Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Mutual Information and Firefly Algorithm for Epilepsy Classification from EEG

Epilepsy is a serious neurological condition with recurrent evoking of seizures. The epileptic seizure has a huge influence on the quality of life for the patients and their respective families. Therefore, it is absolutely necessary to detect, classify, diagnose and cure epilepsy with high accuracy and efficiency. Electroencephalography (EEG) is a powerful technique which gives a fundamental understanding about the potential generated on the brain surface thereby helping in understanding the basic functionalities and problems associated with brain, especially epilepsy. As the recordings of the EEG signals are too long and hectic to analyze, dimensionality reduction is necessary. In this work, Fuzzy Mutual Information (FMI) is used as a dimensionality reduction technique and then it is classified with the help of Firefly Algorithm. Results show that when FMI is classified with Firefly algorithm, an average classification accuracy of 96.07% is obtained.
Sperm storage by males causes changes in sperm phenotype and influences the reproductive fitness of males and their sons

Recent studies suggest that environmentally induced effects on sperm phenotype can influence offspring phenotype beyond the classic Mendelian inheritance mechanism. However, establishing whether such effects are conveyed purely through ejaculates, independently of maternal environmental effects, remains a significant challenge. Here, we assess whether environmentally induced effects on sperm phenotype affects male reproductive success and offspring fitness. We experimentally manipulated the duration of sperm storage by males, and thus sperm age, in the internally fertilizing fish Poecilia reticulata. We first confirm that sperm ageing influences sperm quality and consequently males reproductive success. Specifically, we show that aged sperm exhibit impaired velocity and are competitively inferior to fresh sperm when ejaculates compete to fertilize eggs. We then used homospermic (noncompetitive) artificial insemination to inseminate females with old or fresh sperm and found that male offspring arising from fertilizations by experimentally aged sperm suffered consistently impaired sperm quality when just sexually mature (four months old) and subsequently as adults (13 months old). Although we have yet to determine whether these effects have a genetic or epigenetic basis, our analyses provide evidence that environmentally induced variation in sperm phenotype constitutes an important source of variation in male reproductive fitness that has far reaching implications for offspring fitness.
Feasibility of Implementing Patient Priorities Care for Older Adults with Multiple Chronic Conditions

Older adults with multiple chronic conditions (MCCs) receive care that is fragmented and burdensome, lacks evidence, and most importantly is not focused on what matters most to them. An implementation feasibility study of Patient Priorities Care (PPC), a new approach to care that is based on health outcome goals and healthcare preferences, was conducted. This study took place at 1 primary care and 1 cardiology practice in Connecticut and involved 9 primary care providers (PCPs), 5 cardiologists, and 119 older adults with MCCs. PPC was implemented using methods based on a practice change framework and continuous plan‐do‐study‐act (PDSA) cycles. Core elements included leadership support, clinical champions, priorities facilitators, training, electronic health record (EHR) support, workflow development and continuous modification, and collaborative learning. PPC processes for clinic workflow and decision‐making were developed, and clinicians were trained. After 10 months, 119 older adults enrolled and had priorities identified; 92 (77%) returned to their PCP after priorities identification. In 56 (46%) of these visits, clinicians documented patient priorities discussions. Workflow challenges identified and solved included patient enrollment lags, EHR documentation of priorities discussions, and interprofessional communication. Time for clinicians to provide PPC remains a challenge, as does decision‐making, including clinicians' perceptions that they are already doing so; clinicians' concerns about guidelines, metrics, and unrealistic priorities; and differences between PCPs and patients and between PCPs and cardiologists about treatment decisions. PDSA cycles and continuing collaborative learning with national experts and peers are taking place to address workflow and clinical decision‐making challenges. Translating disease‐based to priorities‐aligned decision‐making appears challenging but feasible to implement in a clinical setting.
Nanoparticle Vaccines Against Infectious Diseases

Due to emergence of new variants of pathogenic micro-organisms the treatment and immunization of infectious diseases have become a great challenge in the past few years. In the context of vaccine development remarkable efforts have been made to develop new vaccines and also to improve the efficacy of existing vaccines against specific diseases. To date, some vaccines are developed from protein subunits or killed pathogens, whilst several vaccines are based on live-attenuated organisms, which carry the risk of regaining their pathogenicity under certain immunocompromised conditions. To avoid this, the development of risk-free effective vaccines in conjunction with adequate delivery systems are considered as an imperative need to obtain desired humoral and cell-mediated immunity against infectious diseases. In the last several years, the use of nanoparticle-based vaccines has received a great attention to improve vaccine efficacy, immunization strategies, and targeted delivery to achieve desired immune responses at the cellular level. To improve vaccine efficacy, these nanocarriers should protect the antigens from premature proteolytic degradation, facilitate antigen uptake and processing by antigen presenting cells, control release, and should be safe for human use. Nanocarriers composed of lipids, proteins, metals or polymers have already been used to attain some of these attributes. In this context, several physico-chemical properties of nanoparticles play an important role in the determination of vaccine efficacy. This review article focuses on the applications of nanocarrier-based vaccine formulations and the strategies used for the functionalization of nanoparticles to accomplish efficient delivery of vaccines in order to induce desired host immunity against infectious diseases.
PHED: Pre-Handshaking Neighbor Discovery Protocols in full duplex wireless ad hoc networks

Neighbor Discovery (ND) is a basic and crucial step for initializing wireless ad hoc networks. A fast, precise, and energy-efficient ND protocol has significant importance to subsequent operations in wireless networks. However, many existing protocols have high probabilities to generate idle slots in their neighbor discovering processes, which extends the executing duration, and thus compromises their performance. In this paper, we propose a novel randomized protocol PHED, Pre-Handshaking Neighbor Discovery Protocol, to initialize synchronous full duplex wireless ad hoc networks. By introducing a pre-communication strategy to help each node be aware of activities of its neighborhood, we significantly reduce the probabilities of generating idle slots and collisions. Moreover, with the development of single channel full duplex communication technology [1, 2], we further decrease the processing time needed in PHED, and construct the first full duplex neighbor discovery protocol. Our theoretical analysis proves that PHED can increase the speed of ND by approximately 98% in comparison with the classical ALOHA-like protocols [3, 4]. In addition, we prove the effectiveness of PHED by simulations.
Adaptive radiation along a deeply conserved genetic line of least resistance in Anolis lizards

On microevolutionary timescales, adaptive evolution depends upon both natural selection and the underlying genetic architecture of traits under selection, which may constrain evolutionary outcomes. Whether such genetic constraints shape phenotypic diversity over macroevolutionary timescales is more controversial, however. One key prediction is that genetic constraints should bias the early stages of species divergence along “genetic lines of least resistance” defined by the genetic (co)variance matrix, G. This bias is expected to erode over time as species means and G matrices diverge, allowing phenotypes to evolve away from the major axis of variation. We tested for evidence of this signal in West Indian Anolis lizards, an iconic example of adaptive radiation. We found that the major axis of morphological evolution was well aligned with a major axis of genetic variance shared by all species despite separation times of 20–40 million years, suggesting that divergence occurred along a conserved genetic line of least resistance. Further, this signal persisted even as G itself evolved, apparently because the largest evolutionary changes in G were themselves aligned with the line of genetic least resistance. Our results demonstrate that the signature of genetic constraint may persist over much longer timescales than previously appreciated, even in the presence of evolving genetic architecture. This pattern may have arisen either because pervasive constraints have biased the course of adaptive evolution or because the G matrix itself has been shaped by selection to conform to the adaptive landscape.
Scarce Attack Datasets and Experimental Dataset Generation

Efficient employability of machine learning and artificial intelligence techniques to automate the process of network forensics for attack identification and attribution, depends largely on the availability of datasets to build up the knowledge base. The requirement in the case of a more proactive approach of incident response and mitigation is also the same. This paper explore the publicly available attack datasets with regard to their employability in ML and AI for network forensics and consider the need for developing ethical hacking techniques for generation of attack datasets through experimentation. We assess the methodology with certain attack scenarios and observe that the same can be adopted for a wide gamut of network attacks. The findings are potentially valuable for researchers and security practitioners to overcome the limitations of the scarce attack datasets.
LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases

Abstract Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut. net/lncrnadisease/.
Data Mining Techniques used in the Recommendation of E-commerce services

Recommender systems and web search engines have gained a lot of importance in today's digital platform. In today's digital world everything (from buying to selling) has come to internet platform. Due to huge amount of data large scale processing is required. Today large amount of data is obtained from e-commerce services, application data, web data etc. This large-scale data processing involves many similarity search algorithms for giving recommendations. Many e-commerce services and applications use similarity search for giving valuable suggestions and showing the related documents. In this paper, we discuss the similarity search algorithms, PathSim and SimRank. We compare and contrast both the algorithms by taking different datasets. We suggest that the efficiency of the website improves if the algorithms are used in respective scenarios. The time complexities of both the algorithms are compared to check.
Support Vector Machine Classification of Crop Lands Using Sentinel-2 Imagery

Crop classification makes it possible to obtain the spatial distribution and planting area information of crops. Meanwhile, a multi-year crop mapping helps to study the dynamics of agricultural fields and may serve as the basis of crop structure adjustment and optimization to assist government decision-making. This study aims to explore the feasibility of Sentinel-2 imagery data for crop type mapping and to evaluate the effectiveness of Support Vector Machine (SVM) in discriminating various crop types using a small training dataset. In doing so, the classical SVM classification method was applied to distinguish major crop types in Plymouth County, Iowa, USA, based on Sentinel-2 time-series Normalized Difference Vegetation Index data in 2016. The results show that the SVM model can effectively classify three major crop types (corn, soybean and alfalfa) in Plymouth with an overall accuracy of >67% and a Kappa coefficient of 0.52, which evaluated the potential of utilizing SVM to obtain the spatial distribution information of crops based on the new Sentinel-2 imagery. Besides, this exploration of new data sources for crop classification can give some guidance and the method can be applied to other regions.
First case of trastuzumab emtansine‐associated hemorrhagic telangiectasias treated with propranolol

Dear Editor Trastuzumab emtansine (T-DM1) is a monoclonal antibody targeting the human epidermal growth factor receptor-2 (HER2) conjugated to a cytotoxic microtubule inhibitor, DM1, indicated in advanced breast cancer overexpressing HER2. Cutaneous toxicities are rare, and mainly include mucositis, hand-foot syndromes, mucosal, and cutaneous telangiectasias (Bhardwaj et al., 2014; Dholaria & Srinivasan, 2015; Verma, 2012). We report the first case of T-DM1-associated hemorrhagic cutaneous telangiectasias treated with propranolol. A 61-year old woman was referred for spontaneous hemorrhage from a presternal spider angioma. It resorbed after a 30 min compression and one hemostatic stitch was applied when the crust came off. She received T-DM1 as a second-line therapy for breast cancer metastatic to the brain, liver, adrenal glands, and lymph nodes for 18 months. She previously received docetaxel and trastuzumab. Multiple slightly pruriginous telangiectasias progressively appeared in a few months on T-DM1 treatment in the superior vena cava area (upper trunk, face, lower lip, shoulders); some of them had the appearance of spider angiomas (Figures 1 and 2a). These lesions progressively increased in size and number. Blood tests showed Grade 1 thrombocytopenia (122,000 cells/mm). Oral propranolol was initiated with increasing dose over 1 month up to 160 mg per day, while T-DM1 treatment was continued. After 6 weeks, the smallest lesions disappeared. An overall 50% improvement in other lesions were observed (Figures 1 and 2b) without appearance of new lesions. Four months later, propranolol was discontinued mistakenly for 2 weeks and telangiectasias relapsed in a few days, with a new therapeutic response after propranolol resumption. The patient reported a single new jugal bleeding episode after 7 months of propranolol that spontaneously resorbed. T-DM1 and propranolol were discontinued after 15 months of propranolol due to cancer progression. Telangiectasias improved then markedly in a few weeks, without total disappearance after 4 months (Figure 1c). In our patient, propranolol led to a significant clinical benefit with partial remission of T-DM1-associated telangiectasias and no recurrence of associated severe hemorrhage. However, the efficacy was only suspensive. Eleven cases of T-DM1-associated cutaneous telangiectasias have already been reported (Sibaud et al., 2015) and 64% were associated with mucosal telangiectasias sometimes mimicking genetic hereditary hemorrhagic telangiectasia, 9% were complicated by cutaneous hemorrhages, as in our patient, and 82% by mucosal hemorrhages (epistaxis, gingivorrhagia, hematochezia). Biologically, 36% were associated with thrombocytopenia (27% of Grade 1 and 9% of Grade 3) and 82% with Grade 1 hepatic cytolysis. Telangiectasias were also located in the superior cava vena area in five of six cases for which the location was specified. The time to onset ranged between 3 and 14 months (median: 3.5 months).
Automated Diagnosis of Breast Cancer Using Wavelet Based Entropy Features

Breast cancer is one of the most common and serious threat around worldwide for women. It is necessary to prognosis of Histopathology image in order to avoid death rate also early detection is required for the best treatment. Histopathology refers to the examination of biopsy sample by a pathologist under microscope. The pathologist locates, analyze and classify the image type and grade the image. The classification depends on structure of tissue, cell distribution, and shape of the cell which decides the histopathology image as benign or malignant. Manual classification of image is most time consuming and prone to error. To overcome this difficulty automated diagnosis using machine learning is needed for accurate and early diagnosis. In this paper k-means clustering is used for segmentation of cell nuclei and DWT (Discrete Wavelet Transform) is applied to the segmented image. Coiflets is one of the discrete wavelets used in this paper it has scaling functions with vanishing moments. Energy, shannon entropy and log energy entropy features are extracted from the LL, LH, HL and HH sub bands. In the present work we have used BreakHis (The Breast Cancer Histopathological Images) dataset. The proposed method result in a accuracy of 93.3% in linear SVM and 92.7% in Quadratic SVM and 91.3% in Fine Gaussian SVM.
Directivity and Frequency-Dependent Effective Sensitive Element Size of a Reflectance-Based Fiber-Optic Hydrophone: Predictions From Theoretical Models Compared With Measurements

The goal of this work was to measure the directivity of a reflectance-based fiber-optic hydrophone at multiple frequencies and to compare it to four theoretical models: rigid baffle (RB), rigid piston (RP), unbaffled (UB), and soft baffle (SB). The fiber had a nominal 105-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter core and a 125-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> overall diameter (core + cladding). Directivity measurements were performed at 2.25, 3.5, 5, 7.5, 10, and 15 MHz from ±90° in two orthogonal planes. Effective hydrophone sensitive element radius was estimated by least-squares fitting the four models to the directivity measurements using the sensitive element radius as an adjustable parameter. Over the range from 2.25 to 15 MHz, the average magnitudes of differences between the effective and nominal sensitive element radii were 59% ± 49% (RB), 10% ± 5% (RP), 46% ± 38% (UB), and 71% ± 19% (SB). Therefore, the directivity of a reflectance-based fiber-optic hydrophone may be best estimated by the RP model.
Topological exploration of artificial neuronal network dynamics

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics. We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike-train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike-train distances systematically improves the performance of our method.
Analytical Evaluation and Reduction of Torque Harmonics in Induction Motor Drives Operated at Low Pulse Numbers

Induction motor drives operated with a low ratio of the switching frequency to the fundamental frequency (i.e., pulse number) are prone to have high magnitudes of low-order torque harmonics. This paper proposes a method to analytically evaluate the harmonic torque spectrum based on the pulsewidth-modulated voltage waveforms for low pulse number operation. The predicted torque harmonic spectra for different pulsewidth-modulated waveforms are validated through simulations and experiments. This paper also reports optimal switching angles to minimize the total rms value of torque harmonics of order 6, 12, 18 <inline-formula><tex-math notation="LaTeX">$,\dots,$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$6(N-1)$</tex-math></inline-formula>, considering the number of switching angles per quarter (<inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>) to be 4 and 5. Compared to sine triangle pulsewidth modulation and selective harmonic elimination pulsewidth modulation with the same pulse number, the proposed optimal switching angles significantly reduce the total rms value of all harmonic torques of order lower than <inline-formula><tex-math notation="LaTeX">$6N$</tex-math></inline-formula>. Extensive simulation and experimental results on a 3.7-kW induction motor drive are presented to demonstrate the superior performance of the proposed optimal pulsewidth modulation.
A novel technique for large and ptotic breast reconstruction using a latissimus dorsi myocutaneous flap set at the posterior aspect, combined with a silicone implant, following tissue expander surgery

Large and ptotic breast reconstruction in patients who are not candidates for a transverse rectus abdominalis myocutaneous flap and revision surgery for the contralateral breast remains challenging. We developed a novel breast reconstruction technique using a latissimus dorsi myocutaneous (LD m-c) flap set at the posterior aspect of the reconstructed breast, combined with an anatomical silicone breast implant (SBI), following tissue expander surgery. We performed the proposed technique in four patients, in whom the weight of the resected tissue during mastectomy was >500 g and the depth of the inframammary fold (IMF) was >3 cm. After over-expansion of the lower portion of the skin envelope by a tissue expander, the LD m-c flap was transferred to cover the lower portion of the breast defect and to achieve a ptotic contour, with the skin paddle set at the posterior aspect of the reconstructed breast. An SBI was then placed in the rest of the breast defect after setting the LD m-c flap. No major complications were observed during the follow-up period. The proposed technique resulted in symmetrical and aesthetically satisfactory breasts with deep IMFs, which allowed proper fitting of the brassiere, following large and ptotic breast reconstruction.