Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Models for Genome Assembly and Analysis

Author(s): Rahman, Atif Hasan | Advisor(s): Pachter, Lior | Abstract: Genome assembly is the process of merging fragments of DNA sequences produced by shotgun sequencing in order to reconstruct the original genome. It is complicated by repeated regions in genomes, sequencing errors, and experimental biases. Here we focus on our efforts to confront some of the challenges in genome assembly and analysis of genomes to find regions associated with phenotypes using statistical models.Assembly algorithms have been extensively benchmarked using simulated data so that results can be compared to ground truth. However, in de novo assembly, only crude metrics such as contig number and size are typically used to evaluate assembly quality. We present CGAL, a novel likelihood-based approach to assembly assessment in the absence of a ground truth. We show that likelihood is more accurate than other metrics currently used for evaluating assemblies, and describe its application to theoptimization and comparison of assembly algorithms.We then extend this to develop a method for ''scaffolding'' i.e. linking contigs using read pairs based on optimizing assembly likelihood. It uses generative models to approximate whether joining contigs would result in an increase in assembly likelihood. The methods are grounded in a rigorous statistical model yet proper approximations make the implementation named SWALO efficient and applicable to practical datasets. We analyze SWALO on real and simulated datasets used previously to evaluate other scaffolding methods and find that it consistently outperforms all other scaffolders.Finally, we focus on the problem of analyzing genomic data to associate regions in the genome to traits or diseases. We present an alignment free method for association studies that is based on counting k-mers in sequencing read, testing for associations directly between k-mers and the trait of interest, and local assembly of the statistically significantk-mers to identify sequence differences. Results with simulated data and an analysis of the 1000 genomes data provide a proof of principle for the approach. In a pairwise comparison of the Toscani in Italia (TSI) and the Yoruba in Ibadan, Nigeria (YRI) populations we find that sequences identified by our method largely agree with results obtained using standard GWAS based on variant calling from mapped reads. However unlike standard GWAS, we find that our method identifies associations with structural variations and sites not present in the reference genome.We also analyze the data from the Bengali from Bangladesh (BEB) population to explore possible genetic basis of high rate of mortality due to cardiovascular diseases (CVD) among South Asians and find significant differences in frequencies of a number of non-synonymous variants in genes linked to CVDs between BEB and TSI samples, including the site rs1042034, which has been associated with higher risk of CVDs previously and the nearby rs676210 in the Apolipoprotein B (ApoB) gene.
Global phylogeography of the leatherback turtle ( Dermochelys coriacea )

Analyses of mitochondrial (mt) DNA control region sequences from 175 leatherback turtles Dermochelys coriacea from 10 nesting colonies revealed shallow phylogenetic structuring of maternal lineages on a global scale. Eleven haplotypes were observed, and mean estimated sequence divergence, p = 0.00581, is much lower than the deepest nodes reported in global mtDNA surveys of the green turtle Chelonia mydas, loggerhead Caretta caretta, and ridley turtles Lepidochelys spp. The leatherback turtle is the product of an evolutionary trajectory originating at least 100 million years ago, yet the intraspecific phylogeny recorded in mitochondrial lineages may trace back less than 900 000 years. The gene genealogy and global distribution of mtDNA haplotypes indicate that leatherbacks may have radiated from a narrow refugium, possibly in the Indian‐Pacific during the early Pleistocene glaciation. Analysis of haplotype frequencies revealed that nesting populations are strongly subdivided globally (FST = 0.415), and within ocean basins (FST = 0.203‐0.253), despite the leatherback’s highly migratory nature. Within the Atlantic significant differences in haplotype frequency distributions and Nm values < 2 are observed in pairwise comparisons between St. Croix (U.S. Virgin Islands) and mainland Caribbean populations, and between Trinidad and the same mainland populations. These findings provisionally support the natal homing hypothesis for leatherback turtles, although several proximal nesting populations were indistinguishable, suggesting recent colonization or less precise natal homing behaviour than documented for other marine turtle species. The evidence of natal homing, manifested on ecological time scales, may be erased in some populations by rapid rookery turnover resulting from climatic fluctuation and the ephemeral nature of nesting habitat on a geological time scale. The evolutionary effective population size (Ne) is estimated from mtDNA data to be between 45 000 and 60 000, a value that exceeds current global census estimates of 26 000 to 43 000 adult females.
Bending properties and strength grading of Norway spruce: variation within and between stands1

Current strength grading of Norway spruce (Picea abies (L.) Karst. ) structural timber is only able to describe parts of the great variability in density and bending properties. This study assesses whether information about the origin of the timber can be used to predict its strength and stiffness, alone or in combination with machine strength grading. Three hundred and seventy-three boards from 45 trees sampled from three stands in eastern Norway were studied. Substantial parts of the variability of density, modulus of elasticity (MOE), and bending strength or modulus of rupture (MOR) of the boards studied were explained by origin (differences between sites, relative tree size (diameter at breast height), and longitudinal position in stem). Origin also gave a reduction in residual variance in addition to what was obtained by machine grading based on resonance frequencies. For MOR, the improvement was larger than what was obtained by adding density, whereas for MOE, the density was more important than info.
Communication in Bacteria

Bacteria communicate with one another using chemical signal molecules. As in higher organisms, the information supplied by these molecules is critical for synchronizing the activities of large groups of cells. In bacteria, chemical communication involves producing, releasing, detecting, and responding to small hormone-like molecules termed autoinducers. This process, termed quorum sensing, allows bacteria to monitor the environment for other bacteria and to alter behavior on a population-wide scale in response to changes in the number and/or species present in a community. Most quorumsensing-controlled processes are unproductive when undertaken by an individual bacterium acting alone but become beneficial when carried out simultaneously by a large number of cells. Thus, quorum sensing confuses the distinction between prokaryotes and eukaryotes because it enables bacteria to act as multicellular organisms. This review focuses on the architectures of bacterial chemical communication networks; how chemical information is integrated, processed, and transduced to control gene expression; how intra- and interspecies cell-cell communication is accomplished; and the intriguing possibility of prokaryote-eukaryote cross-communication.
Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page: https://github.com/liubing220524/ MAPC-DRF-HSI.
Spectrum-Efficient Transmission Mode Selection for Full-Duplex-Enabled Two-Way D2D Communications

Device-to-device (D2D) communications and full-duplex (FD) communications, which can improve the spectrum efficiency (SE) of mobile communications, have received much attention. To further improve the SE, some researchers have begun to integrate FD into D2D communications, which will generate more transmission modes. Since different modes have different advantages and disadvantages, the mode selection problem needs further study. In this paper, we investigate the mode selection problem for FD-enabled two-way D2D communications to improve the SE. Four transmission modes, i.e., FD underlay mode, half-duplex (HD) underlay mode, FD overlay mode and HD overlay mode, are considered. For each mode, we maximize the system SE while fulfilling the minimum rate requirements and maximum transmit power constraints for both cellular and D2D users. For FD underlay mode, the optimization problem can be transformed into a difference of convex functions (D.C.) programming and the concave-convex procedure (CCCP) algorithm can solve the problem efficiently. For HD underlay mode and FD overlay mode, we can use search plus CCCP algorithm to solve the optimization problem. For HD overlay mode, we only need to search for the optimal solution. After obtaining the maximum SE of four modes, we can select the maximum one as the optimal mode. Numerical results are presented to illustrate the effects of the channel gains and self-interference cancellation ability on the maximum SE of four modes and the transmission mode selection.
Pre-Processing Technique for Compass-less Madgwick in Heading Estimation for Industry 4.0

The high quality of heading orientation is a key factor to monitor the industrial robot arms in the Industry 4.0. The paper proposes a new algorithm ‘No Motion No Integration’ (NMNI) to enhance the gyroscope performance via signal processing and specifically improve the Madgwick filter for heading estimation to overcome the weakness of magnetometer and Global Navigation Satellite System (GNSS). The experiment was carried out to characterize the pros and cons of traditional Madgwick and new approached Madgwick. The results show that the proposed method displayed high effective measurements. The gyroscope drift was minimized to attain a good yaw angle without a magnetometer or GNSS.
INVESTIGATIONS ON FLIGHT HABITS OF THE PECAN WEEVIL, CURCULIO CARYAE (COLEOPTERA: CURCULIONIDAE)

Abstract Research over a 2-year period indicated that female pecan weevils, Curculio caryae Horn, tended to fly higher than males. Male pecan weevils were observed to be clumsy flyers and crawled to the pecan tree more often than females. Significant differences were noted among replicates, height flown by sexes, and distances from the tree where weevils were released. Pecan weevils do have definite habits in reaching the tree and this study seems to indicate that further investigations might be conducted to establish a method of control for the less mobile male weevils as well as for the higher flying females.
Noncooperative Resource optimization for NOMA Based Fog Radio Access Network

Fog-computing radio access network (F-RAN) and non-orthogonal multiple access (NOMA) have been recognized as the promising technologies with high mobility and low delay support. In this paper, we propose a new network architecture of the NOMA based F-RAN, and investigate the noncooperative radio resource optimization for time-varying wireless network environment. The subchannel assignment is modeled as the two-side matching issue, and deal with by the matching theory. Then, the dynamic power allocation is deal with the Lyapunov theory, which is decoupled into there subproblems. Simulations results illustrates that the dynamic resource management scheme can obtain the high utility performance gain of communication systems.
Evolutionary Computing Approaches for Solving Multi-Objective and Many-Objective Optimization Problems: A Review

In today world, there exists different algorithmic approaches in Computer Science to solve the complex real-world optimization problems around us. Based on the number of objectives they are classified as single-objective, multi-objective or many-objective optimization problems. Though many approaches are available for solving single-objective optimization problems, they are not directly scalable to solve Multi-objective and Many-objective optimization problems. However, the Evolutionary Computing (EC) based approaches are becoming most popular and common to solve all the types of optimization problems, due to their simplicity and wide applicability. This paper presents the details of the Evolutionary Algorithms (EAs) to solve the optimization problem with the category of EAs for Multi-objective and EAs for Many-Objective optimization problems. Insight about the Hybridization of EAs is also added. The details about the algorithmic components required for designing EAs is also presented along with the Benchmarking Problems and Performance Metrics available to validate the newly designed EAs.
Types of Data with Algorithms for Assessing Mental Health Conditions

Mental Health assessment is done by psychiatrists. With the advent of technology several types of data have been used to detect mental health illness and provide an access to the people. Paper tries to give an overall view of the types of data used for monitoring mental health by various authors. The types of data taken for studies in assessing mental health conditions are speech/audio clips, email conversations, wrist sensors, f-MRI scans, EOG data, structural-MRI scans, resting state EEG, questionnaire based/survey data, Twitter data, wireless monitoring with the help of computer algorithms. This paper gives a survey of kinds of data and the algorithms studied and applied by multiple authors.
Genética, filogeografia e fertilidade de populações de Vriesea gigantea Gaud. (Bromeliaceae)

10 Capítulo 1. Introdução 12 I) A Família Bromeliaceae 13 II) Vriesea gigantea 15 III) Variabilidade genética em populações naturais de bromélias 17 IV) Genética de populações e filogeografia na Mata Atlântica 21 V) Marcadores Moleculares microssatélites 22 a) Microssatélites nucleares 23 b) Microssatélites plastidiais 24
Lysozyme Separation from Egg White by Cation Exchange Column Chromatography

Cation exchange column chromatography was investigated for simple and efficient separation of lysozyme from homogenized egg white. A macroporus weak acid type resin, Duolite C-464, was selected based on high lysozyme recovery in the90–95% range, retention of whipping and gelling properties in the lysozyme-separated egg white, and ease of column operation. Polyacrylamide gel electrophoresis in the presence of sodium dodecyl sulfate indicated high purity of the recovered lysozyme and simultaneous recovery of avidin under the eluting conditions used. The process for lysozyme separation from egg white using Duolite C-464 is suitable for operation in an automated, continuous mode.
Dynamic Multi-RAT Access for Ultra Dense 5G and Beyond: A Mean Field Perspective

In this paper, we investigate the uplink power allocation problem in a large scale environment for user devices with multi-homing capabilities. We introduce an analytical model for multi-homing ultra-dense heterogeneous networks, which takes into account spatial randomness and user device diversity. Coupling stochastic geometry analysis and mean-field approximation, we formulate the problem as a mean-field optimal control with two populations. Then, the optimality conditions are derived using Lagrangian dual formulation to obtain the mean-field equilibrium. Finally, by using a finite difference method, we illustrate the optimal transmit power for both uni-homed and dual-homed devices.
A Low Power Radix-4 Booth Multiplier With Pre-Encoded Mechanism

The radix-4 Booth algorithm is widely used to improve the performance of multiplier because it can reduce the number of partial products by half. However, numerous additional encoders and decoders would cause the power consumption of the Booth multiplier to be considerable. In this paper, a new radix-4 Booth pre-encoded mechanism is proposed to reduce the power consumption of the Booth multiplier. The proposed design can effectively reduce the power of the Booth multiplier dissipated in the redundant activities by disabling the Booth encoders and decoders from unnecessary working. Particularly, since the control signals are generated early at the pipeline input register before the multiplier, the performance of our design is better than the traditional Booth multiplier. Based on the TSMC 40 nm technology, the simulation results show that the proposed pre-encoded mechanism can reduce the dynamic and static power by 45% and 65%, respectively, compared to the traditional 16-bit radix-4 Booth multiplier. Compared to the previous designs, the proposed design keeps the feature of race-free and has lower power consumption. Even compared to the approximate design, the proposed design has better power efficiency and can provide the exact products.
Activity Recommendation: Optimizing Life in the Long Term

College students every day decide and plan how to best spend their time to balance academic, physical, and social goals under uncertainty. This process is likely suboptimal where long-term life satisfaction and success is not guaranteed, and poor decision-making may lead to longer-term problems like depression. To support everyday planning, we introduce activity recommendation, a novel method that combines artificial intelligence, machine learning, and a psychology-informed approach to automatically generate activity-recommendations that optimize long-term life satisfaction. We tested our method with an existing dataset and derived activity recommendations for depressed and non-depressed students. We evaluated the recommendations through interviews with college students who rated the suggestions positively. Our model can be optimized for different goals and domains and is easy to interpret. Our results demonstrate the feasibility of our approach and lay the groundwork towards implementing a live system.
Comparison of morphogenic and plant regeneration ability of some explants of teak (Tectona grandis Linn. F)

To search out the explants with higher regeneration potential-mature cotyledons, cotyledonary nodes and epicotyls of teak were cultured on MS basal medium with different types of auxins and cytokinins in varying concentrations and combinations. Mature cotyledon explants exhibited higher in vitro morphogenesis closely followed by cotyledonary node and epicotyls cultured on medium fortified with 4.0 mg.l BA and 2.0 mg.l NAA. However, maximum plantlets were recovered from cotyledonary nodes on same medium. Phenotypically normal plants were obtained after hardening in green house.
Spatial-Related Correlation Network for 3D Point Clouds

Due to the irregularity and inconsistency of 3D point clouds, it is difficult to extract features directly from them. Existing methods usually extract point features independently and then use the max-pooling operation to aggregate local features, which limits the feature representation capability of their models. In this work, we design a novel spatial-related correlation path, which considers both spatial information and point correlations, to preserve high dimensional features, thereby capturing fine-detail information and long-distance context of the point cloud. We further propose a new network to aggregate the spatial aware correlations with point-wise features and global features in a learnable way. The experimental results show that our method can achieve better performance than the state-of-the-art approaches on challenging datasets. We can achieve 0.934 accuracy on ModelNet40 dataset and 0.875 mean IoU (Intersection over Union) on ShapeNet dataset with only about 2.42 million parameters.
Unsupervised Flight Phase Recognition with Flight Data Clustering based on GMM

Currently, with the rapid development of the aviation industry, researchers are paying more attention to the improvement of aviation safety. Aviation safety mainly includes flight safety, aviation ground safety, and air defense safety. In terms of flight safety, the analysis of large amounts of flight data has gradually become a useful tool for timely detection of potential dangers at various stages of flight. As a result, flight data analysis has been one of the hot topics in aviation. However, due to the complexity of the aircraft operating conditions throughout the aircraft, if the data is analyzed at the entire flight phase, it is very difficult and time consuming to identify the problematic fight phase. Therefore, flight phase recognition for civil aircraft is implemented in this study. A flight phase recognition method based on Gaussian Mixture Model (GMM) is proposed in this work, which is the important foundation for timely detecting the abnormal event and improving the system safety and reliability. Firstly, the FDR data are preprocessed by spline interpolation and normalization, and then a GMM-based flight phase clustering is realized. In addition, a set of evaluation method is developed to evaluate the quality of flight phase recognition result. Finally, the effectiveness of the method is verified by using real FDR data from NASA's open database.
Analysis for Rank Distribution of BATS Codes under Time-Variant Channels

A batched sparse (BATS) code provides a novel two-stage coding structure for the multi-hop network, in which the outer code performed at the source node generates a potentially unlimited number of batches and the inner code at the intermediate network nodes applies network coding on packets belonging to the same batch. Previous works have studied the performance of BATS codes in the erasure channels, in which the packet loss rate $\varepsilon$ is always assumed to be a constant on each link. However, in some application scenarios such as the Industrial Internet of Things (IIoTs) where there are a number of mobile nodes in the networks, the channel conditions could be time-variant due to the mobility of nodes, resulting the packet loss rate $\varepsilon$ varying over time as well. Therefore this paper studies the rank distribution which is one of the most significant performance metric of BATS codes under time-variant channels by assuming the packet loss between links modeled as a random variable instead of a constant value. Closed-form expressions of rank distribution are obtained with the packet loss rate $\varepsilon$ following two typical types of distributions. Both numerical and simulation results are provided to verify our analysis.