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Articular cartilage has a limited capacity to self-heal once damaged. Tissue-specific stem cells are a solution for cartilage regeneration; however

Semi-Supervised Domain Adaptation has been widely studied with various approaches to address domain shift with labeled source-domain data combined with scarcely labeled target-domain data. Model adaptation is becoming promising with a paradigm of source pre-training and target fine-tuning, which eliminates the simultaneous availability of data from both domains and makes for data privacy. Among the model adaptation methods, Entropy Minimization (EM) is popularly incorporated to encourage a low-density separation on target samples. However, EM tends to brutally force models to make over-confident predictions, which could make the models collapse with deteriorated performance. In this paper, we first study the over-confidence of EM with a quantitative analysis, which shows the importance of capturing the dependency among labels. To address this issue, we propose to guide EM via longitudinal self-distillation. Specifically, we produce a dynamic "teacher" label distribution during training by constructing a graph on target data and perform pseudo-label propagation to encourage the "teacher" distribution to capture context category dependency based on a global data structure. Then EM is guided longitudinally by distilling the learned label distribution to combat the brute-force over-confidence. Extensive experiments demonstrate the effectiveness of our methods.

In a system with energy-constrained sensors, each transmitted observation comes at a price. The price is the energy the sensor expends to obtain and send a new measurement. The system has to ensure that sensors' updates are timely, i.e., their updates represent the observed phenomenon accurately, enabling services to make informed decisions based on the information provided. If there are multiple sensors observing the same physical phenomenon, it is likely that their measurements are correlated in time and space. To take advantage of this correlation to reduce the energy use of sensors, in this paper we consider a system in which a gateway sets the intervals at which each sensor broadcasts its readings. We consider the presence of battery-powered sensors as well as sensors that rely on Energy Harvesting (EH) to replenish their energy. We propose a Deep Reinforcement Learning (DRL)-based scheduling mechanism that learns the appropriate update interval for each sensor, by considering the timeliness of the information collected measured through the Age of Information (AoI) metric, the spatial and temporal correlation between readings, and the energy capabilities of each sensor. We show that our proposed scheduler can achieve near-optimal performance in terms of the expected network lifetime. European Regional Development Fund through the SFI Research Centres Programme [13/RC/2077_P2 SFI CONNECT]; SFI-NSFC Partnership Programme [17/NSFC/5224]; Commonwealth Cyber Initiative at Virginia Tech Published version This work was funded in part by the European Regional Development Fund through the SFI Research Centres Programme under Grant No. 13/RC/2077_P2 SFI CONNECT and by the SFI-NSFC Partnership Programme Grant Number 17/NSFC/5224. It is also supported by the Commonwealth Cyber Initiative at Virginia Tech.

Spinal cord injury (SCI) is a common disease of the nervous system, including primary and secondary injuries. Neuronal inflammation after SCI is the most important pathological process of SCI and a chemical barrier to nerve function recovery after injury. Ski, an evolutionarily conserved functional transcriptional regulator protein, is upregulated in reactive astrocytes after SCI and regulates the biological characteristics of astrocytes. However, its role in the glial inflammatory response triggered by reactive astrocytes after spinal cord ischemia and its exact mechanism remains unclear. This study investigated the role and mechanism of Ski in the inflammatory response triggered by reactive astrocytes induced by oxygen and sugar deprivation/reoxygenation (OGD/R) model in vitro. In the ODG/R model, Ski expression was upregulated. In contrast, Ski upregulation was accompanied by increased levels of iNOS, IL-1β, IL-6, TNF-α, and other inflammation-related factors. These results indicated that the inflammatory response triggered by astrocytes was significantly enhanced in OGD/R-stimulated astrocytes. Astrocytes were transfected with Ski specific siRNA to knock out Ski and subsequently attenuate OGD-induced astrocyte-triggered inflammation. Our results also suggest that Ski downregulation downregulates the expression of iNOS, IL-1β, IL-6, and TNF-α in OGD/R-induced reactive astrocytes by inhibiting the activity of the NF-κB signaling pathway. In conclusion, downregulation of Ski can effectively inhibit glial inflammation in SCI by inhibiting the activity of the NF-κB pathway. These findings suggest that Ski is a promising therapeutic target for inflammatory responses after SCI.In conclusion, Ski downregulation can effectively inhibit glial inflammation in SCI by inhibiting the activity of the NF-κB pathway. These findings suggest that Ski might serve as a promising target for the treatment of inflammatory responses after SCI.

Basement membrane proteins are known to guide cell structures, differentiation, and tissue repair. Although there is a wealth of knowledge on the functions of laminins, perlecan, and type IV collagen in maintaining tissue homeostasis, not much is known about nidogen. As a key molecule in the basement membrane, nidogen contributes to the formation of a delicate microenvironment that proves necessary for stem cell lineage-specific differentiation. In this review, the expression of nidogen is delineated at both cellular and tissue levels from embryonic to adult stages of development; the effect of nidogens is also summarized in the context of musculoskeletal development and regeneration, including but not limited to adipogenesis, angiogenesis, chondrogenesis, myogenesis, and neurogenesis. Furthermore, potential mechanisms underlying the role of nidogens in stem cell-based tissue regeneration are also discussed. This concise review is expected to facilitate our existing understanding and utilization of nidogen in tissue engineering and regeneration.

Exocyst complex component 3 Sec6 of mammals, one of the components of the exocyst complex, participates in numerous cellular functions, such as promoting cell migration and inhibiting apoptosis. In this study, the Sec6 was obtained from Epinephelus coioides, an economically important cultured fish. The full length of E. coioides Sec6 was 2655 bp including a 245 bp 5' UTR, a 154 bp 3' UTR, and a 2256 bp open reading frame (ORF) encoding 751 amino acids, with a molecular mass of 86.76 kDa and a theoretical pI of 5.57. Sec6 mRNA was detected in all the tissues examined, but the expression level is different in these tissues. Using fluorescence microscopy, Sec6 were distributed in both the nucleus and the cytoplasm. After SGIV infection, the expression of E. coioides Sec6 was significantly up-regulated in both trunk kidney and spleen response to Singapore grouper iridovirus (SGIV), an important pathogens of E. coioides. Sec6 could increase the SGIV-induced cytopathic effects (CPE), the expression of the SGIV genes VP19, LITAF, MCP, ICP18 and MCP, and the viral titers. Besides, E. coioides Sec6 significantly downregulated the promoter of NF-κB and AP-1, and inhibited the SGIV-induced apoptosis. The results demonstrated that E. coioides Sec6 might play important roles in SGIV infection.

Over the last decade, the electricity sector has seen a significant increase in the number of residential battery systems, as well as increasing interest in using them to reduce demand during network peaks. Although there is an abundance of literature assessing this ability using modelled residential batteries, there is a lack of detailed assessment using deployed residential batteries. This paper analyses 1-min resolution data from 15 non-coordinated residential batteries deployed in Australia across 6 network peak demand periods. A novel metric was used to quantify errors in BESS load-following, which occurred when the batteries did not completely mitigate grid import and export even when they had sufficient energy capacity and rated power. On average the 15 batteries discharged around 25% of their rated power during network demand peaks, whereas those that load-followed discharged around 40%. Despite the small sample size, these results suggest that the outcomes from modelled batteries represent the ideal upper bound and the actual performance of some batteries is likely to be lower. This there is a need for more research into the actual operation of deployed batteries, and what this means for the current modelled findings regarding their ability to reduce demand during network peaks.

In social advertising, the social platform host may run marketing campaigns for multiple competing clients simultaneously. In this case, each client comes up with a budget and an influence spread requirement. The host runs campaigns by allocating a set of seed nodes for each client. If the influence spread triggered by a seed set meets the requirement, the host can earn the budget from the corresponding client. In this paper, we study the problem of Profit Maximization, considering that different seeds incur different costs. Given all the clients' requirements met, we aim to find the optimal seed allocation with minimum cost. Under the competitive K-LT propagation model, we show the Profit Maximization problem is NP-hard and NP-hard to approximate with any factor. To find a feasible solution, we propose an effective algorithm that iteratively selects a candidate set and obtains an approximate allocation. The experimental results over a real-world dataset validate the effectiveness of the proposed methods.

China's high-speed railway (HSR) is booming recently, the HSR’s performance of energy conservation and carbon reduction has attracted much attention. This study developed a new life cycle model of energy consumption and greenhouse gas (GHG) emissions on China’s HSR by life cycle analysis (LCA), covering the stages of infrastructure, HSR train, and operation, based on the TLCAM (Tsinghua-LCA Model). A case of the Beijing–Shanghai HSR has been studied to show that the full life cycle energy consumption and GHG emissions of HSR transportation are 0.4 MJ km−1 per capita and 0.04 kg CO2e km−1 per capita, respectively, which are far less than aviation, gasoline vehicles, diesel vehicles, electric vehicles and public vehicles. With the cleaner power structure and the progress of HSR train technology, the energy consumption and carbon emissions of HSR in 2020 could be reduced by 20% compared to 2015. This study indicates that electricity generation mix structure and full load rate are important factors influencing the life-cycle energy consumption and GHG emissions of HSR transportation. It is recommended to improve the coverage of HSR network, accelerate train upgrades, improve the full load rate of HSR trains, and promote the low-carbon development of electricity supply to strengthen and realize the low-carbon advantage of HSR transport mode in China. HSR transportation can be used to achieve the low carbon transformation of China's transportation sector and improve oil supply safety situation.

Efficient and affordable electrocatalysts for reversible oxygen reduction and oxygen evolution reactions (ORR and OER, respectively) are highly sought-after for use in rechargeable metal-air batteries. However, the construction of high-performance electrocatalysts that possess both largely accessible active sites and superior ORR/OER intrinsic activities is challenging. Herein, we report the design and successful preparation of a 3D hierarchically porous graphene framework with interconnected interlayer macropores and in-plane mesopores, enriched with pyridinic-nitrogen-cobalt (pyri-N-Co) active sites, namely, CoFe/3D-NLG. The pyri-N-Co bonding significantly accelerates sluggish oxygen electrocatalysis kinetics, in turn substantially improving the intrinsic ORR/OER activities per active site, while copious interlayer macropores and in-plane mesopores enable ultra-efficient mass transfer throughout the graphene architecture, thus ensuring sufficient exposure of accessible pyri-N-Co active sites to the reagents. Such a robust catalyst structure endows CoFe/3D-NLG with a remarkably enhanced reversible oxygen electrocatalysis performance, with the ORR half-wave potential identical to that of the benchmark Pt/C catalyst, and OER activity far surpassing that of the noble-metal-based RuO2 catalyst. Moreover, when employed as an air electrode for a rechargeable Zn-air battery, CoFe/3D-NLG manifests an exceedingly high open-circuit voltage (1.56 V), high peak power density (213 mW cm−2), ultra-low charge/discharge voltage (0.63 V), and excellent charge/discharge cycling stability, outperforming state-of-the-art noble-metal electrocatalysts.

Long noncoding RNAs (lncRNAs) are molecules more than 200 nucleotides in length. They play roles in various cells, mainly regulating cell growth, differentiation, and apoptosis. They also participate in the pathogenesis of many diseases. In fact, several studies have shown that lncRNAs function as cancer or tumor suppressor genes and play important roles in the occurrence and development of cancer in humans. New evidence has shown that lncRNA heart and neural crest derivatives expressed 2-antisense RNA 1 (lncRNA HAND2-AS1) hinders the occurrence and development of various tumors. Overexpression of HAND2-AS1 was found to be significantly related to the clinical and pathological characteristics of cancer patients, as well as the regulation of cell proliferation, apoptosis, invasion, metastasis, and energy metabolism through several possible mechanisms. Therefore, HAND2-AS1 may be a promising tumor biomarker and therapeutic target. Here, we review the biological functions, mechanisms, and potential clinical significance of HAND2-AS1 in numerous human tumors.

A growing body of evidence suggests that long non-coding RNAs (lncRNAs), a novel class of non-coding endogenous single-stranded RNA, play a key role in multiple physiological and pathological processes through transcriptional interference, post-transcriptional regulation, and epigenetic modification. Furthermore, many studies have shown that lncRNAs-as oncogenes or tumour suppressors-play an important role in the occurrence and development of human cancers. Small nucleolar RNA host gene 16 (SNHG16) was initially identified as an oncogenic lncRNA in neuroblastoma, and has since been identified as a carcinogenic regulator of various malignant tumours. Overexpression of SNHG16 is associated with clinical and pathological characteristics of cancer patients, and regulates cell proliferation, apoptosis, invasion and metastasis through a variety of potential mechanisms. Therefore, SNHG16 may be a promising biomarker and therapeutic target for cancers. In this review, we summarize the biological function, related mechanisms and potential clinical significance of SNHG16 in multiple human cancers.