-related disorders.

debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues.

SUMMARY: We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/debCAM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues.

Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised methods to deconvolve tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we develop a novel unsupervised deconvolution method, Convex Analysis of Mixtures (CAM), within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tissue samples. To facilitate the utility of this method, we implement an R-Java CAM package that provides comprehensive analytic functions and graphic user interface (GUI).

Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources.

Blind Source Separation (BSS) is a powerful tool for analyzing composite data patterns in many areas, such as computational biology. We introduce a novel BSS method, Convex Analysis of Mixtures (CAM), for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter plot. We propose and prove a sufficient and necessary condition for identifying the mixing matrix through edge detection in the noise-free case, which enables CAM to identify the mixing matrix not only in the exact-determined and over-determined scenarios, but also in the under-determined scenario. We show the optimality of the edge detection strategy, even for cases where source well-groundedness is not strictly satisfied. The CAM algorithm integrates plug-in noise filtering using sector-based clustering, an efficient geometric convex analysis scheme, and stability-based model order selection. The superior performance of CAM against a panel of benchmark BSS techniques is demonstrated on numerically mixed gene expression data of ovarian cancer subtypes. We apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging data taken from breast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration in mice, both producing biologically plausible decomposition results.

Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.

BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor.

Most published copy number datasets on solid tumors were obtained from specimens comprised of mixed cell populations, for which the varying tumor-stroma proportions are unknown or unreported. The inability to correct for signal mixing represents a major limitation on the use of these datasets for subsequent analyses, such as discerning deletion types or detecting driver aberrations. We describe the BACOM2.0 method with enhanced accuracy and functionality to normalize copy number signals, detect deletion types, estimate tumor purity, quantify true copy numbers, and calculate average-ploidy value. While BACOM has been validated and used with promising results, subsequent BACOM analysis of the TCGA ovarian cancer dataset found that the estimated average tumor purity was lower than expected. In this report, we first show that this lowered estimate of tumor purity is the combined result of imprecise signal normalization and parameter estimation. Then, we describe effective allele-specific absolute normalization and quantification methods that can enhance BACOM applications in many biological contexts while in the presence of various confounders. Finally, we discuss the advantages of BACOM in relation to alternative approaches. Here we detail this revised computational approach, BACOM2.0, and validate its performance in real and simulated datasets.

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

SUMMARY: We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. AVAILABILITY AND IMPLEMENTATION: UNDO is available at http://bioconductor.org/packages.

Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics.

With the existence of biologically distinctive malignant cells originated within the same tumor, intratumor functional heterogeneity is present in many cancers and is often manifested by the intermingled vascular compartments with distinct pharmacokinetics. However, intratumor vascular heterogeneity cannot be resolved directly by most in vivo dynamic imaging. We developed multi-tissue compartment modeling (MTCM), a completely unsupervised method of deconvoluting dynamic imaging series from heterogeneous tumors that can improve vascular characterization in many biological contexts. Applying MTCM to dynamic contrast-enhanced magnetic resonance imaging of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. MTCM is readily applicable to other dynamic imaging modalities for studying intratumor functional and phenotypic heterogeneity, together with a variety of foreseeable applications in the clinic.

Synthesis of haptens and selective enzyme-linked immunosorbent assay of octachlorostyrene.

A sensitive, competitive indirect enzyme-linked immunosorbent assay (ELISA) was developed for the detection of octachlorostyrene (OCS), a persistent and bioaccumulative toxicant. To achieve the most sensitive antibody, several haptens with different linkers that simulated the special structure of OCS were synthesized and conjugated to carrier proteins. Polyclonal rabbit antibodies against different immunizing antigens were obtained and screened against different coating antigens. Under the optimized conditions, this indirect ELISA shows a linear detection range from 1.4 to 86.3 ng/mL, with an IC50 value of 4.46 ng/mL and a limit of detections (LOD) of 0.1 ng/mL. Twelve kinds of compounds were tested for calculating cross-reactivities, and almost all of them showed little cross-reactivity (<5%). Water and sera samples spiked with OCS were analyzed by ELISA and the achieved recoveries were satisfied with a mean recovery of 92%. This immunoassay can be used as a rapid and convenient tool to monitoring OCS in environmental samples.

A photoelectrochemical immunosensor for tris(2,3-dibromopropyl) isocyanurate detection with a multiple hybrid CdTe/Au-TiO2 nanotube arrays.

In this paper, tris(2,3-dibromopropyl) isocyanurate (TBC) is for the first time as far as we know determined by ultrasensitive photoelectrochemical (PEC) immunoassay using an antibody-modified ternary hybrid CdTe/Au-TiO2 nanotube arrays (NTAs) photoelectrode, developed by the pulse electrodeposition technique. The as-prepared hybrid shows enhanced photon absorption and photocurrent response, which subsequently increased the photoelectrical conversion efficiency in the visible region. TBC-antibody (Ab) was developed in rabbits as a result of immunization with the BSA-TBC conjugate and covalently cross-linked onto the CdTe/Au-TiO2 NTAs. Since the photocurrent is highly dependent on the TiO2 surface properties, the specific interaction between TBC and the antibody results in a sensitive change in the photocurrent, which displayed a linear range of 5.0 × 10(-11) to 5.0 × 10(-5) M and a low detection limit of 5.0 × 10(-11) M for TBC determination. This proposed strategy highlights the application of TiO2 nanotube in visible-light-activated photoelectrochemical biosensing, which could largely reduce the destructive effect of UV light on biomolecules.

Label-free photoelectrochemical immunosensor based on CdTe/CdS co-sensitized TiO2 nanotube array structure for octachlorostyrene detection.

An octachlorostyrene (OCS) photoelectrochemical (PEC) immunosensor was developed by cross-linking anti-OCS antibody onto a CdTe/CdS-sensitized TiO2 nanotube arrays (NTAs). The anti-OCS polyclonal antibody was developed in rabbit as a result of immunization with BSA-OCS hapten conjugate. TiO2 NTAs were immobilized firstly with hydrothermally synthesized CdTe quantum dots (QDs), and then CdS which filled the spaces within the CdTe-TiO2 composite and encapsulated the CdTe QDs, forming an ideal stepwise bandedge structure, which benefited the light harvesting. The PEC immunosensor therefore shows high specificity and high sensitivity with a limit of detection of 2.58 pM, and a linear range from 5 pM to 50 nM. The testing time is 4 min. The analysis of river water reveals that the proposed sensor can be applied in the analysis of OCS in real water samples without complicated pre-treatments.

Electrogenerated chemiluminescence detection of trace level pentachlorophenol using carbon quantum dots.

Pentachlorophenol (PCP) is an environmental pollutant of serious concern due to its high toxicity and long persistence property. Fast and sensitive detection of PCP is therefore of great interest. In this work, carbon quantum dots (QDs) are synthesized by hydrothermal reaction, and characterized by fluorescence spectrophotometer, fourier transform infrared (FT-IR) spectroscopy, UV/vis/NIR spectrophotometer and scanning electron microscopy (SEM). The carbon QDs show stable and intensive electrogenerated chemiluminescence (ECL) in the presence of the coreactant S2O8(2-). Under the scanning potential of -1.2 to 0.5 V, PCP reacts with the excited C˙(-), resulting in a decrease in ECL. The detection of trace level PCP is therefore achieved using the carbon QDs and Pt working electrode. Parameters that may affect the ECL intensity including the pH of solution, ionic strength, concentrations of coreactant and carbon QDs are optimized. Under the optimal conditions, a detection limit of 1.3 × 10(-12) g L(-1) is achieved with a linear range of 10 pg L(-1)∼1.0 µg L(-1).

Fourier transform infrared and physicochemical analyses of roasted coffee.

In this study, Brazilian coffee beans processed to different stages of roast at 210, 220, 230, and 240 °C were analyzed for pH value, titratable acidity, moisture content, and color lightness. Fourier transform infrared (FTIR) spectroscopy, in conjunction with principal component analysis, was conducted to study the effects of process time and temperature on the IR-active components of the acetyl acetate extract of the roasted coffee. The results showed that high-temperature-short-time resulted in higher moisture content, higher pH value, and higher titratable acidity when the beans were roasted beyond the start-of-second-crack stage, as compare to low-temperature-long-time process (LTLT). The LTLT process also resulted in greater IR absorbance for aldehydes, ketones, aliphatic acids, aromatic acids, and caffeine carbonyl bands on the FTIR spectra. Clusters for principal component score plots were well separated, indicating that the changes IR-active components in the coffee extracts, due to the different roasting treatments, can be discriminated by the FTIR technique. On the basis of the loading plots of principal components, changes of IR-active compounds in the coffee extract at various stages of roasting were discussed.