Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning.

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.

Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning.

Behavior classification and recognition of sheep are useful for monitoring their health and productivity. The automatic behavior classification of sheep by using wearable devices based on IMU sensors is becoming more prevalent, but there is little consensus on data processing and classification methods. Most classification accuracy tests are conducted on extracted behavior segments, with only a few trained models applied to continuous behavior segments classification. The aim of this study was to evaluate the performance of multiple combinations of algorithms (extreme learning machine (ELM), AdaBoost, stacking), time windows (3, 5 and 11 s) and sensor data (three-axis accelerometer (T-acc), three-axis gyroscope (T-gyr), and T-acc and T-gyr) for grazing sheep behavior classification on continuous behavior segments. The optimal combination was a stacking model at the 3 s time window using T-acc and T-gyr data, which had an accuracy of 87.8% and a Kappa value of 0.836. It was applied to the behavior classification of three grazing sheep continuously for a total of 67.5 h on pasture with three different sward surface heights (SSH). The results revealed that the three sheep had the longest walking, grazing and resting times on the short, medium and tall SHH, respectively. These findings can be used to support grazing sheep management and the evaluation of production performance.

Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.

The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification.

Progressive Transfer Learning.

Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between each mini-batch's distribution and the whole dataset's distribution when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the dataset's global information when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the dataset's global information into a latent state and uses it to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by jointly optimizing BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can greatly improve the ReID model's performance on MSMT17, Market-1501, CUHK03, and DukeMTMC-reID datasets. Moreover, we extend our proposal to the general image classification task. The experiments in several image classification benchmark datasets demonstrate that our proposal can significantly improve baseline models' performance. The code has been released at https://github.com/ZJULearning/PTL.

Association of polymorphisms in C1orf106, IL1RN, and IL10 with post-induction infliximab trough level in Crohn's disease patients.

BACKGROUND: Trough levels of the post-induction serum infliximab (IFX) are associated with short-term and long-term responses of Crohn's disease patients to IFX, but the inter-individual differences are large. We aimed to elucidate whether single gene polymorphisms (SNPs) within FCGR3A, ATG16L1, C1orf106, OSM, OSMR, NF-κB1, IL1RN, and IL10 partially account for these differences and employed a multivariate regression model to predict patients' post-induction IFX levels. METHODS: The retrospective study included 189 Crohn's disease patients undergoing IFX therapy. Post-induction IFX levels were measured and 41 tag SNPs within eight genes were genotyped. Associations between SNPs and IFX levels were analysed. Then, a multivariate logistic-regression model was developed to predict whether the patients' IFX levels achieved the threshold of therapy (3 µg/mL). RESULTS: Six SNPs (rs7587051, rs143063741, rs442905, rs59457695, rs3213448, and rs3021094) were significantly associated with the post-induction IFX trough level (P = 0.015, P < 0.001, P = 0.046, P = 0.022, P = 0.011, P = 0.013, respectively). A multivariate prediction model of the IFX level was established by baseline albumin (P = 0.002), rs442905 (P = 0.025), rs59457695 (P = 0.049), rs3213448 (P = 0.056), and rs3021094 (P = 0.047). The area under the receiver operating characteristic curve (AUROC) of this prediction model in a representative training dataset was 0.758. This result was verified in a representative testing dataset, with an AUROC of 0.733. CONCLUSIONS: Polymorphisms in C1orf106, IL1RN, and IL10 play an important role in the variability of IFX post-induction levels, as indicated in this multivariate prediction model of IFX levels with fair performance.

SIF: Self-Inspirited Feature Learning for Person Re-identification.

The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. Most of these networks are trained using the classic stochastic gradient descent optimizer. However, limited efforts have been made to explore potential performance of existing ReID networks directly by better training scheme, which leaves a large space for ReID research. In this paper, we propose a Self-Inspirited Feature Learning (SIF) method to enhance performance of given ReID networks from the viewpoint of optimization. We design a simple adversarial learning scheme to encourage a network to learn more discriminative person representation. In our method, an auxiliary branch is added into the network only in the training stage, while the structure of the original network stays unchanged during the testing stage. In summary, SIF has three aspects of advantages: (1) it is designed under general setting; (2) it is compatible with many existing feature learning networks on the ReID task; (3) it is easy to implement and has steady performance. We evaluate the performance of SIF on three public ReID datasets: Market1501, DuckMTMC-reID, and CUHK03(both labeled and detected). The results demonstrate significant improvement in performance brought by SIF. We also apply SIF to obtain state-of-the-art results on all the three datasets. Specifically, mAP / Rank-1 accuracy are: 87.6% / 95.2% (without re-rank) on Market1501, 79.4% / 89.8% on DuckMTMC-reID, 77.0% / 79.5% on CUHK03 (labeled) and 73.9% / 76.6% on CUHK03 (detected), respectively. The code of SIF will be available soon.

Determination of apatinib and its three active metabolites by UPLC-MS/MS in a Phase IV clinical trial in NSCLC patients.

Aim: To develop and validate a simple method using UPLC-MS/MS for determination of apatinib and its three active metabolites in a Phase IV clinical trial. Materials & methods: All compounds were separated on a Hypersil GOLD™ aQ C18 Polar Endcapped LC column (50 × 2.1 mm, 1.9 µm, Thermo) using 5 mmol/l ammonium acetate with 0.1% formic acid:acetonitrile (20:80, v/v) as the mobile phase after a rapid liquid-liquid extraction. This method was validated over the linear concentration range of 1.00-1000 ng/ml for each compound. Results: The interassay precision and accuracy were less than ±15%. The validated method was successfully applied to determine concentrations of clinical samples in non-small-cell lung cancer patients.

Quantitative bioanalytical LC-MS/MS assay for S130 in rat plasma-application to a pharmacokinetic study.

Aim: An innovative Atg4B inhibitor, S130, exhibited a negative influence on colorectal cancer cells in vitro and in vivo. To assist reliable toxicodynamic and pharmacokinetic evaluation, an LC-MS/MS assay of S130 in rat plasma must be necessary. Results: An LC-MS/MS assay for determination of S130 in rat plasma has been first developed and fully verified whose values met the admissible limits as per the US FDA guidelines. Chromatographic separation was achieved by using an isocratic elution after 3 min. MS was conducted under the ESI+ mode fitted with selected reaction monitoring. The calibration curve proved acceptable linearity over 0.50-800 ng/ml. Conclusion: The developed LC-MS/MS assay of S130 in rat plasma is easily applicable in pharmacokinetics study and the further toxicological evaluation.

Sparse Learning with Stochastic Composite Optimization.

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

Fast and accurate hashing via iterative nearest neighbors expansion.

Recently, the hashing techniques have been widely applied to approximate the nearest neighbor search problem in many real applications. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. Given a query, instead of performing a linear scan of the entire data base, the hashing method can perform a linear scan of the points whose hamming distance to the query is not greater than rh , where rh is a constant. However, in order to find the true nearest neighbors, both the locating time and the linear scan time are proportional to O(∑i=0(rh)(c || i)) ( c is the code length), which increase exponentially as rh increases. To address this limitation, we propose a novel algorithm named iterative expanding hashing in this paper, which builds an auxiliary index based on an offline constructed nearest neighbor table to avoid large rh . This auxiliary index can be easily combined with all the traditional hashing methods. Extensive experimental results over various real large-scale datasets demonstrate the superiority of the proposed approach.

Density sensitive hashing.

Nearest neighbor search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, for example, locality sensitive hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbor search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called density sensitive hashing (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.

In vitro investigation of friction under edge-loading conditions for ceramic-on-ceramic total hip prosthesis.

Edge-loading generates higher wear rates in ceramic-on-ceramic total hip prosthesis (THP). To investigate the friction coefficient (FC) in these conditions, three alumina ceramic (Biolox Forte) 32 mm-diameter components were tested using a hip friction simulator. The cup was positioned with a 75 degrees abduction angle to achieve edge-loading conditions. The motion was first applied along the edge and then across the edge of the cup. First, tests were conducted under lubricated conditions with 25% bovine serum. Next, to simulate an extremely high contact pressure, the tests were run with the addition of a third body alumina ceramic chip inserted between the edge of the cup and the head. Engineering blue was used to analyze the contact area. Reference values were determined using a 0 degrees cup abduction angle. Edge loading was achieved. The FC increased by three- to sixfold when the motion was applied along the edge, and by 70% when the motion was applied across the edge. However, the FC value was still low (about 0.1), which is similar to metal-on-metal THP. With the third body alumina ceramic particle inserted, the FC was 26 times higher than in the ideal conditions and intermittent squeaking occurred. High cup abduction angles may generate edge-loading and an increase in the friction coefficient for ceramic THP.

Effect of bearing geometry and structure support on transient elastohydrodynamic lubrication of metal-on-metal hip implants.

An effective lubrication can significantly reduce wear of metal-on-metal artificial hip joints. The improvement of the lubrication can be achieved through the optimisation of the bearing geometry in terms of a small clearance and/or the structural support such as a polyethylene backing underneath a metallic bearing in a sandwich acetabular cup form. The separate effects of these two factors on fluid film lubrication of 28 mm diameter metal-on-metal total hip joints under walking conditions were numerically investigated in this paper. The results show that a larger lubricant film due to the polyethylene backing can be significantly enhanced by the transient squeeze-film action, particularly during the stance phase, and a similar lubricant film can be developed for both the monolithic cup relying on the smaller clearance and the sandwich cup benefiting from the polyethylene backing. Both cup systems can function in a wide range of lubrication regimes, covering both mixed and fluid film, under the current design and manufacture conditions.