Dynamic immunoediting by macrophages in homologous recombination deficiency-stratified pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, notably resistant to existing therapies. Current research indicates that PDAC patients deficient in homologous recombination (HR) benefit from platinum-based treatments and poly-ADP-ribose polymerase inhibitors (PARPi). However, the effectiveness of PARPi in HR-deficient (HRD) PDAC is suboptimal, and significant challenges remain in fully understanding the distinct characteristics and implications of HRD-associated PDAC. We analyzed 16 PDAC patient-derived tissues, categorized by their homologous recombination deficiency (HRD) scores, and performed high-plex immunofluorescence analysis to define 20 cell phenotypes, thereby generating an in-situ PDAC tumor-immune landscape. Spatial phenotypic-transcriptomic profiling guided by regions-of-interest (ROIs) identified a crucial regulatory mechanism through localized tumor-adjacent macrophages, potentially in an HRD-dependent manner. Cellular neighborhood (CN) analysis further demonstrated the existence of macrophage-associated high-ordered cellular functional units in spatial contexts. Using our multi-omics spatial profiling strategy, we uncovered a dynamic macrophage-mediated regulatory axis linking HRD status with SIGLEC10 and CD52. These findings demonstrate the potential of targeting CD52 in combination with PARPi as a therapeutic intervention for PDAC.

A frozen suspension external calibration strategy for elemental quantitative imaging of fresh plant soft tissues by LA-ICP-MS with cryogenic ablation cell.

A simple and reliable external calibration strategy of LA-ICP-MS for fresh plant soft tissues was developed. The prepared plant suspension was frozen by the designed cryogenic ablation cell and used as external standard for quantitative elemental imaging analysis of fresh plant tissues. The controllable water content of the prepared external standards provides a similar matrix with fresh soft tissues, and a homogeneous elemental distribution could be ensured due to the fine grinding particle sizes. More interestingly, the presence of water increased the signal intensity produced by the suspension by a factor of 1.6 (Pb) to 66.6 (La) compared to that of the pressed cake. The excellent dispersing property and advantage of long-term use were achieved owing to the employment of 0.1% PAANa as suspending agent. A series of plant reference materials were analyzed, and the relative errors of most elements were less than 10 %, indicating that there is a reliable accuracy of the proposed method. The limits of detection (LODs) ranged from 0.1 ng·g-1 (La) to 1279 ng·g-1 (S). This method was used for elemental imaging analysis in rice leaves under arsenic stress, and the results were consistent with previous studies, which mean that the proposed method could provide technical support for researchers in the fields of agriculture and environment.

Direct multi-elemental analysis of cerebrospinal fluid samples by LA-ICP-MS employing an aerosol local extraction cryogenic ablation cell.

A novel method for direct high-throughput analysis of multi-elements in cerebrospinal fluid (CSF) samples by laser ablation inductively coupled plasma mass spectrometry with an aerosol local extraction cryogenic ablation cell (ALEC-LA-ICP-MS) was developed. Microliter-level CSF samples were frozen by a designed cryogenic ablation cell and directly analyzed by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) without time-consuming pretreatment. Compared with the precision obtained at room temperature (20℃), that obtained at low temperature (- 25℃) was significantly improved; the RSDs were reduced from 8.3% (Zn) to 32.6% (Mn) to 2.2% (Pb) to 6.5% (Mn) with six times parallel determination. To meet the analytical requirement of the micro-volume CSF samples, the laminar flow aerosol local extraction strategy was adopted to improve the transmission efficiency of aerosols, and the signal intensity was increased by four times compared with the standard commercial ablation cell. The standard solution with 0.4% bovine serum albumin (BSA) matrix was used as matrix-match external standard, and Rh was added into the samples as internal standard. The limits of detection (LODs) ranged from 0.17 µg·L-1 (Mn) to 8.67 µg·L-1 (Mg). Standard addition recovery experiments and the determination of CRM serum L-1 and L-2 were carried out to validate the accuracy of the method; all results indicated there were excellent accuracy and precision in the proposed method. The matrix-scanning function in the GeoLas software combined with the microwell plate realizes the high-throughput automatic analysis. Twenty-four CSF samples from different patients were determined; the results showed that there might be a correlation between the metal elements in CSF and the diseases, which means that the proposed method has potential in the diagnosis of neurological diseases.

A Synchronous Magnitude Estimation with P-Wave Phases' Detection Used in Earthquake Early Warning System.

How to estimate an earthquake's magnitude rapidly and accurately is a challenge for any earthquake early warning system. In order to reach a balance between accuracy and timeliness, a synchronous magnitude estimation method with P-wave phases' detection is proposed. In this method, the P-wave phases are detected by the changes of the signal-to-noise ratio (SNR) of the seismic records, where the SNRs are calculated by the short-term power and long-term power ratio (STP/LTP). Meanwhile, the variations of the SNR are applied to estimate the magnitude of the earthquake. By the statistics of some earthquake cases, a synchronous magnitude estimation model of the variation of the P-wave phases' SNR, the earthquake magnitude, and the hypocentral distance was built. Compared with some other magnitude estimation methods, the suggested method inherits the robustness of the STP/LTP method and is more accurate and rapid than the peak displacement (Pd) method.

Mitigation of sand liquefaction under static loading condition using biogas bubbles generated by denitrifying bacteria.

Nitrogen bubbles that is generated by microbial denitrification process is a new pollution-free clean material for mitigation of sand liquefaction. The current study aims to assess sustainability as well as distribution character of biogas bubbles in sand column under the condition of hydrostatics along with its performance of mitigating sand liquefaction under static loading. A series of laboratory experiments were conducted, and test results indicate that biogas bubbles has excellent sustainability in sand pores, and after 92 weeks, an increase of saturation from 84.5 to 85.1% marking only 0.6% rise. The volume of biogas generated by bacteria increases linearly with decrease of depth. Under undrained condition, if saturation of sample decreased from 100% to around 92.4%, strain softening behavior will transfer to strain hardening, and undrained shear strength can be increased by approximately two times in both of compression and extension tests. The excess pore water pressure ratio and liquefaction potential index have significant reduction with the decrease of saturation, and the magnitude of impact on compression is comparatively bigger than the extension tests. This study validates that as a new material, biogas bubbles are very stable in soil, desaturation using nitrogen bubbles is an effective method for mitigating the liquefaction of sand under static loading conditions. Moreover, the study provides support for the desaturation mitigating static liquefaction of sand to prevent geological disasters and reveals its potential engineering practical value.

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.

BACKGROUND: Atrial fibrillation (AF) is one of the most prevalent sustained arrhythmias, however, epidemiological data may understate its actual prevalence. Meanwhile, AF is considered to be a major cause of ischemic strokes due to irregular heart-rhythm, coexisting chronic vascular inflammation, and renal insufficiency, and blood stasis. We studied co-expressed genes to understand relationships between atrial fibrillation (AF) and stroke and reveal potential biomarkers and therapeutic targets of AF-related stroke. METHODS: AF-and stroke-related differentially expressed genes (DEGs) were identified via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE79768 and GSE58294, respectively. Subsequently, extensive target prediction and network analyses methods were used to assess protein-protein interaction (PPI) networks, Gene Ontology (GO) terms and pathway enrichment for DEGs, and co-expressed DEGs coupled with corresponding predicted miRNAs involved in AF and stroke were assessed as well. RESULTS: We identified 489, 265, 518, and 592 DEGs in left atrial specimens and cardioembolic stroke blood samples at < 3, 5, and 24 h, respectively. LRRK2, CALM1, CXCR4, TLR4, CTNNB1, and CXCR2 may be implicated in AF and the hub-genes of CD19, FGF9, SOX9, GNGT1, and NOG may be associated with stroke. Finally, co-expressed DEGs of ZNF566, PDZK1IP1, ZFHX3, and PITX2 coupled with corresponding predicted miRNAs, especially miR-27a-3p, miR-27b-3p, and miR-494-3p may be significantly associated with AF-related stroke. CONCLUSION: AF and stroke are related and ZNF566, PDZK1IP1, ZFHX3, and PITX2 genes are significantly associated with novel biomarkers involved in AF-related stroke.

Analysis of Genes Involved in Persistent Atrial Fibrillation: Comparisons of 'Trigger' and 'Substrate' Differences.

BACKGROUND/AIMS: Recent research has improved our understanding of the pulmonary vein and surrounding left atrial (LA-PV) junction and the left atrial appendage (LAA), which are considered the 'trigger' and 'substrate' in the development of atrial fibrillation (AF), respectively. Herein, with the aim of identifying the underlying potential genetic mechanisms, we compared differences in gene expression between LA-PV junction and LAA specimens via bioinformatic analysis. METHODS: Microarray data of AF (GSE41177) were downloaded from the Gene Expression Omnibus database. In addition, linear models for microarray data limma powers differential expression analyses and weighted correlation network analysis (WGCNA) were applied. RESULTS: From the differential expression analyses, 152 differentially expressed genes and hub genes, including LEP, FOS, EDN1, NMU, CALB2, TAC1, and PPBP, were identified. Our analysis revealed that the maps of extracellular matrix (ECM)-receptor interactions, PI3K-Akt and Wnt signaling pathways, and ventricular cardiac muscle tissue morphogenesis were significantly enriched. In addition, the WGCNA results showed high correlations between genes and related genetic clusters to external clinical characteristics. Maps of the ECM-receptor interactions, chemokine signaling pathways, and the cell cycle were significantly enriched in the genes of corresponding modules and closely associated with AF duration, left atrial diameter, and left ventricular ejection function, respectively. Similarly, mapping of the TNF signaling pathway indicated significant association with genetic traits of ischemic heart disease, hypertension, and diabetes comorbidity. CONCLUSIONS: The ECM-receptor interaction as a possible central node of comparison between LA-PV and LAA samples reflected the special functional roles of 'triggers' and 'substrates' and may be closely associated with AF duration. Furthermore, LEP, FOS, EDN1, NMU, CALB2, TAC1, and PPBP genes may be implicated in the occurrence and maintenance of AF through their interactions with each other.

Uncertainty Modeling for Gaze Estimation.

Gaze estimation is an important fundamental task in computer vision and medical research. Existing works have explored various effective paradigms and modules for precisely predicting eye gazes. However, the uncertainty for gaze estimation, e.g., input uncertainty and annotation uncertainty, have been neglected in previous research. Existing models use a deterministic function to estimate the gaze, which cannot reflect the actual situation in gaze estimation. To address this issue, we propose a probabilistic framework for gaze estimation by modeling the input uncertainty and annotation uncertainty. We first utilize probabilistic embeddings to model the input uncertainty, representing the input image as a Gaussian distribution in the embedding space. Based on the input uncertainty modeling, we give an instance-wise uncertainty estimation to measure the confidence of prediction results, which is critical in practical applications. Then, we propose a new label distribution learning method, probabilistic annotations, to model the annotation uncertainty, representing the raw hard labels as Gaussian distributions. In addition, we develop an Embedding Distribution Smoothing (EDS) module and a hard example mining method to improve the consistency between embedding distribution and label distribution. We conduct extensive experiments, demonstrating that the proposed approach achieves significant improvements over baseline and state-of-the-art methods on two widely used benchmark datasets, GazeCapture and MPIIFaceGaze, as well as our collected dataset using mobile devices.

Capsule Networks With Residual Pose Routing.

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture, which is desirable for high performance in the deep learning era, due to the complex capsule routing algorithms. In this article, we present a simple yet effective capsule routing algorithm, which is presented by a residual pose routing. Specifically, the higher-layer capsule pose is achieved by an identity mapping on the adjacently lower-layer capsule pose. Such simple residual pose routing has two advantages: 1) reducing the routing computation complexity and 2) avoiding gradient vanishing due to its residual learning framework. On top of that, we explicitly reformulate the capsule layers by building a residual pose block. Stacking multiple such blocks results in a deep residual CapsNets (ResCaps) with a ResNet-like architecture. Results on MNIST, AffNIST, SmallNORB, and CIFAR-10/100 show the effectiveness of ResCaps for image classification. Furthermore, we successfully extend our residual pose routing to large-scale real-world applications, including 3-D object reconstruction and classification, and 2-D saliency dense prediction. The source code has been released on https://github.com/liuyi1989/ResCaps.

Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images.

Deep reinforcement learning (DRL) has demonstrated impressive performance in medical image segmentation, particularly for low-contrast and small medical objects. However, current DRL-based segmentation methods face limitations due to the optimization of error propagation in two separate stages and the need for a significant amount of labeled data. In this paper, we propose a novel deep generative adversarial reinforcement learning (DGARL) approach that, for the first time, enables end-to-end semi-supervised medical image segmentation in the DRL domain. DGARL ingeniously establishes a pipeline that integrates DRL and generative adversarial networks (GANs) to optimize both detection and segmentation tasks holistically while mutually enhancing each other. Specifically, DGARL introduces two innovative components to facilitate this integration in semi-supervised settings. First, a task-joint GAN with two discriminators links the detection results to the GAN's segmentation performance evaluation, allowing simultaneous joint evaluation and feedback. This ensures that DRL and GAN can be directly optimized based on each other's results. Second, a bidirectional exploration DRL integrates backward exploration and forward exploration to ensure the DRL agent explores the correct direction when forward exploration is disabled due to lack of explicit rewards. This mitigates the issue of unlabeled data being unable to provide rewards and rendering DRL unexplorable. Comprehensive experiments on three generalization datasets, comprising a total of 640 patients, demonstrate that our novel DGARL achieves 85.02% Dice and improves at least 1.91% for brain tumors, achieves 73.18% Dice and improves at least 4.28% for liver tumors, and achieves 70.85% Dice and improves at least 2.73% for pancreas compared to the ten most recent advanced methods, our results attest to the superiority of DGARL. Code is available at GitHub.

Pixel Distillation: Cost-flexible Distillation across Image Sizes and Heterogeneous Networks.

Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size. Therefore, we propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources. Specifically, we first propose an input spatial representation distillation (ISRD) mechanism to transfer spatial knowledge from large images to student's input module, which can facilitate stable knowledge transfer between CNN and ViT. Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge. Finally, we adapt pixel distillation to object detection via an aligned feature for preservation (AFP) strategy for TAS, which aligns output dimensions of detectors at each stage by manipulating features and anchors of the assistant. Comprehensive experiments on image classification and object detection demonstrate the effectiveness of our method.

Position-based anchor optimization for point supervised dense nuclei detection.

Nuclei detection is one of the most fundamental and challenging problems in histopathological image analysis, which can localize nuclei to provide effective computer-aided cancer diagnosis, treatment decision, and prognosis. The fully-supervised nuclei detector requires a large number of nuclei annotations on high-resolution digital images, which is time-consuming and needs human annotators with professional knowledge. In recent years, weakly-supervised learning has attracted significant attention in reducing the labeling burden. However, detecting dense nuclei of complex crowded distribution and diverse appearances remains a challenge. To solve this problem, we propose a novel point-supervised dense nuclei detection framework that introduces position-based anchor optimization to complete morphology-based pseudo-label supervision. Specifically, we first generate cellular-level pseudo labels (CPL) for the detection head via a morphology-based mechanism, which can help to build a baseline point-supervised detection network. Then, considering the crowded distribution of the dense nuclei, we propose a mechanism called Position-based Anchor-quality Estimation (PAE), which utilizes the positional deviation between an anchor and its corresponding point label to suppress low-quality detections far from each nucleus. Finally, to better handle the diverse appearances of nuclei, an Adaptive Anchor Selector (AAS) operation is proposed to automatically select positive and negative anchors according to morphological and positional statistical characteristics of nuclei. We conduct comprehensive experiments on two widely used benchmarks, MO and Lizard, using ResNet50 and PVTv2 as backbones. The results demonstrate that the proposed approach has superior capacity compared with other state-of-the-art methods. In particularly, in dense nuclei scenarios, our method can achieve 95.1% performance of the fully-supervised approach. The code is available at https://github.com/NucleiDet/DenseNucleiDet.

Walnut-Derived Peptides Alleviate Learning and Memory Impairments in a Mice Model via Inhibition of Microglia Phagocytose Synapses.

Microglia phagocytose synapses have an important effect on the pathogenesis of neurological disorders. Here, we investigated the neuroprotective effects of the walnut-derived peptide, TWLPLPR(TW-7), against LPS-induced cognitive deficits in mice and explored the underlying C1q-mediated microglia phagocytose synapses mechanisms in LPS-treated HT22 cells. The MWM showed that TW-7 improved the learning and memory capacity of the LPS-injured mice. Both transmission electron microscopy and immunofluorescence analysis illustrated that synaptic density and morphology were increased while associated with the decreased colocalized synapses with C1q. Immunohistochemistry and immunofluorescence demonstrated that TW-7 effectively reduced the microglia phagocytosis of synapses. Subsequently, overexpression of C1q gene plasmid was used to verify the contribution of the TW-7 via the classical complement pathway-regulated mitochondrial function-mediated microglia phagocytose synapses in LPS-treated HT22 cells. These data suggested that TW-7 improved the learning and memory capability of LPS-induced cognitively impaired mice through a mechanism associated with the classical complement pathway-mediated microglia phagocytose synapse.

Integrin α6-containing extracellular vesicles promote lymphatic remodelling for pre-metastatic niche formation in lymph nodes via interplay with CD151.

Heterogeneous extracellular vesicles (EVs) from various types of tumours are acknowledged for inducing the formation of pre-metastatic "niches" in draining lymph nodes (LNs) to promote lymphatic metastasis. In order to identify the specific subpopulations of EVs involved, we performed high-resolution proteomic analysis combined with nanoflow cytometry of bladder cancer (BCa) tissue-derived EVs to identify a novel subset of tumour-derived EVs that contain integrin α6 (ITGA6+EVs) and revealed the positive correlation of ITGA6+EVs with the formation of pre-metastatic niche in draining LNs and lymphatic metastasis in multicentre clinical analysis of 820-case BCa patients. BCa-derived ITGA6+EVs induced E-selectin (SELE)-marked lymphatic remodelling pre-metastatic niche and promoted metastasis in draining LNs through delivering cargo circRNA-LIPAR to lymphatic endothelial cells in vivo and in vitro. Mechanistically, LIPAR linked ITGA6 to the switch II domain of RAB5A and sustained RAB5A GTP-bound activated state, thus maintaining the production of ITGA6+EVs loaded with LIPAR through endosomal trafficking. ITGA6+EVs targeted lymphatic vessels through ITGA6-CD151 interplay and released LIPAR to induce SELE overexpression-marked lymphatic remodelling pre-metastatic niche. Importantly, we constructed engineered-ITGA6 EVs to inhibit lymphatic pre-metastatic niche, which suppressed lymphatic metastasis and prolonged survival in preclinical models. Collectively, our study uncovers the mechanism of BCa-derived ITGA6+EVs mediating pre-metastatic niche and provides an engineered-EV-based strategy against BCa lymphatic metastasis.

Pyruvate dehydrogenase complex E1 subunit α crotonylation modulates cocaine-associated memory through hippocampal neuron activation.

Neuronal activation is required for the formation of drug-associated memory, which is critical for the development, persistence, and relapse of drug addiction. Nevertheless, the metabolic mechanisms underlying energy production for neuronal activation remain poorly understood. In the study, a large-scale proteomics analysis of lysine crotonylation (Kcr), a type of protein posttranslational modification (PTM), reveals that cocaine promoted protein Kcr in the hippocampal dorsal dentate gyrus (dDG). We find that Kcr is predominantly discovered in a few enzymes critical for mitochondrial energy metabolism; in particular, pyruvate dehydrogenase (PDH) complex E1 subunit α (PDHA1) is crotonylated at the lysine 39 (K39) residue through P300 catalysis. Crotonylated PDHA1 promotes pyruvate metabolism by activating PDH to increase ATP production, thus providing energy for hippocampal neuronal activation and promoting cocaine-associated memory recall. Our findings identify Kcr of PDHA1 as a PTM that promotes pyruvate metabolism to enhance neuronal activity for cocaine-associated memory.

Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization.

Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet, and then, aggregates the snippet-level classification scores to obtain the video-level classification score. In contrast, the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same-to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier. This achieves the novel Equivalent Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal classifier may possess two characteristics: 1) the frame-level classification scores obtained from the pre-classification stream and the feature aggregation weights in the post-classification stream should be consistent; and 2) the classification results of these two streams should be identical. Based on these two characteristics, we further introduce a weight-transition module and an equivalent training strategy into the proposed learning framework, which assists to thoroughly mine the equivalence mechanism. Comprehensive experiments are conducted on three benchmarks and ECM achieves accurate action localization results.

CLRNet: Component-Level Refinement Network for Deep Face Parsing.

Face parsing aims to assign pixel-wise semantic labels to different facial components (e.g., hair, brows, and lips) in given face images. However, directly predicting pixel-level labels for each facial component over the whole face image would obtain limited accuracy, especially for tiny facial components. To address this problem, some recent works propose to first crop tiny patches from the whole face image and then predict masks for each facial component. However, such cropping-and-segmenting strategy consists of two independent stages, which cannot be jointly optimized. Besides, as one valuable piece of information for parsing the highly structured facial components, context cues are not elaborately explored by the existing works. To address these issues, we propose a component-level refinement network (CLRNet) for precisely segmenting out each facial component. Specifically, we introduce an attention mechanism to bridge the two independent stages together and form an end-to-end trainable pipeline for face parsing. Furthermore, we incorporate the global context information into the refining process for each cropped facial component patch, providing informative cues for accurate parsing. Extensive experiments are carried out on two benchmark datasets, LFW-PL and HELEN. The results demonstrate the superiority of the proposed CLRNet over other state-of-the-art methods, especially for tiny facial components.

Facile Preparation of Dense Polysulfone UF Membranes with Enhanced Salt Rejection by Post-Heating.

Polysulfone (PSf) membranes typically have a negligible rejection of salts due to the intrinsic larger pore size and wide pore size distribution. In this work, a facile and scalable heat treatment was proposed to increase the salt rejection. The influence of heat treatment on the structure and performance of PSf membranes was systematically investigated. The average pore size decreased from 9.94 ± 5.5 nm for pristine membranes to 1.18 ± 0.19 nm with the increase in temperature to 50 °C, while the corresponding porosity decreased from 2.07% to 0.13%. Meanwhile, the thickness of the sponge structure decreased from 20.20 to 11.5 µm as the heat treatment temperature increased to 50 °C. The MWCO of PSf decreased from 290,000 Da to 120 Da, whereas the membrane pore size decreased from 5.5 to 0.19 nm. Correspondingly, the water flux decreased from 1545 to 27.24 L·m-2·h-1, while the rejection ratio increased from 3.1% to 74.0% for Na2SO4, from 1.3% to 48.2% for MgSO4, and from 0.6% to 23.8% for NaCl. Meanwhile, mechanism analysis indicated that the water evaporation in the membranes resulted in the shrinkage of the membrane pores and decrease in the average pore size, thus improving the separation performance. In addition, the desalting performance of the heat-treated membranes for real actual industrial wastewater was improved. This provides a facile and scalable route for PSf membrane applications for enhanced desalination.

A review on the identification of transgenic oilseeds and oils.

Transgenic technology can increase the quantity and quality of vegetable oils worldwide. However, people are skeptical about the safety of transgenic oil-bearing crops and the oils they produce. In order to protect consumers' rights and avoid transgenic oils being adulterated or labeled as nontransgenic oils, the transgenic detection technology of oilseeds and oils needs careful consideration. This paper first summarized the current research status of transgenic technologies implemented at oil-bearing crops. Then, an inspection process was proposed to detect a large number of samples to be the subject rapidly, and various inspection strategies for transgenic oilseeds and oils were summarized according to the process sequence. The detection indicators included oil content, fatty acid, triglyceride, tocopherol, and nucleic acid. The detection technologies involved chromatography, spectroscopy, nuclear magnetic resonance, and polymerase chain reaction. It is hoped that this article can provide crucial technical reference and support for staff engaging in the supervision of transgenic food and for researchers developing fast and efficient monitoring methods in the future.

Self-Supervised Interactive Embedding for One-Shot Organ Segmentation.

One-shot organ segmentation (OS2) aims at segmenting the desired organ regions from the input medical imaging data with only one pre-annotated example as the reference. By using the minimal annotation data to facilitate organ segmentation, OS2 receives great attention in the medical image analysis community due to its weak requirement on human annotation. In OS2, one core issue is to explore the mutual information between the support (reference slice) and the query (test slice). Existing methods rely heavily on the similarity between slices, and additional slice allocation mechanisms need to be designed to reduce the impact of the similarity between slices on the segmentation performance. To address this issue, we build a novel support-query interactive embedding (SQIE) module, which is equipped with the channel-wise co-attention, spatial-wise co-attention, and spatial bias transformation blocks to identify "what to look", "where to look", and "how to look" in the input test slice. By combining the three mechanisms, we can mine the interactive information of the intersection area and the disputed area between slices, and establish the feature connection between the target in slices with low similarity. We also propose a self-supervised contrastive learning framework, which transforms knowledge from the physical position to the embedding space to facilitate the self-supervised interactive embedding of the query and support slices. Comprehensive experiments on two large benchmarks demonstrate the superior capacity of the proposed approach when compared with the current alternatives and baseline models.