A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

Calcium Signals during SARS-CoV-2 Infection: Assessing the Potential of Emerging Therapies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense single-stranded RNA virus that causes coronavirus disease 2019 (COVID-19). This respiratory illness was declared a pandemic by the world health organization (WHO) in March 2020, just a few weeks after being described for the first time. Since then, global research effort has considerably increased humanity’s knowledge about both viruses and disease. It has also spawned several vaccines that have proven to be key tools in attenuating the spread of the pandemic and severity of COVID-19. However, with vaccine-related skepticism being on the rise, as well as breakthrough infections in the vaccinated population and the threat of a complete immune escape variant, alternative strategies in the fight against SARS-CoV-2 are urgently required. Calcium signals have long been known to play an essential role in infection with diverse viruses and thus constitute a promising avenue for further research on therapeutic strategies. In this review, we introduce the pivotal role of calcium signaling in viral infection cascades. Based on this, we discuss prospective calcium-related treatment targets and strategies for the cure of COVID-19 that exploit viral dependence on calcium signals.

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

Multi-day evaluation of space domain awareness architectures via decision analysis and multi-objective optimization

The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.

Gene-Based Therapeutics for Inherited Retinal Diseases

Inherited retinal diseases (IRDs) are a heterogenous group of orphan eye diseases that typically result from monogenic mutations and are considered attractive targets for gene-based therapeutics. Following the approval of an IRD gene replacement therapy for Leber’s congenital amaurosis due to RPE65 mutations, there has been an intensive international research effort to identify the optimal gene therapy approaches for a range of IRDs and many are now undergoing clinical trials. In this review we explore therapeutic challenges posed by IRDs and review current and future approaches that may be applicable to different subsets of IRD mutations. Emphasis is placed on five distinct approaches to gene-based therapy that have potential to treat the full spectrum of IRDs: 1) gene replacement using adeno-associated virus (AAV) and nonviral delivery vectors, 2) genome editing via the CRISPR/Cas9 system, 3) RNA editing by endogenous and exogenous ADAR, 4) mRNA targeting with antisense oligonucleotides for gene knockdown and splicing modification, and 5) optogenetic approaches that aim to replace the function of native retinal photoreceptors by engineering other retinal cell types to become capable of phototransduction.

Research Progress and Key Issues of Hydrodebenzylation of Hexabenzylhexaazaisowurtzitane (HBIW) in the Synthesis of High Energy Density Material Hexanitrohexaazaisowurtzitane (HNIW)

High energy density materials (HEDM) are the subject of an extensive research effort in relation to the use of these compounds as components of rocket propellants, powders, and formulations of high-performance explosives. Hexanitrohexaazaisowurtzitane (HNIW, i.e., CL-20) has received much attention in these research fields for its specific impulse, burning rate, ballistics, and detonation velocity. In this paper, the development and performances of the explosives from the first to the fourth generation are briefly summarized, and the synthesis status of the fourth-generation explosive, HNIW, is reviewed. The key issues that restrict the development of industrial amplification synthesis of HNIW are analyzed, and the potential directions of development are proposed. It is pointed out that to synthesize new and efficient catalysts is the key to making the cost-effective manufacturing of CL-20 a reality.

Relating Ndesign to Field Compaction: A Case Study in Minnesota

High field density helps in increasing the durability of asphalt pavements. In a current research effort, the University of Minnesota and the Minnesota Department of Transportation (MnDOT) have been working on designing asphalt mixtures with higher field densities. One critical issue is the determination of the Ndesign values for these mixtures. The physical meaning of Ndesign is discussed first. Instead of the traditional approach, in which Ndesign represents a measure of rutting resistance, Ndesign is interpreted as an indication of the compactability of mixtures. The field density data from some recent Minnesota pavement projects are analyzed. A clear negative correlation between Ndesign and field density level is identified, which confirms the significant effect of Ndesign on the compactability and consequently on the field density of mixtures. To achieve consistency between the laboratory and field compaction, it is proposed that Ndesign should be determined to reflect the real field compaction effort. A parameter called the equivalent number of gyrations to field compaction effort (Nequ) is proposed to quantify the field compaction effort, and the Nequ values for some recent Minnesota pavement projects are calculated. The results indicate that the field compaction effort for the current Minnesota projects evaluated corresponds to about 30 gyrations of gyratory compaction. The computed Nequ is then used as the Ndesign for a Superpave 5 mixture placed in a paving project, for which field density data and laboratory performance test results are obtained. The data analysis shows that both the field density and pavement performance of the Superpave 5 mixture are significantly improved compared with the traditional mixtures. The results indicate that Nequ provides a reasonable estimation of field compaction effort, and that Nequ can be used as the Ndesign for achieving higher field densities.

Estuarine Infauna Within Incidentally Retained Sediment in Artificial Rockpools

Artificial coastal structures (ACSs) are primarily designed to provide services for human use, such as flood defence or shipping, and are generally poor for marine biodiversity. Consequently, there has been significant research effort to enhance these hard structures to increase biodiversity and habitat availability via eco-engineering. On seawalls and breakwaters, this has included the creation of habitats for benthic species found on natural rocky shores, including the provision of cracks, crevices and water retaining features, such as artificial rockpools. When sediment retention in these features has occurred, it has often been deemed detrimental to the overarching aim of the intervention. Yet, it is soft sediment habitat that is impacted the most through coastal construction. As ecological enhancement of a flood defence scheme, nine concrete retrofit rockpools were installed at three different tidal elevations between mean high water neap tide and mean tide level on steel sheet piling on the Arun Estuary in Littlehampton Harbour, United Kingdom, which naturally filled with mud 1 year after installation. To explore how analogous the faunal assemblages and sediment profile of rockpool mud were to two local mudflats, core samples were taken and analysed for species richness, abundance, biomass, assemblage structure, median grain size, and organic matter content. More benthic species were observed in the artificial rockpool than in the local mudflats. Although the rockpools were placed at higher tidal levels than the lower shore mudflat, their assemblage structure and species richness were more similar to the lower shore mudflat at the base of the sheet piling than the upper shore mudflat. This study demonstrates that retained sediment within eco-engineered features on hard ACSs can create habitat for benthic assemblages. Providing sediment-retentive features on ACSs has the potential to provide a novel eco-engineering option that may be appropriate for some heavily modified waterbodies on sheltered, depositional coasts.

Development of Novel Pasta Products with Evidence Based Impacts on Health—A Review

Pasta made from durum wheat is a widely consumed worldwide and is a healthy and convenient food. In the last two decades, there has been much research effort into improving the nutritional value of pasta by inclusion of nonconventional ingredients due to the demand by health-conscious consumers for functional foods. These ingredients can affect the technological properties of the pasta, but their health impacts are not always measured rather inferred. This review provides an overview of pasta made from durum wheat where the semolina is substituted in part with a range of ingredients (barley fractions, dietary fibre sources, fish ingredients, herbs, inulin, resistant starches, legumes, vegetables and protein extracts). Impacts on pasta technological properties and in vitro measures of phytonutrient enhancement or changes to starch digestion are included. Emphasis is on the literature that provides clinical or animal trial data on the health benefits of the functional pasta.

How to write a research study protocol

A study protocol is an important document that specifies the research plan for a clinical study. Many funders such as the NHS Health Research Authority encourage researchers to publish their study protocols to create a record of the methodology and reduce duplication of research effort. In this paper, we will describe how to write a research study protocol.