Evidence is mounting that the gut-brain axis plays an important role in mental diseases fueling mechanistic investigations to provide a basis for future targeted interventions. However, shotgun ...metagenomic data from treatment-naïve patients are scarce hampering comprehensive analyses of the complex interaction between the gut microbiota and the brain. Here we explore the fecal microbiome based on 90 medication-free schizophrenia patients and 81 controls and identify a microbial species classifier distinguishing patients from controls with an area under the receiver operating characteristic curve (AUC) of 0.896, and replicate the microbiome-based disease classifier in 45 patients and 45 controls (AUC = 0.765). Functional potentials associated with schizophrenia include differences in short-chain fatty acids synthesis, tryptophan metabolism, and synthesis/degradation of neurotransmitters. Transplantation of a schizophrenia-enriched bacterium, Streptococcus vestibularis, appear to induces deficits in social behaviors, and alters neurotransmitter levels in peripheral tissues in recipient mice. Our findings provide new leads for further investigations in cohort studies and animal models.
Accumulating evidence suggests that gut microbiota plays a role in the pathogenesis of schizophrenia via the microbiota-gut-brain axis. This study sought to investigate whether transplantation of ...fecal microbiota from drug-free patients with schizophrenia into specific pathogen-free mice could cause schizophrenia-like behavioral abnormalities. The results revealed that transplantation of fecal microbiota from schizophrenic patients into antibiotic-treated mice caused behavioral abnormalities such as psychomotor hyperactivity, impaired learning and memory in the recipient animals. These mice also showed elevation of the kynurenine-kynurenic acid pathway of tryptophan degradation in both periphery and brain, as well as increased basal extracellular dopamine in prefrontal cortex and 5-hydroxytryptamine in hippocampus, compared with their counterparts receiving feces from healthy controls. Furthermore, colonic luminal filtrates from the mice transplanted with patients' fecal microbiota increased both kynurenic acid synthesis and kynurenine aminotransferase II activity in cultured hepatocytes and forebrain cortical slices. Sixty species of donor-derived bacteria showed significant difference between the mice colonized with the patients' and the controls' fecal microbiota, highlighting 78 differentially enriched functional modules including tryptophan biosynthesis function. In conclusion, our study suggests that the abnormalities in the composition of gut microbiota contribute to the pathogenesis of schizophrenia partially through the manipulation of tryptophan-kynurenine metabolism.
Innate immune cells such as macrophages are abundantly present within malignant ascites, where they share the microenvironment with ovarian cancer stem cells (CSC).
To mimic this malignant ascites ...microenvironment, we created a hanging-drop hetero-spheroid model to bring CSCs and macrophages in close association. Within these hetero-spheroids, CD68
macrophages (derived from U937 or peripheral blood monocytes) make up ~ 20% of the population, while the rest are ovarian cancer cells and ovarian cancer stem cells (derived from the high grade serous ovarian cancer cell line, OVCAR3).
Our results indicate that CSCs drive the upregulation of M2 macrophage marker CD206 within hetero-spheroids, compared to bulk ovarian cancer cells, implying an inherently more immuno-suppressive program. Moreover, an increased maintenance of elevated aldehyde dehydrogenase (ALDH) activity is noted within hetero-spheroids that include pre-polarized CD206
M2 macrophages, implying a reciprocal interaction that drives pro-tumoral activation as well as CSC self-renewal. Consistent with enriched CSCs, we also observe increased levels of pro-tumoral IL-10 and IL-6 cytokines in the CSC/M2-macrophage hetero-spheroids. CSC/M2-macrophage hetero-spheroids are also less sensitive to the chemotherapeutic agent carboplatin and are subsequently more invasive in transwell assays. Using inhibitors of WNT secretion in both CSCs and macrophages, we found that CSC-derived WNT ligands drove CD206
M2 macrophage activation, and that, conversely, macrophage-derived WNT ligands enriched ALDH
cells within the CSC compartment of hetero-spheroids. Upon examination of specific WNT ligand expression within the monocyte-derived macrophage system, we observed a significant elevation in gene expression for WNT5B. In CSCs co-cultured with macrophages within hetero-spheroids, increases in several WNT ligands were observed, and this increase was significantly inhibited when WNT5B was knocked down in macrophages.
Our data implies that macrophage- initiated WNT signaling could play a significant role in the maintenance of stemness, and the resulting phenotypes of chemoresistance and invasiveness. Our results indicate paracrine WNT activation during CSC/M2 macrophages interaction constitutes a positive feedback loop that likely contributes to the more aggressive phenotype, which makes the WNT pathway a potential target to reduce the CSC and M2 macrophage compartments in the tumor microenvironment.
Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune ...landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The practical application of supercapacitors requires high mass loading electrodes to enhance the energy density, which would limit a slow electron and ion migration dynamics in a thick electrode and ...result in poor electrochemical capacitive properties. Here, porous carbon fibers (PCFs) were prepared from natural hollow cattail fibers by optimizing the carbonization temperature prior to KOH chemical activation. The results show that the PCF-C600 prepared by the carbonization temperature of 600 °C possesses not only high specific surface area of 1298.2 m
2
g
− 1
and pore volume of 0.555 cm
3
g
− 1
, but also high gravimetric capacitance (242.7 F g
− 1
at 0.05 A g
− 1
), excellent rate performance and cycle stability. For commerical supercapacitor applications, the effect of mass loading of the PCF-C600 electrode on electrochemical performance is in-depth investigated. The PCF-C600 electrode with a high mass loading of 16.0 mg cm
− 2
exhibits a high areal capacitance of 2.79 F cm
− 2
(174.3 F g
− 1
) at 0.1 A g
− 1
and high utilization of the active material owing to its large specific surface area and uniform pore distribution. The excellent electrochemical performance indicated that the obtained porous carbon fibers could be a promising electrode material for commerical supercapacitor applications.
Many approaches, including traditional parametric modeling and machine learning techniques, have been proposed to estimate propensity scores. This paper describes a new model averaging approach to ...propensity score estimation in which parametric and nonparametric estimates are combined to achieve covariate balance. Simulation studies are conducted across different scenarios varying in the degree of interactions and nonlinearities in the treatment model. The results show that, based on inverse probability weighting, the proposed propensity score estimator produces less bias and smaller standard errors than existing approaches. They also show that a model averaging approach with the objective of minimizing the average Kolmogorov–Smirnov statistic leads to the best performing IPW estimator. The proposed approach is also applied to a real data set in evaluating the causal effect of formula or mixed feeding versus exclusive breastfeeding on a child’s body mass index Z-score at age 4. The data analysis shows that formula or mixed feeding is more likely to lead to obesity at age 4, compared to exclusive breastfeeding.
In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, ...existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.
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Energy modeling, energy engineering, energy systems
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis ...tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
Highlights • The presence of effector immune cells is associated with better clinical response. • The effector function of TILs is compromised in comparison with peripheral blood counterparts. • Type ...1 interferon (IFN-I) signaling in cancer cells modulate tumor immunogenicity. • Autophagy in cancer cells interfere with immunogenic cytotoxicity. • Cancer Initiating Cells may represent a population with a suppressed immunogenic profile.
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