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In recent years, analog circuits have received extensive attention and are widely used in many emerging applications. The high demand for analog circuits necessitates shorter circuit design cycles. To achieve the desired performance and specifications, various geometrical symmetry constraints must be carefully considered during the analog layout process. However, the manual labeling of these constraints by experienced analog engineers is a laborious and time-consuming process. To handle the costly runtime issue, we propose a graph-based learning framework to automatically extract symmetric constraints in analog circuit layout. The proposed framework leverages the connection characteristics of circuits and the devices’ information to learn the general rules of symmetric constraints, which effectively facilitates the extraction of device-level constraints on circuit netlists. The experimental results demonstrate that compared to state-of-the-art symmetric constraint detection approaches, our framework achieves higher accuracy and F1-score.

With the widespread use of deep neural networks (DNNs) in intelligent systems, DNN accelerators with high performance and energy efficiency are greatly demanded. As one of the feasible processing-in-memory (PIM) architectures, 3-D stacked-DRAM-based PIM (DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solution for DNN accelerators with better performance and energy efficiency. However, the low-access-cost characteristics of stacked DRAM and the distributed manner of memory access and data storing require us to rebalance the hardware design and DNN mapping. In this article, we propose NicePIM to efficiently explore the design space of hardware architecture and DNN mapping of DRAM-PIM-based DNN inference accelerators, which consists of three key components: 1) PIM-Tuner; 2) PIM-Mapper; and 3) data-scheduler. PIM-Tuner optimizes the hardware configurations leveraging a DNN model for classifying area-compliant PIM-node designs and a deep kernel learning model for identifying better-hardware parameters. PIM-Mapper explores a variety of DNN mapping configurations, including parallelism between branches of DNN, DNN layer partitioning, DRAM capacity allocation, and data layout pattern in DRAM, to generate high-hardware-utilization DNN mapping schemes for various hardware configurations. The data-scheduler employs an integer-linear-programming-based data scheduling algorithm to alleviate the inter-PIM-node communication overhead of data-sharing brought by DNN layer partitioning. Experimental results demonstrate that NicePIM can optimize hardware configurations for DRAM-PIM systems effectively and can generate high-quality DNN mapping schemes with latency and energy cost reduced by 37% and 28% on average, respectively, compared to the baseline method.

ObjectiveHirschsprung disease (HSCR) is a severe congenital disorder affecting 1:5000 live births. HSCR results from the failure of enteric nervous system (ENS) progenitors to fully colonise the gastrointestinal tract during embryonic development. This leads to aganglionosis in the distal bowel, resulting in disrupted motor activity and impaired peristalsis. Currently, the only viable treatment option is surgical resection of the aganglionic bowel. However, patients frequently suffer debilitating, lifelong symptoms, with multiple surgical procedures often necessary. Hence, alternative treatment options are crucial. An attractive strategy involves the transplantation of ENS progenitors generated from human pluripotent stem cells (hPSCs).DesignENS progenitors were generated from hPSCs using an accelerated protocol and characterised, in detail, through a combination of single-cell RNA sequencing, protein expression analysis and calcium imaging. We tested ENS progenitors’ capacity to integrate and affect functional responses in HSCR colon, after ex vivo transplantation to organotypically cultured patient-derived colonic tissue, using organ bath contractility.ResultsWe found that our protocol consistently gives rise to high yields of a cell population exhibiting transcriptional and functional hallmarks of early ENS progenitors. Following transplantation, hPSC-derived ENS progenitors integrate, migrate and form neurons/glia within explanted human HSCR colon samples. Importantly, the transplanted HSCR tissue displayed significantly increased basal contractile activity and increased responses to electrical stimulation compared with control tissue.ConclusionOur findings demonstrate, for the first time, the potential of hPSC-derived ENS progenitors to repopulate and increase functional responses in human HSCR patient colonic tissue.

Purpose: This study aims to raise awareness of the disparities in survival predictions among races in head and neck cancer (HNC) patients by developing and validating population-based prognostic models specifically tailored for Taiwanese and Asian populations. Methods: A total of 49,137 patients diagnosed with HNCs were included from the Taiwan Cancer Registry (TCR). Six prognostic models, divided into three categories based on surgical status, were developed to predict both overall survival (OS) and cancer-specific survival using the registered demographic and clinicopathological characteristics in the Cox proportional hazards model. The prognostic models underwent internal evaluation through a tenfold cross-validation among the TCR Taiwanese datasets and external validation across three primary racial populations using the Surveillance, Epidemiology, and End Results database. Predictive performance was assessed using discrimination analysis employing Harrell’s c-index and calibration analysis with proportion tests. Results: The TCR training and testing datasets demonstrated stable and favorable predictive performance, with all Harrell’s c-index values ≥ 0.7 and almost all differences in proportion between the predicted and observed mortality being < 5%. In external validation, Asians exhibited the best performance compared with white and black populations, particularly in predicting OS, with all Harrell’s c-index values > 0.7. Conclusions: Survival predictive disparities exist among different racial groups in HNCs. We have developed population-based prognostic models for Asians that can enhance clinical practice and treatment plans.

Bit-serial architectures can handle Neural Networks (NNs) with different weight precision, achieving higher resource efficiency compared with bit-parallel architectures. Besides, the weights contain abundant zero bits owing to the fault tolerance of NNs, indicating that bit sparsity of NNs can be further exploited for performance improvement. However, the irregular proportion of zero bits in each weight causes imbalanced workloads in the Processing Element (PE) array, which degrades performance or induces overhead for sparse processing. Thus, this article proposed a channel-wise bit-sparsity quantization method that keeps the non-zero bit number of each weight in each channel from exceeding a certain threshold and clusters the channels with the same threshold to balance the workloads in PE array with little accuracy loss. Then, we co-designed a sparse bit-serial architecture, called Bit-balance, to improve overall performance, supporting weight-bit sparsity and adaptive bitwidth computation. The whole design was implemented with 65 nm technology at 1 GHz and performs at 447-, 37-, 59-, 240-, and 19-frame/s for AlexNet, VGG-16, ResNet-50, GoogleNet, and Yolo-v3 respectively. Compared with sparse bit-serial accelerator, Bitlet, Bit-balance achieves 1.6$\boldsymbol{\times}$× 2.1$\boldsymbol{\times}$× energy efficiency (frame/J) and 2.3$\boldsymbol{\times}$× 3.6$\boldsymbol{\times}$× resource efficiency (frame/mm${}^{\mathbf{2}}$2).

Deep learning models can automatically capture the state in a device from complex data. However, it is typically used for end-to-end fault diagnosis, and the visualization results of the convolutional layer are abstract. A convolutional neural network model with a maximization of mutual information (MMI) between inputs and outputs is proposed. It is the basic idea to enhance the feature extraction capability of the model through improved loss deconstraints and improve the interpretability of the model at the visualization level. First, the bearing vibration data are converted to an envelope spectrum signal, which is used as an input to the model. This process can be viewed as the model conducting feature extraction on the patterns of distinct characteristic spectral lines. Then, the multilayer convolution used for feature extraction is treated as a system; and the output of the system is designed to be the same size as the input of the model, so that the model is easy to understand; second, an attention mechanism (AM) is added to enhance the obvious features. Finally, a loss function is designed to drive the convolutional system based on the mutual information (MI) rule between the signal before and after noise reduction to extract more frequency components of the fault features. The simulated signals and real cases show that the proposed method has achieved certain effect in the fault feature extraction of bearings and also has this certain interpretability in the level of feature visualization.

Although surgical resection provides a straightforward and effective treatment for most malignant solid tumors, tumor recurrence and acute postoperative pain continue to be two big problems associated with this treatment. To resolve these problems, a nanocrystal composite slow-releasing ropivacaine and doxorubicin was fabricated in this study. Briefly, a self-assembling peptide was used to form nanoparticle complexes with the two drugs, based on which homogeneous nanocrystals were obtained by adjusting the pH. In cultured human melanoma cells, the nanocrystals exhibited improved antitumor activity due to a synergistic effect and enhanced cellular uptake of the two drugs. On the other hand, the nanocrystals could slowly release ropivacaine in vitroand in vivo, generating long-acting analgesia on the rat sciatic nerve block model and incisional pain model. On a nude mouse tumor resection model, the nanocrystals simultaneously suppressed the recurrence of solid tumor and relieved postoperative pain, indicating a potential postoperative treatment for tumor resection patients. This nanocrystal system also suggested a promising and facile strategy for developing multifunctional formulations combining different drugs, which could achieve better therapeutic outcomes in a synergistic and sustained manner.

ABSTRACT In this study, five different concentrations of solidification solution (0.5, 1.0, 1.5, 2.0 and 2.5 mol/L urea- Ca[Cl.sub.2]) were used to solidify desert aeolian sand by microbially induced calcite [...]

During Shanghai’s two-month lockdown in 2022, residential community members coped with stress collectively. For many, this experience of intensive community engagement was a learning process with regard to the deliberation of community affairs and collective problem-solving. This article explores how an embryonic sense of collectivity manifested in the expression of shared and solitary sentiments in community engagement and how this collective sense easily fractured during concrete community deliberation and collective decision-making. Despite problems common to collective action, these residents’ barriers to community engagement were largely shaped by extant government policy and grassroots institutional structure, sparking conflicts, cynicism, and mistrust, as evidenced by their narratives.

Abstract:By examining the case of rural Guangdong, this research elaborates on the practice of party-led social capital building (PSCB), which provides a Janus-faced mechanism for creating synergies between the local party-state and society: the Primary Party Organizations cultivate bonding, bridging, and linking social capital to enhance community engagement while deliberately limiting the democratizing potential of this process by selecting vetted nonstate actors, including social workers, private entrepreneurs, and official delegates from higher administrative levels, to serve as key nodes in the community networks. These delegates are tasked with gathering information, building trust and reciprocity, and influencing public opinion to ensure that mass participation in community affairs produces the results desired by the local party-state. The state-vetted key players are mobilized and controlled by the grassroots cadres through well-targeted actions, including party discipline and various carrot-and-stick incentives in the private and tertiary sectors. Reinvigorating the mass line by integrating party-building with the control of the nonstate sector, grid management, and cultural governance, PSCB calls for our reimagination of the governance nexus in grassroots China.

Coarse-grained reconfigurable array (CGRA) has gained popularity in compute-intensive applications because of its flexibility and energy-efficiency. When mapping the data flow graph (DFG) abstracted from the kernel loop of the application to CGRA, for data routing especially DFGs with high fan-out operations, data transfer through the global register file (GRF) and insert routing/recomputation operations in the DFG are two ways that can serve as routing paths, but with increased power cost. For the data routing in these complex DFGs, we propose a new routing mode, bus routing, to exploit the buses as routing paths between PEs without GRF access and inserted operations. A mapping method incorporating bus routing, BusMap, is proposed to map the DFG onto CGRA by solving the maximum independent set (MIS) on a quadruple resource occupation conflict graph. The experiment results show BusMap can achieve fewer GRF access and inserted operations while having the same or even smaller initiation interval (II) compared to previous works.

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have provided a promising solution for neural network acceleration. However, stuck-at faults(SAFs) and process variations in memristor devices significantly degrade the computing accuracy of NCS. In this brief, we propose a unified robust network training framework for a memristive crossbar-based NCS, simultaneously taking the impacts of SAFs and variations into account. In order to incorporate SAFs and variations into the training process, an effective sampling strategy for SAF and an efficient variation injection technique based on the local reparameterization method are developed. Experimental results clearly demonstrate that the proposed training framework can boost the computation accuracy of NCS.

Sparsity is an intrinsic property of convolutional neural networks (CNNs), worth exploiting for CNN accelerators. However, the extra processing involved comes with hardware overhead, resulting in only marginal profits for most architectures. Meanwhile, systolic arrays have become increasingly competitive on CNN acceleration for its high spatiotemporal locality and low hardware overhead. However, the irregularity of sparsity induces imbalanced workloads under the rigid systolic dataflow, causing performance degradation. Thus, this article proposed a systolic-array-based architecture, called Sense, for sparse CNN acceleration by model-hardware codesign, enabling large performance gains. To balance input feature map (IFM) and weight loads across the processing element (PE) array, we applied channel clustering to gather IFMs with approximate sparsity for array computation and codesigned a load-balancing weight pruning method to keep the sparsity ratio of each kernel at a certain value with little accuracy loss, improving PE utilization and overall performance. In addition, adaptive dataflow configuration was applied to determine the computing strategy based on the storage ratio of IFMs and weights, lowering $1.17\times $ – $1.8\times $ dynamic random access memory (DRAM) access compared with Swallow and further reducing system energy consumption. The whole design was implemented on ZynqZCU102 with 200 MHz and performs at 471, 34, 53, and 191 image/s for AlexNet, VGG-16, ResNet-50, and GoogleNet, respectively. Compared with sparse systolic-array-based accelerators, Swallow, fusion-enabled systolic architecture (FESA), and SPOTS, Sense achieves $0.97\times $ – $2.18\times $ , $1.3\times $ – $1.67\times $ , and $0.94\times $ – $1.82\times $ energy efficiency (image/J) on these CNNs, respectively.

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have provided a promising solution to the acceleration of neural networks. However, stuck-at faults (SAFs) in the memristor devices significantly degrade the computing accuracy of NCS. Besides, the memristor suffers from the process variations, causing deviation of the actual programming resistance from its target resistance. In this paper, we propose a reliability-driven design framework for a memristive crossbar-based NCS in combination with general and chip-specific design optimizations. First, we design a general reliability-aware training scheme to enhance the robustness of NCS to SAFs and device variations; a dropconnect-inspired approach is developed to alleviate the impact of SAFs; a new weighted error function, including cross-entropy error (CEE), the $l_{2}$ -norm of weights, and the sum of squares of first-order derivatives of CEE with respect to weights, is proposed to obtain a smooth error curve, where the effects of variations are suppressed. Second, given the neural network model generated by the reliability-aware training scheme, we exploit chip-specific mapping and re- training to further improve computation accuracy loss incurred by SAFs. Experimental results show that the proposed method can boost the computation accuracy of NCS and improve the NCS robustness. Note to Practitioners—This work is motivated by the manufacturing reliability problem in a memristive crossbar-based NCS. To enhance the robustness of an NCS to SAFs and device variations, this paper presents a reliability-driven design framework with taking account of both general and chip-specific design optimizations. The experimental results have demonstrated that the proposed framework is superior to the prior arts, and can be easily integrated with existing industrial hardware-based fault tolerance solutions for higher accuracy at lower overhead. Memristive crossbar-based computing system gives hope for the anticipated efficient implementation of artificial neuromorphic networks. With the help of the reliability-driven designs, the computation accuracy is restored, and hence we can expect the wide use of memristive crossbar-based computing system in neuromorphic computing applications.

In this brief, an ADC-free SRAM-based IMC macro is proposed, which enables energy-efficient and high-precision MAC operation by brain-inspired computing. We identify two key features that support IMC macro to achieve high energy efficiency and high precision MAC calculation. First, the temporal-coding spiking neuron circuit is used to replace the analog-to-digital converter (ADC) to achieve high-efficiency data conversion. Second, the digital adder tree logic eliminates the cost of moving partial sums between PEs and increases the parallelism of the calculations. The mixed-signal SRAM-based IMC macro is designed for processing artificial intelligence (AI) algorithms with reconfigurable precisions based on bit-wise input and weight. In an experiment, the proposed SRAM-based IMC macro with an area of 3.41 mm2 was designed in 0.18 $\mu {\mathrm{ m}}$ CMOS technology. Post-layout simulation results indicate that the 16Kb IMC macro achieves 10.8-13.5 TOPS/W with 4-b inputs, 4-b weights, and 14-b MAC-value outputs.

Ciprofol (HSK 3486, C14H20O), a novel 2,6-disubstituted phenol derivative similar to propofol, is a new type of intravenous general anaesthetic. We found that the exhaled ciprofol concentration could be measured online by ultraviolet time-of-flight mass spectrometry (UV-TOFMS), which could be used to predict the plasma concentration and anaesthetic effects of ciprofol. In this study, we present the calibration method and validation results of UV-TOFMS for the quantification of ciprofol gas. Using a self-developed gas generator to prepare different concentrations of ciprofol calibration gas, we found a linear correlation between the concentration and intensity of ciprofol from 0 parts per trillion by level (pptv) to 485.85 pptv (R2= 0.9987). The limit of quantification was 48.59 pptv and the limit of detection was 7.83 pptv. The imprecision was 12.44% at 97.17 pptv and was 8.96% at 485.85 pptv. The carry-over duration was 120 seconds. In addition, we performed a continuous infusion of ciprofol in beagles, measured the exhaled concentration of ciprofol by UV-TOFMS, determined the plasma concentration by high-performance liquid chromatography, and monitored the anaesthetic effects as reflected by the bispectral index value. The results showed that the exhaled and plasma concentrations of ciprofol were linearly correlated. The exhaled ciprofol concentration correlated well with the anaesthetic effect. The study showed that we could use UV-TOFMS to provide a continuous measurement of gaseous ciprofol concentration at 20 second intervals.

The high systemic blood glucose concentration of hyperglycemic wound microenvironment (WME) severely impedes the disinfection and healing of infected skin wounds. Herein, a WME-activated smart natural product, integrated GOx–GA–Fe nanozyme (GGFzyme), is engineered, which combines a nanozyme and natural enzyme to promote reactive oxygen species (ROS) generation in situ for hyperglycemic wound disinfection. GGFzyme can consume a high concentration of glucose in hyperglycemia wounds and generate H2O2. The conversion of glucose into gluconic acid not avails starvation treatment but reduces the pH of WME to elevate the catalytic activities of both the nanozyme (GA–Fe) and natural enzyme (GOx). And H2O2is then high efficiently catalyzed into •OH and O2•–in situ to combat pathogenic bacteria and promote wound disinfection. The high catalytic antibacterial capacity and superior biosafety, combined with beneficial WME modulation, demonstrate that GGFzyme is a promising therapeutic agent for hyperglycemic wounds.

Scedosporium boydiiis considered as emerging opportunists affecting both immunocompromised and immunocompetent individuals. Diagnosis and treatment of Scedosporiuminfections can be challenging, and the outcome is frequently fatal, especially in patients with disseminated infections. Metagenomic next-generation sequencing (mNGS) is a promising adjunctive diagnostic approach for uncommon pathogens. Here, we report a rare case of brain abscess caused by S. boydiiin a systemic lupus erythematosus (SLE) patient. This case highlights the importance of mNGS in the early diagnosis of S. boydii. Additionally, the combined application of culture and molecular tools may be promising in the clinical diagnosis of fungal disease.

Graphical Abstract:

A novel system multiplexing and program element identification scheme is proposed. Multiplexer periodically inserts Program Element Information Table (PELT) that records position of each transport packet and associated program element identifier. De-multiplexer parses incoming PElT and extracts needed transport packets based on position offsets. Elementary streams are identified, de-multiplexed, and sent to respective decoders for further processing. Packet link table is introduced to improve system error resilience. Compared to Packet Identifier (PID) match filtering in MPEG-2 Systems, proposed PEIT match filtering scheme has advantages in reducing memory access and bandwidth overhead, and is suitable for implementation with a general purpose processor. Index Terms--Match filtering, Multiplexing, Systems, Transport stream.

The clean production of hydrogen from water using sunlight has emerged as a sustainable alternative toward large-scale energy generation and storage. However, designing photoactive semiconductors that are suitable for both light harvesting and water splitting is a pivotal challenge. Atomically thin transition metal dichalcogenides (TMD) are considered as promising photocatalysts because of their wide range of available electronic properties and compositional variability. However, trade-offs between carrier transport efficiency, light absorption, and electrochemical reactivity have limited their prospects. We here combine two approaches that synergistically enhance the efficiency of photocarrier generation and electrocatalytic efficiency of two-dimensional (2D) TMDs. The arrangement of monolayer WS2and MoS2into a heterojunction and subsequent nanostructuring into a nanoscroll (NS) yields significant modifications of fundamental properties from its constituents. Spectroscopic characterization and ab initiosimulation demonstrate the beneficial effects of straining and wall interactions on the band structure of such a heterojunction-NS that enhance the electrochemical reaction rate by an order of magnitude compared to planar heterojunctions. Phototrapping in this NS further increases the light–matter interaction and yields superior photocatalytic performance compared to previously reported 2D material catalysts and is comparable to noble-metal catalyst systems in the photoelectrochemical hydrogen evolution reaction (PEC-HER) process. Our approach highlights the potential of morphologically varied TMD-based catalysts for PEC-HER.

Study Design: Network meta-analysis of randomized controlled trials.Objectives: Lumbar spondylolisthesis is a common indication for spinal fusion. Lumbar interbody fusion (LIF) is popular method to achieve arthrodesis, but previous syntheses usually used head-to-head comparison of 2 surgical methods, and no of them pooled analysis with high-quality. This network meta-analysis of randomized controlled trials was carried out to simultaneously compare fusion techniques in the treatment of lumbar spondylolisthesis.Methods: Three databases were searched for randomized controlled trials (RCTs) on this topic. After critical appraisal, fusion rate, overall adverse events, operative time, Oswestry Disability Index, and pain were extracted for analysis. We conduced network meta-analysis using contrast-based method. Primary outcomes were reported as risk ratio (RR) with 95% confidence interval (CI).Results: Fifteen RCTs (n = 992) met our eligibility criteria. The RCTs treated patients posterolateral fusion (PLF), posterior LIF (PLIF), transforaminal LIF (TLIF), minimally invasive (MIS) TLIF, extreme lateral LIF (XLIF), and circumferential fusion. The pooled estimate showed that circumferential fusion led to significantly higher fusion rate than PLF (RR = 2.15, 95%CI:1.41–3.28), PLIF (RR = 2.11, 95%CI:1.38–3.22), TLIF (RR = 2.13, 95%CI:1.39–3.27), MIS-TLIF (RR = 2.13, 95%CI:1.35–3.35), and XLIF (RR = 2.01, 95%CI: 1.25–3.22). Moreover, circumferential fusion exhibited the best balance in probability between fusion rate and adverse event rate. No evidence showed inconsistency or small-study effect in the results.Conclusions: Collectively, circumferential fusion might be worth to be recommended because it exhibits the best balance between fusion rate and overall adverse event. PLF is still an inferior procedure and requires shorter operative time.

Quantum operations by utilizing the underlying geometric phases produced in physical systems are favoured due to their potential robustness. When a system in a non-degenerate eigenstate undergoes an adiabatically cyclic evolution dominated by its Hamiltonian, it will get a geometric phase, referred to as the Berry Phase. While a non-adiabatically cyclic evolution produces an Aharonov-Anandan geometric phase. The two types of Abelian geometric phases are extended to the non-Abelian cases, where the phase factors become matrix-valued and the transformations associated with different loops are non-commutable. Abelian and non-Abelian (holonomic) operations are prevalent in discrete variable systems, whose limited (say, two) energy levels, form the qubit. While their developments in continuous systems have also been investigated, mainly due to that, bosonic modes (in, such as, cat states) with large Hilbert spaces, provide potential advantages in fault-tolerant quantum computation. Here we propose a feasible scheme to realize non-adiabatic holonomic quantum logic operations in continuous variable systems with cat codes. We construct arbitrary single-qubit (two-qubit) gates with the combination of single- and two-photon drivings applied to a Kerr Parametric Oscillator (KPO) (the coupled KPOs). Our scheme relaxes the requirements of the previously proposed quantum geometric operation strategies in continuous variable systems, providing an effective way for quantum control.

Introduction: Balancing the benefits and risks of antimicrobials in health care requires an understanding of their effects on antimicrobial resistance at the population scale. Therefore, we aimed to investigate the association between the population antibiotics use and resistance rates and further identify their critical thresholds. Methods: Data for monthly consumption of six antibiotics (daily defined doses [DDDs]/1000 inpatient-days) and the number of cases caused by five common drug-resistant bacteria (occupied bed days [OBDs]/10,000 inpatient-days) from inpatients during 2009–2020 were retrieved from the electronic prescription system at Nanjing Drum Tower Hospital, a tertiary hospital in Jiangsu Province, China. Then, a nonlinear time series analysis method, named generalized additive models (GAM), was applied to analyze the pairwise relationships and thresholds of these antibiotic consumption and resistance. Results: The incidence densities of carbapenem-resistant Acinetobacter baumannii(CRAB), carbapenem-resistant Klebsiella pneumoniae(CRKP), and aminoglycoside-resistant Pseudomonas aeruginosawere all strongly synchronized with recent hospital use of carbapenems and glycopeptides. Besides, the prevalence of carbapenem-resistant Escherichia coliwas also highly connected the consumption of carbapenems and fluoroquinolones. To lessen resistance, we determined a threshold for carbapenem and glycopeptide usage, where the maximum consumption should not exceed 31.042 and 25.152 DDDs per 1000 OBDs, respectively; however, the thresholds of fluoroquinolones, third-generation cephalosporin, aminoglycosides, and β-lactams have not been identified. Conclusions: The inappropriate usage of carbapenems and glycopeptides was proved to drive the incidence of common drug-resistant bacteria in hospitals. Nonlinear time series analysis provided an efficient and simple way to determine the thresholds of these antibiotics, which could provide population-specific quantitative targets for antibiotic stewardship.

To meet the visual characteristics of human eyes, high resolution imaging technology came into being. In this paper, a high resolution reconstruction method of ghost imaging via SURF-NSML is proposed. Using the image registration method of Speeded Up Robust Features (SURF) and the fusion algorithm of New Sum of Modified Laplacian (NSML), a series of low-resolution images obtained by the ghost imaging system were registered and fused to obtain high-resolution images. This high resolution image reconstruction method does not need the use of spectroscopic devices, filters and other devices, simplifying the experimental equipment; and it is received by a bucket detector, which greatly increases the utilization of experimental equipment. By the reconstruction analysis of images from natural scenes and from the medical field, it is proved that this method combining the device platform with the language algorithm can reconstruct the target image with better visual characteristics and richer details, which has a good promotion effect on the research of image reconstruction.

Study Design: A network meta-analysis.Objectives: Lumbar degenerative disc disease (LDDD) is an important issue in aging population, for which lumbar interbody fusion (LIF) is a feasible management in cases refractory to conservative therapy. There are various techniques available to perform LIF, including posterior (PLIF), transforaminal (TLIF), and anterior (ALIF) approaches. However, the comparative safety profile of these procedures remains controversial. Our study aimed to evaluate comparative adverse events of the LIF procedures in patients with LDDD.Methods: We searched 5 databases for relevant prospective cohort studies and randomized clinical trials. After quality assessments, we extracted neural, spinal, vascular, and wound events for conducting contrast-based network meta-analysis. Results were reported in risk ratio (RR), 95% confidence interval (CI), and surface under the cumulative ranking (SUCRA).Results: We identified 14 studies involving 921 participants with LDDD. Pooled result showed that open PLIF (OPLIF) leads to significantly higher overall adverse event rate than does open TLIF (OTLIF; RR = 3.43, 95% CI = 1.21-9.73). OTLIF confers the highest SUCRA in neural (78.7) and spinal (80.8) event rates. Minimally invasive TLIF has the highest SUCRA in vascular event (84.2), and minimally invasive PLIF has the highest SUCRA in wound event (88.1). No inconsistency or publication bias was detected in the results.Conclusions: Based on our results, perhaps OPLIF should be avoided in the management of LDDD due to the inferiority of overall complications. Specifically, TLIF seems to have the safest profile in terms of neural, spinal, and vascular events. Nevertheless, shared decision making is still mandatory when choosing the proper LIF procedure for patients with LDDD in clinical practice.

To develop highly sensitive updatable hologram materials, an azobenzene monomer was synthesized herein that supports effective photoisomerization under visible green beam irradiation. Furthermore, an acetylene group was introduced via Sonogashira coupling for higher birefringence. Polymerization was performed on the synthesized azobenzene monomer and on the methyl methacrylate (MMA) monomer, which serves as the skeleton of the film. The structures of all molecules were characterized by nuclear magnetic resonance (NMR) spectroscopy, and UV-Visible absorption spectroscopy was performed to analyze their optical properties. The holographic writing and rewriting capabilities of the newly synthesized azobenzene side-chain polymer were characterized by using a green laser as a writing beam, which showed that a high diffraction efficiency was reliably achievable under low-energy laser irradiation when evaluated with a four-wave-mixing optical scheme.

Methyl groups are ubiquitous in natural products and biologically active compounds, but methods for their selective transformation in such structures are limited. For example, terpenoids contain many methyl groups, due to their biosynthetic pathways, but few reactions of these groups in such structures have been reported. We demonstrate that the combination of methyl C–H silylation and oxidation proximal to native hydroxyl or carbonyl groups occurs in a range of terpenoids and show that the installed hydroxyl group serves as a toehold to enable substitution, elimination, or integration of the methyl carbon into the terpenoid skeleton by the cleavage of C–C bonds. In one case, substitution of the entire methyl group occurs by further oxidation and decarboxylative coupling. In a second, substitution of the methyl group with hydrogen occurs by photochemical hydrodecarboxylation or epimerization by retro-Claisen condensation. In a third, photocatalytic decarboxyolefination formally eliminates methane from the starting structure to generate a terminal olefin for further transformations. Finally, a Dowd–Beckwith-type rearrangement cleaves a nearby C–C bond and integrates the methyl group into a ring, forming derivatives with unusual and difficult-to-access expanded rings. This strategy to transform a methyl group into a synthon marks a distinct approach to restructuring the skeletons of complex architectures and adding functional groups relevant to medicinal chemistry.

Cell adhesion plays a key role in biomaterial development. Self-assembled monolayers (SAMs) provide a convenient and versatile means of modifying surface properties to study how environmental cues affect the cell adhesion process. Serial ζ-potential surfaces can be realized by introducing various ratios of oppositely charged functional groups on a gold surface. A quartz crystal microbalance with dissipation monitoring (QCM-D) has advantages for examining real-time viscoelastic changes on surfaces. This surface-sensitive technique can be applied in cell adhesion studies to investigate the cell–surface interactions. In this work, HEK293T epithelial cells were used to examine the adhesion kinetics of semiadherent cells on NH2–COOH binary SAM-modified surfaces with serial surface potential. Immunofluorescence staining was used to examine focal adhesion sites after a 4-h cell adhesion process. Combined with optical microscopy, QCM-D was used to record in situ and real-time viscoelastic and morphological changes. It was found that HEK293T cells were prone to spread and form more focal adhesion sites on surfaces with more positive charge (more NH2groups) but aggregated and remained highly mobile on surfaces with more negative charge (more COOH groups). On NH2-rich surfaces, cells underwent three-phase kinetics during the adhesion process. Initially, cells adhered and spread quickly on the NH2-rich surfaces with little or no extracellular matrix (ECM) by the attractive interaction between the positively charged amine groups and negatively charged cell membrane. The epithelial cells then shrank their filopodia in the second phase to normalize their size. In the final phase, cells underwent ECM remodeling and formed matured ECM. On COOH-rich surfaces, four phases were identified during the cell adhesion process. Initially, due to electrostatic repulsion between the negatively charged cell membrane and surfaces, direct cell adhesion and spreading were restricted. However, ECM was quickly deposited. In the second phase, cells adhered on and interacted with the surface through the ECM layer. In the third phase, cells underwent ECM remodeling, and additional ECM was deposited on the surfaces. Finally, instead of cell–surface interactions, the cells aggregated to form cell–cell junctions. In summary, the cell adhesion process shifted from direct cell–surface interaction to cell–ECM–surface interaction and cell–cell junctions when the surface potential shifted from positive to negative.

Flexible pressure sensors have attracted intense attention because of their widespread applications in electronic skin, human–machine interfaces, and healthcare monitoring. Conductive porous structures are always utilized as active layers to improve the sensor sensitivities. However, flexible pressure sensors derived from traditional foaming techniques have limited structure designability. Besides, random pore distribution causes difference in structure and signal repeatability between different samples even in one batch, therefore limiting the batch production capabilities. Herein, we introduce a structure designable lattice structure pressure sensor (LPS) produced by bottom-up digital light processing (DLP) 3D printing technique, which is capable of efficiently producing 55 high fidelity lattice structure models in 30 min. The LPS shows high sensitivity (1.02 kPa–1) with superior linearity over a wide pressure range (0.7 Pa to 160 kPa). By adjusting the design parameters such as lattice type and layer thickness, the electrical sensitivities and mechanical properties of LPS can be accurately controlled. In addition, the LPS endures up to 60000 compression cycles (at 10 kPa) without any obvious electrical signal degradation. This benefits from the firm carbon nanotubes (CNTs) coating derived from high-energy ultrasonic probe and the subsequent thermal curing process of UV-heat dual-curing photocurable resin. For practical applications, the LPS is used for real time pulse monitoring, voice recognition and Morse code communication. Furthermore, the LPS is also integrated to make a flexible 4 × 4 sensor arrays for detecting spatial pressure distribution and a flexible insole for foot pressure monitoring.

Abstract:Local cadres in China form innovation partnerships with social workers from Hong Kong (HK) to learn new knowledge regarding public service delivery. How do cadres perceive and process information from HK social workers? How do cadres influence meaning construction towards a preferred redefinition? How do cadres reproduce new knowledge to guide real practice? This study answers these questions by scrutinizing the interactions between local cadres and HK social workers in their sense-making of three concepts, namely, innovation, indigenization, and professionalism. This study finds that mainland local cadres' views regarding the three concepts considerably differ from the views held by HK social workers. Thus, cadres exert strenuous efforts to reshape social workers' understanding to ensure that the introduction of new knowledge by this group of professionals conforms to the government's prior-set objectives and agenda. The dominance of cadres reduces the uncertainty involved in learning and, thus, enhances their receptivity to new knowledge but simultaneously curtails diversity and elasticity in government knowledge absorption, which decreases the prospect of double loop learning. Moreover, the serious power asymmetry between government cadres and social workers (both overseas and local) is reinforced in the knowledge absorption and reproduction processes.

Background: To comprehensively evaluate the efficacy and safety of Retzius sparing (RS) for men undergoing robot-assisted laparoscopic prostatectomy (RARP). Methods: We searched four electronic databases and reference lists of relevant studies for eligible research published before March 11, 2019. After quality assessment, eligible studies were synthesized for relevant outcomes, including positive surgical margin (PSM), continence, incontinence, complication, console time, and hospital stay. Results: Two randomized clinical trials and four observational studies were included in this study. Quantitative syntheses revealed significantly higher PSM rates in RS-RARP compared with conventional RARP (c-RARP) (odds ratio [OR] 1.68, p = 0.02). Furthermore, we found significantly higher PSM rates at the anterior site in RS-RARP compared with c-RARP (OR 4.34, p = 0.03) and significantly lower incontinence rates in RS-RARP in the first month (OR 0.30, p < 0.001) and 12th month (OR 0.25, p < 0.001). Conclusions: Our syntheses revealed higher PSM rates in the RS-RARP group, especially in the anterior aspect. However, RS-RARP had superior functional outcome of urinary continence and lower console time than did c-RARP with equivalent complication rates. Thus, we suggest that operators pay more attention to making clear surgical margins if the lesion is in anterior prostate when performing RS-RARP.

We present a scheme to realize precise discrimination of chiral molecules in a cavity. Assisted by additional laser pulses, cavity fields can evolve into different coherence states with contrary-sign displacements according to the handedness of molecules. Consequently, the handedness of molecules can be read out with homodyne measurement on the cavity, and the successful probability is nearly unity without very strong cavity fields. Numerical results show that the scheme is insensitive to errors, noise, and decoherence. Therefore, the scheme may provide helpful perspectives for accurate discrimination of chiral molecules.

In this work, a series of water-soluble propofol prodrugs were synthesized, and their propofol release rate and pharmacodynamic characteristics were measured. We found that inserting glycolic acid as a linker between propofol and the cyclic amino acid accelerated the release of propofol from prodrugs into the plasma while preserving its safety. In animal experiments, prodrugs (3e, 3g, and 3j) were significantly better than fospropofol (the only water-soluble propofol prodrug that has been used clinically) in terms of safety, onset, and duration time of anesthesia. Their molar dose, onset time, and anesthesia duration time were comparable to those of propofol, helping to maintain the clinical benefits of propofol. The experimental results showed the potential of such compounds as water-soluble prodrugs of propofol.

We demonstrated that ultrabroadband noise-like pulses (NLPs) spanning from below 1600 nm to beyond 2300 nm can be generated in Tm-doped fiber lasers enabled by an optical microfiber. Meanwhile, pronounced red light around 660 nm was also observed, which was attributed to the intracavity third harmonic generation (THG) of ultrashort pulses by harnessing the intermodal phase matching in the optical microfiber. As far as we know, it is the first time to simultaneously observe the ultrabroadband NLPs and the intracavity THG in ultrafast fiber lasers, and it is anticipated that these ultrabroadband NLPs can be adopted as a compact broadband source for optical spectroscopy around 2 µm and a wonderful potential seed for supercontinuum generation in the mid-infrared. Moreover, the THG of the intracavity high peak power NLPs in ultrafast fiber lasers provides a new kind of fiber-format visible light source.

We research a system of compressed-sensing computational ghost imaging (CSCGI) based on the intensity fluctuation brought by turbulence. In this system, we used the gamma-gamma intensity-fluctuation model, which is commonly used in transmission systems, to simulate the CSCGI system. By setting proper values of the parameters such as transmission distance, refractive-index structure parameter, and sampling rates, the peak signal-to-noise ratio (PSNR) performance and bit-error rate (BER) performance are obtained to evaluate the imaging quality, which provides a theoretical model to further research the ghost-imaging algorithm.

BackgroundThe only widely accepted, effective treatment for open-angle glaucoma (OAG) is to reduce the intraocular pressure (IOP), with medical therapy being the typical first-line therapy. Notably, an alternative therapy is selective laser trabeculoplasty (SLT), which is safe and effective in lowering the IOP. Nonetheless, whether SLT could replace medication as the first-line therapy for OAG is still under debate.MethodsStudies involving randomised controlled trials conducted before August 2019 that compared the efficacy of SLT-related and medication-only treatments for OAG were selected from PubMed, Embase, Cochrane Library and Web of Science. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) methodology was employed to rate the quality of the body of evidence.Results1229 patients in eight trials were included. The overall results revealed no significant differences between SLT-related and medication-only treatments regarding the IOP reduction (mean difference (MD): 0.18, 95% CI −0.72 to 1.07, p=0.70, I2=73%) and the success rate of IOP control (risk ratio: 1.02, 95% CI 0.99 to 1.04, p=0.74, I2=0%). The SLT-related therapy group required significantly fewer medications compared with the medication-only group (MD: −1.06, 95% CI −1.16 to −0.96, p<0.0000, I2=5%). A quantitative analysis was not performed concerning adverse events and quality of life because of the limited data available.ConclusionSLT is safe and has a lower incidence of ocular side effects. SLT can be the choice of first-line therapy for OAG. However, clinicians should consider the cost-effectiveness, as well as the patient’s characteristics, before deciding on the therapeutic option.

Various metallurgical dust contains Fe2O3and CaO, which catalyze the carbon–oxygen reaction and exhibit variable distributions and relative contents. Herein, we probe the interactions between Fe2O3and CaO in a typical dust collection in the steelmaking process [basic oxygen furnace dust (BOF dust)] and their effects on BOF dust-catalyzed graphite combustion. Evaluation of composite catalysts with several Fe2O3to CaO ratios and three types of BOF dust shows that high levels of Fe2O3significantly inhibit the combustion process. These catalysts with variable Fe2O3to CaO ratios undergo two sintering reactions at carbon–oxygen combustion temperatures of 650–900 °C to afford calcium ferrite (CaO·Fe2O3) or dicalcium ferrite (2CaO·Fe2O3). The former compound features a lower formation temperature and a faster reaction rate than the latter. Analysis of the Fe2O3–CaO binary phase diagram demonstrates that combustion is inhibited in the range of CaO·Fe2O3formation, whereas increased catalytic activity is observed in the range of 2CaO·Fe2O3formation, implying that catalyst sintering results in deactivation, and first-principles calculations suggest that the diffusion of carbon in CaO·Fe2O3is most likely to be the limiting factor for the catalytic reaction. The formation of CaO·Fe2O3can be suppressed (and hence, catalytic activity can be increased) by reducing the catalyst operating temperature and modulating the distribution ratio.

Peer-assisted learning has been regarded as an adjunct to teaching modalities. It remains inconclusive regarding the benefits of peer observation in skills learning. Hence, we investigated whether the active engagement (AE) of peer observation in addition to expert demonstration would facilitate the performance in the virtual reality (VR) tasks.

ABSTRACTCerebral ischaemiareperfusion injury is an important pathological process in nervous system diseases during which neurons undergo oxygenglucose deprivation and reoxygenation (OGD/R) injury. No study has used epitranscriptomics to explore the characteristics and mechanism of injury. N6methyladenosine (m6A) is the most abundant epitranscriptomic RNA modification. However, little is known about m6A modifications in neurons, especially during OGD/R. m6A RNA immunoprecipitation sequencing (MeRIPseq) and RNA-sequencing data for normal and OGD/R-treated neurons were analysed by bioinformatics. MeRIP quantitative real-time polymerase chain reaction was used to determine the m6A modification levels on specific RNAs. We report the m6A modification profiles of the mRNA and circRNA transcriptomes of normal and OGD/R-treated neurons. Expression analysis revealed that the m6A levels did not affect m6A mRNA or m6A circRNA expression. We found crosstalk between m6A mRNAs and m6A circRNAs and identified three patterns of m6A circRNA production in neurons; thus, distinct OGD/R treatments induced the same genes to generate different m6A circRNAs. Additionally, m6A circRNA biogenesis during distinct OGD/R processes was found to be time specific. These results expand our understanding of m6A modifications in normal and OGD/R-treated neurons, providing a reference to explore epigenetic mechanisms and potential treatments for OGD/R-related diseases.