Black Phosphorus/Ferroelectric P(VDF-TrFE) Field-Effect Transistors with High Mobility for Energy-Efficient Artificial Synapse in High-Accuracy Neuromorphic Computing.

The neuromorphic system is an attractive platform for next-generation computing with low power and fast speed to emulate knowledge-based learning. Here, we design ferroelectric-tuned synaptic transistors by integrating 2D black phosphorus (BP) with a flexible ferroelectric copolymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)). Through nonvolatile ferroelectric polarization, the P(VDF-TrFE)/BP synaptic transistors show a high mobility value of 900 cm2 V-1 s-1 with a 103 on/off current ratio and can operate with low energy consumption down to the femtojoule level (∼40 fJ). Reliable and programmable synaptic behaviors have been demonstrated, including paired-pulse facilitation, long-term depression, and potentiation. The biological memory consolidation process is emulated through ferroelectric gate-sensitive neuromorphic behaviors. Inspiringly, the artificial neural network is simulated for handwritten digit recognition, achieving a high recognition accuracy of 93.6%. These findings highlight the prospects of 2D ferroelectric field-effect transistors as ideal building blocks for high-performance neuromorphic networks.

Effects of traction methods in inexperienced endoscopists during colorectal endoscopic submucosal dissection.

BACKGROUND: colorectal endoscopic submucosal dissection (ESD) remains a technical challenge, but traction devices show promise in making this procedure easier. However, the efficacy of traction techniques for colorectal ESD is still unknown for inexperienced endoscopists. METHODS: We selected 400 patients who underwent colorectal ESD performed by four inexperienced endoscopists. Each patient in the traction-assisted ESD (TA-ESD) group was matched to a patient in the conventional ESD (C-ESD) group according to propensity scores. RESULTS: One-to-one propensity score-matching analysis created 87 matched pairs. The self-completion rate in the TA-ESD group is significantly higher than that in the C-ESD group (100% [87/87] vs. 92% [80/87], p < 0.001). The median resection speed was significantly faster in the TA-ESD group than that in the C-ESD group (27 mm2/min [IQR, 19.5-47.3] vs.18 mm2/min [IQR, 13.5-33.8], p < 0.001) and the procedure time in the TA-ESD group was significantly shorter than that in the C-ESD group (33 min [IQR, 27-47] vs.53 min [IQR, 38-73], p < 0.001). However, the histologic complete resection rate was not significantly different between the TA-ESD and C-ESD groups (93.1% [6/87]) vs. 96.6% [3/87], p < 0.1888, respectively). The en bloc resection rate (96.6%) and perforation rate (4.6%) were equivalent between the TA-ESD group and the C-ESD group. CONCLUSION: Traction techniques seem to improve resection speed and self-completion rate of colorectal ESD for inexperienced endoscopists.

Machine learning based marine water quality prediction for coastal hydro-environment management.

During the past three decades, harmful algal blooms (HAB) events have been frequently observed in marine waters around many coastal cities in the world including Hong Kong. The increasing occurrence of HAB has caused acute influences and damages on water environment and marine aquaculture with millions of monetary losses. For example, the Tolo Harbour is one of the most affected areas in Hong Kong, where more than 30% HAB occurred. In order to forewarn the potential HAB incidents, the machine learning (ML) methods have been increasingly resorted in modelling and forecasting water quality issues. In this study, two different ML methods - artificial neural networks (ANN) and support vector machine (SVM) - are implemented and improved by introducing different hybrid learning algorithms for the simulations and comparative analysis of more than 30-year measured data, so as to accurately forecast algal growth and eutrophication in Tolo Harbour in Hong Kong. The application results show the good applicability and accuracy of these two ML methods for the predictions of both trend and magnitude of the algal growth. Specifically, the results reveal that ANN is preferable to achieve satisfactory results with quick response, while the SVM is suitable to accurately identify the optimal model but taking longer training time. Moreover, it is demonstrated that the used ML methods could ensure robustness to learn complicated relationship between algal dynamics and different coastal environmental variables and thereby to identify significant variables accurately. The results analysis and discussion of this study also indicate the potentials and advantages of the applied ML models to provide useful information and implications for understanding the mechanism and process of HAB outbreak and evolution that is helpful to improving the water quality prediction for coastal hydro-environment management.

Isolation, Structural Elucidation of Three New Triterpenoids from the Stems and Leaves of Schisandra chinensis (Turcz) Baill.

Schisandra chinensis (Turcz) Baill. is sufficiently well known as a medicinal plant worldwide, which modern research shows has many pharmacological activities such as hepatoprotective, anti-inflammatory effect, potent anti-HIV-1 activity, anti-tumor effect, and activity on the central nervous system. With considerable chemical investigation, three new triterpenoids (1⁻3), together with four known triterpenoids were isolated from the S. chinensis (Turcz) Baill. Their structures were elucidated by 1D- and 2D-NMR spectroscopic analyses, single-crystal X-ray diffraction and high-resolution mass spectroscopy, which were identified as Schisanlactone I (1), Schinalactone D, (2), Schisanlactone J, (3) Kadsuphilactone B (4), Schisanlactone C (5), Schisphendilactone B (6), and Schinchinenlactone A (7). The cytotoxicity of those compounds (1⁻7) was tested against Hep-G2 cell lines, but no apparent antitumor activity was observed at 50 µg/mL using MTT method.

Water exchange capability induced by seasonal and regional variability: Assessment of Hong Kong waters.

Assessing water exchange capability is an essential step to understand the vulnerability of waterbody to potential pollution. To characterize the spatiotemporal variations of water exchange rate in Hong Kong waters, a three-dimensional hydrodynamic and transport model was implemented. In this study, water residence time (Tre) representing the water exchange rate was <10 days in open waters but exceeded 60 days in weakly-flushed Tolo Harbour. Sensitivity experiments revealed that river discharge accelerated water exchange in western areas, especially during the wet season, causing 45 % ~ 37 % declines in regional Tre. Conversely, tidal currents were the primary limiting factor, particularly during winter. Furthermore, the seasonal reversal of vertical Tre difference in Mirs Bay demonstrated how intrusion water facilitates water exchange by dilution effects. This comprehensive assessment of coastal water exchange capability not only enhances understanding of local marine pollution distribution but also informs coastal management strategies, which can be applied to other coastal water areas.

Effects of modular nursing model for typical issues on enteral nutrition status, immune function, and quality of life in colon cancer patients.

Objective: This study aimed to analyze the effect of modular nursing model for typical issues on enteral nutrition status, immune function, and quality of life in patients with colon cancer. Methods: The clinical data of 106 colorectal cancer patients who came to our hospital from January 2020 to January 2022 were retrospectively analyzed. The patients were randomized into the control and observation group based on the different nursing models, with 53 cases in each group. The patients in the control group received a simple enteral nutrition nursing model, while these in the observation group were administrated with a modular nursing model for typical issues on the basis of the control group. The differences in enteral nutrition status, immune function, and quality of life indicators of patients before and after nursing were counted and compared between the two groups. Results: After the nursing, the contents of albumin, serum albumin, and transferrin were all elevated in both two groups compared with these before the nursing (P < 0.001), and these contents in observation group was markedly higher than these in the control group after the nursing (P < 0.001). The expressions of immune function indicators, including CD3+, CD4+, CD4+/CD8+, and SIgA of the two groups after the nursing, were much higher than these before the nursing (P < 0.05), while the contents of CD8+ and IgG were sharply decreased in comparison with these before the nursing (P < 0.05). The improvement of immune indicators in the observation group after the nursing was strongly better than that in the control (P < 0.01). The proportion of the total nursing satisfaction was significantly higher in the observation group than that in the control (P < 0.05). After the nursing, the life quality scores of two groups were both strongly elevated (P < 0.05), and the improvement of life quality scores were memorably better in the observation group after nursing than these in the control (P < 0.01). Conclusion: For patients undergoing radical colon cancer resection, modular nursing model for typical issues in the early postoperative period is not only safe, but also improves enteral nutrition, can better maintain immune function in the early postoperative period, improve nursing satisfaction, improve patient prognosis, and promote the improvement of the condition, which is worthy of popularization and application.

A novel electrospun nanofiber system with PEGylated paclitaxel nanocrystals enhancing the transmucus permeability and in situ retention for an efficient cervicovaginal cancer therapy.

Overcoming the vaginal barrier to achieve sufficient drug penetration and retention is a huge obstacle for drug delivery in chemotherapeutics for cervical cancer. In this study, we investigate the feasibility of a novel composite nanocrystal/nanofiber system for improving the transmucus penetration and, thus, enhancing retention and drug delivery to the lesion of a cervicovaginal tumor. Herein, paclitaxel (PTX) was sequentially formulated in the form of nanocrystals, coated with polydopamine (PDA), and modified with PEG. The nanocrystals (NCs@PDA-PEG) were creatively fabricated to create a composite nanofibrous membrane (NCs@PDA-PEG NFs) by using an electrospinning technique. The morphology, size distribution, drug loading, encapsulation efficiency, X-ray powder diffraction (XRD), Fourier transform infrared (FTIR) spectra, in vitro release, in vivo vaginal retention, apoptosis index, anti-tumor efficacy in a murine cervicovaginal tumor model, and local irritation were characterized. The NCs@PDA-PEG were formulated in a cube-like shape with an average size of 385.6 ± 35.47 nm; they were dispersed in electrospun nanofibers, and the drug loading was 7.94 %. The XRD curves indicated that the phase state of PTX changed after the creation of the nanocrystals. The FTIR spectra showed that the drug and the excipients were compatible with each other. In vitro delivery showed that the dissolution of PTX in the electrospun nanofibers was significantly faster than that when using bulk PTX. Compared with the PTX NC NFs, the NC@PDA-PEG NFs exhibited prolonged vaginal residence, superior transmucus penetration, minimal mucosal irritation, and significant tumor inhibition efficacy after the intravaginal administration of the NFs in tumor-bearing mice. In conclusion, by acting as novel pharmaceutical repositories, NCs@PDA-PEG NFs can be promising candidates for non-invasive local treatment, leading to efficient tumor inhibition in cervicovaginal cancer.

Memristors based on 2D MoSe2 nanosheets as artificial synapses and nociceptors for neuromorphic computing.

Neuromorphic computing inspired by the human brain is highly desirable in the artificial intelligence age. Thus, it is essential to comprehensively investigate the neuromorphic characteristics of artificial synapses and neurons which are the unit cells in an artificial neural network (ANN). Memristors are considered ideal candidates to serve as artificial synapses and neurons in the ANN. Herein, two-terminal memristors based on two-dimensional (2D) MoSe2 nanosheets are fabricated, demonstrating analog resistive switching (RS) behaviors. Unlike the digital RS behaviors with a sharp transition between the two resistance states, the analog RS provides a series of tunable resistance states, which is more suitable for the realization of synaptic plasticity. Thus, the fabricated memristors successfully implement the synaptic functions, such as paired-pulse facilitation, long-term potentiation and long-term depression. The analog memristors can be utilized to construct the ANN for image recognition, leading to a high recognition accuracy of 92%. In addition, the synaptic memristors can emulate the "learning-forgetting" experience of the human brain. Furthermore, to demonstrate the ability of single neuron learning in our devices, the memristors are studied as artificial nociceptors to recognize noxious stimuli. Our research provides comprehensive investigations on the neuromorphic characteristics of artificial synapses and nociceptors, suggesting promising prospects for applications in neuromorphic computing based on 2D MoSe2 nanosheets.

Functional Ultrathin Separators Proactively Stabilizing Zinc Anodes for Zinc-Based Energy Storage.

Ultrathin separators are indispensable to high-energy-density zinc-ion batteries (ZIBs), but their easy failure caused by zinc dendrites poses a great challenge. Herein, 23 µm-thick functional ultrathin separators (FUSs), realizing superb electrochemical stability of zinc anodes and outstanding long-term durability of ultrathin separators, are reported. In the FUSs, an ultrathin but mechanically strong nanoporous membrane substrate benefits fast and flux-homogenized Zn2+ transport, while a metal-organic framework (MOF)-derived C/Cu nanocomposite decoration layer provides rich low-barrier zinc nucleation sites, thereby synergistically stabilizing zinc anodes to inhibit zinc dendrites and dendrite-caused separator failure. Investigation of the zinc affinity of the MOF-derived C/Cu nanocomposites unravels the high zincophilicity of heteroatom-containing C/Cu interfaces. Zinc anodes coupled with the FUSs present superior electrochemical stability, whose operation lifetime exceeds 2000 h at 1 mA cm-2 and 600 h at 10 mA cm-2 , 40-50 times longer than that of the zinc anodes using glass-fiber separators. The reliability of the FUSs in ZIBs and zinc-ion hybrid supercapacitors is also validated. This work proposes a new strategy to stabilize zinc anodes and provides theoretical guidance in developing ultrathin separators for high-energy-density zinc-based energy storage.

Controllable digital and analog resistive switching behavior of 2D layered WSe2 nanosheets for neuromorphic computing.

Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe2 nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 104. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe2 nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.