Miracle-dz/UniFAR-FTUs huggingface dataset:
Solution of taxonomic status of Unio mongolicus Middendorff, 1851 (Bivalvia: Unionidae) from the type locality in Transbaikalia and history of its taxonomy

Unio mongolicus Middendorff, 1851 (Bivalvia: Unionidae) was described from Gorbitza mountain stream (in the east of Trans-Baikal Territory, (Russia) and the taxonomy has changed over the past century and a half. A specimen of Middendorffinaia mongolica from the Gorbitza stream collected 160 years later is a match with the type illustration of Unio mongolicus from the type locality. Comparison of the shell morphology of the type specimen of U. mongolicus with the modern specimen collected from this location, combined with our analyses of molecular, anatomical and conchological data of M. cf. mongolica species from rivers of the Upper Amur River Basin and the far eastern Russia, revealed that these taxa all belong to a single polymorphic species. The molecular analysis of the COI gene fragment of mtDNA of examined Comparatory Species assigned to Middendorffinaia Moskvicheva et Starobogatov, 1973 confirmed they are only intraspecific forms of M. mongolica . Morphological and genetic distances between M. mongolica and Nodularia douglasiae (Griffith et Pidgeon, 1833) confirmed their assignment to two independent genera. Based on conchological characteristics and anatomical features, the differences between morphologically similar species M. mongolica, N. douglasiae and Unio crassus Retzius, 1788 were determined. History of the taxonomic changes for Unio mongolicus is discussed.
Gabor Filter Initialization And Parameterization Strategies In Convolutional Neural Networks

Convolutional neural networks (CNN) have been widely known in literature to be extremely effective for classifying images. Some of the filters learned during training of the first layer of a CNN resemble the Gabor filter. Gabor filters are extremely good at extracting features within an image. We have taken this as an incentive by replacing the first layer of a CNN with the Gabor filter to increase speed and accuracy for classifying images. We created two simple 5-layer AlexNet-like CNNs comparing grid-search to random-search for initializing the Gabor filter bank. We trained on MNIST, CIFAR-10, and CIFAR-100 as well as a rock dataset created at Western University to study the classification of rock images using a CNN. When training on this rock dataset, we use an architecture from literature and use our Gabor filter substitution method to show the usage of the Gabor filter. Using the Gabor convolutional neural network (GCNN) showed improvements in the training speed across all datasets tested. We also found that the GCNN underperforms when dropout is added, even when overfitting becomes an issue. The size of the Gabor filter bank becomes a hyperparameter that can be tuned per dataset. Applying our Gabor filter replacement method to a 3-layer CNN reduced final accuracy at epoch 200 by 1.16% but showed large improvements in the speed of convergence during training with 93.44% accuracy on a validation set after 10 epochs compared to the original network’s 82.19%.
Matching communication pattern with underlying hardware architecture

Due to the advent of modern hardware architectures of high-performance computers, the way the parallel applications are laid out is of paramount importance for performance. This abstract presents several techniques and algorithms that efficiently address this issue: the mapping of the application’s virtual topology (for instance its communication pattern) onto the underlying physical topology. Using such strategy improves the application overall execution time significantly. We have developed two approaches. One regards the mapping of application processes during their deployment and the other consists in load-balancing the execution at runtime to match the dynamic application needs to the topology. All this work is based on the TreeMatch library [2].
Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM

The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML) notation. Extract transform load (ETL) is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP) to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC) architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence
SIRPα is transcriptionally downregulated by epigenetic silencing in medulloblastoma

Signal regulatory protein α (SIRP α ) is a transmembrane protein that is commonly expressed in cells of the hematopoietic system and brain. Its function is not fully understood but it includes tumor suppressor properties and effects on differentiation. SIRP α may play a role in the development of medulloblastoma (MB), a WHO grade IV brain tumor, which is the most common malignant brain tumor in childhood. The aim of the current study was to determine the possible role of SIRP α in MB cells. Interestingly, in contrast to normal cerebellum, SIRP α mRNA was strongly downregulated in MB and its protein was not detectable in MB tissues. This down-regulation in MB cells was associated with transcriptional silencing of SIRP α via CpG island promoter hypermethylation. Furthermore, Oncomir cluster miR17-92 and miR-106a were correlated with SIRP α gene silencing in MB tumor specimens and cell lines. Histone modiﬁcation and inhibition of DNA methylation using TSA (20 nM) for 24 hrs and 5-AZA (5 µ M) and DZnep (2.5 µ M) for 72 hrs, respectively, increased SIRP α expression 25-40 fold and resulted in 90% cytotoxicity of MB tumor cell lines D283-med and D458-med. Remarkably, forced upregulation of SIRP α by viral transduction in MB cell lines did not affect cell growth. In conclusion, SIRP α is epigenetically silenced in MB cells and tumor specimens by promoter hypermethylation and possibly by miRNA expression. SIRP α hypermethylation in MB might reﬂect the precursor cell state of these cells, rather than being a tumor-speciﬁc event, since SIRP α overexpression did not inﬂuence MB cell viability. The mechanism of the anti-MB action of epigenetic therapy requires further investigation since our ﬁndings indicate that this effect is independent of SIRP α upregulation.
An extended-2D CNN approach for diagnosis of Alzheimer's disease through structural MRI : Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Alzheimer’s disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer’s and its early-diagnosis has been a challenging task. The current techniques for Alzheimer’s disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer’s disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian dataset.
RF MEMS: DEVELOPMENT OF DESIGN RULES

The modeling of MEMS passive electrical components is presented. The devices are modeled using the MRTD (multiresolution time-domain) and FDTD (finite-difference time-domain) electromagnetic modeling techniques. Methods are presented that allow these time-domain electromagnetic models to be combined with time-domain motion models of MEMS devices.
Asynchronous flowering in clonal seed orchards - An effective strategy for alternative management

Tropical forests have long been of interest to biologists because of their high species diversity and their complicated patterns of community organization. The recent ecological studies which have demonstrated that tropical trees are diverse in their reproductive biology and dynamic population structure. Asynchronous fl owering among the clones in a clonal seed orchard is an inherent problem resulting in poor seed and fruit set in them. These results in the complete defeat of the prime objective of establishment of clonal seed orchards (CSOs) i.e. abundant quality seed production poor fl owering and asynchronous fl owering between the clones are a major bottleneck in higher seed set in these clonal seed orchards across the country. Asynchrony found between clones may be attributed to the origin of clones, which are collected from different zonal populations as revealed by isoenzyme and DNA markers studies. This article reviews the work done in CSOs across the world and suggests an alternate strategy in designing the clonal seed orchards of the future. Research Article Asynchronous fl owering in clonal seed orchards An effective strategy for alternative management Soma Mondal1,2*, Ashutosh Srivastava1, Geeta Joshi1 and HC Yashavantha Rao3 1Department of Tree Improvement and Genetics, Institute of Wood Science and Technology, Bengaluru, Karnataka-560003, India 2Department of Studies in Microbiology, Manasagangotri, University of Mysore, Mysore, Karnataka-570 006, India 3Department of Biochemistry, Indian Institute of Science, Bengaluru, Karnataka-560012, India *Address for Correspondence: Soma Mondal, Department of Tree Improvement and Genetics Division, Institute of Wood Science and Technology, Bangalore, Karnataka-560003, India, Email: kumar.soma@yahoo.com Submitted: 09 April 2019 Approved: 14 May 2019 Published: 15 May 2019 Copyright: © 2019 Mondal S, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Effect of Xylanase and Phytase Supplementation on Goat's Performance in Early Lactation.

BACKGROUND AND OBJECTIVES Supplementing diets of dairy animals with phytase and xylanase can enhance phosphorus availability and fiber degradation in the rumen and positively affect animal's health and productivity. In vitro and in vivo trials have been conducted to define the optimal addition level of xylanase and phytase to lactating Baldi goat's rations and investigate effects of these enzymes on animal's nutrients digestibility, blood chemistry, milk production and milk composition. MATERIALS AND METHODS In vitro batch culture technique was used to evaluate the effect of phytase and xylanase supplementation at different levels (0, 1, 2 and 3 g kg-1 DM) on rumen fermentation characteristics. Eighteen early lactating Baldi goats were randomly assigned into three groups and fed 4% dry matter according to their body weight. The first group was fed control ration (35% yellow corn, 20% corn stalks, 20% berseem hay, 12.5% soybean meal and 12.5% wheat bran), the second group fed control ration+Penizyme at 2 g kg-1 DM (R1), while the third group fed control ration+Phtase-Plus® at 1 g kg-1 DM (R2). RESULTS Xylanase and phytase supplementation increased the in vitro DM and OM degradability and ruminal NH3-N and total volatile fatty acids (TVFA) concentrations, with no effect on total gas production (TGP) volume. All nutrients digestibility (except CP), blood serum glucose concentration, milk production and milk components yields were increased for enzymes supplemented goats than control. CONCLUSION Inclusion of xylanase and phytase in lactating goat's rations improved their productive performance with no deleterious effects on their health.
Real-Time Scheduling on Asymmetric Multiprocessor Platforms

Kecheng Yang: Real-Time Scheduling on Asymmetric Multiprocessor Platforms (Under the direction of James H. Anderson) Real-time scheduling analysis is crucial for time-critical systems, in which provable timing guarantees are more important than observed raw performance. Techniques for real-time scheduling analysis initially targeted uniprocessor platforms but have since evolved to encompass multiprocessor platforms. However, work directed at multiprocessors has largely focused on symmetric platforms, in which every processor is identical. Today, it is common for a multiprocessor to include heterogeneous processing elements, as this offers advantages with respect to size, weight, and power (SWaP) limitations. As a result, realizing modern real-time systems on asymmetric multiprocessor platforms is an inevitable trend. Unfortunately, principles and mechanisms regarding real-time scheduling on such platforms are relatively lacking. The goal of this dissertation is to enrich such principles and mechanisms, by bridging existing analysis for symmetric multiprocessor platforms to asymmetric ones and by developing new techniques that are unique for asymmetric multiprocessor platforms. The specific contributions are threefold. First, for a platform consisting of processors that differ with respect to processing speeds only, this dissertation shows that the preemptive global earliest-deadline-first (G-EDF) scheduler is optimal for scheduling soft real-time (SRT) task systems. Furthermore, it shows that semi-partitioned scheduling, which is a hybrid of conventional global and partitioned scheduling approaches, can be applied to optimally schedule both hard real-time (HRT) and SRT task systems. Second, on platforms that consist of processors with different functionalities, tasks that belong to different functionalities may process the same source data consecutively and therefore have producer/consumer relationships among them, which are represented by directed acyclic graphs (DAGs). End-to-end responsetime bounds for such DAGs are derived in this dissertation under a G-EDF-based scheduling approach, and it is shown that such bounds can be improved by a linear-programming-based deadline-setting technique. Third, processor virtualization can lead a symmetric physical platform to be asymmetric. In fact, for a designated virtual-platform capacity, there exist an infinite number of allocation schemes for virtual processors
Triggered Electrical Stimulation Cueing to Address Hypofunctioning Peroneus Muscle in the Treatment of Chronic Ankle Instability: Two Repeated Single Case Designs

Background: Approximately two million ankle sprains occur in the United States annually. Lateral ankle sprains (LAS) account for 85% of all ankle sprains, 74% of those individuals report continued symptoms following the initial insult. Persistent pain and instability following ankle sprain is indicative of chronic ankle instability (CAI). There are many interventions to address hypofunctioning muscle including electrical stimulation (ES) and it is known that peripheral ES changes muscle behavior. The purpose of this pilot study was to investigate the effect of electrical stimulation of the peroneal muscles combined with 3 exercises for the treatment of CAI. Methods: Two participants with CAI were recruited for this repeated single case design. The Cumberland Ankle Instability Tool (CAIT), the modified Star Excursion Balance Test (mSEBT), and center of pressure (CoP) was measured. A single limb hop to stabilization protocol and single limb stance activities were combined with manual triggered electrical stimulation for six sessions. Results: Improvements in CAIT scores for both patients were observed. There was no statistically significant change in mSEBT scores. Center of Pressure measurement improvements varied between subjects. For subject 1, there was no significant change in CoP in eyes open and eyes closed conditions. For subject 2, there was a significant change in the anterior/posterior CoP data points in both conditions. Discussion: In individuals with CAI, the addition of electrical stimulation to a single limb stance and hop stabilization protocol for hypofunctioning peroneal muscles led to positive CAI outcomes as measured by improved CAIT scores and improved CoP measurement in one subject.
Photon-counting cine-cardiac CT in the mouse

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projection sampling was performed with 10 ms temporal resolution, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40, 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8-3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ∼1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.
Methods for Personalized Design of Data Sonification

This thesis examines the process of designing personalized soni cations – audio representations of data – from the perspective of the everyday end user, who is the ultimate listener and interpreter of that data. Designing soni cations is a complex task that involves domain knowledge of the data, principles of audio and soni cation design, and the technical aspects of connecting the data to the sound. Typically, this knowledge is distributed between sound designers, programmers, and the end users, and the resulting soni cations require time to be developed and tested thoroughly. However, in many cases once the soni cation has been created and deployed, the end user no longer has the opportunity to make changes to the design. Furthermore, if the design cannot be changed, it can be challenging for different end users to use, as each of them has individual preferences as to what is aesthetically pleasing, and individual di ferences in how audio representations of data are perceived and interpreted. In this thesis, we address these challenges via several approaches. We leverage natural soundscapes from the environment (sounds of the weather, animal vocalizations, etc.) that have already been shown to have a pleasant aesthetic for conveying information and can be easily recognized and described by everyday people. We investigate the techniques for algorithmically generating and evaluating soni cation designs. We develop user interfaces to allow end users to explore the soni cation design space and assume ownership of their own design process. The primary contributions of this work include: (1) novel models for representing data with environmental soundscapes that incorporate user interaction to make soni cation de-
IMPROVING AUTOMATIC RECONSTRUCTION OF INTERIOR WALLS FROM POINT CLOUD DATA

Abstract. In this paper we deal with the automatic reconstruction of interior walls from point clouds, an active research topic with several practical applications. We propose an improved version of the method presented in (Magri and Fusiello, 2018), where the overall structure of the environment is extracted by fitting lines to the main building features, using Min-hashed J-Linkage as a multi-model fitting technique. Our variation has the merit of producing more accurate results, both in terms of wall reconstruction and room segmentation, and greatly reducing the need for user-defined thresholds.
Design and validation of computer protocols

Part 1 Basic: introduction protocol structure error control flow control. Part 2 Specification and modeling: validation models correctness requirements protocol design finite state machines. Part 3 Conformance testing synthesis and validation: conformance testing protocol synthesis protocol validation. Part 4 Design tools: a protocol simulator a protocol validator using the validator.
A Hail Mary Diagnostic Success

With the advent of newer methodologies, it has become possible to detect previously unsuspected pathogens. We wish to present a case of human granulocytic anaplasmosis that was diagnosed using 16S ribosomal RNA gene sequencing while originally investigating possible culture-negative endocarditis. This case is the first reported in humans from the province of Quebec. A brief review of means of transmission and clinical and laboratory findings as well as treatment is provided.