A comprehensive and conceptual overview of omics-based approaches for enhancing the resilience of vegetable crops against abiotic stresses.

MAIN CONCLUSION: Abiotic stresses adversely affect the productivity and production of vegetable crops. The increasing number of crop genomes that have been sequenced or re-sequenced provides a set of computationally anticipated abiotic stress-related responsive genes on which further research may be focused. Knowledge of omics approaches and other advanced molecular tools have all been employed to understand the complex biology of these abiotic stresses. A vegetable can be defined as any component of a plant that is eaten for food. These plant parts may be celery stems, spinach leaves, radish roots, potato tubers, garlic bulbs, immature cauliflower flowers, cucumber fruits, and pea seeds. Abiotic stresses, such as deficient or excessive water, high temperature, cold, salinity, oxidative, heavy metals, and osmotic stress, are responsible for the adverse activity in plants and, ultimately major concern for decreasing yield in many vegetable crops. At the morphological level, altered leaf, shoot and root growth, altered life cycle duration and fewer or smaller organs can be observed. Likewise different physiological and biochemical/molecular processes are also affected in response to these abiotic stresses. In order to adapt and survive in a variety of stressful situations, plants have evolved physiological, biochemical, and molecular response mechanisms. A comprehensive understanding of the vegetable's response to different abiotic stresses and the identification of tolerant genotypes are essential to strengthening each vegetable's breeding program. The advances in genomics and next-generation sequencing have enabled the sequencing of many plant genomes over the last twenty years. A combination of modern genomics (MAS, GWAS, genomic selection, transgenic breeding, and gene editing), transcriptomics, and proteomics along with next-generation sequencing provides an array of new powerful approaches to the study of vegetable crops. This review examines the overall impact of major abiotic stresses on vegetables, adaptive mechanisms and functional genomic, transcriptomic, and proteomic processes used by researchers to minimize these challenges. The current status of genomics technologies for developing adaptable vegetable cultivars that will perform better in future climates is also examined.

Evaluation of Bioactive Attributes and Emulsification Potential of Exopolysaccharide Produced by a Brown-rot Fungus Fomitopsis meliae AGDP-2.

Mushrooms possess wide array of biologically active secondary metabolites and have been traditionally used for their medicinal properties. Exopolysaccharide (EPS) is one of such bioactive metabolites. The bioactive attributes and emulsification capabilities of the exopolysaccharides produced by a novel brown-rot fungus Fomitopsis meliae AGDP-2 under submerged fermentation has been thoroughly investigated in the present study. Exopolysaccharide displayed anti-oxidant activities in dose dependent manner with the maximum scavenging of ABTS radicals (42.45%), DPPH radicals (75.34%), Hydroxyl radicals (63.64%), Superoxide anion radical (76.54%) and Ferric Reducing Antioxidant Power with IC50 value of 231 µg/mL. Additionally, evaluation of anti-proliferative properties revealed that EPS significantly inhibited the proliferation of HepG2 and HT-29 cancer cells followed by moderate inhibition of HeLa and MCF-7 cancer cell lines and quite less inhibition of L-132 and KB cell lines. The IC50 values of EPS for the abovementioned cell lines are 9.465 µg/mL, 11.25 µg/mL, 38.98 µg/mL, 87.78 µg/mL, 2061 µg/mL and 2361 µg/mL respectively. Moreover EPS also possess good anti-microbial as well as anti-biofilm properties. The studies on emulsification potential described that EPS is good emulsifier of different vegetable oils and the emulsion formed was quite stable up to 144 h.

Aircraft visibility in view from below in the long-wave infrared band using infrared cross section.

Aircraft low observables' features are crucial in the long-wave infrared (LW-IR) band, due to imaging sensors used in IR search and track and in the latest generation of IR-guided missiles. Earthshine irradiance on the aircraft bottom surface is an important source; hence, it is derived using data for atmospheric transmission. Emission due to skin-friction heating (important at high εbot) and earthshine reflection (important at low εbot) are compared by a dimensionless ratio for different bottom surface emissivities (εbot). The infrared cross section of aircraft in direct view from below is obtained in the LW-IR band, which shows that aircraft is seen also due to negative contrast.

Natural Language Processing-Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study.

BACKGROUND: The process of documentation in electronic health records (EHRs) is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, natural language processing (NLP)-enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user's experience. OBJECTIVE: The objective of this study is evaluate the comparative effectiveness of an NLP-enabled data capture method using dictation and data extraction from transcribed documents (NLP Entry) in terms of documentation time, documentation quality, and usability versus standard EHR keyboard-and-mouse data entry. METHODS: This formative study investigated the results of using 4 combinations of NLP Entry and Standard Entry methods ("protocols") of EHR data capture. We compared a novel dictation-based protocol using MediSapien NLP (NLP-NLP) for structured data capture against a standard structured data capture protocol (Standard-Standard) as well as 2 novel hybrid protocols (NLP-Standard and Standard-NLP). The 31 participants included neurologists, cardiologists, and nephrologists. Participants generated 4 consultation or admission notes using 4 documentation protocols. We recorded the time on task, documentation quality (using the Physician Documentation Quality Instrument, PDQI-9), and usability of the documentation processes. RESULTS: A total of 118 notes were documented across the 3 subject areas. The NLP-NLP protocol required a median of 5.2 minutes per cardiology note, 7.3 minutes per nephrology note, and 8.5 minutes per neurology note compared with 16.9, 20.7, and 21.2 minutes, respectively, using the Standard-Standard protocol and 13.8, 21.3, and 18.7 minutes using the Standard-NLP protocol (1 of 2 hybrid methods). Using 8 out of 9 characteristics measured by the PDQI-9 instrument, the NLP-NLP protocol received a median quality score sum of 24.5; the Standard-Standard protocol received a median sum of 29; and the Standard-NLP protocol received a median sum of 29.5. The mean total score of the usability measure was 36.7 when the participants used the NLP-NLP protocol compared with 30.3 when they used the Standard-Standard protocol. CONCLUSIONS: In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality. Future research will evaluate the NLP-based EHR data capture approach in a clinical setting. It is reasonable to assert that EHRs will increasingly use NLP-enabled data entry tools such as MediSapien NLP because they hold promise for enhancing the documentation process and end-user experience.