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The requirement for alternatives in roll-to-roll (R2R) processing to expand thin film inspection in wider substrates at lower costs and reduced dimensions, and the need to enable newer control feedback options for these types of processes, represents an opportunity to explore the applicability of newer reduced-size spectrometers sensors. This paper presents the hardware and software development of a novel low-cost spectroscopic reflectance system using two state-of-the-art sensors for thin film thickness measurements. The parameters to enable the thin film measurements using the proposed system are the light intensity for two LEDs, the microprocessor integration time for both sensors and the distance from the thin film standard to the device light channel slit for reflectance calculations. The proposed system can deliver better-fit errors compared with a HAL/DEUT light source using two methods: curve fitting and interference interval. By enabling the curve fitting method, the lowest root mean squared error (RMSE) obtained for the best combination of components was 0.022 and the lowest normalised mean squared error (MSE) was 0.054. The interference interval method showed an error of 0.09 when comparing the measured with the expected modelled value. The proof of concept in this research work enables the expansion of multi-sensor arrays for thin film thickness measurements and the potential application in moving environments.

Fake news dissemination is a critical issue in today’s fast-changing network environment. The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. This paper deals with the categorical cyber terrorism threats on social media and preventive approach to minimize their issues. Misleading or unreliable information in form of videos, posts, articles, URLs are extensively disseminated through popular social media platforms such as Facebook, Twitter, etc. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model, Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.

Background: Iron-deficiency anemia among school-aged children is widespread in India. The efficacy of micronutrient and iron fortified school-served meals in reducing iron deficiency anemia has been demonstrated in randomized controlled trials in other parts of the globe. The current study evaluates its effectiveness in real-world Indian settings. Methods: Mid-day-Meal (MDM) programme provides free lunch to students of grade 1 to 8 in all public-funded Indian schools. An implementation research project fortified MDM of all public schools of 4 out of 8 sub-districts (“blocks”) of Dhenkanal district of Odisha state with fortified rice kernel (FRK). All the schools of the other 4 blocks fortified with micronutrient powders (MNP)—both FRK and MNP containing equal amounts of supplementary iron and other micronutrients. Schools of 4 matched blocks of neighboring nonimplementing Angul district served as control. Cross-sectional representative samples of students were drawn from the 3 arms, before and after intervention (n = 1764 and n = 1640 respectively). Pre-post changes in anemia prevalence and hemoglobin levels were estimated in the sampled children using difference-in-difference analysis after controlling for inter-arm differences in socioeconomic status, and iron and deworming tablet consumptions. Results: Factoring in pre-post changes in control and adjusting for potential confounders, the proportion of children without anemia and mean hemoglobin improved by 1.93 (1.38, 2.24, P < .001) times and 0.24 (−0.03, 0.51, P = .083) g/dL in MNP; and 1.63 (1.18, 2.24, P = .002) times and 0.18 (−0.09, 0.45, P = .198) g/dL in FRK arms. Conclusions: Fortified MDM could effectively improve anemia status among Indian school-aged children under real-world conditions.

Additive manufacturing (AM) is emerging within many industrial applications due to inherent advantages such as rapid prototyping and produc- tion. However, the correlation of process parameters across modules and their impacts on product quality are not yet fully understood. This article presents a system built out of Internet of Things (IoT) and edge computing technologies to collect and analyze AM process in-situ. An IoT thermal camera platform was developed, and integrated within an Laser Based Additive Manufacturing (LBAM) system to collect information that could be used to characterize the thermal distribution surrounding the melt pool. Machine learning techniques were utilised to identify the occurrence of defects using the collected low-resolution thermal images.

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.

The digital twin (DT) concept has a key role in the future of the smart manufacturing industry. This review paper aims to investigate the development of the digital twin concept, its maturity and its vital role in the fourth industrial revolution. Having identified its potential functionalities for the digitalisation of the manufacturing industry, the digital twin concept, its origin and perspectives from both the academic and industrial sectors are presented. The identified research gaps, trends and technical limitations hampering the implementation of digital twins are also discussed. In particular, this review attempts to address the research question on how the digital twin concept can support the realisation of an integrated, flexible and collaborative manufacturing environment which is one of the goals projected by the fourth industrial revolution. To address this, a conceptual framework supporting an integrated product-process digital twin for application in digitised manufacturing is proposed. The application and benefits of the proposed framework are presented in three case studies.

A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy.

Treatment of tuberculosis (TB) involves the administration of anti-mycobacterial drugs for several months. The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb, the causative agent) together with increased disease severity in people with co-morbidities such as diabetes mellitus and HIV have hampered efforts to reduce case fatality. In severe disease, TB pathology is largely attributable to over-exuberant host immune responses targeted at controlling bacterial replication. Non-resolving inflammation driven by host pro-inflammatory mediators in response to high bacterial load leads to pulmonary pathology including cavitation and fibrosis. The need to improve clinical outcomes and reduce treatment times has led to a two-pronged approach involving the development of novel antimicrobials as well as host-directed therapies (HDT) that favourably modulate immune responses to Mtb. HDT strategies incorporate aspects of immune modulation aimed at downregulating non-productive inflammatory responses and augmenting antimicrobial effector mechanisms to minimise pulmonary pathology and accelerate symptom resolution. HDT in combination with existing antimycobacterial agents offers a potentially promising strategy to improve the long-term outcome for TB patients. In this review, we describe components of the host immune response that contribute to inflammation and tissue damage in pulmonary TB, including cytokines, matrix metalloproteinases, lipid mediators, and neutrophil extracellular traps. We then proceed to review HDT directed at these pathways.

Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

Electrical machines have recently received a lot of attention due to a variety of applications in several industries. Although advances in digital technologies have enabled more efficient production of electrical machines, faults are still identified at the end of the line tests. In order to avoid accumulation of defects during the production chain, it is desirable to identify faults early in the process. This can be achieved by identifying how critical process parameters and the interdependencies between them influence the occurrence of faults. This poses a challenge in electrical machine manufacturing because of the complexity involved in various manufacturing steps involving deformable material, an example is coil winding. This paper proposes a computational framework to model interdependencies in a complex electrical machine manufacturing process involving deformable material. A Discrete Event Simulation model representing the coil winding process demonstrated that input parameters such as wire tension and winding speed influence physical and electrical properties of the coil (enamelled copper wire) leading to generation of defects in the final product.

The market for electric motors is experiencing a step-growth due to their adoption across a range of industrial sectors. This increased demand also highlights the importance of end-of-life management of electric motors and a requirement for appropriate strategies for the high value materials embedded in them. This paper aims to offer a holistic view on the circular economy research for electric motors and the role of Industry 4.0 technologies by presenting the state-of-the-art available in literature and comparing it with the industrial perspective. The literature review revealed the absence of a methodology for selecting the best end-of-life scenario for industrial electric motors. Recycling, which is an end-of-product-life strategy, was found to be the key focus area of research. Reuse, which is a better strategy in terms of waste hierarchy, was the least researched area due to lack of information about the condition and availability of returned products. In order to capture the current landscape within the UK for the repair, remanufacture and recycling of electrical machines, a structured survey of UK based companies was conducted. The survey revealed that nearly half of the companies do not undertake any repair strategies for electrical machine components, however, there was an aspiration from the respondents to migrate their companies towards more sustainable activities. The industry survey and the review of existing literature led to the identification of research trends, challenges and recommendations for future research.

Studying latent Mycobacterium tuberculosis (Mtb) infection has been limited by the lack of a suitable mouse model. We discovered that transient depletion of biotin protein ligase (BPL) and thioredoxin reductase (TrxB2) results in latent infections during which Mtb cannot be detected but that relapse in a subset of mice. The immune requirements for Mtb control during latency, and the frequency of relapse, were strikingly different depending on how latency was established. TrxB2 depletion resulted in a latent infection that required adaptive immunity for control and reactivated with high frequency, whereas latent infection after BPL depletion was independent of adaptive immunity and rarely reactivated. We identified immune signatures of T cells indicative of relapse and demonstrated that BCG vaccination failed to protect mice from TB relapse. These reproducible genetic latency models allow investigation of the host immunological determinants that control the latent state and offer opportunities to evaluate therapeutic strategies in settings that mimic aspects of latency and TB relapse in humans.

Organic mulches are easily available and cheap in North Bihar region. Therefore, an experiment was conducted to find out the suitability of different types of organic mulches in tomato cultivation. Among the vegetables tomato cultivation faces challenges in producing crop without chemicals for effective weed control. Use of several kinds of mulches gives the opportunity to control weeds effectively, regulate soil temperature and also lower down evaporation from the soil. The experiment was conducted in Krishi Vigyan Kendra, Jalalgarh, Purnea during 2015 and 2016. The treatments were designed by using different organic materials as mulch i.e. pea straw, dry neem leaves, paddy straw and one control without mulch. The observations were recorded on plant height (cm), number of branches per plant, days to 50% flowering, average fruit weight (gm) and total fruit yield (q/ha). The mulches were spread after twenty days of the transplanting of tomato seedlings in 10 cm thick layer. The result of the experiment indicates that organic mulches reduced soil temperature and weed infestation. All these enhanced the growth and fruit yield of tomato as well as weed control, soil moisture conservation and temperature modification resulted good crop growth with more quality fruit yield.

Organic agriculture seeks to augment ecological process that foster plant nutrition while conserving soil and water resources. Organic systems eliminate agrichemicals and reduce other external inputs to improve the environment as well as farm economics. It is a production system which favours maximum use of organic materials like crop residues, FYM, compost, green manures, oil cakes, biodynamic preparations and bio fertilizers etc. to enhance crop production, carbon sequestration and improve soil health. Organic production system is based on specific and precise standards of production which aim to achieving agro-ecosystems which are socially and ecologically sustainable. As demand for organically grown food has been growing rapidly and significant proportion of consumers believe that organic food is qualitatively better than non organic, the present study showed not only the quality of the produce but also quantitatively higher yield that touched the world record production (660 q/ha) in Nalanda Bihar.

A dynamic and flexible manufacturing environment presents many challenges in the movement of Autonomous Mobile Robots (AMRs), leading to delays due to the complexity of operations while negotiating even a simple route. Therefore, an understanding of rules related to AMR movement is important both from a utility perspective as well as a safety perspective. Our survey from literature and industry has revealed a gap in methodology to test rules related to AMR movement in a factory environment. Testing purely through simulations would not able to capture the nuances of shop floor interactions whereas physical testing alone would be incredibly time-consuming and potentially hazardous. This work presents a new methodology\ud that can make use of observations of AMR behaviour on selected cases on the shop floor and build up the fidelity of those simulations based on observations.\ud \ud This paper presents the development of a Highway Code for AMRs, development of simulation models for an ideal-AMR (based on the rules from the Highway Code) and physical testing of real-AMR in an industrial environment. Finally, a behavioural comparison of an ideal-AMR and a real-AMR in five scenarios (taken from the shop floor of an industrial partner) is presented. This work could enable informed decisions regarding the implementation of AMRs through identification of any adverse behaviours which could then be mitigated either through\ud improvements on the AMR or through establishing shop floor protocols that reduce the potential impact of these behaviours.

Studying latent Mycobacterium tuberculosis (Mtb) infection has been limited by the lack of a suitable mouse model. We discovered that transient depletion of protein biotin ligase (BPL) and thioredoxin reductase (TrxB2) results in latent infections during which Mtb cannot be detected but that relapse in a subset of mice. The immune requirements for Mtb control during latency, and the frequency of relapse, were strikingly different depending on how latency was established. TrxB2 depletion resulted in a latent infection that required adaptive immunity for control and reactivated with high frequency, whereas latent infection after BPL depletion was independent of adaptive immunity and rarely reactivated. We identified immune signatures of T cells indicative of relapse and demonstrated that BCG vaccination failed to protect mice from TB relapse. These reproducible genetic latency models allow investigating the host immunological determinants that control the latent state and offer opportunities to evaluate therapeutic strategies in settings that mimic aspects of latency and TB relapse in humans.

While the majority of literature on remanufacturing operations examines an end-of-life (EOL) strategy which is both manual and mechanised, authors generally agree that digitalisation of remanufacturing is expected to increase in the next decade. Subsequently, a new research area described as digitally-enabled remanufacturing, remanufacturing 4.0 or smart remanufacturing is emerging. This is an automated, data-driven system of remanufacturing by means of Industry 4.0 (I4.0) paradigms. Insights into smart remanufacturing can be provided through simulation modelling of the remanufacturing process. While the use of simulation modelling in order to predict responses and behaviour is prevalent in remanufacturing, the use of these tools in smart remanufacturing is still limited in literature. The goal of this research is to present, as a first of its kind, a comparative understanding of simulation modelling in remanufacturing in order to suggest the ideal modelling tool for smart remanufacturing. The proposed comparison includes system dynamics, discrete event simulation and agent based modelling techniques. We apply these modelling techniques on a smart remanufacturing space of a sensor-enabled product and use assumptions derived from industry experts. We then proceed to model the remanufacturing operation from sorting and inspection of cores to final inspection of the remanufactured product. Through our analysis of the assumptions utilised and simulation modelling results we conclude that, while individual modelling techniques present important strategic and operational insights, their individual use may not be sufficient to offer comprehensive knowledge to remanufacturers due to the challenge of data complexity that smart remanufacturing offers.

Hospital mortality rates have frequently been improved by identifying diagnostic groups with high mortality and targeting interventions to those specific groups. We found that high residual inpatient mortality persisted after targeted measures had achieved an initial reduction, and that the causes were spread across a wide range of diagnostic groups. Further interventions were put in place consisting of a structured electronic mortality form and systematised mortality scrutiny and reporting (primary intervention) accompanied by a number of quality improvement interventions arising from the mortality analysis (secondary interventions). We found that those interventions were associated with progressive improvements in mortality rates and average lengths of inpatient stay over the 5-year study period. Winter quarter mortality improvements reached a high level of statistical significance but could not be attributed to changes in any particular diagnostic groups. We conclude that progress with mortality improvements is probably best achieved by applying both code-targeted and general interventions simultaneously.

Protein kinase G (PknG) in Mycobacterium tuberculosis has been shown to modulate phagosome-lysosome fusion. The protein has three distinct domains, an N-terminal Trx domain, a kinase domain, and a C-terminal TPR domain. The present study extensively analyzes the roles of these domains in regulating PknG kinase activity and function. We find that the kinase domain of PknG by itself is inactive, signifying the importance of the flanking domains. Although the deletion of the Trx domain severely impacts the activity of the protein, the C-terminal region also contributes significantly in regulating the activity of the kinase. Apart from this, PknG kinase activity is dependent on the presence of threonine 309 in the p + 1 loop of the activation segment. Mutating the conserved cysteine residues in the Trx motifs makes PknG refractory to changes in the redox environment. In vitro experiments identify threonine 63 as the major phosphorylation site of the protein. Importantly, we find that this is the only site in the protein that is phosphorylated in vivo. Macrophage infection studies reveal that the first 73 residues, the Trx motifs, and the threonine 63 residue are independently essential for modulating PknG-mediated survival of mycobacteria in its host. We have extended these studies to investigate the role of PknG and PknG mutants in the pathogenesis of mycobacteria in mice. Our results reinforce the findings from the macrophage infection experiments, and for the first time demonstrate that the expression of PknG in non-pathogenic mycobacteria allows the continued existence of these bacteria in host tissues.

The viability of Mycobacterium tuberculosis (Mtb) depends on energy generated by its respiratory chain. Cytochrome bc1-aa3 oxidase and type-2 NADH dehydrogenase (NDH-2) are respiratory chain components predicted to be essential, and are currently targeted for drug development. Here we demonstrate that an Mtb cytochrome bc1-aa3 oxidase deletion mutant is viable and only partially attenuated in mice. Moreover, treatment of Mtb-infected marmosets with a cytochrome bc1-aa3 oxidase inhibitor controls disease progression and reduces lesion-associated inflammation, but most lesions become cavitary. Deletion of both NDH-2 encoding genes (Δndh-2 mutant) reveals that the essentiality of NDH-2 as shown in standard growth media is due to the presence of fatty acids. The Δndh-2 mutant is only mildly attenuated in mice and not differently susceptible to clofazimine, a drug in clinical use proposed to engage NDH-2. These results demonstrate the intrinsic plasticity of Mtb’s respiratory chain, and highlight the challenges associated with targeting the pathogen’s respiratory enzymes for tuberculosis drug development., New tuberculosis therapies, targeting respiratory chain components of Mycobacterium tuberculosis, are under development. Here the authors show that, contrary to common belief, some of these components are not essential for pathogen viability and/or virulence in animal models of infection.

Background: Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy.Methods: RESTART was a prospective, randomised, open-label, blinded-endpoint, parallel-group trial at 122 hospitals in the UK that assessed whether starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. For this prespecified subgroup analysis, consultant neuroradiologists masked to treatment allocation reviewed brain CT or MRI scans performed before randomisation to confirm participant eligibility and rate features of the intracerebral haemorrhage and surrounding brain. We followed participants for primary (recurrent symptomatic intracerebral haemorrhage) and secondary (ischaemic stroke) outcomes for up to 5 years (reported elsewhere). For this report, we analysed eligible participants with intracerebral haemorrhage according to their treatment allocation in primary subgroup analyses of cerebral microbleeds on MRI and in exploratory subgroup analyses of other features on CT or MRI. The trial is registered with the ISRCTN registry, number ISRCTN71907627.Findings: Between May 22, 2013, and May 31, 2018, 537 participants were enrolled, of whom 525 (98%) had intracerebral haemorrhage: 507 (97%) were diagnosed on CT (252 assigned to start antiplatelet therapy and 255 assigned to avoid antiplatelet therapy, of whom one withdrew and was not analysed) and 254 (48%) underwent the required brain MRI protocol (122 in the start antiplatelet therapy group and 132 in the avoid antiplatelet therapy group). There were no clinically or statistically significant hazards of antiplatelet therapy on recurrent intracerebral haemorrhage in primary subgroup analyses of cerebral microbleed presence (2 or more) versus absence (0 or 1) (adjusted hazard ratio [HR] 0·30 [95% CI 0·08–1·13] vs 0·77 [0·13–4·61]; pinteraction=0·41), cerebral microbleed number 0–1 versus 2–4 versus 5 or more (HR 0·77 [0·13–4·62] vs 0·32 [0·03–3·66] vs 0·33 [0·07–1·60]; pinteraction=0·75), or cerebral microbleed strictly lobar versus other location (HR 0·52 [0·004–6·79] vs 0·37 [0·09–1·28]; pinteraction=0·85). There was no evidence of heterogeneity in the effects of antiplatelet therapy in any exploratory subgroup analyses (all pinteraction>0·05).Interpretation: Our findings exclude all but a very modest harmful effect of antiplatelet therapy on recurrent intracerebral haemorrhage in the presence of cerebral microbleeds. Further randomised trials are needed to replicate these findings and investigate them with greater precision.Funding: British Heart Foundation.

This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between &ldquo, go&rdquo, or &ldquo, no go&rdquo, for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product&mdash, that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product&rsquo, s life cycle.

Background: Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects.Methods: FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762.Findings: Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months.Interpretation: Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function.Funding: UK Stroke Association and NIHR Health Technology Assessment Programme.

The main challenge aerospace industries are facing in recent times has been triggered by the remarkable increase in commercial aircraft demand. To address this challenge, aircraft manufacturers need to explore ways to increase capacity and workflow through process optimisation and automation. This study focusses on the optimisation of component flow and inventory during the assembly of the A320 Family wings’ at Airbus (Broughton, UK) plant through Discrete Event Simulation (DES).

Successful drug treatment for tuberculosis (TB) depends on the unique contributions of its component drugs. Drug resistance poses a threat to the efficacy of individual drugs and the regimens to which they contribute. Biologically and chemically validated targets capable of replacing individual components of current TB chemotherapy are a major unmet need in TB drug development. We demonstrate that chemical inhibition of the bacterial biotin protein ligase (BPL) with the inhibitor Bio-AMS (5′-[N-(d-biotinoyl)sulfamoyl]amino-5′-deoxyadenosine) killed Mycobacterium tuberculosis (Mtb), the bacterial pathogen causing TB. We also show that genetic silencing of BPL eliminated the pathogen efficiently from mice during acute and chronic infection with Mtb. Partial chemical inactivation of BPL increased the potency of two first-line drugs, rifampicin and ethambutol, and genetic interference with protein biotinylation accelerated clearance of Mtb from mouse lungs and spleens by rifampicin. These studies validate BPL as a potential drug target that could serve as an alternate frontline target in the development of new drugs against Mtb.

The use of optical fibre Bragg gratings (FBGs) to monitor the Interface Pressure Distribution (IPD) on an automotive disc brake pad under a variety of loading conditions is studied. The results demonstrate successful strain transfer from the brake pads to the attached FBG sensors under static loading, with a linear response to increasing pressure, and with the measured IPD showing good agreement with that recorded using pressure sensitive paper. Results are also presented demonstrating that changes in the IPD as a result of torque acting on the brake pads can be monitored by the FBG sensors.

The use of an optical fibre long period grating (LPG) as a soil moisture sensor is reported. Characterization of the device in both clay and sandy soils revealed a sensitivity to moisture levels in the range 10-50%, and the results were compared with the output from a Theta probe, the standard soil moisture sensor, which measures the impedance of the soil. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

An investigation on river Pandu in the year 2009-2010 was carried out to explore its current status in terms of physico-chemical and phycological profile. The study revealed that algal spectrum of the river was connected with the intensity of pollution in the river. River, at present is a perturbed ecosystem due to heavy anthropogenic stress on it. Despite abundant nutrients and other factors responsible for propping algal growth, river had a meak algal population and low diversity as compared to other perennial rivers indicating the grossly polluted unhealthy condition of the river. Algae are natural inhabitants of aquatic environment; and act as natural purifiers due to their nutrient gleaning and oxygenating capabilities; they are more sensitive than animals to industrial and municipal wastes. Maximum number of species existed at station-1 followed by stations-4 and 6 and then at station-5. Lowest species representation has been noticed at stations-2 and 3. Species number and algal population too declined sharply at stations-2 and 3 as compared to station-I. Sharp decline in algal population at stations-2 and 3 may be attributed to certain obvious reasons such as low transparency and reduced illumination as a consequence of residual fly ash discharged by PTPP drain at station-2. Except station-1, all downstream stations show grossly polluted condition of the river.DOI: http://dx.doi.org/10.3126/eco.v20i0.11325Ecoprint: An International Journal of EcologyVol. 20, 2013Page : 9-17

In the unreliable newsboy problem, firms sometimes split their orders among multiple suppliers to manage supply risks. In this paper, we discuss how ordering time can be used as a mechanism to mitigate the supply risks of an unreliable newsboy. We consider a situation where a firm has an option of placing a series of sequential orders such that a new order is placed only after the yield from the previous order is known. An optimal order quantity for each ordering stage is derived by assuming a Bernoulli (all or nothing) supply yield distribution. By comparing the expected cost of the sequential orderingbased unreliable newsboy model with the expected cost of the deterministic newsboy, we demonstrate that the adverse effect of supply side risks can be almost completely negated by adopting a sequential ordering policy when the combined purchasing and holding costs for a low cost supplier at an earlier stage are equal to those of a high cost supplier at the next stage. Our computational results suggest that the sequential ordering policy performs better than a simultaneous ordering strategy when customer service level norms become stringent and inventory holding costs decrease.

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, is the leading cause of bacterial infectious disease mortality. Biotin protein ligase (BirA) globally regulates lipid metabolism in Mtb through the posttranslational biotinylation of acyl coenzyme A carboxylases (ACCs) involved in lipid biosynthesis and is essential for Mtb survival. We previously developed a rationally designed bisubstrate inhibitor of BirA that displays potent enzyme inhibition and whole-cell activity against multidrug resistant and extensively drug resistant Mtb strains. Here we present the design, synthesis, and evaluation of a focused series of inhibitors, which are resistant to cyclonucleoside formation, a key decomposition pathway of our initial analogue. Improved chemical stability is realized through replacement of the adenosyl N-3 nitrogen and C-5′ oxygen atom with carbon as well as incorporation of a bulky group on the nucleobase to prevent the required syn-conformation necessary for proper alignment of N-...

We performed a retrospective cohort comparison study to look at the processes for concentrating geriatric resources in the acute admissions area in a general hospital in the UK and compare key outcomes. The number of consultant geriatricians and other staff working at the 'front door' - acute medical unit (AMU) and short stay ward (SSW) - was increased. We compared 'front door' outcomes with whole department outcomes in 2013 and 2014, looking at the proportion of patients discharged within 3 and 5 days of admission, the proportion discharged from the 'front door', mean lengths of stay (LOS) and readmissions within 28 days of discharge. There were 1,147 and 1,381 discharge episodes in 2013 and 2014, respectively. 'Front door' discharges rose from 36% to 46% (p