Many studies were done in the development of drought stress-tolerant transgenic plants, including crop plants. Rice is considered to be a vital crop target for improving drought stress tolerance. Much transgenic rice showed improved drought stress tolerance was reported to date. They are genetically engineered plants that are developed by using genes that encode proteins involved in drought stress regulatory networks. These proteins include protein kinases, transcription factors, enzymes related to osmoprotectant or plant hormone synthesis, receptor-like kinase. Of the drought stress-tolerant transgenic rice plants described in this review, most of them display retarded plant growth. In crop crops, plant health is a fundamental agronomic trait that can directly affect yield. By understanding the regulatory mechanisms of retarded plant growth under drought stress, conditions are necessary precursors to developing genetically modified plants that result in high yields.

In spite of the significant progress towards sustainable cosmetics, mass-produced sustainable packaging has proven to be a challenge. The complexity of environmental, economic, social, technological and policy considerations in conjunction with varying consumer behaviors and corporate goals can make it difficult to select an optimal strategy across heterogeneous supply-chain components spread over the globe; and the cost and effort of developing, testing and validating alternative strategies discourages empirical exploration of potential alternatives. This review discusses the challenges that can be expected in the context of broader sustainability efforts, as well as the experience gained in related fields such as sustainable cosmetics and sustainable packaging, to identify potential pitfalls as well as promising trends towards development of sustainable cosmetics packaging. The findings suggest there may be little to be gained from attempting to induce customers to change their behavior; waiting for a significant increase in global recycling infrastructure; or expecting regulatory constraints to substitute for the lack of technological and business solutions. A research strategy is delineated towards development of sustainable packaging that, with appropriate policy support, could minimize externalities and provide mass-produced packaging that is acceptable to both consumers and producers.

The aim of this paper is the presentation of an analytical model of insulator flashover and its application for air at atmospheric pressure and pressurized SF6. After a review of the main existing models in air and compressed gases, we develop a relationship of flashover voltage on the basis of an electrical equivalent circuit and the thermal properties of the discharge. The model includes the discharge resistance, the insulator impedance, and the gas interface impedance. The application of this model to a cylindrical resin-epoxy insulators in air medium and SF6 gas with different pressures gives results close to the experimental measurements.

Phylogenetics is a powerful tool for understanding the evolutionary history of organisms and for informing conservation and management of species. Among the strepsirrhine primates, the slender lorises are a threatened genus of small, nocturnal animals confined to India and Sri Lanka. The grey slender loris (Loris lydekkerianus) is divided into several subspecies based on the morphological and geographical variation but not supported by molecular data. We investigated the genetic basis of taxonomic and biogeographic variation as well as the phylogenetic divergence of two subspecies of the grey slender loris in southern India: the Mysore slender loris (Loris lydekkerianus ssp. lydekkerianus) and the Malabar slender loris (Loris lydekkerianus ssp. malabaricus). We sequenced and assembled the whole mitochondrial genomes of three representative individuals from their distribution in southern India and compared them with publicly available mitogenomes of other lorises. We found that the two Indian subspecies vary by 2.09% in the COX1 and CYTB gene regions and form distinct monophyletic clades that diverged about 1.049 million years ago. Our results support the morphological classification of these two subspecies in southern India and have implications for their conservation and management in captivity and in the wild.

Consumption is thus fundamentally related to sustainably because every decision pertaining to what to buy, what quantity the buy, how many to consume, or how to dispose of things has a direct influence on the natural world and the generations to come, and the overall impact of every single the customer's purchase is disastrous. A framework-based systematic literature review (SLR) was conducted to better comprehend the body of research on digital innovation and sustainability driving consumer behavior. This was done in order to acquire a deeper understanding of the studies that have already been done in this area. to provide (i) a comprehensive review of the current research landscape and (ii) a rich roadmap for future research. This study evaluated the literature especially in terms of theory, context, traits, and methodology. A total of 107 research articles published between 2013 and 2022 were found using scientific methods in the Scopus database. The findings showed that most research appears to have employed qualitative methodologies and was grounded in the setting of China and Italy. This review could help scholars better grasp the relationship between digital innovation and sustainability, opening the door for additional study and advancement in the field. Additionally, by extending the literature study and emphasizing the elements that can improve digital innovation and sustainability, practitioners will be given a better overall understanding of how to approach the problem.

UEDVT still remains an underdiagnosed entity due to a significant percentage of patients who remain asymptomatic. The etiology of UEDVT is overwhelmingly attributed to secondary causes including but not limited to central venous access devices and malignancy. Treatment modalities include anticoagulation, catheter directed thrombolysis, percutaneous mechanical thrombectomy or a multimodality approach along with the management of underlying etiology. We describe a case of a female with breast cancer, who came with left UEDVT and CVC malfunction and was successfully treated using the ClotTriever system to maintain continuous intravenous access.

Human tuberculosis (TB) is caused mainly by Mycobacterium tuberculosis (MTB) and M. africanum (MAF) remains a major global health threat. The varying response of different host to contact with the TB bacteria, indicates the importance of host genetics in susceptibility to TB disease. We explored the association among selected human/host genomic variants and disease caused by the two causative pathogens in Ghana through a case control study. MTBC isolates (323) recovered from pulmonary TB patients recruited between 2016 and 2018 were genotyped using spoligotyping. A selection of 29 SNPs from MTB-related genes with high frequency among African populations were genotyped using a TaqMan® SNP Genotyping Assay and iPLEX Gold Sequenom Mass Genotyping Array. Associations between MTBC lineages and host variables were assessed using univariate and multivariate logistic regression. The prevalence of MTB and Maf among the participants were 79% and 21% respectively. Association analysis between the controls and MAF showed that rs2695342 variant on the SIC11A1 gene have the propensity to confer susceptibility to MAF infections (P = 0.093, OR = 8.35, 95% CI = 0.70 – 99.24) whilst the rs17048476 (P = 0.088, OR = 1.57, 95% CI = 0.93 – 2.63) and rs1482868 (P = 0.095, OR = 0.60, 95% CI = 0.33 – 1.09) were observed to be only suggestive. Our findings implicate SLC11A1 as a potential susceptibility gene of substantial interest for TB caused by MAF which is an important pathogen in West Africa and highlight the need for in-depth host pathogen studies in West Africa.

OBJECTIVE A quality improvement study to assess catheter-associated urinary tract infection (CAUTI) rate post-implementation of a bladder catheter with integrated active drain line urine clearance and automated intra-abdominal pressure monitoring in a burn intensive care unit (ICU). DESIGN Eight-year retrospective before and after study (2015–2022). SETTING A single American Burn Association verified Burn Center with fourteen inpatient beds. PATIENTS Patients meeting criteria for admission to a Burn Center METHODS Retrospective cohort study following the implementation of a novel urine output monitoring system with integrated drain line and urine clearance. Data from a 48-month (from January 2015-December 2018) historical control (period 1) were compared to data from a 28-month (from January 2020 to April 2022) post-implementation period (period 2). Pre- and post-implementation CAUTI event incidences were compared. Charts were reviewed to characterize the patients. RESULTS A total of 42 CAUTIs in 2243 patients were identified using the National Health and Safety Network (NHSN) definition during the analyzed period. There were 40 CAUTI events in period 1 and two CAUTIs in period 2. The incidence of CAUTI events pre-implementation was 0.030 (mean of 10 CAUTI events per year) compared to 0.002 (mean of 1 CAUTI event per year) post-implementation of an automatic drain line clearing UO monitoring system showing a significant reduction in CAUTI events (P<0.01, risk ratio novel vs. gravity bladder catheter 0.071, 95% confidence interval: 0.017-0.294). CONCLUSIONS CAUTIs were reduced in the period following the implementation of a novel urinary catheter system with an integrated active drain line and urine clearance in burn patients.

TThe following three objectives were tested in this study: (1) investigate the utility of low-altitude remote sensing using UAS technology to compare the effects of different N application systems in rice production; (2) use spatial extrapolation to scale up plot-level generated to farmer field rice yield data based on crop spectral signatures, and (3) predict and map out rice productivity as a function of N placement systems. Images were captured on a UAV platform at midseason of the rice crop. Orthomosaics were developed for selected fields in rice-producing zones. Grain yields were assessed from low, medium, and high crop health plots delineated based on NDVI values. On the plot scale, UDP outyielded non-UDP by 0.84%. Individual plot yield data were scaled up to the farmer field level through Jenks natural breaks classification and es-tablishing an empirical relationship between OSAVI and plot yields. Assessment of the scaled-up field levelfield-level data also confirmed the superiority of UDP N man-agement over the non-UDP systems in promoting rice yields. Scaling up plot scale da-ta to whole field levels also facilitated generating and mapping expected yield maps for individual farmer fields in the three zones studied. This study has established a tangible simple but tangible protocol protocol for predicting and mapping rice yields in small-scale farmer fields using UAS data.

Plants are immobile, and, to overcome harsh environmental conditions, such as drought, salt, and cold, they have evolved complex signaling pathways. Abscisic acid (ABA), an isoprenoid phytohormone, is a critical signaling mediator that regulates diverse biological processes in various organisms. Significant progress has been made in the determination and characterization of key ABA-mediated molecular factors involved in different stress responses, including stomatal closure and developmental processes, such as seed germination and bud dormancy. Since ABA-signaling is a complex signaling network that integrates with other signaling pathways, the dissection of its intricate regulatory network is necessary to understand the function of essential regulatory genes involved in ABA signaling. In the present review, we focus on two aspects of ABA signaling. First, the perception of the stress signal (abiotic and biotic) and the response network of ABA-signaling components that transduce the signal to the downstream pathway to respond to stress tolerance, regulation of stomata, and ABA signaling component ubiquitination. Second, ABA-signaling in plant development processes, such as lateral root growth regulation, seed germination, and flowering time regulation. Examining such diverse signal integration dynamics could enhance our understanding of the underlying genetic, biochemical, and molecular mechanisms of ABA signaling networks in plants.

Despite the important role of offshore renewable energies in the energy transition, the economical viability is still unclear. Therefore, an appropriate site selection is crucial. Besides the energy potential, the impact of operation and maintenance (O&M) aspects on the location can be critical. Traditional accessibility assessment metrics do not allow a comprehensive evaluation. Therefore, the present paper suggests a novel, technology-informed metric, incorporating the overall set of most critical aspects, i.e. metocean conditions, visibility due to sunlight and sea fog, system failures, and O&M logistics. Among the different aspects, limited visibility is shown to be highly relevant with a reduction of up to 60% in accessibility. The study assesses accessibility in 5 different locations across Europe. On the one hand, accessibility is shown to be less sensitive to long-term resource variations with a reduction of 5% in the last 6 decades. On the other hand, accessibility is shown to be inversely proportional to the energy potential overall, meaning that as energy potential increases, accessibility is reduced, increasing downtime, reducing final energy generation and increasing the final cost of energy. As a consequence, site selection should combine energy potential and accessibility assessments, which is enabled by the technology-informed metric presented here.

Two putative boar pheromones (Androstenone and Androstenol) were discovered in endangered mouse deer during captive breeding program. This study further examined the molecular characteristics, pheromone synthesis pathway, and the functional role of these pheromones in reproduction of mouse deer. CYP17A1 and CYB5 genes were cloned and expressed in HEK-293, COS-7 cell lines and gonads of mouse deer to investigate CYP17A1 gene’s andien-β-synthase activity towards synthesis of sex pheromones in mouse deer. An enzyme immunoassay was also developed and standardized to measure the fecal androstenone during reproductive cycles of mouse deer. Results showed that mouse deer’s CYP17A1 gene possesses andien-β-synthase activity and could transform pregnenolone into 5,16-androstadien-3β-ol. The expression of CYP17A1 gene upregulated in the testis and ovary, compared to other tissues in mouse deer. Significantly elevated pheromones and estrogens were recorded prior to delivery and postpartum estrus / mating in mouse deer. Further, there were weak correlations between fecal pheromones and estrogens/ androgens in mouse deer during breeding season. The findings suggest that the boar pheromones might play a direct role in the reproductive activities of mouse deer which might be used for breeding of mouse deer elsewhere.

Objectives To develop and validate a prediction model for 28-day in-hospital mortality among adult patients critically ill with COVID-19 in the UK. Design Observational cohort study. Setting 287 adult critical care units in England, Wales and Northern Ireland, of which 260 admitted at least one eligible patient. Participants 10,933 patients with confirmed COVID-19 of whom 10,401 were eligible (excluding 532 patients with a duration of critical care less than 24 hours and 1 patient with unknown 28-day outcome): 8,666 development (March-April 2020) and 1,735 temporal validation (May-August 2020). Main outcome measures 28-day in-hospital mortality from start of critical care. Results Two models were developed using 14 patient level predictors selected from 30 candidate predictors, with and without adjustment for calendar time. In the temporal validation data, the model discrimination was maintained (c index 0.78) but calibration was poor, particularly for the model not adjusted for calendar time (ratio of observed to predicted mortality 0.74 versus 0.88 for the model adjusted for calendar time). Conclusions We developed and validated a prediction model for 28-day in-hospital mortality for patients critically ill with COVID-19. Although absolute predictions were inaccurate due to changing outcomes, the models will support risk-adjustment in analyses and monitoring changes in risk-adjusted outcome over time.

Rationale: Examining trends in patient characteristics, processes of care and outcomes, across an epidemic, provides important opportunities for learning. Objectives: To report and explore changes in admission rates, patient characteristics, processes of care and outcomes for all patients with COVID-19 admitted to intensive care units (ICUs) in England, Wales and Northern Ireland. Methods: Population cohort of 10,287 patients with COVID-19 in the Case Mix Programme national clinical audit from 1 February to 2 July, 2020. Analyses were stratified by time period (pre-peak, peak, post-peak) and geographical region. Multivariable logistic regression was used to estimate differences in 28-day mortality, adjusting for patient characteristics over time. Main results: Admissions to ICU peaked simultaneously across regions on 1 April, with ongoing admissions peaking ten days later. Compared with pre- and post-peak periods, patients admitted during the peak were slightly younger but had greater respiratory and renal dysfunction. Use of invasive ventilation and renal replacement reduced over time. Twenty-eight-day mortality reduced from 43.5% (95% CI 41.6% to 45.5%) pre-peak to 34.3% (95% CI 32.3% to 36.2%) post-peak; a difference of −8.8% (95% CI: −5.2%, −12.3%) after adjusting for patient characteristics. London experienced the highest admission rate and had higher mortality during the peak period but a greater reduction in post-peak mortality. Conclusion: This study highlights changes in patient characteristics, processes of care and outcomes, during the UK COVID-19 epidemic. After adjusting for the changes in patient characteristics and first 24-hour physiology, there was substantial improvement in 28-day mortality over the course of the epidemic.

The rapidly evolving understanding of Coronavirus Disease 2019 (COVID-19) respiratory failure pathogenesis, limited disease-specific evidence and demand-resource imbalances have posed significant challenges for intensive care clinicians. In this single-centre retrospective cohort study we describe the outcomes of COVID-19 patients admitted to Guy’s and St. Thomas’ NHS Foundation Trust (GSTT) critical care service. Patients were managed according to a local respiratory failure management pathway that was predicated on timely invasive ventilation when indicated and tailored ventilatory strategies according to pulmonary mechanics. Between 2nd March and 25th May 2020 GSTT critical care service admitted 316 patients with confirmed COVID-19. Of the 201 patients admitted directly through the Emergency Department with a completed critical care outcome, 71.1% survived to critical care discharge. These favourable outcomes may serve to inform the wider debate on the optimal ventilatory management in COVID-19.

In this work performance improvements are described of a low power consumption non-dispersive infrared (NDIR) methane (CH₄) gas sensor using customised optical thin film bandpass filters (BPF). BPF’s shape the spectral characteristic of the combined mid infrared III-V based light emitting diode (LED)/ photodiode (PD) light source/detector optopair, enhancing NDIR CH₄ sensor performance. The BPF, deposited using a novel microwave plasma assisted pulsed DC sputter deposition process, is deposited at room temperature directly onto the temperature sensitive PD heterostructure. BPF’s comprise germanium (Ge) and niobium pentoxide (Nb₂O₅) alternating high and low refractive index layers respectively. Two different optical filter designs are progressed; with BPF bandwidths (BWs) of 160 nm and 300 nm. Comparison of modelled and measured NDIR sensor performance is described, highlighting maximized signal to noise ratio (SNR) and minimized cross talk performance benefits. BPF spectral stability for various environmental temperature and humidity conditions is demonstrated.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

We describe the innate and adaptive immune system trajectory in Multi-system inflammatory syndrome of childhood (MIS-C), at acute(within 72 hours of hospitalization), resolution (at clinical improvement) and convalescent phase. In our cohort, in the acute phase, 68% of the children were SARS-CoV-2 seropositive, with hypercytokinenemia (high interleukin(IL)-1beta,IL-6,IL-8,IL-10,IL-17, interferon gamma), procoagulant state, myocardial dysfunction, activated neutrophils and monocytes; differential T and B cell subset lymphopenia; activated chemokine receptor type-7 positive and gamma-delta T cell subsets; antigen presenting cells had reduced HLA-DR expression; and B-cell class-switch responses occurred with illness resolution. MIS-C is an immunopathogenic illness associated with SARS-CoV-2 infections in children.

Radiomics involves the extraction of information from medical images not visible to the human eye. There is evidence these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations namely Matlab toolkit and Pyradiomics when applied on independent datasets of CT scans of patients i) the open access RIDER dataset containing a set of repeat CT scans taken 15 minutes apart for 31 patients (RIDER Scan 1 and Scan 2 respectively) treated for lung cancer and ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV) manually outlined by an experienced observer available on both datasets was used. 43 common radiomics features available on Matlab and Pyradiomics were calculated using 2 intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters and the Spearman’s rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset and vice versa. 29 out of 43 reported stable features were found to be highly reproducible between Matlab and Pyradiomics implementations, having consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs &gt; 0.8). 18/43 reported features were common in RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

In the current scenario of changing climatic conditions and rising global population, there is always a need to explore novel, efficient, and economical natural products for the benefit of human kind. Biosurfactant is one of the latest explored microbial biomolecules that have been used in numerous fields, including agriculture, pharmaceuticals, cosmetics, food processing, and environment-cleaning industries as raw material, lubrication, wetting, foaming softening, making emulsions, and stabilizing dispersions. The amphiphilic nature of biosurfactant biomolecules showed great advantage, distributing themselves into two immiscible surfaces by reducing interfacial surface tension and increasing the solubility of hydrophobic compounds. Furthermore, their eco-friendly nature, low or even no toxic nature, durability at higher temperatures, and wide range of pH fluctuations making the microbial surfactants preferable compared to their chemical counterparts. Additionally, the biosurfactants can obviate the oxidation flow by eliciting the antioxidant property, antimicrobial, anticancer activity, and drug delivery system, further broadening their applicability in the food, pharmaceutical, and pharma industries. Nowadays, biosurfactant has been broadly utilized to improve the soil quality by improving the concentration of trace elements and mixed with pesticides or applied singly on the plant surfaces for plant disease management. In the present review, we summarise the latest aspect of microbial synthesized biosurfactant compounds, limiting factors of biosurfactant production, and their application in improving soil quality, plant disease management.

Among magnetoelectric (ME) heterostructures, ME laminates of the type Metglas-like / PVDF (magnetostrictive+piezoelectric constituents) have shown the highest induced ME voltages, usually detected at the magnetoelastic resonance of the magnetostrictive constituent. This ME coupling happens because of the high cross-correlation coupling between magnetostrictive and piezoelectric material, and is usually associated with a promising application scenario for sensors or actuators. In this work we detail the basis of the operation of such devices, as well as some arising questions (as size effects) concerning their best performance. Also, some examples of their use as very sensitive magnetic fields sensors or innovative energy harvesting devices will be presented At the end, the challenges, future perspectives and technical difficulties that will determine the success of ME composites for sensor applications are discussed.

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than fully-connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multi-features in multidimensional data. In this paper, we present a multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multi-feature quantum convolution with average and Euclidean pooling and thus adapting the CNN structure to a variational quantum algorithm (VQA). Average pooling is based on the quantum Haar transform (QHT) and Euclidean pooling is based on partial quantum measurement. The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results showed favorable characteristics of our proposed techniques compared to existing work with respect to a number of quantitative metrics such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.