Miracle-dz/UniFAR-FTUs huggingface dataset:
The Progression of Posterior Cortical Atrophy to Corticobasal Syndrome: Lumping or Splitting Neurodegenerative Diseases?

Background Posterior cortical atrophy is a clinical syndrome that is characterized by the progressive loss of visuospatial integration and is associated with neurodegenerative conditions. Case Report We describe a 60-year-old female with simultanagnosia, oculomotor apraxia, and optic ataxia for which she received an initial clinical diagnosis of posterior cortical atrophy. Three years later, she developed Balint's syndrome, Gerstmann's syndrome, left alien hand syndrome, smooth asymmetric (left) rigidity, cortical sensory loss, and spontaneous myoclonic jerks of the left arm, which suggested a final diagnosis of corticobasal syndrome. Discussion This case report indicates that corticobasal syndrome may present with visuospatial deficits.
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery

Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor specific datasets is time intensive and cost prohibitive, remote sensing researchers have embraced deep unsupervised feature extraction. Although these methods have pushed state-of-the-art performance on current HSI benchmarks, many of these tools are not readily accessible to many researchers. In this letter, we introduce a software pipeline, which we call EarthMapper, for the semantic segmentation of non-RGB remote sensing imagery. It includes self-taught spatial-spectral feature extraction, various standard and deep learning classifiers, and undirected graphical models for post-processing. We evaluated EarthMapper on the Indian Pines and Pavia University datasets and have released this code for public use.
High frequency oscillatory ventilation in a cohort of children with respiratory failure

Our objective was to determine survival and variables associated with poor outcomes for patients requiring high frequency oscillatory ventilator (HFOV) support. We tested the ability of markers of oxygenation to predict outcome, specifically, the oxygenation index (OI), both prior to and after initiation of HFOV. We also aimed to examine the effect of immunocompromised condition (IC), and specifically stem cell transplant (SCT), on outcome.
Identification and classification of eyes movement using EEG signals

This work investigates the eye movement of 20 participants using the EEG signals measured using the headset wireless EMOTIV EPOC+ to collect EEG signals. The eye movement is detected analyzing the electroencephalographic (EEG) signals corresponding to four experiments: the participant follows with his eyes the movement of a circle on the screen. These movements are (i) from left to right (ii) from right to left (iii) from top to bottom and (iv) from bottom to top. The EEG signals obtained are classify using Support Vector Machine and Principal Component Analysis. The results obtained presents an accuracy between 81% and 91%. The above results show us that are possible to identify and classify the eye movements using EEG signals.
ON THE USE OF OPTICALLY TRAPPED DUST PARTICLES AS MICRO-PROBES IN PROCESS PLASMAS

In this paper we outline the progress in the development of an optical system for particle manipulation as a plasma diagnostics method. We demonstrate basic principles and preliminary experimental results for optical trapping of microparticles in a plasma. A counter-propagating laser beam was used to trap particles in water as well as in an RF discharge. The experiments indicate that it is possible to manipulate particles, which are levitating in the plasma sheath, to obtain information on the sheath and plasma parameters. PACS: 42.50. Wk, 52.27. Lw, 52.70. -m, 87.80. Cc
Face Recognition Research Based on the Fusion of Layered LBP Feature

Local Binary Pattern(LBP) is an operator to describe the local texture of face, which has the characteristics of rotation invariance and grayscale invariance. This paper proposed a face recognition arithmetic based on the fusion of layered LBP feature(F-LLBP), using the LBP operator to extract the layered texture feature, fusing the characteristics of each layer, reducing the dimension with Principal Component Analysis and Linear Discriminant Analysis(PCA-LDA) to get a set of low dimensional characteristic which are easy to classify, finally using the KNN arithmetic to classify. The experiments on ORL face database verifies the effectiveness of the F-LLBP, the experimental results indicate that the recognition rate of FLLBP increased 8% relative to the traditional LBP feature combined with PCA dimension reduction arithmetic.
Smart monitoring via participatory BLE relaying

We espouse the vision of a smart object/campus architecture where sensors attached to smart objects use BLE as communication interface, and where smartphones act as opportunistic relays to transfer the data. We explore the feasibility of the vision with real-world Wi-Fi based location traces from our university campus. Our feasibility studies establish that redundancy exists in user movement within the indoor spaces, and that this redundancy can be exploited for collecting sensor data in an opportunistic, yet fair manner. We develop a couple of alternative heuristics that address the BLE energy asymmetry challenge by intelligently duty-cycling the scanning actions of individual devices. We evaluate the efficacy and tradeoffs of the proposed approaches by simulation experiments with real-world location traces.
Evolution and Steady State of a Long-Range Two-Dimensional Schelling Spin System

We consider a long-range interacting particle system in which binary particles are located at the integer points of a flat torus. Based on the interactions with other particles in its "neighborhood" and on the value of a common intolerance threshold $\tau$, every particle decides whether to change its state after an independent and exponentially distributed waiting time. This is equivalent to a Schelling model of self-organized segregation in an open system, a zero-temperature Ising model with Glauber dynamics, or an Asynchronous Cellular Automaton (ACA) with extended Moore neighborhoods. We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics. Second, we show that when the process stops, for all ${\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}}$ where ${\tau^* \approx 0.488}$, and when the size of the neighborhood of interaction $N$ is sufficiently large, every particle is contained in a large "monochromatic region" of size exponential in $N$, almost surely. When particles are placed on the infinite lattice $\mathbb{Z}^2$ rather than on a flat torus, for the values of $\tau$ mentioned above, sufficiently large $N$, and after a sufficiently long evolution time, every particle is contained in a large monochromatic region of size exponential in $N$, almost surely.
FBSGraph: Accelerating Asynchronous Graph Processing via Forward and Backward Sweeping

Graph algorithm is pervasive in many applications ranging from targeted advertising to natural language processing. Recently, Asynchronous Graph Processing (AGP) is becoming a promising model to support graph algorithm on large-scale distributed computing platforms because it enables faster convergence speed and lower synchronization cost than the synchronous model for no barrier between iterations. However, existing AGP methods still suffer from poor performance for inefficient vertex state propagation. In this paper, we propose an effective and low-cost forward and backward sweeping execution method to accelerate state propagation for AGP, based on a key observation that states in AGP can be propagated between vertices much faster when the vertices are processed sequentially along the graph path within each round. Through dividing graph into paths and asynchronously processing vertices on each path in an alternative forward and backward way according to their order on this path, vertex states in our approach can be quickly propagated to other vertices and converge in a faster way with only little additional overhead. In order to efficiently support it over distributed platforms, we also propose a scheme to reduce the communication overhead along with a static priority ordering scheme to further improve the convergence speed. Experimental results on a cluster with 1,024 cores show that our approach achieves excellent scalability for large-scale graph algorithms and the overall execution time is reduced by at least 39.8 percent, in comparison with the most cutting-edge methods.
A Metric for Measuring IoT Devices Security Levels

As the number of IoT devices continues to increase, so does the number of challenges we need to face. Security is a big concern since the number of attacks to IoT devices keeps rising. We show that currently available Security metrics do not correctly apply to IoT devices, and propose an appropriate metric. As a proof-of-concept, we apply our metric to three IoT devices and show that it reacts to the presence of vulnerabilities as well as actual exploits.
The Effectiveness of Classification on Information Retrieval System (Case Study)

Large amount of unstructured designed information is difficult to deal with. Obtaining specific information is a hard mission and takes a lot of time. Information Retrieval System (IR) is a way to solve this kind of problem. IR is a good mechanism but does not give the perfect solution. Other techniques have been added to IR to develop the result. One of the techniques is text classification. Text classification task is to assign a document to one or more category. It could be done manually or algorithmically. Text classification enhances the output of this process by reducing the results. This study proved that text classification has a positive influence on Information Retrieval Systems.
Electrocardiogram‐gated coronary CT angiography dose estimates using ImPACT

The primary study objective was to assess radiation doses using a modified form of the Imaging Performance Assessment of Computed Tomography (CT) scanner (ImPACT) patient dosimetry for cardiac applications on an Aquilion ONE ViSION Edition scanner, including the Ca score, target computed tomography angiography (CTA), prospective CTA, continuous CTA/cardiac function analysis (CFA), and CTA/CFA modulation. Accordingly, we clarified the CT dose index (CTDI) to determine the relationship between heart rate (HR) and X‐ray exposure. As a secondary objective, we compared radiation doses using modified ImPACT, a whole‐body dosimetry phantom study, and the k‐factor method to verify the validity of the dose results obtained with modified ImPACT. The effective dose determined for the reference person (4.66 mSv at 60 beats per minute (bpm) and 33.43 mSv at 90 bpm) were approximately 10% less than those determined for the phantom study (5.28 mSv and 36.68 mSv). The effective doses according to the k‐factor (0.014 mSv·mGy−1·cm−1; 2.57 mSv and 17.10 mSv) were significantly lower than those obtained with the other two methods. In the present study, we have shown that ImPACT, when modified for cardiac applications, can assess both absorbed and effective doses. The results of our dose comparison indicate that modified ImPACT dose assessment is a promising and practical method for evaluating coronary CTA. PACS number(s): 87.57. Q‐, 87.59. Dj, 87.57. uq
Impact of F1Fo-ATP-synthase dimer assembly factors on mitochondrial function and organismic aging

In aerobic organisms, mitochondrial F1Fo-ATP-synthase is the major site of ATP production. Beside this fundamental role, the protein complex is involved in shaping and maintenance of cristae. Previous electron microscopic studies identified the dissociation of F1Fo-ATP-synthase dimers and oligomers during organismic aging correlating with a massive remodeling of the mitochondrial inner membrane. Here we report results aimed to experimentally proof this impact and to obtain further insights into the control of these processes. We focused on the role of the two dimer assembly factors PaATPE and PaATPG of the aging model Podospora anserina. Ablation of either protein strongly affects mitochondrial function and leads to an accumulation of senescence markers demonstrating that the inhibition of dimer formation negatively influences vital functions and accelerates organismic aging. Our data validate a model that links mitochondrial membrane remodeling to aging and identify specific molecular components triggering this process.
A framework for real-time spam detection in Twitter

With the increased popularity of online social networks, spammers find these platforms easily accessible to trap users in malicious activities by posting spam messages. In this work, we have taken Twitter platform and performed spam tweets detection. To stop spammers, Google SafeBrowsing and Twitter's BotMaker tools detect and block spam tweets. These tools can block malicious links, however they cannot protect the user in real-time as early as possible. Thus, industries and researchers have applied different approaches to make spam free social network platform. Some of them are only based on user-based features while others are based on tweet based features only. However, there is no comprehensive solution that can consolidate tweet's text information along with the user based features. To solve this issue, we propose a framework which takes the user and tweet based features along with the tweet text feature to classify the tweets. The benefit of using tweet text feature is that we can identify the spam tweets even if the spammer creates a new account which was not possible only with the user and tweet based features. We have evaluated our solution with four different machine learning algorithms namely — Support Vector Machine, Neural Network, Random Forest and Gradient Boosting. With Neural Network, we are able to achieve an accuracy of 91.65% and surpassed the existing solution [1] by approximately 18%.
Barriers and Facilitators of Partner Treatment of Chlamydia: A Qualitative Investigation with Prescribers and Community Pharmacists

Chlamydia trachomatis is the most frequently-notified sexually transmitted infection in Australia. Effective and timely partner treatment of chlamydia is essential to reduce overall prevalence and the burden of infection. Currently in most of Australia, the only avenue for partner treatment of chlamydia (“standard partner therapy”) is a tedious, and often inconvenient, process. The barriers and facilitators of standard partner therapy, and newer models of accelerated partner therapy (APT), need to be identified in the Australian setting. Additionally, the potential role of community pharmacists need to be explored. Semi-structured interview guides for two key stakeholder groups (prescribers and pharmacists) were developed and piloted. Eleven prescribers (general practitioners, sexual health clinicians and nurse practitioners) and twelve pharmacists practicing in the Perth metropolitan region were interviewed. Key reported barriers to standard partner therapy were lack of or delayed chlamydia testing. Key facilitators included ability to test and educate sexual partner. Key barriers for APT included prescribers’ legal responsibility and potential for medication-related adverse effects. Healthcare provider consultation and chlamydia testing were seen as potential facilitators of APT. Pharmacists were receptive to the idea of expanding their role in chlamydia treatment, however, barriers to privacy must be overcome in order to be acceptable to prescribers and pharmacists.
Wireless Spoofing-Attack Prevention Using Radio-Propagation Characteristics

A spoofing attack is a critical issue in wireless communication in embedded systems in which a malicious transmitter outside a system attempts to be genuine. As a countermeasure against this, we propose a device-authentication method based on position identification using radio-propagation characteristics (RPCs). Since RPCs are natural phenomena, this method does not depend on information processing such as encryption technology. We call the space from which attacks achieve success "attack space". By formulating the relationship between combinations of transceivers and the attack space, this method can be used in embedded systems. In this research, we consider two RPCs, the received signal strength ratio (RSSR) and the time difference of arrival (TDoA), and construct the attack-space model which use these RPCs simultaneously for preventing wireless spoofing-attacks. We explain the results of a validity evaluation for the proposed model based on radio-wave-propagation simulation assuming free space and a noisy environment.
A study on image quality provided by a kilovoltage cone‐beam computed tomography

The image‐guided radiotherapy technique (IGRT) makes use of imaging devices to verify the positions of the target volume and organs at risk during the treatment sessions. In this work we evaluate the image quality provided by an imaging system based on a kilovoltage cone‐beam CT, and explore its ability to perform IGRT and adaptive radiotherapy. We analyze the accuracy of the image slice width, the spatial resolution using the MTF function, the image uniformity, the signal‐to‐noise ratio, the contrast‐to‐noise ratio, the low‐contrast sensitivity, and the HU linearity with density. The studied parameters are evaluated in an objective and quantitative way, allowing for a direct comparison with other imaging devices. We conclude that the analyzed cone‐beam imaging system is adequate to accurately perform IGRT within its clinical use, despite the high level of noise present in a cone beam caused by scatter. We also point out the presence of a bowtie wobble artifact in the reconstructed images. Nevertheless, we conclude that these features do not limit the capability of the system to perform adaptive radiotherapy in most cases. PACS number: 87.57. ‐s
Family level phylogenies reveal modes of macroevolution in RNA viruses

Despite advances in understanding the patterns and processes of microevolution in RNA viruses, little is known about the determinants of viral diversification at the macroevolutionary scale. In particular, the processes by which viral lineages assigned as different “species” are generated remain largely uncharacterized. To address this issue, we use a robust phylogenetic approach to analyze patterns of lineage diversification in five representative families of RNA viruses. We ask whether the process of lineage diversification primarily occurs when viruses infect new host species, either through cross-species transmission or codivergence, and which are defined here as analogous to allopatric speciation in animals, or by acquiring new niches within the same host species, analogous to sympatric speciation. By mapping probable primary host species onto family level viral phylogenies, we reveal a strong clustering among viral lineages that infect groups of closely related host species. Although this is consistent with lineage diversification within individual hosts, we argue that this pattern more likely represents strong biases in our knowledge of viral biodiversity, because we also find that better-sampled human viruses rarely cluster together. Hence, although closely related viruses tend to infect related host species, it is unlikely that they often infect the same host species, such that evolutionary constraints hinder lineage diversification within individual host species. We conclude that the colonization of new but related host species may represent the principle mode of macroevolution in RNA viruses.
Distinct transcriptomic and exomic abnormalities within myelodysplastic syndrome marrow cells

Abstract To provide biologic insights into mechanisms underlying myelodysplastic syndromes (MDS) we evaluated the CD34+ marrow cells transcriptome using high-throughput RNA sequencing (RNA-Seq). We demonstrated significant differential gene expression profiles (GEPs) between MDS and normal and identified 41 disease classifier genes. Additionally, two main clusters of GEPs distinguished patients based on their major clinical features, particularly between those whose disease remained stable versus patients who transformed into acute myeloid leukemia within 12 months. The genes whose expression was associated with disease outcome were involved in functional pathways and biologic processes highly relevant for MDS. Combined with exomic analysis we identified differential isoform usage of genes in MDS mutational subgroups, with consequent dysregulation of distinct biologic functions. This combination of clinical, transcriptomic and exomic findings provides valuable understanding of mechanisms underlying MDS and its progression to a more aggressive stage and also facilitates prognostic characterization of MDS patients.
Reduced Expression of the Retinoblastoma Protein Shows That the Related Signaling Pathway Is Essential for Mediating the Antineoplastic Activity of Erufosine

Erufosine is a new antineoplastic agent of the group of alkylphosphocholines, which interferes with signal transduction and induces apoptosis in various leukemic and tumor cell lines. The present study was designed to examine for the first time the mechanism of resistance to erufosine in malignant cells with permanently reduced expression of the retinoblastoma (Rb) protein. Bearing in mind the high number of malignancies with reduced level of this tumor-suppressor, this investigation was deemed important for using erufosine, alone or in combination, in patients with compromised RB1 gene expression. For this purpose, clones of the leukemic T-cell line SKW-3 were used, which had been engineered to constantly express differently low Rb levels. The alkylphosphocholine induced apoptosis, stimulated the expression of the cyclin dependent kinase inhibitor p27Kip1 and inhibited the synthesis of cyclin D3, thereby causing a G2 phase cell cycle arrest and death of cells with wild type Rb expression. In contrast, Rb-deficiency impeded the changes induced by eru-fosine in the expression of these proteins and abrogated the induction of G2 arrest, which was correlated with reduced antiproliferative and anticlonogenic activities of the compound. In conclusion, analysis of our results showed for the first time that the Rb signaling pathway is essential for mediating the antineoplastic activity of erufosine and its efficacy in patients with malignant diseases may be predicted by determining the Rb status.