Miracle-dz/UniFAR-FTUs huggingface dataset:
TI-RADS Diagnostic Performance: Which Algorithm is Superior and How Elastography and 4D Vascularity Improve the Malignancy Risk Assessment

Given the increased prevalence of thyroid nodules in the general population (~50%), the real challenge resides in correctly recognizing the suspicious ones. This study proposes to compare four important Thyroid Imaging and Reporting Data Systems (TI-RADS) and evaluate the contribution of elastography and 4D Color Doppler assessment of vascularity in estimating the risk of malignancy. In the study, 133 nodules with histopathological examination were included. Of these, 35 (26.31%) proved to be malignant. All nodules were classified using the four selected systems and our proposed improved score. The American College of Radiology (ACR) and EU TI-RADS had good sensitivity (94.28%, 97.14%) and NPV (93.33%, 95.83%), but fairly poor specificity (31.81%, 23.46%) and PPV (35.48%, 31.19%), with an accuracy of 42.8% and 45.8%, respectively. Horvath TI-RADS had better accuracy of 66.9% and somewhat improved specificity (62.24%), but poorer sensitivity (80%). Russ’ French TI-RADS includes elastography in the risk assessment strategy. This classification proved superior in all aspects (Se: 91.42%, Sp:82.65%, NPV:96.42%, PPV:65.30%, and Acc of 84.96%). The mean strain ratio (SR) value for malignant lesions was 5.56, while the mean SR value for benign ones was significantly lower, 2.54 (p < 0.05). It also correlated well with the response variable: histopathological result (p < 0.001). Although, adding 4D vascularity to the French score generated a similar calculated accuracy and from a statistical point of view, the parameter itself proved beneficial for predicting the malignancy risk (p < 0.001) and may add important knowledge in uncertain situations. Advanced ultrasound techniques definitely improved the risk estimation and should be used more extensively.
Ecofriendly Route for Waste Upcycling and Silver Nanoparticles Synthesis from Citrus reticulata Peel

In this work, green biosynthesis and upcycling waste were combined for silver nanoparticles (AgNP's) production from citrus reticulata peels as a new natural factories by bottom-up approaches, it was accomplished using a hot aqueous extract of citrus reticulata peels as a rapid, one-step, safe, economically and ecofriendly route. This work proved the capability of plant mediated PNgA ' s production firstly by color change of the extract after two hours, then the well dispersed spherical PNgA ' s production with size 27 nm was proved by using techniques;ultra violet-visiable spectroscopy (UV-vis), X-ray diffraction (XRD) and scanning electronic microscope (SEM) coupled with energy dispersion x-ray (EDX).
Zebrafish Microenvironment Elevates EMT and CSC-Like Phenotype of Engrafted Prostate Cancer Cells

To visually and genetically trace single-cell dynamics of human prostate cancer (PCa) cells at the early stage of metastasis, a zebrafish (ZF) xenograft model was employed. The phenotypes of intravenously transplanted fluorescent cells were monitored by high-resolution, single-cell intravital confocal and light-sheet imaging. Engrafted osteotropic, androgen independent PCa cells were extravasated from caudle vein, invaded the neighboring tissue, proliferated and formed experimental metastases around caudal hematopoietic tissue (CHT) in four days. Gene expression comparison between cells in culture and in CHT revealed that engrafted PCa cells responded to the ZF microenvironment by elevating expression of epithelial–mesenchymal transition (EMT) and stemness markers. Next, metastatic potentials of ALDHhi cancer stem-like cells (CSCs) and ALDHlow non-CSCs were analyzed in ZF. Engraftment of CSCs induced faster metastatic onset, however after six days both cell subpopulations equally responded to the ZF microenvironment, resulting in the same increase of stemness genes expression including Nanog, Oct-4 and Cripto. Knockdown of Cripto significantly reduced the vimentin/E-cadherin ratio in engrafted cells, indicating that Cripto is required for transduction of the microenvironment signals from the ZF niche to increase mesenchymal potential of cells. Targeting of either Cripto or EMT transcriptional factors Snail 1 and Zeb1 significantly suppressed metastatic growth. These data indicated that zebrafish microenvironment governed the CSC/EMT plasticity of human PCa cells promoting metastasis initiation.
Injury to retinal ganglion cell axons increases polysialylated neural cell adhesion molecule (PSA-NCAM) in the adult rodent superior colliculus

Article history: Accepted 21 May 2007 Available online 14 June 2007 The adult mammalian central nervous system (CNS) exhibits a limited regenerative response to injury. It is well established that polysialylated neural cell adhesion molecule (PSA-NCAM) contributes to nervous system plasticity. In the visual system, PSA-NCAM participates in retinal ganglion cell (RGC) axon growth during development and specifically influences RGC innervation of its principle target tissue, the superior colliculus (SC). The goals of this study were to determine whether PSA-NCAM is expressed in the normal adult mouse SC and to evaluate PSA-NCAMexpression following RGC injury. In the normal rostral, but not caudal, SC we find that PSA-NCAM is present in the retinorecipient layers; however, PSA-NCAM and RGC axons do not co-localize. In the deeper collicular layers, PSA-NCAM is observed as several distinct patches that occur at the same depth along the medial–lateral axis throughout the colliculus. RGC axotomy denervates predominantly the contralateral colliculus, where increased PSA-NCAM levels are seen at 7 and 10 days after the injury. Further evaluation of the retinorecipient layers of the partially denervated SC reveals that some intact CTB-traced RGC axons (less than 5%) labeled from the ipsilateral eye do colocalize with PSA-NCAM. This study is the first characterization of PSA-NCAM expression in the normal and partially denervated adult SC and may indicate that PSA-NCAM is involved in attempted visual system remodeling after injury. © 2007 Elsevier B.V. All rights reserved.
A SECURE TWITTER TRENDING MANIPULATION AND TOPIC CLASSIFICATION

-Twitter trends, a timely updated set of top terms in Twitter, have the ability to affect the public agenda of the community and have attracted much attention. Unfortunately, in the wrong hands, Twitter trends can also be abused to mislead people. Twitter drifts, an opportune refreshed arrangement of best terms in Twitter, can influence people in general motivation of the group and have pulled in much consideration. In this project, we will verify whether Twitter patterns are secure from the control of pernicious clients. We gather more than 69 million tweets from 5 million records. Utilizing the gathered tweets, we initially lead an information investigation to know on what basis data is getting manipulated and find proof of Twitter slant control. In this project, we do research at the subject level data and induce the key factors that can decide if a theme begins slanting because of its notoriety, scope, transmission, potential scope, or notoriety.
Energy Performance of a Combined Horizontal and Vertical Compression Approach in Cluster-based WSNS

A BSTRACT Energy efficiency is an essential issue to be reckoned in wireless sensor networks development. Since the low-powered sensor nodes deplete their energy in transmitting the collected information, several strategies have been proposed to investigate the communication power consumption, in order to reduce the amount of transmitted data without affecting the information reliability. Lossy compression is a promising solution recently adapted to overcome the challenging energy consumption, by exploiting the data correlation and discarding the redundant information. In this paper, we propose a hybrid compression approach based on two dimensions specified as horizontal (HC) and vertical compression (VC), typically implemented in cluster-based routing architecture. The proposed scheme considers two key performance metrics, energy expenditure, and data accuracy to decide the adequate compression approach based on HC-VC or VC-HC configuration according to each WSN application requirement. Simulation results exhibit the performance of both proposed approaches in terms of extending the clustering network lifetime.
A Literature Review of Empirical Studies of Recommendation Systems

In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in e-commerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user’s needs and preferences, and Utility-Based Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.
Anti PD-1 monoclonal antibody induced autoimmune diabetes mellitus: a case report and brief review

Nowadays, immune checkpoint inhibitor therapy has been used in more and more cancer patients. These agents were associated with immune-related adverse effects, and autoimmune diabetes mellitus is one of them. And it is not common but can be potentially fatal. Anti PD-1 monoclonal antibody is a humanized IgG4 antibody against PD-1, which has been applied in advanced non-small cell lung cancer (NSCLC) treatment. In this paper, we reported the case of autoimmune diabetes mellitus induced by anti PD-1 monoclonal antibody in NSCLC treatment. Here is a 73-year-old male patient with no diabetes history who had anti PD-1 monoclonal antibody 200 mg every 3 weeks for NSCLC treatment. After 10 cycles of the therapy, his blood glucose level elevated and he suffered diabetic ketoacidosis (DKA). And his C-peptide was significantly decreased with negative relative auto-antibodies. Combined with his medical history and the laboratory examination, anti PD-1 monoclonal antibody induced autoimmune diabetes mellitus was diagnosed. After recovering from DKA and controlling his blood glucose, his anti PD-1 therapy was continued and he still got some benefit. This report suggested that glycemic monitoring is imperative during this anti PD-1 monoclonal antibody treatment. Moreover, after controlling the blood glucose level, continuing the immune therapy could still be benefit and safe for the patient.
Manipulation of biosynthesis of aliphatic glucosinolates by gene replacement and RNAi gene silencing

Glucosinolates are a large group of plant secondary metabolites found mainly in the order Capparales. Manipulation of biosynthesis of aliphatic glucosinolates was attempted using gene replacement and RNAi gene silencing. Experiments were conducted to replace the functional Alk allele in Brassica rapa with the non functional Alk allele from Brassica oleracea (broccoli) to develop glucoraphanin-rich B. rapa. Gene introgression lines but no gene replacement lines were obtained. Four MAM-like genes involved in glucosinolate biosyntheses, MAM1(At5g23010), MAML(At5g23020), MAML-3 (At1g18500) and MAML-4 (At1g74040) were silenced by RNAi techniques in Arabidopsis and the resulting glucosinolate profiles were studied. All four MAM-like genes, MAM1, MAML, MAML-3 and MAML-4 were shown to be involved in the aliphatic glucosinolate elongation pathway.
Glaucoma Screening Based On Super pixel Classification

Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma given patient.
SIMDive: Approximate SIMD Soft Multiplier-Divider for FPGAs with Tunable Accuracy

The ever-increasing quest for data-level parallelism and variable precision in ubiquitous multimedia and Deep Neural Network (DNN) applications has motivated the use of Single Instruction, Multiple Data (SIMD) architectures. To alleviate energy as their main resource constraint, approximate computing has re-emerged, albeit mainly specialized for their Application-Specific Integrated Circuit (ASIC) implementations. This paper, presents for the first time, an SIMD architecture based on novel multiplier and divider with tunable accuracy, targeted for Field-Programmable Gate Arrays (FPGAs). The proposed hybrid architecture implements Mitchell's algorithms and supports precision variability from 8 to 32 bits. Experimental results obtained from Vivado, multimedia and DNN applications indicate superiority of proposed architecture (both in SISD and SIMD) over accurate and state-of-the-art approximate counterparts. In particular, the proposed SISD divider outperforms the accurate Intellectual Property (IP) divider provided by Xilinx with 4x higher speed and 4.6x less energy and tolerating only 0.8% error. Moreover, the proposed SIMD multiplier-divider supersede accurate SIMD multiplier by achieving up to 26%, 45%, 36%, and 56% improvement in area, throughput, power, and energy, respectively.
The Nutritive Value and PalatabilityofSelected Browse Forages Mixtures from Arid and Semi-Arid Area of Kenya When Fed to Growing Small East African Goats

In this study the chemical composition, in vitro drymatter digestibility (IVDMD) and palatabilityof five browse forage mixtures were studied. The five forage mixtures were selected based ontheir nutritive value and IVDMD and from reported previous studies of single species.The species included Acacia brevispica, Acacia mellifera, Acacia nilotica, Zizyphusmucronata and Berchemia discolor. In vitro gas production technique was used to determine the rumen fermentation characteristics. Palatabilityof the browse forage mixtures was determined based on the mixture’s voluntary intakebya cafeteria approach on growing small East African goats. The crude protein (CP) content ranged from 153.9 g/kg DM in B. discolor &A. nilotica forage mixture to 184.4 g/kg dry matter (DM) in Z. mucronata& A. mellifera forage mixture, while the total fibre content as measured by NDF ranged from 313.7 g/kg DM in Z. mucronata& A. mellifera forage mixture to 440.4 g/kg DM in the forage mixture containing Z. mucronata, A. brevispica, A. mellifera& A. nilotica. The IVDMD ranged from 57.1% in B. discolor & A. nilotica to 64.0 % in the browse mixture containing Z. mucronata, A. brevispica, A. mellifera& A. nilotica. Browse mixture containing Z. mucronata, A. brevispica, A. mellifera, A. nilotica and mixture containing B. discolor, Z. mucronata, A. mellifera and A. nilotica were the most preferred by the animals and had a coefficient of preference (CoP) of 1.51 and 1.02 respectively. The two browse forage mixtures also had the highest (p<0.05) intakes (168.6 and 113.3 g DMI/6h respectively) by the goats. The results of this study indicates that browse foragemixtures are highly palatable andhave great potential as supplements for poorquality basal dietsin the marginal areas of the tropicsand can enhance performance of livestock especially the small ruminants.
Efficient Online Context-Aware Machine Learning Algorithm Mobile Communication Based 5G

Millimeter-Wave (mmWave) groups have turned into the true possibility for 5G vehicle- to-everything (V2X) since future vehicular frameworks request Gbps connections to gain the fundamental tactile data for (semi)- self-sufficient driving. By the by, the directionality of mmWave communications and its defenselessness to blockage bring up serious issues on the plausibility of mmWave vehicular communications. The dynamic idea of 5G vehicular situations and the intricacy of directional mmWave correspondence call for higher context-awareness and versatility. To this point, we propose an online learning algorithm tending to the issue of shaft choice with condition awareness in mmWave vehicular frameworks. Specifically, we demonstrate this issue as a contextual multi-furnished desperado issue. Next, we propose a lightweight context-aware online learning algorithm; specifically fast machine learning (FML), with demonstrated execution bound and ensured combination. FML misuses coarse client area data and totals the got information to gain from and adjust to its condition. Besides, we exhibit the practicality of genuine usage of FML by proposing a standard-consistent convention dependent on the current engineering of cell systems and the inevitable highlights of 5G. We likewise play out a broad assessment utilizing practical traffic designs got from Google Maps. Our assessment demonstrates that FML empowers mmWave base stations to accomplish close ideal execution by and large inside 33 mins of arrangement by learning from the accessible context. Besides, FML stays inside ∼ 5% of the ideal execution by quick adjustment to framework changes (i.e., blockage, traffic).
An IoT Based Health Care and Patient Monitoring System to Predict Medical Treatment using Data Mining Techniques: Survey

: The majority of patients in the hospital are non-ambulatory and would benefit from predictive and personalized monitoring system. Technology plays the major role in healthcare not only for sensory devices but also in communication, recording and display device. It is very important to monitor various medical parameters and post operational days. Hence the latest trend in Healthcare communication method using IOT is adapted. Internet of things serves as a catalyst for the healthcare and plays prominent role in wide range of healthcare applications. In this project the AVR-328 microcontroller(Arduino board) is used as a gateway to communicate to the various sensors such as temperature sensor, heartbeat sensor, ECG sensor, sensor for keeping a track of drip levels(blood or saline). The microcontroller picks up the sensor data and sends it to the network through Wi-Fi and hence provides real time monitoring of the health care parameters for doctors. The data can be accessed anytime by the doctor. The controller is also connected with buzzer to alert the caretaker about variation in sensor output. At the time of extremity situation alert message is sent to the doctor through the android application connected to the cloud server. Hence quick provisional medication can be easily done by this system. Also an additional subsystem is provided for prediction of heart disease for the patient based on his/her health parameters. This system is efficient with low power consumption capability, easy setup, high performance and time to time response.
Validation of subgrid-scale models for large-eddy simulations of turbulent premixed flames by dual-plane stereo-PIV

In this study we present an experimental strategy for the validation of subgrid-scale (SGS) models for large-eddy simulations (LES) of turbulent premixed flames. The approach is based on the application of a dual-plane stereo-PIV (DPSPIV) technique which enables , the measurement of the 3D flow field in two parallel planes. This allows the determination of velocity gradients in all three directions. A vector resolution of 118 μm was achieved. In contrast to other studies where spatial derivatives of the flow field are analyzed, the calculation of velocity gradients is performed subsequent to a spatial averaging operation which reproduces the application of the filter function in LES. Additionally, the flame front position is deduced from the clearly observable step in the particle number density between burnt and unburnt regions of the flame. This facilitates the single-shot based evaluation of an important quantity for reacting flows, the density weighted rate-of-strain tensor. Moreover, the simultaneous, highly spatially resolved measurement of flame front position and flow field enables the direct determination of the SGS scalar flux of the reaction progress variable by spatial averaging over several regions of interest. The measured data allows the direct interpretation of SGS model formulations since based on the resolved data, the SGS model expressions can be derived also. For two operating-points of a turbulent premixed V-shaped flame we will demonstrate pure experimentally based a-priori tests between the directly measured SGS scalar flux and different closure models in form of a statistical analysis and a direct single-shot based two-dimensional comparison. Furthermore, we can show for the first time, to the best of our knowledge, an experimentally derived singleshot based distribution of density weighted rate-of-strain tensor in a reactive flow.
A generalization of the ElGamal public-key cryptosystem

Abstract The ElGamal cryptosystem is one of the most widely used public-key cryptosystems that depends on the difficulty of computing the discrete logarithms over finite fields. Over the years, the original system has been modified and altered in order to achieve a higher security and efficiency. In this paper, a generalization for the original ElGamal system is proposed which also relies on the discrete logarithm problem. The encryption process of the scheme is improved such that it depends on the prime factorization of the plaintext. Modular exponentiation is taken twice during the encryption; once with the number of distinct prime factors of the plaintext and then with the secret encryption key. If the plaintext consists of only one distinct prime factor, then the new method is similar to that of the basic ElGamal algorithm. The proposed system preserves the immunity against the Chosen Plaintext Attack (CPA).