Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.

Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.

Utilizing Entropy of Cadence to Optimize Cycling Rehabilitation in Individuals With Parkinson's Disease.

BACKGROUND: Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression. OBJECTIVES: The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function. METHODS: Twenty-two participants were randomized into either patient-specific adaptive dynamic cycling (PSADC) or non-adaptive (NA) group. SampEn of cadence was calculated after each of the 12 sessions, and motor function was evaluated using the Kinesia test. Pearson's correlation coefficient was used to analyze the relationship between SampEn of cadence and motor function improvement. Multiple linear regression (MLR) was used to identify the strongest predictors of motor function improvement. RESULTS: Pearson's correlation coefficient revealed a significant correlation between SampEn of cadence and motor function improvements (R2 = -.545, P = .009), suggesting that higher SampEn of cadence led to greater motor function improvement. MLR demonstrated that SampEn of cadence was the strongest predictor of motor function improvement (ß = -8.923, t = -2.632, P = .018) over the BMI, Levodopa equivalent daily dose, and effort. CONCLUSIONS: The findings show that PSADC paradigm promoted a greater improvement in motor function than NA dynamic cycling. These data will be used to develop a predictive model to optimize motor function improvement after cycling in individuals with PD.

Image-Based High-Throughput Phenotyping in Horticultural Crops.

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Comparison between Conventional Breath-hold and Respiratory-triggered Magnetic Resonance Cholangiopancreatography with and without Compressed Sensing: Cross-sectional Study.

AIM: The application of compressed sensing (CS) has enabled breath-hold 3D-MRCP with a shorter acquisition time in clinical practice. INTRODUCTION: To compare the image quality of breath-hold (BH) and respiratory-triggered (RT) 3D-MRCP with or without CS application in the same study population. METHODS: In this retrospective study, from February to July 2020, a total of 98 consecutive patients underwent four different acquisition types of 3D-MRCP.; 1) BH MRCP with the generalized autocalibrating partially parallel acquisition (GRAPPA) (BH-GRAPPA), 2) RT-GRAPPA-MRCP, 3) RT-CS-MRCP and 4) BH-CS-MRCP. Relative contrast of common bile duct, 5-scale visibility score of biliary pancreatic ducts, 3-scale artifact score and 5-scale overall image quality score were evaluated by two abdominal radiologists. RESULTS: Relative contrast value was significantly higher in BH-CS or RT-CS than in RT-GRAPPA (0.90 ± 0.057 and 0.89 ± 0.079, respectively, vs. 0.82 ± 0.071, p < 0.01) or BH-GRAPPA (vs. 0.77 ± 0.080, p < 0.01). The area affected by artifact was significantly lower in BH-CS among 4 MRCPs (p < 0.08). Overall image quality score in BH-CS was significantly higher than BH-GRAPPA (3.40 vs. 2.71, p < 0.01). There were no significant differences between RT-GRAPPA and BH-CS (vs. 3.13, p = 0.67) in overall image quality. CONCLUSION: In this study, our results revealed BH-CS had higher relative contrast and comparable or superior image quality among four MRCP sequences.

Usefulness of the acromioclavicular joint cross-sectional area as a diagnostic image parameter of acromioclavicular osteoarthritis.

BACKGROUND: Acromioclavicular joint (ACJ) space narrowing has been considered to be an important diagnostic image parameter of ACJ osteoarthritis (ACJO). However, the morphology of the ACJ space is irregular because of osteophyte formation, subchondral irregularity, capsular distention, sclerosis, and erosion. Therefore, we created the ACJ cross-sectional area (ACJCSA) as a new diagnostic image parameter to assess the irregular morphologic changes of the ACJ. AIM: To hypothesize that the ACJCSA is a new diagnostic image parameter for ACJO. METHODS: ACJ samples were obtained from 35 patients with ACJO and 30 healthy individuals who underwent shoulder magnetic resonance (S-MR) imaging that revealed no evidence of ACJO. Oblique coronal, T2-weighted, fat-suppressed S-MR images were acquired at the ACJ level from the two groups. We measured the ACJCSA and the ACJ space width (ACJSW) at the ACJ on the S-MR images using our imaging analysis program. The ACJCSA was measured as the cross-sectional area of the ACJ. The ACJSW was measured as the narrowest point between the acromion and the clavicle. RESULTS: The average ACJCSA was 39.88 ± 10.60 mm2 in the normal group and 18.80 ± 5.13 mm2 in the ACJO group. The mean ACJSW was 3.51 ± 0.58 mm in the normal group and 2.02 ± 0.48 mm in the ACJO group. ACJO individuals had significantly lower ACJCSA and ACJSW than the healthy individuals. Receiver operating characteristic curve analyses demonstrated that the most suitable ACJCSA cutoff score was 26.14 mm2, with 91.4% sensitivity and 90.0% specificity. CONCLUSION: The optimal ACJSW cutoff score was 2.37 mm, with 88.6% sensitivity and 96.7% specificity. Even though both the ACJCSA and ACJSW were significantly associated with ACJO, the ACJCSA was a more sensitive diagnostic image parameter.

MR Imaging Characteristics of Primary T-Cell Lymphoma of the Cauda Equina: A Case Report and Literature Review.

Primary central nervous system lymphoma is a rare form of extranodal non-Hodgkin lymphoma, and primary T-cell lymphoma of the cauda equina is extremely rare. We describe a case involving a 56-year-old female who presented with low back pain and radiating leg pain for 4 months. MRI of the lumbar spine revealed an elongated, multinodular intradural lesion of approximately 10 cm from the L4 body to the S2 body level with iso-signal intensity on T1-weighted imaging, heterogeneous iso- and high-signal intensity on T2-weighted imaging, and a heterogeneous intense enhancement on gadolinium contrast-enhanced T1-weighted imaging. A peripheral T-cell lymphoma of the cauda equina was diagnosed on the basis of immunohistochemical and T-cell receptor gamma gene rearrangement analysis after intradural biopsy of the mass.

Compressive Strength, Chloride Ion Penetrability, and Carbonation Characteristic of Concrete with Mixed Slag Aggregate.

The shortage of natural aggregates has recently emerged as a serious problem owing to the tremendous growth of the concrete industry. Consequently, the social interest in identifying aggregate materials as alternatives to natural aggregates has increased. In South Korea's growing steel industry, a large amount of steel slag is generated and discarded every year, thereby causing environmental pollution. In previous studies, steel slag, such as blast furnace slag (BFS), has been used as substitutes for concrete aggregates; however, few studies have been conducted on concrete containing both BFS and Ferronickel slag (FNS) as the fine aggregate. In this study, the compressive strength, chloride ion penetrability, and carbonation characteristic of concrete with both FNS and BFS were investigated. The mixed slag fine aggregate (MSFA) was used to replace 0, 25%, 50%, 75%, and 100% of the natural fine aggregate volume. From the test results, the highest compressive strength after 56 days was observed for the B/F100 sample. The 56 days chloride ion penetrability of the B/F75, and B/F100 samples with the MSFA contents of 75% and 100% were low level, approximately 34%, and 54% lower than that of the plain sample, respectively. In addition, the carbonation depth of the samples decreased with the increase in replacement ratio of MSFA.

A novel approach to predict ingress/egress discomfort based on human motion and biomechanical analysis.

This study proposes an ingress/egress discomfort prediction algorithm using an in-depth biomechanical method and motion capture database. The ingress/egress motion of the subject was captured using an optical motion capture system and physically adjustable vehicle mock-up. The subjective discomfort evaluation data were also recorded at the same time. The inverse kinematics and inverse dynamics were performed to analyze captured ingress/egress motion. These procedure provide motion and joint torque information on each subject. Based on the analysis results, this study proposes the following novel features: accumulated movement of joint and sum of rectified joint torque. This study conducted a feature selection procedure to identify a relevant feature subset. Recursive feature selection and optimal feature selection methods found the most relevant feature subset with collected subjective responses. Finally, we constructed the prediction model using support vector machine. The prediction model was evaluated through prediction accuracy and statistical analysis. For comparison with the previous study, this study implemented two representative models and compare the result with those of the previous studies using the identical dataset. The effectiveness of proposed algorithm was demonstrated in comparison with previous studies.

A novel approach to predicting human ingress motion using an artificial neural network.

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.

Complementation of a mutation in CpSRP43 causing partial truncation of light-harvesting chlorophyll antenna in Chlorella vulgaris.

Photosynthesis of microalgae enables conversion of light energy into chemical energy to produce biomass and biomaterials. However, the efficiency of this process must be enhanced, and truncation of light-harvesting complex (LHC) has been suggested to improve photosynthetic efficiency. We reported an EMS-induced mutant (E5) showing partially reduced LHC in Chlorella vulgaris. We determined the mutation by sequencing the whole genome of WT and E5. Augustus gene prediction was used for determining CDS, and non-synonymous changes in E5 were screened. Among these, we found a point mutation (T to A) in a gene homologous to chloroplast signal recognition particle 43 kDa (CpSRP43). The point mutation changed the 102nd valine to glutamic acid (V102E) located in the first chromodomain. Phylogenetic analyses of CpSRP43 revealed that this amino acid was valine or isoleucine in microalgae and plants, suggesting important functions. Transformation of E5 with WT CpSRP43 showed varying degrees of complementation, which was demonstrated by partial recovery of the LHCII proteins to the WT level, and partially restored photosynthetic pigments, photosynthetic ETR, NPQ, and growth, indicating that the V102E mutation was responsible for the reduced LHC in E5.

Small-scale noise-like moiré pattern caused by detector sensitivity inhomogeneity in computed tomography.

We report a new type of moiré pattern caused by inhomogeneous detector sensitivity in computed tomography. Defects in one or a few detector bins or miscalibrated detectors induce well-known ring artifacts. When detector sensitivity is not homogenous over all detector bins, these ring artifacts occur everywhere as distributed rings in reconstructed images and may cause a moiré pattern when combined with insufficient view sampling, which induces a noise-like pattern or a subtle texture in the reconstructed images. Complete correction of the inhomogeneity in detectors can remove the pattern and improve image quality. This paper describes several properties of moiré patterns caused by detector sensitivity inhomogeneity.

Ring artifact correction using detector line-ratios in computed tomography.

Ring artifacts in computed tomography (CT) images degrade image quality and obscure the true shapes of objects. While several correction methods have been developed, their performances are often task-dependent and not generally applicable. Here, we propose a novel method to reduce ring artifacts by calculating the ratio of adjacent detector elements in the projection data, termed the line-ratio. Our method estimates the sensitivity of each detector element and equalizes them in sinogram space. As a result, the stripe pattern can be effectively removed from sinogram data, thereby also removing ring artifacts from the reconstructed CT image. Numerical simulations were performed to evaluate and compare the performance of our method with that of conventional methods. We also tested our method experimentally and demonstrated that our method has superior performance to other methods.

Physiochemical properties of digested sewage sludge with ultrasonic treatment.

We investigated the dewaterability and physiochemical properties of digested sludge after treatment with ultrasonic energy for the purpose of reducing sludge. The study involved laboratory experimentation under varying test conditions of treatment time, volume of sludge and ultrasonic energy, which combined can be denoted as specific supplied energy (E(v)). Results of the experiments show that particle size (dp(50), dp(10), U) of the ultrasonically treated sludge decreases due to the separation of sludge flocs. Capillary suction times (CSTs) decrease significantly, while turbidity, VDSs/VS and SCODs/TCOD increase with ultrasonic treatment. From these results, it was found that the ultrasonic treatment specified by the supplied energy (E(v)) can not only improve dewaterability but also reduce the volume and mass and change the chemical properties of sludge.

Subsurface biobarrier formation by microorganism injection for contaminant plume control.

The concept of an in situ mixture of residual soil and aerobic microorganisms as a biobarrier for controlling contaminant plume was evaluated in this study. Azotobacter chroococcum was inoculated into soil with oxygen as the electron acceptor and appropriate substrate to induce biofilm clog soil pores. The hydraulic conductivity of soil decreased by 1/8000 while substrate and oxygen were provided to the injected microorganism, and increased by 400% when no substrate was provided. A series of column experiments were carried out to measure the hydraulic conductivity of soil specimens. The results showed that the highest hydraulic conductivity reduction occurred when the substrate and electron acceptors were first introduced, and this reduction increased toward the outlet of the column. The substrate was consumed mostly at the inlet and was distributed with time. The analysis of volatile substances after the test showed that the inlet had a high organic content and the outlet had a low organic content.