Deconvolution of monocyte responses in inflammatory bowel disease reveals an IL-1 cytokine network that regulates IL-23 in genetic and acquired IL-10 resistance.

OBJECTIVE: Dysregulated immune responses are the cause of IBDs. Studies in mice and humans suggest a central role of interleukin (IL)-23-producing mononuclear phagocytes in disease pathogenesis. Mechanistic insights into the regulation of IL-23 are prerequisite for selective IL-23 targeting therapies as part of personalised medicine. DESIGN: We performed transcriptomic analysis to investigate IL-23 expression in human mononuclear phagocytes and peripheral blood mononuclear cells. We investigated the regulation of IL-23 expression and used single-cell RNA sequencing to derive a transcriptomic signature of hyperinflammatory monocytes. Using gene network correlation analysis, we deconvolved this signature into components associated with homeostasis and inflammation in patient biopsy samples. RESULTS: We characterised monocyte subsets of healthy individuals and patients with IBD that express IL-23. We identified autosensing and paracrine sensing of IL-1α/IL-1ß and IL-10 as key cytokines that control IL-23-producing monocytes. Whereas Mendelian genetic defects in IL-10 receptor signalling induced IL-23 secretion after lipopolysaccharide stimulation, whole bacteria exposure induced IL-23 production in controls via acquired IL-10 signalling resistance. We found a transcriptional signature of IL-23-producing inflammatory monocytes that predicted both disease and resistance to antitumour necrosis factor (TNF) therapy and differentiated that from an IL-23-associated lymphocyte differentiation signature that was present in homeostasis and in disease. CONCLUSION: Our work identifies IL-10 and IL-1 as critical regulators of monocyte IL-23 production. We differentiate homeostatic IL-23 production from hyperinflammation-associated IL-23 production in patients with severe ulcerating active Crohn's disease and anti-TNF treatment non-responsiveness. Altogether, we identify subgroups of patients with IBD that might benefit from IL-23p19 and/or IL-1α/IL-1ß-targeting therapies upstream of IL-23.

An Efficient, Anonymous and Robust Authentication Scheme for Smart Home Environments.

In recent years, the Internet of Things (IoT) has exploded in popularity. The smart home, as an important facet of IoT, has gained its focus for smart intelligent systems. As users communicate with smart devices over an insecure communication medium, the sensitive information exchanged among them becomes vulnerable to an adversary. Thus, there is a great thrust in developing an anonymous authentication scheme to provide secure communication for smart home environments. Most recently, an anonymous authentication scheme for smart home environments with provable security has been proposed in the literature. In this paper, we analyze the recent scheme to highlight its several vulnerabilities. We then address the security drawbacks and present a more secure and robust authentication scheme that overcomes the drawbacks found in the analyzed scheme, while incorporating its advantages too. Finally, through a detailed comparative study, we demonstrate that the proposed scheme provides significantly better security and more functionality features with comparable communication and computational overheads with similar schemes.

Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India.

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability (VS) , vulnerability to property (VP) , and vulnerability to the location in terms of awareness (VA) . The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zones-namely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real-life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.

Interlining Cr(VI) remediation mechanism by a novel bacterium Pseudomonas brenneri isolated from coalmine wastewater.

A bioremedial approach was investigated on the removal of Cr(VI) from aqueous solution using a novel chromium reducing bacteria isolated from coalmine wastewater. Cr(VI) removal efficacy of the bacterium was determined in a series of batch studies under the influence of various parameters viz., pH (1-7), temperature (20-40 °C), initial metal concentration (1-150 mg/L), agitation speed (80-150 rpm) and substrate concentration (1-5 mg/L). Oxygen involvement in the removal process was determined by different incubation conditions. Substrate consumption and its resultant biomass generation were considered for determining the viability of the microbe under varied metal concentration. The microbial isolate survived in Cr(VI) tainted solution with initial concentration of 1-140 mg/L, among which maximum remediation was found in 60 mg/L Cr(VI) loaded solution. The bacterial species also survived in other metal solution viz., Fe(II), As(V), Cu(II), Pb(II), Zn(II), Mg(II), Mn(II) apart from Cr(VI). Multiple approaches were tested to facilitate understanding of the bacterial Cr(VI) removal mechanism. The bacteria accumulated metal ions in the exponential growth phase both on and within the cell. Underlying latent factors which governed the bacterial growth and its removal activity was determined with the classical Monod equation. The isolated bacterium also survived in the bimetallic solutions with significant removal of Cr(VI). The microbial species isolated from mining area was identified as Pseudomonas brenneri by 16s rRNA molecular characterization. Hence, the isolated novel bacterium illustrated promising involvement towards bio-treatment of Cr(VI) laden wastewater.

Optimization of Fe2+ Removal from Coal Mine Wastewater using Activated Biochar of Colocasia esculenta.

The present study investigates the sorptive removal of Fe2+ from simulated coal mine waste water using steam activated biochar (SABC) developed from the roots of Colocasia esculenta. The process was optimized by response surface methodology (RSM) under the influence of pH, temperature, adsorbent dosage and contact time at a constant shaking speed of 180 rpm with an initial concentration of 3 mg/L. The uptake performance of the biosorbent was assessed following a 24 full factorial experimental matrix developed by central composite design approach. Adsorbent was characterised by SEM, EDAX, XRD and B.E.T surface area analyzer. Maximum removal of 72.96% of Fe2+ was observed at pH 7.75, temperature 37.5 °C, adsorbent dosage 1.5 g/L for a time period of 180 mins. The study suggested that SABC prepared from roots of Colocasia esculenta could be used as an efficient and cost effective sorbent for removal of Fe2+ from coal mine wastewater.

A spatial model of the efficiency of T cell search in the influenza-infected lung.

Emerging strains of influenza, such as avian H5N1 and 2009 pandemic H1N1, are more virulent than seasonal H1N1 influenza, yet the underlying mechanisms for these differences are not well understood. Subtle differences in how a given strain interacts with the immune system are likely a key factor in determining virulence. One aspect of the interaction is the ability of T cells to locate the foci of the infection in time to prevent uncontrolled expansion. Here, we develop an agent based spatial model to focus on T cell migration from lymph nodes through the vascular system to sites of infection. We use our model to investigate whether different strains of influenza modulate this process. We calibrate the model using viral and chemokine secretion rates we measure in vitro together with values taken from literature. The spatial nature of the model reveals unique challenges for T cell recruitment that are not apparent in standard differential equation models. In this model comparing three influenza viruses, plaque expansion is governed primarily by the replication rate of the virus strain, and the efficiency of the T cell search-and-kill is limited by the density of infected epithelial cells in each plaque. Thus for each virus there is a different threshold of T cell search time above which recruited T cells are unable to control further expansion. Future models could use this relationship to more accurately predict control of the infection.

A bioorthogonal small-molecule-switch system for controlling protein function in live cells.

Chemically induced dimerization (CID) has proven to be a powerful tool for modulating protein interactions. However, the traditional dimerizer rapamycin has limitations in certain in vivo applications because of its slow reversibility and its affinity for endogenous proteins. Described herein is a bioorthogonal system for rapidly reversible CID. A novel dimerizer with synthetic ligand of FKBP' (SLF') linked to trimethoprim (TMP). The SLF' moiety binds to the F36V mutant of FK506-binding protein (FKBP) and the TMP moiety binds to E. coli dihydrofolate reductase (eDHFR). SLF'-TMP-induced heterodimerization of FKBP(F36V) and eDHFR with a dissociation constant of 0.12 µM. Addition of TMP alone was sufficient to rapidly disrupt this heterodimerization. Two examples are presented to demonstrate that this system is an invaluable tool, which can be widely used to rapidly and reversibly control protein function in vivo.

Trio preserves motor synapses and prolongs motor ability during aging.

The decline of motor ability is a hallmark feature of aging and is accompanied by degeneration of motor synaptic terminals. Consistent with this, Drosophila motor synapses undergo characteristic age-dependent structural fragmentation co-incident with diminishing motor ability. Here, we show that motor synapse levels of Trio, an evolutionarily conserved guanine nucleotide exchange factor (GEF), decline with age. We demonstrate that increasing Trio expression in adult Drosophila can abrogate age-dependent synaptic structural fragmentation, postpone the decline of motor ability, and maintain the capacity of motor synapses to sustain high-intensity neurotransmitter release. This preservative activity is conserved in transgenic human Trio, requires Trio Rac GEF function, and can also ameliorate synapse degeneration induced by depletion of miniature neurotransmission. Our results support a paradigm where the structural dissolution of motor synapses precedes and promotes motor behavioral diminishment and where intervening in this process can postpone the decline of motor function during aging.

dsSurvival 2.0: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system.

OBJECTIVE: Survival models are used extensively in biomedical sciences, where they allow the investigation of the effect of exposures on health outcomes. It is desirable to use diverse data sets in survival analyses, because this offers increased statistical power and generalisability of results. However, there are often challenges with bringing data together in one location or following an analysis plan and sharing results. DataSHIELD is an analysis platform that helps users to overcome these ethical, governance and process difficulties. It allows users to analyse data remotely, using functions that are built to restrict access to the detailed data items (federated analysis). Previous works have provided survival modelling functionality in DataSHIELD (dsSurvival package), but there is a requirement to provide functions that offer privacy enhancing survival curves that retain useful information. RESULTS: We introduce an enhanced version of the dsSurvival package which offers privacy enhancing survival curves for DataSHIELD. Different methods for enhancing privacy were evaluated for their effectiveness in enhancing privacy while maintaining utility. We demonstrated how our selected method could enhance privacy in different scenarios using real survival data. The details of how DataSHIELD can be used to generate survival curves can be found in the associated tutorial.

Food gels: gelling process and new applications.

Food gels are viscoelastic substances and several gelled products are manufactured throughout the world. The gelling agents in foods are usually polysaccharides and proteins. In food gels, the polymer molecules are not cross-linked by covalent bonds with the exception of disulphide bonds in some protein gels. Instead, the molecules are held together by a combination of weak inter-molecular forces like hydrogen bonds, electrostatic forces, Van der Waals forces, and hydrophobic interactions. Polysaccharides including hydrocolloids are strongly hydrated in aqueous medium but they tend to have less ordered structures. The mechanism of gelation depends on the nature of the gelling agent(s) and on the conditions of gel formation like the temperature, the presence of ions, the pH, and the concentration of gelling agents, etc. Characterization of gels can be performed in several ways of which rheological measurements are frequently practiced. Multi-component or mixed gel system is an important area of interest in which two or more gelling components are simultaneously used to achieve certain specific structural and functional characteristics. We here discuss about the different gels and gelling agents, the characterization of gels, and the mechanism of gelation with an emphasis on mixed or multi-component gels that would have significant commercial applications.

dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system.

OBJECTIVE: Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. RESULTS: We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures.

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies.

Artificial intelligence (AI) is increasingly taking on a greater role in healthcare. However, hype and negative news reports about AI abound. Integrating patient and public involvement (PPI) in healthcare AI projects may help in adoption and acceptance of these technologies. We argue that AI algorithms should also be co-designed with patients and healthcare workers. We specifically suggest (1) including patients with lived experience of the disease, and (2) creating a research advisory group (RAG) and using these group meetings to walk patients through the process of AI model building, starting with simple (e.g., linear) models. We present a framework, case studies, best practices, and tools for applying participative data science to healthcare, enabling data scientists, clinicians, and patients to work together. The strategy of co-designing with patients can help set more realistic expectations for all stakeholders, since conventional narratives of AI revolve around dystopia or limitless optimism.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD.

OBJECTIVE: Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. RESULTS: We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.

Intact Drosophila central nervous system cellular quantitation reveals sexual dimorphism.

Establishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system (CNS). Currently, however, strict quantitation of cell numbers has been achieved only for the nervous system of Caenorhabditis elegans. Here, we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact larval Drosophila CNS, revealing fewer neurons and more glial cells than previously predicted. We also discover an unexpected divergence between the sexes at this juvenile developmental stage, with the female CNS having significantly more neurons than that of males. Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of CNS organisation. We additionally extended our analysis to quantitate the expression of voltage-gated potassium channel family genes throughout the CNS and uncover substantial differences in abundance. Our methodology enables robust and accurate quantification of the number and positioning of cells within intact organs, facilitating sophisticated analysis of cellular identity, diversity, and gene expression characteristics.

A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness.

Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as 'black boxes', producing a prediction from input data without a clear explanation of their workings. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable. Here, we apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm and other statistical learning techniques to predict the risk of death. The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.80 (95% confidence intervals [0.78, 0.82]). We used class-contrastive analysis to produce explanations for the model predictions. We outline the scenarios in which class-contrastive analysis is likely to be successful in producing explanations for model predictions. Our aim is not to advocate for a particular model but show an application of the class-contrastive analysis technique to electronic healthcare record data for a disease of public health significance. In patients with schizophrenia, our work suggests that use or prescription of medications like antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with schizophrenia and the need to manage co-morbidities in these patients. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. Our approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with schizophrenia and potentially other diseases.

Miniature neurotransmission is required to maintain Drosophila synaptic structures during ageing.

The decline of neuronal synapses is an established feature of ageing accompanied by the diminishment of neuronal function, and in the motor system at least, a reduction of behavioural capacity. Here, we have investigated Drosophila motor neuron synaptic terminals during ageing. We observed cumulative fragmentation of presynaptic structures accompanied by diminishment of both evoked and miniature neurotransmission occurring in tandem with reduced motor ability. Through discrete manipulation of each neurotransmission modality, we find that miniature but not evoked neurotransmission is required to maintain presynaptic architecture and that increasing miniature events can both preserve synaptic structures and prolong motor ability during ageing. Our results establish that miniature neurotransmission, formerly viewed as an epiphenomenon, is necessary for the long-term stability of synaptic connections.

Simulating a Community Mental Health Service During the COVID-19 Pandemic: Effects of Clinician-Clinician Encounters, Clinician-Patient-Family Encounters, Symptom-Triggered Protective Behaviour, and Household Clustering.

Objectives: Face-to-face healthcare, including psychiatric provision, must continue despite reduced interpersonal contact during the COVID-19 (SARS-CoV-2 coronavirus) pandemic. Community-based services might use domiciliary visits, consultations in healthcare settings, or remote consultations. Services might also alter direct contact between clinicians. We examined the effects of appointment types and clinician-clinician encounters upon infection rates. Design: Computer simulation. Methods: We modelled a COVID-19-like disease in a hypothetical community healthcare team, their patients, and patients' household contacts (family). In one condition, clinicians met patients and briefly met family (e.g., home visit or collateral history). In another, patients attended alone (e.g., clinic visit), segregated from each other. In another, face-to-face contact was eliminated (e.g., videoconferencing). We also varied clinician-clinician contact; baseline and ongoing "external" infection rates; whether overt symptoms reduced transmission risk behaviourally (e.g., via personal protective equipment, PPE); and household clustering. Results: Service organisation had minimal effects on whole-population infection under our assumptions but materially affected clinician infection. Appointment type and inter-clinician contact had greater effects at low external infection rates and without a behavioural symptom response. Clustering magnified the effect of appointment type. We discuss infection control and other factors affecting appointment choice and team organisation. Conclusions: Distancing between clinicians can have significant effects on team infection. Loss of clinicians to infection likely has an adverse impact on care, not modelled here. Appointments must account for clinical necessity as well as infection control. Interventions to reduce transmission risk can synergize, arguing for maximal distancing and behavioural measures (e.g., PPE) consistent with safe care.

Atmospheric Pressure Plasma Deposition of TiO2: A Review.

Atmospheric pressure plasma (APP) deposition techniques are useful today because of their simplicity and their time and cost savings, particularly for growth of oxide films. Among the oxide materials, titanium dioxide (TiO2) has a wide range of applications in electronics, solar cells, and photocatalysis, which has made it an extremely popular research topic for decades. Here, we provide an overview of non-thermal APP deposition techniques for TiO2 thin film, some historical background, and some very recent findings and developments. First, we define non-thermal plasma, and then we describe the advantages of APP deposition. In addition, we explain the importance of TiO2 and then describe briefly the three deposition techniques used to date. We also compare the structural, electronic, and optical properties of TiO2 films deposited by different APP methods. Lastly, we examine the status of current research related to the effects of such deposition parameters as plasma power, feed gas, bias voltage, gas flow rate, and substrate temperature on the deposition rate, crystal phase, and other film properties. The examples given cover the most common APP deposition techniques for TiO2 growth to understand their advantages for specific applications. In addition, we discuss the important challenges that APP deposition is facing in this rapidly growing field.

Optogenetic Tuning Reveals Rho Amplification-Dependent Dynamics of a Cell Contraction Signal Network.

Local cell contraction pulses play important roles in tissue and cell morphogenesis. Here, we improve a chemo-optogenetic approach and apply it to investigate the signal network that generates these pulses. We use these measurements to derive and parameterize a system of ordinary differential equations describing temporal signal network dynamics. Bifurcation analysis and numerical simulations predict a strong dependence of oscillatory system dynamics on the concentration of GEF-H1, an Lbc-type RhoGEF, which mediates the positive feedback amplification of Rho activity. This prediction is confirmed experimentally via optogenetic tuning of the effective GEF-H1 concentration in individual living cells. Numerical simulations show that pulse amplitude is most sensitive to external inputs into the myosin component at low GEF-H1 concentrations and that the spatial pulse width is dependent on GEF-H1 diffusion. Our study offers a theoretical framework to explain the emergence of local cell contraction pulses and their modulation by biochemical and mechanical signals.