Dynamic Sequences Research Articles

Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology.

To investigate the diagnostic performance of a multiparametric magnetic resonance imaging (MRI) protocol comprising quantitative MRI (diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) perfusionMRI) and conventional MRI in the characterization of gallbladder wall thickening (GWT). This prospective study comprised consecutive adults with GWT who underwent multiparametric MRI between July 2020 and April 2022. Two radiologistsevaluated the MRI independently. The final diagnosis was based on surgical histopathology. The association of MRI parameters with malignant GWT was evaluated. The area under the curve (AUC) for the quantitative MRI parameters and diagnostic performance of conventional, and multiparametric MRI were compared. The interobserver agreementbetween two radiologists was calculated. Thirty-five patients (mean age, 56years, 23 females) with GWT (25 benign and ten malignant) were evaluated. The quantitative MRI parameters significantly associated with malignant GWT were apparent diffusion coefficient on DWI (p = 0.007) and mean diffusivity (MD) on DTI (p = 0.013), perfusion fraction (f) on IVIM (p = 0.033), time to peak enhancement (TTP, p = 0.008), and wash in rate (p = 0.049) on DCE-MRI. TTP had the highest AUC of 0.790, followed by MD (0.782) and f (0.742) (p = 0.213) for predicting malignant GWT. Multiparametric MRI had significantly higher sensitivity (90% vs. 80%, p = 0.045) than conventional MRI for diagnosing malignant GWT. The two radiologists' reading had substantial to near-perfect agreement (kappa = 0.639-1) and moderate to strong correlation (interclass correlation coefficient = 0.5-0.88). Multiparametric protocol incorporating advanced sequences improved the diagnostic performance of MRI for differentiating benign and malignant GWT. • Multiparametric MRI had 90% sensitivity and 88% specificity for diagnosing malignant GWT, compared to 80% sensitivity and 88% specificity for conventional CE-MRI. • Among the quantitative MRI parameters, TTP (perfusion-MRI) had the highest AUC of 0.790, followed by MD (0.782) and IVIM-f (0.742). • For most quantitative MRI parameters, there was moderate to strong agreement (ICC = 0.5-0.88).

Spinsim: A GPU optimized python package for simulating spin-half and spin-one quantum systems

Spinsim simulates the quantum dynamics of unentangled spin-1/2 and spin-1 systems evolving under time-dependent control. While other solvers for the time-dependent Schrödinger equation optimize for larger state spaces but less temporally-rich control, spinsim is optimized for intricate time evolution of a minimalist system. Efficient simulation of individual or ensemble quanta driven by adiabatic sweeps, elaborate pulse sequences, complex signals and non-Gaussian noise is the primary target application. We achieve fast and robust evolution using a geometric integrator to bound errors over many steps, and split the calculation parallel-in-time on a GPU using the numba just-in-time compiler. Speed-up is three orders of magnitude over QuTip's sesolve and Mathematica's NDSolve, and four orders over SciPy's ivp_solve for equal accuracy. Interfaced through python, spinsim should be useful for simulating robust state preparation, inversion and dynamical decoupling sequences in NMR and MRI, and in quantum control, memory and sensing applications with two- and three-level quanta. Program summaryProgram Title: SpinsimCPC Library link to program files:https://doi.org/10.17632/f6rdk4gyxr.1Developer's repository link:https://github.com/alexander-tritt-monash/spinsimLicensing provisions: BSD 3-clauseProgramming language: Python (3.7 or greater)Nature of problem: Quantum sensing is a domain of quantum technology where the dynamics of quantum systems are used to infer properties of the systems' environments. The development of quantum sensing protocols is greatly sped-up by software simulations of the new techniques. Quantum sensing simulation benefits from temporally-rich control of individual quanta. However, current specialized time-dependent Schrödinger equation solvers are instead optimized only for simple pulses in large Hilbert spaces. Thus, there is a need for efficient simulation of individual or ensemble quanta driven by adiabatic sweeps, elaborate pulse sequences, complex signals and non-Gaussian noise.Solution method: Spinsim simulates the quantum dynamics of spin-1/2 and spin-1 systems evolving under time-dependent control. We first speed up the integration of the time-dependent Schrödinger equation by splitting the calculation parallel-in-time on a GPU using the numba [1] just-in-time compiler. We achieve fast and robust evolution using a geometric integrator to bound errors over many steps. A dynamic rotating frame transformation and Lie-Trotter decomposition are used to decrease effective step sizes on long and short time scales, respectively. Hence, each individual step is more accurate. Spinsim is interfaced via a python package, meaning it can be used by researchers inexperienced with geometric integrators and GPU parallelism.Additional comments including restrictions and unusual features: To use the (default) Nvidia cuda GPU parallelization, one needs to have a cuda compatible Nvidia GPU [2]. For cuda mode to function, one also needs to install the Nvidia cuda toolkit [3]. If cuda is not available on the system, the simulator will automatically parallelize over multicore CPUs instead. Developed and tested on Windows 10; tested on Windows 11. CPU functionality tested on MacOS 10.16 Big Sur (note that MacOS 10.14 Mojave and higher are not compatible with cuda hardware and software). The package (including cuda functionality) is in principle compatible with Linux, but functionality has not been tested.

An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments

The visual simultaneous localization and mapping (SLAM) method under dynamic environments is a hot and challenging issue in the robotic field. The oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one of the most effective methods. However, the traditional ORB-SLAM algorithm cannot perform well in dynamic environments due to the feature points of dynamic map points at different timestamps being incorrectly matched. To deal with this problem, an improved visual SLAM method built on ORB-SLAM3 is proposed in this paper. In the proposed method, an improved new map points screening strategy and the repeated exiting map points elimination strategy are presented and combined to identify obvious dynamic map points. Then, a concept of map point reliability is introduced in the ORB-SLAM3 framework. Based on the proposed reliability calculation of the map points, a multi-period check strategy is used to identify the unobvious dynamic map points, which can further deal with the dynamic problem in visual SLAM, for those unobvious dynamic objects. Finally, various experiments are conducted on the challenging dynamic sequences of the TUM RGB-D dataset to evaluate the performance of our visual SLAM method. The experimental results demonstrate that our SLAM method can run at an average time of 17.51 ms per frame. Compared with ORB-SLAM3, the average RMSE of the absolute trajectory error (ATE) of the proposed method in nine dynamic sequences of the TUM RGB-D dataset can be reduced by 63.31%. Compared with the real-time dynamic SLAM methods, the proposed method can obtain state-of-the-art performance. The results prove that the proposed method is a real-time visual SLAM, which is effective in dynamic environments.

Ensemble perspective for understanding temporal credit assignment.

Recurrent neural networks are widely used for modeling spatiotemporal sequences in both nature language processing and neural population dynamics. However, understanding the temporal credit assignment is hard. Here, we propose that each individual connection in the recurrent computation is modeled by a spike and slab distribution, rather than a precise weight value. We then derive the mean-field algorithm to train the network at the ensemble level. The method is then applied to classify handwritten digits when pixels are read in sequence, and to the multisensory integration task that is a fundamental cognitive function of animals. Our model reveals important connections that determine the overall performance of the network. The model also shows how spatiotemporal information is processed through the hyperparameters of the distribution, and moreover reveals distinct types of emergent neural selectivity. To provide a mechanistic analysis of the ensemble learning, we first derive an analytic solution of the learning at the infinitely large network limit. We then carry out a low-dimensional projection of both neural and synaptic dynamics, analyze symmetry breaking in the parameter space, and finally demonstrate the role of stochastic plasticity in the recurrent computation. Therefore, our study sheds light on mechanisms of how weight uncertainty impacts the temporal credit assignment in recurrent neural networks from the ensemble perspective.

MRI for Cushing Disease: A Systematic Review.

MR imaging is key in the diagnostic work-up of Cushing disease. The sensitivity of MR imaging in Cushing disease is not known nor is the prognostic significance of "MR imaging-negative" disease. Our aim was to determine the overall sensitivity and prognostic significance of MR imaging localization of Cushing disease. We performed a systematic review of the MEDLINE and PubMed databases for cohort studies reporting the sensitivity of MR imaging for the detection of adenomas in Cushing disease. This study included 57 studies, comprising 5651 patients. Risk of bias was assessed using the methodological index for non-randomized studies criteria. Meta-analysis of proportions and pooled subgroup analysis were performed. Overall sensitivity was 73.4% (95% CI, 68.8%-77.7%), and the sensitivity for microadenomas was 70.6% (66.2%-74.6%). There was a trend toward greater sensitivity in more recent studies and with the use of higher-field-strength scanners. Thinner-section acquisitions and gadolinium-enhanced imaging, particularly dynamic sequences, also increased the sensitivity. The use of FLAIR and newer 3D spoiled gradient-echo and FSE sequences, such as spoiled gradient-echo sequences and sampling perfection with application-optimized contrasts by using different flip angle evolutions, may further increase the sensitivity but appear complementary to standard 2D spin-echo sequences. MR imaging detection conferred a 2.63-fold (95% CI, 2.06-3.35-fold) increase in remission for microadenomas compared with MR imaging-negative Cushing disease. Pooled analysis is limited by heterogeneity among studies. We could not account for variation in image interpretation and tumor characteristics. Detection on MR imaging improves the chances of curative resection of adenomas in Cushing disease. The evolution of MR imaging technology has improved the sensitivity for adenoma detection. Given the prognostic importance of MR imaging localization, further effort should be made to improve MR imaging protocols for Cushing disease.

Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study

IntroductionBreast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies. MethodsMRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method. ResultsFollowing surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence. ConclusionTexture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis. Implications for practiceThe results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.

Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination.

To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions. Clinical and MRI data of 158 patients with breast lesions who underwent dynamic contrast-enhanced MRI (DCE-MRI), syMRI, and MUSE sequences between September 2019 and December 2020 were retrospectively collected. The apparent diffusion coefficient (ADC) values of MUSE and quantitative relaxation parameters (longitudinal and transverse relaxation times [T1, T2], and proton density [PD] values) of syMRI were measured, and the parameter variation values and change in their ratios were calculated. The patients were randomly divided into training (n = 111) and validation (n = 47) groups at a ratio of 7:3. A nomogram was built based on univariate and multivariate logistic regression analyses in the training group and was verified in the validation group. The discriminatory and predictive capacities of the nomogram were assessed by the receiver operating characteristic curve and area under the curve (AUC). The AUC was compared by DeLong test. In the training group, univariate analysis showed that age, lesion diameter, menopausal status, ADC, T2pre, PDpre, PDGd, T2Delta, and T2ratio were significantly different between benign and malignant breast lesions (P < 0.05). Multivariate logistic regression analysis showed that ADC and T2pre were significant variables (all P < 0.05) in breast cancer diagnosis. The quantitative model (model A: ADC, T2pre), BI-RADS model (model B), and multi-parameter model (model C: ADC, T2pre, BI-RADS) were established by combining the above independent variables, among which model C had the highest diagnostic performance, with AUC of 0.965 and 0.986 in the training and validation groups, respectively. The prediction model established based on syMRI, MUSE sequence, and BI-RADS is helpful for clinical differentiation of breast tumors and provides more accurate information for individualized diagnosis.

A dual-attention based coupling network for diabetes classification with heterogeneous data

Diabetes Mellitus (DM) is a group of metabolic disorders characterized by hyperglycaemia in the absence of treatment. Classification of DM is essential as it corresponds to the respective diagnosis and treatment. In this paper, we propose a new coupling network with hierarchical dual-attention that utilizes heterogeneous data, including Flash Glucose Monitoring (FGM) data and biomarkers in electronic medical records. The long short-term memory-based FGM sub-network extracts the time-dependent features of dynamic FGM sequences, while the biomarkers sub-network learns the features of static biomarkers. The convolutional block attention module (CBAM) for dispersing the feature weights of the spatial and channel dimensions is implemented into the FGM sub-network to endure the variability of FGM and allows us to extract high-level discriminative features more accurately. To better adjust the importance weights of the characteristics of the two sub-networks, self-attention is introduced to integrate the characteristics of heterogeneous data. Based on the dataset provided by Peking University People’s Hospital, the proposed method is evaluated through factorial experiments of multi-source heterogeneous data, ablation studies of various attention strategies, time consumption evaluation and quantitative evaluation. The benchmark tests reveal the proposed network achieves a type 1 and 2 diabetes classification accuracy of 95.835% and the comprehensive performance metrics, including Matthews correlation coefficient, F1-score and G-mean, are 91.333%, 94.939% and 94.937% respectively. In the factorial experiments, the proposed method reaches the maximum area under the receiver operating characteristic curve of 0.9428, which indicates the effectiveness of the coupling between the nominated sub-networks. The coupling network with a dual-attention strategy performs better than the one without or only with a single-attention strategy in the ablation study as well. In addition, the model is also tested on another data set, and the accuracy of the test reaches 94.286%, reflecting that the model is robust when it is transferred to untrained diabetes data. The experimental results show that the proposed method is feasible in the classification of diabetes types. The code is available at https://github.com/bitDalei/Diabetes-Classification-with-Heterogeneous-Data.

Cylindrical ionization chamber response in static and dynamic 6 and 15 MV photon beams

Purpose. To investigate the response of the CC13 ionization chamber under non-reference photon beam conditions, focusing on penumbra and build-up regions of static fields and on dynamic intensity-modulated beams. Methods. Measurements were performed in 6 MV 100 × 100, 20 × 100, and 20 × 20 mm2 static fields. Monte Carlo calculations were performed for the static fields and for 6 and 15 MV dynamic beam sequences using a Varian multi-leaf collimator. The chamber was modelled using EGSnrc egs_chamber software. Conversion factors were calculated by relating the absorbed dose to air in the chamber air cavity to the absorbed dose to water. Correction and point-dose correction factors were calculated to quantify the conversion factor variations. Results. The correction factors for positions on the beam central axis and at the penumbra centre were 0.98–1.02 for all static fields and depths investigated. The largest corrections were obtained for chamber positions beyond penumbra centre in the off-axis direction. Point-dose correction factors were 0.54–0.71 at 100 mm depth and their magnitude increased with decreasing field size and measurement depth. Factors of 0.99–1.03 were obtained inside and near the integrated penumbra of the dynamic field at 100 mm depth, and of 0.92–0.94 beyond the integrated penumbra centre. The variations in the ionization chamber response across the integrated dynamic penumbra qualitatively followed the behaviour across penumbra of static fields. Conclusions. Without corrections, the CC13 chamber was of limited usefulness for profile measurements in 20-mm-wide fields. However, measurements in dynamic small irregular beam openings resembling the conditions of pre-treatment patient quality assurance were feasible. Uncorrected ionization chamber response could be applied for dose verification at 100 mm depth inside and close to large gradients of dynamically accumulating high- and low-dose regions assuming 3% tolerance between measured and calculated doses.

Monte Carlo simulation of the nuclear spin decoherence process in Eu3+ : Y2SiO5 crystals

${\mathrm{Eu}}^{3+}$:${\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ crystals are promising candidates for quantum memories because their nuclear spin coherence lifetime can be extended to 47 seconds at a particular magnetic field. Although an extended coherence lifetime of 6 hours can be achieved with a large number of dynamic decoupling sequences, these pulses will also introduce unwanted noise. To realize quantum storage with a lifetime of hours, it is necessary to quantitatively characterize the spin decoherence process of ${\mathrm{Eu}}^{3+}$ ions to guide the experimental efforts. In practice, a number of limiting factors could impose a limit on the achievable spin coherence lifetime, which includes the spin inhomogeneous broadening of the ${\mathrm{Eu}}^{3+}$ ensemble and the homogeneity and stability of the external magnetic field. Here, we provide a Monte Carlo simulation model of the nuclear spin decoherence process of ${\mathrm{Eu}}^{3+}$ ions in ${\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ crystals, and these limiting factors are considered to provide an accurate prediction of the spin coherence lifetimes. This method is universal and can be applied to other rare-earth-ion-doped crystals.

Real-Time Polarimetry of Hyperpolarized 13C Nuclear Spins Using an Atomic Magnetometer.

We introduce a method for nondestructive quantification of nuclear spin polarization, of relevance to hyperpolarized spin tracers widely used in magnetic resonance from spectroscopy to in vivo imaging. In a bias field of around 30 nT we use a high-sensitivity miniaturized 87Rb-vapor magnetometer to measure the field generated by the sample, as it is driven by a windowed dynamical decoupling pulse sequence that both maximizes the nuclear spin lifetime and modulates the polarization for easy detection. We demonstrate the procedure applied to a 0.08 M hyperpolarized [1-13C]-pyruvate solution produced by dissolution dynamic nuclear polarization, measuring polarization repeatedly during natural decay at Earth's field. Application to real-time and continuous quality monitoring of hyperpolarized substances is discussed.

Multistep sequence-controlled supramolecular polymerization by the combination of multiple self-assembly motifs

The precise sequence control of polymer chain is an important research topic of polymer chemistry. Although some methods such as iterative synthesis and supramolecular polymerization have been developed to fabricate sequence-controllable polymer, it is still a great challenge to consecutively prepare multiple supramolecular polymers with different sequence structures. In this work, through the reasonable utilization of assembly motifs, we integrated multiple host-guest recognitions and metal coordination interactions to prepare different sequence-controlled supramolecular polymers by a multistep assembly strategy. This research provides inspiration for the design and preparation of supramolecular polymers with different sequence structures.

Impact of Prostate Urethral Lift Device on Prostate Magnetic Resonance Image Quality.

Prostatic urethral lift with UroLift is a minimally invasive approach to treat symptomatic benign prostatic hypertrophy. This device causes artifacts on prostate magnetic resonance images. Our aim was to evaluate the impact of artifact on prostate magnetic resonance image quality. This was a single-center retrospective review of patients with UroLift who subsequently had prostate magnetic resonance imaging. Two readers graded UroLift artifact on each pulse sequence using a 5-point scale (1-nondiagnostic; 5-no artifact). Prostate Imaging Quality scores were assigned for the whole data set. The volume of gland obscured by artifact was measured. Linear and logistic regression models were used to identify predictors of poor image quality. Thirty-seven patients were included. Poor image quality occurs more in the transition zone than the peripheral zone (15% vs 3%), at base/mid regions vs the apex (13%, 9%, and 5%, respectively) and on diffusion-weighted images vs T2-weighted and dynamic contrast-enhanced sequences (27%, 0.3%, 0%, respectively; P < .001). Suboptimal image quality (ie, Prostate Imaging Quality score <2) was found in 16%-24% of exams. The percentage of gland obscured by the UroLift artifact was higher on diffusion-weighted images and dynamic contrast-enhanced sequences than T2-weighted (32%, 9%, and 6%, respectively; P < .001). UroLift artifact negatively affects prostate magnetic resonance image quality with greater impact in the mid-basal transition zone, obscuring a third of the gland on diffusion-weighted images. Patients considering this procedure should be counseled on the impact of this device on image quality and its potential implications for any image-guided prostate cancer workup.

Feasibility of Non-Gated Dynamic Fetal Cardiac MRI for Identification of Fetal Cardiovascular Anatomy

Introduction: The aim of this study was to evaluate the feasibility of identifying the fetal cardiac and thoracic vascular structures with non-gated dynamic balanced steady-state free precession (SSFP) MRI sequences. Methods: We retrospectively assessed the visibility of cardiovascular anatomy in 60 fetuses without suspicion of congenital heart defect. Non-gated dynamic balanced SSFP sequences were acquired in three anatomic planes of the fetal thorax. The images were analyzed following a segmental approach in consensus reading by an experienced pediatric cardiologist and radiologist. An imaging score was defined by giving one point to each visualized structure, yielding a maximum score of 21 points. Image quality was rated from 0 (poor) to 2 (excellent). The influence of gestational age (GA), field strength, placenta position, and maternal panniculus on image quality and imaging score were tested. Results: 30 scans were performed at 1.5T, 30 at 3T. Heart position, atria, and ventricles could be seen in all 60 fetuses. Basic diagnosis (>12 points) was achieved in 54 cases. The mean imaging score was 16.8+/−3.8. Maternal panniculus (r = −0.3; p = 0.015) and GA (r = 0.6; p < 0.001) correlated with imaging score. Field strength influenced image quality, with 1.5T being better than 3T images (p = 0.012). Imaging score or quality was independent of placenta position. Conclusion: Fetal cardiac MRI with non-gated SSFP sequences enables recognition of basic cardiovascular anatomy.

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning.

Recently, personality trait recognition, which aims to identify people's first impression behavior data and analyze people's psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods.

Face expression image detection and recognition based on big data technology

This research addresses the deficiencies in current dynamic sequence facial expression recognition methods, which suffer from limited accuracy and effectiveness. The primary objective is to introduce an innovative approach that leverages big data technology for improved facial expression detection and recognition. The methodology encompasses several vital steps. The integral graph method is employed to capture dynamic sequences of facial expressions, and a weak facial feature classifier is utilized for image preprocessing. To enhance accuracy, a dynamic sequence model is devised for feature extraction. The study combines the personalized learning algorithm with the optical flow technique to pinpoint critical facial expression junctures and facilitate dynamic sequence recognition. The investigation reveals the inadequacy of current dynamic sequence facial expression recognition methods in accurately categorizing expressions. The proposed approach yields promising results, achieving a peak expression division accuracy of 91.78% in simulations. Notably, the personalized learning recognition method demonstrates enhanced robustness in categorizing expressions, effectively capturing intricate facial details and augmenting overall recognition efficacy. This research thus contributes to advancing facial expression recognition technology, addressing critical shortcomings in current methods.

CenterTube: Tracking Multiple 3D Objects With 4D Tubelets in Dynamic Point Clouds

3D Multi-Object Tracking (MOT) in dynamic point cloud sequences is a fundamental research problem for several downstream tasks such as motion planning and action recognition. Existing methods usually rely on the traditional tracking-by-detection (TBD) paradigm, which performs the tracking based on the results achieved by dedicated detectors. However, this two-stage framework usually cannot sufficiently exploit spatial-temporal information and end-to-end optimization, leading to sub-optimal tracking performance, especially when the object is partially or completely occluded. In this paper, we propose a joint detection and tracking framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CenterTube</i> for dynamic point cloud sequences. The key to our approach is to formulate the problem of multiple object trajectory predictions as 4D tubelet detections. In particular, the proposed CenterTube is composed of three head branches, including a center branch, a regression branch, and a movement branch for the estimation of object center, object size, instance movement, and frame interval, respectively. Additionally, a Tube BEV-IoU (TB-IoU) is also presented to link the generated clip-level tubelets and form the final tracks. Extensive experiments conducted on the KITTI-MOT and nuScenes datasets demonstrate that our model achieves competitive performances even if no ready-made detection results is adopted.

Experimental Evaluation of Analgesics and Anti-Inflammatory Activity of Punarnavadi Guggulu and Panchavalkaladi Taila

Background: Inflammation is a dynamic sequence as a protective response against various etiological agents either infectious or noninfectious by the body. Control of inflammation is necessary when inflammation becomes severe. Many herbs work as anti-inflammatory in ayurveda classics. Punarnavadi Guggulu and Panchavalkaladi Taila is said to be efferive in reducing inflammation. Present experimental traial was conducted to revalidate this claim. The study aimed to evaluate the anti-inflammatory and analgesics activity of Punarnavadi Guggulu and Panchavalkaladi Taila in experimental animals. Materials and Methods: In this trail, Wistar strain albino rats (weighing 200 ± 20 g) of either sex and pharmacologically validates model for analgesic and anti-inflammatory was used to evaluate activity. Punarnavadi Guggulu (270 mg/kg body weight) and Panchavalkaldi Taila (q.s.) locally were administered in the experimental group. Results: Punarnavadi Guggulu with Panchavalkaladi Taila was found significantly inhibited the inflammation on carrageenan-induced paw edema (P &lt; 0.001) at 1, 3, and 5 h. In analgesic activity, Punarnavadi Guggulu with Panchavalkaladi Taila was not significantly inhibit the early and late phases of pain. Conclusion: The results revealed that Punarnavadi Guggulu with Panchavalkaladi Taila has anti-inflammatory activity but insignificant analgesic activity. Hence, Punarnavadi Guggulu with Panchavalkaladi Taila can be effective in reducing inflammatory conditions.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

DeepPCT: Single Object Tracking in Dynamic Point Cloud Sequences

Three-dimensional single object tracking (SOT) in dynamic point cloud sequences is a fundamental problem for numerous intelligent systems. In this article, we propose a deep neural architecture named DeepPCT to achieve accurate and robust tracking in sequential point clouds. Instead of relying on the bounding box classification and regression to localize the object, the proposed framework directly formulates the intersection-over-union (IoU) prediction as its objectives for accurate 3-D localization. A Center IoU (CIoU) is further proposed to mitigate the ambiguity of standard IoU. In addition, we also investigate the impact of an additional re-detection module to the overall tracking performance, since tracking failures frequently occur due to the large variations and error accumulation. Experimental results on the KITTI benchmark demonstrate that the proposed tracker achieves competitive performance, with a 3-D precision rate of 63.7% and a 3-D success rate of 45.0%. Moreover, our tracker achieves compelling performance in the wild with severe illumination variations and occlusions, and even outperforms several 2-D counterpart trackers.