Miracle-dz/UniFAR-FTUs huggingface dataset:
Granulomatous Pulmonary Pneumocystis jirovecii in patients with solid and lymphoproliferative neoplasms: Clinicopathologic characterization of 9 cases

To better characterize the clinical and pathologic features of granulomatous reaction to Pneumocystis jirovecii in non-HIV/AIDS patients, nine cases of this uncommon pathology were reviewed. Patients included 7 males and 2 females (mean age, 63 years). The most common symptom was dyspnea (4/9). Primary medical diagnoses included lymphomas (5/9) and solid malignancies (4/9). Radiology findings included nodular (4/8) and diffuse (3/8) infiltrates, and solitary nodule (1/8). Diagnostic procedures with the highest yield were open lung biopsy (5/9) and autopsy (3/9) and transbronchial biopsy (1/9); one false negative result was initially obtained with bronchoalveolar lavage. Follow-up showed death from disease (5/7) and resolution of disease (2/7). Histologically, clusters of Gomori methenamine silver-positive Pneumocystis organisms were identified in all cases. Organisms were identified within well(5/9) and poorly(4/9) formed necrotizing (6/9) and non-necrotizing (3/9) granulomas ranging in size from 0.1 to 2.5 cm (mean, 0.7cm); granulomas were multiple (8/9) or single (1/9). Eosinophils (6/9), giant cells (5/9), and a fibrous rim around granulomas (4/9) were seen. Foamy pink exudates were present centrally within two granulomas. Only one case demonstrated the classic intraalveolar foamy exudates containing Pneumocystis. In non-HIV/AIDS patients, granulomatous Pneumocystis jirovecii pneumonia occurs most commonly in males with lymphoproliferative and solid malignancies. The diagnosis may be overlooked as conventional radiologic and pathologic features are absent. When suspected, open lung biopsy is most likely to yield diagnostic material. Attention to organism morphology avoids misdiagnosis as Histoplasma. *Correspondence to: Paul H Hartel, Sligo University Hospital, Histology, Level 4, The Mall Sligo, Ireland, E-mail: phartel16@gmail.com
Alternative drop-set – occlusion eccentric-ballistic method

Annotation. This article talks about a method that combines the following elements: 1) the pace of execution; 2) the KAATSU method; 3) drop-set. The rate of movement is one of the most important training variables. After all, many motor activities, whether in sports of higher achievements or in physical culture, take place in a shorter time interval than the muscles on the way to obtaining maximum production of force. The pace of implementation is an very important factor, related to the development of power capabilities, muscle hypertrophy, the time of finding the muscle under the training load, metabolic and hormonal reactions, as well as to muscle activation. Occlusive training has a high potential for application not only in general physical culture, but also in physical rehabilitation. Modern fundamental and clinical studies demonstrate the effectiveness of occlusive training, which is able to increase strength capabilities, increase muscle size. With the use of occlusive training, accumulation of metabolites leads to an increase in the recruitment of muscle fibers of the second type, and increased activation and synthesis of muscle protein stimulate the production of growth hormone. The KAATSU provides the right degree of tension, along with this without provoking an increase in blood pressure. Low-intensity training combined with muscle occlusion provides stimulation of mTORC1 activity and muscle protein synthesis in elderly men. Drop-sets are popular in the practice of strength training and it has very logical arguments: 1) an increase in the volume of the load; 2) Greater recruitment of muscle fibers. Of course, from the security point of view, training to failure is not a correct and effective way to achieve results in force training. But conventionally the concept of "failure" can be divided into several types: the natural limit (this is the termination of the approach for 1-2 repetitions before the failure), failure and super-powerful failure (for example, the method of forced repetitions implies such execution). The first type is the most optimal on the way to the development of muscle strength and hypertrophy. In this brief report proposed a modified version of the drop-sets.
Le diacétyle, les composés dicarbonyles et leurs produits de réduction dans le vin

Le diacetyle et les substances acetoiniques, acetoine et butanediol-2,3, sont des composes bien connus du vin, mais seul le diacetyle a veritablement une importance organoleptique. L'evolution de ces composes est observee lors des fermentations alcoolique et malolactique et differents parametres sont analyses. L'importance de la synthese du diacetyle par Saccharomyces cerevisiae est mise en evidence ainsi que la diminution de sa teneur. Il a ete possible de determiner, grâce a l'analyse par chromatographie en phase gazeuse et la spectrometrie de masse, la presence dans le vin d'autres molecules chimiquement proches dont le metabolisme est similaire. Il s'agit de la pentanedione-2,3, de l'hydroxy3 pentanone-2, du pentanediol-2,3, du methylglyoxal, de l'acetol (hydroxy-1 propanone-2) et du propanediol-1,2. La reduction enzymatique de ces composes est etudiee chez Saccharomyces cerevisiae et plus particulierement l'activite diacetyle reductase et acetoine reductase qui sont comparees chez plusieurs autres souches de levures. La mise au point du dosage du methylglyoxal et du glyoxal par une technique de derivation au PFBOA (chlorohydrate de O-(pentafluoro-2,3,4,5,6 benzyl) hydroxylamine) a egalement permis de determiner certains aldehydes, satures ou insatures, a l'aide de la chromatographie en phase gazeuse couplee a la spectrometrie de masse ou a un detecteur a capture d'electrons.
Serum Insulin Like Growth Factor-1– A Skeletal Maturity Indicator for the Assessment of Orthopedic Treatment Timing of Skeletal Class II Malocclusion

Orthodontic treatment planning decisions are dependent on the assessment of growth status. Skeletal maturity could be assessed with various radiologic and non-radiologic biologic markers.The purpose of our study was to investigate the reliability of serum Insulin like Growth Factor – 1(IGF-1) as a skeletal maturity indicator in comparison to the stages in cervical vertebral maturation (CVM). Twenty five subjects with skeletal class II malocclusion in the age range of 11-19 years were recruited for the study. The cervical vertebrae staging was assessed by lateral cephalograms. Blood was collected aseptically and serum was separated. The serum levels of IGF- 1 were quantitatively assessed using a commercial ELISA kit, IGF-1 600 ELISA (DRG Instruments GmbH, Germany). There was a significant decline in the mean IGF-1 levels with increase in age in both the sexes. Statistically significant difference was observed in the concentration of IGF-1 among the different age groups. There was a significant decline in the mean IGF-1 levels with increase in CVM staging from stage 2 to stage 4. IGF-1 could be a more reliable biologic skeletal maturity indicator in comparison to cervical vertebral maturity indicator and thus could predict more accurately the growth status of the mandible to facilitate correction of skeletal class II jaw discrepancies.
An experimental investigation of cognitive radar

The dynamically changing environment in which future radar systems must operate will be increasingly demanding due a number of factors. The rising congestion in the RF space, reduced availability of dedicated RF spectrum, the requirement to detect low radar cross section targets such as drones, the proliferation of wind turbines causing false detections and target masking, and ever more sophisticated countermeasures all adversely affect performance. Cognitive radar is a relatively new branch of radar research drawing inspiration from the study of brain and sensory system functions in the field of neuroscience. It is well known that bats and marine mammals such as dolphins employ echo-location for navigation and hunting, providing a remarkable degree of success in challenging environments. It is anticipated that adding cognitive capabilities to radar systems will enable performance gains to be achieved by better utilising the limited resources available and optimising the system’s interactions with the environment. This thesis addresses two topics in cognitive radar research. The first topic is the absence of any common scheme for classifying the levels of cognitive ability exhibited in radar systems by the development of a ‘scale of cognition’ explicitly targeted towards the radar problem. The second is to address the limited experimental evidence available in the radar literature by reporting on a range of practical experiments based on scenarios which may benefit from the inclusion of some level of cognition. The experiments were implemented on two experimental radar systems, UCL’s NetRAD system which has no closed-loop adaptive capability, and The Ohio State University’s (OSU) Cognitive Radar Engineering Workspace (CREW), designed for adaptive and cognitive radar research.
MORTALITY AMONG PFOA-EXPOSED WORKERS

Background and Aims: PFOA is ubiquitous at low levels in the serum of residents of industrial countries, and is biopersistent. It is a suspected human carcinogen based on animal data, and some human evidence suggest it causes increased cholesterol. Few human data exist on health effects. Our aim is to determine if PFOA workers with high exposures show excess mortality from any cause. Among 1025 current workers in 2004 at the plant we have studied, those working in PFOA area had a median PFOA serum level of 494 ng/ml, while those who had never worked in a PFOA area had a median level of 114 ng/ml (Sakr et al. 2007) By comparison the US median serum level in the general population is 4 ng/ml). Previously mortality findings in this cohort have shown an excess of diabetes and kidney cancer compared to other Dupont workers, and some positive trends for heart disease mortality (Leonard et al. 2008, Sakr et al. 2009). Methods: We have studied mortality through 2008 of 6027 workers occupationally exposed to high .
Metadata Connector: Exploiting Hashtag and Tag for Cross-OSN Event Search

Social media has revolutionized the way people understand and keep track of real-world events. Various related multimedia information in different modalities such as texts, images and videos is updated on social media and reflects the events. These quantities of information distributes on different Online Social Networks (OSNs), which provides rich, wide coverage, comprehensive information about the trending events. Faced with such large amounts of data, searching has become a handy tool for event understanding and tracking on social media. However, existing single-OSN search mainly involves with single modality on single platform. Moreover, most OSNs usually focus on biased perspective of events, which significantly limits the coverage and diversity of single-OSN based event search. In this paper, we introduce a novel cross-OSN framework to help integrate these cross-OSN information regarding the same event and provide an immersive experience for information retrieval. Since social media information is widely distributed in different OSNs where semantic gap exists among these heterogeneous spaces, we propose to utilize hashtag and tag, which are user-generated metadata for organizing and labeling in many OSNs, as bridges to connect between different OSNs. In our four-stage solution framework, various methods are adopted for hashtag and tag filtering, search results representation, clustering and demonstration. Given an event query, in the first stage we generate related items with corresponding tags and hashtags from OSNs and filter the hashtags and tags we need. Then, topical representation is generated for hashtag and tag. The third stage leverages the derived representation for cross-OSN hashtag and tag clustering. Finally, demonstration for each query is produced and the results are organized hierarchically. Experiments on a dataset containing hundreds of search queries and related items demonstrate the effectiveness of our cross-OSN event search framework.
Feature Selection Algorithms as One of the Python Data Analytical Tools

With the current trend of rapidly growing popularity of the Python programming language for machine learning applications, the gap between machine learning engineer needs and existing Python tools increases. Especially, it is noticeable for more classical machine learning fields, namely, feature selection, as the community attention in the last decade has mainly shifted to neural networks. This paper has two main purposes. First, we perform an overview of existing open-source Python and Python-compatible feature selection libraries, show their problems, if any, and demonstrate the gap between these libraries and the modern state of feature selection field. Then, we present new open-source scikit-learn compatible ITMO FS (Information Technologies, Mechanics and Optics University feature selection) library that is currently under development, explain how its architecture covers modern views on feature selection, and provide some code examples on how to use it with Python and its performance compared with other Python feature selection libraries.
Screening for new primary cancers in patients with metastatic breast cancer: a provincial analysis of the Choosing Wisely Canada recommendations.

Introduction Patients with metastatic cancer have a decreased life expectancy, and with screening and surveillance for new primary cancers, they run the risk of immediate harm with little chance of any benefit. Choosing Wisely Canada therefore recommends that such investigations be avoided in patients with metastatic disease. Methods We examined cancer screening practices in a subset of patients with metastatic cancer in Newfoundland and Labrador. Patients with metastatic breast cancer seen at the provincial cancer clinic during 2014-2016 were identified from the Newfoundland and Labrador Cancer Registry. For each patient, we assessed whether any one or a combination of screening mammography, Pap (Papanicolaou) test, flexible sigmoidoscopy or colonoscopy, or fecal immunohistochemical test were performed at any point after the diagnosis of metastatic disease. Results Of 305 patients with metastatic breast cancer, 114 (37.4%) underwent at least 1 screening investigation (mean: 2.92 investigations per screened patient). The most common screening investigations were mammography (n = 197) and Pap test (n = 107). Primary care providers ordered most of the screening investigations (70%); oncology specialists ordered 14%, and other specialists, 12%. Median overall survival for patients with breast cancer after a diagnosis of metastatic disease was 42 months, with a 5-year overall survival of 35.9%. Conclusions A significant proportion of patients with metastatic breast cancer in Newfoundland and Labrador are still undergoing screening for new primary malignancies, which is discordant with oncology guidelines from Choosing Wisely Canada. Increased education strategies are needed if the Choosing Wisely Canada recommendations are to be implemented into routine clinical practice.
PG2S+: Stack Distance Construction Using Popularity, Gap and Machine Learning

Stack distance characterizes temporal locality of workloads and plays a vital role in cache analysis since the 1970s. However, exact stack distance calculation is too costly, and impractical for online use. Hence, much work was done to optimize the exact computation, or approximate it through sampling or modeling. This paper introduces a new approximation technique PG2S that is based on reference popularity and gap distance. This approximation is exact under the Independent Reference Model (IRM). The technique is further extended, using machine learning, to PG2S+ for non-IRM reference patterns. Extensive experiments show that PG2S+ is much more accurate and robust than other state-of-the-art algorithms for determining stack distance. PG2S+ is the first technique to exploit the strong correlation among reference popularity, gap distance and stack distance.
A cross-layer implementation of Ad hoc On-demand Distance Vector routing (AODV) protocol

Mobile Ad hoc Networks (MANETs) are networks which will form the basis for the ubiquitous data access because of their ease of deployment. Due to the dynamic nature of a MANET, routing is one of the most critical elements of MANET. Routing protocols for MANET can be broadly classified as a proactive routing protocol or a reactive routing protocol. In the proactive routing protocols, mobile nodes periodically exchange routing information among themselves. Hence proactive routing protocols generate high overhead messages in the network. On the other hand, reactive routing protocols work on-demand. Thereby generating less number of overhead messages in the network compared to proactive routing protocols. But reactive routing protocols use a global search mechanism called 'flooding' during the route discovery process. 'Flooding' generates a huge number of overhead messages in the network. Those overhead messages affect the performance of reactive routing protocols in term of network throughput. That kind of performance problem is called 'scaling' problem. Ad hoc On-demand Distance Vector Routing with Cross-Layer Design (AODV-CL) protocol has been proposed to solve this scaling problem. The AODV routing protocol has been modified to implement AODV-CL protocol. AODV-CL protocol reduces 'flooding' problem of reactive routing protocols by limiting the number of nodes that should participate in route discovery process based on their status in the network and also avoiding congested area of the network. It is shown that AODV-CL protocol reduces overhead messages by 73% and reduces end-to-end delay per packet by 32% compared to regular AODV protocol.
Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework

Computer aided diagnosis systems are used to assist radiologists in their decision making. The sensitivity of these systems is hindered by the complexity of the structures inside the lungs. Several systems and methods have been proposed to detect and classify lung nodules, but all of them have their strengths and weaknesses. One way to overcome the weaknesses is to combine multiple systems. Systems based on handcrafted features capture a limited set of characteristics from the image, while deep learning based classifiers can deal with a wider range of structures. In this work, several ways to combine a handcrafted feature based classifier with four convolutional neural network are explored. The systems were combined merging the probabilities assigned to the detections in several ways. Support-vector machine, multilayer perceptron and random forest classifiers were used to combine the selected classifiers. The LUNA16 Challenge was used to evaluate the performance of the resulting hybrid systems. In all cases, the hybrid systems outperformed the individual systems. Although the average of sensitivities are similar for most of the combinations, the best hybrid system achieves a gain of 35 extra nodules at 4 FP per scan.
Loop Detection via Symbolic Constraint Compression

Symbolic analysis of a program involves the evaluation of assembly instructions with arguments that aren’t specific constants, but instead symbolic values defined by Constraints. As instructions are executed symbolically, these constraints are updated to reflect restrictions on what values a variable can hold, and how different variables relate to one another. On some level, the constraints upon the state of the program are a reflection of its behavior, capturing the relation between inputs and outputs as a complex mathematical expression. By using a modification of existing Frequent Pattern Mining techniques, it is possible to find repeating structures in these constraints, and from them discover characteristics of the original program’s behavior. In this report we present our definitions and algorithm, and demonstrate using it to detect loops in several example C programs. The method presented shows promise against binary obfuscation techniques, and with further development could be a valuable tool for analyzing binaries that are problematic for static analysis techniques.
Clinical Evaluation of Nisha Katakadi Kashaya and Yashada Bhasma in the Management of Type-2 Diabetes Mellitus (Madhumeha) : A Multicentre, Open Label Prospective Study

Corresponding Author: Pitta Srinivas, Research Officer (S-2), CCRAS, Dr Achanta Lakshmipathi Research Centre for Ayurveda, Chennai, Tamil Nadu, India, Phone: 044 22541536, e-mail: drsrinivaspitta@yahoo.co.in ABSTRACT Introduction: Diabetes mellitus (DM) is a metabolic disorder characterized by abnormal carbohydrate, fat, and protein metabolism, which is clinically diagnosed based on hyperglycemia. Ayurveda has explained Avyayama (lack of exercise) as the first and foremost cause for diabetes.
Steerable Self-Driving Data Visualization

In this work, we present a self-driving data visualization system, called DeepEye, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DeepEye to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.
Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.