A density correction method for radioactive waste drum based on SRGS technology.

The segmented ringed gamma scanning (SRGS) technique represents an advancement in segmented gamma scanning (SGS) technology used for detecting the density of radioactive waste drums, offering enhanced measurement accuracy. However, significant occur errors in the reconstruction of matrix densities due to the non-uniform distribution of density in radioactive waste and the conical beam emitted from the transmission source collimator. This paper proposes a density correction method based on dichotomy to address this issue. The efficacy of this method was verified through both simulations and experiments on a sample containing five different materials, utilizing 137Cs and 60Co for transmission and emission measurements, respectively. The experimental results demonstrate that the errors in the corrected matrix densities are reduced, falling within a margin of 16.8%. Additionally, the corrected reconstruction error of the activity is approximately 25% of the uncorrected results.

Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment.

While pre-training large-scale video-language models (VLMs) has shown remarkable potential for various downstream video-language tasks, existing VLMs can still suffer from certain commonly seen limitations, e.g., coarse-grained cross-modal aligning, under-modeling of temporal dynamics, detached video-language view. In this work, we target enhancing VLMs with a fine-grained structural spatio-temporal alignment learning method (namely Finsta). First of all, we represent the input texts and videos with fine-grained scene graph (SG) structures, both of which are further unified into a holistic SG (HSG) for bridging two modalities. Then, an SG-based framework is built, where the textual SG (TSG) is encoded with a graph Transformer, while the video dynamic SG (DSG) and the HSG are modeled with a novel recurrent graph Transformer for spatial and temporal feature propagation. A spatial-temporal Gaussian differential graph Transformer is further devised to strengthen the sense of the changes in objects across spatial and temporal dimensions. Next, based on the fine-grained structural features of TSG and DSG, we perform object-centered spatial alignment and predicate-centered temporal alignment respectively, enhancing the video-language grounding in both the spatiality and temporality. We design our method as a plug&play system, which can be integrated into existing well-trained VLMs for further representation augmentation, without training from scratch or relying on SG annotations in downstream applications. On 6 representative VL modeling tasks over 12 datasets in both standard and long-form video scenarios, Finsta consistently improves the existing 13 strong-performing VLMs persistently, and refreshes the current state-of-the-art end task performance significantly in both the fine-tuning and zero-shot settings.

Direct Seawater Electrolysis: From Catalyst Design to Device Applications.

Direct seawater electrolysis (DSE) for hydrogen production, using earth-abundant seawater as the feedstock and renewable electricity as the driving source, paves a new opportunity for flexible energy conversion/storage and smooths the volatility of renewable energy. Unfortunately, the complex environments of seawater impose significant challenges on the design of DSE catalysts, and the practical performance of many current DSE catalysts remains unsatisfactory on the device level. However, many studies predominantly concentrate on the development of electrocatalysts for DSE without giving due consideration to the specific devices. To mitigate this gap, the most recent progress (mainly published within the year 2020-2023) of DSE electrocatalysts and devices are systematically evaluated. By discussing key bottlenecks, corresponding mitigation strategies, and various device designs and applications, the tremendous challenges in addressing the trade-off among activity, stability, and selectivity for DSE electrocatalysts by a single shot are emphasized. In addition, the rational design of the DSE electrocatalysts needs to align with the specific device configuration, which is more effective than attempting to comprehensively enhance all catalytic parameters. This work, featuring the first review of this kind to consider rational catalyst design in the framework of DSE devices, will facilitate practical DSE development.

Joint polarization detection and degradation mechanisms for underwater image enhancement.

Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15 g/ml, 0.30 g/ml, and 0.50 g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10 cm, 20 cm, 30 cm, and 40 cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper's algorithm is improved by about 69.26% over the original S0 image.

Albumin pre-opsonized membrane-active iPep nanomedicine potentiates chemo to immunotherapy of cancer.

Chemotherapy-conjugated immunotherapy in clinical oncology conceptually resembles the combined effects of cytoreduction and immunostimulation in membrane targeted cell killings mediated by pore-forming proteins or host defense peptides. Of the similar concept, targeting cancer cell membrane using membrane active peptides is a hopeful therapeutic modality but had long been hindered from in vivo application. Here we report an enabling strategy of pre-opsonizing a membrane penetrating Ir-complexed octa-arginine peptide (iPep) with serum albumin via intrinsic amphipathicity-driven bimodal interactions into nanoparticles (NP). We found that NP triggered stress-mediated 4T1 cell oncosis which induced potent immunological activation, surpassing several well-known immunogenic medicines. Vested with albumin-enhanced in vivo tumor targeting specificity and pharmacokinetic properties, NP showed combined chemo to immunotherapies of s. c. tumors in mice, with decreased percentages of MDSC, Treg, M2-like macrophage and improved infiltration of CTLs in tumor site, caused complete regression of 4T1 and CT26 tumors, outperforming clinical medicines. In a challenging orthotopic breast cancer model, boost i. v. injections of NP acted as in situ tumor vaccine that drastically enhanced 4T1-specific cellular and humoral immunities to reverse disease progression. Thus, with combined effects of direct cytoreduction, immune activation and tumor vaccine, iPep-NP presents the promise and potential of a new modality of cancer medicine.

A Three-Dimensional Vibration Theory for Ultralight Cellular Sandwich Plates Subjected to Linearly Varying In-Plane Distributed Loads.

Thin structural elements such as large-scale covering plates of aerospace protection structures and vertical stabilizers of aircraft are strongly influenced by gravity (and/or acceleration); thus, exploring how the mechanical behaviors of such structures are affected by gravitational field is necessary. Built upon a zigzag displacement model, this study establishes a three-dimensional vibration theory for ultralight cellular-cored sandwich plates subjected to linearly varying in-plane distributed loads (due to, e.g., hyper gravity or acceleration), with the cross-section rotation angle induced by face sheet shearing accounted for. For selected boundary conditions, the theory enables quantifying the influence of core type (e.g., close-celled metal foams, triangular corrugated metal plates, and metal hexagonal honeycombs) on fundamental frequencies of the sandwich plates. For validation, three-dimensional finite element simulations are carried out, with good agreement achieved between theoretical predictions and simulation results. The validated theory is subsequently employed to evaluate how the geometric parameters of metal sandwich core and the mixture of metal cores and composite face sheets influence the fundamental frequencies. Triangular corrugated sandwich plate possesses the highest fundamental frequency, irrespective of boundary conditions. For each type of sandwich plate considered, the presence of in-plane distributed loads significantly affects its fundamental frequencies and modal shapes.

Metal Oxide-Supported Metal Catalysts for Electrocatalytic Oxygen Reduction Reaction: Characterization Methods, Modulation Strategies, and Recent Progress.

The sluggish kinetics of the oxygen reduction reaction (ORR) with complex multielectron transfer steps significantly limits the large-scale application of electrochemical energy devices, including metal-air batteries and fuel cells. Recent years witnessed the development of metal oxide-supported metal catalysts (MOSMCs), covering single atoms, clusters, and nanoparticles. As alternatives to conventional carbon-dispersed metal catalysts, MOSMCs are gaining increasing interest due to their unique electronic configuration and potentially high corrosion resistance. By engineering the metal oxide substrate, supported metal, and their interactions, MOSMCs can be facilely modulated. Significant progress has been made in advancing MOSMCs for ORR, and their further development warrants advanced characterization methods to better understand MOSMCs and precise modulation strategies to boost their functionalities. In this regard, a comprehensive review of MOSMCs for ORR is still lacking despite this fast-developing field. To eliminate this gap, advanced characterization methods are introduced for clarifying MOSMCs experimentally and theoretically, discuss critical methods of boosting their intrinsic activities and number of active sites, and systematically overview the status of MOSMCs based on different metal oxide substrates for ORR. By conveying methods, research status, critical challenges, and perspectives, this review will rationally promote the design of MOSMCs for electrochemical energy devices.

Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction.

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.

Chimonanthus nitens Oliv. Leaf Granule Ameliorates DSS-Induced Acute Colitis Through Treg Cell Improvement, Oxidative Stress Reduction, and Gut Microflora Modulation.

The rising incidence of ulcerative colitis has become a new challenge for public health. Chimonanthus nitens Oliv. leaf granule (COG) is a natural medicine used for the treatment of respiratory diseases, which has excellent anti-inflammatory and antioxidant effects. However, the therapeutic effect of COG in ulcerative colitis (UC) has not been reported. Here, the experimental colitis was treated with dextran sodium sulfate (DSS) and COG. After treatment with high (30 g/kg), medium (15 g/kg), and low (7.5 g/kg) doses of COG for 11 consecutive days, the body weight, disease activity index (DAI) score, colon length, colon weight index, and the pathological score of mice were effectively improved. COG significantly reduced the levels of inflammatory cytokines in UC mice in vitro and in vivo and restored the secretion levels of IL-6 and IL-10 in the colon. Meanwhile, compared to mice with colitis, COG-treated mice showed lower levels of MDA, MPO, NO, and eNOS and higher levels of GSH-Px and MAO, which indicated that oxidative stress damage in colitic mice was alleviated by COG. Moreover, less Th17 and more Tregs were observed in the COG-treated groups. In addition, COG improved the diversity and relative abundance of gut microflora in the colon of colitic mice, and Lachnospiraceae_NK4A136_group and Lachnospiraceae_UCG-006 were obviously regulated at the genus level. In summary, COG has a protective effect on DSS-induced experimental colitis, mainly through inhibition of immune-inflammatory responses and oxidative stress and regulation of mTreg cell responses and intestinal flora composition.

Optimizing Attention for Sequence Modeling via Reinforcement Learning.

Attention has been shown highly effective for modeling sequences, capturing the more informative parts in learning a deep representation. However, recent studies show that the attention values do not always coincide with intuition in tasks, such as machine translation and sentiment classification. In this study, we consider using deep reinforcement learning to automatically optimize attention distribution during the minimization of end task training losses. With more sufficient environment states, iterative actions are taken to adjust attention weights so that more informative words receive more attention automatically. Results on different tasks and different attention networks demonstrate that our model is of great effectiveness in improving the end task performances, yielding more reasonable attention distribution. The more in-depth analysis further reveals that our retrofitting method can help to bring explainability for baseline attention.

Highly Efficient Electrocatalytic N2 Reduction to Ammonia over Metallic 1T Phase of MoS2 Enabled by Active Sites Separation Mechanism.

The 1T phase of MoS2 has been widely reported to be highly active toward the hydrogen evolution reaction (HER), which is expected to restrict the competitive nitrogen reduction reaction (NRR). However, in this work, a prototype of active sites separation over 1T-MoS2 is proposed by DFT calculations that the Mo-edge and S atoms on the basal plane exhibit different catalytic NRR and HER selectivity, and a new role-playing synergistic mechanism is also well enabled for the multistep NRR, which is further experimentally confirmed. More importantly, a self-sacrificial strategy using g-C3 N4 as templates is proposed to synthesize 1T-MoS2 with an ultrahigh 1T content (75.44%, named as CNMS, representing the composition elements of C, N, Mo, and S), which yields excellent NRR performances with an ammonia formation rate of 71.07 µg h-1 mg-1 cat. at -0.5 V versus RHE and a Faradic efficiency of 21.01%. This work provides a promising new orientation of synchronizing the selectivity and activity for the multistep catalytic reactions.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat.

Background: The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method. Materials and methods: In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ. Results: The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle. Conclusion: Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Hydrophobicity-tuned anion responsiveness underlies endosomolytic cargo delivery mediated by amphipathic vehicle peptides.

Peptide conformation can change subject to environment cues. This concept also applies to many cationic amphipathic peptides (CAPs) known to have cell membrane lytic or penetrative activities. Well-conditioned CAPs can match the properties of the target membrane to support their intended biological functions, e.g., intracellular cargo delivery; however, the intricacy in such conditioning surpasses our current understanding. Here we focused on hydrophobicity, a key biophysical property that dictates the membrane activity of CAPs, and applied a structure-function strategy to evolve a template peptide for endosomolytic cargo delivery. The template was subjected to iterative adjustment to balance hydrophobicity between its N-terminal linear and C-terminal helical domains. We demonstrate that the obtained peptide, LP6, could dramatically promote cargo cell entry and facilitate cytosolic delivery of biomacromolecules such as FITC-dextran, saporin, and human IgG. Among the evolved peptide series, LP6 has low cytotoxicity and moderate hydrophobicity, exhibits maximum change in helical conformation in response to negatively charged phospholipids, and also shows an apparent aggregational behavior in response to sialic acid enrichment. These attributes of LP6 collectively indicate that its anion-responsive conformational change is a critical underlining of its endosomolytic cargo delivery capability. Our results also suggest that modulation of hydrophobicity serves as a key to the precise tuning of CAP's membrane activity for future biomedical applications.

Effect of Anti-Infective Reconstituted Bone Xenograft Combined with External Fixator on Serum CRP and PCT Levels and Prognosis of Patients with Bone Infection after Lower Extremity Long Bone Trauma.

Bone infection is one of the common complications of orthopedic surgery. After bone trauma occurs in the human body, the infection of Staphylococcus aureus and Gram-negative bacteria into the fracture area can lead to double infection of the soft tissue and bone tissue at the fracture site, leading to a variety of complications, mostly in the lower extremities. Bone infection easily causes bone destruction, bone nonunion, and bone defect, seriously affecting the quality of life of patients. The traditional treatment method of bone infection is to control the infection first and then repair the bone graft, but this method has a long course, poor efficacy, and high disability rate. In this study, anti-infective reconstituted bone xenograft (ARBX) combined with external fixation was used to treat patients with posttraumatic bone infections of the long bones of the lower extremities, to explore its efficacy, and to analyze its effects on serum CRP, PCT levels, and prognosis. Our results showed that ARBX combined with the external fixator had a good effect on the treatment of patients with bone infection after lower extremity long bone trauma, which could effectively enhance the repair and functional recovery of the limb bone, significantly alleviate the infection degree of patients, reduce the inflammatory response of the body, and have a good prognosis.

A span-graph neural model for overlapping entity relation extraction in biomedical texts.

MOTIVATION: Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint models are built upon sequential scheme, and fail to detect overlapping entity and relation, which are ubiquitous in biomedical texts. The main reason is that sequential models have relatively weaker power in capturing long-range dependencies, which results in lower performance in encoding longer sentences. In this article, we propose a novel span-graph neural model for jointly extracting overlapping entity relation in biomedical texts. Our model treats the task as relation triplets prediction, and builds the entity-graph by enumerating possible candidate entity spans. The proposed model captures the relationship between the correlated entities via a span scorer and a relation scorer, respectively, and finally outputs all valid relational triplets. RESULTS: Experimental results on two biomedical entity relation extraction tasks, including drug-drug interaction detection and protein-protein interaction detection, show that the proposed method outperforms previous models by a substantial margin, demonstrating the effectiveness of span-graph-based method for overlapping relation extraction in biomedical texts. Further in-depth analysis proves that our model is more effective in capturing the long-range dependencies for relation extraction compared with the sequential models. AVAILABILITY AND IMPLEMENTATION: Related codes are made publicly available at http://github.com/Baxelyne/SpanBioER.

Enriching contextualized language model from knowledge graph for biomedical information extraction.

Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.

Toggling Preassembly with Single-Site Mutation Switches the Cytotoxic Mechanism of Cationic Amphipathic Peptides.

Precise regulation of membrane-active peptide activity is a frontier of research to facilitate its applicational translation. A clear understanding of how a peptide's physicochemical properties determine its mode of action (MOA) will aid the process. Herein, anionic glutamate residue-based scanning was applied to the hydrophobic surface of a self-assembling lysine-rich cationic amphipathic peptide (CAP) KL1. Single-site mutations from leucine to glutamate dramatically changed the MOA of all mutants from membranolytic to nonlytic. An apoptosis-inducing mutant L2E unable to self-assemble under extracellular anions exhibited a different conformational transformation process in the amphiphilic environment than KL1. Further adjustment of the overall positive charge allowed regulation of cytotoxic potency without affecting the MOA determined by the lack of preassembly formation. Compared with KL1, hemolytic toxicities of nonmembranolytic peptides were greatly reduced, with safety indices increased. This work thus provided novel insights into and integrated rationales on the improvement of CAPs for both anticancer activity and safety profile.

Structure-tuned membrane active Ir-complexed oligoarginine overcomes cancer cell drug resistance and triggers immune responses in mice.

The development of chemotherapy, an important cancer treatment modality, is hindered by the frequently found drug-resistance phenomenon. Meanwhile, researchers have been enthused lately by the synergistic use of chemotherapy with emerging immunotherapeutic treatments. In an effort to address both of the two unmet needs, reported herein is a study on a series of membrane active iridium(iii) complexed oligoarginine peptides with a new cell death mechanism capable of overcoming drug resistance as well as stimulating immunological responses. A systematic structure-activity relationship study elucidated the interdependent effects of three structural factors, i.e., hydrophobicity, topology and cationicity, on the regulation of the cytotoxicity of the Ir(iii)-oligoarginine peptides. With the most prominent toxicities, Ir-complexed octaarginines (R8) were found to display a progressive oncotic cell death featuring cell membrane-penetration and eruptive cytoplasmic content release. Consequently, this membrane-centric death mechanism showed promising potential in overcoming multiple chemical drug-resistance of cancer cells. More interestingly, the eruptive mode of cell death proved to be immunogenic by stimulating the dendritic cell maturation and inflammatory factor accumulation in mice tumours. Taking these mechanisms together, this work demonstrates that membrane active compounds may become the next generation chemotherapeutics because of their combined advantages.

Asymmetric Side-Chain Engineering of Isoindigo-Based Polymers for Improved Stretchability and Applications in Field-Effect Transistors.

Thus far, there is still no study systematically investigating the influence of asymmetric side-chain design on a polymer's stretchability and its associated stretchable device applications. Herein, three kinds of asymmetric side chains consisting of carbosilane side chain (Si-C8), siloxane-terminated side chain (SiO-C8), and decyltetradecane side chain (DT) are engineered in isoindigo-bithiophene (PII2T, P1-P3) and isoindigo-difluorobithiophene (PII2TF, P4-P6) conjugated polymers, and their structure-stretchability correlation is explored in field-effect transistor characterization. It is revealed that owing to the geometric difference between the side chains, different asymmetric side-chain combinations impose distinct influences on the molecular stacking and orientation of the derived polymers. Surprisingly, the combination of asymmetric side chains and backbone fluorination is shown to deliver the best stretchability and mechanical durability of the derived polymer. Consequently, P6 consisting of asymmetric Si-C8/DT side chains and fluorinated backbone possesses the best mobility preservation of 81% at 100% strain with the stretching force perpendicular to the charge-transporting direction. Moreover, it presents 90% mobility retention after 400 stretching-releasing cycles with 60% strain, greatly exceeding the value (36%) of the non-fluorinated counterpart (P3). Our results suggest that the rational design of asymmetric side chains and backbone fluorination provides an efficient way to enhance the intrinsic stretchability of conjugated polymers.