Vantage Power (United Kingdom)

It is well established that Cutibacterium acnes (C. acnes) is a common skin commensal microorganism that has been linked to acne. In acne flare-ups, C. acnes can be found in abundant levels within the inflammatory lesions (called comedones) associated with the skin disease. Recently, it was reported that 3D reconstructed human epidermis (RHE) treated with viable cultures of C. acnes can elicit β-defensin antimicrobial peptide responses in the skin and can weaken the skin barrier of the RHE after three days of exposure to C. acnes. Employing a modification of this in vitro assay, RHE was pretreated with C. acnes for 48 hours, then further treated with a mixture of water-soluble polysaccharides (STRATAPHIX™ POLY, "Polysaccharide Blend") previously shown to reduce inflammasome-mediated inflammatory responses in normal human epidermal keratinocytes (NHEK). Two inflammasome-mediated inflammation markers were tested, including caspase-1, a potent protease enzyme activated by NOD-like receptor protein (NLRP)-induced inflammasome activation, and interleukin-1β (IL-1β), a cytokine which is activated from inactive pro-IL-1β by caspase-1. The treatment of the RHE tissues with C. acnes for 48 hours elicited an inflammatory response measured with both markers compared against untreated tissues. Treatment of the tissues with 1% and 2% salicylic acid for 24hours after C. acnes treatment increased the inflammatory response measured with both markers. Application of the water-soluble polysaccharides in combination with 1% and 2% of salicylic acid significantly reduces expression of both active caspase-1 and IL-1β compared against the tissues treated with C. acnes and salicylic acid alone. The results lend further support to previously reported work which was done on NHEKs treated with ultraviolet B (UVB) light and adenosine triphosphate (ATP) and demonstrate that the same mixture of polysaccharides can have a modulating effect against bacterial and chemical induced inflammation in RHE.

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.

ABSTRACT BACKGROUND Due to uncertainties encompassing the transmission dynamics of COVID-19, mathematical models informing the trajectory of disease are being proposed throughout the world. Current pandemic is also characterized by surge in hospitalizations which has overwhelmed even the most resilient health systems. Therefore, it is imperative to assess supply side preparedness in tandem with demand projections for comprehensive outlook. OBJECTIVE Hence, we attempted this study to forecast the demand of hospital resources for one year period and correspondingly assessed capacity and tipping points of Indian health system to absorb surges in demand due to COVID-19. METHODS We employed age-structured deterministic SEIR model and modified it to allow for testing and isolation capacity to forecast the demand under varying scenarios. Projections for documented cases were made for varying degree of mitigation strategies of a) No-lockdown b) Moderate-lockdown c) Full-lockdown. Correspondingly, data on a) General beds b) ICU beds and c) Ventilators was collated from various government records. Further, we computed the daily turnover of each of these resources which was then adjusted for proportion of cases requiring mild, severe and critical care to arrive at maximum number of COVID-19 cases manageable by health care system of India. FINDINGS Our results revealed pervasive deficits in the capacity of public health system to absorb surge in demand during peak of epidemic. Also, continuing strict lockdown measures was found to be ineffective in suppressing total infections significantly, rather would only push the peak by a month. However, augmented testing of 500,000 tests per day during peak (mid-July) under moderate lockdown scenario would lead to more reported cases (5,500,000–6,000,000), leading to surge in demand for hospital resources. A minimum allocation of 10% public resources and 30% private resources would be required to commensurate with demand under that scenario. However, if the testing capacity is limited to 200,000 tests per day under same scenario, documented cases would plummet by half.

A study was undertaken to investigate the CO & soot emissions generated by a partially-fueled 9- element LDI (Lean-Direct Injection) combustor configuration operating in the idle range of jet engine conditions. In order to perform the CFD analysis, several existing soot/chemistry models were implemented into the OpenNCC (Open National Combustion Code). The calculations were based on a Reynolds-Averaged Navier Stokes (RANS) simulation with standard k-epsilon turbulence model, a 62- species jet-a/air chemistry, a 2-equation soot model, & a Lagrangian spray solver. A separate transport equation was solved for all individual species involved in jet-a/air combustion. In the test LDI configuration we examined, only five of the nine injectors were fueled with the major pilot injector operating at an equivalence ratio of near one and the other four main injectors operating at an equivalence ratio near 0.55. The calculations helped to identify several reasons behind the soot & CO formation in different regions of the combustor. The predicted results were compared with the reported experimental data on soot mass concentration (SMC) & emissions index of CO (EICO). The experimental results showed that an increase in either T3 and/or F/A ratio lead to a reduction in both EICO & SMC. The predicted results were found to be in reasonable agreement. However, the predicted EICO differed substantially in one test condition associated with higher F/A ratio.

This article develops the critical reviews of the leadership development terrain and argues for the necessity to consider and explore the pedagogic spaces of leadership development and its constitutive dynamics . It first synthesises the theoretical debates on pedagogic space by identifying five relevant themes and how pedagogic and learning spaces are differentiated. It locates itself within these debates, focusing on assemblage, embodiment and enactment. It then draws on a case study of the South African retail banking sector to suggest a sectoral analysis. The value and contribution of this is the attention to different levels of analysis and the theme of journeys. It allows for the consideration of, and deliberation on, expanding and opening up pedagogic and learning spaces.