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Abstract Imaging abnormal copper/iron with effective fluorescent tools is essential to comprehensively put insight into many pathological events. However, conventional coordination‐based detection is mired in the fluorescence quenching induced by paramagnetic Cu(II)/Fe(III). Moreover, the strong chelating property of the probe will consume dissociative metal ions and inevitably interfere with the physiological microenvironment. Here, a new strategy is developed by employing this aberrant Cu(II)/Fe(III) to catalyze bond cleavage for fluorescent imaging of them. A short series of near‐infrared fluorescent molecules (NIRB1−NIRB6) is devised as substrates, wherein the specific C═C bonds can be effectively cleaved to activate red fluorophore by Cu(II)/Fe(III) catalyzing. Representatively, NIRB1 is applied for fluorescent imaging of Cu(II)/Fe(III) in living cells, zebrafish, and Alzheimer's disease (AD)‐afflicted mouse brains which is of significance to monitor metal safety. The successful cleavage of C═C bonds catalyzed by Cu(II)/Fe(III) enriches the application of abiotic bond cleavage reactions in metal detection, and may also inspire the development of fluorescent tools for the future diagnosis and therapy of diseases.

Abstract Background Removal of heavy metals from water and soil is a pressing challenge in environmental engineering, and biosorption by microorganisms is considered as one of the most cost-effective methods. In this study, the metal-binding proteins MerR and ChrB derived from Cupriavidus metallidurans were separately expressed in Escherichia coli BL21 to construct adsorption strains. To improve the adsorption performance, surface display and codon optimization were carried out. Results In this study, we constructed 24 adsorption engineering strains for Hg2+ and Cr6+, utilizing different strategies. Among these engineering strains, the M’-002 and B-008 had the strongest heavy metal ion absorption ability. The M’-002 used the flexible linker and INPN to display the merR opt at the surface of the E. coli BL21, whose maximal adsorption capacity reached 658.40 μmol/g cell dry weight under concentrations of 300 μM Hg2+. And the B-008 overexpressed the chrB in the intracellular, its maximal capacity was 46.84 μmol/g cell dry weight under concentrations 500 μM Cr6+. While in the case of mixed ions solution (including Pb2+, Cd2+, Cr6+ and Hg2+), the total amount of ions adsorbed by M’-002 and B-008 showed an increase of up to 1.14- and 4.09-folds, compared to the capacities in the single ion solution. Conclusion The construction and optimization of heavy metal adsorption strains were carried out in this work. A comparison of the adsorption behavior between single bacteria and mixed bacteria systems was investigated in both a single ion and a mixed ion environment. The Hg2+ absorption capacity is reached the highest reported to date with the engineered strain M’-002, which displayed the merR opt at the surface of chassis cell, indicating the strain’s potential for its application in practical environments.

Abstract Practical photodynamic therapy calls for high-performance, less O2-dependent, long-wavelength-light-activated photosensitizers to suit the hypoxic tumor microenvironment. Iridium-based photosensitizers exhibit excellent photocatalytic performance, but the in vivo applications are hindered by conventional O2-dependent Type-II photochemistry and poor absorption. Here we show a general metallopolymerization strategy for engineering iridium complexes exhibiting Type-I photochemistry and enhancing absorption intensity in the blue to near-infrared region. Reactive oxygen species generation of metallopolymer Ir-P1, where the iridium atom is covalently coupled to the polymer backbone, is over 80 times higher than that of its mother polymer without iridium under 680 nm irradiation. This strategy also works effectively when the iridium atom is directly included (Ir-P2) in the polymer backbones, exhibiting wide generality. The metallopolymer nanoparticles exhibiting efficient O2 •− generation are conjugated with integrin αvβ3 binding cRGD to achieve targeted photodynamic therapy.

The developments of communication technologies, Internet of Things (IoT), and Artificial Intelligence (AI) have significantly accelerated the advancement of Intelligent Transportation Systems (ITS) and Autonomous Driving (AD) in recent years. The exchange of sensed information by widely deployed radars, cameras, and other sensors on vehicles and roadside infrastructure can improve the traffic awareness of drivers and pedestrians. However, wireless data transmission in vehicular networks is challenged by highly dynamic path loss due to utilized frequency bands, weather conditions, traffic overheads, and geographical conditions. In this paper, we propose an Integrated Sensing and Communication System (ISAC) based path loss prediction approach to improve the knowledge of wireless data transmissions in vehicular networks, which utilizes multi-modal data collected by millimeter-wave (mmWave) radars, laser radars, and cameras to forecast the end-to-end path loss distribution. By leveraging a generative adversarial network for parameter initialization coupled with fine-tuning through supervised learning, the model's accuracy can be significantly improved. To increase the model's scalability, the effects of weather conditions, geographical conditions, traffic overheads, and frequency bands are all analyzed. According to the simulation results, our model achieves excellent accuracy with Mean Squared Error (MSE) of the predicted path loss distribution below $3e^{-3}$ across five different scenarios.

Abstract This study explores and explains how duality enables disruptive innovation to encroach on the market and redefine its boundaries under constraints of consumer preferences, purchasing power, technological performance, and complementary technologies. The findings indicate (1) disruptive innovation introduces a new value dimension into the market and enhances the heterogeneity of consumers’ demand, which creates prerequisites for its market encroachment while avoiding competing directly with incumbent enterprises; (2) when considering purchasing power constraints, the disadvantage of disruptive innovation in the preexisting value dimension becomes a price advantage of encroaching on the low-end market; (3) under the constraint of complementary technologies, disruptive innovation can open up new markets that incumbent enterprises have not yet touched by virtue of its advantages in the new value dimension; (4) disruptive innovation does not rely on technological performance to encroach on the market, indicating technological performance is not a necessity for identifying disruptive innovations.

On the backdrop of dwindling conventional reserves, unconventional reservoirs have emerged as a pivotal chapter in resource extraction. Despite their challenges, such as low permeability, complex fluid storage, and flow mechanisms, hydraulic fracturing technology has underpinned the development of unconventional reservoirs. Consequently, this has brought about a shift in the sequence of flow regimes, e.g., the transient radial flow regime has been largely shortened by the lengthy transient linear flow regime due to the low permeability of unconventional reservoirs. Moreover, straight-line analysis (SLA), the simplest technique in rate transient analysis (RTA), is a fundamental and potent tool for swiftly extracting reservoir and hydraulic fracture information, estimating oil and gas reserves, and furnishing crucial initial data for subsequent historical matching processes. However, there is currently a dearth of review papers pertaining to a necessary guide of applying SLA in various transient linear flow (TLF) regimes and different unconventional reservoirs. Hence, this paper commences by elucidating the classification of TLF regimes, commonly used methods for recognizing flow regimes, and the diverse SLA methods used for different TLF regimes. Subsequently, it delves into a discussion of different modification techniques for variable rate/flowing pressure, gas phase, complex reservoir characteristics in unconventional reservoirs, and dynamic drainage area concepts etc. Furthermore, the application of SLA in specific domains, namely core analysis and the flowback period, is described. It culminates by surveying the advancements through an integration of novel technologies to enhance estimation accuracy. The paper also highlights certain drawbacks of current SLA technology and proposes new research directions. Ultimately, this paper would serve as an indispensable resource, offering foundational knowledge for the application of SLA in TLF to promote the production of global unconventional resources in a cost-effective and environmentally sustainable fashion in the face of a climate-resilient world.

Abstract Deep NIR organic phototheranostic molecules generally have large π‐conjugation structures and show highly hydrophobic properties, thus, forming strong π–π stacking in the aqueous medium, which will affect the phototheranostic performance. Herein, an end‐group strategy is developed to lift the performance of NIR‐II emitting photosensitizers. Extensive characterizations reveal that the hydrogen‐bonding interactions of the hydroxyl end group can induce a more intense π–π electronic coupling than the chlorination‐mediated intermolecular forces. The results disclose that π–π stacking will lower fluorescence quantum yield but significantly benefit the photodynamic therapy (PDT) efficiency. Accordingly, an asymmetrically substituted derivative (BTIC‐δOH‐2Cl) is developed, which shows balanced phototheranostic properties with excellent PDT efficiency (14.6 folds of ICG) and high NIR‐II fluorescence yield (2.27%). It proves the validity of the end‐group strategy on controlling the π–π interactions and rational tuning the performance of NIR‐II organic phototheranostic agents.

Magnetic resonance imaging (MRI) is a well-established non-invasive medical imaging technology. Here, to improve the performance of the technique, the authors describe the design of a pH-responsive T1-T2 dual-modal MRI contrast agent, showing enhanced imaging sensitivity in preclinical cancer models.

Background and purpose: With deep-learning, gross tumour volume (GTV) auto-segmentation has substantially been improved, but still substantial manual corrections are needed. With interactive deep-learning (iDL), manual corrections can be used to update a deep-learning tool while delineating, minimising the input to achieve acceptable segmentations. We present an iDL tool for GTV segmentation that took annotated slices as input and simulated its performance on a head and neck cancer (HNC) dataset. Materials and methods: Multimodal image data of 204 HNC patients with clinical tumour and lymph node GTV delineations were used. A baseline convolutional neural network (CNN) was trained (n = 107 training, n = 22 validation) and tested (n = 24). Subsequently, user input was simulated on initial test set by replacing one or more of predicted slices with ground truth delineation, followed by re-training the CNN. The objective was to optimise re-training parameters and simulate slice selection scenarios while limiting annotations to maximally-five slices. The remaining 51 patients were used as an independent test set, where Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD95%) were assessed at baseline and after every update. Results: Median segmentation accuracy at baseline was DSC = 0.65, MSD = 4.3 mm, HD95% = 17.5 mm. Updating CNN using three slices equally sampled from the craniocaudal axis of the GTV in the first round, followed by two rounds of annotating one extra slice, gave the best results. The accuracy improved to DSC = 0.82, MSD = 1.6 mm, HD95% = 4.8 mm. Every CNN update took 30 s. Conclusions: The presented iDL tool achieved substantial segmentation improvement with only five annotated slices.

Meat plays a significant role in human diets, providing a rich source of high-quality protein. With advancements in technology, research in the field of meat preservation has been undergoing dynamic evolution. To gain insights into the development of this discipline, the study conducted an analysis and knowledge structure mapping of 1672 papers related to meat preservation research within the Web of Science Core Collection (WOSCC) spanning from 2001 to 2023. And using software tools such as VOSviewer 1.6.18 and CiteSpace 5.8.R3c allowed for the convenient analysis of the literature by strictly following the software operation manuals. Moreover, the knowledge structure of research in the field of meat preservation was synthesized within the framework of “basic research—technological application—integration of technology with fundamental research,” aligning with the research content. Co-cited literature analysis indicated that meat preservation research could be further categorized into seven collections, as well as highlighting the prominent role of the antibacterial and antioxidant properties of plant essential oils in ongoing research. Subsequently, the future research direction and focus of the meat preservation field were predicted and prospected. The findings of this study could offer valuable assistance to researchers in swiftly comprehending the discipline’s development and identifying prominent research areas, thus providing valuable guidance for shaping research topics.

The world is endowed with a tremendous amount of coal resources, which are unevenly distributed in a few nations. While sustainable energy resources are being developed and deployed, fossil fuels dominate the current world energy consumption. Thus, low-carbon clean technologies, like underground coal gasification (UCG), ought to play a vital role in energy supply and ensuring energy security in the foreseeable future. This paper provides a state-of-the-art review of the world's development of UCG for enhanced hydrogen production. It is revealed that the world has an active interest in decarbonizing the coal industry for hydrogen-oriented research in the context of UCG. While research is ongoing in multiple coal-rich nations, China dominates the world's efforts in both industrial-scale UCG pilots and laboratory experiments. A variety of coal ranks were tested in UCG for enhanced hydrogen output, and the possibilities of linking UCG with other prospective technologies had been proposed and critically scrutinized. Moreover, it is found that transborder collaborations are in dire need to propel a faster commercialization of UCG in an ever-more carbon-conscious world. Furthermore, governmental and financial support is necessary to incentivize further UCG development for large-scale hydrogen production.

In order to explore the quality variation and flavor formation of dry-cured donkey leg, the changes in physical–chemical composition, lipolytic, free amino acids content and volatile flavor compounds were investigated in this study. Six fresh, trimmed hind legs with average weight of 8.12 ± 0.8 kg were taken from male Dezhou donkeys slaughtered at the age of 24 months with the average live weight of 240 kg. The entire processing time was eight months long including six stages, specifically: cooling, salting, air-drying, fermenting and aging. Samples were collected at 0 d, 10 d, 20 d, 30 d, 65 d, 105 d and 165 d of processing. The results showed that the pH value remained stable in the range of 6.2~6.6. The moisture and water activity significantly decreased (p < 0.05) during processing. The chloride content, ash, total volatile basic nitrogen (TVB-N) and peroxide value (POV) significantly increased (p < 0.05), from 0.45% to 12.39%, from 3% to 17%, from 1.43 mg/kg to 8.98 mg/kg and from 1.39 g/100 g to 5.26 g/100 g, respectively. The thiobarbituric acid (TBARS) value reached its highest value of 0.39 mg MDA/kg at the end of the salting stage and then decreased to 0.34 mg MDA/kg. Eighteen free amino acids and fifteen free fatty acids were detected, and their contents were significantly increased during processing (p < 0.05). Volatile compounds were analyzed using solid-phase microextraction (SPME) and gas chromatography–mass spectrometry (GC–MS). Among 114 volatile compounds detected in dry-cured donkey leg, aldehydes, esters, alkane and alcohols were more abundant in the final products, with relative concentrations of 41.88%, 5.72%, 5.35% and 5.25%, respectively. Processing significantly affected the physical–chemical properties, which could contribute to the formation of flavor substances of dry-cured donkey leg.

Foreign objects such as kites, nests and balloons, etc., suspended on transmission lines may shorten the insulation distance and cause short-circuits between phases. A detection method for foreign objects on transmission lines is proposed, which combines multi-network feature fusion and random forest. Firstly, the foreign object image dataset of balloons, kites, nests and plastic was established. Then, the Otus binarization threshold segmentation and morphology processing were applied to extract the target region of the foreign object. The features of the target region were extracted by five types of convolutional neural networks (CNN): GoogLeNet, DenseNet-201, EfficientNet-B0, ResNet-101, AlexNet and then fused by concatenation fusion strategy. Furthermore, the fused features in different schemes were used to train and test random forest, meanwhile, the gradient-weighted class activation mapping (Grad-CAM) was used to visualize the decision region of each network, which can verify the effectiveness of the optimal feature fusion scheme. Simulation results indicate that the detection accuracy of the proposed method can reach 95.88%, whose performance is better than the model of a single network. This study provides references for detection of foreign objects suspended on transmission lines.

Donkey (Equus asinus L.) is an important source of medicinal material in China. Both the hide and meat of donkeys have a variety of physiological activities such as the hide is sufficiently employed for making Ejiao (Colla Corii Asini). The physiological efficacy value of donkey meat is not maximized due to lack of advanced extracting methodologies. In this study, an innovative extracting technology was established for the extraction of antioxidant oligopeptides from donkey meat by two-step enzymatic hydrolysis. The influence of enzymolysis on the degree of hydrolysis (DH) and protein recovery (PR) were evaluated, and the chemical composition, antioxidant activity, and molecular weight distribution of oligopeptides in hydrolysates were studied. The optimum enzymatic hydrolysis conditions were identified at 5.96 pH, 0.7% enzyme-substrate ratio, 55°C temperature and 3 h time. Under these optimum conditions, DH of 25.4%, PR of 95% was obtained. Moreover, hydrolysates were rich in oligopeptides, especially di- and tri-peptides and demonstrated good antioxidant activities. These results introduce a new methodology with the provision of fundamental data for the production of antioxidant oligopeptides from donkey meat. [ABSTRACT FROM AUTHOR]