Hyperspectral imaging is a non-destructive imaging technique used in plant phenotyping to collect and analyze an array of electromagnetic information in visible (380-700 nm) and near-infrared wavelengths region (700-2,500 nm). Hyperspectral imaging can provide information of plant responses under various biotic and abiotic stress, e.g., drought, temperature rising, disease, and nutrition deficiency. We present a hyperspectral data processing pipeline designed for the data collected at Ag Alumni Seed Phenotyping Facility (AAPF) in Purdue University, USA. The procedure consists of initializing a processing session, radiometric calibration with white and dark references, geometric calibration (registration) of visible and near infrared (VNIR) and shortwave infrared (SWIR) images, vegetation and non-vegetation classification, vegetation indices calculation of a plant area, exporting data products, and quality control. In concern of large data size of hyperspectral data, we highlight the need to save memory usage during computation and save disk space for data products. We also address the need of human interpretable images in the hyperspectral data products for plant scientists without experiences in hyperspectral imaging. We expect the developed procedure could improve robustness of large hyperspectral data processing and promote the usage of hyperspectral data by increasing interpretability.

Global wheat production needs to increase by 60% to ensure food security in the future. Radiation use efficiency (RUE), defined as dry matter production per unit of light energy consumption, is an important trait that contributes to wheat yield potential. Traditionally, RUE is estimated through sequential biomass cuts evaluated against cumulative light interception, which is less precise and non-specific to genotypes. 3D models have recently been shown promise in estimating light interception when used along with ray tracing algorithms, mostly deployed in single plant-based models, while light interception at the canopy level remains to be explored. In this study, a mobile robotic phenotyping platform equipped with dual multispectral laser sensors was used to generate canopy 3D data. Using this platform, 100 spring wheat genotypes were scanned at heading stage to understand the genetic variation for RUE and its associated traits under field conditions. Ray-tracing algorithms were used to estimate the fraction of intercepted photosynthetically active radiation (FIPAR) for all genotypes, validated through a hand-held light ceptometer. Genotype-specific RUE was calculated as a slope between dry biomass and accumulated PAR. 3D model-based FIPAR was in close agreement with ceptometer-derived FIPAR. 3D model-derived RUE showed a large genetic variation across 100 wheat genotypes. It explained a higher variation in grain yield than ceptometer-derived RUE. These results indicate that canopy 3D models can be used as a rapid method for estimating canopy RUE in wheat, and potentially are extendable to other cereals.

Grain and seed properties can be evaluated using near-infrared spectroscopy and other methods for post-harvest quality assessment. Hyperspectral imaging combines spectroscopy with spatial information, which provides additional features that may improve predictive models of seed traits. To assess the ability of deep learning models to use hyperspectral data for predicting phenotypes, we first aimed to predict the genotype of maize seeds. Previous work achieved high identification accuracy between a small set of genotypes using either RGB images or hyperspectral data, and we hypothesized that high spectral resolution (350-1000nm) hyperspectral data would outperform simple RGB data in our study. Our dataset consisted of hyperspectral images of maize seeds from 47 inbred lines, including the 26 NAM lines, with 96 individual seeds per genotype. We evaluated the difference in genotype identification accuracy using three different representations of the individual seed data: 1) using the whole scan, containing the reflectance at 580 different wavelengths, 2) using a subset containing the reflectance at 3 different wavelengths corresponding to a pseudo-RGB image, and 3) a gray-scale image derived from the pseudo-RGB image. We fine-tuned VGG11, a popular convolutional neural network, using 85% of the individual seed data for each of the representations. We obtained around 90% genotype prediction accuracy on the unseen data for both the whole scan and the pseudo-RGB data, and 72% genotype prediction accuracy using the gray-scale data. The results indicate that the shape and color information contained in RGB images might be sufficient for the task of maize seed genotype identification.

Holistic assessment of fruit quality is an essential component of producing Strawberry varieties that will succeed in the marketplace and improve consumer satisfaction. However, several key quantitative traits are notoriously slow and expensive to assess using standard procedures, namely acidity and aroma, which require titration and gas chromatography and mass spectroscopy compared to others: brix, anthocyanins, and vitamin C, which are measured by refractometer and parallelized plate reader assays. Scaling up evaluations for acidity and aroma has been difficult as the techniques require 5 and 40 mins/sample, respectively, and sample preparation is equally intense, requiring multiple trained hands working for 10-hour sessions to create the sample series for 100 entries. We evaluated the ability (R 2 , RMSE) of a handheld near infrared (NIR) spectrometer, measuring 125 wavelengths between 800 and 1600 nm, and an electronic nose, measuring the reaction of 32 electrochemical sensors that respond to various compounds in gas samples, on 4,000 diverse strawberry accessions to determine if the 5 and 40 min/sample assays can be replaced with a 1 (0.33%) sec/sample (NIR) and 2 (5%) min/sample (E-nose) assay that require no additional sample prep. We also assess the NIR's ability to predict brix, anthocyanins, and vitamin C. With these two sensors, we will be able to increase the scale of early generation evaluation from hundreds to thousands of samples in early generations, produce full datasets prior to deadlines in the breeding program, and make more reliable genetic gains for quality traits affecting marketability and consumer acceptance.

The California strawberry industry generated more than 2 billion dollars in revenue in 2020 (USDA-ERS). Strawberry breeders develop new varieties to increase productivity in the face of shifting biotic and abiotic stresses. The University of California Davis maintains a strawberry breeding program that evaluates >10,000 entries yearly to meet the demand for new improved varieties, focusing on plant productivity, fruit quality, and resistance to soil borne pathogens. One challenge to a breeding program of this scale is efficiently scoring and collecting detailed information on cultivar performance. Traits like plant size and growth rate are rarely collected. It takes a crew of 4 people 20-25 hours to score fruit count, so it is currently done once per week. Correlated traits, e.g., plant size and vigor, assessed by drone imagery could provide high-quality information and replace labor intensive assessments of phenotypic traits and yield. In 2022 we deployed drones to generate research grade imagery of nearly 10,000 entries at Wolfskill Experimental Orchard in Winters, CA and another 3,000 entries under induced disease pressure to determine the best predictors of productivity and disease severity from drone imagery. We applied image analytics tools developed by HIPHEN to extract ground coverage, plant height, biovolume and a range of visual indices from multiple sensors to assess cultivar performance. The extracted traits were then used as independent variables to predict either yield or visual disease severity. We report our initial findings, examine the successes and learnings, and propose solutions to ongoing challenges in strawberry breeding.

ORCiD: [https://orcid.org/0000-0001-6665-6094] Plant phenotyping has been an essential aspect of crop science analytics that is saddled with tasks such as providing critical information about plants' genetics, traits, productivity, and other intricate details to gain insights about their survival under certain conditions and in a specific environment for various analyses. Various methods have been quantifying this information using various models culled from several kinds of datasets. In this study, we extract various phenotypic information about the soybean using UAS-based images captured over the growing fields within the selected experiment field. The DJI M300 unmanned aerial systems were equipped with the Zenmuse P1 and L1 sensors; both used to capture RGB and LiDAR images. In addition, the DJI P4 multispectral UAS was also used to collect multispectral information over these fields at various date intervals. The data captured is being processed using custom-developed algorithms and automated workflows to obtain biomass, vegetation indices, canopy cover, canopy height, and canopy volume. These indices would show variations in the traits of the crop under study as related to the soybean. This phenotypic information would be compared against the field measurements for validation.

Interactive Annotation for object delineation can be considered as a semi-supervised few-shot learning problem where machine learning models learn from a small set of annotated pixels and generalize to the entire picture to extract the object of interest. One aim of interactive annotation is to reduce the effort of manually labeling data. Some existing works attempted to address this problem with deep metric learning so that the encoding layers in the network are able to extract features that boost discriminability among pixels belonging to different classes. To keep the data structure in the embedding space, metric loss with prototypes has been proposed. In our work, we improved the existing methods by developing a new objective function to update the network and prototypes simultaneously. The prototypes are optimized based on the loss that enhances their dissimilarity instead of clustering or sampling from the dataset. Moreover, we designed a GUI with the proposed method for interdisciplinary collaboration of image-support plant phenotyping studies.

Trichosanthes dioica, also referred as “Sespadula” in English and “Parwal” in Hindi, is found abundantly throughout India. T. dioica leaf juice is used as a febrifuge, tonic in alopecia, and in subacute liver enlargement instances. Leaf and fruit remedies for drunkenness and jaundice are mentioned in the Charaka Samhita. The immature fruits are eaten fried and as dorma with roe stuffing, as well as used as a vegetable in soup, stew, curry, and sweet dishes. Apart from the fruits, other parts of the plant, such as the leaves and tender shoots, have been used in traditional medicine since ancient times. When shade-dried fruits were mixed in the food of nondiabetic animals, specific medicinal properties such as hypocholesterolemic, hypoglyceridimic were discovered. Its seeds and leaves have recently been discovered to be anti-diabetic agents. Numerous pharmaceutical studies have scientific research on several T. dioica components, but some other historically significant therapeutic uses are also up till now scientifically unproven. The plant can be used as an anti-inflammatory, anti-cancer, hypolipidemic, cardiotonic, diuretic, ulcer-preventive, antidiabetic etc. The plant shows good antioxidant activity. The different chemical components that are present in the plant are vitamin A, vitamin C, tannins, saponins, alkaloids, peptides, tetra and pentacyclic triterpenes, etc. The present review describes about the various parts of the plant which can be used for the research, the different phytochemicals present in the plant and pharmacological activity of the plant. Keywords: Trichosanthes dioica, Cucurbitaceae, Hepatoprotective, Anticancer, Antidiabetic, Antioxidant.

Urban agriculture has been broadly acknowledged for its potential to reduce carbon emissions, increase food security, and improve economic growth in some of the most vulnerable communities in the United States. Collard (B.oleracea var. viridis) is a diploid leafy green, grown on urban farms and community gardens across the country, including the St. Louis Metro region. Beyond their nutritional importance, collards provide urban and commercial agronomic systems with a plethora of important ecosystem services. They scavenge nitrogen and available resources, suppress weeds, and act as a biofumigant to control soil-borne pests and pathogens. Recently, The Heirloom Collard Project characterized the above-ground growth habits of 18 landrace collard varieties across 250 organic gardens and farms. Little work has been published to investigate collard root system architecture, which influences both quality traits and ecosystem services that contribute to sustainable crop production. The objectives of this research are to 1) quantify root spatial and temporal diversity across 18 landrace collard varieties, and 2) evaluate the relationship between root phenotype and urban farmer crowd-sourced data for key traits such as germination rate, disease resistance, vigor, yield, flavor, and winter hardiness. This work will lead to the development of a participatory framework for urban farmers and chefs to select varieties with improved root architecture based on regional needs.

Background and Purpose: Realistic models predicting hepatobiliary processes in health and disease are lacking. We therefore aimed to develop a physiologically relevant human liver model consisting of normothermic machine perfusion (NMP) of explanted diseased human livers that can be used to investigate hepatic first-pass, clearance, biliary excretion and drug-drug interactions. Experimental approach: Eleven livers were included in the study, seven with a cirrhotic and four with a non-cirrhotic disease background. After explantation of the diseased liver, the liver artery and portal vein were reconstructed followed by NMP. After 120 minutes of perfusion, a drug cocktail (rosuvastatin, digoxin, metformin and furosemide) was administered to the portal vein and 120 minutes later, a second bolus of the drug cocktail was co-administered with drug inhibitors to study relevant drug-drug interactions. Key results: The explanted livers showed good viability and functionality after explantation and 360 minutes of NMP. Hepatic first-pass and clearance of rosuvastatin and digoxin showed to be the most affected by cirrhosis with an increase in Cmax of 10.03 and 2.89 times, respectively, compared to non-cirrhotic livers. No major differences were observed for metformin and furosemide. Drug-drug interaction of rosuvastatin or digoxin with inhibitors were more pronounced in non-cirrhotic livers compared to cirrhotic livers. Conclusions and Implications: Our results demonstrated that explanted cirrhotic and non-cirrhotic livers were suitable for NMP and we demonstrated the applicability to study hepatic first pass, clearance, biliary excretion and drug-drug interaction. This model can be applied in a variety of research settings for hepatology, transplantation and pharmacology

In this article, the Lorentz Factor is reviewed in terms of Heisenberg's Uncertainty Principle in the light of Einstein's relativity equations. It is shown that the relative speed between any two objects in Space can not be zero under any circumstances. Therefore the Lorentz Factor can never be equal to 1. And the velocity vector of an object is unique with respect to any reference frame. These provide a new perspective to improve the general understanding about some basic concepts of Physics including relative motion, time and distance perception, exclusion principle, attributes of an object, volume, singularities, particle-wave duality and the nature of light which are discussed in the paper.

Background: Not being well controlled by therapy with inhaled corticosteroids and long-acting β2 agonist bronchodilators is a major concern for severe-asthma patients. Current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response. Methods: Two novel computational algorithms, machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based algorithm Logic Explainable Networks (LEN ) were used on open accessible mRNA expression data from moderate-to-severe asthma patients to identify genes as predictors of omalizumab response Results: With REFS, the number of features were reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy of 0.975. The 5 responsiveness predictive genes encode for the following proteins: Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113 ,SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4 identified responsiveness predicting genes are associated with: mucosal immunity, cell metabolism, and airway remodeling. Conclusion and clinical relevance: Both computational methods show 4 identical genes as predictors of omalizumab response in moderate-to-severe asthma patients. The obtained high accuracy indicates that our approach has potential for clinical settings. Future studies in relevant cohort data should validate our computational approach.

Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health. We developed a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a corn field across multiple diurnal cycles throughout a growing season. Using the polarization data, we present preliminary results demonstrating the potential to use polarization to de-couple light reflected from the surface versus light scattered from the tissues, thus enabling time of day, solar incidence angle, and viewing angle to be reduced as confounding factors for the spectral measurement. We present two approaches for polarization correction of our image data. The first is by using ground truth Normalized Difference Vegetation Index (NDVI) with linear regression and convolutional neural networks to train a deep learning model capable of compensating for the leaf normal relative to the camera and sun angle. The second approach involves using a recently constructed instrument which fits a scattering model of corn leaves by measuring the Bidirectional Reflectance Distribution Function (BRDF). This function models the behavior of light reflected off a leaf relative to its spectrum, polarization, and angle of incidence. Incorporating this model with data collected by the HIP, we estimate that the system will be able to distinguish leaves with surface normals facing towards the camera from leaves facing away from the camera. Preliminary results demonstrate a promising solution to reduce confounding factors in high-throughput systems for applications in plant phenomics and remote sensing.

A high-voltage, external capacitor-less low-dropout regulator (HVLDO) with a transient enhancement loop is presented in this work. The proposed HVLDO is designed with high withstand voltage LDMOS transistors and a transient enhancement loop is proposed to properly inject or sink current to/from the gate and output nodes of the power transistors to achieve fast transient response under wide load range conditions and high stability. This HVLDO is fabricated in 0.5 μm SOI BCD process with an active area of 0.29 mm2. It operates over an input voltage range of 5.2 to 20 V, provides an output voltage of 5 V and a maximum load of 100 mA, while supporting load capacitances from 0 pF to 1 μF. Measurements show that this design has a line regulation of 0.88 mV/V and a load regulation of 0.22 mV/mA. The proposed HVLDO features fast line transient response of 60/20 mV@9.8 V/µs, fast load transient response of 30/70 mV@100 mA/µs, and recovery time of 2 µs without external capacitors. Compared with the prior art, this work achieves the best transient FOM of 12.19 fs.

In this short paper we consider a cell in the network of mobile users. It is proved that the mean value of distance between a user and a base station and the mean value of emission power to a user are calculated in a different manner. These two values are calculated for three characteristic user density distributions in a cell. In another way defined, the mean value of emission power to a user differs from the emission power to a user positioned on the mean distance from a base station. The results are verified by simulation.

Michelle Cho1, Zhengbin Liu2, Dhineshkumar Thirupatthi3, Tim Parker3, Shayla Gunn4,

Kinetic modeling of thermal degradation process by pyrolysis as an alternative for energy recovery of used masks generated by the COVID-19 pandemic. The masks were isolated for 72 h for virus inactivation and characterized by FTIR-ATR spectroscopy, elemental analysis, and higher heating value. Thermal degradation was performed by thermogravimetric analysis at different heating rates on an inert atmosphere. The gases produced were characterized by gas chromatography and mass spectrometry. The kinetic model was developed based on weight loss and calculated activation energies, reaction orders, pre-exponential factors, and thermodynamic parameters. The best fit models were established between the experimental and calculated data. Composition of the mask samples were polypropylene, polyethylene terephthalate, nylon 6, and Spandex, with higher calorific values than traditional fuels. The kinetic and thermodynamic parameters of the pyrolysis processes demonstrated the feasibility and high potential of recovery of these residues with conversions higher than 89.26 %.

Alagille syndrome is a genetic disease with multi-organ involvement and can cause various congenital cardiac lesions. This syndrome is caused by mutations in the JAG1 and NOTCH gene pathways. As a result of these lesions, pulmonary hypertension can be seen in patients. While pulmonary hypertension is observed especially due to pulmonary valve stenosis; It may be due to various causes such as tetralogy of Fallot. Currently, pulmonary hypertension has not been reported in Alagille patients without cardiac anomaly in the literature.

Introduction: Monoclonal gammopathies are a group of disorders associated with monoclonal proliferation of plasma cells that produces a monoclonal protein. To describe the epidemiological and immunochemical characteristics of monoclonal gammopathies diagnosed during a nineteen-year period in a Moroccan teaching hospital was the main objective of this study. Methods: This study was performed from January 2000 to August 2019. It was a retrospective study that included of 545 Moroccan patients with monoclonal gammopathy. Results: The patients who participated in the study, 374 (68.6%) were male and 171(31.4%) were female, with a mean ±SD age of 62.24±13.14 years. The most frequent reasons for admission were bone pain (41,60%), renal failure (19.08%), alteration of the general condition (12.21%) and anemia (10.69). Plasma cell proliferative disorders in our study were as follow, multiple myeloma (MM) (45.65%), Monoclonal gammopathies of undetermined significance (MGUS) (39.05%), Waldenstrom’s macroglobulinemia(5.58%), Lymphoma (2.27%+1.2%), Chronic Lymphocytic Leukemia (2.48%), Plasma cell leukemia (1.86%), Plasmacytoma (0.62%), POEMS syndrome (0.41%), and Amyloidosis (0.84%). The most frequent isotypes in MM were the IgGκ (62) 36.5%, IgGλ(52)30.6%, IgAκ(27)15.9% and the IgAλ (19)11.2%. It is also worthy of note, that Free light chain MM represents 20% of all cases of MM. Conclusions: This is the largest Moroccan cohort, it included 545 patients. The results of this study point to the need for an early diagnosis of monoclonal gammopathies in the Moroccan population

Objective: Since the outbreak of COVID-19, wearing masks and frequent hand washing have become common phenomena. The purpose of this study was to explore the impact of such lifestyle changes on adenoid hypertrophy in children in Beijing. Methods: Baidu Index platform was used to search with adenoid hypertrophy as the keyword, and the search volume of terms from 2017 to 2021 was recorded weekly. Meanwhile, the visits to adenoid hypertrophy in the otolaryngology department of Children’s Hospital, Capital Institute of Pediatrics in the same period were collected and compared, and analyzed. Results: (1) Baidu index indicated that the following group of adenoid hypertrophy was mainly parents of childbearing age, and female parents paid more attention;  (2) From 2017 to 2019, the online attention and outpatient visits to adenoid hypertrophy increased year by year. After the COVID-19 outbreak, the increasing trend declined and stagnated. Conclusions: After the outbreak of COVID-19, epidemic prevention policies (wearing masks, hand hygiene, reducing movement of people, etc.) have a certain inhibitory effect on adenoid hypertrophy.  Keywords: adenoid hypertrophy, COVID-19, Baidu Index platform, masks, hand hygiene Key points: Adenoid hypertrophy may be associated with recurrent respiratory infections in childhood. After the onset of COVID-19, China enacted many epidemic prevention policies. Wearing masks and hand hygiene may reduce the incidence of respiratory infections. Web data provides insight into the real needs of the population. After the onset of COVID, there was a stagnation in network attention and outpatient visit rates for adenoid hypertrophy.

Direct sequencing can be used for characterizing mutagenicity in complex biological models, e.g., in various tissues of mammalian species. We employed whole-genome High-Fidelity Sequencing (HiFi Sequencing) for detecting mutations induced in male CD-1 mice by N-ethyl-N-nitrosourea (ENU). Mice were treated by gavage with a single 40 mg/kg dose of ENU at 12 weeks of age; negative control mice were untreated. Peripheral blood and solid tissues (liver, spleen, and kidney) were harvested 4 weeks post-exposure; the erythrocyte Pig-a assay was used to measure mutant frequencies in peripheral blood, while mutations were evaluated in the solid tissues by HiFi Sequencing. The frequency of Pig-a phenotypically mutant total red blood cells and reticulocytes in ENU-treated mice increased 7- and 30-fold, while the frequency of mutations in the genomic DNA of solid tissues increased up to 7-fold, with the greatest increase observed in the spleen and the smallest increase in the liver. The most common mutations detected by HiFi Sequencing in ENU-treated mice were T>A transitions and T>C transversions. The data suggest that HiFi Sequencing complements the Pig-a reporter gene mutation assay, detecting mutagenicity in tissues where performing the Pig-a assay is impossible and providing information on the spectra of mutations which potentially may be useful for characterizing the genotoxicity of novel compounds.

Liu in 2007 has introduced the theory of uncertainty [[20]](#ref-0020). The structure of the present paper is to define some new type of generalized ∆- operator on this theory of uncertainty by employing the notion of sequence spaces. Some basic structures will be computed. Also, some sharp inclusion relations with counter examples concerning to the newly constructed spaces of this paper will be computed. Mathematical Subject Classification 2010: 46A45; 60F17.

As the COVID-19 continues to mutate, the number of infected people is increasing dramatically, and the vaccine is not enough to fight the mutated strain. In this paper, a SEIR-type fractional model with reinfection and vaccine inefficacy is proposed, which can successfully capture the mutated COVID-19 pandemic. The existence, uniqueness, boundedness and nonnegativeness of the fractional model are derived. Based on the basic reproduction number R 0 , locally stability and globally stability are analyzed. The sensitivity analysis evaluate the influence of each parameter on the R 0 and rank key epidemiological parameters. Finally, the necessary conditions for implementing fractional optimal control are obtained by Pontryagin's Maximum Principle, and the corresponding optimal solutions are derived for mitigation COVID-19 transmission. The numerical results show that humans will coexist with COVID-19 for a long time under the current control strategy. Furthermore, it is particularly important to develop new vaccines with higher protection rates.

Using magnetic resonance imaging (MRI), our established root phenotyping platform (van Dusschoten et al., 2016) can visualize and analyze plant roots in natural soil nondestructively (Pflugfelder et al., 2017). Using plant pots with 9 cm diameter and 30cm height, a root system can be scanned within 1h while roots down to diameters of 300µm can be detected and analyzed using our in-house root extraction software NMRooting (van Dusschoten et al., 2016). Thanks to automation with a pick-and-place robot the platform routinely achieves a throughput of 24 plants per day. All these values, however, are based on compromises between imaging speed and quality. In our system, the root detection limit is determined by the signal to noise ratio (SNR) of our images. The SNR can be increased by using smaller plant pots or by increasing the imaging time. In this contribution we investigate the potential gain in the root detection limit when sacrificing plant throughput in favor of image quality. We acquired low noise root images using repeated signal averaging during the measurement process. Using this approach, the root detection limit could be lowered, visualizing roots not detected by the standard imaging protocol.