ORCiD: https://orcid.org/0000-0001-7766-3775 The expanding geographic range of Phyllachora maydis, the fungus that induces Tar Spot infection on corn foliage, is increasingly threatening a Michigan industry that contributes over $1 billion to the state's economy annually. Foliar infection of maize by P. maydis is often difficult to detect early. Visible lesions initially appear tiny, ambiguous, and sparse, making them difficult to identify with the naked eye. Both farmers and breeders of corn desperately need better tools that allow early, definitive detection of lesions and provide more time for management decisions. This tool must verify presence of P. maydis and quantify infection severity as quickly as possible to allow growers the most options for treatment. Advances in machine learning now enable quantification of crop infection presence and severity using powerful object detection packages. With the growing availability of open-source tools, such as the Mask Region-Based Convolutional Neural Network (Mask R-CNN) and PlantCV, the field of plant disease phenotyping has more options for methods than ever before. I propose comparing the accuracy of two potential pipelines to quantify tar spot infection severity: one based on heuristic methods, involving techniques such as dynamic image colorspace thresholding, and the other based on the use of annotations, such as object detection and contour analysis. Comparison of these two methods will provide insight into challenges involved with phenotyping in the field as well as phenotyping foliar diseases using automated methods.

X-ray tomography (XRT) is a powerful and versatile tool for generating detailed non-destructive three-dimensional (3D) image data of large and complicated structures. In particular, excavated, cleaned and dried maize root crowns can be rapidly scanned, and the resulting 3D volumes processed in a computational feature extraction pipeline to provide a wide range of root trait measurements. These measurements provide rich data that give insights into how roots occupy 3D space in ways not possible with any 2D imaging/measurement systems. Hundreds of root crowns can be scanned in a moderate-throughput system, and multivariate statistical analyses can provide valuable insight into the role that genes and quantitative trait loci play in selected root traits. Research presented will describe XRT scan parameter optimization and its impact on root trait data generated by the feature extraction pipeline.

Imaging of plants using multi-camera arrays in high-density growth environments is a strategy for affordable high-throughput phenotyping. In multi-camera systems, simultaneous imaging of hundreds to thousands of plants eliminates the time delay in measurements between plants seen in plant-to-camera or camera-to-plant systems, which allows for the analysis of plant growth, development, and environmental responses at a high temporal resolution. On the other hand, high plant density, camera-to-camera variation, and other trade-offs increase the complexity of data analysis. Here we present two recent updates to the PlantCV image analysis package to improve usability when working with multi-plant datasets. First, we introduce a method to automate detection of plants organized in a grid layout, reducing the need to make separate workflows for each camera in a multi-camera system. Second, we reduced the number of input and output parameters for functions handling the shape and location of plants and introduce automatic iteration over multiple objects of interest (e.g. plants), reducing the level of programming needed to build workflows.

In natural ecosystems and low-input agriculture systems often the main source of nitrogen is biological nitrogen fixation by symbiotic coexistence with root colonizing microorganisms such as in root nodules in legumes. In return for this nutrient supply, plants allocate significant amount of photosynthetically fixed carbon (C) belowground, fueling activity and growth of the nodules. However, there is still a lack in understanding how plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. Traditional approaches based on destructive sampling make investigations of localized carbon allocation dynamics difficult. Non-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants. MRI allows for repetitive measurements and quantification of root system architecture traits nodule structures while growing. PET was employed to follow the spatial distribution of leaf-supplied 11 C tracer to nodules and roots. Using Pisum sativum as model for legumes and applying nitrate as an additional N source we investigated short term C allocation dynamics in the root system. We found that the fraction of 11 C tracer arriving in the most active nodules decreased by almost 40% and remained stable between 16h and 42h after the N application. Our results highlight that the combination of MRI-PET enables deeper insights into short term C dynamics of roots and interactions with colonizing microbes. We expect that this modality has high potential for revealing mechanisms that relate to dynamic fitness traits supporting breedingprograms for future crops.

This paper attempted to use the RVB method to find that there is a constant that cannot be crossed out in the calculation of the curvature term representing the geometric invariant. The constant may be a parameter item.

Introduction: Sickle cell disease is debilitating hereditary disorder affecting large tribal population in certain parts of India. Complications of sickle cell disease are veno-occlusive crisis, gall stones, leg ulcers, stroke, anemia requiring transfusions adding to the decreasing quality of life. Usage of hydroxyurea increase value of HbF and thereby decreases the complications. To assess the real world scenario of SCD patients, particularly, there levels of HbF, a random clinical examination with blood sampling was initiated and compared to baseline available data. Methods : Confirmed cases of sickle cell disease attended special clinical camps. Patients of all age group were invited to attend the camp. To assess the real world scenario of SCD patients, particularly the levels of HbF, a questionnaire and a random clinical examination with blood sampling was initiated and compared to baseline available data. Results: One hundred fourteen patients attended the camp. There were 68 males and 46 female patients with a median age group of 19 years (2-70 years). HU was prescribed on average considering age of the patient and average weight. 104 children were taking hydroxyurea. Exact dose calculations were not used. The dose averages between 10 mg/kg to15 mg/kg. As there was only one formulation available , the required daily dosing was changed to fixed dose scheduling There was significant change in the levels of HbF in the patients on hydroxyurea in all age groups using fixed dose combination. Conclusions: The achievement of desired HbF target levels using “real-world” scenario, compels us to think about HU strategies. The practice of using fixed dose schedules, in real-time clinical practice, with minimal follow up deserves a serious discussion and could be of great use in low resource countries.

A document by Callum Haseler. Click on the document to view its contents.

Cup Like acute myeloid leukemia is the rarest form of children leukemia. We present a case of Cup Like Acute Myeloid Leukemia (AML) with t(4;12) (q12;p13) associated with two other clones t(1;16)(q12;q24) and t(12;13)(p13;q13). Cytogenetic and iFISH are highly relevant for the prognosis of and therapeutic decisions in LAM.

To encode their knowledge, societies without writing have developed memory techniques based on rhythm, narrative, identification, body participation and collective emotion. In On the other hand, with the rise of writing, knowledge could partially detaching from personal identities or collective, become more "critical", aim for a certain objectivity and a theoretical range " universal". It's not just the modes of media-dependent knowledge information and communication techniques. This are also, through cognitive ecologies they condition, the values and criteria of company judgments. But these are recisely the knowledge assessment criteria (in the broadest sense of this term) which are brought into play by the extension of the cyberculture, with the probable decline, already observable, values that prevailed in civilization structured by static writing. Not that these values are called to disappear but rather to become secondary, to lose their power of command.

A feeling of vertigo seizes our contemporaries, torn as they are between this globalization of which they see and sometimes support the manifestations and their quest for roots, references, belonging. Education must face this problem, because it is situated, more than ever, in view of the painful delivery of a global society, at the heart of the development of the person as communities. Its mission is to enable everyone, without exception, to bring to fruition all their talents and all their poten tialities of creation, which implies for everyone the ability to take charge and carry out his personal project.

Ontogenetic colour change in animals is an interesting evolutional question, which has been studied by evolutionary biologist for decades. However, the main challenge is how to measure the colour quantitively and continuously in a full life cycle for lizards. We used the spectrometer approach to measure the tail colour of Blue-tailed Skink (Plestiodon elegans) from birth to sexual mature. The spectrometer approach is simple, fast, and accurate depending on animals’ visual sense, to measure the tail colour of skink. We showed a strong relationship between colour indexes (values of L*, a*, b*) and growth time of skink. Moreover, we found colour rhythms are different between sexes, which may influenced by their unique behavior strategies between sexes. Therefore, our study carried out a continuously measuring the tail colour change from larva to adults to investigate the mechanism for ontogenetic colour change in reptiles and to explain the potential factors that driving the dichromatism between sexes in lizards.

Proper concentrations of several nutrients, such as iron (Fe), zinc (Zn) and potassium (K) in the soil are needed for plant growth. Thus, farmers sometimes test soils to determine fertiliser application rates. However, measuring soil and plant nutrient concentrations is relatively time-consuming and expensive. Therefore, usually, only a few samples are collected and analysed. This limits the scale of research but also poses some limitations on farming practices, as farmers cannot sample fields at high density in order to customize application rates. Cheap, fast, and high spatial resolution methods to measure soil nutrient concentrations would alleviate some of these limitations. This work aimed to determine the potential of hyperspectral imaging (HI) to predict the concentration of some soil nutrients. Soil samples were scanned by visible and near-infrared imaging systems with a total wavelength range of 450-1700 nm. Fe, Zn, and K were analyzed. Partial least-square regression models (PLSR) were used to correlate the relative reflectance of total Fe, Zn and K in the soil samples. The PLSR models could highly predict Fe concentration (R2 0.81, RMSE% 16.6) and performed moderately well for Zn (R2 0.30, RMSE% 0.9) and K (R2 0.47, RMSE% 5.58) concentrations. The overall results indicated that the hyperspectral technique coupled with PLSR could be an accurate and reliable method for determining soil nutrient concentrations.

Community colleges and minority-serving institutions are often ill-equipped with research facilities necessary for providing post-secondary students with opportunities for engagement in authentic research that would equip them with practical skills. Partnerships with research organizations can address gaps in professional training of post-secondary students to better equip them with skills aligned with industry needs, such as data handling. Through collaboration between the Donald Danforth Plant Science Center (DDPSC) and Harris-Stowe State University (HSSU), a local Historically Black College and University, a plant data science Course-based Undergraduate Research Experience (CURE) was developed to improve undergraduate access to research experiences and equip racially minoritized students with cutting-edge data science techniques. Biology and mathematics majors at HSSU used DDPSC researcher-generated plant image data to immerse themselves in real-world plant biology research. Students enrolled in this course used PlantCV, a Python-based image data processing software, to analyze the phenotypes of plants subjected to abiotic stresses. They interacted with image data to investigate heat stress responses in Zea mays (maize) and were exposed to complex interdisciplinary concepts that challenged their understanding of how biology and data science intersect. Students learned to analyze image data to extract conclusions based upon student-generated questions. Qualitative data from students' weekly surveys reveal the building of data science knowledge within students engaged in the CURE, providing insights for educators involved in planning and assessing undergraduate learning experiences at minority-serving institutions. This course

Retroperitoneal sarcoma is relatively uncommon. We share our experience in encountering retroperitoneal sarcoma with vascular and urethral adhesion in a 46-year-old woman. Given the rarity of these tumors, evaluation and management should ideally be performed in a center equipped with multidisciplinary expertise in treating sarcomas.

Speech emotion recognition plays an important role in many applications, but the task is challenging due to various factors such as background noise, different speaker speech characteristics, etc. The well known speech emotion recognition system ACRNN uses CNN to extract local features of speech signals and attention mechanism focuses on the parts with prominent emotions. However, it has no ability to capture long-term global information and it also has no ability to jointly attend to the information from different representation subspaces at different positions because only one single attention module is used. In order to settle out the drawbacks of ACRNN, CoRNN is proposed in this letter by applying Conformer to replace the modules of CNN and attention module. The experimental results on IEMOCAP dataset demonstrate the unweighted average recall of the proposed CoRNN can achieve 65.53%, which improves 0.79% comparing with ACRNN.

In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.

A document by Rachel Penczykowski. Click on the document to view its contents.

Image based high throughput plant phenotyping is a powerful tool to capture and quantify diverse plant traits. The available commercial platforms are often cost-prohibitive. This study describes the development of a low cost, automated plant phenotyping platform, which can acquire images, transfer data, segment the images, extract the traits and perform data analysis using low-cost microcomputers, cameras and IoT irrigation system. Quantifiable plant traits (e.g., shape, area, height, color) were extracted from the plant images using an in-house pipeline developed in R language. An experiment of water stress (waterlogging and drought) on Mentha arvensis (Menthol mint) crop (cv. CIM-Kosi) was conducted to demonstrate image traits being used as a proxy for plant response to water stress. It was found that the effect of drought stress on plant height and number of secondary branches could be correlated to color traits of plant canopy images. Also, the effect of waterlogging stress on chlorophyll and flavonoid content could be related to the shape traits of plant canopy images and effect on waterlogging on plant height and canopy width could be associated with color and texture traits. The imaging platforms could successfully demonstrate a viable low-cost solution for incorporating high-throughput plant phenotyping in various plant stress related research applications.

Branching patterns in plant roots are associated with complex traits such as stress-tolerance, yield, and the ability for carbon sequestration. The capability of the root system to branch allows the plant to search the soil for water and nutrients. For example, a reduction of higher order roots may determine how well a crop plant tolerates drought, whereas the ability to develop more higher order roots determines how well a crop plant tolerates a nutrient deficient soil. Measurements of traits such as rooting depth, root width or specific root length, however, often fail to capture the complex morphological arrangement of the root system. Therefore, a more rigorous analysis of root branching patterns is highly relevant as they are linked to the ability of plants to respond to abiotic stresses, such as drought and nutrient deficiency. Despite the need, it remains a challenge to extract information about branching patterns due to intersecting and overlapping roots in 2D and 3D imaging data. Such occlusion problems add ambiguity and outliers to root trait measurements. We present an algorithm to resolve such intersections in a globally optimal way based on simple heuristics such as straightness of roots - thus being dimension independent. This will enable quantitative analysis of how root branching patterns change in response to abiotic stress using shape descriptors. The possibility to computationally measure very dense branching structures with thousands of intersections will support the breeding of plants that withstand increasing areas of drought and nutrient deficiencies in the world.

Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.

To study how plants respond to their environment researchers use imaging phenotyping technologies. The use of image-based phenotyping has enabled researchers to analyse plants and produce data at a large scale. However, this large influx of data has created a 'big data' problem to emerge causing researchers to search for new innovative ways to tackle the challenges of processing their data in a reasonable timeframe. To address such issues, deep learning and data science techniques are being used to perform a comprehensive analysis. Here we use a Plant Screen™ compact system to image a series of barley plants using two different imaging sensors. This compact system contains an RGB top and side view camera and a hyperspectral visible near infrared (VNIR) camera. To streamline the processing and analysis of RGB and hyperspectral imaging, we are building a pipeline using a lightweight implementation of the U-Net architecture to improve the accuracy of semantic segmentation based on the raw images captured via the compact system. Several models were designed and developed, each of which was tailored to either the type of imaging sensor being used or the angle for which the images been provided were taken (e.g., top-down, side-view). Results showed that each model regardless of sensor or perspective produced an accuracy greater than 90% and could accurately segment cereal crops regardless of their size, shape or colour. These results demonstrate the feasibility of using DL models to semantically segment cereal crops imaged using either RGB or hyperspectral imaging sensors.

ORCiD: https://orcid.org/0000-0002-0550-7682 Keywords: cover crops, root system architecture, ecosystem services, root traits, gel imaging system. Cover crops are an emerging solution to the negative impacts of conventional agricultural practices. Through their essential ecosystem functions, cover crops can improve soil health and biodiversity during fallow periods in conventional crop rotation systems. Hairy vetch (Vicia villosa), winter barley (Hordeum vulgare), and purple top turnip (Brassica campestris) are cover crops that provide a variety of ecosystem services such as nitrogen fixation, nutrient capture, and soil remediation. Using a 4D gel imaging system, we were able to evaluate 3D root system architecture over time of these three cover crops in order to further understand root growth and development. The collected traits allowed us to compare root growth and RSA across the plant species and better understand how certain root traits are linked in ecosystem functions. The long, fibrous root system found in winter barley allows the plant to effectively catch nutrients and water in the soil. The large taproot and secondary roots found in turnip are able to break up compacted soil while maintaining a network of finer roots to scavenge for nutrients. Similar to purple top turnip, the taproot in hairy vetch may provide soil remediation, but the deeper roots in vetch allow for the plant to provide increased acquisition and fixation of atmospheric nitrogen.

Developing smart luminescent materials, especially stimulus-responsive fluorescent materials, is a goal of great importance, as well as a challenge. Mechano-responsive fluorescence is a property whereby the fluorescence characteristics (i.e., emission color, quantum yield, or lifetime) of a species change as a result of mechanical stimulation. In general, the said mechanical stimulation causes a phase transition in the material, and it simultaneously induces a fresh interaction of the luminous, resulting in a change in the color of the photoluminescence emission. Therefore, the transition of a material from crystalline-to-amorphous, from amorphous-to-crystalline, or from a crystalline to another, as well as the phase transformation of liquid crystals, can contribute to changes in fluorescence emission triggered by a mechanic stimulus. This article briefly reviews the development of such mechano-responsive fluorescent compounds, which consist of organic or organometallic molecules, and the emerging trends in this research field.

Crop breeding relies on the numbers game. The higher the number of locations and entries evaluated, the higher the probability of developing superior cultivars. One major challenge facing breeders of perennial cool season forage crops is the number of biomass harvests per season. In regions with mild winters, alfalfa is harvested every four weeks, six to seven times a year. This creates an operational bottleneck limiting the number of entries and testing locations. Substantial investments were made in the development of automated solutions for precision Ag for row crops. The adoption of these platforms to forage crops rests on their accuracy in estimating biomass yield and cost-effectiveness. This work focused on evaluating the sensitivity of popular unmanned aerial vehicles (UAVs) and imaging strategies for optimal real-time biomass estimates in perennial forage crops. Experimental plots consisting of single plants, row plots, and sward plots were used for a hybrid data collection approach including direct measurements and remote sensing. UAV platforms equipped with a 42-megapixel RGB camera (Sony Alpha 7Rii), a five-band multispectral system (MicaSense RedEdge MX), a hyperspectral sensor (Resonon-Pika L), and a LiDAR (LiDARUSA Revolution 120) were tested. Images were used to generate 3D canopy models of vegetation in the field and to compute morphometric and spectral indices descriptive of vegetation coverage, health and vigor. Harvested biomass yield was used to validate the values derived from UAVs. Preliminary results suggest that the simple red-green vegetation index may be sufficient to give a reliable estimate of biomass yield.