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The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection. Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images. Several works utilized computer vision technologies effectively and contributed a lot in this domain. This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects. A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented. The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories. Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions. The survey would help researchers to gain understanding of computer vision applications in plant disease detection.

In eukaryotic translation initiation, AUG recognition of the mRNA requires accommodation of Met-tRNAi in a ‘PIN’ state, which is antagonized by the factor eIF1. eIF5 is a GTPase activating protein (GAP) of eIF2 that additionally promotes stringent AUG selection, but the molecular basis of its dual function was unknown. We present a cryo-electron microscopy (cryo-EM) reconstruction of a yeast 48S pre-initiation complex (PIC), at an overall resolution of 3.0 Å, featuring the N-terminal domain (NTD) of eIF5 bound to the 40S subunit at the location vacated by eIF1. eIF5 interacts with and allows a more accommodated orientation of Met-tRNAi. Substitutions of eIF5 residues involved in the eIF5-NTD/tRNAi interaction influenced initiation at near-cognate UUG codonsin vivo, and the closed/open PIC conformation in vitro, consistent with direct stabilization of the codon:anticodon duplex by the wild-type eIF5-NTD. The present structure reveals the basis for a key role of eIF5 in start-codon selection.

Recently, several studies have indicated an increased interest in the scientific community regarding the application of Cannabis sativa plants, and their extracts, for medicinal purposes. This plant of enormous medicinal potential has been legalised in an increasing number of countries globally. Due to the recent changes in therapeutic and recreational legislation, cannabis and cannabinoids are now frequently permitted for use in clinical settings. However, with their highly lipophilic features and very low aqueous solubility, cannabinoids are prone to degradation, specifically in solution, as they are light-, temperature-, and auto-oxidation-sensitive. Thus, plant-derived cannabinoids have been developed for oral, nasal-inhalation, intranasal, mucosal (sublingual and buccal), transcutaneous (transdermal), local (topical), and parenteral deliveries. Among these administrations routes, topical and transdermal products usually have a higher bioavailability rate with a prolonged steady-state plasma concentration. Additionally, these administrations have the potential to eliminate the psychotropic impacts of the drug by its diffusion into a nonreactive, dead stratum corneum. This modality avoids oral administration and, thus, the first-pass metabolism, leading to constant cannabinoid plasma levels. This review article investigates the practicality of delivering therapeutic cannabinoids via skin in accordance with existing literature.

Ultrasound-based engineering of carrier-free nanodrugs by supramolecular self-assembly has recently emerged as an innovative and environmentally friendly synthetic approach. By applying high-frequency sound waves (490 kHz) in aqueous solutions, the transformation of small chemotherapeutic and antibiotic drug molecules into carrier-free nanodrugs with anticancer and antimicrobial activities was recently achieved. The transformation of the antibiotic drug molecules, i.e., doxycycline, into stable nanodrugs (~130 nm) with selective anticancer activity was achieved without requiring organic solvents, chemical agents, or surfactants. The obtained nanodrug exhibited reactive oxygen species (ROS)-mediated cytotoxicity on human breast cancer (MDA-MB 231 cells) but a negligible antiproliferative effect on healthy fibroblast cells. Imaging by super-resolution microscopy (STORM) provided insights into the intracellular trafficking and endosomal escape of the nanodrugs. Overall, these findings suggest that small antibiotic drugs can be transformed into chemotherapeutic nanodrugs with high selectivity against cancer cells.

Most people in European countries die in hospitals outside of specialist palliative care wards. Healthcare professionals of all disciplines are therefore often involved in the care for dying patients. Healthcare professionals' perception of quality of care in the dying phase as well as its predictors are of interest to improve quality of care on non-palliative care hospital wards. Identification of personal and organizational predictors of healthcare professionals' perceived quality of care in the dying phase. Cross-sectional online survey with healthcare professionals of ten non-palliative care hospital wards of two university medical centers. Descriptive statistics were used to describe the data. A hierarchical linear regression model with ten theoretically derived personal (gender, age, profession, palliative care training, spirituality, two self-care items, general self-efficacy, thanatophobia, burden factors when caring for dying patients) and two organizational predictors (type of ward, interprofessional patient-centered teamwork) was developed. The dependent variable was an eleven-point Likert-scaled item (0 = extremely bad, 10 = ideal) measuring the quality of care in the dying phase at the respective ward, perceived by healthcare professionals. Predictors were categorized as modifiable and non-modifiable. Most of the n = 201 participants were female (64.7%), nurses (57.2%) and 30-50 years old (53.2%). The regression model was statistically significant (p < 0.001) and explained 30.7% of the total variance. Lower perceived quality of care in the dying phase was associated with younger age (β = 0.15, ρ = 0.020), being a nurse (β = 0.29, ρ < 0.001), and lower perception of interprofessional patient-centered teamwork on their ward (β = 0.37, ρ < 0.001). Perceived quality of interprofessional patient-centered teamwork was the most clinically relevant predictor in this model, as it had the strongest association and was modifiable. Age and profession were significant, non-modifiable predictors but can be considered when implementing interventions. As improving the perceived quality of care in the dying phase could be beneficial for dying patients, interventions strengthening interprofessional patient-centered teamwork should be implemented on non-palliative care hospital wards.

Ultrasonically synthesized core-shell microcapsules can be made of synthetic polymers or natural biopolymers, such as proteins and polysaccharides, and have found applications in food, drug delivery and cosmetics. This study reports on the ultrasonic synthesis of microcapsules using unmodified (natural) and biodegradable glycogen nanoparticles derived from various sources, such as rabbit and bovine liver, oyster and sweet corn, for the encapsulation of soybean oil and vitamin D. Depending on their source, glycogen nanoparticles exhibited differences in size and ‘bound’ proteins. We optimized various synthetic parameters, such as ultrasonic power, time and concentration of glycogens and the oil phase to obtain stable core-shell microcapsules. Particularly, under ultrasound-induced emulsification conditions (sonication time 45 s and sonication power 160 W), native glycogens formed microcapsules with diameter between 0.3 μm and 8 μm. It was found that the size of glycogen as well as the protein component play an important role in stabilizing the Pickering emulsion and the microcapsules shell. This study highlights that native glycogen nanoparticles without any further tedious chemical modification steps can be successfully used for the encapsulation of nutrients.

Introduction . Globally, hospitals are an important place in end‐of‐life care and most frequent place of death in Germany (47%), but at the same time, the least preferred one—both for patients and their informal caregivers. As hospital care in the dying phase on non‐palliative care wards has rarely been studied systematically, we assessed the current state of care in the dying phase in hospitals as a first step. Methods . In an online survey, N  = 165 national health experts were invited to answer eight open questions on care aspects, facilitators, barriers, and needs for improvement as well as COVID‐19 pandemic specifics regarding hospital care in the dying phase. Sociodemographic data were analysed descriptively, and responses were analysed using qualitative thematic analysis. Results . Of n  = 65 experts, 52% work as nursing staff and 30% as physicians. We identified facilitators, barriers, and needs for improvement regarding 11 topics on the following three levels: institutional level (general institutional conditions, hospital culture, and integration of specialist palliative care), team level (attitude towards and dealing with death and dying, competencies, communication, and teamwork) and care level (dying phase, symptom control, patient centredness, and involvement of informal caregivers). Conclusion . Improving care in the dying phase has to overcome barriers on various levels. We assume that rather “small” measures will find their way into clinical routine and contribute to the improvement of hospital care in the dying phase.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative treatment, which carries a high risk of complications and mortality, highlighting the necessity of discussions about life threat. This study explores the perspectives of patients, informal caregivers (ICs), and healthcare professionals (HCPs) regarding the timing, content, and challenges of these discussions. Multicenter cross-sectional survey in comprehensive care centers across Germany, involving patients with allo-HSCT, ICs, and HCPs. Questionnaires assessed perceived concerns about life threat, communication needs, and attitudes toward death. Statistical analyses included descriptive statistics, group comparisons, and multivariate logistic regressions. A total of 61 patients, 31 ICs and 125 HCPs participated in this study. Patients and ICs had a high need for discussion about life threat at diagnosis, which was corroborated by HCPs. At further time points in the transplant trajectory, there were more discrepancies between patients‘/ICs‘ and HCPs’ perceptions towards conversations about life threat. While 61.7% of patients preferred having their ICs present during discussions, ICs often felt overlooked, with only 50% finding conversations with HCPs helpful. HCPs’ avoidance of death was associated with a reduced likelihood of reporting a need for conversation in patients in the event of severe complications. These findings highlight a discrepancy between patients’ and ICs’ preferred timing for conversations about life threat and HCPs’ perceptions of when patients actually express such a need. To bridge this gap, earlier and ongoing conversations are essential. Training programs should address HCPs’ discomfort in discussing prognosis and improve interdisciplinary teamwork to standardize end-of-life communication. Trial registration number : DRKS00027290 (German Clinical Trials Register) on 10.01.2022.

Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad‐spectrum activity and minimum resistance development against the rapidly evolving antibiotic‐resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high‐ranking motifs into new peptide sequences. The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non‐AMPs. The generated peptide sequences are experimentally validated against the bacterial pathogens, and an accuracy of ≈60% is achieved. To refine the algorithm, the physicochemical features are analyzed, particularly charge and hydrophobicity of experimentally validated peptides. The peptides with specific range of charge and hydrophobicity are then removed, which lead to a substantial increase in an experimental accuracy, from ≈60% to ≈80%. Furthermore, generated peptides are active against different fungal strains with minimal off‐target toxicity. In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens. A machine learning‐based in silico predictive and generative model is developed to engineer highly effective antimicrobial peptides (AMPs) for combating multidrug‐resistant pathogens. An iterative algorithm involving peptide generation, scoring, and motif identification produces AMPs with high theoretical scores. Experimental validation against microbial pathogens and algorithm improvement based on these results demonstrates 80% accuracy showcasing its effectiveness and translational potential.

Peanut ( Arachis hypogaea L.) growers use different tillage systems in the Southeastern United States, the impact of which needs to be assessed with regard to evapotranspiration (ET), carbon uptake, and water-use efficiency (WUE). The eddy-covariance method was used to measure these fluxes in peanut in two common tillage systems (strip tillage vs. conventional tillage) over the course of three consecutive growing seasons (2019–2021). Results suggest that during the dry year of 2019 with rainfall of only 30 cm, strip tillage peanut had a significantly higher daytime ecosystem WUE, 105%, 51%, and 32% higher than that of the conventional tillage in early, mid, and late growth stages, respectively. In 2020, with mean rainfall the overall difference in average WUE was nonsignificant between the tillage systems. Heavy rainfall of 112 cm in 2021 led to waterlogged conditions in the conventional tillage field due to poorer infiltration. This likely reduced the CO 2 uptake. Waterlogging did not occur in the strip tillage field due to improved infiltration. As a result, in 2021, 18%, 33%, and 48% greater ecosystem WUE in strip tillage during early, mid, and later stages was found. Thus, this study suggests that strip tillage fields can achieve higher net CO 2 uptake and WUE in Georgia during dry or very wet years. However, no difference in WUE was found between different tillage systems in a typical year with average rainfall for Georgia. The present study has implications for regions characterized by long growing seasons and low rainfall.