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Single-layer isophorone diisocyanate (IPDI) are one of the most popular self-healing microcapsules but suffers from low shell strength, poor heat resistance, stability and aging properties. In this paper, IPDI microcapsules were encapsulated into double-layer phenolic (PF)/polyurethane (PU) by a two-step process involving interfacial polymerization and in-situ polymerization. The prepared microcapsule composites were comprehensively characterized for their physical and chemical properties using optical microanalysis, scanning electron microscope, Fourier transform infrared spectroscopy, thermal gravimetric analysis and depth-sensing indentation analysis. Compared with the single-layer PU-IPDI microcapsule counterpart, the mechanical performance, thermal resistance, aging property and environmental stability of double-layer PF/PU-IPDI microcapsules were significantly improved. The epoxy coating was enhanced with the incorporation of 10 wt.% PF/PU-IPDI microcapsules, whose self-healing performance was evaluated by scratch corrosion test. The results demonstrated successful repair of coating scratches, along with the absence of corrosion on the coated steel substrate soaked in a 10 wt.% NaCl solution for 7 days. By comparing the tensile strength of epoxy coating before and after crack formation, it could be found that the self-healing efficiency was 57.9% when loaded with 10 wt.% of PF/PU-IPDI microcapsules in coating. This study highlights that the rational design of double-layer microcapsules integrated into the epoxy coating matrix could provide excellent anti-corrosion and self-healing properties. [ABSTRACT FROM AUTHOR]

The exploration of low permittivity and dielectric loss microwave dielectric ceramics is particularly important in order to meet the requirements of high-speed massive communication. In this work, new CaSn (BO3)2 ceramics were fabricated by using solid-phase sintering method, and their phase structure, microstructure and dielectric properties were investigated. The sintering temperature has an important effect on microwave dielectric performances of the ceramics. The CaSn (BO3)2 ceramic sintered at 1040 °C exhibited the optimal dielectric properties of εr = 5.20, Q × ƒ = 47,725 GHz, τƒ = − 11 ppm/ °C. [ABSTRACT FROM AUTHOR]

Cam-lobe radial-piston hydraulic motors are widely used as rotation driving units for various marine machinery owing to their ultrahigh output torque (more than 100 kN m). A multi-row cam roller bearing (MCRB) is the key component that directly determines the fatigue life of a cam-lobe radial-piston hydraulic motor. However, compact geometry and complex loads render MCRB susceptible to fatigue failure, highlighting the need for an optimized MCRB to achieve longer fatigue life and higher reliability. Therefore, this study proposes an innovative geometry optimization method for an MCRB to improve its fatigue life. In this method, a quasi-static model was developed to calculate the load distribution, with the fatigue life of the MCRB calculated using both basic dynamic loading and load distribution. Subsequently, a genetic algorithm was used to obtain the optimized geometry parameters, which significantly improved the fatigue life of the MCRB. Finally, a loading test was conducted on a hydraulic motor installed with both the initial and optimized MCRB to validate the effectiveness of the proposed optimization method. This study provides a theoretical guideline for optimizing the design of MCRB, thereby increasing the fatigue life of hydraulic motors. [ABSTRACT FROM AUTHOR]

In manufacturing, causal relations between components have become crucial to automate assembly lines. Identifying these relations permits error tracing and correction in the absence of domain experts, in addition to advancing our knowledge about the operating characteristics of a complex system. This paper is motivated by a case study focusing on deciphering the causal structure of a wafer manufacturing system using data from sensors and abnormality monitors deployed within the assembly line. In response to the distinctive characteristics of the wafer manufacturing data, such as multimodality, high-dimensionality, imbalanced classes, and irregular missing patterns, we propose a hierarchical ensemble approach. This method leverages the temporal and domain constraints inherent in the assembly line and provides a measure of uncertainty in causal discovery. We extensively examine its operating characteristics via simulations and validate its effectiveness through simulation experiments and a practical application involving data obtained from Seagate Technology. Domain engineers have cross-validated the learned structures and corroborated the identified causal relationships. [ABSTRACT FROM AUTHOR]

As an important indicator of the surface quality of workpieces, surface roughness has a great impact on production costs and the quality performance of the finished components. Effective surface roughness prediction can not only increase productivity but also reduce costs. However, the current methods for surface roughness prediction have some limitations. On the one hand, the prediction accuracy of classical experimental and statistical-based surface roughness prediction methods is low. On the other hand, the results of deep learning-based surface roughness prediction methods are uninterpretable due to their black-box learning mechanism. Therefore, this paper presents an ensemble learning with a differential evolution algorithm, applies it to the prediction of surface roughness of abrasive water jet polishing (AWJP), and conducts an interpretability analysis to identify key factors contributing to the prediction accuracy of surface roughness. First, we proposed automatically constructing features by an Evolution Forest algorithm to train the base regression models. The differential evolution algorithm with a simplified encoding mechanism was then used to search for the best weighted-ensemble to integrate the base regression models for obtaining highly accurate prediction results. Extensive experiments have been conducted on AWJP to validate the effectiveness of our proposed methods. The results show that the prediction accuracy of our proposed method is higher than the existing machine learning algorithms. In addition, this is the first of its time for the contributions of machining parameters (i.e., features) on surface roughness prediction by using interpretable analysis methods. The analysis results can provide a reference basis for subsequent experiments and studies. [ABSTRACT FROM AUTHOR]

Macrocyclic conformations play a crucial role in regulating their properties. Our understanding of the determinants to control macrocyclic conformation interconversion is still in its infancy. Here we present a macrocycle, octamethyl cyclo[4](1,3-(4,6)-dimethylbenzene)[4]((4,6-benzene)(1,3-dicarboxylate) (OC-4), that can exist at 298 K as two stable atropisomers with C2v and C4v symmetry denoted as C2v-OC-4 and C4v-OC-4, respectively. Heating induces the efficient stepwise conversion of C2v- to C4v-OC-4 via a Cs-symmetric intermediate (Cs-OC-4). It differs from the typical transition state-mediated processes of simple C–C single bond rotations. Hydrolysis and further esterification with a countercation dependence promote the generation of C2v- and Cs-OC-4 from C4v-OC-4. In contrast to C2v-OC-4, C4v-OC-4 can bind linear guests to form pseudo-rotaxans, or bind C60 or C70 efficiently. The present study highlights the differences in recognition behavior that can result from conformational interconversion, as well as providing insights into the basic parameters that govern coupled molecular rotations. The three-dimensional conformation of macrocycles determine their chemical and recognition properties and the factors that influence the interconversion between macrocyclic forms are poorly understood. Here the authors describe a macrocycle existing as two stable atropisomers and the mechanism behind the thermally-induced transition between them. [ABSTRACT FROM AUTHOR]

The coronavirus disease-2019 (COVID-19) pandemic has impaired the quality of life (QoL) for many due to its extensive impacts. However, few studies have addressed the specific impact of COVID-19 on the mental health of adolescents, particularly post-traumatic stress disorder (PTSD). This study considered the impact of COVID-19-related PTSD on the QoL of adolescents in China, the mediating effects of insomnia, and the moderating effects of resilience. Participants included 50,666 adolescents aged 12–18 years selected using a comprehensive sampling method. We performed data collection from January 8th to January 18th, 2023, using the Children's Revised Impact of Event Scale, Pittsburgh Sleep Quality Index, Ten-item Connor-Davidson Resilience Scale, and Screening for and Promotion of Health-related QoL in Children and Adolescents Questionnaire for data collection. Male adolescents exhibited significantly lower levels of PTSD and insomnia compared to females and scored significantly higher in psychological resilience and overall QoL. Insomnia played a mediating role between PTSD and QoL. Psychological resilience moderated the impact of COVID-19-related stress on adolescents' QoL through its influence on insomnia. PTSD resulting from the COVID-19 pandemic affects the QoL of adolescents through the presence of insomnia. Psychological resilience plays a moderating role in this process. Cultivating psychological resilience in adolescents can effectively enhance their ability to cope with the impacts of sudden public events. [ABSTRACT FROM AUTHOR]

Cu catalyses electrochemical CO2 reduction to valuable multicarbon products but understanding the structure-function relationship has remained elusive due to the active Cu sites being heterogenized and under dynamic re-construction during electrolysis. We herein coordinate Cu with six phenyl-1H-1,2,3-triazole derivatives to form stable coordination polymer catalysts with homogenized, single-site Cu active sites. Electronic structure modelling, X-ray absorption spectroscopy, and ultraviolet–visible spectroscopy show a widely tuneable Cu electronics by modulating the highest occupied molecular orbital energy of ligands. Using CO diffuse reflectance Fourier transform infrared spectroscopy, in-situ Raman spectroscopy, and density functional theory calculations, we find that the binding strength of *CO intermediate is positively correlated to highest occupied molecular orbital energies of the ligands. As a result, we enable a tuning of C–C coupling efficiency—a parameter we define to evaluate the efficiency of C2 production—in a broad range of 0.26 to 0.86. This work establishes a molecular platform that allows for studying structure-function relationships in CO2 electrolysis and devises new catalyst design strategies appliable to other electrocatalysis. Understanding the structure-function relationship in Cu-catalyzed CO2 reduction is challenging due to dynamic active sites. This study introduces stable coordination polymer catalysts with homogenized, single-site Cu, enabling the study and tuning of C–C coupling efficiency in CO2 electroreduction. [ABSTRACT FROM AUTHOR]

Given the existing uncertainty regarding the effectiveness and safety of switching from low-molecular-weight heparin (LMWH) to direct oral anticoagulants (DOACs) in patients with cancer-associated venous thrombosis (CAT), we conducted a comprehensive population-based cohort study utilizing electronic health database in Hong Kong. A total of 4356 patients with CAT between 2010 and 2022 were included, with 1700 (39.0%) patients switching to DOAC treatment. Compared to continuous LMWH treatment, switching to DOACs was associated with a significantly lower risk of hospitalization due to venous thromboembolism (HR: 0.49 [95% CI = 0.35–0.68]) and all-cause mortality (HR: 0.67 [95% CI = 0.61–0.74]), with no significant difference in major bleeding (HR: 1.04 [95% CI = 0.83–1.31]) within six months. These findings provide reassurance regarding the effectiveness and safety of switching from LMWH to DOACs among patients with CAT, including vulnerable patient groups. Whether switching from low-molecular-weight heparin to direct oral anticoagulants (DOACs) in cancer-associated thrombosis patients is safe and effective is unknown. Here, the authors show that switching to DOACs reduced venous thromboembolism and mortality without increasing bleeding. [ABSTRACT FROM AUTHOR]

The failure characteristics of roof strata in coal mining and the accurate prediction of mine water inflow are prerequisites for mine safety. Taking a typical coal mine in Southwest China as a case, a new type of similar material suitable for coal mine in exposed karst areas was developed. Through the physical and numerical simulation, the failure characteristics of overlaying rock in karst areas were analyzed. The results showed that the plastic zone developed and accumulated from the roof. After mining, the stress and displacement of overlying rock were divided into three areas along the mining direction. The middle area experienced the maximum change. Overall, the height of falling and water-conducting fracture zones in karst exposed areas was slightly lower than that in other areas. And then, the hydrogeological structure considering the water-conducting fracture zone was rationally generalized, and a three-dimensional numerical model for predicting mine water inflow was built. It was found that the numerical simulation method had the minimum error and the maximum accuracy than other traditional methods, and its results were the closest to the measured value, which could more truly reflect the dynamic formation process of mine water inflow. [ABSTRACT FROM AUTHOR]

Previous studies have shown that scutellarin inhibits the excessive activation of microglia, reduces neuronal apoptosis, and exerts neuroprotective effects. However, whether scutellarin regulates activated microglia-mediated neuronal apoptosis and its mechanisms remains unclear. This study aimed to investigate whether scutellarin can attenuate PC12 cell apoptosis induced by activated microglia via the JAK2/STAT3 signalling pathway. Microglia were cultured in oxygen–glucose deprivation (OGD) medium, which acted as a conditioning medium (CM) to activate PC12 cells, to investigate the expression of apoptosis and JAK2/STAT3 signalling-related proteins. We observed that PC12 cells apoptosis in CM was significantly increased, the expression and fluorescence intensity of the pro-apoptotic protein Bax and apoptosis-related protein cleaved caspase-3 were increased, and expression of the anti-apoptotic protein B-cell lymphoma-2 (Bcl-2) was decreased. Phosphorylation levels and fluorescence intensity of the JAK2/STAT3 signalling pathway-related proteins JAK2 and STAT3 decreased. After treatment with scutellarin, PC12 cells apoptosis as well as cleaved caspase-3 and Bax protein expression and fluorescence intensity decreased. The expression and fluorescence intensity of Bcl-2, phosphorylated JAK2, and STAT3 increased. AG490, a specific inhibitor of the JAK2/STAT3 signalling pathway, was used. Our findings suggest that AG490 attenuates the effects of scutellarin. Our study revealed that scutellarin inhibited OGD-activated microglia-mediated PC12 cells apoptosis which was regulated via the JAK2/STAT3 signalling pathway. [ABSTRACT FROM AUTHOR]

With the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack samples, such as zero-day, leaves a short response time for security protectors, making it substantially more difficult to protect industrial control systems from new types of malicious attacks. Traditional supervised intrusion detection models rely on a large number of samples for training and their performance needs to be improved. Therefore, there is an urgent need for few-shot intrusion detection. Aiming at the above problems, this paper proposes a detection model based on a meta-learning framework, which aims to effectively improve the accuracy and real-time performance of intrusion detection, and designs a meta-learning intrusion detection model containing a sample generation module, a feature mapping module and a feature metric module. Among them, the sample generation module introduces the residual block into the Natural GAN and proposes a new method to generate high-quality antagonistic samples—Res-Natural GAN, which is used to enhance the antagonism of the generated samples and the feature mining degree, to improve the accuracy of malicious traffic detection; the feature mapping module proposes a new attention mechanism, the multi-head fast attention mechanism, which is applied to the encoder structure of the transformer and combined with a parameter optimization algorithm based on particle swarm mutation to shorten the mapping time and improve the real-time performance of the model while mapping the features effectively; the feature metric module proposes a prototype structure based on a prototype storage update algorithm and combines it with a prototype network to achieve correct classification by measuring the Euclidean distance between the detected samples and the class of prototypes, and to shorten the inference time while ensuring the detection accuracy; finally, the three modules are combined to form a real-time meta-learning intrusion detection model. To evaluate the proposed model, five different types of experiments are conducted on multiple public datasets. The experimental results show that the model has higher detection accuracy than the traditional model for both few-shot and zero-shot malicious attacks, and is not only applicable to 5G-enabled industrial internet, but also generalized to different network environments and attack types. [ABSTRACT FROM AUTHOR]

We have observed spin splitting and band gap structure in the Si[110] direction by doping with impurities, which is manifested by a significant opening of the spin-splitting energy level in the localized states. Interestingly, the opening effect occurs such that the electronic spin states are embedded in the localized states on the surface. Here, the broken symmetry obviously appears in the system. In the simulation calculation, it is observed that the spin-splitting gap of the electronic spin levels can be opened over 500 meV in the localized states for the Si=O double bond for doped Si[110] nanowires with a diameter of 0.4 nm. Si[110] nanowire structures doped with oxygen were prepared on silicon wafers using a combination of nanosecond pulsed laser deposition (PLD) and coherent electron beam irradiation studied comparatively in experiments. By combining the experimental and computational results, the physical model on the coupling between the electronic spin states and the localized states was constructed, which will have good application potential in the fields of the electronic spin qubit, quantum information storage, and quantum information processing. [ABSTRACT FROM AUTHOR]

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language. [ABSTRACT FROM AUTHOR]

Climate change and disease threaten shrimp farming. Here, we studied the beneficial properties of a phytogenic formulation, Shrimp Best (SB), in whiteleg shrimp. Functional studies showed that SB dose-dependently increased shrimp body weight and decreased feed conversion ratio. We found that SB protected against Vibrio parahaemolyticus as evidenced by survival rate, bacterial load, and hepatopancreatic pathology in shrimp. Finally, we explored the likely mechanism by which SB affects growth performance and vibriosis in shrimp. The 16S rRNA sequencing data showed that SB increased 6 probiotic genera and decreased 6 genera of pathogenic bacteria in shrimp. Among these, SB increased the proportion of Lactobacillus johnsonii and decreased that of V. parahaemolyticus in shrimp guts. To dissect the relationship among SB, Lactobacillus and Vibrio, we investigated the in vitro regulation of Lactobacillus and Vibrio by SB. SB at ≥ 0.25 μg/mL promoted L. johnsonii growth. Additionally, L. johnsonii and its supernatant could inhibit V. parahaemolyticus. Furthermore, SB could up-regulate five anti-Vibrio metabolites of L. johnsonii, which caused bacterial membrane destruction. In parallel, we identified 3 fatty acids as active compounds from SB. Overall, this work demonstrated that SB improved growth performance and vibriosis protection in shrimp via the regulation of gut microbiota. [ABSTRACT FROM AUTHOR]

Video object segmentation (VOS) has made significant progress with matching-based methods, but most approaches still show two problems. Firstly, they apply a complicated and redundant two-extractor pipeline to use more reference frames for cues, increasing the models' parameters and complexity. Secondly, most of these methods neglect the spatial relationships (inside each frame) and do not fully model the temporal relationships (among different frames), i.e., they need adequate modeling of spatial-temporal relationships. In this paper, to address the two problems, we propose a unified transformer-based framework for VOS, a compact and unified single-extractor pipeline with strong spatial and temporal interaction ability. Specifically, to slim the common-used two-extractor pipeline while keeping the model's effectiveness and flexibility, we design a single dynamic feature extractor with an ingenious dynamic input adapter to encode two significant inputs, i.e., reference sets (historical frames with predicted masks) and query frame (current frame), respectively. Moreover, the relationships among different frames and inside every frame are crucial for this task. We introduce a vision transformer to exploit and model both the temporal and spatial relationships simultaneously. By the cascaded design of the proposed dynamic feature extractor, transformer-based relationship module, and target-enhanced segmentation, our model implements a unified and compact pipeline for VOS. Extensive experiments demonstrate the superiority of our model over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. We also explore potential solutions, such as sequence organizers, to improve the model's efficiency. On DAVIS17 validation, we achieve ∼ 50% faster inference speed with only a slight 0.2% ( J & F ) drop in segmentation quality. Codes are available at https://github.com/sallymmx/TransVOS.git. [ABSTRACT FROM AUTHOR]

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.Understanding the decentralized self-organization in animal groups helps design swarm robotics, yet the underlying mechanism remains elusive. Xiao et al. analyze collective motions of three large bird-flocking datasets and translate their findings to guide evacuation of a swarm of miniature robots in confinement. [ABSTRACT FROM AUTHOR]

Collective cooperation is essential for many social and biological systems, yet understanding how it evolves remains a challenge. Previous investigations report that the ubiquitous heterogeneous individual connections hinder cooperation by assuming individuals update strategies at identical rates. Here we develop a general framework by allowing individuals to update strategies at personalised rates, and provide the precise mathematical condition under which universal cooperation is favoured. Combining analytical and numerical calculations on synthetic and empirical networks, we find that when individuals’ update rates vary inversely with their number of connections, heterogeneous connections actually outperform homogeneous ones in promoting cooperation. This surprising property undercuts the conventional wisdom that heterogeneous structure is generally antagonistic to cooperation and, further helps develop an efficient algorithm OptUpRat to optimise collective cooperation by designing individuals’ update rates in any population structure. Our findings provide a unifying framework to understand the interplay between structural heterogeneity, behavioural rhythms, and cooperation.Collective cooperation is found across many social and biological systems. Here, the authors find that infrequent hub updates promote the emergence of collective cooperation and develop an algorithm that optimises collective cooperation with update rates. [ABSTRACT FROM AUTHOR]

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities. Predicting the colonization of exogenous species in complex communities is a challenge in ecology. Here, the authors propose a data-driven approach to predict colonization outcomes and perform validation experiments in human gut microbial communities. [ABSTRACT FROM AUTHOR]

Observationally, kilonovae are astrophysical transients powered by the radioactive decay of nuclei heavier than iron, thought to be synthesized in the merger of two compact objects1–4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2,5. On timescales of weeks to months, its behaviour is predicted to differ depending on the ejecta composition and the merger remnant6–8. Previous work has shown that the kilonova associated with gamma-ray burst 230307A is similar to kilonova AT2017gfo (ref. 9), and mid-infrared spectra revealed an emission line at 2.15 micrometres that was attributed to tellurium. Here we report a multi-wavelength analysis, including publicly available James Webb Space Telescope data9 and our own Hubble Space Telescope data, for the same gamma-ray burst. We model its evolution up to two months after the burst and show that, at these late times, the recession of the photospheric radius and the rapidly decaying bolometric luminosity (Lbol ∝ t−2.7±0.4, where t is time) support the recombination of lanthanide-rich ejecta as they cool.A modelling analysis shows that an unusually long gamma-ray burst gave rise to a lanthanide-rich kilonova following the merger of a neutron star–neutron star or of a neutron star–black hole. [ABSTRACT FROM AUTHOR]

Membrane biofouling poses an ongoing challenge in seawater reverse osmosis (SWRO) desalination. Here we delved into the impact of seasonal variations in microbial communities on membrane biofouling and innovatively fabricated quorum sensing inhibitors (acylase (AC) and methyl anthranilate (MA))-modified membranes to combat it. Results indicated that Proteobacteria dominated in seawater and membrane biofilm across all seasons, while other phyla varied seasonally. At the class level, the two leading bacteria on the membrane were Gamma- and Alphaproteobacteria, constituting 14–48% and 4–27%, respectively. Genera like Arcobacter, Vibrio, and Rhodobacteraceae were identified as keystone species that exhibited a significant positive correlation with extracellular polymeric substance (EPS) and biofilm formation, leading to a substantive reduction in membrane flux by 70 to 77%. The introduction of AC and MA inhibitors on the membrane suppressed keystone bacteria Rhodobacteraceae and Arcobacter and affected their metabolism, thereby significantly reducing EPS by 65–69% and 55–59%, respectively, and alleviating membrane flux decline by 30–32% and 18–22%, respectively, compared to the pristine membrane. These findings shed new light on the seasonal patterns of membrane biofouling and provide valuable insights into anti-biofouling strategies based on QS inhibition for collaborative biofilm formation. [ABSTRACT FROM AUTHOR]

With the advancement of metal additive manufacturing (AM), lattice structures become a promising solution to situations that require lightweight design and yet maintain adequate mechanical strength. Limited by the quality of feedstock materials, the layer-wise process characteristic and the dynamic nature of thermal environment, lattice structures made by AM often suffer from process-induced imperfections such as poor surface finish and notable geometric deviation. In this study, systematic quantitative characterisation methods are developed to address surface quality and geometric discrepancy of NiTi lattice struts made by laser powder bed fusion (LPBF), with a special focus on fatigue-related features such as stress concentration factors. The results show that for the examined strut diameters and inclination angles, the strut diameter plays a significant role in geometry inaccuracy and the inclination angle has a greater effect on surface texture and stress concentration factor distribution on the surface. Lattice struts with diameters greater than 0.7 mm and inclination angles over 40° with respect to the platform exhibit superior manufacturing quality among all configurations of the struts. The proposed approach not only opens a new avenue to evaluate μ-CT data in a more quantitative way but also offers opportunities to develop guidelines for lattice structure design. [ABSTRACT FROM AUTHOR]

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms. [ABSTRACT FROM AUTHOR]

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The Zhurong rover of the Tianwen-1 mission successfully landed in the southern part of the Utopian Planitia and the northern region of the dichotomy boundary. Craters within a ~ 134 km2 region surrounding the Zhurong rover were identified and divided into seven degradation classes based on their preservation states and morphological details. Assessing how craters have degraded over time provides insight into local surface processes and then speculates on the climate evolution of the study area. The small depth/diameter (d/D) of craters in the study area may be caused by the rapid filling of sediments or by impact processes occurring in poorly cohesive weathering layers, and may also be associated with the volatile material alteration. As time went by, the process of crater degradation is nonlinear, and the degradation rate of the fresh crater in the study area at the initial stage of degradation may be as high as 0.2 m/Myr. The calculated surface erosion rate for the study area is ~ 10–2–10–3 m/Myr, indicating that the erosion of the Martian surface since the Middle Amazonian occurred in the dry environment dominated by wind-sand erosion. [ABSTRACT FROM AUTHOR]

The use of cement-based composites (CBC) with high thermal conductivity for geothermal well cementing is extremely important for the efficient development and use of geothermal energy. Accurate prediction of thermal conductivity can save a lot of experimental costs and time. At present, there is no specific calculation model for the thermal conductivity of CBC. In this study, the microstructure, thermal conductivity model and influencing factors of CBC were investigated by experimental tests, theoretical analysis and numerical simulation. The results showed that the cement-based material could be simplified into a two-layer structure of hydrated and unhydrated layers. Mathematical and numerical models based on the coupled Series model and the Maxwell–Eucken model were established to calculate the thermal conductivity for CBC. The mathematical and numerical models were found to be more accurate by comparison with the conventional models and experimental test results. The cubic packing was more favorable than the spherical packing to improve the thermal conductivity of CBC. The plate material had significant anisotropy. The thermal conductivity of CBC showed a rapid decrease followed by a slow decrease, a decrease followed by a slow increase and finally a rapid decrease, a rapid increase followed by an up and down fluctuation and finally a plateau, respectively, with the increase of filler particle diameter, spacing and curing temperature. Based on these results, the effective methods and future research directions were proposed to maximize the thermal conductivity of geothermal well cementing materials in actual engineering applications. The research findings can provide some technical references for the efficient development of geothermal energy and research on CBC with high thermal conductivity. [ABSTRACT FROM AUTHOR]

The application of carbon fiber epoxy resin composite (CFRP) in the aerospace industry is limited by the laser treatment, because of the poor oxidation resistance and anti-laser ablation performance of the composites. The application of a coating on components is an effective method to improve their laser ablation resistance. The nitride coatings, consisting of Si3N4, AlN, BN, and an epoxy resin, were applied onto the CFRP using brush painting. When irradiating at 100 W/cm2 for 5 s, the damage has occurred on the coating with Si3N4 and AlN, and the BN coating remains original morphology at 100 W/cm2 for 20 s. With the power density has increased to 200 W/cm2, the serious ablation damage occurs in the BN coating irradiated for 10 s. Due to the heat absorption of resin pyrolysis and the low thermal conductivity of CFRP, the highest back-surface temperature is only 75 °C. The laser protection performance of CFRP should mainly improved by increasing the reflectivity of the coating. [ABSTRACT FROM AUTHOR]

Traditional Chinese landscape painting is prone to low-resolution image issues during the digital protection process. To reconstruct high-quality images from low-resolution landscape paintings, we propose a novel Chinese landscape painting generation diffusion probabilistic model (CLDiff), which is similar to the Langevin dynamic process, and realizes the transformation of the Gaussian distribution into the empirical data distribution through multiple iterative refinement steps. The proposed CLDiff can provide ink texture clear super-resolution predictions by gradually transforming the pure Gaussian noise into a super-resolution landscape painting condition on a low-resolution input through a parameterized Markov Chain. Moreover, by introducing an attention module with an energy function into the U-Net architecture, we turn the denoising diffusion probabilistic model into a powerful generator. Experimental results show that CLDiff achieves better visual results and highly competitive performance in traditional Chinese Landscape painting super-resolution tasks. [ABSTRACT FROM AUTHOR]

Fraxinus hubeiensis is a plant endemic to China and widely used as folk medicine to treat various diseases. However, its chemical constituents have never been reported sufficiently. Thus, the primary objective of this study was to investigate the phytochemical constituents and biological activities of F. hubeiensis leaves. Hence, combined column chromatographic and spectroscopic techniques were used to identify and characterize the secondary metabolites such as a pair of 3-keto-glycoside epimers (1) and (2), along with five known compounds (3 ~ 7). The results of α-glucosidase inhibitory activity exhibited that 1 and 2 had moderate activity with IC50 values of 359.50 and 468.43 µM, respectively, compared to a positive control acarbose with the IC50 value of 164.08 µM. However, Compounds 1–6 were shown to be inactive against the tested microbes. [ABSTRACT FROM AUTHOR]

Molecular folding regulation with environmental stimuli is critical in living and artificial molecular machine systems. Herein, we described a macrocycle, cyclo[4] (1,3-(4,6-dimethyl)benzene)[4](1,3-(4,6-dimethyl)benzene)(4-pyridine). Under 298 K, it has three stable stiff atropisomers with names as 1 (Cs symmetry), 2 (Cs symmetry), and 3 (C4v symmetry). At 393 K, 1 can reversibly transform into 2, but at 473 K, it can irrevocably transform into 3. At 338 K, 3 and (PhCN)2PdCl2 complex to produce the metal-organic cage 4. Only at 338 K does the combination of 1 or 2 and (PhCN)2PdCl2 create a gel-like structure. Heating both gels to 473 K transforms them into 4. In addition to offering a thermally accelerated method for modifying self-assembled systems using macrocyclic building blocks, this study also has the potential to develop the nanoscale transformation material with a thermal response. While heat modification is ubiquitous in biologic systems, it remains a significant challenge in artificial systems. Here the authors demonstrate thermal regulation of conversion between different rigid macrocycle atropisomers and related self-assemblies. [ABSTRACT FROM AUTHOR]

A set of high-quality pan-genomes would help identify important genes that are still hidden/incomplete in bird reference genomes. In an attempt to address these issues, we have assembled a de novo chromosome-level reference genome of the Silkie (Gallus gallus domesticus), which is an important avian model for unique traits, like fibromelanosis, with unclear genetic foundation. This Silkie genome includes the complete genomic sequences of well-known, but unresolved, evolutionarily, endocrinologically, and immunologically important genes, including leptin, ovocleidin-17, and tumor-necrosis factor-α. The gap-less and manually annotated MHC (major histocompatibility complex) region possesses 38 recently identified genes, with differentially regulated genes recovered in response to pathogen challenges. We also provide whole-genome methylation and genetic variation maps, and resolve a complex genetic region that may contribute to fibromelanosis in these animals. Finally, we experimentally show leptin binding to the identified leptin receptor in chicken, confirming an active leptin ligand-receptor system. The Silkie genome assembly not only provides a rich data resource for avian genome studies, but also lays a foundation for further functional validation of resolved genes. A chromosome-level genome for the Silkie chicken (Gallus gallus domesticus) provides insight into both the basis of fibromelanosis in these animals and, more broadly, unresolved genes in genome assemblies for related chicken subspecies. [ABSTRACT FROM AUTHOR]

Core genome multilocus sequence typing (cgMLST) is commonly used to classify bacterial strains into different types, for taxonomical and epidemiological applications. However, cgMLST schemes require central databases for the nomenclature of new alleles and sequence types, which must be synchronized worldwide and involve increasingly intensive calculation and storage demands. Here, we describe a distributed cgMLST (dcgMLST) scheme that does not require a central database of allelic sequences and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. We classify 69,994 worldwide Neisseria strains into multi-level clusters that assign species, lineages, and local disease outbreaks. We divide Neisseria meningitidis into 168 endemic lineages and three epidemic lineages responsible for at least 9 epidemics in the past century. According to our analyses, the epidemic and endemic lineages experienced very different population dynamics in the past 100 years. Epidemic lineages repetitively emerged from endemic lineages, disseminated worldwide, and apparently disappeared rapidly afterward. We propose a stepwise model for the evolutionary trajectory of epidemic lineages in Neisseria, and expect that the development of similar dcgMLST schemes will facilitate epidemiological studies of other bacterial pathogens.Core genome multilocus sequence typing (cgMLST) is used to classify bacterial strains for epidemiological applications. Here, the authors describe a distributed cgMLST scheme that does not require a central database of allelic sequences, and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. [ABSTRACT FROM AUTHOR]

The pathological features of Alzheimer's disease are the formation of amyloid plaques and entanglement of nerve fibers. Studies have shown that Cu may be involved in the formation of amyloid plaques. However, their role has been controversial. The aim of this study was to explore the role of Cu in AD. We applied the "R" software for our differential analysis. Differentially expressed genes were screened using the limma package. Copper metabolism-related genes and the intersection set of differential genes with GSE5281 were searched; functional annotation was performed. The protein–protein interaction network was constructed using several modules to analyse the most significant hub genes. The hub genes were then qualified, and a database was used to screen for small-molecule AD drugs. We identified 87 DEGs. gene ontology analysis focused on homeostatic processes, response to toxic substances, positive regulation of transport, and secretion. The enriched molecular functions are mainly related to copper ion binding, molecular function regulators, protein-containing complex binding, identical protein binding and signalling receptor binding. The KEGG database is mainly involved in central carbon metabolism in various cancers, Parkinson's disease and melanoma. We identified five hub genes, FGF2, B2M, PTPRC, CD44 and SPP1, and identified the corresponding small molecule drugs. Our study identified key genes possibly related to energy metabolism in the pathological mechanism of AD and explored potential targets for AD treatment by establishing interaction networks. [ABSTRACT FROM AUTHOR]

The asymmetric texture on the cutting performance of the tool and its influence on the derivative cutting were studied. Based on the Johnson–Cook model, the finite element is used to simulate and analyze the cutting 45# steel of non-woven tool (NT), forward asymmetric texture tool (FT), and reverse asymmetric texture tool (RT), the corresponding cutting experiments are carried out, and the cutting force, cutting temperature, chip morphology, derivative cutting, and tool wear of NT tool, FT tool, and RT tool are compared and analyzed. It is found that the cutting performance of asymmetric texture tools is better than that of NT tool, but the existence of texture will also induce derivative cutting phenomena, which will have a certain negative impact on cutting performance and aggravate tool wear. The average main cutting forces of the NT tool, RT tool, and FT tool are 601.9 N, 196.4 N, and 419.1 N, respectively, and the cutting force of texture tools is reduced by about 30.4–67.4% compared with the NT tool. The results show that the introduction of texture is conducive to increasing the shear angle, reducing the cutting force, improving the temperature distribution of the tooltip, reducing the adhesion of chips, and improving the cutting stability. However, the texture-induced derivative cutting will aggravate the wear of the cutter surface of the texture at the chip outflow end, which will affect the cutting temperature to a certain extent and increase the cutting force, and the coupling effect of texture and derivative cutting makes the cutting performance of RT tool better than FT tool. [ABSTRACT FROM AUTHOR]

Heavy metals content in tires affects the safety of soil and agricultural products. The digestion method is a pretreatment for determining heavy metals in tire samples, and will affect the efficiency and accuracy of the heavy metal determination. The microwave digestion process and reagents for tire samples are not currently standardized. Therefore, this study attempts to provide an appropriate method of resolution for scholars. All digestion processes were performed in Mars One. We tested 15 different acid mixtures to determine the best reagent type and dose and then investigated the effect of maximum temperature, holding time, and sample grams on the degree of digestion. In summary, the best condition to digest the tire sample was a mixture of 3 ml HNO3 and 7 ml H2SO4, taking 0.1 (± 0.0005) g tire sample, at the maximum digestion temperature of 220 °C for 25 min. The experimental conclusion will provide a reliable experimental method for scientists using MARS One to study heavy metals in tires. At the same time, researchers using the MARS series can also find valuable references in this paper. [ABSTRACT FROM AUTHOR]

Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation. [ABSTRACT FROM AUTHOR]

Background: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI). Methods: We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2–3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. Results: ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32−43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). Conclusions: The optimum ACS acceleration factors for ankle MRI were found to be 3.2–3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. Relevance statement: AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. Key points: • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed. [ABSTRACT FROM AUTHOR]

Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation. [ABSTRACT FROM AUTHOR]

Nitrite is widely used due to its personal properties. It not only exists in the food processing and chemical industry but also in every corner of the natural world. Nevertheless, excessive use of nitrite can cause serious environmental and human health hazards. Therefore, the efficient and sensitive detection of nitrite content has become a problem that must be faced. Among numerous methods, the electrochemical method has been widely studied for its advantages, such as fast reaction speed, high sensitivity, and miniaturization. The principle of operation of the nitrite electrochemical sensor is as follows. A modified layer located on the electrode surface oxidizes nitrite to nitrate. The sensing signal generated during oxidation can be converted into a measurable electrochemical signal. The electrochemical signal is proportional to nitrite concentration within a certain range, so that nitrite can be quantitatively detected. However, the sensor's performance is affected by the high oxidation potential of the bare electrode in electrochemistry. Therefore, the electrode is often modified to improve the sensor's performance in the study. In this review, the research progress of materials other than precious metals is discussed in the electrochemical detection of nitrite in recent years (2023–2013). The properties of carbon materials, non-noble metals, metal-organic framework compounds, conductive polymers, and their composites in electrochemical sensors are discussed in detail. Besides, it also looks forward to the challenges and prospects of nanomaterials in electrochemical sensor applications. Article overall summary diagram [ABSTRACT FROM AUTHOR]

Falls are a serious public health problem in the aging population because of the associated clinical and socioeconomic impact. Although previous studies have investigated fall-risk-increasing drugs (FRIDs), few studies have focused on dosage among adult inpatients. This study aimed to evaluate associations between fall risk and dosage of different FRIDs classes in hospital inpatients. Inpatients who experienced falls at medical or surgical wards of Changhua Christian Hospital from January 2017 to December 2021 were identified and matched by age, sex, and hospital ward to randomly selected controls (four per case). Anonymous patient data were extracted from the hospital medical data repository, including demographic characteristics, comorbidities, fall-risk scores, and drug prescriptions. Medication dosages were computed using the anatomical therapeutic chemical classification and the defined daily dose system of the World Health Organization. A total of 852 cases and 3408 controls were identified as eligible. Reducing the use of CNS-active medications, administering lower doses of sedative-hypnotics, prescribing sufficient dopaminergic anti-Parkinson agents, and using NSAIDs instead of opioids are imperative in preventing falls among hospitalized patients according to the findings in the study. [ABSTRACT FROM AUTHOR]

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles. Here, leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, the authors introduce MAP2B, a metagenomic profiler, showing it can significantly remove false-positive identification and generate highly accurate taxonomic profiling results. [ABSTRACT FROM AUTHOR]

White rot fungi is a kind of filamentous fungi which can degrade lignin, hemicellulose and cellulose effectively. In this study, a wild white rot fungi collected from Pingba Town, Bijie City of China was identified as Coprinellus disseminatus (fruiting body) based on morphological and molecular identification. The mycelium of C. disseminatus cultured in the medium supplemented xylan as carbon showed the higher xylanase (XLE) and cellulase (CLE) activity. Further, the activities of tissue degradation-related enzymes including XLE, CLE, acetyl xylanesterase (AXE) and α-l-arabinofuran glycosidase (α-l-AF) were determined after fermenting Eucommia ulmoides leaves by inoculating C. disseminatus mycelium. The results showed that the activities of XLE, CLE, AXE and α-l-AF of mycelium cultured in xylan-contained medium reached the maximum at 5 d after inoculation, which were 777.606 ± 4.248 U mL−1, 9.594 ± 0.008 U mL−1, 4.567 ± 0.026 U mL−1 and 3.497 ± 0.10 U mL−1 respectively. Also, the activities of AXE and α-l-AF both reached the maximum in C. disseminatus mycelium cultured in glucose-contained medium. By comparing the yield of E. ulmoides gum under different fermentation treatments, the extraction yield of E. ulmoides gum were 2.156 ± 0.031% and 2.142 ± 0.044% at 7 d and 14 d after fermentation with mycelium supplemented xylan as carbon source, which were significantly higher than other groups. This study provides a theoretical reference for the preparation of E. ulmoides gum by large-scale fermentation of E. ulmoides leaves with C. disseminatus. [ABSTRACT FROM AUTHOR]