Triple-task mutual consistency for semi-supervised 3D medical image segmentation.

Semi-supervised deep learning algorithm is an effective means of medical image segmentation. Among these methods, multi-task learning with consistency regularization has achieved outstanding results. However, most of the existing methods usually simply embed the Signed Distance Map (SDM) task into the network, which underestimates the potential ability of SDM in edge awareness and leads to excessive dependence between tasks. In this work, we propose a novel triple-task mutual consistency (TTMC) framework to enhance shape and edge awareness capabilities, and overcome the task dependence problem underestimated in previous work. Specifically, we innovatively construct the Signed Attention Map (SAM), a novel fusion image with attention mechanism, and use it as an auxiliary task for segmentation to enhance the edge awareness ability. Then we implement a triple-task deep network, which jointly predicts the voxel-wise classification map, the Signed Distance Map and the Signed Attention Map. In our proposed framework, an optimized differentiable transformation layer associates SDM with voxel-wise classification map and SAM prediction, while task-level consistency regularization utilizes unlabeled data in an unsupervised manner. Evaluated on the public Left Atrium dataset and NIH Pancreas dataset, our proposed framework achieves significant performance gains by effectively utilizing unlabeled data, outperforming recent state-of-the-art semi-supervised segmentation methods. Code is available at https://github.com/Saocent/TTMC.

General Hyperspectral Image Super-Resolution via Meta-Transfer Learning.

Recent advances in deep learning-based methods have led to significant progress in the hyperspectral super-resolution (SR). However, the scarcity and the high dimension of data have hindered further development since deep models require sufficient data to learn stable patterns. Moreover, the huge domain differences between hyperspectral image (HSI) datasets pose a significant challenge in generalizability. To address these problems, we present a general hyperspectral SR framework via meta-transfer learning (MTL). We randomly sample various spectral ranges for SR tasks during MTL, allowing the model to accumulate diverse task experiences. Additionally, we implement a task schedule to gradually expand the number of bands, bridging the significant domain differences between datasets. By leveraging multiple datasets, we are able to achieve better performance and greater generalizability, making it applicable under various circumstances. Meanwhile, as a general framework, our scheme can be applied to existing methods to obtain performance improvements. In addition, we design an advanced network architecture based on the multifusion features to further improve the performance. Experiments demonstrate that our method not only achieves superior performance in both qualitative and quantitative terms but also can adapt robustly to a new and difficult sample, where few epochs can yield quite considerable results.

Custom-Design of Multi-Stimuli-Responsive Degradable Silica Nanoparticles for Advanced Cancer-Specific Chemotherapy.

Chemotherapy is crucial in oncology for combating malignant tumors but often encounters obatacles such as severe adverse effects, drug resistance, and biocompatibility issues. The advantages of degradable silica nanoparticles in tumor diagnosis and treatment lie in their ability to target drug delivery, minimizing toxicity to normal tissues while enhancing therapeutic efficacy. Moreover, their responsiveness to both endogenous and exogenous stimuli opens up new possibilities for integrating multiple treatment modalities. This review scrutinizes the burgeoning utility of degradable silica nanoparticles in combination with chemotherapy and other treatment modalities. Commencing the elucidation of degradable silica synthesis and degradation mechanisms, emphasis is placed on the responsiveness of these materials to endogenous (e.g., pH, redox reactions, hypoxia, and enzymes) and exogenous stimuli (e.g., light and high-intensity focused ultrasound). Moreover, this exploration delves into strategies harnessing degradable silica nanoparticles in chemotherapy alone, coupled with radiotherapy, photothermal therapy, photodynamic therapy, gas therapy, immunotherapy, starvation therapy, and chemodynamic therapy, elucidating multimodal synergies. Concluding with an assessment of advances, challenges, and constraints in oncology, despite hurdles, future investigations are anticipated to augment the role of degradable silica in cancer therapy. These insights can serve as a compass for devising more efficacious combined tumor treatment strategies.

Evaluation of the mechanism of Gong Ying San activity on dairy cows mastitis by network pharmacology and metabolomics analysis.

OBJECTIVES: The goal of this investigation was to identify the main compounds and the pharmacological mechanism of the traditional Chinese medicine formulation, Gong Ying San (GYS), by infrared spectral absorption characteristics, metabolomics, network pharmacology, and molecular-docking analysis for mastitis. The antibacterial and antioxidant activities were determined in vitro. METHODS: The chemical constituents of GYS were detected by ultra-high-performance liquid chromatography Q-extractive mass spectrometry (UHPLC-QE-MS). Related compounds were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php) and the Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.tcmip.cn/ETCM/index.php/Home/) databases; genes associated with mastitis were identified in DisGENT. A protein-protein interaction (PPI) network was generated using STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment screening was conducted using the R module. Molecular-docking analyses were performed with the AutoDockTools V1.5.6. RESULTS: Fifty-four possible compounds in GYS with forty likely targets were found. The compound-target-network analysis showed that five of the ingredients, quercetin, luteolin, kaempferol, beta-sitosterol, and stigmasterol, had degree values >41.6, and the genes TNF, IL-6, IL-1ß, ICAM1, CXCL8, CRP, IFNG, TP53, IL-2, and TGFB1 were core targets in the network. Enrichment analysis revealed that pathways associated with cancer, lipids, atherosclerosis, and PI3K-Akt signaling pathways may be critical in the pharmacology network. Molecular-docking data supported the hypothesis that quercetin and luteolin interacted well with TNF-α and IL-6. CONCLUSIONS: An integrative investigation based on a bioinformatics-network topology provided new insights into the synergistic, multicomponent mechanisms of GYS's anti-inflammatory, antibacterial, and antioxidant activities. It revealed novel possibilities for developing new combination medications for reducing mastitis and its complications.

MERF: A Practical HDR-Like Image Generator via Mutual-Guided Learning Between Multi-Exposure Registration and Fusion.

In this paper, we present a novel high dynamic range (HDR)-like image generator that utilizes mutual-guided learning between multi-exposure registration and fusion, leading to promising dynamic multi-exposure image fusion. The method consists of three main components: the registration network, the fusion network, and the dual attention network which seamlessly integrates registration and fusion processes. Initially, within the registration network, the estimation of deformation fields among multi-exposure image sequences is conducted following an exposure-invariant feature extraction phase. This leads to enhanced accuracy by mitigating discrepancies across domains. Subsequently, the fusion network utilizes a progressive frequency fusion module in two distinct stages, addressing color correction and detail preservation within low and high-frequency domains, respectively. To facilitate the mutual enhancement of the registration and fusion networks, we undertake a mutual-guided learning strategy encompassing their physical connection and constraint paradigm. Firstly, a dual attention network bridges the registration and fusion networks, addressing ghosting, which is beyond the scope of registration and facilitates information exchange between input images. Secondly, a meticulously designed generative adversarial network-like iterative training schema guides the overall network framework, thereby yielding high-quality HDR-like images through mutual enhancement. Comprehensive experiments on publicly available datasets validate the superiority of our method over existing state-of-the-art approaches.

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images. The code will be available at https://github.com/guobaoxiao/LSC.

Application of ultra-low-volume spray for the control of vegetable disease in greenhouse: Investigation of formulation performance and potential dermal exposure.

BACKGROUND: The use of pesticides in greenhouse vegetable cultivation is necessary and significant. However, traditional pesticide application methods such as the use of backpack sprayers with water-diluted pesticides have certain drawbacks, e.g., uneven distribution, high labor intensity, and safety risks. RESULTS: In this work, fluazinam ultra-low-volume liquids (Flu-ULVs) were prepared using oily solvents as carriers. The effects of different oils on the physical properties of the preparations were investigated. The Flu-ULV can be sprayed directly using a hand-held ultra-low-volume (ULV) sprayer without dilution with water. Compared with commercial water-based suspension concentrates of fluazinam, the Flu-ULV samples showed better wetting of tomato leaves, better atomization, and more uniform droplet distribution. At a dosage of 300 mL/ha, the coverage rate of tomato leaves ranged from 32.47% to 79.3%, with a droplet deposition density of 556 to 2017 droplets/cm2. Application of Flu-ULVs provided 70.86% control efficacy against gray mold in tomatoes, which was higher than those achieved with reference products. Dermal exposure to Flu-ULVs was also evaluated in greenhouse experiments. The coverage rates for all parts of the operator's body ranged from 0.02% to 0.07%, with deposition volumes of 2.23 to 12.26 µg/cm2. CONCLUSION: Ground ULV spraying of fluazinam was proved to be an effective and safe management option for the control of tomato gray mold in greenhouses. This study laid a foundation for the use of ultra-low volume spray to control vegetable diseases in greenhouse, especially those induced by high humidity environment. © 2024 Society of Chemical Industry.

Dietary antarctic krill improves antioxidant capacity, immunity and reduces lipid accumulation, insights from physiological and transcriptomic analysis of Plectropomus leopardus.

BACKGROUND: Due to its enormous biomass, Antarctic krill (Euphausia superba) plays a crucial role in the Antarctic Ocean ecosystem. In recent years, Antarctic krill has found extensive application in aquaculture, emerging as a sustainable source of aquafeed with ideal nutritional profiles. However, a comprehensive study focused on the detailed effects of dietary Antarctic krill on aquaculture animals, especially farmed marine fishes, is yet to be demonstrated. RESULTS: In this study, a comparative experiment was performed using juvenile P. leopardus, fed with diets supplemented with Antarctic krill (the krill group) or without Antarctic krill (the control group). Histological observation revealed that dietary Antarctic krill could reduce lipid accumulation in the liver while the intestine exhibited no obvious changes. Enzyme activity measurements demonstrated that dietary Antarctic krill had an inhibitory effect on oxidative stress in both the intestine and the liver. By comparative transcriptome analysis, a total of 1,597 and 1,161 differentially expressed genes (DEGs) were identified in the intestine and liver, respectively. Functional analysis of the DEGs showed multiple enriched terms significantly related to cholesterol metabolism, antioxidants, and immunity. Furthermore, the expression profiles of representative DEGs, such as dhcr7, apoa4, sc5d, and scarf1, were validated by qRT-PCR and fluorescence in situ hybridization. Finally, a comparative transcriptome analysis was performed to demonstrate the biased effects of dietary Antarctic krill and astaxanthin on the liver of P. leopardus. CONCLUSIONS: Our study demonstrated that dietary Antarctic krill could reduce lipid accumulation in the liver of P. leopardus, enhance antioxidant capacities in both the intestine and liver, and exhibit molecular-level improvements in lipid metabolism, immunity, and antioxidants. It will contribute to understanding the protective effects of Antarctic krill in P. leopardus and provide insights into aquaculture nutritional strategies.

Image Deblurring by Exploring In-Depth Properties of Transformer.

Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the pretrained transformers to address this problem. In particular, we leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. The pretrained transformer can capture the global topological relations (i.e., self-similarity) of image, and we observe that the captured topological relationships about the sharp image will change when blur occurs. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information, which is critical in measuring the sharpness of the deblurred image. On the basis of the advantages, we present two types of novel perceptual losses to guide image deblurring. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space. The other type considers the features extracted from an image as a distribution and compares the distribution discrepancy between recovered image and target one. We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores peak signal-to-noise ratio (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks. The code is available at https://github. com/erfect2020/TransformerPerceptualLoss.

Fructus Ligustri Lucidi inhibits ferroptosis in ovariectomy­induced osteoporosis in rats via the Nrf2/HO­1 signaling pathway.

Postmenopausal osteoporosis (PMOP) has increased in prevalence in recent years, thus researchers have evaluated alternative medicine therapies. Fructus Ligustri Lucidi (FLL) can inhibit bone loss, and ferroptosis serves an important role in osteoporosis. Therefore, the present study assessed the presence of ferroptosis in PMOP and whether FLL could inhibit ferroptosis to improve bone microstructure in ovariectomized rats. Ovariectomized rats were treated with FLL (1.56 g/kg/day) for 12 weeks. Micro-CT was performed to evaluate the bone microstructure and bone mineral density. Western blotting and reverse transcription-quantitative PCR were performed to assess the relative expression levels of proteins and mRNA. Subsequently, malondialdehyde (MDA) and Fe2+ assay kits were used to quantify the MDA and Fe2+ content, respectively. The results demonstrated that ovariectomy (OVX) resulted in iron overload and the accumulation of lipid peroxide. Furthermore, the expression of key factors that inhibited ferroptosis, glutathione peroxidase 4 and solute carrier family 7 member 11 was significantly downregulated in ovariectomized rats, which was significantly reversed by FLL treatment. Furthermore, bone formation was assessed using the expression of osteogenesis-related genes, runt-related transcription factor 2 and osterix, which revealed significantly higher levels in FLL-treated rats compared with ovariectomized rats. The levels of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) were also significantly recovered following FLL treatment. In the present study, OVX of postmenopausal osteoporotic rats was found to induce ferroptosis by enhancing lipid peroxidation and Fe2+ levels. FLL significantly suppressed ferroptosis, protected the osteogenic ability of ovariectomized rats and promoted the Nrf2/HO-1 signaling pathway.

MEDAG expression in vitro and paeoniflorin alleviates bone loss by regulating the MEDAG/AMPK/PPARγ signaling pathway in vivo.

Objectives: Osteoporosis (OP) is characterized by reduced bone mass and impaired bone microstructure. Paeoniflorin (PF) is isolated from peony root with anti-inflammatory, immunomodulatory, and bone-protective effects. Up to now, the mechanism of anti-OP in PF has not been completely clarified. Methods: The expression of MEDAG in osteoclasts, osteoblasts and adipocytes was detected by RT-qPCR. The OVX mouse model was constructed, and oral administration of PF was performed for 15 weeks. Bone microstructure was detected by H&E staining and a micro-CT system, and expression of signaling proteins examined by Western blot and immunohistochemical staining. ELISA and biochemical kits were used to quantify serum metabolite levels. Key findings: MEDAG were upregulated in osteoclasts and adipocytes, and downregulated in osteoblasts. PF administration effectively alleviated OVX-induced bone loss, and histological changes in femur tissues. Moreover, PF significantly reduced serum TRAP, CTX-1, P1NP, BALP, and LDL-C levels and increased HDL-C. In addition, PF inhibited the expression of MEDAG, cathepsin K, NFATc1, PPARγ, and C/EBPα and increased p-AMPKα, OPG and Runx2. Conclusions: MEDAG is a potential target for bone diseases, and PF might attenuate OVX-induced osteoporosis via MEDAG/AMPK/PPARγ signaling pathway.

Severe hepatic encephalopathy with mechanical ventilation may inform waitlist priority in acute liver failure: A UNOS database analysis.

BACKGROUND & AIMS: Patients with acute liver failure (ALF) awaiting liver transplantation (LT) may develop multiorgan failure, but organ failure does not impact waitlist prioritization. The aim of this study was to examine the impact of organ failure on waitlist mortality risk and post LT outcomes in patients with ALF. METHODS: We studied adults waitlisted for ALF in the United Network for Organ Sharing (UNOS) database (2002-2019). Organ failures were defined using a previously described Chronic Liver Failure modified sequential organ failure score assessment adapted to UNOS data. Regression analyses of the primary endpoints, 30-day waitlist mortality (Competing risk), and post-LT mortality (Cox-proportional hazards), were performed. Latent class analysis (LCA) was used to determine the organ failures most closely associated with 30-day waitlist mortality. RESULTS: About 3212 adults with ALF were waitlisted, for hepatotoxicity (41%), viral (12%) and unspecified (36%) etiologies. The median number of organ failures was three (interquartile range 1-3). Having ≥3 organ failures (vs. ≤2) was associated with a sub hazard ratio (HR) of 2.7 (95%CI 2.2-3.4)) and a HR of 1.5 (95%CI 1.1-2.5)) for waitlist and post-LT mortality, respectively. LCA identified neurologic and respiratory failure as most impactful on 30-day waitlist mortality. The odds ratios for both organ failures (vs. neither) were higher for mortality 4.5 (95% CI 3.4-5.9) and lower for delisting for spontaneous survival .5 (95%CI .4-.7) and LT .6 (95%CI .5-.7). CONCLUSION: Cumulative organ failure, especially neurologic and respiratory failure, significantly impacts waitlist and post-LT mortality in patients with ALF and may inform risk-prioritized allocation of organs.

ConvMatch: Rethinking Network Design for Two-View Correspondence Learning.

Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits its performance although many context-capturing modules are appended in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage a convolutional neural network (CNN) as the backbone, inherently capable of context aggregation. Specifically, with the observation that sparse motion vectors and a dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating the dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Moreover, we propose global information injection and bilateral convolution, to fit the overall spatial transformation better and accommodate the discontinuities of the motion field in case of large scene disparity. Extensive experiments reveal that ConvMatch consistently outperforms state-of-the-arts for relative pose estimation, homography estimation, and visual localization.

The Safe Use of Analgesics in Patients with Cirrhosis: A Narrative Review.

Pain is prevalent in patients with cirrhosis. Due to potential alterations in drug metabolism, risk for adverse effects, and complications from cirrhosis, physicians are often faced with difficult choices when choosing appropriate analgesics in these patients. Overall, acetaminophen remains the preferred analgesic. Despite its potential for intrinsic liver toxicity, acetaminophen is safe when used at 2 g/d. In contrast, non-selective nonsteroidals should be avoided due to their multiple side effects, including worsening renal function, blunting diuretic response, and increasing risk of portal hypertensive and peptic ulcer bleeding. Celecoxib can be administered for short term (≤5 days) in patients with Child's A and Child's B cirrhosis (50% dose reduction). Opioids carry the risk of precipitating hepatic encephalopathy and should generally be avoided, when possible. If clinical situation demands their use, opioid use should be limited to short-acting agents for short duration. Gabapentin and pregabalin are generally safe. Duloxetine should be avoided in hepatic impairment. Topical diclofenac and lidocaine seem to be safe in patients with cirrhosis.

Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism.

BACKGROUND: Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients. METHOD: In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS: A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma. SIGNIFICANCE AND NOVELTY: In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.

Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models.

Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. This paper addresses the above issue by proposing a novel method with diffusion models, termed as Dif-Fusion, to generate the distribution of the multi-channel input data, which increases the ability of multi-source information aggregation and the fidelity of colors. In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process. Then, we use the the denoising network to extract the multi-channel diffusion features with both visible and infrared information. Finally, we feed the multi-channel diffusion features to the multi-channel fusion module to directly generate the three-channel fused image. To retain the texture and intensity information, we propose multi-channel gradient loss and intensity loss. Along with the current evaluation metrics for measuring texture and intensity fidelity, we introduce Delta E as a new evaluation metric to quantify color fidelity. Extensive experiments indicate that our method is more effective than other state-of-the-art image fusion methods, especially in color fidelity. The source code is available at https://github.com/GeoVectorMatrix/Dif-Fusion.

Density-Guided Incremental Dominant Instance Exploration for Two-View Geometric Model Fitting.

Existing two-view multi-model fitting methods typically follow a two-step manner, i.e., model generation and selection, without considering their interaction. Therefore, in the first step, these methods have to generate a considerable number of instances in order to cover all desired ones, which not only offers no guarantees, but also introduces unnecessary expensive calculations. To address this challenge, this study presents a new algorithm, termed as D2Fitting, that incrementally explores dominant instances. Particularly, rather than viewing model generation and selection as two disjoint parts, D2Fitting fully considers their interaction, and thus performs these two subroutines alternatively under a simple yet effective optimization framework. This design can avoid generating too many redundant instances, thus reducing computational overhead and allowing the proposed D2Fitting being real-time. Meanwhile, we further design a novel density-guided sampler to sample high-quality minimal subsets during the model generation process, so as to fully exploit the spatial distribution of the input data. Also, to mitigate the influence of noise on the subsets sampled by the proposed sampler, a global-residual optimization strategy is investigated for the minimal subset refinement. With all the ingredients mentioned above, the proposed D2Fitting can accurately estimate the number and parameters of geometric models and efficiently segment the input data simultaneously. Extensive experiments on several public datasets demonstrate the significant superiority of D2Fitting over several state-of-the-arts.

Nifuratel reduces Salmonella survival in macrophages by extracellular and intracellular antibacterial activity.

Salmonella are intracellular bacterial pathogens for which, as with many of the other Enterobacteriaceae, antibiotic resistance is becoming an increasing problem. New antibiotics are being sought as recommended by the World Health Organization and other international institutions. These must be able to penetrate macrophages, and infect the major host cells and the Salmonella-containing vacuole. This study reports screening a small library of Food and Drug Administration (FDA)-approved drugs for their antibacterial effect in macrophages infected with a rapid-multiplying mutant of Salmonella Enteritidis. The most effective drug that was least toxic for macrophages was Nifuratel, a nitrofuran antibiotic already in use for parasitic infections. In mice, it provided 60% protection after oral infection with a lethal S. Enteritidis dose with reduced bacterial numbers in the tissues. It was effective against different serovars, including multidrug-resistant strains of Salmonella Typhimurium, and in macrophages from different host species and against Listeria monocytogenes and Shigella flexneri. It reduced IL-10 and STAT3 production in infected macrophages which should increase the inflammatory response against Salmonella. IMPORTANCE Salmonella can keep long-term persistence in host's macrophages to evade cellular immune defense and antibiotic attack and exit in some condition and reinfect to cause salmonellosis again. In addition to multidrug resistance, this infection circle causes Salmonella clearance difficult in the host, and so there is a great need for new antibacterial agents that reduce intramacrophage Salmonella survival to block endogenous Salmonella reinfection.

Organic ultraviolet absorbents in soils and typical plants from an industrial metropolis in China: Concentrations, profiles and environmental implications.

There is accumulating evidence of the toxicity of organic ultraviolet absorbers (OUVAs); however, limited information is available regarding the presence of OUVAs in terrestrial environments and organisms. Therefore, this study was conducted to investigate the occurrence of 11 OUVAs in soils and typical plant species from an industrial metropolis in China. Total OUVA concentrations in soils ranged from 1.30 to 80.3 ng g-1 DW. Based on comparison with previously reported data, OUVA contamination in soil was not severe. Benzophenone and octocrylene were the dominant OUVAs in soils, with median contributions to total concentrations of 25% and 15%, respectively. Source assessment revealed that the observed OUVA contamination primarily originated from industrial activities and the use of personal care products. The concentration of 11 OUVAs in plants ranged from 159 to 4470 ng g-1 DW, at high levels. Our findings imply that great attention should be given to the presence of these chemicals in plants because of the risk they could pose as well as the potential for biomagnification as plants are eaten by insects and birds. Our results also indicate the necessity to further study the geochemical behavior of these chemicals in urban ecosystems in order to better manage the harmfulness to terrestrial ecological health caused by their exposure through the food chains.

Low-dose metronomic chemotherapy triggers oxidized mtDNA sensing inside tumor cells to potentiate CD8+T anti-tumor immunity.

Low-dose metronomic (LDM) chemotherapy, the frequent and continuous use of low doses of conventional chemotherapeutics, is emerging as a promising form of chemotherapy utilization. LDM chemotherapy exerts immunomodulatory effects. However, the underlying mechanism is not fully understood. Here we found that suppressing tumor growth by LDM chemotherapy was dependent on the activation of CD8+T cells. LDM chemotherapy potentiated the cytotoxic function of CD8+T cells by stimulating cancer-cell autonomous type I interferon (IFN) induction. Mechanistically, LDM chemotherapy evoked mitochondrial dysfunction and increased reactive oxygen species (ROS) production. ROS triggered the oxidation of cytosolic mtDNA, which was sensed by cGAS-STING, consequently inducing type I IFN production in the cancer cells. Moreover, the cGAS-STING-IFN axis increased PD-L1 expression and predicted favorable clinical responses to chemoimmunotherapy. Antioxidant N-acetylcysteine inhibited oxidized mtDNA-induced type I IFN production and attenuated the efficacy of combination therapy with LDM chemotherapy and PD-L1 blockade. This study elucidates the critical role of intratumoral oxidized mtDNA sensing in LDM chemotherapy-mediated activation of CD8+T cell immune response. These findings may provide new insights for designing combinatorial immunotherapy for cancer patients.