Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of PCR with standard culture of fine needle aspiration samples in the diagnosis of tuberculosis lymphadenitis.

INTRODUCTION Lymphadenopathy is the commonest form of extrapulmonary tuberculosis (TB) Clinical diagnosis of TB in lymph nodes requires aspiration of the material and isolation of mycobacteria. Bacterial culture is the gold standard for detection of tubercle bacilli, but it is time-consuming and requires specialized safety procedures and a BSL3 laboratory. However, PCR is a rapid method which requires small volumes of samples and can also be performed on killed bacilli to ensure safety. This project was designed to compare direct fine needle aspirate (FNA) PCR with culture in the diagnosis of tuberculosis lymphadenitis. METHODOLOGY Direct examination of samples with EZN staining, culture, cytology and PCR was performed on previously collected FNA from the patients with suspected tuberculosis lymphadenitis. RESULTS In total, 38% of the samples were positive for TB by culture, 11.8% by EZN staining, 23.4% by PCR, and 59.8% by cytology. Cytology had the highest sensitivity (81%) and EZN stain the least (22.9%). The specificity of EZN stain was the highest (92.4%) while cytology was the lowest (50%). In this study, out of 50 culture-positive samples, 21 (42%) were positive by PCR while 8 (10.8%) out of 74 culture-negative samples were positive by PCR. CONCLUSIONS Although PCR is a sensitive diagnostic method, its sensitivity was shown to be low in this study. Therefore, we recommend that further studies should be conducted on fresh aspirate samples to investigate for possible PCR inhibitors which may limit the sensitivity of PCR diagnosis.
A Proposed, Evidence-based Approach to the Treatment of Chronic Hepatitis B

Patients with chronic hepatitis B virus infection are at increased risk for the development of cirrhosis and hepatocellular carcinoma. Viral suppression with antiviral therapy has been shown to decrease the risk of these complications. Criteria for initiation of antiviral therapy have evolved over time to include serum alanine aminotransferase elevation, serum hepatitis B virus DNA elevations, and histologic assessment. Current societal guidelines and a treatment algorithm have been developed to guide decision-making as regards to antiviral therapy. More recent data has shown the importance of basal core/core promoter mutations, serum albumin, and platelet count in predicting complications of chronic hepatitis B. We present a new treatment strategy for determining the need for antiviral therapy in patients with chronic hepatitis B.
Controlled Revision - An algorithmic approach for belief revision

This paper provides algorithmic options for belief revision of a database receiving an infinite stream of inputs. At stage , the database is , receiving the input . The revision algorithms for moving to the new database take into account the history of previous revisions actually executed as well as possible revision options which were discarded at the time but may now be pursued. The appropriate methodology for carrying this out is Labelled Deductive Systems.
Switching characteristics of submicron cobalt islands

The magnetic characteristics of 0.2×0.4×0.02 μm3 cobalt islands were investigated using magnetic force microscopy in the presence of an applied field. The islands were noninteracting and showed a wide variety of single and multidomain configurations. The distribution of magnetization directions supports earlier models which suggest that crystalline anisotropy plays a dominant role in establishing a dispersion of easy axis directions about the long axis of the particles. The magnetic evolution, involving rotation and switching of individual islands, was observed at various points along the microscopic magnetization curve. A magnetization curve of an ensemble of islands was derived from the images and compares remarkably well with macroscopic M–H measurements.
Neural Networks through Shared Maps in Mobile Devices

We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition.
Transmit power and other-cell interference reduction via successive interference cancellation with imperfect channel estimation

Successive interference cancellation (SIC), in conjunction with orthogonal convolutional codes, has been shown to approach the Shannon capacity for an AWGN channel. However, multi-cell fading wireless channels induce error in the amplitude and phase estimates required to perform perfect cancellation of the interference. By combining SIC with a previously developed power control algorithm, we show that other-cell interference and transmit power can be reduced by an order of magnitude or more relative to a conventional (IS-95 style) CDMA system. In addition, even as estimation error becomes large, the capacity is more than doubled over systems without interference cancellation or those that employ SIC without optimum power control.
Robust Spatial Autoregressive Modeling for Hardwood Log Inspection

We explore the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. The application of CT to such industrial vision problems requires efficient and robust image analysis methods. This paper addresses one particular aspect of the problem of creating such a computer vision system, namely, the use of image texture modeling for wood defect recognition. In particular, we contribute the first application of spatial autoregressive (SAR) modeling to wood-grain texture analysis of CT images of hardwood logs. Thereto a circularly shifted correlation approach is developed to discriminate the circular texture patterns on the cross-sectional CT images of logs. A robust algorithm for parameter estimation is applied to obtain model parameters associated with individual defects occurring inside a log. Based on the estimated model features, a simple minimum distance correlationclassifier is constructed which classifies an unknown defect into one of the prototypical defects. Experimental results from the proposed method, applied to CT images from different red oak wood, are given and show the efficacy of our approach. © 1994
Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks

We introduce a new method for proving explicit upper bounds on the VC dimension of general functional basis networks and prove as an application, for the first time, that the VC dimension of analog neural networks with the sigmoidal activation function? (y)=1/1+e? yis bounded by a quadratic polynomialO((lm)2) in both the numberlof programmable parameters, and the numbermof nodes. The proof method of this paper generalizes to much wider class of Pfaffian activation functions and formulas and gives also for the first time polynomial bounds on their VC dimension. We present also some other applications of our method.
Pyramidal cell specialization in the occipitotemporal cortex of the vervet monkey

Pyramidal cells were injected intracellularly in fixed, flat-mounted cortical slices taken from the first and fourth visual areas (V1 and V4, respectively) and cytoarchitectonic areas TEO and TE of two age and gender-matched vervet monkeys and the size, branching complexity and spine density of their basal dendritic trees determined. In both animals, we found marked differences in the pyramidal cell phenotype between cortical areas. More specifically, a consistent trend for larger, more branched and more spinous pyramidal cells with progression through V1, V4, TEO and TE was observed. These findings support earlier reports of interareal specialization in pyramidal cell structure in occipitotemporal visual areas in the macaque monkey.
A systematic method of designing control systems for service and field robots

The paper presents a systematic approach to robot control system design. The robot is treated as an embodied agent decomposed into effectors, receptors, both real and virtual, and a control subsystem. Those entities communicate through communication buffers. The activities of those entities are governed by FSMs that invoke behaviours formulated in terms of transition functions taking as arguments the contents of input buffers and producing the values inserted into output buffers. The method is exemplified by applying it to a design of a control system of a robot capable of locating an open box and covering it with a lid. Other systems that have been designed in a similar way are presented as well, to demonstrate the versatility of the approach.
Useful Clustering Outcomes from Meaningful Time Series Clustering

Clustering time series data using the popular subsequence (STS) technique has been widely used in the data mining and wider communities. Recently the conclusion was made that it is meaningless, based on the findings that it produces (a) clustering outcomes for distinct time series that are not distinguishable from one another, and (b) cluster centroids that are smoothed. More recent work has since showed that (a) could be solved by introducing a lag in the subsequence vector construction process, however we show in this paper that such an approach does not solve (b). Motivating the terminology that a clustering method which overcomes (a) is meaningful, while one which overcomes (a) and (b) is useful, we propose an approach that produces useful time series clustering. The approach is based on restricting the clustering space to extend only over the region visited by the time series in the subsequence vector space. We test the approach on a set of 12 diverse real-world and synthetic data sets and find that (a) one can distinguish between the clusterings of these time series, and (b) that the centroids produced in each case retain the character of the underlying series from which they came.
Interference detection and estimation for spatial filtering: Application to radio interferometry

The increasing spectrum occupancy is a serious threat for highly sensitive astronomical measurement at radio frequencies. The deployment of array radio telescopes allows the exploitation of spatial information regarding sources of radio frequency interference (RFI) in order to filter them out of astronomical data and theoretical recover uncorrupted time and frequency data. This paper introduces the corrupted array radio telescope data model, and gives an overview of array signal processing techniques considered for performing spatial RFI detection and mitigation.
Information about foregone rewards impedes dynamic decision-making in older adults

“Making an informed decision” implies that more information leads to better decisions, yet it may be the case that additional information biases decisions in a systematic and sometimes detrimental manner. In the present study, we examined the effect of additional information on older adults’ decision-making using a task for which available rewards were dependent on the participant’s recent pattern of choices. The optimal strategy was to forego the immediately rewarding option in favor of the option that yielded larger delayed reward. We found that providing information about true foregone rewards – the reward that would have been received had the participant chosen the other option – significantly reduced older adults’ decision-making performance. However, false foregone rewards – foregone rewards manufactured to make the long-term option appear more immediately rewarding – led older adults to perform at a level equal to younger adults. We conclude that providing information about foregone rewards biases older adults toward immediate rewards at a greater rate than younger adults, leading to poorer older adult performance when immediate rewards and long-term rewards conflict, but intact performance when immediate rewards and long-term rewards appear to align.
The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder

Major depressive disorder (MDD) is marked by disturbances in brain functional connectivity. This connectivity is modulated by rhythmic oscillations of brain electrical activity, which enable coordinated functions across brain regions. Oscillatory activity plays a central role in regulating thinking and memory, mood, cerebral blood flow, and neurotransmitter levels, and restoration of normal oscillatory patterns is associated with effective treatment of MDD. Repetitive transcranial magnetic stimulation (rTMS) is a robust treatment for MDD, but the mechanism of action (MOA) of its benefits for mood disorders remains incompletely understood. Benefits of rTMS have been tied to enhanced neuroplasticity in specific brain pathways. We summarize here the evidence that rTMS entrains and resets thalamocortical oscillators, normalizes regulation and facilitates reemergence of intrinsic cerebral rhythms, and through this mechanism restores normal brain function. This entrainment and resetting may be a critical step in engendering neuroplastic changes and the antidepressant effects of rTMS. It may be possible to modify the method of rTMS administration to enhance this MOA and achieve better antidepressant effectiveness. We propose that rTMS can be administered: (1) synchronized to a patient's individual alpha frequency (IAF), or synchronized rTMS (sTMS); (2) as a low magnetic field strength sinusoidal waveform; and, (3) broadly to multiple brain areas simultaneously. We present here the theory and evidence indicating that these modifications could enhance the therapeutic effectiveness of rTMS for the treatment of MDD.
Recent mechanical weakening of the Arctic sea ice cover as revealed from larger inertial oscillations

[1] We present a simple and analytical ocean boundary layer-sea ice coupled dynamical model that we apply to the modeling of Arctic sea ice motion in the frequency domain, and particularly in the inertial range. This study further complements our related work in an unpublished paper where the sea ice cover response to the Coriolis forcing has been studied. This analytical model allows interpretation of the spatial, seasonal and pluriannual dependence of the magnitude of the inertial oscillations detailed in terms of mechanical behavior of the ice cover. In this model, the sea ice mechanical response is simplified through the introduction of a linear internal friction term K. A dependence of K allows us to explain the associated dependence of the seasonal and regional Arctic sea ice inertial motion. In addition, a significant decrease of K, i.e., a mechanical weakening of the sea ice cover, is observed for the period 2002–2008 compared to 1979–2001, for the entire Arctic in both seasons. These results show that the regional, seasonal and pluriannual variations of sea ice inertial motion are not only the trivial consequence of simultaneous variations of thickness and concentration (and so of ice mass per unit area). Instead, the shrinking and thinning of the Arctic sea ice cover over the last few decades has induced a mechanical weakening, which in turns has favored sea ice fracturing and deformation.
Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate FDR to identify the genes increased in expression up-regulated or decreased in expression down-regulated. In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered subnetworks the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.
Assessment and Comparison of Functionalities of Telemedical Applications

services on the Internet are modern alternative to traditional medical care supporting many functions of healthcare process. In order to improve quality and usability of upcoming tools, evaluation of existing solutions may provide valuable insight for further developments. Therefore the objective of this study was to analyze functionalities of selected telemedical applications. Our analysis provided overview of most widely- used tools and their functionalities. We discriminated three important groups of telemedical applications which were further characterized by ascribing specific functionalities considering also main and supporting role of these functionalities in each group. Our analysis resulted in extensive yet non-exhaustive list of functionalities. Based on data ontology commonly used in medical applications we distinguish three main groups of applications: Communication and social networks, Self-observation, monitoring and diagnosis, Support for medical professionals. Functional analysis for each group of medical applications was performed based on selected cases. According to the findings from our study the major criterion that allows to disentangle the heterogeneity of medical application functionalities is professionalization of the medical application. It is plausible that in future different groups of applications will become even less distinguishable due to increasing applicability of social network features in web applications but also because of increasing popularity of e- health add-ons and tools which are becoming increasingly integrated with complementary solutions. We also identified some unexplored potential of medical applications in the field of disease prevention. General Terms Information Personalization, Data modeling, Data ontology Keywordsmedical application, ontology, functionalities, disease monitoring, telecare
Application of Adaptive Neuro Fuzzy Inference System for Designing Square Monopole Antenna

Design and application of a very effective and convenient hybrid method based on Adaptive Neuro Fuzzy Inference System (ANFIS) has been explained in this paper. ANFIS has been implemented to determine three design parameters of square monopole antenna. This hybrid method is much superior compared with other traditional methods in terms of number of data and time required for training. Moreover, the method is efficient enough to reduce the error percentage less than 5%. Results given by ANFIS are matched with simulated model given by IE3D and thus error percentage is calculated.
Exploiting hierarchical and temporal information in building predictive models from EHR data

Clinical predictive modeling has the potential to revolutionize healthcare by allowing caregivers to allocate resources effectively, resulting not only in lower costs but also in better patient outcomes. Electronic health records (EHR), which contain large volumes of detailed patient information, are a great resource for learning accurate predictive models using advanced machine learning and data mining techniques. In this thesis we develop techniques that can exploit available patient information to learn clinical predictive models while tackling the challenges inherent in the data. We present our work in the context of predicting disease progression in chronic diseases. We present a novel feature representation that exploits hierarchical relationships between high cardinality categorical variables to tackle the challenge of high dimensionality. Our approach reduces feature dimensionality while maintaining variablespecific predictive information to yield more accurate predictive models than methods that ignore the hierarchy. For predicting incident heart failure, we show that by leveraging hierarchy in the diagnosis and procedure codes, we can improve the area under the receiver operating characteristic curve from 0.65 to 0.70 and the F-score from 0.37 to 0.40. Using simulation, we further analyzed the properties of the data that affect the amount of improvement obtained by leveraging hierarchy. We also present a method to exploit temporal information in longitudinal EHR data. Our multitask-based machine learning approach captures time varying effects of a predictor. Our results show that by exploiting temporal information, our approach can improve risk stratification of patients with compromised kidney function over a model that only use the most recent patient information. Specifically, our proposed approach is able to boost sensitivity from 37.5% to 55.1% (for a precision of ≈ 50%) when identifying patients at high risk of rapid progression of renal dysfunction. Thesis Supervisor: John Guttag Title: Dugald C. Jackson Professor
Platform-Based Design Approach for Embedded Vision Applications

Implementation of an efficient, complex real-time image and video processing system has many different contrary design needs. These systems require managing and processing a large amount of data in a timely manner. The robust implementation requires design reuse and flexibility, which is very crucial for an ever-increasing application need. A combination of hardware and software which form a platform fulfills the demands of the continuously developing application domain. The platform-based design has recently gained in importance which allows to explore the trade-offs between various designs requirements for the hardware and software implementation of real-time image and video processing algorithms and its systems. In this paper, we present the exploration of platform-based design approach for implementing a real-time vision system. We have used the platform-based design approach to design a configurable real-time image and video streaming module for vision-based systems on an Virtex-5 FPGA. We have also implemented a real-time image and video acquisition module for a smart camera system. 
TASM: Top-k Approximate Subtree Matching

We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree, e.g., a DBLP article with 15 nodes, in a large document tree, e.g., DBLP with 26M nodes, using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space complexity, and, thus, do not scale. Our solution is TASM-postorder, a memory-efficient and scalable TASM algorithm. We prove an upper-bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML documents confirms our analytic results.