PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration.

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC. Our source code is available for reference at https://github.com/clearxu/PSTNet.

SkinFormer: Learning Statistical Texture Representation With Transformer for Skin Lesion Segmentation.

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer's global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future. Our code is available at https://github.com/Rongtao-Xu/SkinFormer.

Mechanistic Studies of Stimulus-Response Integrated Catalysis of Single-Atom Alloys under Electric Fields for Electrochemical Nitrogen Reduction.

The present work introduces a novel catalytic strategy to promote the nitrogen reduction reaction (NRR) by employing a cooperative Cu-based single-atom alloy (SAA) and oriented external electric fields (OEEFs) as catalysts. The field strength (F)-dependent reaction pathways are investigated by means of first-principles calculations. Different dipole-induced responses of intermediates to electric fields break the original scaling relationships and effectively tune not only the activity but also the product selectivity of the NRR. When the most active Os1Cu SAA is taken as an example, in the absence of an OEEF, the overpotential (η) of the NRR is 0.62 V, which is even larger than that of the competitive hydrogen evolution reaction (HER). A negative field not only reduces η but switches the preference to the NRR over the HER. In particular, η at F = -1.14 V/Šreaches the bottom of 0.18 V, which is 70% lower than that in the field-free state.

Azetidines Kill Multidrug-Resistant Mycobacterium tuberculosis without Detectable Resistance by Blocking Mycolate Assembly.

Tuberculosis (TB) is the leading cause of global morbidity and mortality resulting from infectious disease, with over 10.6 million new cases and 1.4 million deaths in 2021. This global emergency is exacerbated by the emergence of multidrug-resistant MDR-TB and extensively drug-resistant XDR-TB; therefore, new drugs and new drug targets are urgently required. From a whole cell phenotypic screen, a series of azetidines derivatives termed BGAz, which elicit potent bactericidal activity with MIC99 values <10 µM against drug-sensitive Mycobacterium tuberculosis and MDR-TB, were identified. These compounds demonstrate no detectable drug resistance. The mode of action and target deconvolution studies suggest that these compounds inhibit mycobacterial growth by interfering with cell envelope biogenesis, specifically late-stage mycolic acid biosynthesis. Transcriptomic analysis demonstrates that the BGAz compounds tested display a mode of action distinct from the existing mycobacterial cell wall inhibitors. In addition, the compounds tested exhibit toxicological and PK/PD profiles that pave the way for their development as antitubercular chemotherapies.

Conformational Preference of Lithium Polysulfide Clusters Li2Sx (x = 4-8) in Lithium-Sulfur Batteries.

Structures are of fundamental importance for diverse studies of lithium polysulfide clusters, which govern the performance of lithium-sulfur batteries. The ring-like geometries were regarded as the most stable structures, but their physical origin remains elusive. In this work, we systematically explored the minimal structures of Li2Sx (x = 4-8) clusters to uncover the driving force for their conformational preferences. All low-lying isomers were generated by performing global searches using the ABCluster program, and the ionic nature of the Li···S interactions was evidenced with the energy decomposition analysis based on the block-localized wave function (BLW-ED) approach and further confirmed with the quantum theory of atoms in molecule (QTAIM). By analysis of the contributions of various energy components to the relative stability with the references of the lowest-lying isomers, the controlling factor for isomer preferences was found to be the polarization interaction. Notably, although the electrostatic interaction dominates the binding energies, it contributes favorably to the relative stabilities of most isomers. The Li+···Li+ distance is identified as the key geometrical parameter that correlates with the strength of the polarization of the Sx2- fragment imposed by the Li+ cations. Further BLW-ED analyses reveal that the cooperativity of the Li+ cations primarily determines the relative strength of the polarization.

Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) is a challenging and promising task that aims to localize objects solely based on the supervision of image category labels. In the absence of annotated bounding boxes, WSOL methods must employ the intrinsic properties of the image classification task pipeline to generate object localizations. In this work, we propose a WSOL method for exploring the Intrinsic Discrimination and Consistency in the image classification task pipeline, and call it as IDC. First, we develop a Triplet Metrics Based Foreground Modeling (TMFM) framework to directly predict object foreground regions using intrinsic discrimination. Unlike Class Activation Map (CAM) based methods that also rely on intrinsic discrimination, our TMFM framework alleviates the problem of only focusing on the most discriminative parts by optimizing foreground and background regions synergistically. Second, we design a Dual Geometric Transformation Consistency Constraints (DGTC2) training strategy to introduce additional supervision and regularization constraints for WSOL by leveraging intrinsic geometric transformation consistency. The proposed pixel-wise and object-wise consistency constraint losses cost-effectively provide spontaneous supervision for WSOL. Extensive experiments show that our IDC method achieves significant and consistent performance gains compared to existing state-of-the-art WSOL approaches. Code is available at: https://github.com/vignywang/IDC.

Compound mutation by ultraviolet and diethyl sulfate of protease producing thermophilic bacteria to hydrolyze excess sludge.

Excess sludge (ES), a resource-rich organic waste, can be solubilized by thermophilic enzymes to extract proteins for sludge reduction and resources recovery. To solve the problems of low hydrolysis effect of ES and low enzyme producing ability of wild thermophilic bacteria, ultraviolet and diethyl sulfate (UV-DES) were adopted to mutate thermophilic bacteria in this study. Mutation sites were detected and annotated by whole genome sequencing analysis. The results showed that UV-DES mutagenesis could effectively improve enzyme-producing capacity of thermophilic bacteria and promote the hydrolysis of ES. The protease activity of the mutant strain KT16 was 46.7 % higher than that of the original strain DC8. The protein extraction rate with enzyme produced by KT16 reached 83.3 %. The total content of proteins recycled through KT16 enzyme solution was 3539.6 mg·L-1, 18.4 % higher than that of DC8. This work provided a theoretical idea and technical guidance for the protein recovery from ES.

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named "soft mask", which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJR TH1Oi1wm/view?usp=sharing).

[Retracted] SB-T-121205, a next-generation taxane, enhances apoptosis and inhibits migration/invasion in MCF-7/PTX cells.

Following the publication of this paper, and subsequently to a corrigendum that was published to take account of errors that had been made concerning the Transwell invasion assay data in Fig. 4B (doi: 10.3892/ijo.2022.5463), it was drawn to the Editor's attention by a concerned reader that the western blots shown in Figs. 3C and 6B shared the same control data; moreover, potential anomalies were identified in the flow cytometric plots shown in Fig. 2A. Although the authors proposed to publish a corrigendum to account for these errors, including new data after having performed certain of the experiments again, given the number of problems that have been identified concerning the assembly of various of the figures in the published paper, the Editor of International Journal of Oncology has decided not to publish a further corrigendum, and has, in fact, determined that this paper should be retracted from the Journal on account of an overall lack of confidence in the originally presented data. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Oncology 50: 893-902, 2017; DOI: 10.3892/ijo.2017.3871].

Optomechanical noise suppression with the optimal squeezing process.

Quantum squeezing-assisted noise suppression is a promising field with wide applications. However, the limit of noise suppression induced by squeezing is still unknown. This paper discusses this issue by studying weak signal detection in an optomechanical system. By solving the system dynamics in the frequency domain, we analyze the output spectrum of the optical signal. The results show that the intensity of the noise depends on many factors, including the degree or direction of squeezing and the choice of the detection scheme. To measure the effectiveness of squeezing and to obtain the optimal squeezing value for a given set of parameters, we define an optimization factor. With the help of this definition, we find the optimal noise suppression scheme, which can only be achieved when the detection direction exactly matches the squeezing direction. The latter is not easy to adjust as it is susceptible to changes in dynamic evolution and sensitive to parameters. In addition, we find that the additional noise reaches a minimum when the cavity (mechanical) dissipation κ(γ) satisfies the relation κ = Nγ, which can be understood as the restrictive relationship between the two dissipation channels induced by the uncertainty relation. Furthermore, by taking into account the noise source of our system, we can realize high-level noise suppression without reducing the input signal, which means that the signal-to-noise ratio can be further improved.

On the nature of inter-anion coinage bonds.

To explore the binding energy profiles and elucidate the bonding nature in counter-intuitive anion⋯anion coinage bonds (CiBs), thirty-one complexes were constructed, and the inter-anion CiBs were studied theoretically. The metastability was evidenced by the characteristic potential wells in six cases, demonstrating that anions [Au(CN)4]-, [Ag(CN)2]- and [AuO]- are appropriate building blocks for CiBs. The kinetic stability was further supported by ab initio molecular dynamics (AIMD) simulations and the analyses based on the local vibrational mode and quantum theory of atoms in molecules (QTAIM) methods. The anion⋯anion CiBs in the dimers of [AuCl4]- and [Au(CN)4]- previously observed in condensed phases were confirmed to be thoroughly repulsive under vacuum, but turned attractive in the crystal environment which was simulated using the solvation model based on density (SMD). However, the intrinsic strength of the inter-anion bonding is barely variated by the environment, as it is the combination of the inter-anion interaction and the environment effect that stabilizes the anion pairs. The block-localized wavefunction (BLW) method and its corresponding energy decomposition (BLW-ED) approach were further employed aiming at a chemically meaningful explanation for these counterintuitive phenomena. By inspecting the profiles of energy components, we identified the vital distinction between inter-anion CiBs and conventional non-covalent interactions lying in the electrostatic interaction, which variates nonmonotonically in the inter-anion complexes. The electrostatic interaction also dominates the depth of potential wells, which is commonly used to evaluate the kinetic stability, while Pauli exchange repulsion is the most repulsive factor preventing the formation of anion adducts. The importance of the Pauli exchange repulsion was further highlighted by comparing cases with and without metastability, in which the absence of a potential well is solely caused by the enhancement of the Pauli exchange repulsion.

RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentation.

High spatial resolution (HSR) remote sensing images contain complex foreground-background relationships, which makes the remote sensing land cover segmentation a special semantic segmentation task. The main challenges come from the large-scale variation, complex background samples and imbalanced foreground-background distribution. These issues make recent context modeling methods sub-optimal due to the lack of foreground saliency modeling. To handle these problems, we propose a Remote Sensing Segmentation framework (RSSFormer), including Adaptive TransFormer Fusion Module, Detail-aware Attention Layer and Foreground Saliency Guided Loss. Specifically, from the perspective of relation-based foreground saliency modeling, our Adaptive Transformer Fusion Module can adaptively suppress background noise and enhance object saliency when fusing multi-scale features. Then our Detail-aware Attention Layer extracts the detail and foreground-related information via the interplay of spatial attention and channel attention, which further enhances the foreground saliency. From the perspective of optimization-based foreground saliency modeling, our Foreground Saliency Guided Loss can guide the network to focus on hard samples with low foreground saliency responses to achieve balanced optimization. Experimental results on LoveDA datasets, Vaihingen datasets, Potsdam datasets and iSAID datasets validate that our method outperforms existing general semantic segmentation methods and remote sensing segmentation methods, and achieves a good compromise between computational overhead and accuracy. Our code is available at https://github.com/Rongtao-Xu/RepresentationLearning/tree/main/RSSFormer-TIP2023.

Attention Weighted Local Descriptors.

Local features detection and description are widely used in many vision applications with high industrial and commercial demands. With large-scale applications, these tasks raise high expectations for both the accuracy and speed of local features. Most existing studies on local features learning focus on the local descriptions of individual keypoints, which neglect their relationships established from global spatial awareness. In this paper, we present AWDesc with a consistent attention mechanism (CoAM) that opens up the possibility for local descriptors to embrace image-level spatial awareness in both the training and matching stages. For local features detection, we adopt local features detection with feature pyramid to obtain more stable and accurate keypoints localization. For local features description, we provide two versions of AWDesc to cope with different accuracy and speed requirements. On the one hand, we introduce Context Augmentation to address the inherent locality of convolutional neural networks by injecting non-local context information, so that local descriptors can "look wider to describe better". Specifically, well-designed Adaptive Global Context Augmented Module (AGCA) and Diverse Surrounding Context Augmented Module (DSCA) are proposed to construct robust local descriptors with context information from global to surrounding. On the other hand, we design an extremely lightweight backbone network coupled with the proposed special knowledge distillation strategy to achieve the best trade-off in accuracy and speed. What is more, we perform thorough experiments on image matching, homography estimation, visual localization, and 3D reconstruction tasks, and the results demonstrate that our method surpasses the current state-of-the-art local descriptors. Code is available at: https://github.com/vignywang/AWDesc.

The strength and selectivity of perfluorinated nano-hoops and buckybowls for anion binding and the nature of anion-π interactions.

Perfluorinated cycloparaphenylenes (F-[n]CPP, n = 5-8), boron nitride nanohoop (F-[5]BNNH), and buckybowls (F-BBs) were proposed as anion receptors via anion-π interactions with halide anions (Cl- , Br- and I- ), and remarkable binding strengths up to -294.8 kJ/mol were computationally verified. The energy decomposition approach based on the block-localized wavefunction method, which combines the computational efficiency of molecular orbital theory and the chemical intuition of ab initio valence bond theory, was applied to the above anion-π complexes, in order to elucidate the nature and selectivity of these interactions. The overall attraction is mainly governed by the frozen energy component, in which the electrostatic interaction is included. Remarkable binding strengths with F-[n]CPPs can be attributed to the accumulated anion-π interactions between the anion and each conjugated ring on the hoop, while for F-BBs, additional stability results from the curved frameworks, which distribute electron densities unequally on π-faces. Interestingly, the strongest host was proved to be the F-[5]BNNH, which exhibits the most significant anisotropy of the electrostatic potential surface due to the difference in the electronegativities of nitrogen and boron. The selectivity of each host for anions was explored and the importance of the often-overlooked Pauli exchange repulsion was illustrated. Chloride anion turns out to be the most favorable anion for all receptors, due to the smallest ionic radius and the weakest destabilizing Pauli exchange repulsion.

Selectivity Rule of Cryptands for Anions: Molecular Rigidity and Bonding Site.

Cryptands utilize inside CH or NH groups as hydrogen bond (H-bond) donors to capture anions such as halides. In this work, the nature and selectivity of confined hydrogen bonds inside cryptands were computationally analyzed with the energy decomposition scheme based on the block-localized wavefunction method (BLW-ED), aiming at an elucidation of governing factors in the binding between cryptands and anions. It was revealed that the intrinsic strengths of inward hydrogen bonds are dominated by the electrostatic attraction, while the anion preferences (selectivity) of inner CH and NH hydrogen bonds are governed by the Pauli exchange repulsion and electrostatic interaction, respectively. Typical conformers of cages are classified into two groups, including the C3(h) -symmetrical conformers, in which all halide anions are located near the centroids of cages, and the "semi-open" conformers, which exhibit shifted bonding sites for different halide anions. Accordingly, the difference in governing factors of selectivity is attributed to either the rigidity of cages or the binding site of anions for these two groups. In details, the C3 conformers of NH cryptands can be enlarged more remarkably than the C3(h) -symmetrical conformers of CH cryptands as the size of anion (ionic radius) increases, resulting in the relaxation of the Pauli repulsion and a dramatic reduction in electrostatic attraction, which eventually rules the selectivity of NH cryptands for halide anions. By contrary, the CH cryptands are more rigid and cannot effectively reduce the Pauli repulsion, which subsequently governs the anion preference. Unlike C3 conformers whose rigidity determines the selectivity, semi-open conformers exhibit different binding sites for different anions. From F- to I- , the bonding site shifts toward the outside end of the pocket inside the semi-open NH cryptand, leading to the significant reduction of the electrostatic interaction that dominates the anion preference. Differently, binding sites are much less affected by the size of anion inside the semi-open CH cryptand, in which the Pauli exchange repulsion remains the key factor for the selectivity of inner hydrogen bonds.

[Corrigendum] SB­T­121205, a next­generation taxane, enhances apoptosis and inhibits migration/invasion in MCF­7/PTX cells.

Subsequently to the publication of the above article, an interested reader drew to the authors' attention that Fig. 4B on p. 899, showing the results of Transwell invasion assay experiments, contained a pair of apparently overlapping panels, such that they may have been derived from the same original source, even though they were intended to show the results from differently performed experiments. After having re­examined their original data, the authors were able to identify that Fig. 4B had been inadvertently assembled incorrectly. The revised version of Fig. 4, featuring the correct data for the SB­T­121205, 10 nM data panel (the lower­left panel in Fig. 4B), is shown on the next page. The authors confirm that these data continue to support the main conclusions presented in their paper, and are grateful to the Editor of International Journal of Oncology for allowing them this opportunity to publish a Corrigendum. They also apologize to the readership for any inconvenience caused. [International Journal of Oncology 50: 893­902, 2017; DOI: 10.3892/ijo.2017.3871].

Strategies for the drug discovery and development of taxane anticancer therapeutics.

INTRODUCTION: Paclitaxel and docetaxel have been extensively used in the clinic over the past three decades. Although the patents of these first-generation taxanes have expired, their clinical applications, particularly new formulations and combination therapies, are under active investigations. Inspired by the notable success of Abraxane and Lipusu, new formulations have been extensively developed. In parallel, to overcome multidrug resistance (MDR) and to eradicate cancer stem cells, immense efforts have been made on the discovery and development of new-generation taxanes with improved potency and superior pharmacological properties. AREAS COVERED: This review covers (a) natural sources of advanced intermediates used for semi-synthesis of taxane API, (b) new formulations, (c) the major issues of FDA approved taxanes, (d) the design and development of next-generation taxanes, (e) new mechanisms of action, and (f) a variety of taxane-based drug delivery systems. EXPERT OPINION: As the highly potent next-generation taxanes can eradicate cancer stem cells and overcome MDR, the priority is to develop these compounds as an integral part of cancer therapy, especially for pancreatic, colon and prostate cancers which hardly respond to checkpoint inhibitors. In order to mitigate undesirable side effects, the exploration of effective nanoformulations and tumor-targeted drug delivery systems are essential.

The role of physical activity promoting thinking skills and emotional behavior of preschool children.

OBJECTIVES: Physical activity is critical, not only for the normal growth and development of children, but also for emotional and social behavior. The purpose of the article is to determine the relationship between physical education and social and emotional development of preschool children. METHODS: The study involved 366 children (188 boys and 178 girls) at the ages of 5 (N = 191) and 6 (N = 174), who study in public kindergartens in Beijing (China). Within 3 months, additional physical education and fitness classes were held. Before and after the study, a test was conducted: Ages & Stages Questionnaires: Social-Emotional (ASQ:SE), which was completed by the parents. The research process did not affect the performance or development of children participants. RESULTS: As a result of the study, the main regularities of the influence of physical education on social and emotional behavior of children were established. Based on the results of the study, it was determined that there is a positive correlation between age, physical education, and social-emotional behavior (r +-= 0.668). CONCLUSION: Gender differences are not statistically significant when it comes to physical activity's effect on social and emotional behavior (p-value = 0.004). The results can be applied to programs for the prevention of psychosocial and social-emotional development delays of children in kindergartens.

Therapeutic efficacy of the novel SHP2 degrader SHP2-D26, alone or in combination, against lung cancer is associated with modulation of p70S6K/S6, Bim and Mcl-1.

SHP2, a protein tyrosine phosphatase, plays a critical role in fully activating oncogenic signaling pathways such as Ras/MAPK downstream of cell surface tyrosine receptors (e.g., EGFR), which are often activated in human cancers, and thus has emerged as an attractive cancer therapeutic target. This study focused on evaluating the therapeutic potential of the novel SHP2 degrader, SHP2-D26 (D26), either alone or in combination, against non-small cell lung cancer (NSCLC) cells. While all tested NSCLC cell lines responded to D26 with IC50s of < 8 µM, a few cell lines (4/14) were much more sensitive than others with IC50s of ≤ 4 µM. There was no clear association between basal levels of SHP2 and cell sensitivities to D26. Moreover, D26 rapidly and potently decreased SHP2 levels in different NSCLC cell lines in a sustained way regardless of cell sensitivities to D26, suggesting that additional factors may impact cell response to D26. We noted that suppression of p70S6K/S6, but not ERK1/2, was associated with cell responses to D26. In the sensitive cell lines, D26 effectively increased Bim levels while decreasing Mcl-1 levels accompanied with the induction of apoptosis. When combined with the third generation EGFR inhibitor, osimertinib (AZD9291), synergistic effects on decreasing the survival of different osimertinib-resistant cell lines were observed with enhanced induction of apoptosis. Although D26 alone exerted moderate inhibition of the growth of NSCLC xenografts, the combination of osimertinib and D26 effectively inhibited the growth of osimertinib-resistant xenografts, suggesting promising efficacy in overcoming acquired resistance to osimertinib.