Search

Abstract Background Bacillus subtilis is well known for promoting plant growth and reducing abiotic and biotic stresses. Mutant gene-defective models can be created to understand important traits associated with rhizosphere fitness. This study aimed to analyze the role of exopolymeric genes in modulating tomato rhizosphere microbiome assembly under a gradient of soil microbiome diversities using the B. subtilis wild-type strain UD1022 and its corresponding mutant strain UD1022eps−TasA, which is defective in exopolysaccharide (EPS) and TasA protein production. Results qPCR revealed that the B. subtilis UD1022eps−TasA− strain has a diminished capacity to colonize tomato roots in soils with diluted microbial diversity. The analysis of bacterial β-diversity revealed significant differences in bacterial and fungal community structures following inoculation with either the wild-type or mutant B. subtilis strains. The Verrucomicrobiota, Patescibacteria, and Nitrospirota phyla were more enriched with the wild-type strain inoculation than with the mutant inoculation. Co-occurrence analysis revealed that when the mutant was inoculated in tomato, the rhizosphere microbial community exhibited a lower level of modularity, fewer nodes, and fewer communities compared to communities inoculated with wild-type B. subtilis. Conclusion This study advances our understanding of the EPS and TasA genes, which are not only important for root colonization but also play a significant role in shaping rhizosphere microbiome assembly. Future research should concentrate on specific microbiome genetic traits and their implications for rhizosphere colonization, coupled with rhizosphere microbiome modulation. These efforts will be crucial for optimizing PGPR-based approaches in agriculture.

Solar irradiance nowcasting and short-term forecasting are important tools for the integration of solar plants into the electricity grid. Understanding the role of clouds and aerosols in those techniques is essential for improving their accuracy. In this study, we introduce improvements in the existing nowcasting and short-term forecasting operational systems SENSE (Solar Energy Nowcasting System) and NextSENSE achieved by using a new configuration and by upgrading cloud and aerosol inputs, and we also investigate the limitations of evaluating such models using surface-based sensors due to cloud effects. We assess the real-time estimates of surface global horizontal irradiance (GHI) produced by the improved SENSE2 operational system at high spatial and temporal resolution (∼ 5 km, 15 min) for a domain including Europe and the Middle East–North Africa (MENA) region and the short-term forecasts of GHI (up to 3 h ahead) produced by the NextSENSE2 system against ground-based measurements from 10 stations across the models' domain for a whole year (2017). Results for instantaneous (every 15 min) comparisons show that the GHI estimates are within ±50 W m−2 (or ±10 %) of the measured GHI for 61 % of the cases after the implementation of the new model configuration and a proposed bias correction. The bias ranges from −12 to 23 W m−2 (or from −2 % to 6.1 %) with a mean value of 11.3 W m−2 (2.3 %). The correlation coefficient is between 0.83 and 0.96 and has a mean value of 0.93. Statistics are significantly improved when integrating on daily and monthly scales (the mean bias is 3.3 and 2.7 W m−2, respectively). We demonstrate that the main overestimation of the SENSE2 GHI is linked with the uncertainties of the cloud-related information within the satellite pixel, while relatively low underestimation, linked with aerosol optical depth (AOD) forecasts (derived from the Copernicus Atmospheric Monitoring Service – CAMS), is reported for cloudless-sky GHI. The highest deviations for instantaneous comparisons are associated with cloudy atmospheric conditions, when clouds obscure the sun over the ground-based station. Thus, they are much more closely linked with satellite vs. ground-based comparison limitations than the actual model performance. The NextSENSE2 GHI forecasts based on the cloud motion vector (CMV) model outperform the persistence forecasting method, which assumes the same cloud conditions for future time steps. The forecasting skill (FS) of the CMV-based model compared to the persistence approach increases with cloudiness (FS is up to ∼ 20 %), which is linked mostly to periods with changes in cloudiness (which persistence, by definition, fails to predict). Our results could be useful for further studies on satellite-based solar model evaluations and, in general, for the operational implementation of solar energy nowcasting and short-term forecasting, supporting solar energy production and management.

Background Neurometabolic diseases are the results of genetic changes that lead to an imbalance in energy utilization and metabolism. Aim Our aim was to explore the update in treatment and diagnosis of neurometabolic disease. Methods PubMed, Scopus, Google scholar, and the web of science were searched for studies reported in the last 20 years (1997–30/10/2020). The data was searched and archived by keywords like “Neurometabolic”, “neurogenesis”, and “role of neuro-degeneration in neurometabolic disease” without narrowing or limiting search items. Only abstracts of searched publications were reviewed. A total of 389 publications were found in the initial research, in which 62 publications were considered for the study and the remaining were excluded because of their specificity to the subject. Study update The neurometabolic disease affects one in 500 newborns, causing a major burden of illness and infant mortality. However, the cause of the disease is unclear in up to 50% of neurological-like cases. Thus, we ask why are they referred to as neurometabolic disorders (NMD), despite extensive genetic and biochemistry investigations? Treatment is possible for some metabolic diseases. For instance, the devastating neurological effects of phenylketonuria have been recognized for many years. Except for some notable exceptions, treatment remains largely unsatisfactory. Therefore, research efforts concentrate on corrective genetic approaches applicable after early detection by newborn screening or before fertilization. We considered recent studies on treating neurometabolic diseases. We focused on the most common neurometabolic diseases and the associated clinical advancements in their therapy.

This research paper demonstrates a metamaterial (MTM) based sensing technique to detect various blood samples by analyzing their dielectric properties. The performance of this MTM-based sensor is evaluated with the help of mimicked human blood samples that closely resemble the dielectric properties of actual human blood samples. Moreover, the ISM band frequency of 2.4 GHz is chosen as one of the reference resonance frequencies due to its various industrial and medical applications. The resonating patch is developed on the FR-4 substrate with a dimension of 10 × 20 mm2 that provides sharp reference resonances of 2.4 and 4.72 GHz for the spectra of the transmission coefficient with a good quality factor (Q-factor). The MTM sensor can detect the mimicked blood samples with a maximum frequency deviation of up to 650 MHz at 2.4 GHz and up to 850 MHz at 4.72 GHz, with maximum sensitivity of 0.917 and 0.707, respectively. The measured results using the prototype of the sensor support the simulation result with good agreement, indicating high sensing capability. Due to its high sensitivity, figure of merit (FoM), and frequency shifting with dielectric property changes in blood samples, the developed MTM-based sensor can be implemented effectively for quick sensing of infected blood samples and biomedical applications.

NADK2 encodes the mitochondrial form of nicotinamide adenine dinucleotide (NAD) kinase, which phosphorylates NAD. Rare recessive mutations in human NADK2 are associated with a syndromic neurological mitochondrial disease that includes metabolic changes, such as hyperlysinemia and 2,4 dienoyl CoA reductase (DECR) deficiency. However, the full pathophysiology resulting from NADK2 deficiency is not known. Here, we describe two chemically induced mouse mutations in Nadk2-S326L and S330P-which cause severe neuromuscular disease and shorten lifespan. The S330P allele was characterized in detail and shown to have marked denervation of neuromuscular junctions by 5 weeks of age and muscle atrophy by 11 weeks of age. Cerebellar Purkinje cells also showed progressive degeneration in this model. Transcriptome profiling on brain and muscle was performed at early and late disease stages. In addition, metabolomic profiling was performed on the brain, muscle, liver and spinal cord at the same ages and on plasma at 5 weeks. Combined transcriptomic and metabolomic analyses identified hyperlysinemia, DECR deficiency and generalized metabolic dysfunction in Nadk2 mutant mice, indicating relevance to the human disease. We compared findings from the Nadk model to equivalent RNA sequencing and metabolomic datasets from a mouse model of infantile neuroaxonal dystrophy, caused by recessive mutations in Pla2g6. This enabled us to identify disrupted biological processes that are common between these mouse models of neurological disease, as well as those processes that are gene-specific. These findings improve our understanding of the pathophysiology of neuromuscular diseases and describe mouse models that will be useful for future preclinical studies.

Abstract Background Autosomal dominant polycystic kidney disease (ADPKD) leads to progressive renal cyst formation and loss of kidney function in most patients. Vasopressin 2 receptor antagonists (V2RA) like tolvaptan are currently the only available renoprotective agents for rapidly progressive ADPKD. However, aquaretic side effects substantially limit their tolerability and therapeutic potential. In a preliminary clinical study, the addition of hydrochlorothiazide (HCT) to tolvaptan decreased 24-h urinary volume and appeared to increase renoprotective efficacy. The HYDRO-PROTECT study will investigate the long-term effect of co-treatment with HCT on tolvaptan efficacy (rate of kidney function decline) and tolerability (aquaresis and quality of life) in patients with ADPKD. Methods The HYDRO-PROTECT study is an investigator-initiated, multicenter, double-blind, placebo-controlled, randomized clinical trial. The study is powered to enroll 300 rapidly progressive patients with ADPKD aged ≥ 18 years, with an eGFR of > 25 mL/min/1.73 m2, and on stable treatment with the highest tolerated dose of tolvaptan in routine clinical care. Patients will be randomly assigned (1:1) to daily oral HCT 25 mg or matching placebo treatment for 156 weeks, in addition to standard care. Outcomes The primary study outcome is the rate of kidney function decline (expressed as eGFR slope, in mL/min/1.73 m2 per year) in HCT versus placebo-treated patients, calculated by linear mixed model analysis using all available creatinine values from week 12 until the end of treatment. Secondary outcomes include changes in quality-of-life questionnaire scores (TIPS, ADPKD-UIS, EQ-5D-5L, SF-12) and changes in 24-h urine volume. Conclusion The HYDRO-PROTECT study will demonstrate whether co-treatment with HCT can improve the renoprotective efficacy and tolerability of tolvaptan in patients with ADPKD.

Wheat breeding enhances wheat crops for better environmental resistance and higher yield potential. Experimental breeding lines are evaluated based on their yield potential, where quantifying spikes per unit area and kernels per spike is crucial for assessment. This study introduces SPINEL (SPIke and kerNEL), a framework that combines unmanned aerial vehicle (UAV)-captured multispectral imaging and field-captured RGB camera imaging for spike and kernel quantification. This approach utilizes YOLOv8 models, each tailored for a specific detection task. The first model detects plots in UAV-captured multispectral images with a mean average precision (mAP) score of 95%, while the second model, trained to detect spikes in the same dataset, demonstrates an mAP score of 86%. The third model detects spikes and kernels in field-captured RGB images with an 85% mAP score. The first two models aid in estimating the spike density in each field plot. The third model provides the estimated number of kernels in spikes of each unique breeding line. Spikes per field plot and kernels per spike serve as key quantification metrics. The SPINEL framework utilizes the geolocation information of the multispectral images and associates these metrics with breeding lines at the field level. This integration provides a clear visual representation of spike count and average kernels per spike for each field plot. SPINEL offers a precise, automated solution for phenotyping in wheat breeding, promising significant advancements in crop improvement strategies.

This study explored how to use UAV-based multispectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data were collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices were collected utilizing portable phenotyping instruments, and agronomic data were collected manually. The YOLOv8 detection model was utilized for field-scale wheat plot detection. Four ML algorithms—decision tree (DT), random forest (RF), gradient boosting (GB), and extreme GB (XGBoost were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm's predicting ability across all growth stages, with a root-mean-square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination ($R^{2}$) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an $R^{2}$ of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an $R^{2}$ value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and ML in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices.

Abstract As human spaceflight increases in duration, cultivation of crops in spaceflight is crucial to protecting human health under microgravity and elevated oxidative stress. Foodborne pathogens (e.g., Salmonella enterica) carried by leafy green vegetables are a significant cause of human disease. Our previous work showed that Salmonella enterica serovar Typhimurium suppresses defensive closure of foliar stomata in lettuce (Lactuca sativa L.) to ingress interior tissues of leaves. While there are no reported occurrences of foodborne disease in spaceflight to date, known foodborne pathogens persist aboard the International Space Station and space-grown lettuce has been colonized by a diverse microbiome including bacterial genera known to contain human pathogens. Interactions between leafy green vegetables and human bacterial pathogens under microgravity conditions present in spaceflight are unknown. Additionally, stomatal dynamics under microgravity conditions need further elucidation. Here, we employ a slow-rotating 2-D clinostat to simulate microgravity upon in-vitro lettuce plants following a foliar inoculation with S. enterica Typhimurium and use confocal microscopy to measure stomatal width in fixed leaf tissue. Our results reveal significant differences in average stomatal aperture width between an unrotated vertical control, plants rotated at 2 revolutions per minute (2 RPM), and 4 RPM, with and without the presence of S. typhimurium. Interestingly, we found stomatal aperture width in the presence of S. typhimurium to be increased under rotation as compared to unrotated inoculated plants. Using confocal Z-stacking, we observed greater average depth of stomatal ingression by S. typhimurium in lettuce under rotation at 4 RPM compared to unrotated and inoculated plants, along with greater in planta populations of S. typhimurium in lettuce rotated at 4 RPM using serial dilution plating of homogenized surface sterilized leaves. Given these findings, we tested the ability of the plant growth-promoting rhizobacteria (PGPR) Bacillus subtilis strain UD1022 to transiently restrict stomatal apertures of lettuce both alone and co-inoculated with S. typhimurium under rotated and unrotated conditions as a means of potentially reducing stomatal ingression by S. typhimurium under simulated microgravity. Surprisingly, rotation at 4 RPM strongly inhibited the ability of UD1022 alone to restrict stomatal apertures and attenuated its efficacy as a biocontrol following co-inoculation with S. typhimurium. Our results highlight potential spaceflight food safety issues unique to production of crops in microgravity conditions and suggest microgravity may dramatically reduce the ability of PGPRs to restrict stomatal apertures.

Validity and reliability are crucial when conducting research to ensure the truthfulness of an instrument. This study investigated the measurement functioning of an instrument on students' awareness of space science. The instrument was administered to 206 secondary school students involved in the Sudden Ionospheric Disturbance p outreach program. Two experts evaluated the content validity of the instrument. Data were analyzed using the Winsteps 3.71.0.1 software to obtain the Rasch model analysis (RMA) on item reliability and persons' separation, item measure, item fit based on PTMEA CORR, polarity items, misfit items, unidimensionality, and a person-item map. The findings revealed that the items are valid, reliable, and appropriate to measure awareness of space science.

This article proposed a labyrinth shape circular split ring resonator (LC-SRR) based microwave sensor for material permittivity characterization at L to S-band applications. The proposed sensor achieved miniaturized dimensions of only 25 × 20 mm2 by utilizing fractal technology. This new design strategy adopts a unique and compact structure and high electromagnetic (EM) field intensity, contributing to a more precise sensing mechanism in the microwave frequency range. The design geometry achieved resonant frequency at 2.57 GHz with a notch depth of −29.5 dB. The design evolution, metamaterial characteristics, equivalent circuit model, and electric (E) and magnetic (H) fields are presented to understand the stopband characteristics at the resonant frequency. A high sensitivity of 10.50 % is achieved for the proposed sensor for the permittivity range from 1 to 2. The reflection and transmission characteristics of the proposed LC-SRR based sensor are validated by fabricating and measuring the prototype. Moreover, the sensor performance is validated using different dielectric materials (Roger RT5880, Roger RO4350B, and FR-4) The measurement results show consistency with simulation results. Finally, the miniaturized low-profile sensor structure and high sensitivity to characterize the permittivity of the material makes the proposed sensor suitable for low-permittivity sensing applications.

This paper explores the implications of Indonesia's agreement to delegate the Flight Information Region (FIR) of air navigation services to Singapore, specifically in the airspace of the Riau and Natuna islands, as stated in Presidential Regulation Number 109 of 2022. Using the normative juridical method, this study examines the intricate details of the agreement, with a focus on its potential legal consequences for Indonesia's national and international obligations.Although Indonesia retains sovereignty over its territory, this analysis scrutinizes the nuanced provisions of the agreement and their implications, particularly in terms of the technical aspects of air traffic services provided by the Singapore Air Traffic Services Provider above Indonesia's territories, which the agreement does not explicitly delineate the responsibilities or shared consequences in law. The purpose of this paper is to elucidate Indonesia's increased responsibility under the new agreement and emphasize the importance of a careful approach to its implementation. By exploring the multifaceted dimensions of national and international interests, this study seeks to highlight the imperative for Indonesia to navigate the agreement prudently.Ultimately, this analysis aims to provide a comprehensive understanding of Indonesia's obligations, potential challenges, and essential considerations under the renewed agreement of 2022, underscoring the significance of a balanced approach in safeguarding Indonesia's interests on both national and international fronts.

BackgroundA low socioeconomic status is associated with a vulnerable health status (VHS) through the accumulation of health-related risk factors, such as poor lifestyle behaviors (eg, inadequate nutrition, chronic stress, and impaired health literacy). For pregnant women, a VHS translates into a high incidence of adverse pregnancy outcomes and therefore pregnancy-related inequity. We hypothesize that stimulating adequate pregnancy preparation, targeting lifestyle behaviors and preconception care (PCC) uptake, can reduce these inequities and improve the pregnancy outcomes of women with a VHS. A nudge is a behavioral intervention aimed at making healthy choices easier and more attractive and may therefore be a feasible way to stimulate engagement in pregnancy preparation and PCC uptake, especially in women with a VHS. To support adequate pregnancy preparation, we designed a mobile health (mHealth) app, Pregnant Faster, that fits the preferences of women with a VHS and uses nudging to encourage PCC consultation visits and engagement in education on healthy lifestyle behaviors. ObjectiveThis study aimed to test the feasibility of Pregnant Faster by determining usability and user satisfaction, the number of visited PCC consultations, and the course of practical study conduction. MethodsWomen aged 18-45 years, with low-to-intermediate educational attainment, who were trying to become pregnant within 12 months were included in this open cohort. Recruitment took place through social media, health care professionals, and distribution of flyers and posters from September 2021 until June 2022. Participants used Pregnant Faster daily for 4 weeks, earning coins by reading blogs on pregnancy preparation, filling out a daily questionnaire on healthy lifestyle choices, and registering for a PCC consultation with a midwife. Earned coins could be spent on rewards, such as fruit, mascara, and baby products. Evaluation took place through the mHealth App Usability Questionnaire (MAUQ), an additional interview or questionnaire, and assessment of overall study conduction. ResultsDue to limited inclusions, the inclusion criterion “living in a deprived neighborhood” was dropped. This resulted in the inclusion of 47 women, of whom 39 (83%) completed the intervention. In total, 16 (41%) of 39 participants visited a PCC consultation, with their main motivation being obtaining personalized information. The majority of participants agreed with 16 (88.9%) of 18 statements of the MAUQ, indicating high user satisfaction. The mean rating was 7.7 (SD 1.0) out of 10. Points of improvement included recruitment of the target group, simplification of the log-in system, and automation of manual tasks. ConclusionsNudging women through Pregnant Faster to stimulate pregnancy preparation and PCC uptake has proven feasible, but the inclusion criteria must be revised. A substantial number of PCC consultations were conducted, and this study will therefore be continued with an open cohort of 400 women, aiming to establish the (cost-)effectiveness of an updated version, named Pregnant Faster 2. International Registered Report Identifier (IRRID)RR2-10.2196/45293