Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolic Disturbances Associated with Systemic Lupus Erythematosus

The metabolic disturbances that underlie systemic lupus erythematosus are currently unknown. A metabolomic study was executed, comparing the sera of 20 SLE patients against that of healthy controls, using LC/MS and GC/MS platforms. Validation of key differences was performed using an independent cohort of 38 SLE patients and orthogonal assays. SLE sera showed evidence of profoundly dampened glycolysis, Krebs cycle, fatty acid β oxidation and amino acid metabolism, alluding to reduced energy biogenesis from all sources. Whereas long-chain fatty acids, including the n3 and n6 essential fatty acids, were significantly reduced, medium chain fatty acids and serum free fatty acids were elevated. The SLE metabolome exhibited profound lipid peroxidation, reflective of oxidative damage. Deficiencies were noted in the cellular anti-oxidant, glutathione, and all methyl group donors, including cysteine, methionine, and choline, as well as phosphocholines. The best discriminators of SLE included elevated lipid peroxidation products, MDA, gamma-glutamyl peptides, GGT, leukotriene B4 and 5-HETE. Importantly, similar elevations were not observed in another chronic inflammatory autoimmune disease, rheumatoid arthritis. To sum, comprehensive profiling of the SLE metabolome reveals evidence of heightened oxidative stress, inflammation, reduced energy generation, altered lipid profiles and a pro-thrombotic state. Resetting the SLE metabolome, either by targeting selected molecules or by supplementing the diet with essential fatty acids, vitamins and methyl group donors offers novel opportunities for disease modulation in this disabling systemic autoimmune ailment.
Continuous Auditing: Technology Involved

This study will concentrate on the latest factor causing changes in the domain of accountancy: technological advances. With a great deal of creativity and ingenuity, accountants around the world were able to find solutions to one of the problems that arose: increased fraudulent behavior. These, at times, involved a level of technology that was still not fully understood by all its users. This paper is going to focus on one of the ways that technology was applied to react to these changes: continuous auditing and monitoring. The idea of continuously auditing/monitoring the events and transactions of companies is not a new one, but innovations in technology have redefined it. Through explanation and demonstration of three continuous auditing models, this paper will attempt to bring some light on the topic and give an insight on the technology required for such a practice to be carried out effectively. Possible drawbacks and obstacles of incorporating the system in a company’s day-to-day activities will be also looked at, and recommendations will be made. CONTINUOUS AUDITING: TECHNOLOGY INVOLVED 3
Prospective epidemiologic study on respiratory diseases in children and immunization against measles.

BACKGROUND A hypothesis that vaccination with live attenuated measles virus performed in early childhood may prevent allergic sensitization has been explored in the course of a 3-year follow-up study in Kraków (Poland) among 1005 school children. MATERIALS AND METHODS The basic respiratory health end-points were chronic respiratory symptoms, allergy, asthma and susceptibility to acute respiratory infections. Information about immunization status of children was extracted from individual vaccination records kept by school nurses. The study showed the highest rates of chronic respiratory symptoms in non-vaccinated children who contracted measles while the lowest rates in those vaccinated who did not contract the disease. RESULTS AND CONCLUSIONS Risk of allergy diagnosed by a physician in vaccinated children after adjustment to potential confounders was about half of that in the reference group (OR = 0.58 95% CI: 0.42-0.80), the same was found for asthma diagnosed by a physician (OR = 0.50 95% CI: 0.24-1.00), and for susceptibility to respiratory infections (OR = 0.51 95% CI: 0.36-0.72). Our data provide indirect evidence that infection with attenuated measles virus is able to alter immunological reactions being responsible for the manifestation of respiratory symptoms and allergy.
Simultaneous Assessment of Soil Microbial Community Structure and Function through Analysis of the Meta-Transcriptome

Background Soil ecosystems harbor the most complex prokaryotic and eukaryotic microbial communities on Earth. Experimental approaches studying these systems usually focus on either the soil community's taxonomic structure or its functional characteristics. Many methods target DNA as marker molecule and use PCR for amplification. Methodology/Principal Findings Here we apply an RNA-centered meta-transcriptomic approach to simultaneously obtain information on both structure and function of a soil community. Total community RNA is random reversely transcribed into cDNA without any PCR or cloning step. Direct pyrosequencing produces large numbers of cDNA rRNA-tags; these are taxonomically profiled in a binning approach using the MEGAN software and two specifically compiled rRNA reference databases containing small and large subunit rRNA sequences. The pyrosequencing also produces mRNA-tags; these provide a sequence-based transcriptome of the community. One soil dataset of 258,411 RNA-tags of ∼98 bp length contained 193,219 rRNA-tags with valid taxonomic information, together with 21,133 mRNA-tags. Quantitative information about the relative abundance of organisms from all three domains of life and from different trophic levels was obtained in a single experiment. Less frequent taxa, such as soil Crenarchaeota, were well represented in the data set. These were identified by more than 2,000 rRNA-tags; furthermore, their activity in situ was revealed through the presence of mRNA-tags specific for enzymes involved in ammonia oxidation and CO2 fixation. Conclusions/Significance This approach could be widely applied in microbial ecology by efficiently linking community structure and function in a single experiment while avoiding biases inherent in other methods.
Brain pattern recognition based classification of neurodegenerative diseases

The visual inspection of the neurodegenerative disease through medical imaging is a tedious and error prone task. Most of the times the radiologist can misunderstand the disorder to be normal aging effect. In this project a system is being introduced which automatically classifies the kind of neurodegenerative disease. Basic image processing like preprocessing followed by feature extraction have been done in input Magnetic Resonance Image (MRI). Neural network methodologies have been used for testing and training the image which is preceded by Gray Level Co Matrix (GLCM). These features undergo a training phase of neural network followed by testing to classify the kind of neurodegenerative disease whether Parkinson or Schizophrenia or Normal. Support Vector Machine (SVM) is the neural network which is opted here.
Electric field-directed fibroblast locomotion involves cell surface molecular reorganization and is calcium independent

Directional cellular locomotion is thought to involve localized intracellular calcium changes and the lateral transport of cell surface molecules. We have examined the roles of both calcium and cell surface glycoprotein redistribution in the directional migration of two murine fibroblastic cell lines, NIH 3T3 and SV101. These cell types exhibit persistent, cathode directed motility when exposed to direct current electric fields. Using time lapse phase contrast microscopy and image analysis, we have determined that electric field-directed locomotion in each cell type is a calcium independent process. Both exhibit cathode directed motility in the absence of extracellular calcium, and electric fields cause no detectable elevations or gradients of cytosolic free calcium. We find evidence suggesting that galvanotaxis in these cells involves the lateral redistribution of plasma membrane glycoproteins. Electric fields cause the lateral migration of plasma membrane concanavalin A receptors toward the cathode in both NIH 3T3 and SV101 fibroblasts. Exposure of directionally migrating cells to Con A inhibits the normal change of cell direction following a reversal of electric field polarity. Additionally, when cells are plated on Con A- coated substrata so that Con A receptors mediate cell-substratum adhesion, cathode-directed locomotion and a cathodal accumulation of Con A receptors are observed. Immunofluorescent labeling of the fibronectin receptor in NIH 3T3 fibroblasts suggests the recruitment of integrins from large clusters to form a more diffuse distribution toward the cathode in field-treated cells. Our results indicate that the mechanism of electric field directed locomotion in NIH 3T3 and SV101 fibroblasts involves the lateral redistribution of plasma membrane glycoproteins involved in cell-substratum adhesion.
Attention driven grasping for clearing a heap of objects

Generation of grasps for automated object manipulation in cluttered scenarios presents major challenges for various modules of the pipeline such as 2D/3D visual processing, 3D modeling, grasp hypothesis generation, grasp planning and path planning. In this paper, we present a solution framework for solving a complex instance of the problem - represented by a heap of unknown and unstructured objects in a bounded environment - in our case, a box; with the goal being removing all objects in the box using an attention driven object modeling approach to cognitive grasp planning. The focus of the algorithm delves on Grasping by Components (GBC), with a prioritization scheme derived from scene based attention and attention driven segmentation. In order to overcome the traditional challenge of segmentation performing poorly in cluttered scenes, we employ a novel active segmentation approach suited to our scenario. While the attention module helps prioritize objects in the heap and salient regions, the GBC scheme segments out parts and generates grasp hypotheses for each part. GBC is a very important component of any scalable and holistic grasping system since it abstracts point cloud object data with parametric shapes and no apriori knowledge (such as 3D models) is required. Earlier work in 3D model building (such as CAD based, simple geometries, bounding boxes, Superquadrics etc.) have depended on precise shape and pose recognition as well as exhaustive training to learn or exhaustive searching in grasp space to generate good grasp hypotheses. These methods are not scalable for real-time scenarios, complex shapes and unknown environments - key challenges in robotic grasping. In order to alleviate this concern, we present a novel parametric algorithm to estimate grasp points and approach vectors from the 3D parametric shape model, along with innovative schemes to optimize the computation of the parametric models as well as to refine the generated grasp hypotheses based on the scene information to aid path planning. We present evaluation of our complex grasping pipeline for cluttered heaps through a series of test sequences involving removal of objects from a box, along with evaluations for our attention mechanisms, active segmentation, 3D model fitting optimizations and quality of our grasp hypotheses.
ULISSE, a network intrusion detection system

In this paper we present a tool for network anomaly detection and network intelligence which we named ULISSE. It uses a two tier architecture with unsupervised learning algorithms to perform network intrusion and anomaly detection. ULISSE uses a combination of clustering of packet payloads and correlation of anomalies in the packet stream. We show the experiments we conducted on such architecture, we give performance results, and we compare our achievements with other comparable existing systems.
An Energy-Efficient Scheme for STT-RAM L1 Cache

Spin-Transfer Torque RAM (STT-RAM) is a promising cache candidate studied frequently in recent years. Compared to the traditional SRAM, The STT-RAM is more promising for future on-chip caches due to STT-RAM's long endurance, low leakage, high density and high access speed. Nevertheless, the major challenges of using STT-RAM as L1 cache are its write energy and write latency. It is feasible to use STT-RAM as L1 cache by reducing data retention time. We find that most data in L1 cache has a life time shorter than STT-RAM data retention time. A refresh scheme that will degrades system performance, and bring more energy consumption is needed to assure data correctness. In this paper, we proposed a counter-controlled scheme to avoid STT-RAM L1 cache data block refreshing. We propose a dead data processing strategy that deals with data block when it exceeds its retention time. Our simulation results show that STT-RAM L1 cache coupled with our counter-controlled scheme can save up to 60% energy consumption, 44% energy consumption on average compared to SRAM L1 cache, achieve slightly performance improvement on average compared to baseline.
MRI Shoulder Complex Segmentation and Classification

This paper deals with a segmentation (classification) probl em which arises in the diagnostic and treatment of shoulder disorders. Classical techniques can be applied successfully to solve the binary problem but they do not provide a suitable method for the multiphase problem w e consider. To this end we compare two different methods which have been applied successfully to o ther medical images modalities and structures. Our preliminary results suggest that a successful segmenta tio and classification has to be based on an hybrid method combining statistical and geometric information.
QoS-Constrained Core Selection Algorithm for Multicast Routing

With the proliferation of multimedia group applications, multicast is becoming increasingly important. We propose a core selection algorithm to the core selection problem in Core-based multicast routing with QOS requirements.we select the smaller set of cores from the set of candidate cores such that the number of group members with satisfied end-to-end QoS requirements is maximized. Simulation results show our algorithms have features of well performance of less selected cores,and are feasible and effective.
An efficient system for the detection of arrhythmic segments in ECG recordings based on non-linear features of the RR interval signal

In this paper we explore the RR interval signal to detect arrhythmic segments in electrocardiograms (ECG) using non-linear analysis. Initially, the RR interval signal is extracted and it is segmented into small segments. Linear (standard deviation), spectral (total energy) and non-linear (approximated entropy and normalized entropy) characteristics are extracted for each segment. Time-frequency analysis is used for the calculation of the total energy. These characteristics are fed into a neural network to classify each segment as normal or arrhythmic. The proposed approach is validated using the MIT-BIH database for various segment sizes (32, 64, 128, 256 and 512 RR intervals). The method results in high sensitivity and specificity (85% sensitivity and 92% specificity) for arrhythmic segment detection.
SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Smartphones

This paper presents SmartMal-A novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce Service Oriented Architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server's main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users' regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices.
PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing

Small RNAs (sRNAs) are a class of short (20–25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete ‘sRNAome’ we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.
Beta Estimation with Stock Return Outliers: The Case of U.S. Pharmaceutical Companies

Efficient estimation of the equity cost of public corporations is an essential component of computing the required rate of return of real investment projects, and therefore the basis for a rational investment policy. The accepted methodology relies on the CAPM model to define the return risk premium, and the OLS method to estimate the beta risk coefficient required for calculating the premium. This study challenges the use of the OLS method for this task by demonstrating its vulnerability to the impact of stock return outliers caused by large, unpredictable, company-specific events. That impact is verified on a sample of U.S. pharmaceutical companies by comparing the OLS estimation performance with that of our proposed method based on Huber’s Robust M (HRM) estimator, a related statistical method that follows a mixed return model identifying regular and outlier return components. Using the HRM-estimated beta as a benchmark, we demonstrate that (1) outliers can substantially bias the OLS beta, (2) the bias is negatively correlated with company size, and (3) the size of the bias is often moderated but not eliminated by extending the estimation period. The latter finding suggests that a robust method like HRM is preferable where estimators ought to represent the behavior of the majority of historical data despite the presence of outliers. The risk of trusting the OLS beta is especially high when estimation must rely on a small sample.
Compact Second-Order Highly Linear Varactor-Tuned Dual-Mode Filters With Constant Bandwidth

This paper presents a compact highly linear tunable second-order quasi-elliptic filter with constant 3-dB bandwidth. The proposed filter is thoroughly analyzed to clearly describe the filter equivalent circuit and the tuning mechanism involved. In addition, the tunable resonator configuration employed is shown to improve filter linearity, especially for low bias voltages where distortion is normally stronger. A quasi-elliptic tunable filter was designed, built, and tested for illustration and verification. With a 3-dB bandwidth variation of only 4.6%, the filter had a frequency coverage from 1.45 to 1.96 GHz, an insertion loss better than 2.5 dB, and measured IIP3 >; 43 dBm throughout. The experimental results are in excellent agreement to theory and simulations.
Towards Private Navigation of Tree Structured Spatial Indexes

With many location-based services, spatial data such as points of interest are indexed at a potentially untrusted host and queries are evaluated by navigating the underlying index structure used to partition the data. While encryption can prevent the host from learning the data content (i.e., what is accessed), it cannot hide the frequency that index nodes are accessed while navigating the index for query processing. Combining the knowledge of such access frequencies with public knowledge readily available about points of interest, the host can infer sensitive information about the indexed data and hence the locations of the users querying it (violating location privacy). In this paper, we propose a technique that hides frequency access to the nodes of tree-structured spatial indexes (e.g., R-tree) from an untrusted server hosting the data. With our approach, each access to an index node requires reading an extra node using a precomputed node-based probability distribution function to guarantee uniform node access at all tree levels. We analytically verify the strong level of privacy achieved with a constant computation and acceptable communication and storage overhead for employing our private index navigation scheme.
Table Ii Comparing Results of the Three Fccs Theoretical Analysis of Bus-invert Coding

theoretical speedup equal to 19=9:5 (= 2), which is almost the same the experimental values shown in Table II. However, hardware overhead is needed to achieve the 2 times speedup. Comparing the sequential and dynamic pipelined datapaths shown in Figs. 2 and 9, the penalty paid for the speedup is extra area overhead including more pipelined registers and a somewhat larger controller. In addition, the total area of one multiplier and one divider used in the dynamic pipelined architecture is larger than the area of one ALU in the sequential architecture that can perform multiplication and division operations. In the future, how to reduce the area of datapath such as using the table look-up method to replace some larger functional units is our next goal. V. CONCLUSION This brief has presented a dynamic pipelined architecture for the fuzzy tree color correction algorithm FCC. It describes the design process of the FCC dynamic pipelined architecture, which performs a nested loop with data-dependent number of iterations and data-dependent branches, using the variable latencies and speculative computation. The pipeline latencies of the designed architecture are depended on the execution length of the inner section and are naturally unfixed. Experimental results show that the dynamic pipelined FCC architecture obtains about 2 times speedup with a slight area overhead. A new efficient fuzzy algorithm for color correction, " IEEE Trans. Color image scanner with an RGB linear image sensor, " " Colorimetric calibration in electronic imaging devices using a look-up-table model and interpolations, " J. Design and implementation of the tree-based fuzzy logic controller, " IEEE Trans. Automatic synthesis of dynamically configured pipelines supporting variable data initiation intervals, " IEEE Trans. PASS: A package for automatic scheduling and sharing pipelined data paths, " in Proc. Abstract—The closed-form formulas derived from the Markov chains depicting the bus-invert coding processes for even and odd bus widths are employed to compute switching activity and weight per bus transfer, to explain the diminishing returns with increasing bus width and to show the nonviability of partitioning a bus into smaller buses of odd number bits, etc. Probing into the codewords generated by the coding process, we obtain the same closed-form formulas as that obtained by the Markov-based approach. Our contributions are forming a superset over the previously published theoretical results and providing comprehensive background information for exploiting the bus-invert method for low-power applications.