A disease-associated XPA allele interferes with TFIIH binding and primarily affects transcription-coupled nucleotide excision repair.

XPA is a central scaffold protein that coordinates the assembly of repair complexes in the global genome (GG-NER) and transcription-coupled nucleotide excision repair (TC-NER) subpathways. Inactivating mutations in XPA cause xeroderma pigmentosum (XP), which is characterized by extreme UV sensitivity and a highly elevated skin cancer risk. Here, we describe two Dutch siblings in their late forties carrying a homozygous H244R substitution in the C-terminus of XPA. They present with mild cutaneous manifestations of XP without skin cancer but suffer from marked neurological features, including cerebellar ataxia. We show that the mutant XPA protein has a severely weakened interaction with the transcription factor IIH (TFIIH) complex leading to an impaired association of the mutant XPA and the downstream endonuclease ERCC1-XPF with NER complexes. Despite these defects, the patient-derived fibroblasts and reconstituted knockout cells carrying the XPA-H244R substitution show intermediate UV sensitivity and considerable levels of residual GG-NER (~50%), in line with the intrinsic properties and activities of the purified protein. By contrast, XPA-H244R cells are exquisitely sensitive to transcription-blocking DNA damage, show no detectable recovery of transcription after UV irradiation, and display a severe deficiency in TC-NER-associated unscheduled DNA synthesis. Our characterization of a new case of XPA deficiency that interferes with TFIIH binding and primarily affects the transcription-coupled subpathway of nucleotide excision repair, provides an explanation of the dominant neurological features in these patients, and reveals a specific role for the C-terminus of XPA in TC-NER.

Noncanonical PDK4 action alters mitochondrial dynamics to affect the cellular respiratory status.

Dynamic regulation of mitochondrial morphology provides cells with the flexibility required to adapt and respond to electron transport chain (ETC) toxins and mitochondrial DNA-linked disease mutations, yet the mechanisms underpinning the regulation of mitochondrial dynamics machinery by these stimuli is poorly understood. Here, we show that pyruvate dehydrogenase kinase 4 (PDK4) is genetically required for cells to undergo rapid mitochondrial fragmentation when challenged with ETC toxins. Moreover, PDK4 overexpression was sufficient to promote mitochondrial fission even in the absence of mitochondrial stress. Importantly, we observed that the PDK4-mediated regulation of mitochondrial fission was independent of its canonical function, i.e., inhibitory phosphorylation of the pyruvate dehydrogenase complex (PDC). Phosphoproteomic screen for PDK4 substrates, followed by nonphosphorylatable and phosphomimetic mutations of the PDK4 site revealed cytoplasmic GTPase, Septin 2 (SEPT2), as the key effector molecule that acts as a receptor for DRP1 in the outer mitochondrial membrane to promote mitochondrial fission. Conversely, inhibition of the PDK4-SEPT2 axis could restore the balance in mitochondrial dynamics and reinvigorates cellular respiration in mitochondrial fusion factor, mitofusin 2-deficient cells. Furthermore, PDK4-mediated mitochondrial reshaping limits mitochondrial bioenergetics and supports cancer cell growth. Our results identify the PDK4-SEPT2-DRP1 axis as a regulator of mitochondrial function at the interface between cellular bioenergetics and mitochondrial dynamics.

Optimal antithrombotic strategy in patients with atrial fibrillation beyond 1 year after drug-eluting stent implantation: Design and rationale of the randomized ADAPT AF-DES trial.

BACKGROUND: Both anticoagulation and antiplatelet therapies are recommended after percutaneous coronary intervention (PCI) in patients with atrial fibrillation (AF). Although contemporary guidelines recommend discontinuation of antiplatelet therapy 1 year after drug-eluting stent (DES) implantation due to excessive bleeding risk, supporting randomized trials are still lacking. METHODS: The ADAPT AF-DES trial is a multicenter, prospective, open-label, randomized, non-inferiority trial, enrolling 960 patients with AF with a CHA2DS2-VASc score > 1, who underwent PCI with DES implantation at least 12 months before enrollment. Eligible patients are randomly assigned to receive either non-vitamin K antagonist oral anticoagulant (NOAC) monotherapy or NOAC plus clopidogrel combination therapy. The primary outcome is net adverse clinical event (NACE) at 1 year after randomization, defined as a composite of all-cause death, myocardial infarction, stent thrombosis, stroke, systemic embolism, and major or clinically relevant non-major bleeding, as defined by the International Society on Thrombosis and Hemostasis criteria. We hypothesize that NOAC monotherapy would be non-inferior to NOAC plus clopidogrel combination therapy for NACE in patients with AF beyond 12 months after DES implantation. CONCLUSIONS: The ADAPT AF-DES trial will evaluate the efficacy and safety of NOAC monotherapy versus NOAC plus clopidogrel combination therapy in patients with AF beyond 12 months after PCI with DES implantation. The ADAPT AF-DES trial will provide robust evidence for an optimal antithrombotic strategy in patients with AF after DES implantation. CLINICAL TRIAL REGISTRATION: https://www. CLINICALTRIALS: gov. Unique identifier: NCT04250116.

Effect of menopausal hormonal therapy on cardiovascular risks in Korean postmenopausal women: A nationwide cohort study.

OBJECTIVE: To evaluate the association between menopausal hormonal therapy (MHT) and the risk of cardiovascular disease (CVD), according to various regimens, dosages, routes of administration and starting ages of MHT. DESIGN: A population-based cohort study using the Korean National Health Insurance Services database. SETTING: Nationwide health insurance database. POPULATION: Women who reported entering menopause at an age of ≥40 years with no history of CVD in the national health examination. METHODS: The study population comprised 1 120 705 subjects enrolled between 2002 and 2019, categorised according to MHT status (MHT group, n = 319 007; non-MHT group, n = 801 698). MAIN OUTCOME MEASURES: Incidence of CVD (a composite of myocardial infarction and stroke). RESULTS: The incidence of CVD was 59 266 (7.4%) in the non-MHT group and 17 674 (5.5%) in the MHT group. After adjusting for confounding factors, an increased risk of CVD was observed with the administration of tibolone (hazard ratio, HR 1.143, 95% CI 1.117-1.170), oral estrogen (HR 1.246, 95% CI 1.198-1.295) or transdermal estrogen (HR 1.289, 95% CI 1.066-1.558), compared with the non-MHT group; the risk was based on an increased risk of stroke. The risk trends were consistent regardless of the age of starting MHT or the physicians' specialty. Among tibolone users, a longer period from entering menopause to taking tibolone and the use of any dosage (1.25 or 2.5 mg) were linked with a higher risk of CVD, compared with non-MHT users. CONCLUSIONS: This nationwide cohort study demonstrated an increased risk of CVD, driven mainly by an increased risk of stroke, among tibolone and oral or transdermal estrogen users, compared with that of non-MHT users.

NSMF promotes the replication stress-induced DNA damage response for genome maintenance.

Proper activation of DNA repair pathways in response to DNA replication stress is critical for maintaining genomic integrity. Due to the complex nature of the replication fork (RF), problems at the RF require multiple proteins, some of which remain unidentified, for resolution. In this study, we identified the N-methyl-D-aspartate receptor synaptonuclear signaling and neuronal migration factor (NSMF) as a key replication stress response factor that is important for ataxia telangiectasia and Rad3-related protein (ATR) activation. NSMF localizes rapidly to stalled RFs and acts as a scaffold to modulate replication protein A (RPA) complex formation with cell division cycle 5-like (CDC5L) and ATR/ATR-interacting protein (ATRIP). Depletion of NSMF compromised phosphorylation and ubiquitination of RPA2 and the ATR signaling cascade, resulting in genomic instability at RFs under DNA replication stress. Consistently, NSMF knockout mice exhibited increased genomic instability and hypersensitivity to genotoxic stress. NSMF deficiency in human and mouse cells also caused increased chromosomal instability. Collectively, these findings demonstrate that NSMF regulates the ATR pathway and the replication stress response network for genome maintenance and cell survival.

ssFPN: Scale Sequence (S2) Feature-Based Feature Pyramid Network for Object Detection.

Object detection is a fundamental task in computer vision. Over the past several years, convolutional neural network (CNN)-based object detection models have significantly improved detection accuracyin terms of average precision (AP). Furthermore, feature pyramid networks (FPNs) are essential modules for object detection models to consider various object scales. However, the AP for small objects is lower than the AP for medium and large objects. It is difficult to recognize small objects because they do not have sufficient information, and information is lost in deeper CNN layers. This paper proposes a new FPN model named ssFPN (scale sequence (S2) feature-based feature pyramid network) to detect multi-scale objects, especially small objects. We propose a new scale sequence (S2) feature that is extracted by 3D convolution on the level of the FPN. It is defined and extracted from the FPN to strengthen the information on small objects based on scale-space theory. Motivated by this theory, the FPN is regarded as a scale space and extracts a scale sequence (S2) feature by three-dimensional convolution on the level axis of the FPN. The defined feature is basically scale-invariant and is built on a high-resolution pyramid feature map for small objects. Additionally, the deigned S2 feature can be extended to most object detection models based on FPNs. We also designed a feature-level super-resolution approach to show the efficiency of the scale sequence (S2) feature. We verified that the scale sequence (S2) feature could improve the classification accuracy for low-resolution images by training a feature-level super-resolution model. To demonstrate the effect of the scale sequence (S2) feature, experiments on the scale sequence (S2) feature built-in object detection approach including both one-stage and two-stage models were conducted on the MS COCO dataset. For the two-stage object detection models Faster R-CNN and Mask R-CNN with the S2 feature, AP improvements of up to 1.6% and 1.4%, respectively, were achieved. Additionally, the APS of each model was improved by 1.2% and 1.1%, respectively. Furthermore, the one-stage object detection models in the YOLO series were improved. For YOLOv4-P5, YOLOv4-P6, YOLOR-P6, YOLOR-W6, and YOLOR-D6 with the S2 feature, 0.9%, 0.5%, 0.5%, 0.1%, and 0.1% AP improvements were observed. For small object detection, the APS increased by 1.1%, 1.1%, 0.9%, 0.4%, and 0.1%, respectively. Experiments using the feature-level super-resolution approach with the proposed scale sequence (S2) feature were conducted on the CIFAR-100 dataset. By training the feature-level super-resolution model, we verified that ResNet-101 with the S2 feature trained on LR images achieved a 55.2% classification accuracy, which was 1.6% higher than for ResNet-101 trained on HR images.

Proposal of a Token-Based Node Selection Mechanism for Node Distribution of Mobility IoT Blockchain Nodes.

Various elements, such as evolutions in IoT services resulting from sensoring by vehicle parts and advances in small communication technology devices, have significantly impacted the mass spread of mobility services that are provided to users in need of limited resources. In particular, business models are progressing away from one-off costs towards longer-term costs, as represented by shared services utilizing kick-boards or bicycles and subscription services for vehicle software. Advances in shared mobility services, as described, are calling for solutions that can enhance the reliability of data aggregated by users leveraging mobility services in the next-generation mobility areas. However, the mining process to renew status ensures continued network communication, and block creation demands high performance in the public block chain. Therefore, easing the mining process for state updates in public blockchains is a way to alleviate the high-performance process requirements of public blockchains. The proposed mechanism assigns token-based block creation authority instead of the mining method, which provides block creation authority to nodes that provide many resources. Blocks are created only by a group of participants with tokens, and after creation, tokens are updated and delivered to new nodes to form a new token group. Additionally, tokens are updated in each block after their initial creation, making it difficult to disguise the tokens and preventing resource-centered centralization.

Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network.

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.

Ewing sarcoma protein promotes dissociation of poly(ADP-ribose) polymerase 1 from chromatin.

Poly(ADP-ribose) polymerase 1 (PARP1) facilitates DNA damage response (DDR). While the Ewing's sarcoma breakpoint region 1 (EWS) protein fused to FLI1 triggers sarcoma formation, the physiological function of EWS is largely unknown. Here, we investigate the physiological role of EWS in regulating PARP1. We show that EWS is required for PARP1 dissociation from damaged DNA. Abnormal PARP1 accumulation caused by EWS inactivation leads to excessive Poly(ADP-Ribosy)lation (PARylation) and triggers cell death in both in vitro and in vivo models. Consistent with previous work, the arginine-glycine-glycine (RGG) domain of EWS is essential for PAR chain interaction and PARP1 dissociation from damaged DNA. Ews and Parp1 double mutant mice do not show improved survival, but supplementation with nicotinamide mononucleotides extends Ews-mutant pups' survival, which might be due to compensatory activation of other PARP proteins. Consistently, PARP1 accumulates on chromatin in Ewing's sarcoma cells expressing an EWS fusion protein that cannot interact with PARP1, and tissues derived from Ewing's sarcoma patients show increased PARylation. Taken together, our data reveal that EWS is important for removing PARP1 from damaged chromatin.

ATAD5 restricts R-loop formation through PCNA unloading and RNA helicase maintenance at the replication fork.

R-loops are formed when replicative forks collide with the transcriptional machinery and can cause genomic instability. However, it is unclear how R-loops are regulated at transcription-replication conflict (TRC) sites and how replisome proteins are regulated to prevent R-loop formation or mediate R-loop tolerance. Here, we report that ATAD5, a PCNA unloader, plays dual functions to reduce R-loops both under normal and replication stress conditions. ATAD5 interacts with RNA helicases such as DDX1, DDX5, DDX21 and DHX9 and increases the abundance of these helicases at replication forks to facilitate R-loop resolution. Depletion of ATAD5 or ATAD5-interacting RNA helicases consistently increases R-loops during the S phase and reduces the replication rate, both of which are enhanced by replication stress. In addition to R-loop resolution, ATAD5 prevents the generation of new R-loops behind the replication forks by unloading PCNA which, otherwise, accumulates and persists on DNA, causing a collision with the transcription machinery. Depletion of ATAD5 reduces transcription rates due to PCNA accumulation. Consistent with the role of ATAD5 and RNA helicases in maintaining genomic integrity by regulating R-loops, the corresponding genes were mutated or downregulated in several human tumors.

Reinforced Palmprint Reconstruction Attacks in Biometric Systems.

Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.

Overexpression of VEGF in the MOPC 315 Plasmacytoma Induces Tumor Immunity in Mice.

Vascular endothelial growth factor (VEGF) has important effects on hematopoietic and immune cells. A link between VEGF expression, tumor progression, and metastasis has been established in various solid tumors; however, the impact of VEGF expression by hematopoietic neoplasias remains unclear. Here, we investigated the role of VEGF in plasma cell neoplasia. Overexpression of VEGF in MOPC 315 tumor cells (MOPCSVm) had no effect on their growth in vitro. However, constitutive ectopic expression of VEGF dramatically reduced tumorigenicity of MOPC 315 when implanted subcutaneously into BALB/c mice. Mice implanted with MOPCSVm effectively rejected tumor grafts and showed strong cytotoxic T lymphocyte (CTL) activity against parental MOPC 315 cells. MOPCSVm implants were not rejected in nude mice, suggesting the process is T-cell-dependent. Adoptive transfer of splenocytes from recipients inoculated with MOPCSVm cells conferred immunity to naïve BALB/c mice, and mice surviving inoculation with MOPCSVm rejected the parental MOPC 315 tumor cells following a second inoculation. Immunohistochemical analysis showed that MOPCSVm induced a massive infiltration of CD3+ cells and MHC class II+ cells in vivo. In addition, exogenous VEGF induced the expression of CCR3 in T cells in vitro. Together, these data are the first to demonstrate that overexpression of VEGF in plasmacytoma inhibits tumor growth and enhances T-cell-mediated antitumor immune response.

Mitoquinone Inactivates Mitochondrial Chaperone TRAP1 by Blocking the Client Binding Site.

Heat shock protein 90 (Hsp90) family proteins are molecular chaperones that modulate the functions of various substrate proteins (clients) implicated in pro-tumorigenic pathways. In this study, the mitochondria-targeted antioxidant mitoquinone (MitoQ) was identified as a potent inhibitor of mitochondrial Hsp90, known as a tumor necrosis factor receptor-associated protein 1 (TRAP1). Structural analyses revealed an asymmetric bipartite interaction between MitoQ and the previously unrecognized drug binding sites located in the middle domain of TRAP1, believed to be a client binding region. MitoQ effectively competed with TRAP1 clients, and MitoQ treatment facilitated the identification of 103 TRAP1-interacting mitochondrial proteins in cancer cells. MitoQ and its redox-crippled SB-U014/SB-U015 exhibited more potent anticancer activity in vitro and in vivo than previously reported mitochondria-targeted TRAP1 inhibitors. The findings indicate that targeting the client binding site of Hsp90 family proteins offers a novel strategy for the development of potent anticancer drugs.

O-GlcNAcylation regulates dopamine neuron function, survival and degeneration in Parkinson disease.

The dopamine system in the midbrain is essential for volitional movement, action selection, and reward-related learning. Despite its versatile roles, it contains only a small set of neurons in the brainstem. These dopamine neurons are especially susceptible to Parkinson's disease and prematurely degenerate in the course of disease progression, while the discovery of new therapeutic interventions has been disappointingly unsuccessful. Here, we show that O-GlcNAcylation, an essential post-translational modification in various types of cells, is critical for the physiological function and survival of dopamine neurons. Bidirectional modulation of O-GlcNAcylation importantly regulates dopamine neurons at the molecular, synaptic, cellular, and behavioural levels. Remarkably, genetic and pharmacological upregulation of O-GlcNAcylation mitigates neurodegeneration, synaptic impairments, and motor deficits in an animal model of Parkinson's disease. These findings provide insights into the functional importance of O-GlcNAcylation in the dopamine system, which may be utilized to protect dopamine neurons against Parkinson's disease pathology.

Outcomes between prediabetes and type 2 diabetes mellitus in older adults with acute myocardial infarction in the era of newer-generation drug-eluting stents: a retrospective observational study.

BACKGROUND: The comparative clinical outcomes between prediabetes and type 2 diabetes mellitus (T2DM) in older adults with AMI in the era of newer-generation drug-eluting stents (DES) are limited. We investigated the 2-year clinical outcomes of these patients. METHODS: A total of 5492 AMI patients aged ≥65 years were classified into three groups according to their glycemic status: normoglycemia (group A: 1193), prediabetes (group B: 1696), and T2DM (group C: 2603). The primary outcome was the occurrence of major adverse cardiac events (MACE), defined as all-cause death, recurrent myocardial infarction (Re-MI), and any repeat revascularization. The secondary outcome was stent thrombosis (ST). RESULTS: The primary and secondary outcomes cumulative incidences were similar between the prediabetes and T2DM groups. In both the prediabetes and T2DM groups, the cumulative incidences of MACE (adjusted hazard ratio [aHR]: 1.373; p = 0.020 and aHR: 1.479; p = 0.002, respectively) and all-cause death or MI (aHR: 1.436; p = 0.022 and aHR: 1.647; p = 0.001, respectively) were significantly higher than those in the normoglycemia group. Additionally, the cumulative incidence of all-cause death in the T2DM group was significantly higher than that in the normoglycemia group (aHR, 1.666; p = 0.003). CONCLUSIONS: In this retrospective study, despite the 2-year clinical outcomes of the patients with prediabetes and T2DM in the older adults were worse than those in the normoglycemia group; they were similar between the prediabetes and T2DM groups. Hence, comparable treatment strategies should be strengthened between prediabetes and T2DM in older adults with AMI. TRIAL REGISTRATION: Retrospectively registered.

CTCF cooperates with CtIP to drive homologous recombination repair of double-strand breaks.

The pleiotropic CCCTC-binding factor (CTCF) plays a role in homologous recombination (HR) repair of DNA double-strand breaks (DSBs). However, the precise mechanistic role of CTCF in HR remains largely unclear. Here, we show that CTCF engages in DNA end resection, which is the initial, crucial step in HR, through its interactions with MRE11 and CtIP. Depletion of CTCF profoundly impairs HR and attenuates CtIP recruitment at DSBs. CTCF physically interacts with MRE11 and CtIP and promotes CtIP recruitment to sites of DNA damage. Subsequently, CTCF facilitates DNA end resection to allow HR, in conjunction with MRE11-CtIP. Notably, the zinc finger domain of CTCF binds to both MRE11 and CtIP and enables proficient CtIP recruitment, DNA end resection and HR. The N-terminus of CTCF is able to bind to only MRE11 and its C-terminus is incapable of binding to MRE11 and CtIP, thereby resulting in compromised CtIP recruitment, DSB resection and HR. Overall, this suggests an important function of CTCF in DNA end resection through the recruitment of CtIP at DSBs. Collectively, our findings identify a critical role of CTCF at the first control point in selecting the HR repair pathway.

A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme.

In recent years, the importance of catching humans' emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual's workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with "functional connectivity", i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network.

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.

Simple Method To Characterize the Ciliary Proteome of Multiciliated Cells.

Motile cilia of multiciliated epithelial cells have important roles in animal development and cell homeostasis. Although several studies have identified and reported proteins localized in this complex organelle and the related immotile primary cilia from various cell types, it is still challenging to isolate high quantities of ciliary proteins for proteomic analysis. In this study, African clawed frog (Xenopus laevis) embryos, which have many multiciliated cells in the epidermis, were treated with a simple ionic buffer to identify 1009 proteins conserved across vertebrates; these proteins were putatively localized in motile cilia. Using two ciliary proteome databases, we confirmed that previously validated cilia-associated proteins are highly enriched in our ciliary proteome. Proteins localized at the transition zone and Ellis-van Creveld zone, which are distinct regions at the base of cilia, near the junction with the apical cell surface, were isolated using our method. Among the newly identified ciliary proteins, we report that KRT17 may have an unrecognized function in motile cilia. Hence, the method developed in this study would be useful for understanding the ciliary proteome.