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Abstract Neurexins are key adhesion proteins that coordinate extracellular and intracellular synaptic components. Nonetheless, the low abundance of these multidomain proteins has complicated any localization and structure-function studies. Here we combine an ALFA tag (AT)/nanobody (NbALFA) tool with classic genetics, cell biology and electrophysiology to examine the distribution and function of the Drosophila Nrx-1 in vivo. We generate full-length and ΔPDZ ALFA-tagged Nrx-1 variants and find that the PDZ binding motif is key to Nrx-1 surface expression. A PDZ binding motif provided in trans, via genetically encoded cytosolic NbALFA-PDZ chimera, fully restores the synaptic localization and function of NrxΔPDZ-AT. Using cytosolic NbALFA-mScarlet intrabody, we achieve compartment-specific detection of endogenous Nrx-1, track live Nrx-1 transport along the motor neuron axons, and demonstrate that Nrx-1 co-migrates with Rab2-positive vesicles. Our findings illustrate the versatility of the ALFA system and pave the way towards dissecting functional domains of complex proteins in vivo.

Abstract Achieving a simple yet sustainable printing technique with minimal instruments and energy remains challenging. Here, a facile and sustainable 3D printing technique is developed by utilizing a reversible salting-out effect. The salting-out effect induced by aqueous salt solutions lowers the phase transition temperature of poly(N-isopropylacrylamide) (PNIPAM) solutions to below 10 °C. It enables the spontaneous and instant formation of physical crosslinks within PNIPAM chains at room temperature, thus allowing the PNIPAM solution to solidify upon contact with a salt solution. The PNIPAM solutions are extrudable through needles and can immediately solidify by salt ions, preserving printed structures, without rheological modifiers, chemical crosslinkers, and additional post-processing steps/equipment. The reversible physical crosslinking and de-crosslinking of the polymer through the salting-out effect demonstrate the recyclability of the polymeric ink. This printing approach extends to various PNIPAM-based composite solutions incorporating functional materials or other polymers, which offers great potential for developing water-soluble disposable electronic circuits, carriers for delivering small materials, and smart actuators.

In light of the rapid advancements in big data and artificial intelligence applications, heterogeneous computing (HGC) platforms that integrate diverse computing units have gained traction with the aim of achieving energy efficiency and high performance. A custom topology-based optical network-on-chip (ONoC) that provides unparalleled diversity between computing nodes is expected to be the next-generation communication infrastructure for meeting the bandwidth and energy efficiency requirements of HGC. One of the recent challenges in the field of ONoCs is to accelerate multicast routing via wavelength division multiplexing (WDM), which dispatches data parallelly across non-interfering wavelengths. The optimization of network throughput and laser power efficiency revolves around two factors: the number of wavelengths and optical signal-to-noise ratio (OSNR). Accordingly, we introduce OSNR-aware wavelength allocation and branching methods for multicast routing (OWBM) tailored to an HGC platform in a customized ONoC. OWBM increases the wavelength resource efficiency by establishing independent routing paths in the partitioned destination nodes such that each routing path is guaranteed to be prevented from overlapping among the partitions. Moreover, the adaptive branching mechanism of OWBM adaptively selects path-based routing and OSNR-aware routing on the fly according to the wavelength allocation cases, further augmenting the throughput and laser power efficiency. Consequently, OWBM outperformed conventional tree- and path-based multicast approaches by elevating the average throughput by 47.39% and curbing the laser power consumption by up to 35.92% in various convolutional neural network benchmarks. Compared with the existing ONoC wavelength allocation techniques, the OWBM demonstrated a maximum of 42.46% enhanced wavelength utilization on a 64-core HGC platform.

Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among them, the ability to discriminate energy exhibits significant potential for clinical applications because it provides material-specific information. That is, material decomposition (MD) can be achieved through energy discrimination. In this study, deep learning-based material decomposition was performed using live animal data. We propose MD-Unet, which is a deep learning strategy for material decomposition based on an Unet architecture trained with data from three energy bins. To mitigate the data insufficiency, we developed a pretrained model incorporating various simulation data forms and augmentation strategies. Incorporating these approaches into model training results in enhanced precision in material decomposition, thereby enabling the identification of distinct materials at individual pixel locations. The trained network was applied to the acquired animal data to evaluate material decomposition results. Compared with conventional methods, the newly generated MD-Unet demonstrated more accurate material decomposition imaging. Moreover, the network demonstrated an improved material decomposition ability and significantly reduced noise. In addition, they can potentially offer an enhancement level similar to that of a typical contrast agent. This implies that it can acquire images of the same quality with fewer contrast agents administered to patients, thereby demonstrating its significant clinical value.

Belady’s algorithm is widely known as an optimal cache replacement policy. It has been the foundation of numerous recent studies on cache replacement policies, and most studies assume this as an upper limit. Despite its widespread adoption, we discovered opportunities to unleash the headroom by addressing cache access types and implementing cache bypass. In this study, we propose Stormbird, a cache replacement policy that synergistically integrates the extensions of Belady’s algorithm and the power of reinforcement learning. Reinforcement learning is well-suited for cache replacement policy problems owing to its ability to interact dynamically with the environment, adapt to changing access patterns, and optimize the maximum cumulative rewards. Stormbird utilizes several selected features from the reinforcement learning model to enhance the instructions per cycle efficiency while maintaining a low hardware area overhead. Furthermore, it considers cache access types and integrates dynamic set dueling techniques to improve the cache performance. For 2 MB last-level cache per core, Stormbird achieves an average instructions per cycle improvement of 0.13% over the previous state-of-the-art on a single-core system and 0.02% on a four-core system while simultaneously reducing hardware overhead by 62.5%. Stormbird incurs a low hardware overhead of only 10.5 KB for 2 MB last-level cache and can be implemented without using program counter values.

One of the vital challenges for the binary neural networks (BNNs) is improving their inference performance by expanding their data representation capabilities for figuring out delicate patterns and nuances in the data. Addressing the explosive computational demands on neural network training is essential to guarantee sustainable development and scalable deployment. However, mitigating the increase in the computational cost during the training phase is critical for ensuring sustainability and scalability during deployment. In this study, an advanced sign-flipping-aware optimizer (SFAO) that focuses on BNNs was introduced to diminish the computational burden. SFAO balanced the model performance and computational cost through sign-flipping-aware updating rules throughout the training of BNNs. SFAO optimizer, tailored for BNNs with binary weight-specific updating rules, considerably reduced the computing resources needed for training on the CIFAR-10 dataset. Specifically, it surpassed the conventional full-precision updating rule by reducing the total instruction count by 21.89%. In contrast, SFAO showed a marginal 0.44% decline in the image classification accuracy relative to the updating rules for the full-precision parameters. Furthermore, the implementation of early stopping using the sign flip rate led to a notable reduction of 9.37% in the average computation time per network for the ImageNet dataset.

As the applications for artificial intelligence are growing rapidly, numerous network compression algorithms have been developed to restrict computing resources such as smartphones, edge, and IoT devices. Knowledge distillation (KD) leverages soft labels derived from a teacher model to a less parameterized model achieving high accuracy with reduced computational burden. Moreover, online KD provides parallel computing through collaborative learning between teacher and student networks, thus enhancing the training speed. A binarized neural network (BNN) offers an intriguing opportunity to facilitate aggressive compression at the expense of drastically degraded accuracy. In this study, two performance improvements are proposed for online KD when a BNN is applied as a student network: 1) parameterized weight clipping (PWC) to reduce dead weights in the student network and 2) quantization gap-aware adaptive temperature scheduling between the teacher and student networks. In contrast to constant weight clipping (CWC), PWC demonstrates a 3.78% top-1 test accuracy enhancement with trainable weight clipping by decreasing the gradient mismatch with CIFAR-10 dataset. Furthermore, the quantization gap-aware temperature scheduling increases the top-1 test accuracy by 0.08% over online KD at a constant temperature. By aggregating both methodologies, the top-1 test accuracy for CIFAR-10 dataset was 94.60%, and that for Tiny-ImageNet dataset was comparable to that of the 32-bit full-precision neural network.

The processing of data-intensive applications, followed by an unprecedented amount of data traffic, drives explosive accesses to the memory subsystem. The overloaded memory subsystem experiences increased data access latency. To expedite data access, a network caching technique that leverages network-on-chip (NoC) virtual channels (VCs) as an expanded memory subsystem has emerged. Previous network caching studies focused on utilizing VCs on the NoC’s local input port as a victim cache to reduce local data access latency. In contrast to previous studies, we explore the opportunity of mitigating problems associated with shared data access via in-network caching for directory entries (NCDE), which can utilize every input port’s VCs to hold directory entries. NCDE exploits VCs as the victim and prefetch buffers of the directory entries, each reducing directory eviction-induced invalidations and simplifying the cache-to-cache (C2C) data transfer. The effectiveness of NCDE was evaluated using a gem5 full-system simulator, and the results show that the average memory access time (AMAT) and workload execution time were reduced by 7.69% and 5.82%, respectively. As a cost for accelerating the data access latency, implementing NCDE incurs a negligible router area overhead of 1.56%.

Many deep learning frameworks utilize GEneral Matrix Multiplication (GEMM)-based convolution to accelerate CNN execution. GEMM-based convolution provides faster convolution yet requires a data conversion process called lowering (i.e., im2col), which incurs significant memory overhead and diminishes performance. This paper proposes a novel hardware mechanism, called On-the-fly Lowering Engine (OLE), to eliminate the lowering overheads. Our goal is to offload the lowering overheads from the GEMM-based convolution. With OLE, the lowered matrix is neither pre-calculated nor stored in the main memory. Instead, a hardware engine generates lowered matrix on-the-fly from the original input matrix to reduce memory footprint and bandwidth requirements. Furthermore, the hardware offloading eliminates CPU cycles for lowering operation and overlaps computation with lowering to hide the performance overhead. Our evaluation shows that OLE can reduce memory footprint of convolutional layer inputs down to $\frac {1}{12.5}\times $ and the overall memory footprint by up to 33.5%. Moreover, OLE can reduce the execution time of convolutional layers by 57.7% on average, resulting in an average speedup of $2.3\times $ for representative CNN models.

Abstract Selective electrochemical oxygen reduction (ORR) toward a two‐electron (2e−) pathway is an eco‐friendly alternative method for H2O2 synthesis to replace the energy‐intensive anthraquinone oxidation process. Carbon‐based electrocatalysts (CBEs) show great potential for practical H2O2 synthesis. However, their complex structures make it challenging to determine the nature of active sites and to precisely control them. Herein, we show that precise modulation of the chemistry and structures of holey graphene with edge sites enriched by oxygen‐containing functional groups can facilitate 2e− ORR. These combined functionalities could improve ORR performance under various pH conditions, for example, resulting in an average of 95% H2O2 selectivity, ~97% Faraday efficiency, high productivity of 2360 mol kgcat−1 h−1 in alkaline media. Density functional theory calculations on the oxygen functional groups at the edge sites revealed the most active site for 2e− ORR is a synergy between ether (COC) and carbonyl (CO) functional groups with nearly zero overpotential.

Liquefied gas electrolytes (LGE) can enable the operation of electrochemical devices in cold conditions but their high vapour pressure poses safety concerns. Here, the authors show that the nano-confinement effect of metal-organic framework allows battery with LGE to work at low temperature and reduced pressure.

Processing-in-memory (PIM) comprises computational logic in the memory domain. It is the most promising solution to alleviate the memory bandwidth problem in deep neural network (DNN) processing. The hybrid memory cube (HMC), a 3D stacked memory structure, can efficiently implement the PIM architecture by maximizing the existing legacy hardware. To accelerate DNN inference, multiple HMCs can be connected, and data-independent tasks can be assigned to processing elements (PEs) within each HMC. However, owing to the packet-switched network structure, inter-HMC interconnects exhibit variable and unpredictable latencies depending on the data transmission path and link contention. A well-designed task schedule using context switching can effectively hide communication latency and improve PE utilization. Nevertheless, as the number of HMC increases, the variability of a wide range of inter-HMC communication latencies causes frequent context switching, degrading overall performance. This paper proposes a DNN task scheduling that can effectively utilize task parallelism by reducing the communication latency variance owing to HMC interconnect characteristics. Task partitions are generated to exploit parallelism while providing inter-HMC traffic within the sustainable link bandwidth. Task-to-HMC mapping is performed to hide the average communication latency of intermediate DNN processing results. A task schedule is generated using retiming to accelerate DNN inference while maximizing resource utilization. The effectiveness of the proposed method was verified through simulations using various realistic DNN applications performed on a ZSim x86-64 simulator. The simulations revealed that DNN processing with the proposed scheduling improved the DNN processing speed by reducing the processing time by 18.19% over conventional methods where each HMC operated independently.

The explosive growth of deep learning (DL)–based artificial intelligence (AI) applications necessitates extraordinary computing capabilities that cannot be achieved using traditional CPU standalone computing. Therefore, the heavy mission-critical DL kernel computing currently relies on a heterogeneous computing (HGC) platform integrated with CPUs, GPUs, and accelerators, as well as substantial data storage elements. However, the metallic electrical interconnection in the existing manycore platform would not be sustainable for handling the massively increasing bandwidth demand of big data driven AI applications. Incorporating an optical network-on-chip (ONoC) for providing ultrahigh bandwidth, we propose a rapid topology generation and core mapping of ONoC (REGO) for energy-efficient HGC multicore architecture. The genetic algorithm (GA)-based REGO utilizes the structural characteristics of the optical router to the fitness function and thus compromises the trade-off between the required throughput, optical signal-to-noise ratio (OSNR), and total energy consumption. Furthermore, the crossover step accelerates the convergence speed by suppressing randomness in the GA, thus significantly reducing excessive running time owing to the NP-hard property. The generated ONoC through REGO demonstrates, on an average, an increase of 63.29 % and 22.80 % in throughput and a decrease of 50.24 % and 9.56 % in energy per bit, in the VGG-16 and VGG-19 compared with the conventional mesh- and torus-topology-based ONoCs, respectively.

Fibers with high torsional strength are of practical interest for artificial muscles, electric generators, actuators, etc. Here, the authors optimize torsional strength by overcoming rheological thresholds of nanocarbon (nanotube/graphene oxide) dopes.

Network-on-chip (NoC) has become the mainstream fabric architecture for chip multiprocessor (CMP) design. Owing to the market-driven advancement of modern applications in CMP, multicast traffic is aggressively increasing to support barrier synchronization, multithreading, and cache coherence protocols. Although multicast by branching of packets in the NoC router facilitates shortest path routing, additional branching-induced deadlocks must be circumvented. Existing NoC studies on deadlock-free minimal path routing in multicast traffic have typically deployed additional virtual channels or large buffers to hold entire packets, thereby significantly increasing the router area. Focusing on the area-efficient solution while sustaining the performance, we propose a novel multicast router using buffer sharing (MRBS) to guarantee deadlock-free multicast routing by exploiting the spatial diversity of the input buffer. MRBS ensures minimal path routing without requiring additional virtual channels or large buffers to hold entire packets. Extensive experiments were conducted by varying the buffer, packet, and network sizes, as well as the number of destinations per packet, under random multicast traffic with diverse injection rates. Simulation results show that MRBS achieves a 39.3 % improvement in the area-delay product on average for various network sizes compared to the conventional tree-based router.

Large-scale production of fibers from two dimensional materials opens a pathway to promising applications. Here the authors report meter-long MXene fibers with high electrical conductivity that are fabricated via continuous wet spinning and demonstrated in electrical wires.

The advances in silicon photonics technology have facilitated the realization of optical network-on-chips (ONoCs) to cope with the physical limitations of metal interconnections in traditional CMOS integrated circuits. As the performance and reliability of optical links are adversely affected by insertion losses and crosstalk, which inevitably occur during the propagation of optical signals, optimizing the power of a laser source requires a sophisticated analysis of the optical signal-to-noise ratio (OSNR). Calculating the worst-case OSNR for all possible communication links in an ONoC is an NP-hard problem even under the assumption of a single-wavelength laser source. Moreover, when expanding the design space by accommodating wavelength-division multiplexing (WDM), semiconductor optical amplifier (SOA), diverse topologies, and the associated router architectures, the computational complexity becomes excessive. Therefore, in this study, we propose an extended worst-case OSNR search algorithm (EWOSA) that significantly reduces the computational burden through a preprocessing algorithm that reduces the number of candidate paths when the combined effect of the insertion loss and crosstalk on the OSNR is considered. Simulation results demonstrate that the EWOSA can identify approximately 0.18 dB lower worst-case OSNR than the existing formal worst-case analysis method in 8 × 8 mesh-based ONoC, and this improvement in OSNR accuracy becomes more apparent (up to 4.81 dB) when SOAs are deployed in ONoCs. Furthermore, the EWOSA can be used for OSNR optimization, owing to its rapid analysis speed and generality.

This study was performed to evaluate the effects of dietary vitamin levels on physiological responses, blood profiles, and reproductive performance in gestating sows. A total of 52 F1 multiparous sows (Yorkshire × Landrace) with an average body weight of 223.5 ± 31.7 kg, an average parity of 6.4 ± 2.7, and an average backfat thickness of 18.5 ± 4.9 mm were divided into four treatment groups considering body weight, backfat thickness, and parity in a completely randomized design with 13 replicates. The treatments were 100% (V1), 300% (V3), 600% (V6) and 900% (V9) of the National Research Council (NRC) Nutrient Requirements of Swine. The gestation diet was formulated based on corn-soybean meal (SBM) and contained 3,265 kcal of metabolizable energy (ME)/kg and 12.00% crude protein. During the lactation period, all sows were fed the same commercial lactation diet. There was no significant difference in body weight of gestating sows. However backfat thickness tended to increase when higher levels of vitamins were provided to gestating sows (p < 0.10). When high levels of dietary vitamins were provided, the body weight change of lactating sows increased (p < 0.01). When sows were fed higher levels of vitamins, the feed intake of lactating sows tended to decrease (p = 0.06). There were no treatment differences in the number of total born, born alive, stillbirth piglets, or the body weight of piglets according to different dietary vitamin level. As dietary vitamin level increased, the serum concentration of 25(OH)D3 in sows at 90 days of gestation linearly increased (p < 0.01). Furthermore, the serum vitamin E level of gestating sows was linearly increased with increasing dietary vitamin level (p < 0.05). The current NRC vitamin requirements are sufficient for gestating sows and higher levels of vitamins in the gestation diet did not show any beneficial effects for gestating and lactating sows.

Graphene oxide sheets have been used as a model system to study how the mechanical properties of individual 2D building blocks scale to their bulk form. Here the authors show that the modulus of multilayer graphene oxide films can be enhanced if some of the sheets are weakened by introducing in-plane porosity.

This experiment was conducted to evaluate the effect of dietary energy and crude protein (CP) levels on growth performance, blood profiles, and carcass traits in growing-finishing pigs. A total of 180 crossbred pigs ([Yorkshire × Landrace] × Duroc) with an average body weight of 30.96 ± 3.068 kg were used for a 12-week feeding trial. Experimental pigs were allotted to a 2 × 3 factorial arrangement using a randomized complete block (RCB) design. The first factor was two levels of dietary metabolizable energy (ME) density (13.40 MJ/kg or 13.82 MJ/kg), and the second factor was three dietary CP levels based on subdivision of growing-finishing phases (high: 18%/16.3%/16.3%/13.2% middle: 17%/15.3%/15.3%/12.2% and low: 16%/14.3%/14.3%/11.2%). Average daily gain (ADG) and gain-feed ratio (G:F ratio) decreased as dietary CP level was decreased linearly (linear, p < 0.05; p < 0.05, respectively) in the early growing period, and G:F ration also decreased as dietary CP level was decreased linearly (linearly, p < 0.05) over the whole growing phase. Over the entire experimental period, G:F ratio decreased as dietary ME level decreased (p = 0.01). Blood urea nitrogen (BUN) concentration was increased as dietary energy level decreased in growing period (p < 0.01). During finishing period, total protein concentration was decreased by lower dietary energy level (p < 0.05). In this study, there were no significant differences in proximate factors, physiochemical properties, muscle TBARS assay results, pH changes, or color of pork by dietary treatments. However, saturated fatty acid (SFA) increased (p < 0.01) and polyunsaturated fatty acid (PUFA) decreased (p < 0.05) when ME was decreased by 0.42 MJ/kg in growing-finishing pig diets. In addition, monounsaturated fatty acid (MUFA) tended to increase when CP level was decreased in growing-finishing pig diets (p = 0.06). A growing-finishing diet of 13.82 MJ/kg diet of ME with the high CP level can improve growth performance and show better fatty acids composition of pork.

Objective This experiment was conducted to investigate the effect of reducing dietary metabolic energy (ME) and crude protein (CP) levels on growth performance, blood profiles, and nutrient digestibility in weaning pigs. Methods A total of 240 crossbred pigs (Duroc×[Landrace×Yorkshire]) with an average body weight of 8.67±1.13 kg were used for a 6-week feeding trial. Experimental pigs were allotted to a 2×3 factorial arrangement using a randomized complete block design. The first factor was two levels of dietary ME density (low ME level, 13.40 MJ/kg or high ME level, 13.82 MJ/kg) and the second factor was three dietary CP levels based on subdivision of early and late weaning phases (low CP level, 19.7%/16.9%; middle CP level, 21.7%/18.9%; or high CP level, 23.7%/20.9%). Results Over the entire experimental period, there were no significant difference in body weight among groups, but a decrease in diet energy level was associated with an increase in average daily feed intake (p = 0.02) and decrease in gain-feed ratio (G:F) ratio (p

Objective This study was conducted to evaluate various wheat supplementation levels on growth performance, blood profiles, nutrient digestibility, and pork quality in growing-finishing pigs. Methods A total of 120 growing pigs ([Yorkshire×Landrace]×Duroc), with an average 27.75± 1.319 kg body weight, were used in growth trial. Pigs were allotted into each treatment by body weight and sex in 4 replicates with 6 pigs per pen in a randomized complete block design. Four-phase feeding programs were used in this experiment. The treatments included the following: i) corn-soybean meal (SBM) – based diet (CON), ii) corn-SBM – based diet+15% of wheat (W15), iii) corn-SBM – based diet+30% of wheat (W30), iv) corn-SBM – based diet+45% of wheat (W45), and 5) corn-SBM–based diet+60% of wheat (W60). Results There was no significant difference in growth performance among the dietary treatments. However, the gain-to-feed (G:F) ratio tended to increase (quadratic, p

As the main symptom of Alzheimer’s disease-related dementia is memory loss, patient compliance for donepezil hydrochloride (donepezil), administered as once-daily oral formulations, is poor. Thus, we aimed to design poly(lactic-co-glycolic acid) (PLGA) microspheres (MS) with alginate-coated large pores as an injectable depot of donepezil exhibiting sustained release over 2–3 weeks. The PLGA MS with large pores could provide large space for loading drugs with high loading capacity, and thereby sufficient amounts of drugs were considered to be delivered with minimal use of PLGA MS being injected. However, initial burst release of donepezil from the porous PLGA MS was observed. To reduce this initial burst release, the surface pores were closed with calcium alginate coating using a spray-ionotropic gelation method. The final pore-closed PLGA MS showed in vitro sustained release for approximately 3 weeks, and the initial burst release was remarkably decreased by the calcium alginate coating. In the prediction of plasma drug concentration profiles using convolution method, the mean residence time of the pore-closed PLGA MS was 2.7-fold longer than that of the porous PLGA MS. Therefore, our results reveal that our pore-closed PLGA MS formulation is a promising candidate for the treatment of dementia with high patient compliance.

Assembly, maintenance and function of synaptic junctions depend on extracellular matrix (ECM) proteins and their receptors. Here we report that Tenectin (Tnc), a Mucin-type protein with RGD motifs, is an ECM component required for the structural and functional integrity of synaptic specializations at the neuromuscular junction (NMJ) in Drosophila. Using genetics, biochemistry, electrophysiology, histology and electron microscopy, we show that Tnc is secreted from motor neurons and striated muscles and accumulates in the synaptic cleft. Tnc selectively recruits αPS2/βPS integrin at synaptic terminals, but only the cis Tnc/integrin complexes appear to be biologically active. These complexes have distinct pre- and postsynaptic functions, mediated at least in part through the local engagement of the spectrin-based membrane skeleton: the presynaptic complexes control neurotransmitter release, while postsynaptic complexes ensure the size and architectural integrity of synaptic boutons. Our study reveals an unprecedented role for integrin in the synaptic recruitment of spectrin-based membrane skeleton.

[This corrects the article DOI: 10.1371/journal.pgen.1005810.].

To address the performance bottleneck in metal-based interconnects, hybrid optical network-on-chip (HONoC) has emerged as a new alternative. However, as the size of the HONoC grows, insertion loss and crosstalk noise increase, leading to excessive laser source output power and performance degradation. Therefore, we propose a low-power scalable HONoC architecture by incorporating semiconductor optical amplifiers (SOAs). An SOA placement algorithm is developed considering insertion loss and crosstalk noise. Furthermore, we establish a worst-case crosstalk noise model of SOA-enabled HONoC and induce optimized SOA gains with respect to power consumption and performance, respectively. Extensive simulations for worst-case signal-to-noise ratio (SNR) and power consumption are conducted under various traffic patterns and different network sizes. Simulation results show that the proposed SOA-enabled HONoC architecture and the associated algorithm help sustain the performance as network size increases without additional laser source power.

We calculated the band structures of a variety of N- and S-doped graphenes in order to understand the effects of the N and S dopants on the graphene electronic structure using density functional theory (DFT). Band-structure analysis revealed energy band upshifting above the Fermi level compared to pristine graphene following doping with three nitrogen atoms around a mono-vacancy defect, which corresponds to p-type nature. On the other hand, the energy bands were increasingly shifted downward below the Fermi level with increasing numbers of S atoms in N/S-co-doped graphene, which results in n-type behavior. Hence, modulating the structure of graphene through N- and S-doping schemes results in the switching of “p-type„ to “n-type„ behavior with increasing S concentration. Mulliken population analysis indicates that the N atom doped near a mono-vacancy is negatively charged due to its higher electronegativity compared to C, whereas the S atom doped near a mono-vacancy is positively charged due to its similar electronegativity to C and its additional valence electrons. As a result, doping with N and S significantly influences the unique electronic properties of graphene. Due to their tunable band-structure properties, the resulting N- and S-doped graphenes can be used in energy and electronic-device applications. In conclusion, we expect that doping with N and S will lead to new pathways for tailoring and enhancing the electronic properties of graphene at the atomic level.

At the Drosophila NMJ, BMP signaling is critical for synapse growth and homeostasis. Signaling by the BMP7 homolog, Gbb, in motor neurons triggers a canonical pathway-which modulates transcription of BMP target genes, and a noncanonical pathway-which connects local BMP/BMP receptor complexes with the cytoskeleton. Here we describe a novel noncanonical BMP pathway characterized by the accumulation of the pathway effector, the phosphorylated Smad (pMad), at synaptic sites. Using genetic epistasis, histology, super resolution microscopy, and electrophysiology approaches we demonstrate that this novel pathway is genetically distinguishable from all other known BMP signaling cascades. This novel pathway does not require Gbb, but depends on presynaptic BMP receptors and specific postsynaptic glutamate receptor subtypes, the type-A receptors. Synaptic pMad is coordinated to BMP's role in the transcriptional control of target genes by shared pathway components, but it has no role in the regulation of NMJ growth. Instead, selective disruption of presynaptic pMad accumulation reduces the postsynaptic levels of type-A receptors, revealing a positive feedback loop which appears to function to stabilize active type-A receptors at synaptic sites. Thus, BMP pathway may monitor synapse activity then function to adjust synapse growth and maturation during development.

In South Korea, WU polyomavirus (WUPyV) was detected in 34 (7%) of 486 children with acute lower respiratory tract infections, 3 (4.2%) of 72 asymptomatic children, and as coinfection with other respiratory viruses in 23 (67.6%) children. Although WUPyV was frequently detected, its clinical role has not been distinguished from that of coinfecting viruses.

This study presents the first evidence of human bocavirus infection in South Korean children. The virus was detected in 27 (8.0%) of 336 tested specimens, including 17 (7.5%) of 225 virus-negative specimens, collected from children with acute lower respiratory tract infection.

Niemann Pick disease type C1 (NPC) is an autosomal recessive disease characterized by progressive neurological deterioration leading to premature death. In this study, we hypothesized that human umbilical cord blood-derived mesenchymal stem cells (hUCB-MSCs) have the multifunctional abilities to ameliorate NPC symptoms in the brain. To test this hypothesis, hUCB-MSCs were transplanted into the hippocampus of NPC mice in the early asymptomatic stage. This transplantation resulted in the recovery of motor function in the Rota Rod test and impaired cholesterol homeostasis leading to increased levels of cholesterol efflux-related genes such as LXRα, ABCA1, and ABCG5 while decreased levels of 3-hydroxy-3-methylglutaryl coenzyme A reductase were observed in NPC mice. In the cerebrum, hUCB-MSCs enhanced neuronal cell survival and proliferation, where they directly differentiated into electrically active MAP2-positive neurons as demonstrated by whole-cell patch clamping. In addition, we observed that hUCB-MSCs reduced Purkinje neuronal loss by suppression of inflammatory and apoptotic signaling in the cerebellum as shown by immunohistochemistry. We further investigated how hUCB-MSCs enhance cellular survival and inhibit apoptosis in NPC mice. Neuronal cell survival was associated with increased PI3K/AKT and JAK2/STAT3 signaling; moreover, hUCB-MSCs modulated the levels of GABA/glutamate transporters such as GAT1, EAAT2, EAAT3, and GAD6 in NPC mice as assessed by Western blot analysis. Taken together, our findings suggest that hUCB-MSCs might play multifunctional roles in neuronal cell survival and ameliorating motor deficits of NPC mice.

BackgroundTo clarify the role of the POU domain transcription factor Oct4 in Adipose Tissue Stromal Cells (ATSCs), we investigated the regulation of Oct4 expression and other embryonic genes in fully differentiated cells, in addition to identifying expression at the gene and protein levels. The ATSCs and several immature cells were routinely expressing Oct4 protein before and after differentiating into specific lineages.Methodology/principal findings and conclusionsHere, we demonstrated the role of Oct4 in ATSCs on cell proliferation and differentiation. Exogenous Oct4 improves adult ATSCs cell proliferation and differentiation potencies through epigenetic reprogramming of stemness genes such as Oct4, Nanog, Sox2, and Rex1. Oct4 directly or indirectly induces ATSCs reprogramming along with the activation of JAK/STAT3 and ERK1/2. Exogenic Oct4 introduced a transdifferentiation priority into the neural lineage than mesodermal lineage. Global gene expression analysis results showed that Oct4 regulated target genes which could be characterized as differentially regulated genes such as pluripotency markers NANOG, SOX2, and KLF4 and markers of undifferentiated stem cells FOXD1, CDC2, and EPHB1. The negatively regulated genes included FAS, TNFR, COL6A1, JAM2, FOXQ1, FOXO1, NESTIN, SMAD3, SLIT3, DKK1, WNT5A, BMP1, and GLIS3 which are implicated in differentiation processes as well as a number of novel genes. Finally we have demonstrated the therapeutic utility of Oct4/ATSCs were introduced into the mouse traumatic brain, engrafted cells was more effectively induces regeneration activity with high therapeutic modality than that of control ATSCs. Engrafted Oct4/ATSCs efficiently migrated and transdifferentiated into action potential carrying, functionally neurons in the hippocampus and promoting the amelioration of lesion cavities.