Totally Corrective Multiclass Boosting with Binary Weak Learners

In this work, we propose a new optimization framework for multiclass boosting learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two successful multiclass boosting algorithms, which can use binary weak learners. We explicitly derive these two algorithms' Lagrange dual problems based on their regularized loss functions. We show that the Lagrange dual formulations enable us to design totally-corrective multiclass algorithms by using the primal-dual optimization technique. Experiments on benchmark data sets suggest that our multiclass boosting can achieve a comparable generalization capability with state-of-the-art, but the convergence speed is much faster than stage-wise gradient descent boosting. In other words, the new totally corrective algorithms can maximize the margin more aggressively.Comment: 11 page

Companion diagnostics and predictive biomarkers for PD-1/PD-L1 immune checkpoint inhibitors therapy in malignant melanoma

Programmed cell death receptor 1 (PD-1), when bound to the ligand programmed death-ligand 1 (PD-L1), can suppress cellular immunity and play a critical role in the initiation and development of cancer. Immune drugs targeting these two sites have been developed for different cancers, including malignant melanoma. The accompanying diagnostic method has been approved by the FDA to guide patient medication. However, the method of immunohistochemical staining, which varies widely due to the antibody and staining cut-off values, has certain limitations in application and does not benefit all patients. Increasing researches begin to focus on new biomarkers to improve objective response rates and survival in cancer patients. In this article, we enumerated three major groups, including tumour microenvironment, peripheral circulation, and gene mutation, which covered the current main research directions. In the future, we hope those biomarkers may be used to guide the treatment of patients with malignant melanoma

Comparison of electrohysterogram characteristics during uterine contraction and non-contraction during labor

Uterine contraction is one of the most important indication in the labor progression. Electrohysterogram (EHG) is a promising method for monitoring uterine contraction and discriminating efficient and inefficient contractions. This study aims to analyze the difference of EHG signals between two groups. EHG signals are recorded with abdominal electrodes from 20 pregnant women, including 10 in term labor group and 10 in non-labor group. Typical linear and nonlinear characteristics of EHG signals, including root mean square (RMS), peak frequency (PF), median frequency (MDF), mean frequency (MNF), parameters from wavelet decomposition (W4, W5) and time reversibility (Tr) are extracted. These characteristics are compared between contraction and non-contraction in term labor group and non-labor group. The result shows that RMS, W4 and W5 of contraction are significantly larger than non-contraction both within term labor group and between two groups (all p<;0.001). However, MDF and MNF are significantly smaller (all p<;0.05). Furthermore, all characteristics of non-contraction show no significant difference between two groups, except MNF. The variability of RMS, W4, W5 and Tr of contraction are significantly larger than non-contraction both within term labor group and between two groups (all p<;0.05, with p<;0.001 for W5 and Tr). However, the variability of MDF, PF and MNF are significantly smaller (all p<;0.05). Moreover, the variability of all characteristics of non-contraction shows no significant difference between two groups, except MNF. We have shown that characteristics of EHG signals and their variability during contraction are quite different from non-contraction. Therefore, it is feasible to separate uterine contractions and monitor uterine activity with EHG signals

A bibliometric analysis on discovering anti-quorum sensing agents against clinically relevant pathogens: current status, development, and future directions

BackgroundQuorum sensing is bacteria’s ability to communicate and regulate their behavior based on population density. Anti-quorum sensing agents (anti-QSA) is promising strategy to treat resistant infections, as well as reduce selective pressure that leads to antibiotic resistance of clinically relevant pathogens. This study analyzes the output, hotspots, and trends of research in the field of anti-QSA against clinically relevant pathogens.MethodsThe literature on anti-QSA from the Web of Science Core Collection database was retrieved and analyzed. Tools such as CiteSpace and Alluvial Generator were used to visualize and interpret the data.ResultsFrom 1998 to 2023, the number of publications related to anti-QAS research increased rapidly, with a total of 1,743 articles and reviews published in 558 journals. The United States was the largest contributor and the most influential country, with an H-index of 88, higher than other countries. Williams was the most productive author, and Hoiby N was the most cited author. Frontiers in Microbiology was the most prolific and the most cited journal. Burst detection indicated that the main frontier disciplines shifted from MICROBIOLOGY, CLINICAL, MOLECULAR BIOLOGY, and other biomedicine-related fields to FOOD, MATERIALS, NATURAL PRODUCTS, and MULTIDISCIPLINARY. In the whole research history, the strongest burst keyword was cystic-fibrosis patients, and the strongest burst reference was Lee and Zhang (2015). In the latest period (burst until 2023), the strongest burst keyword was silver nanoparticle, and the strongest burst reference was Whiteley et al. (2017). The co-citation network revealed that the most important interest and research direction was anti-biofilm/anti-virulence drug development, and timeline analysis suggested that this direction is also the most active. The key concepts alluvial flow visualization revealed seven terms with the longest time span and lasting until now, namely Escherichia coli, virulence, Pseudomonas aeruginosa, virulence factor, bacterial biofilm, gene expression, quorum sensing. Comprehensive analysis shows that nanomaterials, marine natural products, and artificial intelligence (AI) may become hotspots in the future.ConclusionThis bibliometric study reveals the current status and trends of anti-QSA research and may assist researchers in identifying hot topics and exploring new research directions

Possible Meissner effect near room temperature in copper-substituted lead apatite

With copper-substituted lead apatite below room temperature, we observe diamagnetic dc magnetization under magnetic field of 25 Oe with remarkable bifurcation between zero-field-cooling and field-cooling measurements, and under 200 Oe it changes to be paramagnetism. A glassy memory effect is found during cooling. Typical hysteresis loops for superconductors are detected below 250 K, along with an asymmetry between forward and backward sweep of magnetic field. Our experiment suggests at room temperature the Meissner effect is possibly present in this material.Comment: 7 pages, 4 figure

UA-Track: Uncertainty-Aware End-to-End 3D Multi-Object Tracking

3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing methods overlook the uncertainty issue, which refers to the lack of precise confidence about the state and location of tracked objects. Uncertainty arises owing to various factors during motion observation by cameras, especially occlusions and the small size of target objects, resulting in an inaccurate estimation of the object's position, label, and identity. To this end, we propose an Uncertainty-Aware 3D MOT framework, UA-Track, which tackles the uncertainty problem from multiple aspects. Specifically, we first introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty in object prediction with probabilistic attention. Secondly, we propose an Uncertainty-guided Query Denoising strategy to further enhance the training process. We also utilize Uncertainty-reduced Query Initialization, which leverages predicted 2D object location and depth information to reduce query uncertainty. As a result, our UA-Track achieves state-of-the-art performance on the nuScenes benchmark, i.e., 66.3% AMOTA on the test split, surpassing the previous best end-to-end solution by a significant margin of 8.9% AMOTA

Observation of diamagnetic strange-metal phase in sulfur-copper codoped lead apatite

By codoping sulfur and copper into lead apatite, the crystal grains are directionally stacked and the room-temperature resistivity is reduced from insulating to $2\times10^{-5}~\Omega\cdot$m. The resistance-temperature curve exhibits a nearly linear relationship at low temperature suggesting the presence of strange-metal phase, and a second-order phase transition is then observed at around 230~K during cooling the samples. A possible Meissner effect is present in dc magnetic measurements. Further hydrothermal lead-free synthesis results in smaller resistance and stronger diamagnetism, demonstrating the essential component might be sulfur-substituted copper apatite and the alkalis matter as well. A clear pathway towards superconductivity in this material is subsequently benchmarked.Comment: 12 pages, 4 figure