A large number of indices for presence-absence data that compare two assemblages have been proposed or reinvented. Interpretation of these indices varies across the literature, despite efforts for clarification and unification. Most effort has focused on the mathematics behind the indices, their relationships with diversity, and with each other. At the same time, the following issues have been largely overlooked: (i) requirement that a small re-arrangement of assemblages should only cause a small change in an index, (ii) inferences from the indices about diversity patterns, (iii) inter-dependence of indices based on their information value, (iv) overlap of the ecological phenomena that the indices aim to capture, and (v) incomparability of measures of different phenomena. Neglecting these issues has resulted in the invention or reinvention of indices without increasing their information value, although this value is crucial for correct interpretation of the indices. We offer a framework for pairwise diversity indices that accounts for these issues. We differentiate between statistical and information dependence of indices and show mathematical links between all indices, even those that have not yet been developed. Using linear algebra, we show (1) which set of indices carries complete information on assemblage arrangement, (2) how to calculate any index from two presence-absence indices, which can be used to standardize and compare different indices across the literature, and (3) what can be inferred about diversity phenomena from different informationally independent indices. It is impossible to purify an index of a single biodiversity phenomenon from the effects of other phenomena, because these phenomena inevitably constrain each other. Consequently, many recently proposed indices do not measure the phenomena that they were intended to measure. In contrast, a proper inference can be made by combining classical indices from different, information independent families.

Biological experiments are often short-lived due to logistical or resource-related challenges, and short-term observations are extrapolated to make long-term predictions. However, the effects of experimental treatments on biological communities and processes take time to develop. Consequently, the robustness of conclusions drawn from observations increases with the duration of the experiment. Scattered throughout central Laos, thousands of large stone jars have been left behind from ancient burial rituals. The most famous sites in the Xiengkhouang province are collectively referred to as the Plain of Jars. These jars form a massive biological experiment: for ~2000 years, rainwater has interacted with geology to create unique yet replicated aquatic ecosystems influenced by different tree coverage. The layout of these jars, with clusters of up to several hundred jars separated by several kilometers, allows for controlled testing of multiple questions within ecology and evolution. Here, we report for the first time how these ancient mesocosms can be used to test ecosystem responses to local abiotic variation and disturbance. We show that tree cover dominates every jar ecosystem’s state, and that variations in tree cover density create gradients in oxygen and nutrient concentrations among jar ecosystems. These findings show that litter contribution to aquatic ecosystems leads to higher nutrient content and reduces oxygen concentration, even in systems under different long-term selection in the oldest man-made ecosystems ever analyzed. This first biological analysis provides a fundamental understanding of a unique environment and offers trajectories for future exploration.

not-yet-known not-yet-known not-yet-known unknown Accurate forecasts of species distributions in response to changing climate is essential for proactive management and conservation decision-making. However, species distribution models (SDMs) often have limited capacity to produce robust forecasts under novel environmental conditions, partly due to limitations in model training data. Model-based approaches that leverage diverse types of data have advanced over the last decade, yet their forecasting skill, especially during episodic climatic events, remains uncertain. Here, we develop a suite of SDMs for a commercially important fishery species, albacore tuna (Thunnus alalunga), to evaluate forecast skill under marine heatwave conditions. We compare models that use different methods to leverage data sources (data pooling vs. joint likelihood) and to address spatial dependence (environmental and spatial effects vs. environmental-only) to assess their relative performance in predicting species distributions under novel environmental conditions. Our results indicate model performance declined across all model types as environmental novelty increased as expected. However, joint-likelihood approaches were more resilient to novel conditions, demonstrating greater predictive skill and ecological realism than traditional SDMs. These results suggest that ecological forecasts under novel environmental conditions are more skillful with a model framework that accounts for unmeasured spatial and temporal variability and uses model-based data integration to explicitly leverage diverse data types. As access to diverse data sources continues to increase, maximizing their utility will be key for delivering accurate forecasts of species distributions and advancing proactive, climate-ready management and conservation strategies.

Tomato cultivation is a significant field crop operation in the world and a primary source of yield. Tomato plants are more susceptible to many diseases, which impact yield and product quality. The detection of disease by conventional methods, such as visual inspection, takes a long time and is unreliable, thus resulting in a delay in management. This study proposes a new method for disease detection of tomato leaves using EfficientNet, a machine learning algorithm that will ensure accurate results without significant computational slowdowns. It uses the PlantVillage dataset, containing images of tomato leaves. Employing a mix of transfer learning and data augmentation, a specialized Convolutional Neural Network model - in this instance, EfficientNet - has been created. The main aim of the model is to successfully identify major diseases like Early Blight, Late Blight, and Septoria Leaf Spot and provide farmers worldwide with a reliable automated system for early diagnosis and treatment. By increasing disease detection, this research reduces crop loss per pesticide applied, thereby achieving more sustainable and precise farming and economic gain for local farmers. The model is an EfficientNet architecture with batch normalization, dropout, and dense layers to ensure optimal feature extraction and sorting into 11 disease classes—the model utilized transfer learning and adaptive optimization algorithms. The model outputs a test accuracy of 98.37% and an F1 Score of 0.9836, indicating that the model is efficient and trustworthy in disease recognition. This performance is significant in agricultural diagnosis. It reduces losses and promotes sustainable agriculture.

not-yet-known not-yet-known not-yet-known unknown In this study, we analyze post-storm satellite imagery to assess solar photovoltaic (PV) damage for over 11400 systems following a catastrophic hailstorm in Austin, TX in September 2023, which produced softball-sized hail; and for over 1500 systems across Puerto Rico and the US Virgin Islands after Hurricanes Irma and Maria in September 2017. Findings show that approximately 5.9% of identified PV sites were damaged in the hailstorm, and approximately 17% of PV installations were damaged after the hurricanes. A weak correlation between hurricane wind gust speed and percent site damage was determined, with installation practices playing a heavy role in site resilience. Additionally, we show that newer module vintages are more susceptible to hail damage than older modules, likely due to decreasing module glass thickness. For hail sizes of 60 mm or greater, consistent hail damage is sustained by PV installations, regardless of system configuration.

not-yet-known not-yet-known not-yet-known unknown Introduction: Myasthenia gravis (MG) is primarily caused by autoantibodies that have detrimental effects on neuromuscular junctions. There are two subtypes of MG, namely generalized MG (GMG) and ocular MG (OMG). Half of the patients with MG have OMG, and more than half of the patients with OMG could progress to having GMG in 2 years. Neurologists are mostly concerned about the possibility of the occurrence of OMG and when it would transition to GMG. Now, there have been numerous studies on experimental autoimmune myasthenia gravis (EAMG) for GMG; however, there have been very few available studies on OMG EAMG, which were focused on anti-muscle nicotinic acetylcholine receptor and involved using the GMG EAMG protocol. Result and Discussion: We summarize the literature on the clinical and basic information in OMG, focusing on their related autoantibodies, conversion, treatment dilemmas, and animal models and the gene, protein, function, and EAMG of low-density lipoprotein receptor-related protein 4 (LRP4). LRP4 is a key protein in the neuromuscular junction. Based on the above evidence, a strong relationship was observed between OMG and LR4, suggesting that the LRP4 EAMG might be a good candidate to mimic OMG.

Recently, the norovirus GII.13[P16] strain, the source of which has not been identified, was detected in epidemiological investigation samples from four regions in South Korea. The norovirus GII.13 genotype is rare worldwide. In this case, the causative pathogen was not identified in the food, and the epidemiological characteristics of the transmission route could not be confirmed. The P2 domain of norovirus is a hypervariable region, and analyzing its sequence analysis is an important approach for identifying transmission routes and epidemiological relationships. Cluster analysis confirmed that the capsid region of the norovirus GII.13 strain detected in samples from the four regions showed some differences from that of the GII.13 strain reported in Germany. Additionally, the differences among the strains from the four regions in the capsid and the nucleotide sequence of the P2 domain were identical. Our findings suggest that the source of contamination in patients in the four regions was the same.

To the EditorIs the potential clinical benefit of allergen immunotherapy underestimated in multicentre studies?The clinical efficacy of allergen immunotherapy (AIT) is documented by clinical studies. The standards for clinical trials study design, patient’s selection, appropriate outcomes and statistical treatment have been described by World Allergy Organization (1). As the primary outcome in patients with allergic rhinoconjunctivitis a combined symptom and medication score are recommended as a simple and standardized method that balances both symptoms and the need for antiallergic medication in an equally weighted manner (2). The latest EAACI Guidelines on Allergen Immunotherapy for allergic rhinoconjunctivitis state the indication for AIT to patients experience moderate-to-severe symptoms which interfere with usual daily activities or sleep despite regular and appropriate pharmacotherapy and/or avoidance strategies (3). A problem in recent AIT studies is that in order to include sufficient numbers of patients, they need to be multicentre trials often involving several countries. A fundamental requirement for being able to documenting a beneficial effect of the intervention (AIT), patients included should have a minimum of rhinoconjunctivitis symptoms.A recent published study of tree-pollen sublingual immunotherapy (4) needs some comments. The severity of the disease in the included patients are reflected in the symptom and medication score of placebo-treated patients. The mean daily symptom score (DSS) in the placebo-group during the birch pollen season was 2.75 (on a scale 0-18). The daily mean medication score in the placebo-group was 2.42 (on a scale 0-20) (Figure 1). The implications of these disease scores are that for symptoms the placebo (or untreated) patients have a mean daily symptom score ≈ 0.5 on a scale 0-3 for 6 individual symptoms and for medication the placebo (untreated) patients have a mean intake of < ½ antihistamine per day. The disease severity in the placebo group shows that: either patients have not been selected as having moderate-to-severe rhinitis or pollen exposure at some centres have been so low that correctly included patients have virtually had no clinical symptoms. A suspicion that the latter is the most likely comes from data on “Severe days” (defined as DSS ≥ 6 and ≥2 moderate symptoms or 1 symptom assessed as severe). The proportion of severe days during the birch season was for the placebo group 0.23 [95% CI: 0.17, 0.29]. The proportion of symptom-free days (no symptoms and no use of rescue medication) was 0.24 [95% CI: 0.23, 0.25] in the placebo group. The implications are that untreated patients (placebo) have 23% of days with severe symptoms (although the definition does not imply disabling symptoms) and 24% symptom-free days.In seasonal allergy trials, the observed treatment effect is highly dependent on pollen exposure with the magnitude of efficacy being greater with higher pollen exposure (5). The treatment effects are therefore diluted by including patients with minimal symptoms. A possible method to circumvent this problem is to group study centres into tertiles according to symptom observed in the placebo patients in each centre (low, middle and high tertiles) (6). This allows an estimation of the efficacy that could be achieved in patients from sites where symptoms were severe during the pollen season.ConclusionThe use of a tertile approach to analyse efficacy of AIT in seasonal allergic rhinitis clinical studies can give a more accurate assessment of the potential maximal clinical benefit. By giving information of efficacy based on tertiles in the key secondary endpoints allow for evaluating the potential benefit of AIT in centres with a low, middle and high symptom-medication score in placebo patients. When only presenting average data using the full analysis set of all subjects the full capacity of the treatment will be diluted due to inclusion of virtually asymptomatic patients.Hans-Jørgen Malling, MD, DMSchttps://orcid.org/0000-0003-4868-6307Allergy Clinic, Copenhagen University Hospital Gentofte, Gentofte Hospitalsvej 8, DK 2900 Hellerup, DenmarkCorresponding author contact: hjmalling@dadlnet.dkNo fundingThe author declare no conflict of interests in relation to this workWord counts 598ReferencesG. W. Canonica, C. E. Baena-Cagnani, J. Bousquet et al. Recommendations for standardization of clinical trials with Allergen Specific Immunotherapy for respiratory allergy. A statement of a World Allergy Organization (WAO) taskforce. Allergy 2007; 62: 317-324.Pfaar O, Demoly P, Gerth van Wijk R et al. Recommendations for the standardization of clinical outcomes used in allergen immunotherapy trials for allergic rhinoconjunctivitis: an EAACI Position Paper. Allergy 2014; 69: 854–867.Roberts G, Pfaar O, Akdis CA. EAACI Guidelines on Allergen Immunotherapy: Allergic rhinoconjunctivitis. Allergy. 2018; 73: 765-798.Gappa M, Gagnon R, Horak F et al. The SQ tree sublingual immunotherapy tablet is effective and well tolerated in children—A pivotal phase III trial. Allergy. 2024 Nov 4.  doi: 10.1111/all.16363. Online ahead of print.Durham SR, Nelson HS, Nolte H et al. Magnitude of efficacy measurements in grass allergy immunotherapy trials is highly dependent on pollen exposure. Allergy 2014; 69: 617–623.Howarth P, Malling H-J, Molimard M, Devillier P. Analysis of allergen immunotherapy studies shows increased clinical efficacy in highly symptomatic patients. Allergy 2012; 67: 321–327.Figure 1

Across a broad spectrum, photonics facilitates thermal management by manipulating electromagnetic waves in energy devices, enhancing energy production and efficiency. The light-matter interaction should be explored not only from the perspective of their individual heating/cooling functions but also for their mutual utilization. Here, we review recent advancements in thermal management strategies from a photonic systems viewpoint, presenting the comprehensive strategy that spans heating and cooling. We focus on strategically designed photonics, considering material properties that interact with the long-wave infrared spectrum at wavelengths from 8 to 13 μm, which are optimized for thermal emissions, and utilize the ultraviolet-visible-near infrared spectrum of the solar irradiance band for photothermal heating. Furthermore, exploring the potential for utilizing multiple wavelengths simultaneously forms the basis of a comprehensive photonics design strategy that leverages a broad spectrum of wavelength bands. This review evaluates the role of photonics in temperature control, thermoelectric generation, steam production, photocatalytic reactions, and radiative cooling, concluding with a discussion of the challenges and future directions in photonics for energy systems, highlighting the importance of a deep understanding of these interactions to maximize their potential.

A document by Ruturaj Shete. Click on the document to view its contents.

Background: Sierra Leone’s public health system struggles with its supply chain, which often leads to frequent stockouts, expiry, and pilferage of healthcare commodities. A well-coordinated supply chain system can minimize these challenges. Our current study explored the facilitators and barriers to pharmaceutical supply chain integration in Sierra Leone. Method: This qualitative study utilised the theory of saturation and a purposive sampling method to recruit 15 key informants from various districts in Sierra Leone. A semi-structured interview guide was used to conduct an in-depth interview using a recorder. The interview process was carried out at the various offices of readily available participants and online for out-of-reach participants who had a stable network. The recordings were transcribed verbatim, and themes and subthemes were identified from the transcripts. Result: The main factors facilitating the integration of the pharmaceutical supply chain included the availability of digital health tools, existing capacity-building initiatives, the presence of well-defined policies and Standard Operating Procedures, good working relationships with partners, and effective communication among stakeholders. Barriers such as poor financial allocation and reliance on external donors for support, Inadequate warehouse space, Fragmented procurement processes and logistical challenges, lack of adequate human resources, inconsistencies in policy implementation across regions, lack of timely information sharing and concerns over data security were the key barriers identified in our study. Conclusion: While there are facilitating factors which drive the supply chain integration process by reducing workload, streamlining data access, enhancing transparency, and fostering collaboration with partners; the existence of systemic barriers hinders the full realization of this process. Addressing these systemic barriers while leveraging the identified facilitators will be a crucial step towards building a functional and more resilient supply chain system with integrated functions to meet the country’s healthcare delivery goals.

Biologists are increasingly interested in the ecological and evolutionary factors that influence microbial communities. Yet compared to bacterial communities, our understanding of viral community ecology remains limited. Here, we investigated the factors influencing viral community composition and structure among wild rhesus macaques (Macaca mulatta) in human-modified environments in Bangladesh, focusing on assemblages of picobirnaviruses (PbVs) as a model system. We found that anthropogenic factors—particularly human and livestock densities—were the strongest predictors of viral community composition. Host social structure played a secondary role, shaping viral communities at the group and site levels. Virus-virus associations influenced co-occurrence patterns primarily within individual hosts but their effect became less evident at broader scales. In contrast, individual host traits (age, sex, and physiological stress) and viral phylogenetic relationships had minimal effects on viral co-occurrence. Notably, anthropogenic factors had opposing effects on different PbV taxa: while some viruses were more prevalent in areas with higher human and livestock densities, others were less common, possibly due to their association with wildlife or natural environments. This suggests that macaques may acquire certain PbVs from anthropogenic sources while experiencing reduced exposure to others in human-modified landscapes. Together, these findings reveal the dominant role of environmental and social factors in shaping viral communities, and highlight the hierarchical nature of virus community assembly – with different ecological processes operating at individual, group, and site scales.

Title:A novel finding of hair growth like vellus hairs on glabrous skin of distal phalanx of thumb in Vogt-Koynagi-Harada DiseaseAuthors:1 Dr. Muhammad Mateen Amir dr.mateen61@gmail.comAl- Khidmat teaching hospital UCMD.2 Bilal Aslamdrbilalaslamsheikh@gmail.comUniversity of Lahore3 Javed IqbalJiqbal3@hamad.qaHamad Medical Corporation Doha4 Syed Muhammad AliSAli35@hamad.qaHamad Medical Corporation Doha

A key requirement for building a machine that can learn is to understand the process by which living beings learn and adapt to their surroundings. This article looks at the process by which living beings adapt to their surroundings.

Dihydrofolate reductase (DHFR) is a critical enzyme for bacterial survival, catalyzing the reduction of folate to tetrahydrofolate (THF), a vital cofactor for DNA synthesis. The significant structural differences between bacterial and human DHFR present an opportunity for targeted drug discovery, especially in combating antibiotic resistance. This study employed virtual screening and molecular dynamics simulations to identify novel inhibitors of DHFR in Klebsiella pneumoniae. Three ligands, specifically 5, 16, and 25, were found to exhibit strong binding affinities within the folate-binding site, effectively competing with the natural substrate. Detailed analyses revealed that ligand 5 engages critical residues such as Met20 and Asp27, indicating a potential mechanism for competitive inhibition. Ligands 16 and 25 also demonstrated substantial interaction stability, disrupting substrate binding through diverse intermolecular forces. Molecular dynamics simulations indicated consistent stability metrics across root-mean-square deviation (RMSD), polar surface area (PSA), and solvent-accessible surface area (SASA) assessments, confirming robust binding characteristics. These findings suggest that ligands 5, 16, and 25 represent promising leads for the development of novel antibacterial agents targeting DHFR, with the potential to circumvent common resistance mechanisms. Future research will be essential to validate these compounds in vitro and in vivo, paving the way for optimized therapeutic options against multidrug-resistant pathogens.

This paper investigates a cell-free massive multiple-input multiple-output (MIMO)-aided binary task offloading mobile edge computing (MEC) system. The goal is to optimize computation task processing for user equipment (UE). We formulate an optimization problem for jointly allocating computation and communication resources under a power constraint. The objective is to minimize weighted energy consumption subject to a maximum delay constraint. We simplify the original problem and transform it into an NP-hard problem under fractional non-convex constraints. A heuristic scheme is proposed based on a genetic algorithm (GA) and alternating iterations. Simulation results show that the proposed algorithm significantly reduces energy consumption in task processing.

Ecosystems within cities can contribute significantly to preserving local biodiversity in the face of urban sprawl, but they face significant threats from anthropogenetic activities and the introduction of alien invasive species (AIS). A comprehensive management plan is required to adequately preserve the biodiversity harboured by urban ecosystems. However, ecological information needed to establish, implement and monitor such plans is often incomplete. In this study, we assessed the application of eDNA metabarcoding in surveying plant biodiversity in an aquatic habitat by collecting water samples at five sites in an urban dam in the City of Johannesburg. Out of 178 reconstructed OTUs, only 43 could be assigned to species level (including four AIS). The remaining OTUs could not be identified below the genus level, indicating that no DNA barcodes have yet been generated for the putative species in question. The spatial distribution of plant diversity across the surveyed dam was heterogeneous. Although this study provides a good overview of plant community structure, it also revealed a gap in the taxonomic coverage of South African plants on public DNA databases. This emphasises the need for increased national barcoding efforts to expand current reference sequence databases.

The balance between supply and demand of ecosystem services (ESs) is the cornerstone for maintaining regional socio-ecological system stability and preventing land degradation. However, current research on the driving mechanisms of ESs mostly focuses on the supply side, and the driving mechanisms of supply-demand balance have not been sufficiently emphasised. Taking the Hotan Oasis as the research region, we measured the ecosystem services supply-demand ratios (ESDR) of four key services, including sand fixation (SF), water yield (WY), soil retention (SR), and food production (FP) in the Hotan Oasis from 2005 to 2020. We utilised the optimal parameter-based geographic detector (OPGD) model to explore the driving mechanisms for the four ESDRs. The results show that: (1) the supply and demand of ESs showed significant spatial and temporal variations; SF and SR were generally in deficit, with SF deficit areas mainly distributed in the northern desert zone and SR deficit concentrated in the southern mountainous areas. WY and FP had quantitative surpluses, but spatial mismatches were prominent; (2) Natural and human activities jointly drove the supply and demand of ESs. Animal husbandry had a higher driving effect on SF, and its interaction with natural factors was significant; Natural factors had the strongest driving effect on ESDRs in WY and SR; the planting industry had the strongest driving effect on FP, and its interaction with population factors was prominent. (3) Aiming at the spatial heterogeneity of the supply and demand of ESs and the driving mechanism, this study proposed a zonal management framework of “source sand control - pathway blocking - system synergy”.

IntroductionGastric volvulus is an uncommon but potentially life-threatening condition characterized by the abnormal twisting of the stomach along its axes, leading to obstruction and compromised blood flow (ischemia)1,2. Initially described by Berti in 1866, this disorder poses significant diagnostic and therapeutic challenges due to its broad spectrum of clinical presentations 3,4. There is no gender predilection, and the highest incidence is observed in individuals in their fifth decade of life, with a secondary peak in infants under one year of age 3. Predisposing factors include diaphragmatic defects such as hiatal or paraesophageal hernias, phrenic nerve dysfunction, congenital anomalies of the stomach or spleen, a history of bariatric surgery, and spinal deformities like kyphoscoliosis 2,3,5.The clinical manifestations of gastric volvulus are highly variable, ranging from vague abdominal discomfort to acute abdominal emergencies, complicating timely diagnosis 2. A classic presentation includes Borchardt’s triad, which is present in 70% of the cases. It is defined as severe epigastric pain, unproductive retching, and the inability to pass a nasogastric tube 6,7. Etiologically, gastric volvulus can be classified as primary, resulting from congenital or acquired abnormalities in the gastric suspensory ligaments, or more commonly as secondary, associated with other anatomical defects such as diaphragmatic hernias 2,5.Intestinal obstruction is the primary complication, which can manifest acutely, recurrently, intermittently, or chronically8. Additionally, the risk of gastric strangulation can lead to severe outcomes, including gastric tissue necrosis, perforation, sepsis, hypovolemic shock, displacement of the other internal organs, and aspiration pneumonia 1,5,8,9. Diagnosis based on the history or physical examination is challenging, and the confirmation typically relies on radiological imaging, with plain radiographs, computed tomography (CT) scans, and upper gastrointestinal series playing crucial roles 5,6. Once diagnosed, surgical intervention is generally the preferred treatment approach8,10.Given the significant morbidity and mortality rates linked to gastric volvulus, early recognition and prompt management are critical. This case series highlights two patients diagnosed with gastric volvulus, illustrating the diverse clinical presentations, diagnostic challenges, and treatment approaches associated with this rare condition.

not-yet-known not-yet-known not-yet-known unknown Background: Acrofacial Dysostosis, Cincinnati Type (AFDC), is a rare autosomal dominant craniofacial disorder associated with mutations in the POLR1A gene. This report describes a family with a novel POLR1A mutation (c.4996T>C), causing progressively severe AFDC phenotypes over three generations. Case Presentation: The proband is a 3-year-old female with severe craniofacial abnormalities,

Objectives. Attachment styles of forensic service users can be unclassifiable, agreement between staff regarding service user attachment styles may be poor. Instability of attachment behaviour may be associated with untoward incidents in a medium secure unit. Methods. In a two-stage study, firstly Fleiss Kappa determined agreement levels in attachment style ratings of 43 service users by 60 nursing staff in a medium secure clinic. In the second stage, nursing observations of attachment and untoward behaviour in 10 female service users were analysed over an average of 24 days using Generalised Estimated Equation modelling. Results. Staff ratings of attachment styles were inconsistent, service users were most frequently categorised in all 4 attachment style categories. Within-day variability in attachment behaviour in 10 female service users was common, increased untoward incidents were associated with the highest possible level of attachment behaviour variability (which included secure attachment behaviours). Conclusions. Fluid, exhaustive changes in attachment behaviour may be more useful than attachment styles for predicting challenging behaviour in secure care. Inclusion of securely attached behaviour in the pattern associated with incidents may be especially problematic for predicting proximal risk. Practitioner points

In this paper, a multiple loaded monopole with ultrawideband characteristic is presented with loaded resistance, inductance and capacitance are modeled in linear frequency dependent models. To have a fast frequency sweeping process covering ultrawideband, antenna is simulated by the method of moments (MoM) with asymptotic waveform evaluation (AWE). For simulating with AWE process properly, loaded parallel circuit of effectively linear frequency dependent components is derived in AWE model. As an example, a loaded monopole antenna covering 30–900 MHz is simulated from genetic algorithm (GA) optimization process, and measured result have a good coherence with optimized result.

not-yet-known not-yet-known not-yet-known unknown Violacein is a natural microbial purple pigment that is used as an easily screenable model compound in synthetic biology. In spite of its broader application potential as an antimicrobial, antiviral and antitumor agent, its low yield and the instability of wild-type production strains preclude its industrial production, while heterologous expression is also still at the laboratory scale. Therefore, more effective methods to synthesize violacein based on rational design have become a focus of research. In this study, the violacein biosynthesis pathway was reconstructed in Saccharomyces cerevisiae, but no production of violacein was initially observed. Analysis of the biosynthetic pathway revealed that low vioC expression was responsible for the low yield of violacein. Through pathway engineering, including multicopy integration and overexpression, followed by optimization of the fermentation medium via Box-Behnken design, the violacein yield in 250 mL shake flasks reached 143.89 mg/L, the highest value reported in S. cerevisiae to date. The heterologous reconstruction of the violacein biosynthesis pathway in S. cerevisiae presented here offers theoretical guidance for the de novo biosynthesis of this bioactive compound and related molecules.

not-yet-known not-yet-known not-yet-known unknown Genome-environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data (N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA (N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.