Miracle-dz/UniFAR-FTUs huggingface dataset:
The investigation on phytochemicals from Ferulago angulata (Schlecht) Boiss, indigenous to central parts of Iran

Ferulago angulata (Schlecht) Boiss (Apiaceae) is a perennial herb distributed in the central parts of Iran. In this work, the essential oil of Ferulago angulat a aerial parts collected from the central parts of Iran was obtained by using hydrodistillation analysed by GC and GC–MS. Air-dried and powdered plant roots were extracted with n-hexane, dichloromethane and methanol, respectively, using a Soxhlet apparatus. The n-hexane extract was subjected to preparative thin-layer chromatography using chloroform–acetone (95:5) as solvent system to yield two linear furanocoumarins, prantschimgin and oxypeucedanin. The structure of theisolated compounds was elucidated by using spectroscopic data such as 1H NMR, 13C NMR, UV and IR. The results of GC–MS analyses showed that γ-terpinolene (11.97%), α-pinene (10.00%), sabinene (6.89%), linalool (5.56%) and cis-ocimene (4.41%) were the main components among 51 constituents characterised in the oil. The oil exhibits considerable phytotoxic activity.
Percutaneous electrical nerve stimulation: a novel analgesic therapy for diabetic neuropathic pain.

OBJECTIVE To evaluate the use of percutaneous electrical nerve stimulation (PENS) in the management of patients with painful diabetic peripheral neuropathy. RESEARCH DESIGN AND METHODS A total of 50 adult patients with type 2 diabetes and peripheral neuropathic pain of >6 months duration involving the lower extremities were randomly assigned to receive active PENS (needles with electrical stimulation at an alternating frequency of 15 and 30 Hz) and sham (needles only) treatments for 3 weeks. Each series of treatments was administered for 30 min three times a week according to a standardized protocol. After a 1-week washout period, all patients were subsequently switched to the other modality. A 10-cm visual analog scale (VAS) was used to assess pain, physical activity, and quality of sleep before each session. The changes in VAS scores and daily requirements for oral analgesic medication were determined during each 3-week treatment period. Patients completed the MOS 36-Item Short-Form Health Survey (SF-36), the Beck Depression Inventory (BDI), and the Profile of Mood States (POMS) before and after completion of each treatment modality. At the end of the crossover study, a patient preference questionnaire was used to compare the effectiveness of the two modalities. RESULTS Compared with the pain VAS scores before active (6.2 +/- 1.0) and sham (6.4 +/- 0.9) treatments, pain scores after treatment were reduced to 2.5 +/- 0.8 and 6.3 +/- 1.1, respectively. With active PENS treatment, the VAS activity and sleep scores were significantly improved from 5.2 +/- 1.0 and 5.8 +/- 1.3 to 7.9 +/- 1.0 and 8.3 +/- 0.7, respectively. The VAS scores for pain, activity, and sleep were unchanged from baseline values after the sham treatments. Patients' daily oral nonopioid analgesic requirements decreased by 49 and 14% after active and sham PENS treatments, respectively. The post-treatment physical and mental components of the SF-36, the BDI, and the POMS all showed a significantly greater improvement with active versus sham treatments. Active PENS treatment improved the neuropathic pain symptoms in all patients. CONCLUSIONS PENS is a useful nonpharmacological therapeutic modality for treating diabetic neuropathic pain. In addition to decreasing extremity pain, PENS therapy improved physical activity, sense of well-being, and quality of sleep while reducing the need for oral nonopioid analgesic medication.
Incidental Focal Thyroid Uptake on FDG Positron Emission Tomographic Scans May Represent a Second Primary Tumor

Purpose This article discusses the value of incidental thyroid uptake of fluorodeoxyglucose (FDG) on whole-body positron emission tomography (PET) scans performed in patients with cancer. This issue has been reported diversely in the literature. Materials and Methods Whole-body PET scans of four patients with cancer (two of them women; age, 49 to 78 years) in whom focal thyroid uptake was visualized and subsequently correlated with thyroid carcinoma based on cytologic or histopathologic data were reviewed. The clinical outcomes of these patients were analyzed retrospectively. Maximum and average standardized uptake values (SUVs) of thyroid accumulation were recalculated in all patients. Results Two of the four patients referred for FDG PET scans had lung adenocarcinomas, one had prostrate carcinoma, and one had an unknown primary tumor. Focal thyroid uptake was visualized, with maximum and average SUVs ranging from 3.7 and 2.3 to 53 and 34, respectively. These findings were correlated with cytologic (two patients) or histopathologic data (two patients) that indicated thyroid carcinoma. In two patients, their treatment was changed and total thyroidectomy was performed; in one of them the SUVs of the focal thyroid accumulation (maximum and average values of 7.9 and 4.8, respectively) were less than the cutoff values for thyroid cancer noted in the literature. The clinical condition of the other two patients did not permit additional investigation or treatment for thyroid carcinoma. Conclusion Increased focal thyroid uptake on whole-body FDG PET scans should not be overlooked, even when it is not marked, and should prompt further investigation to rule out cancer.
Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data

Bioinformatics is a data intensive field of research and development. DNA microarray used to better understand form of saccharomyces cerevisiae disease such as cancer. Microarray allows us to diagnose and treat patients more successfully. Statistical method devoted to detection in DNA from microarray data, the inherent challenges in data quality associated with most filter techniques remains a challenging problem in microarray association studies. Applying methods of simulation studies and a genome-wide association microarray study in saccharomyces cerevisiae, that find current approach significantly improve DNA microarray cell reduces the yeast value rates and false positive genes variation. Clustering is the one of the main techniques for data mining. Microarray is the evolutionary history for a set of evolutionary related genes expression data. There are number of different distance based methods of which two are dealt with here: Euclidean method and Manhattan method. A method for construction of distance based gens expression using clustering is proposed and implemented on different saccharomyces cerevisiae samples. Evolutionary distances between two or more genes are calculated using p-distance method. Multiple samples are applied on different datasets. Hierarchical clustering and k-mean clustering are constructed for different datasets from available data using both the distance based methods. Then, final cluster is constructed using these closely related filter dataset. General Terms Thresholding, Bioinformatics, Gene’s expression, Microarray, Data mining, Series analysis.
Effect of Drug and Diet Treatment of Mild Hypertension on Diastolic Blood Pressure

The Trial of Antihypertensive Interventions and Management is a multicenter randomized trial designed to examine the diastolic blood pressure response of various combinations of pharmacological and dietary interventions in the treatment of mild hypertension (diastolic blood pressure 90-100 mm Hg). Eight hundred and seventy-eight participants at 110-160% of ideal weight were randomly allocated to nine drug/diet treatment groups receiving either a placebo, chlorthalidone (25 mg), or atenolol (50 mg), combined with a usual, a weight loss, or a low sodium/high potassium diet The primary outcome was diastolic blood pressure change from baseline to 6 months. Seven hundred and eighty-seven participants had follow-up data. The mean baseline diastolic blood pressure was 93.8 mm Hg; 55.9% of the participants were male, and the weight loss diet group lost an average of 4.7 kg. Multiple comparisons were accounted for in the analysis. A significantly greater lowering of diastolic blood pressure (12.4 mm Hg) was achieved in the atenolol group compared with either the low sodium/high potassium diet group (7.9 mm Hg, p=0.001) or weight loss group (8.8 mm Hg, p=0.006). Adding weight loss to chlorthalidone significantly enhanced blood pressure lowering (15.1 mm Hg) when compared with the diuretic alone (10.8 mm Hg, p=0.002), but adding a low sodium/high potassium diet (12.2 mm Hg, p=0.029) did not In the short-term treatment of mild hypertension where diastolic blood pressure is the sole consideration, drugs outperform diet, and weight loss is beneficial, especially with diuretics.
Portioned EDF-based scheduling on multiprocessors

This paper presents an EDF-based algorithm, called Earliest Deadline Deferrable Portion (EDDP), for efficient scheduling of recurrent real-time tasks on multiprocessor systems. The design of EDDP is based on the portioned scheduling technique which classifies each task into a fixed task or a migratable task. A fixed task is scheduled on the dedicated processor without migrations. A migratable task is meanwhile permitted to migrate between the particular two processors. In order to curb the cost of task migrations, EDDP makes at most M -- 1 migratable tasks on M processors. The scheduling analysis derives the condition for a given task set to be schedulable. It is also proven that no tasks ever miss deadlines, if the system utilization does not exceed 65%. Beyond the theoretical analysis, the effectiveness of EDDP is evaluated through simulation studies. Simulation results show that EDDP achieves high system utilization with a small number of preemptions, compared with the traditional EDF-based algorithms.
Protein Sub-Nuclear Localization Prediction Using SVM and Pfam Domain Information

The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139. 227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server.
Practical augmented Lagrangian methods for constrained optimization

This book focuses on Augmented Lagrangian techniques for solving practical constrained optimization problems. The authors rigorously delineate mathematical convergence theory based on sequential optimality conditions and novel constraint qualifications. They also orient the book to practitioners by giving priority to results that provide insight on the practical behavior of algorithms and by providing geometrical and algorithmic interpretations of every mathematical result, and they fully describe a freely available computational package for constrained optimization and illustrate its usefulness with applications. Audience: The book is aimed at engineers, physicists, chemists, and other practitioners interested in full access to comprehensive and well-documented software for large-scale optimization as well as up-to-date convergence theory and its practical consequences. It will also be of interest to graduate and advanced undergraduate students in mathematics, computer science, applied mathematics, optimization, and numerical analysis.
Statistical analysis of MR imaging and its applications in image modeling

This paper presents a statistical description of MR imaging, from the imaging equation to the image random field. Both thermal noise and object variability are considered in the pixel images generated by Fourier transform reconstruction algorithm. The Gaussianity, stationarity, dependence and ergodicity of MR image random field are characterized as the standard problems of statistics, and justified to form the basis for establishing the stochastic image model and conducting the statistical image analysis. An application of these properties to the finite normal mixture modeling of MR images is demonstrated, and a new mathematical understanding is discussed based on some new findings. <<ETX>>
Virtual infrastructure for collision-prone wireless networks

Wireless ad hoc networks pose several significant challenges: devices are unreliable; deployments are unpredictable; and communication is erratic. One proposed solution is Virtual Infrastructure, an abstraction in which unpredictable and unreliable devices are used to emulate reliable and predictable infrastructure. In this paper, we present a new protocol for emulating virtual infrastructure in collision-prone wireless networks. At the heart of our emulation is a convergent history agreement protocol that tolerates lost messages and crash failures. It is designed specifically for ad hoc deployments, for example, the set of participants a priori unknown. The convergent history agreement protocol is quite efficient, as each agreement instance completes in a constant number of communication rounds, and the size of the messages is constant, independent of the length of the execution. Building on the convergent history agreement protocol, our virtual infrastructure emulation introduces only constant overhead per virtual round emulated. We believe that the techniques developed in this paper help to bring virtual infrastructure one step closer to a reality.
Interactive beautification: a technique for rapid geometric design

We propose interactive beautification, a technique for rapid geometric design, and introduce the technique and its algorithm with a prototype system Pegasus. The motivation is to solve the problems with current drawing systems: too many complex commands and unintuitive procedures to satisfy geometric constraints. Interactive beautification system receives the user's freestroke and beautifies it by considering geometric constraints among segments. A single stroke is beautified one after another, preventing accumulation of recognition errors or catastrophic deformation. Supported geometric constraints includes perpendicularity, congruence, symmetry, etc., which were not seen in existing freestroke recognition systems. In addition, the system generates multiple candidates as a result of beautification to solve the problem of ambiguity. Using the technique, the user can draw precise diagrams rapidly satisfying geometric relations without using any editing commands.Interactive beautification is achieved by three sequential processes; 1) inferring underlining geometric constraints based on the spatial relationships among the input stroke and the existing segments, 2) generating multiple candidates combining inferred constraints appropriately, and 3) evaluating the candidates to find the most plausible candidate and to remove the inappropriate candidates. An user study was performed using the prototype system, a commercial CAD, and an OO-based drawing system. The result showed that the users can draw required diagrams more rapidly and more precisely using the prototype system.
P and S wave tomography of the mantle beneath the United States

Mantle seismic structure beneath the United States spanning from the active western plate margin to the passive eastern margin was imaged with teleseismic P and S wave traveltime tomography including USArray data up to May 2014. To mitigate artifacts from crustal structure 5–40 s, Rayleigh wave phase velocities were used to create a 3‐D starting model. Major features of the final P and S models include two distinct low‐velocity anomalies at depths of ~60–300 km beneath the central and northern Appalachians and passive margin. The central Appalachian low‐velocity anomaly coincides with Eocene basaltic magmatism, and the northern anomaly is located along the Cretaceous track of the Great Meteor hot spot. At depths of ~300–700 km beneath the central and eastern U.S. large high‐velocity anomalies are inferred to be remnants of the Farallon slab that subducted prior to ~40 Ma during the Laramide orogeny.
Mobile call graphs: beyond power-law and lognormal distributions

We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distributions of the number of phone calls per customer; the total talk minutes per customer; and the distinct number of calling partners per customer. We find that these distributions are skewed, and that they significantly deviate from what would be expected by power-law and lognormal distributions. To analyze our observed distributions (of number of calls, distinct call partners, and total talk time), we propose PowerTrack , a method which fits a lesser known but more suitable distribution, namely the Double Pareto LogNormal (DPLN) distribution, to our data and track its parameters over time. Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe. Furthermore, we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours. We discuss the application of those results to our model and to forecasting.
Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments

Cloud computing is the latest computing paradigm that delivers IT resources as services in which users are free from the burden of worrying about the low-level implementation or system administration details. However, there are significant problems that exist with regard to efficient provisioning and delivery of applications using Cloud-based IT resources. These barriers concern various levels such as workload modeling, virtualization, performance modeling, deployment, and monitoring of applications on virtualized IT resources. If these problems can be solved, then applications can operate more efficiently, with reduced financial and environmental costs, reduced under-utilization of resources, and better performance at times of peak load. In this paper, we present a provisioning technique that automatically adapts to workload changes related to applications for facilitating the adaptive management of system and offering end-users guaranteed Quality of Services (QoS) in large, autonomous, and highly dynamic environments. We model the behavior and performance of applications and Cloud-based IT resources to adaptively serve end-user requests. To improve the efficiency of the system, we use analytical performance (queueing network system model) and workload information to supply intelligent input about system requirements to an application provisioner with limited information about the physical infrastructure. Our simulation-based experimental results using production workload models indicate that the proposed provisioning technique detects changes in workload intensity (arrival pattern, resource demands) that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.
Implicit Parallelism through Deep Language Embedding

The appeal of MapReduce has spawned a family of systems that implement or extend it. In order to enable parallel collection processing with User-Defined Functions (UDFs), these systems expose extensions of the MapReduce programming model as library-based dataflow APIs that are tightly coupled to their underlying runtime engine. Expressing data analysis algorithms with complex data and control flow structure using such APIs reveals a number of limitations that impede programmer's productivity. In this paper we show that the design of data analysis languages and APIs from a runtime engine point of view bloats the APIs with low-level primitives and affects programmer's productivity. Instead, we argue that an approach based on deeply embedding the APIs in a host language can address the shortcomings of current data analysis languages. To demonstrate this, we propose a language for complex data analysis embedded in Scala, which (i) allows for declarative specification of dataflows and (ii) hides the notion of data-parallelism and distributed runtime behind a suitable intermediate representation. We describe a compiler pipeline that facilitates efficient data-parallel processing without imposing runtime engine-bound syntactic or semantic restrictions on the structure of the input programs. We present a series of experiments with two state-of-the-art systems that demonstrate the optimization potential of our approach.
Diacritization as a Machine Translation and as a Sequence Labeling Problem

In this paper we describe and compare two techniques for the automatic diacritization of Arabic text: First, we treat diacritization as a monotone machine translation problem, proposing and evaluating several translation and language models, including word and character-based models separately and combined as well as a model which uses statistical machine translation (SMT) to post-edit a rule-based diacritization system. Then we explore a more traditional view of diacritization as a sequence labeling problem, and propose a solution using conditional random fields (Lafferty et al., 2001). All these techniques are compared through word error rate and diacritization error rate both in terms of full diacritization and ignoring vowel endings. The empirical experiments showed that the machine translation approaches perform better than the sequence labeling approaches concerning the error rates.
Speeding up SAT for EDA

This paper presents performance results for a new SAT solver designed specifically for EDA applications. The new solver significantly outperforms most efficient SAT solvers-Chaff, SATO, and GRASP-on a large set of benchmarks. Performance improvements for standard benchmark groups vary from 1.5/spl times/ to 60/spl times/. They were achieved through a new decision-making strategy and more efficient Boolean constraint propagation (BCP).