Notes

Coarse-grained simulations discover Gram-negative bacterial protection towards polymyxins from the external membrane layer.
Superpixels are widely used in computer vision applications. Most of the existing superpixel methods use established criteria to indiscriminately process all pixels, resulting in superpixel boundary adherence and regularity being unnecessarily inter-inhibitive. This study builds upon a previous work by proposing a new segmentation strategy that classifies image content into meaningful areas containing object boundaries and meaningless parts that include color-homogeneous and texture-rich regions. Based on this classification, we design two distinct criteria to process the pixels in different environments to achieve highly accurate superpixels in content-meaningful areas and keep the regularity of the superpixels in content-meaningless regions. Additionally, we add a group of weights when adopting the color feature, successfully reducing the undersegmentation error. The superior accuracy and the moderate compactness achieved by the proposed method in comparative experiments with several state-of-the-art methods indicate that the content-adaptive criteria efficiently reduce the compromise between boundary adherence and compactness.Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction. Current gesture recognition methods have focused on recognising isolated gestures, and existing continuous gesture recognition methods are limited to two-stage approaches where independent models are required for detection and classification, with the performance of the latter being constrained by detection performance. In contrast, we introduce a single-stage continuous gesture recognition framework, called Temporal Multi-Modal Fusion (TMMF), that can detect and classify multiple gestures in a video via a single model. This approach learns the natural transitions between gestures and non-gestures without the need for a pre-processing segmentation step to detect individual gestures. To achieve this, we introduce a multi-modal fusion mechanism to support the integration of important information that flows from multi-modal inputs, and is scalable to any number of modes. Additionally, we propose Unimodal Feature Mapping (UFM) and Multi-modal Feature Mapping (MFM) models to map uni-modal features and the fused multi-modal features respectively. To further enhance performance, we propose a mid-point based loss function that encourages smooth alignment between the ground truth and the prediction, helping the model to learn natural gesture transitions. We demonstrate the utility of our proposed framework, which can handle variable-length input videos, and outperforms the state-of-the-art on three challenging datasets EgoGesture, IPN hand and ChaLearn LAP Continuous Gesture Dataset (ConGD). Furthermore, ablation experiments show the importance of different components of the proposed framework.It is theoretically insufficient to construct a complete set of semantics in the real world using single-modality data. As a typical application of multi-modality perception, the audio-visual event localization task aims to match audio and visual components to identify the simultaneous events of interest. Although some recent methods have been proposed to deal with this task, they cannot handle the practical situation of temporal inconsistency that is widespread in the audio-visual scene. Inspired by the human system which automatically filters out event-unrelated information when performing multi-modality perception, we propose a discriminative cross-modality attention network to simulate such a process. Similar to human mechanism, our network can adaptively select "where" to attend, "when" to attend and "which" to attend for audio-visual event localization. In addition, to prevent our network from getting trivial solutions, a novel eigenvalue-based objective function is proposed to train the whole network to better fuse audio and visual signals, which can obtain discriminative and nonlinear multi-modality representation. In this way, even with large temporal inconsistency between audio and visual sequence, our network is able to adaptively select event-valuable information for audio-visual event localization. Liproxstatin-1 concentration Furthermore, we systemically investigate three subtasks of audio-visual event localization, i.e., temporal localization, weakly-supervised spatial localization and cross-modality localization. The visualization results also help us better understand how our network works.New therapeutic strategies are direly needed in the fight against cancer. Over the last decade, several tumor ablation strategies have emerged as stand-alone or combination therapies. Histotripsy is the first completely non-invasive, non-thermal, and non-ionizing tumor ablation method. Histotripsy can produce consistent and rapid ablations, even near critical structures. Additional benefits include real-time image-guidance, high precision, and the ability to treat tumors of any predetermined size and shape. link2 Unfortunately, the lack of clinically and physiologically relevant pre-clinical cancer models is often a significant limitation with all focal tumor ablation strategies. The majority of studies testing histotripsy for cancer treatment have focused on small animal models, which have been critical in moving this field forward and will continue to be essential for providing mechanistic insight. While these small animal models have notable translational value, there are significant limitations in terms of scale and anatomical relevance. To address these limitations, a diverse range of large animal models and spontaneous tumor studies in veterinary patients have emerged to complement existing rodent models. These models and veterinary patients are excellent at providing realistic avenues for developing and testing histotripsy devices and techniques designed for future use in human patients. Here, we provide a review of animal models used in preclinical histotripsy studies and compare histotripsy ablation in these models using a series of original case reports across a broad spectrum of preclinical animal models and spontaneous tumors in veterinary patients.Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle this issue, unsupervised domain adaptation (UDA) transfers the knowledge learned from a well-labeled source domain to a different but related target domain where labeled data is unavailable. In this paper, we consider a more practical yet challenging UDA setting where either the source domain data or the target domain data are unknown. Technically, we investigate UDA from a novel view --- adversarial attack --- and tackle the divergence-agnostic adaptive learning problem in a unified framework. Specifically, we first report the motivation of our approach by investigating the inherent relationship between UDA and adversarial attacks. Then we elaborately design adversarial examples to attack the training model and harness these adversarial examples. We argue that the generalization ability of the model would be significantly improved if it can defend against our attack, so as to improve the performance on the target domain. Theoretically, we analyze the generalization bound for our method based on domain adaptation theories. Extensive experimental results verify that our method is able to achieve a favorable performance compared with previous ones.In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. link3 Conclusion The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.
While the history of pulmonary tuberculosis (PTB) is a risk factor for developing both chronic obstructive pulmonary disease (COPD) and lung cancer, it remains unclear whether the history of PTB affects lung cancer development in COPD patients.

To investigate whether a history of PTB is associated with an increased risk of lung cancer development in a population with COPD.

This cohort study included a nationwide representative sample of 13,165 Korean men and women with COPD, aged between 50-84 years. In addition, to assess whether the relationship between PTB and lung cancer risk differs between participants with and without COPD, a matched cohort without COPD was included. Participants were matched 13 for age, sex, smoking history, and PTB status based on the index health screening exam of corresponding participants with COPD. The two cohorts were followed up for 13 years (January 1st, 2003, to December 31st, 2015). PTB was diagnosed based on the results of chest radiography, and incident lung cancer wmong COPD patients in our country with an intermediate TB burden. COPD patients with a history of PTB, particularly the never-smokers, might benefit from periodical screening or assessment for lung cancer development.
The history of PTB was associated with an increased risk of developing lung cancer among COPD patients in our country with an intermediate TB burden. COPD patients with a history of PTB, particularly the never-smokers, might benefit from periodical screening or assessment for lung cancer development.Facing the increasing threat of multi-drug antimicrobial resistance (AMR), humans strive to search for antibiotic drug candidates and antibacterial alternatives from all possible places, from soils in remote areas to deep in the sea. In this "gold rush for antibacterials," researchers turn to the natural enemy of bacterial cells, bacteriophage (phages), and find them a rich source of weapons for AMR bacteria. Endolysins (lysins), the enzymes phages use to break the bacterial cells from within, have been shown to be highly selective and efficient in killing their target bacteria from outside while maintaining a low occurrence of bacterial resistance. In this review, we start with the structures and mechanisms of action of lysins against Gram-positive (GM+) bacteria. The developmental history of lysins is also outlined. Then, we detail the latest preclinical and clinical research on their safety and efficacy against GM+ bacteria, focusing on the formulation strategies of these enzymes. Finally, the challenges and potential hurdles are discussed.
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