Zinc oxide and ferric oxide nanoparticles combination increase plant growth, yield, and quality of soybean under semiarid region.

Zinc (Zn) and iron (Fe) malnutrition are global health challenges that need immediate attention. Hence, to address these issues, a two-pronged approach involving the development and application of novel Zn and Fe products for crop fertilization may be a potential solution. Therefore, zinc oxide (ZnO) (∼13.2 nm) and ferric oxide (Fe2O3) (∼15 nm) nanoparticles (NPs) were synthesized and characterized. Seven nutrients treatments viz, control, ZnO- NPs (25 mg kg-1), Fe2O3-NPs (25 mg kg-1), ZnO + Fe2O3-NPs (25 mg kg-1each), ZnSO4 (55.8 mg kg-1), FeSO4 (60.4 mg kg-1) and ZnSO4+ FeSO4 (55.8 and 60.4 mg kg-1) were arranged in five-time replicated Completely Randomized Design model to test the effectiveness of ZnO and Fe2O3 NPs in two soybean cultivars over conventional zinc sulfate (ZnSO4) and ferrous sulfate (FeSO4) fertilizers. The results indicated that the photosynthetic rate (Pn) and chlorophyll content increased (33.9-86.2%) significantly at the flowering stage with ZnO and Fe2O3 NPs applications, compared to their conventional counterparts. Likewise, the combined application of ZnO and Fe2O3 NPs reduced H2O2 production by 17-19% and increased the superoxide dismutase (SOD) and catalase (CAT) activities by 15-17% and 9.6-11.4% over the combined use of ZnSO4 and FeSO4, respectively. The normalized difference vegetation index (NDVI) showed an increase of 6.9-44.2% under ZnO and Fe2O3 NPs, as well as ZnSO4 and FeSO4. Furthermore, the combined application of NPs enhanced soybean seed yield by 4.6-18.3% compared to conventional Zn and Fe fertilizers. Concerning seed Zn and Fe density, conjoint application of ZnO and Fe2O3 NPs increases Zn by 1.8-2.2-fold and Fe by 19.22-22.58% over the combined application of Zn SO4 and FeSO4, respectively. While the application of NPs significantly decreased seed phytic acid concentrations by 7.3-59.9% compared to the control. These findings suggest that the combined application of ZnO and Fe2O3 NPs effectively enhances soybean productivity, seed nutrient density, and overall produce quality. Therefore, the combined application of ZnO and Fe2O3 -NPs in soybean can be a potential approach for sustainable soybean production and to reduce/arrest Zn and Fe malnutrition in a growing population.

Studies on (polytrimethylene terephthalate)/graphene oxide/f-MWCNT hybrid nanocomposites.

Natural resource-driven approaches to bioengineering plastics are being developed to compete in the automobiles, power, and other sectors. Polytrimethylene terephthalate (PTT) is a particular of them, and it was chosen for the current investigation to build an advanced nanocomposite material. Using a twin-screw micro compounder, injection moulded PTT/Graphene-Oxide (GO)/Carboxyl functionalized Multiwall Carbon nanotube (f-MWCNT) hybrid nanocomposites were prepared. The impact of GO and f-MWCNT reinforcement on the composite's thermal and mechanical characteristics of hybrid nanocomposites was examined. GO was synthesized from the graphite powder by modified Hummer's method and MWCNTs were functionalized using the concentrated sulfuric acid (H2SO4) and nitric acid (HNO3) with a volume ratio of 3:1 in an ultrasonic bath at room temperature. In all formulations, the investigation was done at a constant filler amount of 2 wt%. To understand the chemical interaction between PTT and nanofiller, Raman spectroscopy was used and to examine the state of dispersion, scanning electron microscopy (SEM) was systematically analysed. In comparison to pristine PTT, the water absorption, tensile strength, flexural strength and impact strength of hybrid nanocomposites were improved marginally. It was also observed that GO has more prominent in increasing the mechanical properties of the hybrid and f-MWCNT in thermal properties. The 3-D geometrical bridge between GO (2-D) and f-MWCNT (1-D) made the hybrid more dispersible and effective for different applications.

Expert panel recommendations for topical hemostatic agent use in varied bleeding sites and situations during neuro-spine surgeries.

Intraoperative bleeding poses a substantial challenge, particularly in neuro-spine surgeries leading to complications such as hematomas, infections, and hemodynamic instability. Despite their proven efficacy, use of topical hemostatic agents (THAs) lacks comprehensive published literature and guidelines particularly in the Indian setting. The present study provides the first-ever Indian expert panel recommendations for effective adjunct THA use in different intraoperative bleeding sites and situations in neuro-spine surgeries. A comprehensive approach, encompassing a literature review, followed by experience sharing in a meeting using a survey helped integrate expert opinions in the form of practical algorithms to guide THA selection. Our survey results revealed a strong inclination towards specific THAs, flowable gelatin + thrombin being choice of THA for difficult to access and problematic bleeding situations during tumor removal/resection, transsphenoidal hypophysectomy and skull-based procedures. Both oxidized regenerated cellulose (ORC)/Fibrillar and flowable gelatin + thrombin were recommended for continuous oozing. ORC/Fibrillar was preferred for arteriovenous and cavernous malformations. This expert-panel guidance on THA use aims to optimize hemostat use practices and improve surgical outcomes in neuro-spine surgery.

Smartphone Usage Patterns Among Postgraduate Medical Students: A Central India Perspective on Adaptive Learning in Medicine.

Introduction Technological developments are drawn on a path of continuous inventions. Smartphones have been used in educational activities to access course content, acquire information related to students' performance, and encourage discussion and sharing between students and teachers. Students as learners are the drivers of using new technology for their learning needs, and this is always evolving. Aim The aim of the study is to assess the usage of smartphones for academic learning among postgraduate medical students in a teaching institute in Central India. Materials and methods This cross-sectional, questionnaire-based study was conducted on 130 postgraduate medical students for a period of four months from 1 January 2022 to 30 April 2022. Results The mean age of the study subjects was 28.34 ± 3.22 years with the range being 23 to 44 years. Smartphones had a significant impact on the academic learning of postgraduate medical students. The major impacts of smartphones on academic learning were in the form of improved learning skills (60.91%), timely completion of assignments (40%), increased participation in discussions (34.55%), enhanced academic performance (33.64%), and development of critical and innovative thinking (29.09%). Conclusion A significant proportion of medical postgraduates are utilizing smartphones and social media for academic purposes. Although this new technology offers the potential to enhance learning and patient care, it has some inherent problems associated with its use. However, it may go a long way in redefining how we manage information in medicine.

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.

Sub-10 nm Mixing and Alloying of Cu-Ag and Cu-Ni via Accelerated Solid Diffusion.

Development of nanoscale multicomponent solid inorganic materials is often hindered by slow solid diffusion kinetics and poor precursor mixing in conventional solid-state synthesis. These shortcomings can be alleviated by combining nanosized precursor mixtures and low temperature reaction, which could reduce crystal growth and accelerate the solid diffusion at the same time. However, high throughput production of nanoparticle mixtures with tunable composition via conventional synthesis is very challenging. In this work, we demonstrate that ∼10 nm homogeneous mixing of sub-10 nm nanoparticles can be achieved via spark nanomixing at room temperature and pressure. Kinetically driven Spark Plasma Discharge nanoparticle generation and ambient processing conditions limit particle coarsening and agglomeration, resulting in sub-10 nm primary particles of as-deposited films. The intimate mixing of these nanosized precursor particles enables intraparticle diffusion and formation of Cu/Ni nanoalloy during subsequent low temperature annealing at 100 °C. We also discovered that cross-particle diffusion is promoted during the low-temperature sulfurization of Cu/Ag which tends to phase-segregate, eventually leading to the growth of sulfide nanocrystals and improved homogeneity. High elemental homogeneity, small diffusion path lengths, and high diffusibility synergically contribute to faster diffusion kinetics of sub-10 nm nanoparticle mixtures. The combination of ∼10 nm homogeneous precursors via spark nanomixing, low-temperature annealing, and a wide range of potentially compatible materials makes our approach a good candidate as a general platform toward accelerated solid state synthesis of nanomaterials.

Rehabilitation medicine in palliative care of chronic neurological conditions.

BACKGROUND: People living with long-term neurological conditions (LTNC) often require palliative care. Rehabilitation medicine specialists often coordinate the long-term care of these patients. OBJECTIVE: The aim of the present review was to undertake systematic literature searches to identify the evidence on palliative care for people with LTNC to guide rehabilitation medicine specialists caring for these patients in the UK. METHODS: We searched for evidence for (1) discussion of end of life, (2) planning for end-of-life care, (3) brief specialist palliative care interventions, (4) support for family and carers, (5) training of rehabilitation medicine specialists in palliative care, and (6) commissioning of services. The databases searched were MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, NHS Economic Evaluation Database and Health Technology Assessment Database. Evidence was assimilated using a simplified version of the Grading of Recommendations Assessment, Development and Evaluation method. RESULTS: We identified 2961 records through database searching for neurological conditions and 1261 additional records through database searches for specific symptoms. We removed duplicate records and conference presentations. We screened 3234 titles and identified 330 potentially relevant abstracts. After reading the abstracts we selected 34 studies for inclusion in the evidence synthesis. CONCLUSIONS: From the evidence reviewed we would like to recommend that we move forward by establishing a closer working relationship with specialists in palliative care and rehabilitation medicine and explore the implications for cross-specialty training.

Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement.

Background: Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted. Materials: To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model. Results: We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel. Conclusion: This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.

Ornamental roses for conservation of leafcutter bee pollinators.

Pollinator conservation is a global priority. Efforts are taken to restore pollinators by improving flower resources, a crucial driver of pollinator diversity and population growth. It helped gardening and landscaping supply chains, which introduced lists of bee-friendly plants and bee hotels, yet, desirable results seem distant. One shortcoming of the present schemes is that they lack a cohesive planning for nesting opportunities and nesting provisions for wild solitary bees, the crucial pollinators of crop and wild plants. We tested whether the world's popular ornamental plant, rose (Rosa chinensis Jacq.)-a hitherto unlisted bee-friendly plant-can aid in conserving leafcutter bees, which require fresh leaves for constructing nest cells. We surveyed 2360 rose plants in 136 sites in rural and urban places and lowlands and highlands of south (8°N-12°N) and northeastern India (26°N-27°N) for the characteristic notches the bees leave on foraged leaves. We reared brood constructed with rose and non-rose leaves to examine the brood success rate. About a quarter of all the roses surveyed had the notches of leafcutter bees on the leaves. However, the proportion of cut roses varied considerably among sites. Bees used roses much higher in urban areas and lowlands than in rural areas and highlands. The selection of plants was negatively associated with pesticide application. The brood success rate was 100% for the brood that was constructed by the leaves of rose and non-rose plants. Rose flowers do not support bees, but rose leaves indeed do. We recommend rose plants in leafcutter bee conservation and restoration schemes, particularly in urban environment.

Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface.

Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.

Applications of Sentinel-2 satellite data for spatio-temporal mapping of deep pools for monitoring the riverine connectivity and assessment of ecological dynamics: a case from Godavari, a tropical river in India (2016-2021).

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e., fortnightly, monthly, seasonal, annual and demi-decadal during 2016-2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016-2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed.

An atypical bilateral trifurcation of recurrent laryngeal nerve.

BACKGROUND: Thyroidectomy is a frequently performed surgery for benign and malignant conditions. Nevertheless, one of the most critical complications of thyroidectomy is recurrent laryngeal nerve (RLN) injury leading to vocal cord paralysis. A thorough knowledge of the anatomical variations of RLN and ligation of the related vessels close to their distal branches is critical to avoid injury. CASE PRESENTATION: Here, we report the first case of bilateral trifurcation of recurrent laryngeal nerve (RLN) in a 40-year old woman with multinodular goitre. Total thyroidectomy was performed and RLN was preserved bilaterally. Followed by a precise dissection, fine branches were traced penetrating the larynx. We did not observe any further post-operative complications and patient was discharged with desired outcomes. CONCLUSIONS: Anatomical variations of the RLN include-bifurcations, trifurcations, relation of RLN with inferior thyroid artery (ITA) and presence of non-recurrent laryngeal nerve. Only RLN dividing at a distance greater than 5 mm (branching point distance) before its entry into the larynx beneath the cricothyroid are said to bifurcate or trifurcate. Approximately 25% of nerves show branching [71%-unilateral and 18%-bilateral bifurcation]. Incidence of unilateral trifurcations have been noted be 0.9% and the rates of bilateral trifurcation and the divisions of the branches is yet to be ascertained. This is the first report of a bilateral trifurcation of RLN, detected in patient with multinodular goitre and hence warrants a precise analysis of variations of the RLN in patients undergoing thyroidectomy, which is critical to prevent RLN injury.

Fabrications of hybrid Polyurethane-Pd doped ZrO2 smart carriers for self-healing high corrosion protective coatings.

The work demonstrates the effective utilization of hybrid Polyurethane - palladium doped zirconium oxide (Pd-ZrO2) as innovative carriers for corrosion protection coatings on steel materials. ZrO2 and Pd-ZrO2 nanoparticles were successfully synthesized using Photodeposition followed by the hydrothermal synthesis method. The synthesized nanoparticles were then incorporated into the polyurethane matrix and characterized using Fourier-transform infrared spectroscopy and scanning electron microscopy (SEM). The FTIR and SEM confirm the presence of ZrO2 and Pd-ZrO2 nanoparticles and their morphologies in polyurethane composites material. The thermogravimetric analysis (TGA) results indicated that the polyurethane matrix remained stable up to 250 °C. At 800 °C, >50% of residues are observed for Pd-ZrO2 - polyurethane in the TGA analysis, which confirms that the primer and nanoparticles addition enhances the thermal stability of the composite. The water contact angle measurement explains the hydrophobic behavior of nanocomposite modified coatings on a mild steel substrate. It indicates that Pd-ZrO2 and primer significantly increase the hydrophobicity of polyurethane. The major advantages of developing water-repellent or hydrophobic surfaces open up a world of possibilities for metals and alloys in terms of corrosion prevention. Electrochemical impedance spectroscopy (EIS) and a salt spray test were used to determine the anti-corrosion behavior of the prepared polymer nanocomposites. The polymer nanocomposite coatings have better anti-corrosive capabilities when compared to pure polyurethane. The corrosion protection efficiency increased from 76.63% to 97.57% upon incorporating 2 wt % of Pd-ZrO2 in the polyurethane matrix. The results confirmed that the modifications on the polyurethane enhanced the hydrophobicity and anti-corrosion properties of the polymer nanocomposite coatings.

Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.

ACTA2-Related Dysgyria: An Under-Recognized Malformation of Cortical Development.

BACKGROUND AND PURPOSE: Pathogenic variants in the ACTA2 gene cause a distinctive arterial phenotype that has recently been described to be associated with brain malformation. Our objective was to further characterize gyral abnormalities in patients with ACTA2 pathogenic variants as per the 2020 consensus recommendations for the definition and classification of malformations of cortical development. MATERIALS AND METHODS: We performed a retrospective, multicentric review of patients with proved ACTA2 pathogenic variants, searching for the presence of malformations of cortical development. A consensus read was performed for all patients, and the type and location of cortical malformation were noted in each. The presence of the typical ACTA2 arterial phenotype as well as demographic and relevant clinical data was obtained. RESULTS: We included 13 patients with ACTA2 pathogenic variants (Arg179His mutation, n = 11, and Arg179Cys mutation, n = 2). Ninety-two percent (12/13) of patients had peri-Sylvian dysgyria, 77% (10/13) had frontal dysgyria, and 15% (2/13) had generalized dysgyria. The peri-Sylvian location was involved in all patients with dysgyria (12/12). All patients with dysgyria had a characteristic arterial phenotype described in ACTA2 pathogenic variants. One patient did not have dysgyria or the characteristic arterial phenotype. CONCLUSIONS: Dysgyria is common in patients with ACTA2 pathogenic variants, with a peri-Sylvian and frontal predominance, and was seen in all our patients who also had the typical ACTA2 arterial phenotype.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Sensitivity analysis of cultivar parameters to simulate wheat crop growth and yield under moisture and temperature stress conditions.

Sensitivity of cultivar input parameters were characterised on the outputs of yield and growth variables using a web based crop simulation model Web InfoCrop Wheat. The crop model was assessed for each combination of seventeen input cultivar parameters tested under moisture and temperatures stress conditions in four different ecological regions. Three model outputs, total dry matter at harvest, grain yield at harvest and duration of the crop were chosen for subsequent evaluation. The most dominant cultivar parameters were identified to be TPOPT (Optimum Temp), TTVG (Thermal time for germination to 50% Flowering), KDFMAX (Extinction coefficient of leaves at flowering), GNOCF (Slope of storage organ number/m2 to dry matter during storage organ formation), POTGWT (Potential storage organ weight) and PHOTOSENS (Photoperiod sensitivity) which were associated with growth, thermal time accumulation, leaf area index, grain number and photosensitivity. Comparison of crop simulations with all the cultivar parameters incorporated from the experimental observations and those with only the most sensitive cultivar parameters incorporated was performed. Outputs of the crop simulation were significantly correlated with results from the field experiments. The present study could save time and effort in generating all the cultivar parameters required to perform the crop simulation under moisture and temperature stress conditions. The most significant cultivar parameters (TPOPT, TTVG, KDFMAX, GNOCF, POTGWT and PHOTOSENS) identified through the sensitivity analysis conducted in this study could significantly simulate the crop growth and yield of wheat.

Interactive effect of elevated tropospheric ozone and carbon dioxide on radiation utilisation, growth and yield of chickpea (Cicer arietinum L.).

An experiment was conducted in the Free Air Ozone and Carbon dioxide Enrichment (FAOCE) facility to study the impact of elevated O3, CO2 and their interaction on chickpea crop (cv. Pusa-5023) in terms of phenology, biophysical parameters, yield components, radiation interception and use efficiency. The crop was exposed to elevated O3 (EO:60ppb), CO2 (EC:550 ppm) and their combined interactive treatment (ECO: EC+EO) during the entire growing season. Results revealed that the crop's total growth period was shortened by 10, 14 and 17 days under elevated CO2, elevated O3 and the combined treatment, respectively. Compared to ambient condition, the leaf area index (LAI) under elevated CO2 was higher by 4 to 28%, whilst it is reduced by 7.3 to 23.8% under elevated O3. The yield based radiation use efficiency (RUEy) was highest under elevated CO2 (0.48 g MJ-1), followed by combined (0.41 g MJ-1), ambient (0.38 g MJ-1) and elevated O3 (0.32 g MJ-1) treatments. Elevated O3 decreased RUEy by 15.78% over ambient, and the interaction results in a 7.8% higher RUEy. The yield was 31.7% more under elevated CO2 and 21.9% lower in elevated O3 treatment as compared to the ambient. The combined interactive treatment recorded a higher yield as compared to ambient by 9.7%. Harvest index (HI) was lowest under elevated O3 (36.10%), followed by ambient (39.18%), combined (40.81%), and highest was under elevated CO2 (44.18%). Chickpea showed a positive response to elevated CO2 resulting a 5% increase in HI as compared to ambient condition. Our findings quantified the positive and negative impacts of elevated O3, CO2 and their interaction on chickpea and revealed that the negative impacts of elevated O3 can be compensated by elevated CO2 in chickpea. This work promotes the understanding of crop behaviour under elevated O3, CO2 and their interaction, which can be used as valuable inputs for radiation-based crop simulation models to simulate climate change impact on chickpea crop.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.