Weak Filament Research Articles

Biological and genomic analyses of Clavispora sputumsp. nov., a novel potential fungal pathogen closely related to Clavispora lusitaniae (syn. Candida lusitaniae) and Candida auris

Several human fungal pathogens, including drug-resistant Candida auris and species of the Candida haemulonii complex, have emerged over the past two decades, posing new threats to human health. In this study, we report the isolation and identification of a novel species belonging to the genus Clavispora, herein named as Clavisporasputum, from a clinical sputum sample of a COVID-19 patient. Cl. sputum is phylogenetically closely related to fungal pathogens Clavispora lusitaniae (syn. Candida lusitaniae) and C. auris. When grown on CHROMagar Candida Plus medium, Cl. sputum exhibited a similar coloration to C. auris strain CBS12372. Cl. sputum was able to develop weak filaments on CM medium. Although Cl. sputum and Cl. lusitaniae are phylogenetically closely related, comparative genomic and synteny analyses indicated significant chromosomal rearrangements between the two species. Although Cl. sputum could not grow at 37 °C under regular culture condition, an increased fungal burden in the lung tissue of a mouse systemic infection model implies that it could be a potential opportunistic pathogenic yeast in humans.

Analog Memory and Synaptic Plasticity in an InGaZnO-Based Memristor by Modifying Intrinsic Oxygen Vacancies.

This study focuses on InGaZnO-based synaptic devices fabricated using reactive radiofrequency sputtering deposition with highly uniform and reliable multilevel memory states. Electron trapping and trap generation behaviors were examined based on current compliance adjustments and constant voltage stressing on the ITO/InGaZnO/ITO memristor. Using O2 + N2 plasma treatment resulted in stable and consistent cycle-to-cycle memory switching with an average memory window of ~95.3. Multilevel resistance states ranging from 0.68 to 140.7 kΩ were achieved by controlling the VRESET within the range of -1.4 to -1.8 V. The modulation of synaptic weight for short-term plasticity was simulated by applying voltage pulses with increasing amplitudes after the formation of a weak conductive filament. To emulate several synaptic behaviors in InGaZnO-based memristors, variations in the pulse interval were used for paired-pulse facilitation and pulse frequency-dependent spike rate-dependent plasticity. Long-term potentiation and depression are also observed after strong conductive filaments form at higher current compliance in the switching layer. Hence, the ITO/InGaZnO/ITO memristor holds promise for high-performance synaptic device applications.

Deep Open-Curve Snake for Discriminative 3D Neuron Tracking.

Open-Curve Snake (OCS) has been successfully used in three-dimensional tracking of neurites. However, it is limited when dealing with noise-contaminated weak filament signals in real-world applications. In addition, its tracking results are highly sensitive to initial seeds and depend only on image gradient-derived forces. To address these issues and boost the canonical OCS tracker to a new level of learnable deep learning algorithms, we present Deep Open-Curve Snake (DOCS), a novel discriminative 3D neuron tracking framework that simultaneously learns a 3D distance-regression discriminator and a 3D deeply-learned tracker under the energy minimization, which can promote each other. In particular, the open curve tracking process in DOCS is formed as convolutional neural network prediction procedures of new deformation fields, stretching directions, and local radii and iteratively updated by minimizing a tractable energy function containing fitting forces and curve length. By sharing the same deep learning architectures in an end-to-end trainable framework, DOCS is able to fully grasp the information available in the volumetric neuronal data to address segmentation, tracing, and reconstruction of complete neuron structures in the wild. We demonstrated the superiority of DOCS by evaluating it on both the BigNeuron and Diadem datasets where consistently state-of-the-art performances were achieved for comparison against current neuron tracing and tracking approaches. Our method improves the average overlap score and distance score about 1.7% and 17% in the BigNeuron challenge data set, respectively, and the average overlap score about 4.1% in the Diadem dataset.

Modeling the conduction mechanisms of intrinsic multi-level states in HfOx-based resistive random access memory

Multi-level cell storage technology based on resistive random access memory (RRAM) with multi-level state characteristics is more attractive in achieving low-cost ultra-high-density nonvolatile memory. Although a large number of literatures have reported the multi-level state characteristics of RRAM, so far there is no unified model that can well explain the intrinsic reasons for the existence of intermediate resistance state (IRS) and the switching mechanism between different resistance states. Multi-level state characteristics are observed by I–V characteristic measurements on RRAM with TiN/HfOx/barrier layer/TiN stacks fabricated using a commercialized 28 nm CMOS process. Compared to other published resistive switching models, the proposed model based on trap-assisted tunneling is more consistent with the measured. The model can reproduce the multi-level state characteristics based on the mechanism that interaction between defects and oxygen vacancies at the interface of HfOx and TiN electrode, resulting in the formation of multiple weak conductive filaments. Furthermore, the wide spread of high resistance state (HRS) and the switching between HRS and IRS are determined by the distance of tunneling gap. As HfOx-based RRAM will soon be commercialized, it is becoming very urgent to clarify the switching mechanisms of multi-level state characteristics and propose a universal model. Consequently, this work satisfied the current demand and significantly advanced the understanding and development of commercialized, cost-effective, high-density multi-bit HfOx-based RRAM technology.

Anisotropic mass transport enables distinct synaptic behaviors on 2D material surface

Anisotropy is an intrinsic property in crystals with low structural symmetry, and such well-textured materials usually show distinct electronic transport and optical properties along different lattice orientations, offering wide applications in electronic and photonic devices. As a typical low-symmetry materials, crystalline GeSe with orthorhombic structure shows large electric and optical anisotropies. In this work, we take advantage of the anisotropic mass transport and filamentary growth of Ag ions on the GeSe surface to fabricate planar memristive devices which show directional memory and transient switching phenomena. The anisotropic switching behaviors stem from the distinct morphology of metallic filaments that are directionally dependent on the mobility of ions, e.g., ions diffusing along the low-barrier direction tend to form stark conductive channels while those with low mobility only entail slim and weak dendrites, which have been clearly observed under electronic microscopy. The functionality could be utilized to mimic various synaptic events, such as long-term memory enabled by stable conductive channels and short-term memory by the spontaneous rupture of weak filaments, all implemented in one physical device. Two integration schemes based on the anisotropic devices are designed and demonstrated for different application scenarios, paving the way for its applications in multifunctional brain-inspired computing systems.

On pulse-to-pulse coupling in low-temperature filamentary plasma-assisted ignition in methane-air flows

This work aims to characterize the effects of pulse repetition rate (PRR) and flow speed on dielectric barrier discharge (DBD) plasma pulse-to-pulse coupling and its ability to ignite methane-air flows. Experiments are performed on a homemade DBD flow reactor with 5 mm discharge gap. Pressure and equivalence ratio are kept constant at 700 mbar and 0.6. First, we perform high-speed intensified imaging to visualize pulse-to-pulse plasma behavior and ignition kernel development. In air flows, plasma morphology changes from multiple weak filaments to a few stronger filaments indicating plasma pulse-to-pulse coupling. This leads to plasma energy addition in nearly the same gas volume as the previous discharge. The study performed in methane-air flows highlights the importance of plasma pulse-to-pulse coupling for ignition. We find a critical PRR and a minimum number of pulses required to achieve a strong enough coupling to develop a successful ignition kernel. Ignition probability and kernel growth are also evaluated for various conditions. Finally, plasma pulse-to-pulse coupling is quantified by measuring the plasma parameters such as gas temperature and reduced electric field from an optical emission spectroscopy.

Highly Reliable Electrochemical Metallization Threshold Switch Through Conductive Filament Engineering Using Two‐Dimensional PtSe2 Insertion Layer

AbstractElectrochemical metallization threshold switch (ECM TS) has received significant attention for various applications owing to its high ON/OFF ratio, fast switching characteristics, and simple active electrode/dielectric layer/inert electrode structures. However, the excess diffusion of active metal ions causes severe variation in threshold voltage and poor endurance. Furthermore, the small room for inert electrode metal is a problem to be solved for various applications of ECM TS. Here, a novel structure is proposed in which a two‐dimensional platinum diselenide (PtSe2) is inserted between the dielectric layer and inert electrode to engineer the conductive filament by controlling the active metal ion concentration. The low work function, superior inertness, layered structure, and smooth surface of PtSe2 insertion layer lead to thin and weak conductive filament with few active metal atoms and allow the use of various metals as inert electrode. Consequently, PtSe2‐inserted ECM TS shows high reliability in both threshold voltage distribution (σ/µ = ≈6.58%) and endurance (>106 cycles). Furthermore, PtSe2‐inserted ECM TS‐based 2 × 2 cross‐bar array is proposed as AND and OR logic circuits. This study is expected to pave the way for the development of ECM TS for next‐generation electronic applications.

Structure-Guided Segmentation for 3D Neuron Reconstruction.

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

Non-star-forming molecular gas in the Abell 1367 intra-cluster multiphase orphan cloud

We report the detection of CO emission in the recently discovered multiphase isolated gas cloud in the nearby galaxy cluster Abell 1367. The cloud is located about 800 kpc in projection from the center of the cluster and at a projected distance of > 80 kpc from any galaxy. It is the first and the only known isolated “intra-cluster” cloud detected in X-ray, Hα, and CO emission. We found a total of about 2.2 × 108 M⊙ of H2 with the IRAM 30-m telescope in two regions, one associated with the peak of Hα emission and another with the peak of X-ray emission surrounded by weak Hα filaments. The velocity of the molecular gas is offset from the underlying Hα emission by > 100 km s−1 in the region where the X-ray peaks. The molecular gas may account for about 10% of the total cloud’s mass, which is dominated by the hot X-ray component. The previously measured upper limit on the star formation rate in the cloud indicates that the molecular component is in a non-star-forming state, possibly due to a combination of low density of the gas and the observed level of velocity dispersion. The presence of the three gas phases associated with the cloud suggests that gas phase mixing with the surrounding intra-cluster medium is taking place. The possible origin of the orphan cloud is a late evolutionary stage of a ram pressure stripping event. In contrast, the nearby ram pressure stripped galaxy 2MASX J11443212+2006238 is in an early phase of stripping and we detected about 2.4 × 109 M⊙ of H2 in its main body.

An O-Shape Neural Network With Attention Modules to Detect Junctions in Biomedical Images Without Segmentation.

Junction plays an important role in biomedical research such as retinal biometric identification, retinal image registration, eye-related disease diagnosis and neuron reconstruction. However, junction detection in original biomedical images is extremely challenging. For example, retinal images contain many tiny blood vessels with complicated structures and low contrast, which makes it challenging to detect junctions. In this paper, we propose an O-shape Network architecture with Attention modules (Attention O-Net), which includes Junction Detection Branch (JDB) and Local Enhancement Branch (LEB) to detect junctions in biomedical images without segmentation. In JDB, the heatmap indicating the probabilities of junctions is estimated and followed by choosing the positions with the local highest value as the junctions, whereas it is challenging to detect junctions when the images contain weak filament signals. Therefore, LEB is constructed to enhance the thin branch foreground and make the network pay more attention to the regions with low contrast, which is helpful to alleviate the imbalance of the foreground between thin and thick branches and to detect the junctions of the thin branch. Furthermore, attention modules are utilized to introduce the feature maps of LEB to JDB, which can establish a complementary relationship and further integrate local features and contextual information between these two branches. The proposed method achieves the highest average F1-scores of 0.82, 0.73 and 0.94 in two retinal datasets and one neuron dataset, respectively. The experimental results confirm that Attention O-Net outperforms other state-of-the-art detection methods, and is helpful for retinal biometric identification.

Controlling Multiple Filaments By Embedding Nano-Seed Layer in Resistive Random Access Memory via Glancing Angle Deposition

Accessing data from the memory units to processing units is a main bottleneck in conventional von Neumann computing, which will give rise to latency and large energy consumption in this era of data explosion. A non-von Neumann computational approach, neuromorphic computing, has been come out recent year to speed out the data movement and alleviate the energy consumption. Resistive switching (RS)-based memristors possess several advantages such as fast switching speed, low power consumption and shrinking size that serve high potential applications in the next generation nonvolatile memory and neuromorphic computing system. However, it is difficult to realize the neuromorphic computing in filamentary resistive random access memory (RRAM). Studies have shown that multiple-filaments type RRAM is beneficial for analog switching. Herein, we developed a nano-seed functional layer embedded memory to confine the conducting filament path and achieve multiple weak filaments type RRAM, which is suggested for realizing analog behavior. Nano-seed Al2O3 architecture was fabricated by glancing angle deposition to confine the oxygen vacancy regions. HfO2 layer was fabricated by atomic layer deposition and served as the main insulator layer. The nano-seed Al2O3 layer embedded HfO2-based memory restricts several conducting filaments of 10–12 nm, which are much thinner than pure HfO2-based memory with a strong conducting filament of around 50 nm. Besides, this nano-seed Al2O3 layer embedded HfO2-based memory demonstrates multilevel cell characteristic and bi-directional analog switching behavior. This novel architecture is believed to provide a direction for realizing artificial synaptic devices in implementation of neuromorphic computing systems. Figure 1

A TaOx-Based RRAM with Improved Uniformity and Excellent Analog Characteristics by Local Dopant Engineering

Resistive random-access memory (RRAM) with the ability to store and process information has been considered to be one of the most promising emerging devices to emulate synaptic behavior and accelerate the computation of intelligent algorithms. However, variation and limited resistance levels impede RRAM as a synapse for weight storage in neural network mapping. In this work, we investigate a TaOx-based RRAM with Al ion local doping. Compared with a device without doping, the device with locally doped Al ion exhibits excellent uniformity and analog characteristics. The operating voltage and resistance states show tighter distributions. Over 150 adjustable resistance states can be achieved through tuning compliance current (CC) and reset stop voltage. Moreover, incremental resistance switching is available under optimized identical pulses. The improved uniformity and analog characteristics can be attributed to the collective effects of reduced oxygen vacancy (Vo) formation energy and weak conductive filaments induced by the local Al ion dopants.

Neuron Image Segmentation via Learning Deep Features and Enhancing Weak Neuronal Structures.

Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47.83% and 34.83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38.74% and 22.53% higher than the detection results without segmentation.

ZrOX insertion layer enhanced switching and synaptic performances of TiOX-based memristive devices

The impact of ZrOx material serving as an electro-thermal modulation layer (ETML) in the TiN/Ti/TiOx/TiN memristive device structure is investigated. Although the introduction of the ETML increases the total thickness of the device resulting in the increase of forming voltage, it helps to generate weak filaments. The formation of weak filaments in analog memristive devices is preferable to ensure stable switching cycles and epoch training. The device made with ETML performs stable endurance for more than 600 cycles with an On/Off ratio of approximately one order of magnitude; moreover, the device exhibits uniform potentiation and depression with low nonlinearity.

Conduction mechanism of Co-doped ZnO transparent memristive devices

The Co dopant substitutes the Zn atomic position in the hexagonal crystal lattice and generates acceptor defects. These defects play significant role in modulating the conduction mechanism of the memristive device. The devices without Co dopant have high concentration of donor defects so that the electron can flow easily through hopping these donor defects; henceforth, only weak filaments can be formed during the set process. Meanwhile, the increase of the acceptor defects in the films enhances the film resistivity. This acceptor defects also contribute to an increase of barrier height at the electrode/dielectric interface where the electrons require higher energy to overcome this barrier and, eventually, induce the formation of strong filaments during the set process.

Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.

High‐Tensile Strength, Composite Bijels through Microfluidic Twisting

AbstractRope making is a millennia old technique to collectively assemble numerous weak filaments into flexible and high tensile strength bundles. However, delicate soft matter fibers lack the robustness to be twisted into bundles by means of mechanical rope making tools. Here, weak microfibers with tensile strengths of a few kilopascals are combined into ropes via microfluidic twisting. This is demonstrated for recently introduced fibers made of bicontinuous interfacially jammed emulsion gels (bijels). Bijels show promising applications in use as membranes, microreactors, energy and healthcare materials, but their low tensile strength make reinforcement strategies imperative. Hydrodynamic twisting allows to produce continuous bijel fiber bundles of controllable architecture. Modelling the fluid flow field reveals the bundle geometry dependence on a subtle force balance composed of rotational and translational shear stresses. Moreover, combining multiple bijel fibers of different compositions enables the introduction of polymeric support fibers to raise the tensile strength to tens of megapascals, while simultaneously preserving the liquid like properties of the bijel fibers for transport applications. Hydrodynamic twisting shows potentials to enable the combination of a wide range of materials resulting in composites with features greater than the sum of their parts.

A Threshold Switching Selector Based on Highly Ordered Ag Nanodots for X-Point Memory Applications.

Leakage interference between memory cells is the primary obstacle for enlarging X‐point memory arrays. Metal‐filament threshold switches, possessing excellent selectivity and low leakage current, are developed in series with memory cells to reduce sneak path current and lower power consumption. However, these selectors typically have limited on‐state currents (≤10 µA), which are insufficient for memory RESET operations. Here, a strategy is proposed to achieve sufficiently large RESET current (≈2.3 mA) by introducing highly ordered Ag nanodots to the threshold switch. Compared to the Ag thin film case, Ag nanodots as active electrode could avoid excessive Ag atoms migration into solid electrolyte during operations, which causes stable conductive filament growth. Furthermore, Ag nanodots with rapid thermal processing contribute to forming multiple weak Ag filaments at a lower voltage and then spontaneous rupture as the applied voltage reduced, according to quantized conductance and simulation analysis. Impressively, the Ag nanodots based threshold switch, which is bidirectional and truly electroforming‐free, demonstrates extremely high selectivity >109, ultralow leakage current <1 pA, very steep slope of 0.65 mV dec−1, and good thermal stability up to 200 °C, and further represents significant suppression of leakage currents and excellent performances for SET/RESET operations in the one‐selector‐one‐resistor configuration.

Revealing conducting filament evolution in low power and high reliability Fe3O4/Ta2O5 bilayer RRAM

In this work, we used the polycrystalline-Fe3O4 to improve the reliability of the Ag/Ta2O5/Pt resistive random access memory (RRAM). In both the Ag/Ta2O5/Fe3O4/Pt and Ag/Fe3O4/Ta2O5/Pt structures, the switching properties for these bilayer RRAMs were measured in atmosphere and vacuum environments. The results demonstrated that the humidity would affect the Ag filament formation in different environments, and the Ta2O5 and Fe3O4 interface in a different sequence would change the performance of the device, particularly the Forming voltage. Furthermore, the switching voltage and reliability of these bilayer RRAMs was better than single-layer RRAM device, which significantly increased endurance, especially in the Ag/Fe3O4/Ta2O5/Pt device. We also observed the conducting filament shape and evolution during Forming via in/ex-situ transmission electron microscopy (TEM) in the Ag/Fe3O4/Ta2O5/Pt system. In low humidity, the conducting filament was composed of many weak filaments in a low-resistance state (LRS), where the grain boundaries in the Fe3O4 layer limited filament size. The results of energy dispersive spectrometry (EDS) analysis demonstrated that the filament was composed of Ag metal. This study provided detailed switching knowledge of the bilayer RRAM for improving the reliability and power consumption of the device and new design viewpoints of the RRAM structure in future applications.

Time-decay Memristive Behavior and diffusive dynamics in one forget process operated by a 3D vertical Pt/Ta2O5\u2212x/W device

A time-decay resistive switching memory using a 3D vertical Pt/Ta2O5−x/W device architecture is demonstrated, in which horizontal W electrodes were fabricated, and vertical Pt electrodes was formed at the sidewall after oxide was deposited. Unlike conventional resistive switching, which usually form a conductive filament connect two electrodes, a weak conductive filament was formed from bottom electrode W to near top electrode Pt. The memory can be recovered with a time scale when the electrical stimulation is removed. However, different decay behaviors were observed in one decay curve, including rapid decay and slow decay processes. This can be a good simulation of different stages of forgetting. By a combination of the current decay fitting and the conductive analysis, the rapid decay and slow decay processes correspond to ion diffusion and electron detrapping, respectively.