Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture.

Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics.

A lightweight cow mounting behavior recognition system based on improved YOLOv5s.

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms.

After 3 months of medication balloon therapy, a patient who had contrast-induced encephalopathy recovered: A case report.

RATIONALE: Iodinated contrast agents are extensively employed in clinical settings, with allergic reactions and renal impairment being the most prevalent adverse events. Contrast-induced encephalopathy (CIE) can present with heterogeneous clinical features, making diagnosis challenging. Prior studies on CIE have primarily documented rapid recovery within several days. However, this paper describes a case of CIE in a patient whose clinical symptoms took 3 months to fully abate. PATIENT CONCERNS: A female patient, aged 54 years, received drug-coated balloon therapy for stenosis in a branch of the anterior descending coronary artery. Unfortunately, the patient developed CIE, which initially manifested as visual disturbances and subsequently progressed to gastrointestinal and limb movement issues, as well as an altered mental status, all of which occurred within a 24-hour period during hospitalization. DIAGNOSES: The patient was diagnosed with CIE after cerebral hemorrhage, and cerebral edema was ruled out based on the history of contrast medium administration and radiographic exams. INTERVENTIONS AND OUTCOMES: Dexamethasone (10 mg/d), mannitol (100 mL/d), betahistine (500 mL), trazodone (25 mg), and hydration supplementation were given to treat CIE-related symptoms. Aspirin and clopidogrel were administered for the management of the cardiovascular ailment. The neurologist prescribed neurotrophic agents, namely, cytarabine and methylcobalamin, based on the cerebral magnetic resonance imaging findings. Despite the treatment, the patient's ocular symptoms, including blurry vision, diplopia, and impaired intraocular retraction, persisted. Furthermore, the patient's mental state was altered, and she continued to exhibit a depressive state during her 1-month follow-up visit. LESSONS: CIE is a comparatively infrequent ailment, and its prompt identification and management are of paramount importance. Although the treatments for CIE are primarily symptomatic, it is crucial to acknowledge that the symptoms may not always subside quickly within a short duration. In conjunction with pharmacotherapy, counseling should be offered to address patients' mental health.

Hemoglobin A1c and Angiographic Severity with Coronary Artery Disease: A Cross-Sectional Study.

BACKGROUND: Many studies have shown that glycated hemoglobin (HbA1c) is associated with coronary artery disease (CAD). HbA1c was independently related to angiographic severity in Chinese patients with CAD after adjusting for other covariates. Some traditional cardiovascular drugs may have an impact on this relationship. METHODS: This retrospective study enrolled a total of 572 CAD patients who underwent their coronary angiography and had their HbA1c levels measured at the Chinese Hospital. The complexity of the coronary artery lesions was evaluated using the Syntax score, and the subjects were divided into 4 inter quartiles according to HbA1c levels. Covariates included history of traditional cardiovascular drugs. RESULTS: The average age of selected participants was 61.00 ± 9.15 years old, and about 54.72% of them were male. Result of fully adjusted linear regression showed that HbA1c was positively associated with Syntax score after adjusting confounders (ß = 1.09, 95% CI: 0.27, 1.91, P = 0.0096). By interaction and stratified analyses, the interactions were observed based on our specification including with the medication history of statins and angiotensin receptor blockers (ARBs) (P values for interaction <0.05). CONCLUSION: In this study, we found a positive correlation between the HbA1c levels and the SYNTAX score among CAD individuals, and oral statins and ARBs medication could affect the correlation. Thus, HbA1c measurement could be used for the evaluation of the severity and complexity of coronary lesions among CAD patients.

Attentive 3D-Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer.

Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB-D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB-D data is available for training so as to attain the complementary information. Fortunately, a kind of method via multimodal training and unimodal testing has been proposed. However, unimodal feature representation and cross-modality transfer still need to be further improved. To this end, this paper proposes a new 3D-Ghost and Spatial Attention Inflated 3D ConvNet (3DGSAI) to extract high-quality features for each modality. The baseline of 3DGSAI network is Inflated 3D ConvNet (I3D), and two main improvements are proposed. One is 3D-Ghost module, and the other is the spatial attention mechanism. The 3D-Ghost module can extract richer features for hand gesture representation, and the spatial attention mechanism makes the network pay more attention to hand region. This paper also proposes an adaptive parameter for positive knowledge transfer, which ensures that the transfer always occurs from the strong modality network to the weak one. Extensive experiments on SKIG, VIVA, and NVGesture datasets demonstrate that our method is competitive with the state of the art. Especially, the performance of our method reaches 97.87% on the SKIG dataset using only RGB, which is the current best result.

Ginsenoside Rb1 Prevents Dysfunction of Endothelial Cells by Suppressing Inflammatory Response and Apoptosis in the High-Fat Diet Plus Balloon Catheter-Injured Rabbit Model via the G Protein-Coupled Estrogen Receptor-Mediated Phosphatidylinositol 3-Kinases (PI3K)/Akt Pathway.

BACKGROUND The initiation of atherosclerosis (AS) is attributed to the dysfunction of endothelial cells (ECs) via the inhibition of g protein-coupled estrogen receptor (GPER). In the current study, we assessed the potential of Ginsenoside Rb1 (Rb1) to attenuate the dysfunction of ECs via GPER-mediated PI3K/Akt pathway. MATERIAL AND METHODS AS was induced in rabbits and then the AS rabbits were treated with Rb1. Thereafter, the ECs were isolated from AS and healthy rabbits, and treated with Rb1. The effect of Rb1 on blood lipid levels in AS rabbits and on apoptosis, inflammatory response, and GPER/PI3K/Akt axis activity in ECs was detected. Furthermore, the activities of GPER and PI3K were modulated to verify the key role of the axis in the anti-AS effect of Rb1. RESULTS The levels of total cholesterol, low-density lipoprotein (LDL), and triglyceride in AS rabbits were suppressed by Rb1 while the high-density lipoprotein (HDL) level was increased. In in vitro assays, Rb1 administration inhibited apoptosis process and the production of pro-inflammation cytokines in AS ECs. The expression levels of GPER, p-PI3K, and p-Akt were upregulated by Rb1, associated with the increased level of Bcl-2 and reduced level of Bax. When the activity of GPER was inhibited by GP-15 in AS ECs, the treatment effect of Rb1 was blocked. However, the activation of PI3K could restore the protective effect of Rb1 after the inhibition of GPER. CONCLUSIONS The anti-AS potential of Rb1 was exerted by restoring the regular function of ECs via the activation of GPER-mediated PI3K/Akt signaling.

The Value of Combining Plasma D-Dimer and Endothelin-1 Levels to Predict No-Reflow After Percutaneous Coronary Intervention of ST-Segment Elevation in Acute Myocardial Infarction Patients with a Type 2 Diabetes Mellitus History.

BACKGROUND No-reflow phenomenon is a well-known problem, often accompanying percutaneous coronary intervention (PCI) for ST-segment elevation acute myocardial infarction (STEAMI). This study investigated the value of plasma D-dimer and Endothelin-1 (ET-1) levels on admission in predicting no-reflow after primary PCI and long-term prognosis in STEAMI patients with type 2 diabetes mellitus (T2DM). MATERIAL AND METHODS There were 822 patients with STEAMI and T2DM undergoing successful primary PCI included in this study: 418 patients showed normal re-flow after PCI, while 404 patients showed no-reflow phenomenon after PCI. The predictive value of plasma ET-1 and D-dimer level, and other clinical parameters for the no-reflow phenomenon were analyzed. RESULTS The high plasma ET-1 and D-dimer levels showed predictive value for the no-reflow phenomenon in STEAMI patients with T2DM. Patients with high D-dimer and ET-1 levels showed higher risk (4.212, with 95%CI of 2.973-5.967 and 2.447 with 95%CI of 1.723-3.476, respectively) of no-reflow phenomenon compared with patients with low plasma D-dimer and ET-1 levels. Sensitivity of high plasma ET-1 and D-dimer levels in predicting no-reflow was 0.766. Both plasma D-dimer and ET-1 were adverse prognosticators for STEAMI patients with a T2DM post PCI (P<0.001). CONCLUSIONS In conclusion, plasma D-dimer and ET-1 levels on admission independently predict no-reflow after PCI in STEAMI patients with T2DM. When combined, the D-dimer and ET-1 levels as predictive and prognostic values are clinically promising. The plasma D-dimer and ET-1 levels provided a novel marker for treatment selection for the STEAIM patients with a T2DM history.

Tetra-kis(di-4-pyridylsulfane)dinitratocopper(II).

In the title complex, [Cu(NO(3))(2)(C(10)H(8)N(2)S)(4)], the Cu(II) atom (site symmetry ) is coordinated by two monodentate nitrate ions and two monodentate di-4-pyridylsulfane ligands, resulting in a slightly distorted trans-arranged CuO(2)N(4) octa-hedral geometry. Intra-molecular C-H⋯O hydrogen bonds are present. In the crystal, adjacent mol-ecules are linked via C-H⋯N hydrogen bonds into chains parallel to the a axis. Inter-molecular C-H⋯O inter-actions also occur.