Explore the vast range of titles available.

Flowering time is a critical phenological trait in maize (Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi‐subjective and labor‐intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as unoccupied aerial vehicles or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high‐throughput detection of flowered plots in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here, a methodology for extracting tasseling from UAS red/green/blue imagery using a CNN‐based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas) and then validated through an unrelated set of hybrids. Overall accuracies of 0.946, 0.911, 0.985, and 0.988 were obtained for classifying maize images with or without tassels from College Station, TX, in 2020; College Station, TX, in 2021; Arlington, WI, in 2021; and Madison, WI, in 2021, respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high‐throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments. Plain Language Summary This study focused on using drone images and artificial intelligence (AI) to track when maize plants grow tassels, an important step in their development. Traditional methods rely on people manually observing plants, which is slow, labor‐intensive, and prone to errors. The authors used a drone to take aerial photos and trained a computer model (a convolutional neural network) to recognize maize plots with tassels in these images. The model was tested on data from different locations and years, showing it could accurately detect flowered plots faster than manual methods without sacrificing accuracy. The AI model was also designed to be simple and efficient, so it could run on a laptop or desktop, making it more accessible. This approach could save time and resources in crop breeding and improve how scientists study plants. Future work could apply this to other traits or use higher‐tech sensors for even more data.

Contamination of farmland with heavy metals (HMs), particularly arsenic, cadmium, and lead, poses significant risks to human health and food security, especially through HM bioaccumulation in rice (Oryza Sativa). Current methods of detection for HMs, such as ICP‐MS, provide accurate measurements but are destructive and labor‐intensive, limiting their feasibility for widespread agricultural use. In this study, we investigated the potential of Raman spectroscopy (RS) as a nondestructive, cost‐effective alternative for the detection of HM stress and thereby uptake in rice. Using a dose–response experimental design, we examined the sensitivity of RS for detecting varying levels of arsenic, cadmium, and lead‐induced stress. Our analyses revealed several dose‐dependent changes in Raman peaks associated with carotenoid and phenylpropanoid abundance. We found these changes were specific to each HM, reflecting the activation of distinct stress‐response mechanisms. We also performed ICP‐MS of harvested rice tissue, allowing us to build Raman‐based calibration curves for predicting the HM concentration within rice. Lastly, we built a machine‐learning algorithm that could interpret the Raman spectra to diagnose the specific type of HM toxicity with an average of 84.5% accuracy after only 1 week of HM stress. These findings highlight the promise of RS as a valuable tool for real‐time, nondestructive monitoring of HM contamination in rice crops. Notably, the dose–response experimental design demonstrated RS's ability to detect HM stress levels that aligned with typical environmental contamination.

Study Design: Narrative review. Objectives: To identify the risk factors and surgical management for recurrent lumbar disc herniation using a systematic review of available evidence. Methods: We conducted a review of PubMed, MEDLINE, OVID, and Cochrane Library databases using search terms identifying recurrent lumbar disc herniation and risk factors or surgical management. Abstracts of all identified articles were reviewed. Detailed information from articles with levels I to IV evidence was extracted and synthesized. Results: There is intermediate levels III to IV evidence detailing perioperative risk factors and the optimal surgical technique for recurrent lumbar disc herniations. Conclusions: Multiple risk factors including smoking, diabetes mellitus, obesity, intraoperative technique, and biomechanical factors may contribute to the development of recurrent disc disease. There is widespread variation regarding optimal surgical management for recurrent herniation, which often include revision discectomies with or without fusion via open and minimally invasive techniques.

Senescence is a highly ordered biological process involving resource redistribution away from ageing tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned/unoccupied/uncrewed aerial system/vehicle (UAS, UAV and drone) flights captured the senescence window across two experiments while functional principal component analysis effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: the red chromatic coordinate (RCC) index and the transformed normalized difference green and red (TNDGR) index. Convolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis. The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1–M6). Model performance was strongest for FPC1 scores from VSRs (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to-moderate for RCC index (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis. This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

PurposeTo describe the presence of congenital scoliosis in a genetically identical population as it relates to the possible genetic vs. environmental etiologic factors.MethodsThe authors describe three cases of congenital scoliosis in monozygotic twins. The first pair includes two 4-year-old girls presenting with mirror curves, one of whom had an associated stage I Chiari malformation. The second pair is a 4-year-old girl who presented with thoracic scoliosis, a T10–11 hemivertebra, and multilevel failure of segmentation in the lumbar spine whose identical sibling is unaffected. The third pair includes a 4-month-old boy with T9 and L4 hemivertebra whose brother is also unaffected.ResultsAll three cases were managed conservatively with observation and remained asymptomatic throughout the duration of follow-up. There were no associations with extraspinal deformities, although one patient presented with concomitant type I Chiari malformation.ConclusionThe variable presentation of congenital scoliosis in a genetically unique population serves as testament to the complexity associated with its development, likely involving both environmental factors and a genetic predisposition.

Study Design: Retrospective review of a prospectively collected database. Objective: To predict the occurrence of hospital-acquired conditions (HACs) 30-days postoperatively and to compare predictors of HACs for spine surgery with other common elective surgeries. Methods: Patients ≥18 years undergoing elective spine surgery were identified in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2005 to 2013. Outcome measures included any HACs: superficial or deep surgical site infection (SSI), venous thromboembolism (VTE), urinary tract infection (UTI). Spine surgery patients were compared with those undergoing other common procedures. Random forest followed by multivariable regression analysis was used to determine risk factors for the occurrence of HACs. Results: A total of 90 551 elective spine surgery patients, of whom 3021 (3.3%) developed at least 1 HAC, 1.4% SSI, 1.3% UTI, and 0.8% VTE. The occurrence of HACs for spine patients was predicted with high accuracy (area under the curve [AUC] 77.7%) with the following variables: female sex, baseline functional status, hypertension, history of transient ischemic attack (TIA), quadriplegia, steroid use, preoperative bleeding disorders, American Society of Anesthesiologists (ASA) class, operating room duration, operative time, and level of residency supervision. Functional status and hypertension were HAC predictors for total knee arthroplasty (TKA), bariatric, and cardiothoracic patients. ASA class and operative time were predictors for most surgery cohorts. History of TIA, preoperative bleeding disorders, and steroid use were less predictive for most other common surgical cohorts. Conclusions: Occurrence of HACs after spine surgery can be predicted with demographic, clinical, and surgical factors. Predictors for HACs in surgical spine patients, also common across other surgical groups, include functional status, hypertension, and operative time. Understanding the baseline patient risks for HACs will allow surgeons to become more effective in their patient selection for surgery.

BackgroundBariatric surgery (BS) is an increasingly common treatment for morbid obesity that has the potential to effect bone and mineral metabolism. The effect of prior BS on spine surgery outcomes has not been well established. The aim of this study was to assess differences in complication rates following spinal surgery for patients with and without a history of BS.MethodsRetrospective analysis of the prospectively collected New York State Inpatient Database (NYSID) years 2004–2013. BS patients and morbidly obese patients (non-BS) were divided into cervical and thoracolumbar surgical groups and propensity score matched for age, gender, and invasiveness and complications compared.ResultsOne thousand nine hundred thirty-nine spine surgery patients with a history of BS were compared to 1625 non-BS spine surgery patients. The average time from bariatric surgery to spine surgery is 2.95 years. After propensity score matching, 740 BS patients were compared to 740 non-BS patients undergoing thoracolumbar surgery, with similar comorbidity rates. The overall complication rate for BS thoracolumbar patients was lower than non-BS (45.8% vs 58.1%, P < 0.001), with lower rates of device-related (6.1% vs 23.2%, P < 0.001), DVT (1.2% vs 2.7%, P = 0.039), and hematomas (1.5% vs 4.5%, P < 0.001). Neurologic complications were similar between BS patients and non-BS patients (2.3% vs 2.7%, P = 0.62). For patients undergoing cervical spine surgery, BS patients experienced lower rates of bowel issues, device-related, and overall complication than non-BS patients (P < 0.05).ConclusionsBariatric surgery patients undergoing spine surgery experience lower overall complication rates than morbidly obese patients. This study warrants further investigation into these populations to mitigate risks associated with spine surgery for bariatric patients.

Study Design: Broad narrative review. Objectives: Translaminar screw (TLS) fixation was first described as a salvage technique for fixation of the axial spine. Better understanding of the spine anatomy allows for advancement in surgical techniques and expansion of TLS indications. The goal of this review is to discuss the anatomic feasibility of the TLS fixation in different region of the spine. Methods: A review of the current literatures on the principles, biomechanics, and clinical application of the translaminar screw technique in the axial, subaxial, and thoracolumbar spine. Results: Anatomic feasibility and biomechanical studies have demonstrated that TLS is a safe and strong fixation methods for fusion beyond just the axial spine. However, not all spine segments have wide enough lamina to accept TLS. Preoperative computed tomography scan can help ensure the feasibility and safety of TLS insertion. Recent clinical reports have validated the application of TLS in subaxial spine, thoracic spine, hangman’s fracture, and pediatric population. Conclusions: TLS can be used beyond axial spine; however, TLS insertion is only warranted when the lamina is thick enough to avoid further complications such as breakage. Preoperative computed tomography scans can be used to determine feasibility of such fixation construct.

The AHRQ (Agency for Healthcare Research and Quality) has requested the correction of the result Tables 1–3 of this study: All stated numbers below 10 shall be modified to read “<10” instead.