Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating DNA: an information treasure trove for cancer medicine Fragments of DNA that are derived from dead tumour cells and shed into a patient’s blood have been utilised as biomarkers for the diagnosis and prognosis of liver cancer

Successful cancer treatment is dependent on many factors, including the correct identification of the type of cancer (diagnosis) and an accurate prediction of its likely course (prognosis). A number of biomarkers are widely used for the diagnosis of cancers today. However, identifying and employing the most accurate biomarker has proven difficult, particularly for cancer detection. In this issue of Nature Materials [1] the group led by Kang Zhang compared the epigenetic profiles of normal versus cancer circulating tumour DNA to select and validate biomarkers for the diagnosis and prognosis of hepatocellular carcinoma (HCC).
Sonographic Assessment of Intravascular Fluid Estimate (SAFE) Score by Using Bedside Ultrasound in the Intensive Care Unit

Objective The objective of the study was to use an ultrasound-based numerical scoring system for assessment of intravascular fluid estimate (SAFE) and test its validity. Methods A prospective, observational study was carried out in the surgical intensive care unit (ICU) of an urban tertiary care teaching hospital. Patient's intravascular volume status was assessed using the standard methods of heart rate, blood pressure, central venous pressure, cardiac output, lactate and saturation of venous oxygen, and others. This was compared with assessment using bedside ultrasound evaluation of the cardiac function, inferior vena cava, lungs, and the internal jugular vein. Applying a numerical scoring system was evaluated by Fisher's exact testing and multinomial logistic model to predict the volume status based on ultrasound scores and the classification accuracy. Results 61 patients in the ICU were evaluated. 21 (34.4% of total) patients diagnosed with hypovolemia, and their ultrasound volume score was −4 in 14 (66.7%) patients, −3 in 5 (23.8%) patients, and 0 in 2 (9.5%) patients (p < 0.001). 18 (29.5% of total) patients diagnosed with euvolemia, and their ultrasound volume score was 0 in 11 (61.1%) patients, +1 in 4 (22.2%) patients, and −1 in 1 (5.6%) patient (p < 0.001). 18 (29.5% of total) patients diagnosed with euvolemia, and their ultrasound volume score was 0 in 11 (61.1%) patients, +1 in 4 (22.2%) patients, and −1 in 1 (5.6%) patient (p < 0.001). 18 (29.5% of total) patients diagnosed with euvolemia, and their ultrasound volume score was 0 in 11 (61.1%) patients, +1 in 4 (22.2%) patients, and −1 in 1 (5.6%) patient (p < 0.001). 18 (29.5% of total) patients diagnosed with euvolemia, and their ultrasound volume score was 0 in 11 (61.1%) patients, +1 in 4 (22.2%) patients, and −1 in 1 (5.6%) patient ( Conclusion Using the SAFE scoring system to identify the IVV status in critically ill patients significantly correlates with the standard measures. A SAFE score of −4 to −2 more likely represents hypovolemia, −1 to +1 more likely represents euvolemia, and +2 to +4 more likely to be hypervolemia.
Over-expression of HDAC8 down-regulate CDKN2A is associated with worse prognosis of esophageal squamous cell carcinoma

Background The epigenetic alteration has an impact on cancer cell cycle regulation. Expression of histone deacetylase 8 (HDAC8) ruled out the expression of cyclin-dependent kinase inhibitor 2A (CDKN2A) and this is linked with the prognosis of Esophageal cancer (EC) patients. Methods By the immunohistochemical staining, we examined the expression status of HDAC8 and CDKN2A protein of 110 esophageal squamous cell carcinoma (ESCC) patients in the tissue microarray. The nuclear staining intensity was counted by immunoreactivity scoring ranging from 0 to 12 and grouped them into two; weak or non-expression and over-expression. Results The median follow-up duration of our study was 71 months postoperatively. Up-regulation of HDAC8 expression and down-regulation of CDKN2A (p16) indicate decreased 5-year overall survival (OS) (P=0.003) and (P=0.001) respectively and progression-free survival (PFS) (P=0.005, P=0.001) respectively, which are statistically significant and determined by Kaplan-Meier estimates using log-rank test. Consequently, HDAC8 and CDKN2A act as an independent prognostic biomarker, for OS and PFS that analyzed by multivariate cox-regression analysis, HR 1.173 with 95% CI: 0.215–6.416 and P=0.003 and HR 1.217 with 95% CI: 0.224–6.600 and P=0.005 respectively. Spearman rank test establish the negative correlation between HDAC8 and CDKN2A. Conclusions Over-expression of HDAC8 and weak or no expression of CDKN2A have a worse impact on EC patients and indicate poor survival and progression of the disease and act as a promising prognostic parameter.
Long Noncoding RNA LINC00467 Promotes Glioma Progression through Inhibiting P53 Expression via Binding to DNMT1

Purpose: This study aimed to investigate whether long noncoding RNA (lncRNA) LINC00467 could regulate proliferative and invasive abilities of glioma cells via p53 and DNA methyltransferase 1 (DNMT1), so as to participate in the occurrence and progression of glioma. Methods: LINC00467 expression in glioma was analyzed by GEPIA database and LINC00467 expression in glioma cell lines was detected by qRT-PCR. The regulatory effects of LINC00467 and p53 on proliferative, invasive capacities and cell cycle were conducted by CCK-8 and EdU assays, transwell assay and flow cytometry, respectively. The binding conditions between LINC00467, DNMT1 and p53 were determined by RNA immunoprecipitation (RIP) and Chromatin immunoprecipitation (ChIP) assays. Western blot was conducted to determine whether LINC00467 could regulate p53 in glioma cells. Finally, rescue experiments were carried out to evaluate whether LINC00467 regulates proliferative and invasive abilities of glioma cells through p53. Results: The expression of LINC00467 was significantly up-regulated in tumor samples than that in normal samples, which was not correlated with patient survival time. Besides, expression of LINC00467 was higher in glioma cells than that of negative control cells. Upregulation of LINC00467 promoted proliferative and invasive abilities, and accelerated cell cycle in G0/G1 phase of U87 and LN229 cells. The results of RIP and ChIP assays demonstrated that LINC00467 could bind to DNMT1 and inhibit p53 expression. Overexpression of p53 partially reversed the enhancement of LINC00467 on proliferative and invasive abilities of glioma cells. Conclusion: These results indicated that high expression of LINC00467 could promote proliferative and invasive abilities of glioma cells through targeting inhibition of p53 expression by binding to DNMT1.
Comparative Study of Neurologic Critical Care in Avicenna Canon and New Medicine

Background and Aim: The ancient medical books are rich in scientific doctrines and those reviews continue to amaze the reader after several years. One of the advanced sciences of the modern era is the neuroscience. Avicenna has dealt with various aspects of neuroscience in Canon, including the care of critically ill patients. The present study explores the hidden aspects of this field of medical science in Avicenna's Canon. Materials and Methods: This study was a descriptive review with a comparative approach. First, neurologic subject were extracted from the Canon book and the related new articles and books were searched; then the contents of the Canon book matched with those from the new medicine resources given. Results: A comparative study of the topics discussed in the field of brain anatomy, signs of neurologic diseases, diagnosis and treatment of headache, seizure, meningitis, stroke, traumatic brain injury and spinal cord injury, reveals many accordance with the modern knowledge, especially in the field of care for patients with head injury. Conclusion: The Canon of Avicenna, like many of the ancient medical books, is still full of novelty and nearness to reality after centuries. It contains many useful recommendations for critical care of neurological patients.
Presence Metadata in the Internet of Things: Challenges and Opportunities

The Internet of Things is an emerging computing paradigm that promises to revolutionise society. The widespread capture and aggregation of data from sensors and smart devices combined with processing using machine learning in cloud computing platforms provides unrivalled insights into our environment. In addition to the numerous benefits (smart healthcare, cities, transportation, etc.) such insights potentially jeopardise the privacy of individuals, organisations, and society as whole. This is despite UK and EU regulations attempting to mitigate the risk of individuals’ data exposure and the impact of it on their security. To demonstrate the exploitation of metadata and its threat to privacy, this paper presents Meta-Blue, a Bluetooth Low Energy metadata capture, analysis, and visualisation tool. The results of a case study are combined with an overview of literature on IoT privacy to provide a holistic overview of the challenges and opportunities presented by IoT metadata.
Is m-RECIST superior to RECIST in the evaluation of tumour response in patients with hepatocellular carcinoma (HCC) and treated with targeted therapies?

Poster: "ECR 2011 / C-2102 / Is m-RECIST superior to RECIST in the evaluation of tumour response in patients with hepatocellular carcinoma (HCC) and treated with targeted therapies? " by: "J. S. Rabouhans, A. Baron, J. Cazejust, L. Fartoux, O. Rosmorduc, L. Arrive, Y. Menu; Paris/FR"
Partial differential equation models for intranuclear diffusion, inverse problems in nanobiology and cell cycle specific effects of anticancer drugs.

Professional and Teaching Experience • August 2014 – present: associate professor of Mathematics, Department of Mathematical Sciences, University of Wisconsin – Milwaukee • August 2009 – May 2014: assistant professor of Mathematics, Department of Mathematical Sciences, University of Wisconsin – Milwaukee • September 2007 – August 2009: postdoctoral associate at the Institute for Mathematics and its Applications, University of Minnesota • January 2002 – April 2007: graduate teaching assistant at University of Wisconsin – Milwaukee and Vanderbilt University • February 2001 – December 2001: Assistant research and development with Deutsche Telekom AG, Darmstadt, Germany
Perception of prosodic variation for speech synthesis using an unsupervised discrete representation of F0

In English, prosody adds a broad range of information to segment sequences, from information structure (e.g. contrast) to stylistic variation (e.g. expression of emotion). However, when learning to control prosody in text-to-speech voices, it is not clear what exactly the control is modifying. Existing research on discrete representation learning for prosody has demonstrated high naturalness, but no analysis has been performed on what these representations capture, or if they can generate meaningfully-distinct variants of an utterance. We present a phrase-level variational autoencoder with a multi-modal prior, using the mode centres as "intonation codes". Our evaluation establishes which intonation codes are perceptually distinct, finding that the intonation codes from our multi-modal latent model were significantly more distinct than a baseline using k-means clustering. We carry out a follow-up qualitative study to determine what information the codes are carrying. Most commonly, listeners commented on the intonation codes having a statement or question style. However, many other affect-related styles were also reported, including: emotional, uncertain, surprised, sarcastic, passive aggressive, and upset.
Control of chaos with minimal information transfer

This paper studies set-invariance and stabilization of hyperbolic sets under communication constraints for discrete-time control systems. We first investigate structural and control-theoretic properties of hyperbolic sets, in particular such that arise by adding small control terms to diffeomorphisms admitting a (classical) hyperbolic set. Then we derive a lower bound on the invariance entropy of a hyperbolic set in terms of the difference between unstable volume growth and measure-theoretic fiber entropy. We also provide heuristic arguments for the tightness of the obtained bound and a rigorous proof for a special case. Moreover, we apply our techniques to the problem of local uniform stabilization at a hyperbolic set of a diffeomorphism and discuss an example built on the Henon horseshoe.
Penalization for a PDE with a nonlinear Neumann boundary condition and measurable coefficients

We consider a system of semi-linear partial differential equations with measurable coefficients and a nonlinear Neumann boundary condition. We then construct a sequence of penalized partial differential equations which converges to a solution of our initial problem. The solution we construct is in the L p --viscosity sense, since the coefficients can be not continuous. The method we use is based on backward stochastic differential equations and their S-tightness. The present work is motivated by the fact that many partial differential equations arising in physics have discontinuous coefficients.
Discrete-valued Preference Estimation with Graph Side Information

Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown preference matrix. In this work, we propose a new model in which the unknown preference matrix can have any discrete values, thereby relaxing the assumptions made in prior work. Under this new model, we fully characterize the optimal sample complexity and develop a computationally-efficient algorithm that matches the optimal sample complexity. We also show that our algorithm is robust to model errors, and it outperforms existing algorithms on both synthetic and real datasets.
Four-Dimensional Dominance Range Reporting in Linear Space

In this paper we study the four-dimensional dominance range reporting problem and present data structures with linear or almost-linear space usage. Our results can be also used to answer four-dimensional queries that are bounded on five sides. The first data structure presented in this paper uses linear space and answers queries in $O(\log^{1+\varepsilon}n + k\log^{\varepsilon} n)$ time, where $k$ is the number of reported points, $n$ is the number of points in the data structure, and $\varepsilon$ is an arbitrarily small positive constant. Our second data structure uses $O(n \log^{\varepsilon} n)$ space and answers queries in $O(\log n+k)$ time. These are the first data structures for this problem that use linear (resp. $O(n\log^{\varepsilon} n)$) space and answer queries in poly-logarithmic time. For comparison the fastest previously known linear-space or $O(n\log^{\varepsilon} n)$-space data structure supports queries in $O(n^{\varepsilon} + k)$ time (Bentley and Mauer, 1980). Our results can be generalized to $d\ge 4$ dimensions. For example, we can answer $d$-dimensional dominance range reporting queries in $O(\log\log n (\log n/\log\log n)^{d-3} + k)$ time using $O(n\log^{d-4+\varepsilon}n)$ space. Compared to the fastest previously known result (Chan, 2013), our data structure reduces the space usage by $O(\log n)$ without increasing the query time.
Chemodynamics of green pea galaxies – I. Outflows and turbulence driving the escape of ionizing photons and chemical enrichment

We investigate the ionized gas kinematics, physical properties, and chemical abundances of Sloan Digital Sky Survey J142947, a Green Pea galaxy at redshift z∼ 0.17 with strong, double-peak Ly α emission and indirect evidence of Lyman continuum (LyC) leakage. Using high-dispersion spectroscopy, we perform a multicomponent analysis of emission-line profiles. Our model consistently fits all lines as a narrow component with intrinsic velocity dispersion σ ∼ 40 km s−1, and two broader blue-shifted components with σ ∼ 90 and ∼ 250 km s−1. We find electron densities and temperatures, ionization conditions, and direct O/H and N/O abundances for each component. A highly ionized, metal-poor, young and compact starburst dominates narrow emission, showing evidence of hard radiation fields and elevated N/O. The blue-shifted broader components are consistent with highly turbulent, possibly clumpy ionized gas at the base of a strong photoionized outflow, which accounts for ≳50 per cent of the integrated emission-line fluxes. The outflow is dense and metal-enriched compared to the H ii regions, with expansion velocities larger than those obtained from UV interstellar absorption lines under standard assumptions. Some of these metals may be able to escape, with outflows loading factors comparable to those found in high-z galaxies of similar SFR/Area. Our findings depict a two-stage starburst picture; hard radiation fields from young star clusters illuminate a turbulent and clumpy ISM that has been eroded by SNe feedback. Whilst UV data suggest an extended Ly α halo with high average H i column density, LyC photons could only escape from SDSS J142947 through low H i density channels or filaments in the ISM approaching density-bounded conditions, traced by outflowing gas.
Characterising hot stellar systems with confidence

Hot stellar systems (HSS) are a collection of stars bound together by gravitational attraction. These systems hold clues to many mysteries of outer space so understanding their origin, evolution and physical properties is important but remains a huge challenge. We used multivariate $t$-mixtures model-based clustering to analyze 13456 hot stellar systems from Misgeld & Hilker (2011) that included 12763 candidate globular clusters and found eight homogeneous groups using the Bayesian Information Criterion (BIC). A nonparametric bootstrap procedure was used to estimate the confidence of each of our clustering assignments. The eight obtained groups can be characterized in terms of the correlation, mass, effective radius and surface density. Using conventional correlation-mass-effective radius-surface density notation, the largest group, Group 1, can be described as having positive-low-low-moderate characteristics. The other groups, numbered in decreasing sizes are similarly characterised, with Group 2 having positive-low-low-high characteristics, Group 3 displaying positive-low-low-moderate characteristics, Group 4 having positive-low-low-high characteristic, Group 5 displaying positive-low-moderate-moderate characteristic and Group 6 showing positive-moderate-low-high characteristic. The smallest group (Group 8) shows negative-low-moderate-moderate characteristic. Group 7 has no candidate clusters and so cannot be similarly labeled but the mass, effective radius correlation for these non-candidates indicates that they zare larger than typical globular clusters. Assertions drawn for each group are ambiguous for a few HSS having low confidence in classification. Our analysis identifies distinct kinds of HSS with varying confidence and provides novel insight into their physical and evolutionary properties.
Edge-Tailored Perception: Fast Inferencing in-the-Edge with Efficient Model Distribution

The rise of deep neural networks (DNNs) is inspiring new studies in myriad of edge use cases with robots, autonomous agents, and Internet-of-things (IoT) devices. However, in-the-edge inferencing of DNNs is still a severe challenge mainly because of the contradiction between the inherent intensive resource requirements and the tight resource availability in several edge domains. Further, as communication is costly, taking advantage of other available edge devices is not an effective solution in edge domains. Therefore, to benefit from available compute resources with low communication overhead, we propose new edge-tailored perception (ETP) models that consist of several almost-independent and narrow branches. ETP models offer close-to-minimum communication overheads with better distribution opportunities while significantly reducing memory and computation footprints, all with a trivial accuracy loss for not accuracy-critical tasks. To show the benefits, we deploy ETP models on two real systems, Raspberry Pis and edge-level PYNQ FPGAs. Additionally, we share our insights about tailoring a systolic-based architecture for edge computing with FPGA implementations. ETP models created based on LeNet, CifarNet, VGG-S/16, AlexNet, and ResNets and trained on MNIST, CIFAR10/100, Flower102, and ImageNet, achieve a maximum and average speedups of 56x and 7x, compared to originals. ETP is an addition to existing single-device optimizations for embedded devices by enabling the exploitation of multiple devices. As an example, we show applying pruning and quantization on ETP models improves the average speedup to 33x.
GeoDA: A Geometric Framework for Black-Box Adversarial Attacks

Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-1 label of the classifier. Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples. We propose an effective iterative algorithm to generate query-efficient black-box perturbations with small p norms which is confirmed via experimental evaluations on state-of-the-art natural image classifiers. Moreover, for p=2, we theoretically show that our algorithm actually converges to the minimal perturbation when the curvature of the decision boundary is bounded. We also obtain the optimal distribution of the queries over the iterations of the algorithm. Finally, experimental results confirm that our principled black-box attack algorithm performs better than state-of-the-art algorithms as it generates smaller perturbations with a reduced number of queries.
A Persistent Homology Approach to Time Series Classification

Topological Data Analysis (TDA) is a rising field of computational topology in which the topological structure of a data set can be observed by persistent homology. By considering a sequence of sublevel sets, one obtains a filtration that tracks changes in topological information. These changes can be recorded in multi-sets known as {\it persistence diagrams}. Converting information stored in persistence diagrams into a form compatible with modern machine learning algorithms is a major vein of research in TDA. {\it Persistence curves}, a recently developed framework, provides a canonical and flexible way to encode the information presented in persistence diagrams into vectors. In this work, we propose a new set of metrics based on persistence curves. We prove the stability of the proposed metrics. Finally, we apply these metrics to the UCR Time Series Classification Archive. These empirical results show that our metrics perform better than the relevant benchmark in most cases and warrant further study.