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Meninges that surround the CNS consist of an outer fibrous sheet of dura mater (pachymeninx) that is also the inner periosteum of the skull. Underlying the dura are the arachnoid and pia mater (leptomeninges) that form the boundaries of the subarachnoid space. In this review we (1) examine the development of leptomeninges and their role as barriers and facilitators in the foetal CNS. There are two separate CSF systems during early foetal life, inner CSF in the ventricles and outer CSF in the subarachnoid space. As the foramina of Magendi and Luschka develop, one continuous CSF system evolves. Due to the lack of arachnoid granulations during foetal life, it is most likely that CSF is eliminated by lymphatic drainage pathways passing through the cribriform plate and nasal submucosa. (2) We then review the fine structure of the adult human and rodent leptomeninges to establish their roles as barriers and facilitators for the movement of fluid, cells and pathogens. Leptomeningeal cells line CSF spaces, including arachnoid granulations and lymphatic drainage pathways, and separate elements of extracellular matrix from the CSF. The leptomeningeal lining facilitates the traffic of inflammatory cells within CSF but also allows attachment of bacteria such as Neisseria meningitidis and of tumour cells as CSF metastases. Single layers of leptomeningeal cells extend into the brain closely associated with the walls of arteries so that there are no perivascular spaces around arteries in the cerebral cortex. Perivascular spaces surrounding arteries in the white matter and basal ganglia relate to their two encompassing layers of leptomeninges. (3) Finally we examine the roles of ligands expressed by leptomeningeal cells for the attachment of inflammatory cells, bacteria and tumour cells as understanding these roles may aid the design of therapeutic strategies to manage developmental, autoimmune, infectious and neoplastic diseases relating to the CSF, the leptomeninges and the associated CNS.

α-Dystrobrevin (α-DB) is a major component of the dystrophin-associated protein complex (DAPC). Knockout (KO) of α-DB in the brain is associated with astrocytic abnormalities and loss of neuronal GABA receptor clustering. Mutations in DAPC proteins are associated with altered dopamine signaling and cognitive and psychiatric disorders, including schizophrenia. This study tested the hypothesis that motivation and associated underlying biological pathways are altered in the absence of α-DB expression. Male wildtype and α-DB KO mice were tested for measures of motivation, executive function and extinction in the rodent touchscreen apparatus. Subsequently, brain tissues were evaluated for mRNA and/or protein levels of dysbindin-1, dopamine transporter and receptor 1 and 2, mu opioid receptor 1 (mOR1) and cannabinoid receptor 1 (CB1). α-DB KO mice had significantly increased motivation for the appetitive reward, while measures of executive function and extinction were unaffected. No differences were observed between wildtype and KO animals on mRNA levels of dysbindin-1 or any of the dopamine markers. mRNA levels of mOR1were significantly decreased in the caudate-putamen and nucleus accumbens of α-DB KO compared to WT animals, but protein levels were unaltered. However, CB1 protein levels were significantly increased in the prefrontal cortex and decreased in the nucleus accumbens of α-DB KO mice. Triple-labelling immunohistochemistry confirmed that changes in CB1 were not specific to astrocytes. These results highlight a novel role for α-DB in the regulation of appetitive motivation that may have implications for other behaviours that involve the dopaminergic and endocannabinoid systems.

[alpha]-Dystrobrevin ([alpha]-DB) is a major component of the dystrophin-associated protein complex (DAPC). Knockout (KO) of [alpha]-DB in the brain is associated with astrocytic abnormalities and loss of neuronal GABA receptor clustering. Mutations in DAPC proteins are associated with altered dopamine signaling and cognitive and psychiatric disorders, including schizophrenia. This study tested the hypothesis that motivation and associated underlying biological pathways are altered in the absence of [alpha]-DB expression. Male wildtype and [alpha]-DB KO mice were tested for measures of motivation, executive function and extinction in the rodent touchscreen apparatus. Subsequently, brain tissues were evaluated for mRNA and/or protein levels of dysbindin-1, dopamine transporter and receptor 1 and 2, mu opioid receptor 1 (mOR1) and cannabinoid receptor 1 (CB1). [alpha]-DB KO mice had significantly increased motivation for the appetitive reward, while measures of executive function and extinction were unaffected. No differences were observed between wildtype and KO animals on mRNA levels of dysbindin-1 or any of the dopamine markers. mRNA levels of mOR1were significantly decreased in the caudate-putamen and nucleus accumbens of [alpha]-DB KO compared to WT animals, but protein levels were unaltered. However, CB1 protein levels were significantly increased in the prefrontal cortex and decreased in the nucleus accumbens of [alpha]-DB KO mice. Triple-labelling immunohistochemistry confirmed that changes in CB1 were not specific to astrocytes. These results highlight a novel role for [alpha]-DB in the regulation of appetitive motivation that may have implications for other behaviours that involve the dopaminergic and endocannabinoid systems. Keywords: [alpha]-Dystrobrevin, Appetitive motivation, Endocannabinoid, Brain

Artificial photosynthesis relies on the availability of semiconductors that are chemically stable and can efficiently capture solar energy. Although metal oxide semiconductors have been investigated for their promise to resist oxidative attack, materials in this class can suffer from chemical and photochemical instability. Here we present a methodology for evaluating corrosion mechanisms and apply it to bismuth vanadate, a state-of-the-art photoanode. Analysis of changing morphology and composition under solar water splitting conditions reveals chemical instabilities that are not predicted from thermodynamic considerations of stable solid oxide phases, as represented by the Pourbaix diagram for the system. Computational modelling indicates that photoexcited charge carriers accumulated at the surface destabilize the lattice, and that self-passivation by formation of a chemically stable surface phase is kinetically hindered. Although chemical stability of metal oxides cannot be assumed, insight into corrosion mechanisms aids development of protection strategies and discovery of semiconductors with improved stability. Metal oxide semiconductors are promising materials for solar energy capture but can suffer from stability problems. Here, the authors present a methodology for evaluating corrosion mechanisms and apply it to BiVO 4 , revealing chemical instabilities that are not predicted from thermodynamic considerations alone.

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage. Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.

The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li 3.3 SnS 3.3 Cl 0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing. Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.

Blocking the enzyme KMO with a small molecule reduces the levels of toxic tryptophan metabolites and reduces multiple extrapancreatic organ failure in a rat model of acute pancreatitis. Acute pancreatitis (AP) is a common and devastating inflammatory condition of the pancreas that is considered to be a paradigm of sterile inflammation leading to systemic multiple organ dysfunction syndrome (MODS) and death 1 , 2 . Acute mortality from AP-MODS exceeds 20% (ref. 3 ), and the lifespans of those who survive the initial episode are typically shorter than those of the general population 4 . There are no specific therapies available to protect individuals from AP-MODS. Here we show that kynurenine-3-monooxygenase (KMO), a key enzyme of tryptophan metabolism 5 , is central to the pathogenesis of AP-MODS. We created a mouse strain that is deficient for Kmo (encoding KMO) and that has a robust biochemical phenotype that protects against extrapancreatic tissue injury to the lung, kidney and liver in experimental AP-MODS. A medicinal chemistry strategy based on modifications of the kynurenine substrate led to the discovery of the oxazolidinone GSK180 as a potent and specific inhibitor of KMO. The binding mode of the inhibitor in the active site was confirmed by X-ray co-crystallography at 3.2 Å resolution. Treatment with GSK180 resulted in rapid changes in the levels of kynurenine pathway metabolites in vivo , and it afforded therapeutic protection against MODS in a rat model of AP. Our findings establish KMO inhibition as a novel therapeutic strategy in the treatment of AP-MODS, and they open up a new area for drug discovery in critical illness.

Immune checkpoint therapy (ICT) has transformed cancer treatment in recent years; however, treatment response is not uniform across tumor types. The tumor immune microenvironment plays a critical role in determining response to ICT; therefore, understanding the differential immune infiltration between ICT-sensitive and ICT-resistant tumor types will help to develop effective treatment strategies. We performed a comprehensive analysis of the immune tumor microenvironment of an ICT-sensitive tumor (melanoma, n = 44) and an ICT-resistant tumor (pancreatic cancer, n = 67). We found that a pancreatic tumor has minimal tomoderate infiltration of CD3, CD4, and CD8 T cells; however, the immune infiltrates are predominantly present in the stromal area of the tumor and are excluded from tumoral area compared with melanoma, where the immune infiltrates are primarily present in the tumoral area. Metastatic pancreatic ductal adenocarcinomas (PDACs) had a lower infiltration of total T cells compared with resectable primary PDACs, suggesting that metastatic PDACs have poor immunogenicity. Further, a significantly higher number of CD68⁺ macrophages and VISTA⁺ cells (also known as V-domain immunoglobulin suppressor of T cell activation) were found in the pancreatic stromal area compared with melanoma. We identified VISTA as a potent inhibitory checkpoint that is predominantly expressed on CD68+ macrophages on PDACs. These data suggest that VISTA may be a relevant immunotherapy target for effective treatment of patients with pancreatic cancer.

The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Oceanic dissolved inorganic carbon (TC) is the largest pool of carbon that substantially interacts with the atmosphere on human timescales. Oceanic TC is increasing through uptake of anthropogenic carbon dioxide (CO2), and seawater pH is decreasing as a consequence. Both the exchange of CO2 between the ocean and atmosphere and the pH response are governed by a set of parameters that interact through chemical equilibria, collectively known as the marine carbonate system. To investigate these processes, at least two of the marine carbonate system's parameters are typically measured – most commonly, two from TC, total alkalinity (AT), pH, and seawater CO2 fugacity (fCO2; or its partial pressure, pCO2, or its dry-air mole fraction, xCO2) – from which the remaining parameters can be calculated and the equilibrium state of seawater solved. Several software tools exist to carry out these calculations, but no fully functional and rigorously validated tool written in Python, a popular scientific programming language, was previously available. Here, we present PyCO2SYS, a Python package intended to fill this capability gap. We describe the elements of PyCO2SYS that have been inherited from the existing CO2SYS family of software and explain subsequent adjustments and improvements. For example, PyCO2SYS uses automatic differentiation to solve the marine carbonate system and calculate chemical buffer factors, ensuring that the effect of every modelled solute and reaction is accurately included in all its results. We validate PyCO2SYS with internal consistency tests and comparisons against other software, showing that PyCO2SYS produces results that are either virtually identical or different for known reasons, with the differences negligible for all practical purposes. We discuss insights that guided the development of PyCO2SYS: for example, the fact that the marine carbonate system cannot be unambiguously solved from certain pairs of parameters. Finally, we consider potential future developments to PyCO2SYS and discuss the outlook for this and other software for solving the marine carbonate system. The code for PyCO2SYS is distributed via GitHub (https://github.com/mvdh7/PyCO2SYS, last access: 23 December 2021) under the GNU General Public License v3, archived on Zenodo , and documented online (https://pyco2sys.readthedocs.io/en/latest/, last access: 23 December 2021).