Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Evolving Mixed Membership BlockModels

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (links structure) can change with time. We propose a model of co-evolving networks where both node attributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are influenced by the changing network structure. We derive an efficient variational inference procedure for our model, and validate the model on both synthetic and real–world data.
FPGA core watermarking based on power signature analysis

This paper introduces a new method to watermark FPGA cores where the signature (watermark) is detected at the power supply pins of the FPGA. This is the first watermarking method, where the signature is extracted in this way. The authors were able to sign cores at the netlist as well as the bitfile level, so a wide spectrum of cores can be protected. The power watermarking method works with all types of FPGAs, but with Xilinx FPGAs, the watermarking algorithms and the signature can be integrated into the functionality of the watermarked core. So it is very hard to remove the watermark without destroying the core. A detection algorithm was introduced which can decode the signature from a voltage trace with high probability. Additionally, a second algorithm is introduced which improves the detection probability in case of considerable noise sources. Using this algorithm, it is possible to decode the signature even if other cores operate on the same device at the same time
The Positron Emission Tomography Ligand DAA1106 Binds With High Affinity to Activated Microglia in Human Neurological Disorders

Chronic microglial activation is an important component of many neurological disorders, and imaging activated microglia in vivo will enable the detection and improved treatment of neuroinflammation. 1-(2-Chlorphenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinoline-carbox-amide (PK11195), a peripheral benzodiazepine receptor ligand, has been used to image neuroinflammation, but the extent to which PK11195 binding distinguishes activated microglia and reactive astrocytes is unclear. Moreover, PK11195 may lack sufficient sensitivity for detecting mild neuroinflammation. We hypothesized that N-(2,5-dimethoxybenzyl)-N-(4-fluoro-2-phenoxyphenyl) acetamide (DAA1106), a new ligand that binds specifically to peripheral benzodiazepine receptor, binds to activated microglia in human neurological diseases with higher affinity than does PK11195. We therefore compared the pharmacological binding properties of [3H](R)-PK11195 and [3H]DAA1106 in postmortem tissues from patients with cerebral infarcts, amyotrophic lateral sclerosis, Alzheimer disease, frontotemporal dementia, and multiple sclerosis (n = 10 each). In all diseases, [3H]DAA1106 showed a higher binding affinity as reflected by lower dissociation constant (KD) values than that of [3H](R)-PK11195. Moreover, specific binding of both ligands correlated with the presence of activated microglia identified by immunohistochemistry in situ. We conclude that 1) ligands that bind peripheral benzodiazepine receptor mainly label activated microglia in human neurological disorders and that 2) DAA1106 may possess binding characteristics superior to those of PK11195, which may be beneficial for in vivo positron emission tomography imaging.
Determining Grasping Regions using Vision

Object grasping is one of the fundamental tasks of personal robots. There are several approaches to this problem, some requiring a 3D model of the object. Saxena et al. [1] proposed a learning algorithm which does not require a 3D model; instead it identifies points on the object corresponding to good grasping locations based on computer vision. Our approach identifies oriented rectangular regions instead of points as this representation is more suitable for a parallel-plate gripper. Since these rectangles can have various locations, sizes and orientations, such a representation is computationally expensive. To tackle this problem, we used histogram features that can be computed in (amortized) constant time for each rectangle. Our learning model is based on a linear SVM ranking model trained using SVM-rank [2]. Results from robotic experiments show that our algorithm performs well on both trained objects and novel objects, indicating that it generalized well to a wider class of objects.
A Novel Approach Towards Clustering Based Image Segmentation

In computer vision, image segmentation is always selected as a major research topic by researchers. Due to its vital rule in image processing, there always arises the need of a better image segmentation method. Clustering is an unsupervised study with its application in almost every field of science and engineering. Many researchers used clustering in image segmentation process. But still there requires improvement of such approaches. In this paper, a novel approach for clustering based image segmentation is proposed. Here, we give importance on color space and choose lab for this task. The famous hard clustering algorithm K-means is used, but as its performance is dependent on choosing a proper distance measure, so, we go for cosine distance measure. Then the segmented image is filtered with sobel filter. The filtered image is analyzed with marker watershed algorithm to have the final segmented result of our original image. The MSE and PSNR values are evaluated to observe the performance.
Detection and Phylogenetic Characterization of Human Hepatitis E Virus Strains, Czech Republic

To determine the origin of hepatitis E virus in the Czech Republic, we analyzed patient clinical samples. Five isolates of genotypes 3e, 3f, and 3g were obtained. Their genetic relatedness with Czech strains from domestic pigs and wild boars and patient recollections suggest an autochthonous source likely linked to consumption of contaminated pork.
The formation of novel social category conjunctions in working memory: A possible role for the episodic buffer?

Recent research (e.g., Hutter, Crisp, Humphreys, Waters, & Moffit; Siebler) has confirmed that combining novel social categories involves two stages (e.g., Hampton; Hastie, Schroeder, & Weber). Furthermore, it is also evident that following stage 1 (constituent additivity), the second stage in these models involves cognitively effortful complex reasoning. However, while current theory and research has addressed how category conjunctions are initially represented to some degree, it is not clear precisely where we first combine or bind existing social constituent categories. For example, how and where do we compose and temporarily store a coherent representation of an individual who shares membership of “female” and “blacksmith” categories? In this article, we consider how the revised multi-component model of working memory (Baddeley) can assist in resolving the representational limitations in the extant two-stage theoretical models. This is a new approach to understanding how novel conjunctions form new bound “composite” representations.
Open access echocardiography

EDITOR,-I was impressed recently while reading a nursing journal to find an article written by an orthopaedic surgeon, Robert Hay. ' In keeping with this spirit of interprofessional communication I have chosen to reply to Hay's article in the BMJ. While delighted that Hay chose to communicate directly with the nursing profession, I am dismayed by his comments conceming nursing in general, nursing research in particular, and specifically the role of men in nursing. For the benefit of readers who do not subscribe to Nursing Standard, Hay contends that an "increasingly male leadership" is "frogmarching" nursing towards clinical autonomy against the wishes of the "rank and file. " He is particularly dismissive of nurses participating in research, apparently because we may not "have got an A-level in maths or science" or be familiar with the term "correlation of bi-variant data. " He suggests that nursing sisters should drop the "pseudo-managerial gibberish" of care plans and total quality care and that "only under very special circumstances" should this post be occupied by a man. I suggest that Hay has not "read and understood all published work in the field" as he exhorts nurses to do. Perhaps he should conduct a further literature search, using such key phrases as "nursing development units" to find examples of nurse led, patient centred clinical innovation and names like Philip Burnard, a male nurse who has had an enormous impact on improving standards of patient care. I fear that Hay's comments may reflect less the concern that he obviously feels about patient care and more the threat that he perceives to the medical profession's traditional but now outdated ascendency over nursing. Collaboration, rather than confrontation, should be our aim. I see Hay's article as divisive and unhelpful. I hope that in future, when nurses and doctors correspond in each other's professional journals, they do so with more respect for each other's efforts towards the common goal ofimproved patient care.
Predictive Biomarkers and Personalized Medicine MutationPro fi ling andMicrosatellite Instability in Stage II and III Colon Cancer : An Assessment of Their Prognostic and Oxaliplatin Predictive Value

Purpose: The purpose of this study was to examine the prognostic and oxaliplatin predictive value of mismatch repair (MMR) status and common hot spot mutations, which we previously identified in stage II and III colon cancer. ExperimentalDesign:Mutations inBRAF,KRAS,NRAS,MET, andPIK3CAwere profiled in 2,299 stage II and III colon tumors fromNational Surgical Adjuvant Breast and Bowel Project (NSABP) clinical trials C-07 (n1⁄4 1,836) and C-08 (n1⁄4 463) with Type Plex chemistry and mass spectrometry. C-07 tested the worth of adding oxaliplatin to 5-fluorouracil plus leucovorin, and C-08 tested the worth of adding bevacizumab to FOLFOX. Cox proportional hazard models were used to assess prognostic or oxaliplatin predictive value of mutations for tumor recurrence, overall survival (OS), and survival after recurrence (SAR). Results: BRAFmutations were associated with MMR-deficient tumors (P < 0.0001), poor OS [HR, 1.46; 95% confidence interval (CI), 1.20–1.79; P 0.0002], and poor SAR (HR, 2.31; 95% CI, 1.83–2.95; P < 0.0001). Mutations in KRAS, NRAS, MET, and PIK3CA were not associated with recurrence, OS, or SAR. MMR-deficient tumorswere associatedwith an improved prognosis based on recurrence (HR, 0.48; 95%CI, 0.33–0.70; P < 0.0001). Mutations and MMR status were not predictive for oxaliplatin benefit. Conclusions: This study shows that BRAFmutations profiled from stage II and III colon cancer tumors were associated with poor SAR and validates and explains, at least in part, previous observations associating it with poor OS. Profiling of all of these mutations is warranted for future clinical trials testing new targeted therapies that block relevant signaling pathways. Such clinical trials are under development at NSABP. Clin Cancer Res; 18(23); 6531–41. 2012 AACR.
Organic labyrinths and mazes

This paper addresses the synthesis of labyrinthine and maze structures, which are represented as curves on 2D manifolds. The curves evolve based on a simulation controlled by spatially varying parameters defined by texture maps. We introduce the graphics community to the fascinating area of maze art and present a model for the automatic generation of organic looking labyrinths and mazes. We also present a framework based on regions and patterns for the interactive artistic control of NPR algorithms, such as ours, that evolve in both space and time. In the context of labyrinths, the framework provides the designer with control over both the path complexity and visual aesthetics as the curves evolve. The resulting labyrinths and mazes range from mathematically simple to intricately complex visual structures. Applications of the resulting curves include NPR, difficult to counterfeit imagery, environmental design and architecture, computer games and 1D parameterization of 2D manifolds.
Dmdmdx/Largemyd: a new mouse model of neuromuscular diseases useful for studying physiopathological mechanisms and testing therapies

SUMMARY Although muscular dystrophies are among the most common human genetic disorders, there are few treatment options available. Animal models have become increasingly important for testing new therapies prior to entering human clinical trials. The Dmdmdx mouse is the most widely used animal model for Duchenne muscular dystrophy (DMD), presenting the same molecular and protein defect as seen in humans with the disease. However, this mouse is not useful for clinical trials because of its very mild phenotype. The mouse model for congenital myodystrophy type 1D, Largemyd, harbors a mutation in the glycosyltransferase Large gene and displays a severe phenotype. To help elucidate the role of the proteins dystrophin and LARGE in the organization of the dystrophin-glycoprotein complex in muscle sarcolemma, we generated double-mutant mice for the dystrophin and LARGE proteins. The new Dmdmdx/Largemyd mouse model is viable and shows a severe phenotype that is associated with the lack of dystrophin in muscle. We tested the usefulness of our new mouse model for cell therapy by systemically injecting them with normal murine mesenchymal adipose stem cells (mASCs). We verified that the mASCs were hosted in the dystrophic muscle. The new mouse model has proven to be very useful for the study of several other therapies, because injected cells can be screened both through DNA and protein analysis. Study of its substantial muscle weakness will also be very informative in the evaluation of functional benefits of these therapies.
Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique

The transverse relaxation time (T2) is a fundamental parameter of magnetic resonance imaging sensitive to tissue microstructure and its water content, thus offering a non-invasive approach to evaluate abnormalities of brain tissue in-vivo. Prevailing hypotheses of two childhood psychiatric disorders were tested using quantitative T2 imaging and automated region of interest analyses. In autism, the under-connectivity theory, which proposes aberrant connectivity within white matter (WM), was assessed, finding T2 to be elevated in the WM of the frontal and parietal lobes, while dividing whole brain data into neurodevelopmentally relevant WM compartments found increased T2 in bridging and radiate WM. In Tourette syndrome, tissue abnormalities of deep gray matter structures implicated in the symptomology of this disorder were evaluated and increased T2 of the caudate was found. Despite the sensitivity of quantitative T2 measurements to underlying pathophysiology, interpretation remain difficult. However, in WM, the compartmentalization of distinct water environments may lead to the detection of multi-exponential T2 decay. The metric of interest is principally the myelin water fraction (MWF), which is the proportion of the T2 signal arising from water trapped within layers of the myelin sheath. As a proof of concept study, the ability to measure the MWF based on T2* decay was evaluated and compared to a MWF measurements obtained from T2 decay. Data were analysed using both non-negative least squares and a two-pool model. Signal losses near sources of magnetic field inhomogeneity, such as the sinuses, rendered T2* components inseparable, invalidating this approach for whole brain MWF measurements. However, this study demonstrated the suitability of a two-pool model to calculate the MWF in WM.
Akid: A Library for Neural Network Research and Production from a Dataism Approach

Neural networks are a revolutionary but immature technique that is fast evolving and heavily relies on data. To benefit from the newest development and newly available data, we want the gap between research and production as small as possibly. On the other hand, differing from traditional machine learning models, neural network is not just yet another statistic model, but a model for the natural processing engine --- the brain. In this work, we describe a neural network library named {\texttt akid}. It provides higher level of abstraction for entities (abstracted as blocks) in nature upon the abstraction done on signals (abstracted as tensors) by Tensorflow, characterizing the dataism observation that all entities in nature processes input and emit out in some ways. It includes a full stack of software that provides abstraction to let researchers focus on research instead of implementation, while at the same time the developed program can also be put into production seamlessly in a distributed environment, and be production ready. At the top application stack, it provides out-of-box tools for neural network applications. Lower down, akid provides a programming paradigm that lets user easily build customized models. The distributed computing stack handles the concurrency and communication, thus letting models be trained or deployed to a single GPU, multiple GPUs, or a distributed environment without affecting how a model is specified in the programming paradigm stack. Lastly, the distributed deployment stack handles how the distributed computing is deployed, thus decoupling the research prototype environment with the actual production environment, and is able to dynamically allocate computing resources, so development (Devs) and operations (Ops) could be separated. Please refer to this http URL for documentation.
Tumor endothelial marker 8 expression in triple-negative breast cancer.

BACKGROUND/AIM Tumor endothelial marker 8 (TEM8) is a tumor endothelial-associated antigen that is having an increasingly recognized role in tumor biology. The expression of TEM8 in triple-negative breast cancer (TNBC) has not yet been characterized. MATERIALS AND METHODS We hypothesize that TEM8 is overexpressed in TNBC and in metastatic TNBC in lymph nodes (LN) compared to normal breast tissue and normal lymphatic tissue, respectively. We studied expression of TEM8 in cases of primary (n=17) and metastatic (n=2) TNBC using immunohistochemical analyses. RESULTS All cases demonstrated increased expression of TEM8 in tumor tissue compared to non-cancerous breast tissue. TEM8 was expressed at a higher level in the stroma adjacent to the TNBC in all cases, with focal immunoreactive areas within the tumor. TEM8 was not expressed in normal lymphoid tissue, but showed expression at sites of LN metastases. CONCLUSION TEM8 would appear to represent a new biologic target for designing novel diagnostic or therapeutic approaches for TNBC.
The severity of anaemia depletes cerebrovascular dilatory reserve in children with sickle cell disease: a quantitative magnetic resonance imaging study

Overt ischaemic stroke is one of the most devastating complications in children with sickle cell disease (SCD). The compensatory response to anaemia in SCD includes an increase in cerebral blood flow (CBF) by accessing cerebrovascular dilatory reserve. Exhaustion of dilatory reserve secondary to anaemic stress may lead to cerebral ischaemia. The purpose of this study was to investigate CBF and cerebrovascular reactivity (CVR) using magnetic resonance imaging (MRI) in children with SCD and to correlate these with haematological markers of anaemia. Baseline CBF was measured using arterial spin labelling. Blood‐oxygen level‐dependent MRI in response to a CO2 stimulus was used to acquire CVR. In total, 28 children with SCD (23 not on any disease‐modifying treatment, 5 on chronic transfusion) and 22 healthy controls were imaged using MRI. Transfusion patients were imaged at two time points to assess the effect of changes in haematocrit after a transfusion cycle. In children with SCD, CBF was significantly elevated compared to healthy controls, while CVR was significantly reduced. Both measures were significantly correlated with haematocrit. For transfusion patients, CBF decreased and CVR increased following a transfusion cycle. Lastly, a significant correlation was observed between CBF and CVR in both children with SCD and healthy controls.
Site-2 protease substrate specificity and coupling in trans by a PDZ-substrate adapter protein

Significance Site-2 proteases are ubiquitously distributed, and yet their regulation and substrate recognition remain poorly understood. Here, we describe a model for site-2 protease regulation in which regulated intramembrane protease 1 (Rip1), a site-2 protease crucial for virulence in Mycobacterium tuberculosis, is tethered to one of its substrates by an adapter protein, PDZ-interacting protease regulator (Ppr) 1. Site-2 proteases (S2Ps) are intramembrane metalloproteases that cleave transmembrane substrates in all domains of life. Many S2Ps, including human S2P and Mycobacterium tuberculosis Rip1, have multiple substrates in vivo, which are often transcriptional regulators. However, S2Ps will also cleave transmembrane sequences of nonsubstrate proteins, suggesting additional specificity determinants. Many S2Ps also contain a PDZ domain, the function of which is poorly understood. Here, we identify an M. tuberculosis protein, PDZ-interacting protease regulator 1 (Ppr1), which bridges between the Rip1 PDZ domain and anti-sigma factor M (Anti-SigM), a Rip1 substrate, but not Anti-SigK or Anti-SigL, also Rip1 substrates. In vivo analyses of Ppr1 function indicate that it prevents nonspecific activation of the Rip1 pathway while coupling Rip1 cleavage of Anti-SigM, but not Anti-SigL, to site-1 proteolysis. Our results support a model of S2P substrate specificity in which a substrate-specific adapter protein tethers the S2P to its substrate while holding the protease inactive through its PDZ domain.
Degraded Gray-Scale Text Recognition Using Pseudo-2D Hidden Markov Models and N-Best Hypotheses

Abstract The pseudo-two-dimensional hidden Markov model (PHMM), which is an extension of the 1D HMM, has been shown to be an effective approach in recognition of highly degraded and connected text (O. E. Agazzi and S. Kuo, AT&T Tech. J. 72, Sept./Oct. 1993, 60-72). In this paper, the PHMM is extended to directly recognize poorly printed gray-level document images. The performance of the system is further enhanced by the N-best hypotheses search (F. K. Soong and E.-F. Huang, Proceedings DARPA Speech and Natural Language Workshop, June 1990, pp. 12-19), coupled with duration constraint. Experimental results show that the new system has significantly improved the performance when compared to a similar system using thresholded binary images as inputs. The recognition rate improves from 97.7% in the binary system to 99.9% in gray level with modified N-best search, over a testing set with similar blur and noise conditions as the training set. For a much more degraded testing set, it improves from 89.59 to 98.51%. This also demonstrates the robustness of the proposed system.