IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing.

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.

Assessing Assembly Errors in Immunoglobulin Loci: A Comprehensive Evaluation of Long-read Genome Assemblies Across Vertebrates.

Long-read sequencing technologies have revolutionized genome assembly producing near-complete chromosome assemblies for numerous organisms, which are invaluable to research in many fields. However, regions with complex repetitive structure continue to represent a challenge for genome assembly algorithms, particularly in areas with high heterozygosity. Robust and comprehensive solutions for the assessment of assembly accuracy and completeness in these regions do not exist. In this study we focus on the assembly of biomedically important antibody-encoding immunoglobulin (IG) loci, which are characterized by complex duplications and repeat structures. High-quality full-length assemblies for these loci are critical for resolving haplotype-level annotations of IG genes, without which, functional and evolutionary studies of antibody immunity across vertebrates are not tractable. To address these challenges, we developed a pipeline, "CloseRead", that generates multiple assembly verification metrics for analysis and visualization. These metrics expand upon those of existing quality assessment tools and specifically target complex and highly heterozygous regions. Using CloseRead, we systematically assessed the accuracy and completeness of IG loci in publicly available assemblies of 74 vertebrate species, identifying problematic regions. We also demonstrated that inspecting assembly graphs for problematic regions can both identify the root cause of assembly errors and illuminate solutions for improving erroneous assemblies. For a subset of species, we were able to correct assembly errors through targeted reassembly. Together, our analysis demonstrated the utility of assembly assessment in improving the completeness and accuracy of IG loci across species.

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing.

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Modeling distracted driving behavior considering cognitive processes.

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Transforming growth factor-ß1-loaded RADA-16 hydrogel scaffold for effective cartilage regeneration.

Cartilage repair remains a major challenge in clinical trials. These current cartilage repair materials can not effectively promote chondrocyte generation, limiting their practical application in cartilage repair. In this work, we develop an implantable scaffold of RADA-16 peptide hydrogel incorporated with TGF-ß1 to provide a microenvironment for stem cell-directed differentiation and chondrocyte adhesion growth. The longest release of growth factor TGF-ß1 release can reach up to 600 h under physiological conditions. TGF-ß1/RADA-16 hydrogel was demonstrated to be a lamellar porous structure. Based on the cell culture with hBMSCs, TGF-ß1/RADA-16 hydrogel showed excellent ability to promote cell proliferation, directed differentiation into chondrocytes, and functional protein secretion. Within 14 days, 80% of hBMSCs were observed to be directed to differentiate into vigorous chondrocytes in the co-culture of TGF-ß1/RADA-16 hydrogels with hBMSCs. Specifically, these newly generated chondrocytes can secrete and accumulate large amounts of collagen II within 28 days, which can effectively promote the formation of cartilage tissue. Finally, the exploration of RADA-16 hydrogel-based scaffolds incorporated with TGF-ß1 bioactive species would further greatly promote the practical clinical trials of cartilage remediation, which might have excellent potential to promote cartilage regeneration in areas of cartilage damage.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing.

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Human-level few-shot concept induction through minimax entropy learning.

Humans learn concepts both from labeled supervision and by unsupervised observation of patterns, a process machines are being taught to mimic by training on large annotated datasets-a method quite different from the human pathway, wherein few examples with no supervision suffice to induce an unfamiliar relational concept. We introduce a computational model designed to emulate human inductive reasoning on abstract reasoning tasks, such as those in IQ tests, using a minimax entropy approach. This method combines identifying the most effective constraints on data via minimum entropy with determining the best combination of them via maximum entropy. Our model, which applies this unsupervised technique, induces concepts from just one instance, reaching human-level performance on tasks of Raven's Progressive Matrices (RPM), Machine Number Sense (MNS), and Odd-One-Out (O3). These results demonstrate the potential of minimax entropy learning for enabling machines to learn relational concepts efficiently with minimal input.

Ni/WS2/WC Composite Nanosheets as an Efficient Catalyst for Photoelectrochemical Hydrogen Peroxide Sensing and Hydrogen Evolution.

It is highly attractive to develop a photoelectrochemical (PEC) sensing platform based on a non-noble-metal nano array architecture. In this paper, a PEC hydrogen peroxide (H2O2) biosensor based on Ni/WS2/WC heterostructures was synthesized by a facile hydrothermal synthesis method and melamine carbonization process. The morphology, structural and composition and light absorption properties of the Ni/WS2/WC catalyst were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and UV-visible spectrophotometer. The average size of the Ni/WS2/WC nanosheets was about 200 nm. Additionally, the electrochemical properties toward H2O2 were studied using an electrochemical workstation. Benefiting from the Ni and C atoms, the optimized Ni/WS2/WC catalyst showed superior H2O2 sensing performance and a large photocurrent response. It was found that the detection sensitivity of the Ni/WS2/WC catalyst was 25.7 µA/cm2/mM, and the detection limit was 0.3 mmol/L in the linear range of 1-10 mM. Simultaneously, the synthesized Ni/WS2/WC electrode displayed excellent electrocatalytic properties in hydrogen evolution reaction (HER), with a relatively small overpotential of 126 mV at 10 mA/cm2 in 0.5 M H2SO4. This novel Ni/WS2/WC electrode may provide new insights into preparing other efficient hybrid photoelectrodes for PEC applications.

Long-range connections damage in white matter hyperintensities affects information processing speed.

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

Temperature and Twist Sensor Based on the Sagnac Interferometer with Long-Period Grating in Polarization-Maintaining Fiber.

We utilized a CO2 laser to carve long-period fiber gratings (LPFGs) on polarization-maintaining fibers (PMFs) along the fast and slow axes. Based on the spectra of LPFGs written along two different directions, we found that when LPFG was written along the fast axis, the spectrum had lower insertion loss and fewer side lobes. We investigated the temperature and twist characteristics of the embedded structure of the LPFG and Sagnac loop and ultimately obtained a temperature sensitivity of -0.295 nm/°C and a twist sensitivity of 0.87 nm/(rad/m) for the LPFG. Compared to the single LPFG, the embedded structure of the LPFG and Sagnac loop demonstrates a significant improvement in temperature and twist sensitivities. Additionally, it also possesses the capability to discern the direction of the twist. The embedded structure displays numerous advantages, including easy fabrication, low cost, good robustness, a wide range, and high sensitivity. These features make it highly suitable for applications in structural health monitoring and other related fields.

The Assessment of Natural Cross Pollination Properties of a Novel Male-Sterile-Female-Fertile Mutation msLC01 in Soybean.

The value of a novel soybean male-sterile mutation msLC01 in breeding practice was determined by its outcrossing properties. Then, the effects of different planting arrangements on the pod set characteristics of male-sterile plants were assessed by using orthogonal experiments at two sites. At the same time, the effects of msLC01 male sterility on other traits were assessed in two C2F2 populations. In addition, the nectar secretion and natural outcross of male-sterile plants from four msLC01 lines were compared with one ms1 line and one ms6 line. The results of the orthogonal experiment showed that the pod numbers and pod set rates of male-sterile plants were decisively different between the two experimental sites but not between the two levels of the other factors. Both increasing the ratio of paternal parent to maternal parent and planting the parental seeds in a mixed way, the proportion of seeds pollinated by the target parent pollen could be increased. Except for the pod number per plant trait, there was no significant difference between male-sterile plants and their fertile siblings. The amount of nectar significantly differed among the lines. Compared with ms1 and ms6 male-sterile plants, the four msLC01 lines possessed significantly more or similar numbers of pod sets. The results of this study lay a foundation for the future use of this mutant in soybean breeding.

Investigating K+ Storage Behavior of Highly Graphitized Carbon Fibers as Anodes for a Potassium-Ion Battery.

Many problems of potassium-ion batteries (PIBs) are hidden under a low mass load of the active material. However, developing research based on areal capacity is challenging for PIBs, due to the lack of an anode capable of delivering a stable capacity of more than 1 mAh cm-2. This work investigates the K+ storage behavior of highly graphitized carbon fibers (HG-CF), which exhibit automatic structural adjustments to mitigate voltage polarization. The created defects and residual K+ in the structure favor the reversible insertion/deinsertion of K+. HG-GF after structural adjustment realizes a capacity of 2 mAh (1.13 cm-2) without K deposition and a stable cyclic stability (>500 h). In situ X-ray diffraction and in situ Raman spectra were used to detect defect formation and structural evolution during cycles. This work demonstrates the feasibility of HG-GF as an anode for PIBs and provides a suitable anode for further research of PIBs based on areal capacity.

High-Sensitivity Temperature Sensor Based on Fiber Fabry-Pérot Interferometer with UV Glue-Filled Hollow Capillary Fiber.

Optical fiber Fabry-Pérot (FP) interferometer sensors have long been the focus of researchers in sensing applications because of their simple light path, low cost, compact size and convenient manufacturing methods. A miniature and highly sensitive optic fiber temperature sensor using an ultraviolet glue-filled FP cavity in a hollow capillary fiber is proposed. The sensor is fabricated by fusion splicing a single-mode fiber with a hollow capillary fiber, which is filled with ultraviolet glue to form a FP cavity. The sensor has a good linear response in the temperature testing and high-temperature sensitivity, which can be increased with the length of the FP cavity. The experimental results show that the temperature sensitivity reaches 1.174 nm/°C with a high linear response in the range of 30-60 °C. In addition, this sensor is insensitive to pressure and can be highly suitable for real-time water temperature monitoring for ocean research. The proposed ultraviolet glue-filled structure has the advantages of easy fabrication, high-temperature sensitivity, low cost and an arbitrary length of capillary, which has broad application prospects for marine survey technology, biological diagnostics and environmental monitoring.

Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis.

BACKGROUND: Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients. OBJECTIVE: We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping. METHODS: Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients. RESULTS: Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients. CONCLUSION: The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.

Combining TSS-MPRA and sensitive TSS profile dissimilarity scoring to study the sequence determinants of transcription initiation.

Cis-regulatory elements (CREs) can be classified by the shapes of their transcription start site (TSS) profiles, which are indicative of distinct regulatory mechanisms. Massively parallel reporter assays (MPRAs) are increasingly being used to study CRE regulatory mechanisms, yet the degree to which MPRAs replicate individual endogenous TSS profiles has not been determined. Here, we present a new low-input MPRA protocol (TSS-MPRA) that enables measuring TSS profiles of episomal reporters as well as after lentiviral reporter chromatinization. To sensitively compare MPRA and endogenous TSS profiles, we developed a novel dissimilarity scoring algorithm (WIP score) that outperforms the frequently used earth mover's distance on experimental data. Using TSS-MPRA and WIP scoring on 500 unique reporter inserts, we found that short (153 bp) MPRA promoter inserts replicate the endogenous TSS patterns of ∼60% of promoters. Lentiviral reporter chromatinization did not improve fidelity of TSS-MPRA initiation patterns, and increasing insert size frequently led to activation of extraneous TSS in the MPRA that are not active in vivo. We discuss the implications of our findings, which highlight important caveats when using MPRAs to study transcription mechanisms. Finally, we illustrate how TSS-MPRA and WIP scoring can provide novel insights into the impact of transcription factor motif mutations and genetic variants on TSS patterns and transcription levels.

Improved GWO and its application in parameter optimization of Elman neural network.

Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

Vertically integrated spiking cone photoreceptor arrays for color perception.

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

Acellular allogenic dermis combined with VSD for repair of abdominal wall defect: a case series.

OBJECTIVE: To explore the clinical application of acellular allogenic dermis combined with VSD in repairing abdominal wall defect combined with abdominal infection. METHODS: Clinical data of 5 cases of abdominal cavity infection with abdominal wall defect admitted in the Burn Department of Quanzhou First Hospital from January 2019 to January 2022 were collected for this study. The abdominal cavity was closed temporarily after debridement and VSD in the early stage, and the abdominal wall defect was repaired by acellular allogeneic dermis combined with autologous split-thickness skin graft in the second stage. The changes of infection indexes (WBC, CRP, PCT, Lac) before and after treatment and the clinical therapeutic effect were observed. RESULTS: In the 5 observed cases, the infection index decreased significantly, the intra-abdominal pressure was normal, and there was no abdominal wall hernia, intestinal adhesion, intestinal obstruction or any other complications. The wound of abdominal wall defect achieved stage 1 healing, the local scar tissue only has slight proliferation, and the appearance was satisfying. There was no recurrence in 6 months follow-up. CONCLUSION: Early use of VSD can effectively control abdominal infection and reduce the occurrence of intestinal fistula or other complications. In the later stage of treatment, acellular allogenic dermis combined with autologous split-thickness skin graft can effectively repair abdominal wall defect.

Application of Autogenous Dermis Combined With Local Flap Transplantation in Repair of Titanium Mesh Exposure After Cranioplasty.

OBJECTIVES: To investigate the clinical outcome of autogenous dermis combined with local flap transplantation in the treatment of titanium mesh exposure after cranioplasty. METHODS: We studied a total of 8 patients with titanium mesh exposure after cranioplasty. After debridement of the head wound, the autogenous dermal tissue from the lateral thigh was transplanted to the surface of titanium mesh, and the local skin flap was then applied after suturing and fixation to repair the wound on the surface of the dermis. To repair the lateral thigh dermal tissue area, a local skin flap was obtained, and a blade thick skin graft was used. RESULTS: Both dermal tissue and local skin flap survived. In the meanwhile, the donor skin area of the lateral thigh healed well, with only slight scar hyperplasia, and the titanium mesh was preserved. There was no recurrence after 6 months of follow-up. CONCLUSIONS: The application of autogenous dermis combined with local skin flap to repair titanium mesh exposure can effectively avoid skin flap necrosis, potential re-exposure of titanium mesh, sub-flap effusion, infection, and other problems. This method has an ideal effect, has easy access to materials, and reduces patients' economic burden. It is worth popularizing.

Exosomal miRNA in early-stage hepatocellular carcinoma.

The incidence and mortality of hepatic carcinoma (HCC) remain high, and early diagnosis of HCC is seen as a key approach in improving clinical outcomes. However, the sensitivity and specificity of current early screening methods for HCC are not satisfactory. In recent years, research around exosomal miRNA has gradually increased, and these molecules have emerged as attractive candidates for early diagnosis and treatment of HCC. This review summarizes the feasibility of using miRNAs in peripheral blood exosomes as early diagnostic tools for HCC.