MDM-YOLO: Research on Object Detection Algorithm Based on Improved YOLOv4 for Marine Organisms

Vision-based underwater object detection technology is a hot topic of current research. In order to address the issues of low accuracy and high missed rate of marine life detection, an object detection algorithm called MDM-YOLO (Marine Detection Model with YOLO) for marine organisms based on improved YOLOv4 is proposed. To improve the network's capacity for feature extraction, a multi-branch architecture CSBM is integrated into the backbone. Based on this, the feature fusion structure introduces shuffle attention to reinforce the focus on important information. The experimental results demonstrate that the MDM-YOLO algorithm increases the mean average precision (mAP) by 2.31 % compared to the YOLOv4 algorithm on the Underwater Robot Picking Contest (URPC) dataset. Moreover, on the RSOD dataset and PASCAL VOC dataset, MDM-YOLO obtained an mAP of 87.54 % and 86.87 %, respectively. According to these advancements, the MDM-YOLO model is more suitable for the identification of items on the seafloor

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds

An improved joint non-negative matrix factorization for identifying surgical treatment timing of neonatal necrotizing enterocolitis

Neonatal necrotizing enterocolitis is a severe neonatal intestinal disease. Timely identification of surgical indications is essential for newborns in order to seek the best time for treatment and improve prognosis. This paper attempts to establish an algorithm model based on multimodal clinical data to determine the features of surgical indications and construct an auxiliary diagnosis model. The proposed algorithm adds hypergraph constraints on the two modal data based on Joint Nonnegative Matrix Factorization (JNMF), aiming to mine the higher-order correlations of the two data features. In addition, the adjacency matrix of the two kinds of data is used as a network regularization constraint to prevent overfitting. Orthogonal and L1-norm regulations were introduced to avoid feature redundancy and perform feature selection, respectively, and confirmed 14 clinical features. Finally, we used three classifiers, random forest, support vector machine, and logistic regression, to perform binary classification of patients requiring surgery. The results show that when the features selected by the proposed algorithm model are classified by random forest, the area under the ROC curve is 0.8, which has high prediction accuracy

The association between serum phosphorus and common carotid artery intima–media thickness in ischemic stroke patients

PurposeAn elevated concentration of phosphorus is associated with an increased risk of atherosclerosis and cardiovascular diseases. Common carotid artery intima–media thickness (cIMT) is an imaging marker of atherosclerosis. However, data on the relationship between phosphorus and cIMT in ischemic stroke are scarce. We aimed to evaluate the association between serum phosphorus levels and cIMT in patients who had experienced ischemic stroke.Patients and methodsA total of 1,450 ischemic stroke patients were enrolled. Participants were divided into four groups (quartiles) according to baseline serum phosphorus level. Carotid atherosclerosis was identified by measurement of cIMT; abnormal cIMT was defined as a maximum cIMT or mean cIMT ≥ 1 mm. Multivariable logistic regression models were used to assess the association between serum phosphorus level and the presence of abnormal cIMT.ResultsIn the multivariable adjusted analysis, falling into the highest quartile for serum phosphorus (Q4) was associated with a 2.00-fold increased risk of having abnormal maximum cIMT [adjusted odds ratio (OR) 2.00; 95% confidence interval (CI) 1.44–2.79] and a 1.76-fold increased risk of having abnormal mean cIMT (adjusted OR 1.76; 95% CI 1.22–2.53) in comparison to Q1. Furthermore, the association between serum phosphorus and abnormal cIMT was confirmed in analyses treating serum phosphorus as a continuous variable and in subgroup analyses.ConclusionIn acute ischemic stroke patients, baseline elevated serum phosphorus level was found to be independently associated with carotid atherosclerosis, as measured by cIMT

The prognostic value of deep earlobe creases in patients with acute ischemic stroke

Background and purposeData on earlobe crease (ELC) among patients with acute ischemic stroke (AIS) are limited. Here, we determined the frequency and characteristics of ELC and the prognostic effect of ELC among AIS patients.MethodsA total of 936 patients with acute AIS were enrolled during the period between December 2018 and December 2019. The patients were divided into those without and with ELC, unilateral and bilateral ELC, and shallow and deep ELC, according to the photographs taken of the bilateral ears. Logistic regression models were used to estimate the effect of ELC, bilateral ELC, and deep ELC on poor functional outcomes at 90 days (a modified Rankin Scale score ≥2) in AIS patients.ResultsAmong the 936 AIS patients, there were 746 (79.7%) patients with ELC. Among patients with ELC, there were 156 (20.9%) patients with unilateral ELC and 590 (79.1%) with bilateral ELC and 476 (63.8%) patients with shallow ELC and 270 (36.2%) with deep ELC. After adjusting for age, sex, baseline NIHSS score, and other potential covariates, patients with deep ELC were associated with a 1.87-fold [odds ratio (OR) 1.87; 95% confidence interval (CI), 1.13–3.09] and 1.63-fold (OR 1.63; 95%CI, 1.14–2.34) increase in the risk of poor functional outcome at 90 days in comparison with those without ELC or shallow ELC.ConclusionELC was a common phenomenon, and eight out of ten AIS patients had ELC. Most patients had bilateral ELC, and more than one-third had deep ELC. Deep ELC was independently associated with an increased risk of poor functional outcome at 90 days

Brain Imaging Signs and Health-Related Quality of Life after Acute Ischemic Stroke: Analysis of ENCHANTED Alteplase Dose Arm.

The influence of specific brain lesions on health-related quality of life (HRQoL) after acute ischemic stroke (AIS) is uncertain. We aimed to identify imaging predictors of poor HRQoL in alteplase-treated participants of the alteplase dose arm of the Enhanced Control of Hypertension and Thrombolysis Stroke Study (ENCHANTED). ENCHANTED was an international trial of low- versus standard-dose intravenous alteplase in AIS patients, with functional outcome (modified Rankin scale [mRS]) and HRQoL on the 5-dimension European Quality of Life Scale (EQ-5D) assessed at 90 days post-randomization. Brain images were analyzed centrally by trained assessors. Multivariable logistic regression was undertaken in the study population randomly divided (2:1) into training (development) and validation (performance) groups, with age (per 10-year increase), ethnicity, baseline National Institutes of Health Stroke Scale (NIHSS) score, diabetes mellitus, premorbid function (mRS score 0 or 1), and proxy respondent, forced into all models. Data are presented with odds ratios (ORs) and 95% confidence intervals (CIs). Eight prediction models were developed and validated in 2,526 AIS patients (median age 67.5 years; 38.4% female; 61.7% Asian) with complete brain imaging and 90-day EQ-5D utility score data. The best performance model included acute ischemic changes in the right (OR 1.69, 95% CI: 1.24-2.29) and deep (OR 1.50, 95% CI: 1.03-2.19) middle cerebral artery (MCA) regions. Several background features of brain frailty - atrophy, white matter change, and old infarcts - were significantly associated with adverse physical but not emotional HRQoL domains. In thrombolysed AIS patients, right-sided and deep ischemia within the MCA territory predict poor overall HRQoL, whilst features of old cerebral ischemia are associated with reduced physical HRQoL. [Abstract copyright: © 2020 S. Karger AG, Basel.