Cancer-associated POT1 mutations lead to telomere elongation without induction of a DNA damage response.

Mutations in the shelterin protein POT1 are associated with chronic lymphocytic leukemia (CLL), Hodgkin lymphoma, angiosarcoma, melanoma, and other cancers. These cancer-associated POT1 (caPOT1) mutations are generally heterozygous, missense, or nonsense mutations occurring throughout the POT1 reading frame. Cancers with caPOT1 mutations have elongated telomeres and show increased genomic instability, but which of the two phenotypes promotes tumorigenesis is unclear. We tested the effects of CAS9-engineered caPOT1 mutations in human embryonic and hematopoietic stem cells (hESCs and HSCs, respectively). HSCs with caPOT1 mutations did not show overt telomere damage. In vitro and in vivo competition experiments showed the caPOT1 mutations did not confer a selective disadvantage. Since DNA damage signaling is known to affect the fitness of HSCs, the data argue that caPOT1 mutations do not cause significant telomere damage. Furthermore, hESC lines with caPOT1 mutations showed no detectable telomere damage response while showing consistent telomere elongation. Thus, caPOT1 mutations are likely selected for during cancer progression because of their ability to elongate telomeres and extend the proliferative capacity of the incipient cancer cells.

Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

Characterization of 3D Organotypic Culture of Mouse Adipose-Derived Stem Cells.

Although stem cells are a promising avenue for harnessing the potential of adipose tissue, conventional two-dimensional (2D) culture methods have limitations. This study explored the use of three-dimensional (3D) cultures to preserve the regenerative potential of adipose-derived stem cells (ADSCs) and investigated their cellular properties. Flow cytometric analysis revealed significant variations in surface marker expressions between the two culture conditions. While 2D cultures showed robust surface marker expressions, 3D cultures exhibited reduced levels of CD44, CD90.2, and CD105. Adipogenic differentiation in 3D organotypic ADSCs faced challenges, with decreased organoid size and limited activation of adipogenesis-related genes. Key adipocyte markers, such as lipoprotein lipase (LPL) and adipoQ, were undetectable in 3D-cultured ADSCs, unlike positive controls in 2D-cultured mesenchymal stem cells (MSCs). Surprisingly, 3D-cultured ADSCs underwent mesenchymal-epithelial transition (MET), evidenced by increased E-cadherin and EpCAM expression and decreased mesenchymal markers. This study highlights successful ADSC organoid formation, notable MSC phenotype changes in 3D culture, adipogenic differentiation challenges, and a distinctive shift toward an epithelial-like state. These findings offer insights into the potential applications of 3D-cultured ADSCs in regenerative medicine, emphasizing the need for further exploration of underlying molecular mechanisms.

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography.

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems.

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.

DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing.

Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency.

Secretory SERPINE1 Expression Is Increased by Antiplatelet Therapy, Inducing MMP1 Expression and Increasing Colon Cancer Metastasis.

Contrary to many reports that antiplatelet agents inhibit cancer growth and metastasis, new solid tumors have been reported in patients receiving long-term antiplatelet therapy. We investigated the effects of these agents directly on cancer cells in the absence of platelets to mimic the effects of long-term therapy. When four antiplatelet agents (aspirin, clopidogrel, prasugrel, and ticagrelor) were administered to colon cancer cells, cancer cell proliferation was inhibited similarly to a previous study. However, surprisingly, when cells were treated with a purinergic P2Y12 inhibitor (purinergic antiplatelet agent), the motility of the cancer cells was significantly increased. Therefore, gene expression profiles were identified to investigate the effect of P2Y12 inhibitors on cell mobility, and Serpin family 1 (SERPINE1) was identified as a common gene associated with cell migration and cell death in three groups. Antiplatelet treatment increased the level of SERPINE1 in cancer cells and also promoted the secretion of SERPINE1 into the medium. Increased SERPINE1 was found to induce MMP1 and, thus, increase cell motility. In addition, an increase in SERPINE1 was confirmed using the serum of patients who received these antiplatelet drugs. With these results, we propose that SERPINE1 could be used as a new target gene to prevent the onset and metastasis of cancer in patients with long-term antiplatelet therapy.

Thoracoscopic repair of esophageal atresia with distal tracheoesophageal fistula: is it a safe procedure in infants weighing less than 2000 g?

BACKGROUND: Since Rothenberg first performed thoracoscopic repair for esophageal atresia with distal tracheoesophageal fistula (EA/TEF) successfully in 2000, thoracoscopic repair has achieved status as a routine procedure worldwide. Previously, an international multicenter study reported that this procedure was not inferior to conventional open surgery. However, thoracoscopic surgery is a highly difficult operation for surgeons and anesthesiologists; as a result, the safety and efficacy of the surgery is still under debate. Considering these circumstances, the purpose of this study was to analyze the results of single-center thoracoscopic surgery and to compare the outcomes relative to the patient's weight at the time of surgery. METHODS: We retrospectively analyzed patients with EA/TEF who underwent thoracoscopic surgery in a single center between October 2008 and February 2017. RESULTS: In total, 41 cases of thoracoscopic repair of EA/TEF were performed. Upon subgrouping by over and under 2000 g of body weight at the time of operation, 34 were found to be over 2000 g and seven were under 2000 g. Intraoperative factors and events were not significantly different between the two groups. Additionally, most of the postoperative outcomes, including the rate of postoperative leakage and strictures, showed no difference. On the other hand, the under 2000 g group had more gastroesophageal reflux requiring fundoplication than did the heavier group (P = 0.04). CONCLUSIONS: The results of this center's thoracoscopic repair of EA/TEF were not inferior to other centers' outcomes. Additionally, the intraoperative and postoperative outcomes were similar despite differences in weight at operation. Therefore, thoracoscopic repair might be a feasible surgical option for infants weighing less than 2000 g when performed by a surgeon and anesthesiologist team who are experienced in pediatric thoracoscopic surgery.

Screening of Drug-Induced Steatosis and Phospholipidosis Using Lipid Droplet-Selective Two-Photon Probes.

Lipid droplets (LDs) are organelles that play a major role in regulating the storage of neutral lipids. Dysregulation of LDs is associated with metabolic disorders, such as fatty liver diseases, obesity, diabetes, and atherosclerosis. We have developed LD-selective small-molecule fluorescence probes (probes 3 and 4) that are available for both one- and two-photon microscopy, employing live or fixed cells. We found that probes 3 and 4 sensitively detect the increased LDs in response to oleic acid or endoplasmic reticulum stress, both in cells and tissues of the liver. The narrow absorption and emission bands of probes 3 and 4 allow multicolor imaging for the study of the role of LDs in pathophysiology and LD-associated signaling by the coapplication of the probes for different organelles or antibodies against specific proteins. In addition, we show here, for the first time, that two-photon microscopy imaging using our LD-selective probes with LysoTracker provides a novel method for screening drugs to potentially induce steatosis and/or phospholipidosis.

Vesicoureteral reflux increases the risk of urinary tract infection prior to corrective surgery in newborn males with anorectal malformation.

PURPOSE: The ideal colostomy type indicated for patients with anorectal malformation (ARM) is disputed. The aim of this study was to analyze the clinical factors associated with urinary tract infection (UTI) prior to corrective surgery in male ARM without perineal fistula having undergone diverting enterostomy. METHODS: A retrospective review of patients diagnosed with ARM and surgically managed at our center from January 2011 to December 2019 was performed. Logistic regression was used to analyze the association between clinical factors and UTI. RESULTS: Eighty boys with ARM without perineal fistula underwent diverting enterostomy and subsequent corrective surgery via laparoscopic-assisted anorectal pull-through. A sigmoid loop colostomy was most often performed (70 patients, 87.5%). Twenty-nine patients (36.3%) were diagnosed with vesicoureteral reflux (VUR), including 14 (48.3%) with febrile UTIs. Six patients had other concomitant genitourinary anomalies excluding VUR. Multivariate logistic regression analysis revealed the presence of VUR as the only independent factor associated with the occurrence of febrile UTI (OR 17.3, 95% CI 3.51-85.26, p < 0.001). CONCLUSION: The development of UTI in newborn males with ARM is associated with the presence of VUR, regardless of stoma type. Voiding cystourethrography should be considered in patients with ARM for early diagnosis of VUR and subsequent antibiotic prophylaxis.

F-DCS: FMI-Based Distributed CPS Simulation Framework with a Redundancy Reduction Algorithm.

A cyber physical system (CPS) is a distributed control system in which the cyber part and physical part are tightly interconnected. A representative CPS is an electric vehicle (EV) composed of a complex system and information and communication technology (ICT), preliminary verified through simulations for performance prediction and a quantitative analysis is essential because an EV comprises a complex CPS. This paper proposes an FMI-based distributed CPS simulation framework (F-DCS) adopting a redundancy reduction algorithm (RRA) for the validation of EV simulation. Furthermore, the proposed algorithm was enhanced to ensure an efficient simulation time and accuracy by predicting and reducing repetition patterns involved during the simulation progress through advances in the distributed CPS simulation. The proposed RRA improves the simulation speed and efficiency by avoiding the repeated portions of a given driving cycle while still maintaining accuracy. To evaluate the performance of the proposed F-DCS, an EV model was simulated by adopting the RRA. The results confirm that the F-DCS with RRA efficiently reduced the simulation time (over 30%) while maintaining a conventional accuracy. Furthermore, the proposed F-DCS was applied to the RRA, which provided results reflecting real-time sensor information.

Thermographic Inspection of Internal Defects in Steel Structures: Analysis of Signal Processing Techniques in Pulsed Thermography.

This study performed an experimental investigation on pulsed thermography to detect internal defects, the major degradation phenomena in several structures of the secondary systems in nuclear power plants as well as industrial pipelines. The material losses due to wall thinning were simulated by drilling flat-bottomed holes (FBH) on the steel plate. FBH of different sizes in varying depths were considered to evaluate the detection capability of the proposed technique. A short and high energy light pulse was deposited on a sample surface, and an infrared camera was used to analyze the effect of the applied heat flux. The three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), pulsed phase thermography (PPT), and principal component thermography (PCT), have been applied to raw thermal images. Then, the performance of each technique was evaluated concerning enhanced defect detectability and signal to noise ratio (SNR). The results revealed that TSR enhanced the defect detectability, detecting the maximum number of defects, PPT provided the highest SNR, especially for the deeper defects, and PCT provided the highest SNR for the shallower defects.

E2F1 Promotes Progression of Bladder Cancer by Modulating RAD54L Involved in Homologous Recombination Repair.

DNA repair defects are important factors in cancer development. High DNA repair activity can affect cancer progression and chemoresistance. DNA double-strand breaks in cancer cells caused by anticancer agents can be restored by non-homologous end joining (NHEJ) and homologous recombination repair (HRR). Our previous study has identified E2F1 as a key gene in bladder cancer progression. In this study, DNA repair genes related to E2F1 were analyzed, and RAD54L involved in HRR was identified. In gene expression analysis of bladder cancer patients, the survival of patients with high RAD54L expression was shorter with cancer progression than in patients with low RAD54L expression. This study also revealed that E2F1 directly binds to the promoter region of RAD54L and regulates the transcription of RAD54L related to the HRR pathway. This study also confirmed that DNA breaks are repaired by RAD54L induced by E2F1 in bladder cancer cells treated with MMC. In summary, RAD54L was identified as a new target directly regulated by E2F1. Our results suggest that, E2F1 and RAD54L could be used as diagnostic markers for bladder cancer progression and represent potential therapeutic targets.

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework.

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Thymoquinone-Induced Tristetraprolin Inhibits Tumor Growth and Metastasis through Destabilization of MUC4 mRNA.

Tristetraprolin (TTP), a well-characterized AU-rich element (ARE) binding protein, functions as a tumor suppressor gene. The purpose of this study was to investigate whether a bioactive substance derived from a natural medicinal plant affects the induction of TTP and to elucidate its mechanism. We examined the effects of natural bioactive materials including Resveratrol (RSV), thymoquinone (TQ) and curcumin on the expression of TTP in cancer cell. TQ derived from a natural plant Nigella sativa increased the expression levels of TTP mRNA and proteins in a dose-dependent manner in gastric and breast cancer cells. TQ-induced TTP increased the instability of MUC4 mRNA by direct binding of TTP to ARE in the 3'UTR of MUC4 mRNA. The induction of TTP by TQ also reduced the proliferation, migration and invasion of cancer cells. The expression of the epithelial-mesenchymal (EMT)-related genes, which were target genes of TTP, was also decreased by the TQ treatment. In the in vivo experiments using mouse melanoma cells, TQ-induced TTP inhibited metastasis of tumor cells. We have found that TQ-induced TTP might inhibit metastasis by reducing tumor cell migration and invasion through destabilization of MUC4 mRNA, which suggest the MUC4 as a novel target to TTP.

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications.

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

The pathogenic human Torsin A in Drosophila activates the unfolded protein response and increases susceptibility to oxidative stress.

BACKGROUND: Dystonia1 (DYT1) dystonia is caused by a glutamic acid deletion (ΔE) mutation in the gene encoding Torsin A in humans (HTorA). To investigate the unknown molecular and cellular mechanisms underlying DYT1 dystonia, we performed an unbiased proteomic analysis. RESULTS: We found that the amount of proteins and transcripts of an Endoplasmic reticulum (ER) resident chaperone Heat shock protein cognate 3 (HSC3) and a mitochondria chaperone Heat Shock Protein 22 (HSP22) were significantly increased in the HTorA(ΔE)- expressing brains compared to the normal HTorA (HTorA(WT)) expressing brains. The physiological consequences included an increased susceptibility to oxidative and ER stress compared to normal HTorA(WT) flies. The alteration of transcripts of Inositol-requiring enzyme-1 (IRE1)-dependent spliced X box binding protein 1(Xbp1), several ER chaperones, a nucleotide exchange factor, Autophagy related protein 8b (ATG8b) and components of the ER associated degradation (ERAD) pathway and increased expression of the Xbp1-enhanced Green Fluorescence Protein (eGFP) in HTorA(ΔE) brains strongly indicated the activation of the unfolded protein response (UPR). In addition, perturbed expression of the UPR sensors and inducers in the HTorA(ΔE) Drosophila brains resulted in a significantly reduced life span of the flies. Furthermore, the types and quantities of proteins present in the anti-HSC3 positive microsomes in the HTorA(ΔE) brains were different from those of the HTorA(WT) brains. CONCLUSION: Taken together, these data show that HTorA(ΔE) in Drosophila brains may activate the UPR and increase the expression of HSP22 to compensate for the toxic effects caused by HTorA(ΔE) in the brains.