Integrated biorefineries for repurposing of food wastes into value-added products

Food waste (FW) generated through various scenarios from farm to fork causes serious environmental problems when either incinerated or disposed inappropriately. The presence of significant amounts of carbohydrates, proteins, and lipids enable FW to serve as sustainable and renewable feedstock for the biorefineries. Implementation of multiple substrates and product biorefinery as a platform could pursue an immense potential of reducing costs for bio-based process and improving its commercial viability. The review focuses on conversion of surplus FW into range of value-added products including biosurfactants, biopolymers, diols, and bioenergy. The review includes in-depth description of various types of FW, their chemical and nutrient compositions, current valorization techniques and regulations. Further, it describes limitations of FW as feedstock for biorefineries. In the end, review discuss future scope to provide a clear path for sustainable and net-zero carbon biorefineries

Networked Markov Decision Processes With Delays

We consider a networked control system, where each subsystem evolves as a Markov decision process with some extra inputs from other systems. Each subsystem is coupled to its neighbors via communication links over which the signals are delayed, but are otherwise transmitted noise-free. A centralized controller receives delayed state information from each subsystem. The control action applied to each subsystem takes effect after a certain delay rather than immediately. We give an explicit bound on the finite history of measurement and control that is required for the optimal control of such networked Markov decision processes. We also show that these bounds depend only on the underlying graph structure as well as the associated delays. Thus, the partially observed Markov decision process associated with a networked Markov decision process can be converted into an information state Markov decision process, whose state does not grow with time

Changes in short-term (in-ICU and in-hospital) mortality following Intensive Care Unit admission in adults Living with HIV: 2000-2019

OBJECTIVE: Limited data suggest intensive care unit (ICU) outcomes have improved in people with HIV (PWH). We describe trends in in-ICU/in-hospital mortality among PWH following admission to ICU in a single UK-based HIV referral centre, from 1 January 2000 to 31 December 2019. METHODS: Modelling of associations between ICU admission and calendar year of admission was done using logistic regression with adjustment for age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, CD4+ T-cell count and diagnosis of HIV at/within the past 3 months. RESULTS: Among 221 PWH (71% male, median [interquartile range (IQR)] age 45 years [38-53]) admitted to ICU, median [IQR] APACHE II score and CD4+ T-cell count were 19 [14-25] and 122 cells/mm3 [30-297], respectively; HIV-1 viral load was ≤50 copies/ml in 46%. The most common ICU admission diagnosis was lower respiratory tract infection (30%).In-ICU and in-hospital, mortality were 29% and 38.5%, respectively. The odds of in-ICU mortality decreased over the 20-year period by 11% per year [odds ratio (OR): 0.89 (95% confidence interval (CI): 0.84-0.94)] with in-hospital mortality decreasing by 14% per year [0.86 (0.82-0.91)]. After adjusting for patient demographics and clinical factors, both estimates were attenuated, however, the odds of in-hospital mortality continued to decline over time [in-ICU mortality: adjusted OR: 0.97 (0.90-1.05); in-hospital mortality: 0.90 (0.84-0.97)]. CONCLUSION: Short-term mortality of critically ill PWH admitted to ICU has continued to decline in the ART era. This may result from changing indications for ICU admission, advances in critical care and improvements in HIV-related immune status

The effect of different COVID-19 public health restrictions on mobility: a systematic review

Background- In response to the COVID-19 pandemic, most countries have introduced non-pharmaceutical interventions, such as stay-at-home orders, to reduce person-to-person contact and break trains of transmission. The aim of this systematic review was to assess the effect of different public health restrictions on mobility across different countries and cultures. The University of Bern COVID-19 Living Evidence database of COVID-19 and SARS-COV-2 publications was searched for retrospective or prospective studies evaluating the impact of COVID-19 public health restrictions on Google Mobility. Titles and abstracts were independently screened by two authors. Information from included studies was extracted by one researcher and double checked by another. Risk of bias of included articles was assessed using the Newcastle Ottowa Scale. Given the heterogeneous nature of the designs used, a narrative synthesis was undertaken. From the search, 1672 references were identified, of which 14 were included in the narrative synthesis. All studies reported data from the first wave of the pandemic, with Google Mobility Scores included from January to August 2020, with most studies analysing data during the first two months of the pandemic. Seven studies were assessed as having a moderate risk of bias and seven as a low risk of bias. Countries that introduced more stringent public health restrictions experienced greater reductions in mobility, through increased time at home and reductions in visits to shops, workplaces and use of public transport. Stay-at-home orders were the most effective of the individual strategies, whereas mask mandates had little effect of mobility. Conclusions- Public health restrictions, particularly stay-at-home orders have significantly impacted on transmission prevention behaviours. Further research is required to understand how to effectively address pandemic fatigue and to support the safe return back to normal day-to-day behaviours

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people