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Epstein-Barr virus (EBV) is an extremely prevalent virus which infects over 90% of the global population at some point in their lifetime. Chronic active EBV infection (CAEBV) is a disease with a high mortality rate with life-threatening complications. Although the main symptom of CAEBV is inflammation, most patients present with fever, hepatic dysfunction with or without hepatomegaly, splenomegaly, lymphadenopathy, thrombocytopenia, anemia, hypersensitivity to mosquito bites, rash, oral ulcers, hemophagocytic syndrome, coronary artery aneurysms, and interstitial pneumonia. Although some cases of CAEBV with other organ symptoms have been reported, CAEBV of external auditory canal and parotid gland is extremely rare. We report a case of CAEBV infection mimicking cellulitis or malignancy diagnosed after a number of biopsies in a patient who presented with a mass accompanied by inflammation in the external auditory canal.

Supplementary Fig. S1 - Generation of inducible TPL2 transgenic (iTPL2 TG) mice. Supplementary Fig. S2 - Percentage of tumor-free for iTPL2 TG mice ON DOX Supplementary Fig. S3 - Detection of transgene TPL2 expression using an antibody against HA epitope tag (A) and three different antibodies against TPL2 (B) in an iTPL2 wt (#100) TG-driven KA-like cSCC ON DOX. Supplementary Fig. S4 - A, effects on TPL2-related signaling pathways with the treatments of indicated inhibitors in stable cell lines, overexpressing either control vector (Vector), TPL2-WT, or TPL2-IN, by western blot. Supplementary Fig. S5 - Western blot analyses of normal mouse skin samples Supplementary Fig. S6 - For In vitro kinase assay Supplementary Fig. S7 -Co-immunofluorescence of TPL2 Supplementary Fig. S8 - Quantification of each staining with iTPL2 TG-driven cSCC

Supplementary Materials and Methods (supplementary information on establsihing cell lines and generating inducible TPL2 transgenic (iTPL2 TG) mice) & Supplementary Figure Legends

Squamous cell carcinoma (SCC) and keratoacanthoma (KA; SCC/KA) research has been hampered mainly by our lack of understanding the underlying genetic and epigenetic alterations associated with SCC/KA development, as well as the lack of animal models that faithfully recapitulate histopathologic features of human SCC/KA. Here, we show that TPL2 overexpression induced both cell transformation in immortalized human keratinocytes and SCC and KA-like cutaneous SCC (cSCC) development in mice. Mechanistically, activation of TPL2 downstream signaling pathways such as MEK/ERK MAPK, mTOR, NF-κB, and p38 MAPK leads to TPL2-mediated cell transformation in immortalized human keratinocytes and tumorigenesis in mice. Most importantly, TPL2 overexpression is required for iTPL2 TG–driven SCC and KA-like cSCC tumor maintenance, validating TPL2 as a possible drug target for the treatment of SCC/KA. Finally, we verified that TPL2 is overexpressed in human cutaneous metastatic SCC and KA clinical specimens compared with normal skin. Taken together, our results establish TPL2 as an oncogenic driver in SCC/KA development. Cancer Res; 76(22); 6712–22. ©2016 AACR.

Some viruses restructure host chromatin, influencing gene expression, with implications for disease outcome. Whether this occurs for SARS-CoV-2, the virus causing COVID-19, is largely unknown. Here, we characterized the 3D genome and epigenome of human cells after SARS-CoV-2 infection, finding widespread host chromatin restructuring that features widespread A compartmental weakening, A-B mixing, reduced intra-TAD contacts and decreased H3K27ac euchromatin modification levels. Such changes were not found following common-cold virus HCoV-OC43 infection. Intriguingly, the cohesin complex was significantly depleted from intra-TAD regions, indicating that SARS-CoV-2 disrupts cohesin loop extrusion. These altered 3D genome/epigenome structures correlated with transcriptional suppression of interferon response genes by the virus, while increased H3K4me3 was found in the promoters of pro-inflammatory genes highly induced during severe COVID-19. These findings show that SARS-CoV-2 acutely rewires host chromatin, facilitating future studies of the long-term epigenomic impacts of its infection.

Human NANOG expression resets stem cells to ground-state pluripotency. Here we identify the unique features of human NANOG that relate to its dose-sensitive function as a master transcription factor. NANOG is largely disordered, with a C-terminal prion-like domain that phase-transitions to gel-like condensates. Full-length NANOG readily forms higher-order oligomers at low nanomolar concentrations, orders of magnitude lower than typical amyloids. Using single-molecule Förster resonance energy transfer and fluorescence cross-correlation techniques, we show that NANOG oligomerization is essential for bridging DNA elements in vitro. Using chromatin immunoprecipitation sequencing and Hi-C 3.0 in cells, we validate that NANOG prion-like domain assembly is essential for specific DNA recognition and distant chromatin interactions. Our results provide a physical basis for the indispensable role of NANOG in shaping the pluripotent genome. NANOG’s unique ability to form prion-like assemblies could provide a cooperative and concerted DNA bridging mechanism that is essential for chromatin reorganization and dose-sensitive activation of ground-state pluripotency.

In order to reduce unnecessary data transmissions from Internet of Things (IoT) sensors, this paper proposes a multivariate time series prediction-based adaptive data transmission period control (PBATPC) algorithm for IoT networks. Based on the spatio-temporal correlation between multivariate time series data, we developed a novel multivariate time series data encoding scheme utilizing the proposed time series distance measure ADMWD. Composed of two significant factors for a multivariate time series prediction, i.e., the absolute deviation from the mean (ADM) and the weighted differential distance (WD), the ADMWD considers both the time distance from a prediction point and a negative correlation between the time series data concurrently. Utilizing the convolutional neural network (CNN) model, a subset of IoT sensor readings can be predicted from encoded multivariate time series measurements, and we compared the predicted sensor values with actual readings to obtain the adaptive data transmission period. Extensive performance evaluations show a substantial performance gain of the proposed algorithm in terms of the average power reduction ratio (approximately 12%) and average data reconstruction error (approximately 8.32% MAPE). Finally, this paper also provides a practical implementation of the proposed PBATPC algorithm via the HTTP protocol under the IEEE 802.11-based WLAN network.

This paper proposes a deep recurrent neural network (RNN)-based traffic classification scheme (deep RNN-TCS) for classifying applications from traffic patterns in a hybrid edge computing and cloud computing architecture.We can also classify traffic from a cloud server, but there will be a time delay when packets transfer to the server. Therefore, the traffic classification is possible almost in realtime when it performed on edge computing nodes. However, training takes a lot of time and needs a lot of computing resources to learn traffic patterns. Therefore, it is efficient to perform training on cloud server and to perform serving on edge computing node. Here, a cloud server collects and stores output labels corresponding to the application packets. Then, it trains those data and generates inferred functions. An edge computation node receives the inferred functions and executes classification. Compared to deep packet inspection (DPI), which requires the periodic verification of existing signatures and updated application information (e.g., versions adding new features), the proposed scheme can classify the applications in an automated manner. Also, deep learning can automatically make classifiers for traffic classification when there is enough data. Specifically, input features and output labels are defined for classification as traffic packets and target applications, respectively, which are created as two-dimensional images. As our training data, traffic packets measured at Universitat Politecnica de Catalunya Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system. Through extensive simulation-based experiments, it is verified that the proposed deep RNN-TCS achieves almost 5% improvement in accuracy (96% accuracy) while operating 500 times faster (elapsed time) compared to the conventional scheme.

The comparative efficacy of microscopic tympanoplasty (MT) and endoscopic tympanoplasty (ET) has been widely studied to some extent through meta-analyses. However, most studies on learning curve comparisons between the two surgeries were performed by experienced ET surgeons. We compared the surgical outcomes of MT and ET and evaluated the difference of learning curve between ET and MT performed by a single unskilled, in both MT and ET, surgeon.A total of 91 patients underwent ET and MT at a tertiary hospital. We reviewed the patients' medical records and analyzed all findings, including otoscopic pictures, pure tone audiometry (PTA) before and after surgery, and operation records. All operations were performed by a single otologist who had an experience of a year of otology fellowship at a tertiary university hospital. We compared the demographic and clinical characteristics, including age, sex, admission duration, and audiological outcomes before and after surgery. We also assessed the difference in the decrease in operation time.Among 91 patients, 44 were in the ET group and 47 were in the MT group. The mean age was 51.15 years, and 37 (40.7%) were men. Eighty-two (90.1%) patients were administered local anesthesia. Graft failure was observed in 19 (20.9%) patients, and the mean postoperative follow-up duration was 66.42 days. There were no statistically significant differences in age, sex, affected side, graft failure rate, and operation time between the ET and MT groups. There was a significant improvement in air conduction hearing and air-bone gap after surgery in both groups. Bone conduction hearing did not change before and after the surgery in either group. However, the improvement in air condition and reduction in the air-bone gap did not differ between the two groups. Multivariate linear regression analysis showed that there were no significant variables that affected operation time among age, sex, operation method (ET or MT), anesthesia, graft material, and technique. The spline regression analysis showed the decrease in operative time in ET was significantly faster than MT in the period from 8th to 19th cases.The surgical outcomes of ET are comparable to those of MT in terms of operation time, graft uptake, and postoperative hearing results, even in surgeons who are not experienced with both MT and ET. The operation time of ET was longer than that of MT in the early phase, and the decrease in the operating time was significantly faster in ET than in MT. Both MT and ET reached a plateau in the operation time, and this plateau appeared to be similar in both surgeries.

Cellulose hydrogels have gained considerable attention compared to other synthetic polymer-based hydrogels due to their eco-friendly and sustainable use. Although the development of the preparation steps enabled the cellulose hydrogels to maintain a three-dimensional network structure with abundant water, overcoming low mechanical strength and limited functionality is still challenging. Here liquid metal particles were introduced as a conductive additive as well as a crosslinking booster, overcoming suggested issues. The liquid metal particles can release gallium ions to the hydrogel nature, offering additional physical crosslinking. The additional crosslinking increases compression stability, electrochemical properties and, cohesion with water molecules, resulting in higher electrical stability after repeated compressions. Also, the liquid metal particles themselves, as chemical crosslinking agents, generate the thicker hydrogel frame. The thickened frame provides higher mechanical and electrical properties. The improved functionalities with biocompatibility allow the liquid metal introduced cellulose hydrogel to use in a biomedical application. We measured electromyography signals using the hydrogel to promote contact quality onto the curvilinear and dynamic skin surface. The measurement results indicate that the robust and conductive hydrogel offers higher signal recording quality and reusable benefit. To our knowledge, this is the first demonstration that the liquid metal is used to cellulose hydrogel to improve mechanical and electrical properties. This study opens up the versatile use of robust and conductive cellulose hydrogels with the aid of liquid metal particles.

Malakoplakia is a rare chronic granulomatous disease found in the genitourinary tract, mainly. It is considered to be related to immunosuppression and/or infectious processes. We would like to present an operative case of cecal malakoplakia in a patient with a history of surgical resection and chemotherapy for cervical cancer. A 74-year-old female patient visited our hospital for 1-year follow-up after operation and chemo-radiotherapy for cervical cancer. An infiltrative mass of 6 cm, between the cecal base and the right psoas muscle, was observed on computed tomography. An ileocectomy was performed for diagnosis. Histopathologic examination revealed cecal malakoplakia. After surgery, based on previous reports, antibiotics therapy was added. Then the patient was discharged and treated in the outpatient clinic. To our knowledge, a rare case has been described of cecal malakoplakia during observation after surgery and chemo-radiotherapy for cervical cancer. Malakoplakia is known to be related to immunosuppressive condition. Therefore, our case suggests that close observation should be made in patients on immunosuppressive condition, such as chemotherapy.

The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

This article studies distributed computing (DC) mechanisms on heterogeneous mobile devices (MDs) for latency reduction (LR) in Internet-of-Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded DC mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs’ participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD’s computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the DC, and it follows the MDs’ decisions to determine the load allocation for each MD. Then, we design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of LR on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs’ social welfare.

In this letter, we propose a novel joint mobile cache and power management scheme for energy-efficient mobile augmented reality (MAR) services in mobile edge computing (MEC). For this purpose, depending on whether the cache is hit or not, analytical models for 1) the energy consumption of an MAR device (MD) and 2) the service latency for the MAR are derived and investigated. Considering a tradeoff between energy and service latency in terms of the cache size in MAR with MEC, we design a theoretical framework for the mobile cache management of the MD to optimize the mobile cache size as well as transmission power while guaranteeing the required service latency. From the numerical experiments, we evaluate the performance of the proposed scheme and demonstrate insights regarding the optimization of the performance of MEC-assisted MAR services with a mobile cache.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (MTB), can affect the lungs (pulmonary TB) as well as other sites (extrapulmonary TB). Nasopharyngeal tuberculosis (NPTB) is a rare type of extrapulmonary TB. Since NPTB has nonspecific clinical presentation with low index of suspicion, it is difficult for clinicians to make an early diagnosis and proper treatment. We recently encountered a 42-year-old woman with NPTB concomitant with middle ear TB, which strongly mimicked nasopharyngeal carcinoma. Since the diagnosis of NPTB was difficult to confirm based on endoscopic findings and imaging studies, this patient underwent nasopharyngeal biopsy, and finally, polymerase chain reaction (PCR) confirmed NPTB. This report describes our NPTB case as well as summarizes all cases of NPTB reported in South Korea. We highlight that active tissue biopsy with antibacterial smear and additional PCR or specific TB blood test should be considered for cases with high suspicion of NPTB.

This article proposes a novel fair and scalable relay control (FSRC) scheme for the Internet-of-Things (IoT) services in long range (LoRa)-based low-power wide-area networks. The proposed FSRC scheme promotes relay operation with low spreading factor (SF) to improve the success probability for distant end-devices (EDs) and the fairness of success probability for each SF region. To achieve this, a theoretical framework for designing the relay operation is analytically developed by considering a practical LoRaWAN MAC protocol as an analytical model. The proposed FSRC scheme encompasses a selective relay operation by considering both signal-to-noise ratio and signal-to-interference ratio and the receive signal strength indicator value for the location-unaware relay selection strategy. Using this model, a genetic algorithm-based relay control strategy is proposed to maximize both coverage probability and minimum success probability for all SF regions by controlling the relay parameters, such as source-relay region and source-relay ratio. Our numerical analysis validates the effectiveness of the proposed FSRC scheme under various parameters in terms of the minimum success probability of each SF region, coverage probability, and fairness. Specifically, we verify that the proposed FSRC scheme achieves a maximum of approximately 37% and 33% improvement of the minimum success probability and coverage probability, respectively, under practical LoRa PHY/MAC parameters, compared to the single-hop environment (without relay operation).

We investigated the effects of different thermal treatment conditions on the surface and internal residual strains of bulk GaN grown by hydride vapor phase epitaxy (HVPE). Thermal annealing was performed at 700–1000 °C for 1–5 h in nitrogen atmosphere. The GaN was characterized by atomic force microscopy, energy dispersive spectrometry, X-ray photoelectron spectroscopy, Raman spectroscopy, and by high-resolution X-ray diffractometer. The experimental results demonstrated that thermal decomposition and oxidation occurred on the surface of GaN when exposed to heat over a long time, or even at a low temperature, as compared to thermal decomposition of GaN in ambient nitrogen. The internal residual stress of GaN was relaxed most effectively when annealing at 900 °C for 3 h, and it was confirmed that the crystal quality is best under this condition. We also confirmed that the effect of annealing was extremely beneficial because native oxide impurities were removed most effectively in this condition. However, Ga metal or oxide could formed due to the occurrence of slight thermal decomposition on the surface.

In this study, we focus on improving the energy efficiency of multiple mobile augmented reality (MAR) devices (MDs) with different MAR applications, which are connected to a single multi-access edge computing (MEC) server, through the centralized orchestration of the MEC server. To achieve this, a trade-off between the accuracy, latency, and energy consumption of each MD is considered as intertwined costs. Accordingly, we minimize a sum of intertwined costs including the energy consumption, latency and accuracy loss of multiple MDs, while satisfying the maximum latency constraint and the minimum accuracy constraint of each MAR application. With rigorous analysis, we design a theoretical framework for the optimization of MD performance by jointly managing the frame resolution of the image to offload to the MEC server from MDs, and the computation capacity distribution over MDs in the case of the limited computation capacity of the MEC server. Our numerical analysis validates that the proposed scheme significantly reduces the number of service-level agreement violations, such as exceeding the maximum latency constraint, compared with the existed algorithm. Specifically, the proposed work significantly improve costs of service latency, accuracy loss, and energy consumption of MDs for MEC-assisted MAR services compared with baseline schemes.

The nuclear pore complex (NPC) comprises more than 30 nucleoporins (NUPs) and is a hallmark of eukaryotes. NUPs have been suggested to be important in regulating gene transcription and 3D genome organization. However, evidence in support of their direct roles remains limited. Here, by CutRun, we find that core NUPs display broad but also cell-type-specific association with active promoters and enhancers in human cells. Auxin-mediated rapid depletion of two NUPs demonstrates that NUP93, but not NUP35, directly and specifically controls gene transcription. NUP93 directly activates genes with high levels of RNA polymerase II loading and transcriptional elongation by facilitating full BRD4 recruitment to their active enhancers. dCas9-based tethering confirms a direct and causal role of NUP93 in gene transcriptional activation. Unexpectedly, in situ Hi-C and H3K27ac or H3K4me1 HiChIP results upon acute NUP93 depletion show negligible changesS2211-1247(22)01437-1 of 3D genome organization ranging from A/B compartments and topologically associating domains (TADs) to enhancer-promoter contacts.

In this article, to meet a delay requirement for data analytics from Internet-of-Things (IoT) devices, we design a novel market model of the distributed learning resource management mechanism for multiple mobile-edge computing (MEC) operators. We consider a hybrid architecture (cloud-MEC) for the distributed learning, which is also known as “federated learning” as one of the practical examples, where a coordinator at the cloud coordinates IoT sensors to efficiently distribute their sensing data over multiple MECs. In this sense, multiple MECs receive a shared model from the coordinator and conduct local training of received partial sensing data from IoT sensors. Then, the coordinator at the cloud merges returned local training results from MECs and generates a global model. To model a hierarchical decision-making structure as a market behavior, we formulate and solve a Stackelberg game model. Specifically, in the case of MEC operators as leaders, we design a pricing scheme for MEC operators to obtain its maximum utility by considering a tradeoff between the revenue and energy consumption. Then, as a follower, while the coordinator aims at achieving a balance between the satisfaction attained from the distributed learning and the costs, it follows the MEC operators’ decisions by coordinating IoT sensors to distribute their sensing data over MECs. A unique Stackelberg equilibrium (SE) point is given as a closed form. Finally, we reveal that the SE solution maximizes the utility of all market participants. This game-theoretic study demonstrates that there is an incentive for utilizing multiple MECs to achieve better satisfaction of IoT services in distributed learning.

This paper proposes a novel social-viewport adaptive caching scheme (SACS) for virtual reality (VR) streaming in an edge-computing platform. In VR contents with 360 degree views where only a part of the entire view (i.e., the viewport) is shown and the remaining parts are decoded but not shown, we collect and record multiple clients’ viewports of the same VR contents in local proximity on the edge-computing platform. We extract a social-viewport map, which represents where most of the local clients are directing their attention. By utilizing the social-viewport map, under our proposed scheme, k-means and mean-shift clustering algorithms are adopted to partition 360 degree views into multiple clusters with the nearest mean of hit-ratios from multiple clients. Accordingly, in order to save cache storage while maintaining a high-quality VR streaming service, we adaptively assign different encoding rates with various levels to multiple viewports. We implement the proposed scheme using a commercial EdgeX foundry edge-computing platform. A measurement-based experiment reveals that the proposed scheme achieves a maximum storage reduction of almost 74%, with a 92% hit-ratio to the highest encoded viewports.

This letter proposes a novel resolution and power control scheme for energy-efficient mobile augmented reality (MAR) services through computation offloading to mobile edge computing (MEC). For this purpose, analytical models for the energy consumption of a MAR device (MD), detection accuracy of MAR objects, and service latency are rigorously formulated. Considering their tradeoff in MAR with MEC, a theoretical framework for the computation offloading problem of the MD that jointly optimizes the transmission power and frame resolution is designed. The proposed scheme significantly reduces energy consumption by 73.64% with an accuracy loss of 1.6% while guaranteeing the required latency constraint.

With the widespread use of Internet of Things (IoT), edge computing has recently emerged as a promising technology to tackle low-latency and security issues with personal IoT data. In this regard, many works have been concerned with computing resource allocation of the edge computing server, and some studies have conducted to the pricing schemes for resource allocation additionally. However, few works have attempted to address the comparison among various kinds of pricing schemes. In addition, some schemes have their limitations such as fairness issues on differentiated pricing schemes. To tackle these limitations, this article considered three dynamic pricing mechanisms for resource allocation of edge computing for the IoT environment with a comparative analysis: BID-proportional allocation mechanism (BID-PRAM), uniform pricing mechanism (UNI-PRIM), and fairness-seeking differentiated pricing mechanism (FAID-PRIM). BID-PRAM is newly proposed to overcome the limitation of the auction-based pricing scheme; UNI-PRIM is a basic uniform pricing scheme; FAID-PRIM is newly proposed to tackle the fairness issues of the differentiated pricing scheme. BID-PRAM is formulated as a noncooperative game. UNI-PIM and FAID-PRIM are formulated as a single-leader–multiple-followers Stackelberg game. In each mechanism, the Nash equilibrium (NE) or Stackelberg equilibrium (SE) solution is given with the proof of existence and uniqueness. Numerical results validate the proposed theorems and present a comparative analysis of three mechanisms. Through these analyses, the advantages and disadvantages of each model are identified, to give edge computing service providers guidance on various kinds of pricing schemes.

This paper presents a study on the development of a fire extinguishing agent and extinguishing system for an energy storage system (ESS) fire. The fire extinguishing agent designed to extinguish an ESS fire is a highly permeable fire extinguisher that reduces the surface tension and viscosity while bringing about cooling action. This is the main extinguishing effect of this type of wetting agent, which displays the characteristics of fire extinguishing agents used for penetrating the battery cells inside the ESS module. For the fire extinguishing system, a local application system was designed to suppress fire on a rack-by-rack basis. A 360° rotating nozzle was inserted into the rear hall of the ESS module, and general nozzles were installed in the rack to maximize the fire extinguishing effect. The fire extinguishing agent was strongly discharged by virtue of the gas release pressure. Experiments on fire suppression performance with ESS module 1 unit and module 3 units showed that all visible flames were extinguished in 8 s and 9 s, respectively, by the fire extinguishing agent. In addition, based on confirming reignition for 600 s after the fire extinguishing agent was exhausted, it was confirmed that the ESS fire was completely extinguished without reignition in all fire suppression performance experiments.

Enhancers are distal genomic elements critical for gene regulation and cell identify control during development and diseases. Many human cancers were found to associate with enhancer malfunction, due to genetic and epigenetic alterations, which in some cases directly drive tumour growth. Conventionally, enhancers are known to provide DNA binding motifs to recruit transcription factors (TFs) and to control target genes. However, recent progress found that most, if not all, active enhancers pervasively transcribe noncoding RNAs that are referred to as enhancer RNAs (eRNAs). Increasing evidence points to functional roles of at least a subset of eRNAs in gene regulation in both normal and cancer cells, adding new insights into the action mechanisms of enhancers. eRNA expression was observed to be widespread but also specific to tumour types and individual patients, serving as opportunities to exploit them as potential diagnosis markers or therapeutic targets. In this review, we discuss the brief history of eRNA research, their functional mechanisms and importance in cancer gene regulation, as well as their therapeutic and diagnostic values in cancer. We propose that further studies of eRNAs in cancer will offer a promising ‘eRNA targeted therapy’ for human cancer intervention.

Purpose Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS. Materials and Methods Sixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred. Results Three radiomic features were significantly associated with KRAS mutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively. Conclusion Using MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.

This letter proposes a novel edge-cloud interworking framework in the video analytics of the Internet of Things (IoT) that consists of cost-effective job load balancing and scheduling schemes for computation-intensive video analytics applications. The proposed framework aims to minimize the cost of cloud resource usage while guaranteeing deadlines when conducting concurrent operations. A formulation of a two-stage mixed-integer problem and its heuristic greedy algorithms is presented, which captures all intertwined goals. From the numerical analysis, we reveal that the proposed framework outperforms the existing schemes in terms of monetary cost and service latency with a practical complexity bound.