Bone marrow necrosis: Facts, controversies, and perspective.

Bone marrow necrosis (BMN) is a clinically and pathologically poorly-defined and readily-overlooked entity. The current facts and guidelines pertaining to this entity are scarce, and there exist controversies. Upon reviewing the literature, we present the facts, analyze these controversies, and discourse on future prospects.

miR-769-3p inhibits cellular proliferation of KSHV-infected SH-SY5Y cells through targeting mTOR.

The infection by Kaposi's sarcoma-associated herpesvirus (KSHV) is one of the most common causes of death in AIDS patients. Our studies have found that KSHV can infect SH-SY5Y cells (named SK-RG) in vivo and mTOR was up-regulated, which results in remarkable enhancement of cell proliferation, migration. But the regulatory role of mTOR in KSHV infected neurons has not yet been fully elucidated. Here, we find that miR-769-3p is decreased in SK-RG cells, which can exert anti-KSHV effect through negatively regulating the expression of mTOR. The knockdown of mTOR or overexpress of miR-769-3p decreased the proliferation, migration ability and cell cycle related protein of SK-RG cells, and the expression of KSHV related genes. In contrast, activating mTOR function by 3BDO treatment weakened the cellular behaviors of miR-769-3p overexpressing cells. Meanwhile, overexpressed miR-769-3p and rapamycin showed a shared inhibition trend in the effects on cell proliferation and motility. Our data indicated that miR-769-3p can inhibit cell proliferation and migration by down regulating mTOR in KSHV infected SH-SY5Y cells, and can be a candidate molecule for anti-KSHV therapy.

A Hybrid Scaffold Induces Chondrogenic Differentiation and Enhances In Vivo Cartilage Regeneration.

Extensively researched tissue engineering strategies involve incorporating cells into suitable biomaterials, offering promising alternatives to boost tissue repair. In this study, a hybrid scaffold, Gel-DCM, which integrates a photoreactive gelatin-hyaluronic acid hydrogel (Gel) with an oriented porous decellularized cartilage matrix (DCM), was designed to facilitate chondrogenic differentiation and cartilage repair. The Gel-DCM exhibited excellent biocompatibility in vitro, promoting favorable survival and growth of human adipose-derived stem cells (hADSCs) and articular chondrocytes (hACs). Gene expression analysis indicated that the hACs expanded within the Gel-DCM exhibited enhanced chondrogenic phenotype. In addition, Gel-DCM promoted chondrogenesis of hADSCs without the supplementation of exogenous growth factors. Following this, in vivo experiments were conducted where empty Gel-DCM or Gel-DCM loaded with hACs/hADSCs were used and implanted to repair osteochondral defects in a rat model. In the control group, no implants were delivered to the injury site. Interestingly, macroscopic, histological, and microcomputed tomography scanning results revealed superior cartilage restoration and subchondral bone reconstruction in the empty Gel-DCM group compared with the control group. Moreover, both hACs-loaded and hADSCs-loaded Gel-DCM implants exhibited superior repair of hyaline cartilage and successful reconstruction of subchondral bone, whereas defects in the control groups were predominantly filled with fibrous tissue. These observations suggest that the Gel-DCM can provide an appropriate three-dimensional chondrogenic microenvironment, and its combination with reparative cell sources, ACs or ADSCs, holds great potential for facilitating cartilage regeneration.

[Effects of Platelet-Rich Plasma-Derived Exosomes on Proliferation and Migration of Tendon Stem/Progenitor Cell].

Objective To investigate the effects of platelet-rich plasma-derived exosomes (PRP-Exos) on the proliferation and migration of tendon stem/progenitor cell (TSPC).Methods PRP-Exos were extracted through the combination of polymer-based precipitation and ultracentrifugation.The morphology,concentration,and particle size of PRP-Exos were identified by transmission electron microscopy and nanoparticle tracking analysis.The expression levels of surface marker proteins on PRP-Exos and platelet membrane glycoproteins were determined by Western blot analysis.Rat TSPC was extracted and cultured,and the expression of surface marker molecules on TSPC was detected using flow cytometry and immunofluorescence staining.The proliferation of TSPC influenced by PRP-Exos was evaluated using CCK-8 assay and EdU assay.The effect of PRP-Exos on the migration of TSPC was evaluated by cell scratch assay and Transwell assay.Results The extracted PRP-Exos exhibit typical saucer-like structures,with a concentration of 4.9×1011 particles/mL,an average particle size of (132.2±56.8) nm,and surface expression of CD9,CD63 and CD41.The extracted TSPC expressed the CD44 protein.PRP-Exos can be taken up by TSPC,and after co-cultured for 48 h,concentrations of 50 and 100 µg/mL of PRP-Exos significantly promoted the proliferation of TSPC (both P<0.001),with no statistical difference between the two concentrations (P=0.283).Additionally,after co-cultured for 24 h,50 µg/mL of PRP-Exos significantly promoted the migration of TSPC (P<0.001).Conclusion Under in vitro culture conditions,PRP-Exos significantly promote the proliferation and migration of rat TSPC.

In-situ gelation of fibrin gel encapsulating platelet-rich plasma-derived exosomes promotes rotator cuff healing.

Although platelet-rich plasma-derived exosomes (PRP-Exos) hold significant repair potential, their efficacy in treating rotator cuff tear (RCT) remains unknown. In light of the potential for clinical translation of fibrin gel and PRP-Exos, we evaluated their combined impact on RCT healing and explored suitable gel implantation techniques. In vitro experiments demonstrated that PRP-Exos effectively enhanced key phenotypes changes in tendon stem/progenitor cells. Multi-modality imaging, including conventional ultrasound, shear wave elastography ultrasound, and micro-computed tomography, and histopathological assessments were performed to collectively evaluate the regenerative effects on RCT. The regenerated tendons exhibited a well-ordered structure, while bone and cartilage regeneration were significantly improved. PRP-Exos participated in the healing process of RCT. In-situ gelation of fibrin gel-encapsulated PRP-Exos at the bone-tendon interface during surgery proved to be a feasible gel implantation method that benefits the healing outcome. Comprehensive multi-modality postoperative evaluations were necessary, providing a reliable foundation for post-injury repair.

An Essential Role of c-Fos in Notch1-mediated Promotion of Proliferation of KSHV-Infected SH-SY5Y Cells.

BACKGROUND: This study aimed to investigate the influence of Notch1 on c-Fos and the effect of c-Fos on the proliferation of Kaposi's sarcoma-associated herpesvirus (KSHV)-infected neuronal cells. METHODS: Real-time PCR and western blotting were used to determine c-Fos expression levels in KSHV-infected (SK-RG) and uninfected SH-SY5Y cells. C-Fos levels were measured again in SK-RG cells with or without Notch1 knockdown. Next, we measured c-Fos and p-c-Fos concentrations after treatment with the Notch1 γ-secretase inhibitor LY-411575 and the Notch1 activator Jagged-1. MTT and Ki-67 staining were used to evaluate the proliferation ability of cells after c-Fos levels downregulation. CyclinD1, CDK6, and CDK4 expression levels and cell cycle were analyzed by western blotting and flow cytometry, respectively. After the c-Fos intervention, the KSHV copy number and gene expression of RTA, LANA and K8.1 were analyzed by real-time TaqMan PCR. RESULTS: C-Fos was up-regulated in KSHV-infected SK-RG cells. However, the siRNA-mediated knockdown of Notch1 resulted in a significant decrease in the levels of c-Fos and p-c-Fos (P <0.01, P <0.001). Additionally, a decrease in Cyclin D1, CDK6, and CDK4 was also detected. The Notch1 inhibitor LY-411575 showed the potential to down-regulate the levels of c-Fos and p-c-Fos, which was consistent with Notch1 knockdown group (P <0.01), whereas the expression and phosphorylation of c-Fos were remarkably up-regulated by treatment of Notch1 activator Jagged-1 (P <0.05). In addition, our data obtained by MTT and Ki-67 staining revealed that the c-Fos down-regulation led to a significant reduction in cell viability and proliferation of the SK-RG cells (P <0.001). Moreover, FACS analysis showed that the cell cycle was arrested in the G0/G1 stage, and the expressions of Cyclin D1, CDK6, and CDK4 were down-regulated in the c-Fos-knockdown SK-RG cells (P <0.05). Reduction in total KSHV copy number and expressions of viral genes (RTA, LANA and K8.1) were also detected in c-Fos down-regulated SK-RG cells (P <0.05). CONCLUSION: Our findings strongly indicate that c-Fos plays a crucial role in the promotion of cell proliferation through Notch1 signaling in KSHV-infected cells. Furthermore, our results suggest that the inhibition of expression of key viral pathogenic proteins is likely involved in this process.

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization.

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency. Model compression has been proposed as a promising technique to improve the running efficiency via parameter and operation reduction, whereas this technique is mainly practiced in ANNs rather than SNNs. It is interesting to answer how much an SNN model can be compressed without compromising its functionality, where two challenges should be addressed: 1) the accuracy of SNNs is usually sensitive to model compression, which requires an accurate compression methodology and 2) the computation of SNNs is event-driven rather than static, which produces an extra compression dimension on dynamic spikes. To this end, we realize a comprehensive SNN compression through three steps. First, we formulate the connection pruning and weight quantization as a constrained optimization problem. Second, we combine spatiotemporal backpropagation (STBP) and alternating direction method of multipliers (ADMMs) to solve the problem with minimum accuracy loss. Third, we further propose activity regularization to reduce the spike events for fewer active operations. These methods can be applied in either a single way for moderate compression or a joint way for aggressive compression. We define several quantitative metrics to evaluate the compression performance for SNNs. Our methodology is validated in pattern recognition tasks over MNIST, N-MNIST, CIFAR10, and CIFAR100 datasets, where extensive comparisons, analyses, and insights are provided. To the best of our knowledge, this is the first work that studies SNN compression in a comprehensive manner by exploiting all compressible components and achieves better results.

Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient.

Spiking neural network (SNN) is broadly deployed in neuromorphic devices to emulate brain function. In this context, SNN security becomes important while lacking in-depth investigation. To this end, we target the adversarial attack against SNNs and identify several challenges distinct from the artificial neural network (ANN) attack: 1) current adversarial attack is mainly based on gradient information that presents in a spatiotemporal pattern in SNNs, hard to obtain with conventional backpropagation algorithms; 2) the continuous gradient of the input is incompatible with the binary spiking input during gradient accumulation, hindering the generation of spike-based adversarial examples; and 3) the input gradient can be all-zeros (i.e., vanishing) sometimes due to the zero-dominant derivative of the firing function. Recently, backpropagation through time (BPTT)-inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given spatiotemporal gradient maps. We propose two approaches to address the above challenges of gradient-input incompatibility and gradient vanishing. Specifically, we design a gradient-to-spike (G2S) converter to convert continuous gradients to ternary ones compatible with spike inputs. Then, we design a restricted spike flipper (RSF) to construct ternary gradients that can randomly flip the spike inputs with a controllable turnover rate, when meeting all-zero gradients. Putting these methods together, we build an adversarial attack methodology for SNNs. Moreover, we analyze the influence of the training loss function and the firing threshold of the penultimate layer on the attack effectiveness. Extensive experiments are conducted to validate our solution. Besides the quantitative analysis of the influence factors, we also compare SNNs and ANNs against adversarial attacks under different attack methods. This work can help reveal what happens in SNN attacks and might stimulate more research on the security of SNN models and neuromorphic devices.

Effective and Efficient Batch Normalization Using a Few Uncorrelated Data for Statistics Estimation.

Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations that are hard to be executed in parallel, which heavily reduces the training speed. To address this issue, in this article, we propose a methodology to alleviate the BN's cost by using only a few sampled or generated data for mean and variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation in sampling while the efficiency expects more regular execution patterns. To this end, we design two categories of approach: sampling or creating a few uncorrelated data for statistics' estimation with certain strategy constraints. The former includes "batch sampling (BS)" that randomly selects a few samples from each batch and "feature sampling (FS)" that randomly selects a small patch from each feature map of all samples, and the latter is "virtual data set normalization (VDN)" that generates a few synthetic random samples to directly create uncorrelated data for statistics' estimation. Accordingly, multiway strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The proposed methods are comprehensively evaluated on various DNN models, where the loss of model accuracy and the convergence rate are negligible. Without the support of any specialized libraries, 1.98× BN layer acceleration and 23.2% overall training speedup can be practically achieved on modern GPUs. Furthermore, our methods demonstrate powerful performance when solving the well-known "micro-BN" problem in the case of a tiny batch size. This article provides a promising solution for the efficient training of high-performance DNNs.

Ultrahigh Performance of n-Type Ag2Se Films for Flexible Thermoelectric Power Generators.

Due to the limited thermoelectric (TE) performance of conducting polymers and rigidity of inorganic materials, it is still a huge challenge to prepare low-cost, highly flexible, and high-performance TE materials. Herein, we fabricated n-type Ag2Se films using a porous nylon membrane as a flexible substrate by vacuum-assisted filtration, followed by hot pressing. A very high power factor of ∼1882 µW m-1 K-2 at room temperature is obtained. The high power factor is mainly the result of the high density of the Ag2Se film and the tuned grain orientation, which is realized by the synthesis of multisized Ag2Se nanostructures. The film also exhibits excellent flexibility with 90.7% retention of the power factor after bending around a rod of 4 mm radius for 1000 times. A four-leg TE generator is assembled with the Ag2Se film, and its maximum output power is up to 3.2 µW at a temperature difference of 30 K, corresponding to the maximum power density of 22.0 W m-2 and a normalized maximum power density of 408 µW m-1 K-2. This work provides an effective route to achieve high-power-factor, high-flexibility, and low-cost TE films.

Domain-adversarial multi-task framework for novel therapeutic property prediction of compounds.

MOTIVATION: With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides significant opportunities to improve pharmaceutical research and development. One important application is the purpose prediction of small-molecule compounds with the objective of specifying the therapeutic properties of extensive purpose-unknown compounds and repurposing the novel therapeutic properties of FDA-approved drugs. Such a problem is extremely challenging because compound attributes include heterogeneous data with various feature patterns, such as drug fingerprints, drug physicochemical properties and drug perturbation gene expressions. Moreover, there is a complex non-linear dependency among heterogeneous data. In this study, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework first uses an adversarial strategy to learn target representations and then models non-linear dependency among several domains. RESULTS: Experiments on two real-world datasets illustrate that our approach achieves an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds that we predicted have been widely reported or brought to clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined herein and can be applied in industry for screening significant numbers of small-molecule drug candidates. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/JohnnyY8/DAMT-Model. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rethinking the performance comparison between SNNS and ANNS.

Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced remarkable success via mature models, various benchmarks, open-source datasets, and powerful computing platforms. Spiking neural networks (SNNs), a category of promising models to mimic the neuronal dynamics of the brain, have gained much attention for brain inspired computing and been widely deployed on neuromorphic devices. However, for a long time, there are ongoing debates and skepticisms about the value of SNNs in practical applications. Except for the low power attribute benefit from the spike-driven processing, SNNs usually perform worse than ANNs especially in terms of the application accuracy. Recently, researchers attempt to address this issue by borrowing learning methodologies from ANNs, such as backpropagation, to train high-accuracy SNN models. The rapid progress in this domain continuously produces amazing results with ever-increasing network size, whose growing path seems similar to the development of deep learning. Although these ways endow SNNs the capability to approach the accuracy of ANNs, the natural superiorities of SNNs and the way to outperform ANNs are potentially lost due to the use of ANN-oriented workloads and simplistic evaluation metrics. In this paper, we take the visual recognition task as a case study to answer the questions of "what workloads are ideal for SNNs and how to evaluate SNNs makes sense". We design a series of contrast tests using different types of datasets (ANN-oriented and SNN-oriented), diverse processing models, signal conversion methods, and learning algorithms. We propose comprehensive metrics on the application accuracy and the cost of memory & compute to evaluate these models, and conduct extensive experiments. We evidence the fact that on ANN-oriented workloads, SNNs fail to beat their ANN counterparts; while on SNN-oriented workloads, SNNs can fully perform better. We further demonstrate that in SNNs there exists a trade-off between the application accuracy and the execution cost, which will be affected by the simulation time window and firing threshold. Based on these abundant analyses, we recommend the most suitable model for each scenario. To the best of our knowledge, this is the first work using systematical comparisons to explicitly reveal that the straightforward workload porting from ANNs to SNNs is unwise although many works are doing so and a comprehensive evaluation indeed matters. Finally, we highlight the urgent need to build a benchmarking framework for SNNs with broader tasks, datasets, and metrics.

High Performance and Flexible Polyvinylpyrrolidone/Ag/Ag2Te Ternary Composite Film for Thermoelectric Power Generator.

In this work, polyvinylpyrrolidone (PVP) coated Ag-rich Ag2Te nanowires (NWs) were synthesized by a wet chemical method using PVP coated Te NWs as templates, and a flexible PVP/Ag/Ag2Te ternary composite film on a nylon membrane was prepared by vacuum assisted filtration, followed by heat treatment. TEM and STEM observations of the focused ion beam prepared sample reveal that the composite film shows a porous network-like structure and that the Ag and Ag2Te exist as nanoparticles and NWs, respectively, both bonded with PVP. The Ag nanoparticles are formed by separation of the Ag-rich Ag2Te NWs during the heat treatment. For the composite film starting from a Ag/Te initial molar ratio of 6:1, a high power factor of 216.5 µW/mK2 is achieved at 300 K, and it increases to 370.1 µW/mK2 at 393 K. Bending tests demonstrate excellent flexibility of the hybrid film. A thermoelectric (TE) prototype composed of five legs of the hybrid film is assembled, and a maximum output power of 469 nW is obtained at a temperature gradient of 39.6 K, corresponding to a maximum power density of 341 µW/cm2. This work provides an effective route to a composite film with high TE performance and excellent flexibility for wearable TE generators.

Good Performance and Flexible PEDOT:PSS/Cu2Se Nanowire Thermoelectric Composite Films.

Herein, poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) coated Cu xSe y (PC-Cu xSe y) nanowires are prepared by a wet-chemical method, and PEDOT:PSS/Cu xSe y nanocomposite films on flexible nylon membrane are fabricated by vacuum assisted filtration and then cold-pressing. XRD analysis reveals that the Cu xSe y with different compositions can be obtained by adjusting the nominal Cu/Se molar ratios of their sources. For the composite film starting from a Cu/Se nominal molar ratio of 3, an optimized power factor of ∼270.3 µW/mK2 is obtained at 300 K. Moreover, the film exhibits a superior flexibility with 85% of the original power factor retention after bending for 1000 cycles around a rod with a diameter of 5 mm. TEM and STEM observations of the focused ion beam (FIB) prepared sample reveal that it is mainly attributed to a synergetic effect of the nylon membrane and the composite film with nanoporous structure formed by the intertwined nanowires, besides the intrinsic flexibility of nylon. Finally, a thermoelectric prototype composed of nine legs of the optimized hybrid film generates a voltage and a maximum power of 15 mV and 320 nW, respectively, at a temperature gradient of 30 K. This work offers an effective approach for high TE performance inorganic/polymer composite film for flexible TE devices.

High performance n-type Ag2Se film on nylon membrane for flexible thermoelectric power generator.

Researches on flexible thermoelectric materials usually focus on conducting polymers and conducting polymer-based composites; however, it is a great challenge to obtain high thermoelectric properties comparable to inorganic counterparts. Here, we report an n-type Ag2Se film on flexible nylon membrane with an ultrahigh power factor ~987.4 ± 104.1 µWm-1K-2 at 300 K and an excellent flexibility (93% of the original electrical conductivity retention after 1000 bending cycles around a 8-mm diameter rod). The flexibility is attributed to a synergetic effect of the nylon membrane and the Ag2Se film intertwined with numerous high-aspect-ratio Ag2Se grains. A thermoelectric prototype composed of 4-leg of the Ag2Se film generates a voltage and a maximum power of 18 mV and 460 nW, respectively, at a temperature difference of 30 K. This work opens opportunities of searching for high performance thermoelectric film for flexible thermoelectric devices.