Miracle-dz/UniFAR-FTUs huggingface dataset:
Altered T-cell subset repertoire affects treatment outcome of patients with myelofibrosis

Phenotypic characterization of T cells in myelofibrosis is intriguing because of increased inflammation, markedly elevated pro-inflammatory cytokines, and altered distribution of T-cell subsets. Constitutive activation of Janus kinase-2 (JAK2) in the majority of patients with myelofibrosis contributes to the expression of the programmed cell death protein-1 (PD1) and T-cell exhaustion. We wondered whether T-cell activation affects treatment outcome of patients with myelofibrosis and sought to determine whether the JAK1/2 inhibitor ruxolitinib affects the activation of T-cell subsets. T cells from 47 myelofibrosis patients were analyzed and the percentages of either helper (CD4+) or cytotoxic (CD8+) naïve, central memory, effector memory, or effector T cells; and fractions of PD1-expressing cells in each subset were assessed. Higher numbers of T cells co-expressing CD4/PD1 and CD8/PD1 were found in myelofibrosis patients than in healthy controls (n=28), and the T cells were significantly skewed toward an effector phenotype in both CD4+ and CD8+ subsets, consistent with a shift from a quiescent to an activated state. Over the course of ruxolitinib treatment, the distribution of aberrant T-cell subsets significantly reversed towards resting cell phenotypes. CD4+ and CD8+ subsets at baseline correlated with monocyte and platelet counts, and their PD1+ fractions correlated with leukocyte counts and spleen size. Low numbers of PD1+/CD4+ and PD1+/CD8+ cells were associated with complete resolution of palpable splenomegaly and improved survival rate, suggesting that low levels of exhausted T cells confer a favorable response to ruxolitinib treatment.
Partial Occlusion Face Recognition Method Based on Acupoints locating through Infrared Thermal Imaging

As a new intelligent technology of identity recognition. , face recognition is an important means of security authentication. However, in real life, the face may be blocked by hats, masks and other occlusions, resulting in the existing face recognition algorithms and methods cannot be applied in practical applications. In order to solve the above problems, this paper proposes a partial occlusion face recognition method based on infrared thermal imaging to locate acupoints. Firstly, the face image is collected and the face features are obtained by Principal component analysis (PCA) algorithm. Secondly, the infrared thermal imaging technology is introduced adaptively by judging the occlusion status of the face, and then the individual facial acupoints are located by BP neural network algorithm to assist in the effective recognition of partially occluded faces. The results of simulation show that this recognition method proposed can increase the accuracy of partial occlusion face recognition, which has practical value.
Lysocardiolipin acyltransferase regulates NSCLC cell proliferation and migration by modulating mitochondrial dynamics

Lysocardiolipin acyltransferase (LYCAT), a cardiolipin (CL)-remodeling enzyme, is crucial for maintaining normal mitochondrial function and vascular development. Despite the well-characterized role for LYCAT in the regulation of mitochondrial dynamics, its involvement in lung cancer, if any, remains incompletely understood. In this study, in silico analysis of TCGA lung cancer data sets revealed a significant increase in LYCAT expression, which was later corroborated in human lung cancer tissues and immortalized lung cancer cell lines via indirect immunofluorescence and immunoblotting, respectively. Stable knockdown of LYCAT in NSCLC cell lines not only reduced CL and increased monolyso-CL levels but also reduced in vivo tumor growth, as determined by xenograft studies in athymic nude mice. Furthermore, blocking LYCAT activity using a LYCAT mimetic peptide attenuated cell migration, suggesting a novel role for LYCAT activity in promoting NSCLC. Mechanistically, the pro-proliferative effects of LYCAT were mediated by an increase in mitochondrial fusion and a G1/S cell cycle transition, both of which are linked to increased cell proliferation. Taken together, these results demonstrate a novel role for LYCAT in promoting NSCLC and suggest that targeting LYCAT expression or activity in NSCLC may provide new avenues for the therapeutic treatment of lung cancer.
Cache-Aided Retrospective Interference Alignment in SISO X Wireless Networks

In this paper, one class of single-input single-output (SISO) X networks with cache at both transmitters and receivers is considered. Specifically, the X networks consist of two transmitters and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> receivers. Furthermore, only the delayed channel state information at transmitters (CSIT) is available. In order to improve degrees of freedoms (DoF) of the system, we propose a cache-aided retrospective interference alignment (RIA) scheme which contains the <italic>Prefetching Phase</italic> and the <italic>Delivery Phase</italic>. In the <italic>Prefetching Phase</italic>, we carefully choose the contents to be cached at both transmitters and receivers. In the <italic>Delivery Phase</italic>, we propose a cache-aided RIA transmission scheme where order-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> symbols are delivered and the cached contents are exploited to cancel interference. Moreover, when <inline-formula> <tex-math notation="LaTeX">$\mu = K-1$ </tex-math></inline-formula>, it is reduced to a blind scheme, which does not need the CSIT. Furthermore, we present one theorem to characterize the sum DoF of the proposed cache-aided RIA scheme. It is shown that the proposed cache-aided scheme outperforms the RIA scheme without cache and the cache-aided interference management scheme for the system configuration considered in this paper.
An Associative Analysis of Recognition Memory: Relative Recency Effects in an Eye-Tracking Paradigm

We report 2 eye-tracking experiments with human variants of 2 rodent recognition memory tasks, relative recency and object-in-place. In Experiment 1 participants were sequentially exposed to 2 images, A then B, presented on a computer display. When subsequently tested with both images, participants biased looking toward the first-presented image A: the relative recency effect. When contextual stimuli x and y, respectively, accompanied A and B in the exposure phase (xA, yB), the recency effect was greater when y was present at test, than when x was present. In Experiment 2 participants viewed 2 identical presentations of a 4-image array, ABCD, followed by a test with the same array, but in which one of the pairs of stimuli exchanged position (BACD or ABDC). Participants looked preferentially at the displaced stimulus pair: the object-in-place effect. Three further conditions replicated Experiment 1’s findings: 2 pairs of images were presented one after the other (AB followed by CD); on a test with AB and CD, relative recency was again evident as preferential looking at AB. Moreover, this effect was greater when the positions of the first-presented A and B were exchanged between exposure and test (BACD), compared with when the positions of second-presented C and D were exchanged (ABDC). The results were interpreted within the theoretical framework of the Sometime Opponent Process model of associative learning (Wagner, 1981).
A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI.

PURPOSE Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic CT (sCT) images from MRI. METHODS This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the U-Net network) without softmax layers and the residual network. Moreover, maxpooling indices and high resolution features from the encoding network were incorporated into the corresponding decoding layers. A dataset containing 15 co-registered MRI-CT pairs of male pelvis (1861 two-dimensional images) were used for training and evaluation of MRI to CT synthesis process using a five-fold cross-validation scheme. The performance of the eCNN model was compared to an atlas-based sCT generation technique as well as the original U-Net model considering CT images as reference. The mean error (ME), mean absolute error (MAE), Pearson correlation coefficient (PCC), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics were calculated between sCT and ground truth CT images. RESULTS The eCNN model exhibited effective learning capability using only 12 training subjects. The model achieved a ME and MAE of 2.8±10.3 and 30.0±10.4 HU, respectively, which is substantially lower than values achieved by the atlas-based (-0.8±35.4 and 64.6±21.2) and U-Net (7.4±11.9 and 44.0±8.8) methods, respectively. CONCLUSION The proposed eCNN model exhibited efficient convergence rate with a low number of training subjects, while providing accurate synthetic CT images. The eCNN model outperformed the original U-Net model and showed superior performance to the atlas-based technique.
Comprehensive Cohort Analysis of Mutational Spectrum in Early Onset Breast Cancer Patients

Early onset breast cancer (EOBC), diagnosed at age ~40 or younger, is associated with a poorer prognosis and higher mortality rate compared to breast cancer diagnosed at age 50 or older. EOBC poses a serious threat to public health and requires in-depth investigation. We studied a cohort comprising 90 Taiwanese female patients, aiming to unravel the underlying mechanisms of EOBC etiopathogenesis. Sequence data generated by whole-exome sequencing (WES) and whole-genome sequencing (WGS) from white blood cell (WBC)–tumor pairs were analyzed to identify somatic missense mutations, copy number variations (CNVs) and germline missense mutations. Similar to regular breast cancer, the key somatic mutation-susceptibility genes of EOBC include TP53 (40% prevalence), PIK3CA (37%), GATA3 (17%) and KMT2C (17%), which are frequently reported in breast cancer; however, the structural protein-coding genes MUC17 (19%), FLG (16%) and NEBL (11%) show a significantly higher prevalence in EOBC. Furthermore, the top 2 genes harboring EOBC germline mutations, MUC16 (19%) and KRT18 (19%), encode structural proteins. Compared to conventional breast cancer, an unexpectedly higher number of EOBC susceptibility genes encode structural proteins. We suspect that mutations in structural proteins may increase physical permeability to environmental hormones and carcinogens and cause breast cancer to occur at a young age.
BUA: A Blockchain-based Unlinkable Authentication in VANETs

Authentication with unlinkability is one of the critical requirements for the security of VANETs. Unlinkability prevents attackers from linking multiple messages to infer vehicular privacy. Pseudonymous authentication schemes are widely adopted to achieve unlinkable authentication. However, they need multiple interactions with a trusted third-party to update pseudonym as well as the attached information. In order to address this issue and provide effective services in distributed systems, we propose a blockchain-based unlinkable authentication protocol called BUA, where Service Manager (SM) of each domain acts as the nodes of consortium blockchain to construct a distributed system. Each SM covers a certain logical area and maintains a sequence of consistent blocks, which hold vehicular registration data. Based on the system, vehicles use homomorphic encryption to self-generate any number of pseudonyms to achieve unlinkability. Pseudonymous validity and ownership can be verified locally by each SM. Performance evaluation results of the proposed scheme show that our protocol provides stronger security with less computation and communication overhead.
Mind the Links! How Hyperlinks Influence Online Reading and Navigation : An Eye Movement Study

In this thesis, the eye movement methodology is used to explore the influence of hyperlinks and reading task on online reading in a navigable Web environment. The amount of research on hypertext reading is limited, and the understanding of hypertext processing is only developing. The previous eye movement studies conducted in a nonnavigable web environment suggest that the hyperlinked words are not more difficult to process than unlinked words. The research also suggests that in the skim reading task, hyperlinked words are less likely to be skipped, and when they are fixated, they are processed more carefully than unlinked words. In this experiment, the possibility to click and navigate the hypertext made it possible to explore how the hyperlinks are processed in a more authentic Web environment. It was expected that the results from this experiment would be similar than those observed in previous studies: hyperlinked words would not be more difficult to process than unlinked words, and in the skim reading task, hyperlinked words would be processed more carefully than unlinked words. In the study design, thirty-two native English speakers read and navigated hypertext in a Wikipedia environment while their eye movements were recorded. The results indicate that hyperlinked words are not more difficult to process than unlinked words, but readers do focus on hyperlinked words. The results were not clear on whether the hyperlinked words were processed differently across the reading for comprehension and skim reading tasks. The early processing of hyperlinks was more careful compared to the unlinked words in the skimming task. Hyperlinks received more fixations, and their later processing was more careful than that of the unlinked words in both tasks. The results add to evidence that the visible form of hyperlink is not problematic for reading processes, but what words are selected as hyperlinks should be considered in user optimal web design. It is necessary to further explore reading and navigation on the web, with realistic tasks and in different web environments. It is also important to investigate how hypertexts are used in wider learning processes. Exploring how cognitive resources and individual differences influence hypertext processing is also crucial to find out how hypertexts can be made accessible and beneficial for all the users. HEADINGS Hypertext, hyperlinks, online reading, skim reading, eye movements, web design
Game-construction students experiences of educational games - gaming outcomes and learning outcomes

Abstract Game-based learning has been a rapidly expanding field in the 21th century, with research reports praising the motivational effects and learning outcomes of games. At the same time there have been doubts on the learning outcomes of even the most seriously designed learning games. The generation that today enrols for university programmes is in general a gaming generation, and game construction students are often so called hard core gamers. What are the game construction students’ experiences of educational games, and what are their perceived learning outcomes? This study is based on student essays on gaming habits and game-based learning outcomes, written by students taking a course on game-based learning. Essays from four course batches have been thematically analysed to answer the two research questions. Findings show that students have a rich experience of various kinds of games, but that they do not make any clear distinction between games developed for educational purposes, and other games. Students also perceive that there are cognitive, behavioural as well as affective learning outcomes from both standard entertainment games, and more serious learning games.
Target Tracking in the Presence of Intermittent Measurements by a Sparsely Distributed Network of Stationary Cameras

An observer and predictor framework is developed for tracking a moving object using a sparsely distributed network of stationary cameras. The sparsity of the camera network is representative of complex environments where continuous image feedback is not available, which occurs when the target is transitioning between fields-of-view in the camera network or the target is occluded by objects in the scene. Using a Lyapunov-based stability analysis, estimates of the object’s pose and motion model are proven to converge, provided the current network configuration satisfies certain dwell-time conditions (i.e., minimum time the object needs to be observed and maximum time the object may remain outside a feedback region). This approach allows for areas of the network that may cause instabilities in the pose estimates, based on the dwell-time conditions, to be identified ahead of time and offers insight into how the network configuration could be augmented to ensure stability of the target’s pose estimates.
Machine Learning based Energy Saving Scheme in Wireless Access Networks

With the deployment of the 5G, lower latency and higher frequency brings not only the users' satisfaction but also the dense deployment of base stations which causes large energy consumption. Thus, energy saving is a challenging issue. Machine learning techniques, which could extract features from raw data in the wireless access network and predict the future state by the previous features, are useful to energy saving. In this paper, we proposed the energy saving scheme based on machine learning techniques to achieve the balance between system performance and energy efficiency. Cell switching-off by load prediction using different algorithms, and dynamic threshold adjustment improve the energy efficiency and thereby reduces the operation cost. By demonstrating the results of experiments in wireless environment, the switch-off duration could reach up to 176% increase, and the energy scheme can save up to 2, 223 RMB electricity saving per week per cell.
Vehicle Trajectory Reconstruction from Global Position System Data with Long Short-term Memory Neural Network

Vehicle trajectory contains massive spatial-temporal information of traffic flow, which is of great significance for the comprehensive deconstruction of urban traffic network. Vehicles equipped with global position system (GPS) detector can collect large-scale, all-weather vehicle trajectory data, which plays an important role in construction of intelligent transportation system (ITS). However, due to the limitation of actual technical level and complex environment, GPS equipment cannot receive valid signals frequently. Therefore, it is necessary to complete missing data so that it can better serve for the intelligent traffic management applications. This article proposes a long shortterm memory neural network (LSTM NN) model to reconstruct vehicle trajectory from GPS data, using LSTM NN to learn the operating pattern of vehicle GPS sequence data. The accuracy of the model is verified on the SUMO microscopic traffic simulation platform. The simulation network is constructed based on the real road network in Zhangjiagang, China, and the signal intersection timing is calibrated according to actual signal timing plan. The results show that the method can achieve satisfactory application effects.
Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example

Edge Computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.
Construction of an Uncertainty to Maximize the Gain at Multiple Frequencies

This paper considers the construction of worst- case perturbations for uncertain systems. The uncertain system is modeled as an interconnection of a linear time-invariant (LTI) system and a norm-bounded LTI uncertainty. The worst-case gain (measured in the norm) for the uncertain system can be assessed via skewed-µ analysis. The standard approach is to compute upper and lower bounds for the worst-case gain on a frequency grid. A worst-case LTI perturbation is then constructed to maximize the gain at a single frequency. This perturbation can be used within a high fidelity nonlinear simulation to further explore system robustness. A drawback of this existing approach is that the worst-case perturbation constructed at a single frequency may not necessarily induce poor time-domain performance. It is beneficial to construct a perturbation that maximizes the gain at multiple frequency points, e.g. where the system is most sensitive or where disturbances have large frequency content. This paper provides an algorithm to construct a single perturbation which causes the uncertain system to achieve its largest possible gain at multiple chosen frequency points. This is achieved by interpolating through worst-case samples at the individual frequencies using the boundary Nevanlinna-Pick interpolation. Simple numerical examples are provided to demonstrate the proposed approach.
Turnip yellow mosaic virus protease binds ubiquitin suboptimally to fine-tune its deubiquitinase activity

Single-stranded, positive-sense RNA viruses assemble their replication complexes in infected cells from a multidomain replication polyprotein. This polyprotein usually contains at least one protease, the primary function of which is to process the polyprotein into mature proteins. Such proteases also may have other functions in the replication cycle. For instance, cysteine proteases (PRO) frequently double up as ubiquitin hydrolases (DUB), thus interfering with cellular processes critical for virus replication. We previously reported the crystal structures of such a PRO/DUB from Turnip yellow mosaic virus (TYMV) and of its complex with one of its PRO substrates. Here we report the crystal structure of TYMV PRO/DUB in complex with ubiquitin. We find that PRO/DUB recognizes ubiquitin in an unorthodox way: It interacts with the body of ubiquitin through a split recognition motif engaging both the major and the secondary recognition patches of ubiquitin (Ile44 patch and Ile36 patch, respectively, including Leu8, which is part of the two patches). However, the contacts are suboptimal on both sides. Introducing a single-point mutation in TYMV PRO/DUB aimed at improving ubiquitin-binding led to a much more active DUB. Comparison with other PRO/DUBs from other viral families, particularly coronaviruses, suggests that low DUB activities of viral PRO/DUBs may generally be fine-tuned features of interaction with host factors.