Data for the gene expression profiling and alternative splicing events during the chondrogenic differentiation of human cartilage endplate-derived stem cells under hypoxia

AbstractThis article contains relevant data of the research article titled Global profiling of the gene expression and alternative splicing events during the hypoxia-regulated chondrogenic differentiation in human cartilage endplate-derived stem cells (Yao et al., 2016) [1]. The data show global profiling of the DEGs (Differentially expressed genes) and AS (Alternative splicing) events during the hypoxia-regulated chondrogenesis of CESCs (human cartilage endplate-derived stem cells) by using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) system. In addition, the enriched GO (Gene Ontology) functions and signaling pathways are listed. The information presented here includes the information of patients from which the clinical samples are obtained, the list of primers used for validation, the identification, GO and KEGG analysis of DEG and AS events

Effects of the combination of loxoprofen sodium and sodium hyaluronate on osteoarthritis and knee function

Purpose: To determine the treatment efficacy of the combination of loxoprofen sodium and sodium hyaluronate in osteoarthritis (OA), and its role in knee joint function. Methods: 98 patients with OA admitted to Guang'an People's Hospital, Sichuan, China were allocated into control group (CNG, given loxoprofen sodium n, = 51) and study group (SG, given loxoprofen sodium and sodium hyaluronate, n = 47). Both groups were compared in terms of the levels of inflammatory factor, Lysholm, VAS, WOMAC scores, treatment effects, serum MDA, NO, SOD levels, adverse effects, and blood rheology indices. Results: The study group had higher SOD levels, and higher BALP and BGP than CNG (p < 0.05). SG had lower TRACP-5b and blood rheological indices than CNG (p < 0.05). The difference in the incidence of adverse reactions was not statistically significant between the two groups (p > 0.05). Conclusion: The combination of loxoprofen sodium and sodium hyaluronate effectively improves the function and blood rheological indices of knee joints. It reduces the occurrence of adverse reactions and the level of pain in patients with OA, and improves OA prognosis. However further clinical trials are required prior to application in clinical practice

A shared neural ensemble links distinct contextual memories encoded close in time.

Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information

Dopamine and Serotonin Modulate Free Amino Acids Production and Na+/K+ Pump Activity in Chinese Mitten Crab Eriocheir sinensis Under Acute Salinity Stress

The Chinese mitten crab Eriocheir sinensis lives in saline or fresh water during different life stages and exhibits a complex life history, making it an ideal model to study the salinity adaptation of euryhaline animals. In this study, RNA-seq techniques, and determinations of free amino acids (FAAs), monoamine neurotransmitters, and Na+/K+ pump activity, were employed to understand the osmoregulatory mechanism in Chinese mitten crab. A total of 15,138 differentially expressed genes were obtained from 12 transcriptome libraries. GO enrichment analysis revealed that the mRNA expression profiles were completely remodeled from 12 to 24 h after salinity stress. The neuroendocrine system was activated under stimulation, and the monoamine neurotransmitters including dopamine (DA) and serotonin (5-HT) were released to modulate osmoregulation. Furthermore, the Na+/K+ pump in crab hemocytes was significantly inhibited post salinity stress, resulting in increased intracellular ion concentrations and osmotic pressure to sustain the osmotic balance. Moreover, six key FAAs, including alanine (Ala), proline (Pro), glycine (Gly), glutamate (Glu), arginine (Arg), and aspartate (Asp), were overexpressed to modulate the extracellular osmotic balance during salinity adaptation. Interestingly, the immune genes were not enriched in the GO analysis, implying that the immune system might not contribute fundamentally to the tolerance upon fluctuating ambient salinity in the Chinese mitten crab. These results collectively demonstrated that the Chinese mitten crab had evolved an efficient regulation mechanism by modulating the FAAs production and Na+/K+ pump activity to sustain the osmotic balance independent of the immune system, in which the neuroendocrine modulation, especially generated by the monoamine neurotransmitter, played an indispensable role

The combination of body mass index and fasting plasma glucose is associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis

BackgroundBody mass index (BMI) and fasting plasma glucose (FPG) are known risk factors for type 2 diabetes mellitus (T2DM), but data on the prospective association of the combination of BMI and FPG with T2DM are limited. This study sought to characterize the association of the combination of BMI and FPG (ByG) with T2DM.MethodsThe current study used the NAGALA database. We categorized participants by tertiles of ByG. The association of ByG with T2DM was expressed with hazard ratios (HRs) with 95% confidence intervals (CIs) after adjustment for potential risk factors.ResultsDuring a median follow-up of 6.19 years in the normoglycemia cohort and 5.58 years in the prediabetes cohort, the incidence of T2DM was 0.75% and 7.79%, respectively. Following multivariable adjustments, there were stepwise increases in T2DM with increasing tertiles of ByG. After a similar multivariable adjustment, the risk of T2DM was 2.57 (95% CI 2.26 - 2.92), 1.97 (95% CI 1.53 - 2.54) and 1.50 (95% CI 1.30 - 1.74) for a per-SD change in ByG in all populations, the normoglycemia cohort and the prediabetes cohort, respectively.ConclusionByG was associated with an increased risk of T2DM in Japan. The result reinforced the importance of the combination of BMI and FPG in assessing T2DM risk

MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods.Comment: 10 pages, 2 figures, accepted as the conference paper of Proceedings of the 27th ACM International Conference on Multimedia (MM'19

ReluDiff: Differential Verification of Deep Neural Networks

As deep neural networks are increasingly being deployed in practice, their efficiency has become an important issue. While there are compression techniques for reducing the network's size, energy consumption and computational requirement, they only demonstrate empirically that there is no loss of accuracy, but lack formal guarantees of the compressed network, e.g., in the presence of adversarial examples. Existing verification techniques such as Reluplex, ReluVal, and DeepPoly provide formal guarantees, but they are designed for analyzing a single network instead of the relationship between two networks. To fill the gap, we develop a new method for differential verification of two closely related networks. Our method consists of a fast but approximate forward interval analysis pass followed by a backward pass that iteratively refines the approximation until the desired property is verified. We have two main innovations. During the forward pass, we exploit structural and behavioral similarities of the two networks to more accurately bound the difference between the output neurons of the two networks. Then in the backward pass, we leverage the gradient differences to more accurately compute the most beneficial refinement. Our experiments show that, compared to state-of-the-art verification tools, our method can achieve orders-of-magnitude speedup and prove many more properties than existing tools.Comment: Extended version of ICSE 2020 paper. This version includes an appendix with proofs for some of the content in section 4.