Segmental infralesional pathological spontaneous activity in subacute traumatic spinal cord injury.

INTRODUCTION/AIMS: There is a dearth of knowledge regarding the status of infralesional lower motor neurons (LMNs) in individuals with traumatic cervical spinal cord injury (SCI), yet there is a growing need to understand how the spinal lesion impacts LMNs caudal to the lesion epicenter, especially in the context of nerve transfer surgery to restore several key upper limb functions. Our objective was to determine the frequency of pathological spontaneous activity (PSA) at, and below, the level of spinal injury, to gain an understanding of LMN health below the spinal lesion. METHODS: Ninety-one limbs in 57 individuals (53 males, mean age = 44.4 ± 16.9 years, mean duration from injury = 3.4 ± 1.4 months, 32 with motor complete injuries), were analyzed. Analysis was stratified by injury level as (1) C4 and above, (2) C5, and (3) C6-7. Needle electromyography was performed on representative muscles innervated by the C5-6, C6-7, C7-8, and C8-T1 nerve roots. PSA was dichotomized as present or absent. Data were pooled for the most caudal infralesional segment (C8-T1). RESULTS: A high frequency of PSA was seen in all infralesional segments. The pooled frequency of PSA for all injury levels at C8-T1 was 68.7% of the limbs tested. There was also evidence of PSA at the rostral border of the neurological level of injury, with 58.3% of C5-6 muscles in those with C5-level injuries. DISCUSSION: These data support a high prevalence of infralesional LMN abnormalities following SCI, which has implications to nerve transfer candidacy, timing of the intervention, and donor nerve options.

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data.

Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.

Image set for deep learning: field images of maize annotated with disease symptoms.

OBJECTIVES: Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers' fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. DATA DESCRIPTION: This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.