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Lignocellulosic materials are valuable resources in today's bioprocess technologies; however, their recalcitrance is a major barrier in industry regarding their conversion to microbial products. For this purpose, in this study, the synthesis of ionic liquids (ILs), its function in the hydrolysis of lignocellulosic materials, its biochemistry and possible toxic effects were investigated. In addition, the bioconversion of lignocellulosic materials pretreated with ionic liquids to biofuels (bioethanol, biobutanol, biogas and hydrogen) and various biochemicals is discussed in detail. For this, the focus is on the potential of ILs for industrial integration and use in large-scale reactors. ILs offer significant advantages due to their potential for ease of use and their features such as recovery and reuse after pretreatment. However, there are economic and technical problems that need to be solved to expand ILs in industrial systems and increase their use potential. To overcome these problems and the usability of ILs technologies in industry, techno-economic analyses has been examined and compared with traditional processes.

Artificial intelligence (AI) and machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. AI and ML are being used to not only predict the structures of the proteins but to edit the protein sequence to give them desired properties and enhance their functions. Thus, there is a need to study how these proteins are interacting with other components in the experimental setup or the human body. With the increasing interest in the above-mentioned research gaps, scientists are working on several wet-lab techniques and adding to the knowledge pool. However, this information is scattered and enormous. Hence, AI and ML come to the rescue. It can handle bulk data and organize and produce models that can make sense of the information. Therefore, the involvement of AI and ML is inevitable, and this review highlights these points.

Ultrasonication using low frequencies of sound can increase cell organogenesis, which is beneficial for various industrial applications. This study demonstrates a novel approach of integrated phycoremediation and ultrasonication-irradiation treatment (iPUT) used for improving the degradation of sulfonamide antibiotics via a cumulative effect of combined treatments. Variable ultrasonication treatment (UT) (20 %-2 min to 40 %-10 min) was given to a model microalga, Chlamydomonas mexicana in two ways, 1) single ultrasonic treatment (SUT) and 2) multiple-intermittent ultrasonic treatments (IUT). The microalgal growth was slightly affected by SUT, while it significantly inhibited by IUT. The removal of sulfacetamide and sulfapyridine was significantly improved by >1.7-fold and >1.95-fold at 20 % of SUT and IUT treatment, respectively, compared to control. In the case of sulfamethazine, the SUT showed maximum removal (33.5 %) at 20 %, whereas IUT could achieve 27.5 % removal at the same ultrasonication conditions compared to 9.5 % removal in control. The IUT accelerated the degradation of sulfamethoxazole and sulfadimethoxine more than SUT showing a 9- fold and 12- fold increase in the removal of sulfamethoxazole and sulfadimethoxine with 20 % and 40 % treatments, respectively. The changes in microalgal cell morphology due to ultrasonication treatment were the main cause of enforced uptake and subsequent degradation of these ECs.

Machine Learning and Artificial intelligence are quickly becoming impending game changers for bioprocessing development. However, its true potential has not been harnessed, and real-time application is still in its interim stage to control most cognitive tasks. Hence, it is imperative to know the state of technology to identify the gaps in the knowledge. In this review, we first give an insight into the basic understanding of the machine learning domain and discuss its complexities for more comprehensive applications. Subsequently, we outline how relevant machine learning models are used to statistically and logically analyze the big datasets generated in the bioprocessing industries to control process operations. While doing so, we provide the state of technology applied in different subfields of the bioprocessing industry. Further, this review also discusses the adoption of hybrid modeling strategies for combining mechanistic models with historical data-driven machine learning models to develop new digital biotechnologies.

Microbial consortia are proven to be one of the most effective tools for the decolorization of textile industrial dyes and effluent. The microbial consortium offers several benefits in bioremediation, some of them include the faster rate of dye degradation and high efficiency, tolerance to drastic environmental changes, possession of concerted metabolic activities due to presence of multiple microbial species that carry out maximum catalysis of dye molecule or sometimes complete mineralization, and its feasibility in practical scale operations due to less requirements of sterile conditions and cost effectiveness. This chapter critically summarizes the basic principles of microbial interactions such as mutualism and commensalism as the most valuable microbial relationships within the consortium that are beneficial for the bioremediation of wastewater. Several different microbial consortia which combined the microbial species of bacteria, fungus, yeast, along with algae and plants which have been used for the treatment of textile dyes/effluent have been thoroughly evaluated. All combinations have their own advantages and disadvantages, which have been explored herein. Bacterial species are fast growing microorganisms while fungal species contain highly catalytic enzymatic system, thus their combinations have been quite successful. Microalgae and plants have recently attracted researcher because of their potential of bioremediation and subsequent utilization of their biomass in biofuel generation which is commercially beneficial along with environmentally sustainable approach. Moreover, this chapter provides a brief introduction on newly emerging high-throughput techniques that deal with the engineering of synthetic microbial consortium for bioremediation and environmental cleanup.

Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.

This review provides an update on the state-of-the art technologies for the valorization of solid waste and its mechanism to generate various bio-products. The organic content of these wastes can be easily utilized by the microbes and produce value-added compounds. Microbial fermentation techniques can be utilized for developing waste biorefinery processes. The utilization of lignocellulosic and plastics wastes for the generation of carbon sources for microbial utilization after pre-processing steps will make the process a multi-product biorefinery. The C1 and C2 gases generated from different industries could also be utilized by various microbes, and this will help to control global warming. The review seeks to expand expertise about the potential application through several perspectives, factors influencing remediation, issues, and prospects.

AbstractWidespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.

Sonic, underexpanded helium jets were transversely injected through a turbulent boundary layer into a supersonic Mach 3 flow. Schlieren images were obtained using a high speed camera to visualise the mean and instantaneous flow features. Wall-pressure fluctuations along the gas injection centerline were measured using flush-mounted high-speed pressure transducers. The jet-to-crossflow momentum ratios ranged from 1.21 – 3.89, while the pressure ratios (jet exit to mean flow) were 17.41 – 56.12. Mean Schlieren images show that the penetration depth increases with momentum flux ratio. Instantaneous images reveal a highly unsteady interaction between shear layer vortices and adjacent shock structures. Periodic shear layer vortices were generated via the Kelvin–Helmholtz instability mechanism and convected downstream. Time varying deformation of the λ shock were due to the separated incoming boundary layer and the interaction of the bow shock with shear layer vortices. Compression waves are also observed propagating along the shear layer in the direction of flow convection.

Measurements of the wall pressure fluctuations near a wing-plate junction were made for wings with three different aspect ratios (AR) of 0.2, 0.5, and 1.0 at several angles of attack. The chord-based Reynolds number for each wing was 274,000. The results show that the wall pressure fluctuations are a function of wing AR for cases where AR≤ 1.0. For each wing, the pressure fluctuations are highest upstream of the wing leading-edge due to three-dimensional flow separation; wings with AR = 1.0 and 0.5 show comparable levels, while those with AR = 0.2 show lower fluctuation levels over a wide frequency range. Downstream of the leading-edge, the pressure fluctuations decay rapidly on both sides of the wing until the maximum thickness location after which little variation is observed. The pressure fluctuations downstream of the leading-edge on the suction-side were observed to be comparable for AR = 0.2 and 0.5, while those for AR = 1.0 were higher in magnitude. On the pressure-side, the pressure fluctuations near the leading-edge are a weak function of AR; however, those further downstream remain independent of AR. The pressure fluctuations aft of the wing on the suction-side are more coherent for lower ARs and show higher convection velocity, possibly due to an interaction between the tip and the junction flows for lower ARs.

The unsteady wake and wall pressures surrounding sonic injection into supersonic crossflow were investigated in experiments and LES simulations. The experiments and simulations were conducted in a Mach 3 flow with a 2 Bar sonic injector. Experimental wall-normal pressure profiles downstream of an jet inlet show lower pressures within the boundary layer region compared to the no-injection case, and an increase in pressure above that of the freestream values in the region passing through the jet plume. Mass flux spectra in the jet plume, and wall pressure spectra around the inlet show a significant broadband increase due to injection. The rise in wall pressure fluctuation intensity was greater closer to the jet, with the spectra returning to undisturbed levels much more gradually downstream of the inlet compared to upstream. Similar trends were also observed in the RMS wall pressure measurements.