ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Gray whale habitat use and reproductive success during seismic surveys near their feeding grounds: comparing state-dependent life history models and field data

We used a stochastic dynamic programming (SDP) model to quantify the consequences of disturbance on pregnant western gray whales during one foraging season. The SDP model has a firm basis in bioenergetics, but detailed knowledge of minimum reproductive length of females (Lmin) and the relationship between length and reproductive success (Rfit) was lacking. We varied model assumptions to determine their effects on predictions of habitat use, proportion of animals disturbed, reproductive success, and the effects of disturbance. Smaller Lmin values led to higher predicted nearshore habitat use. Changes in Lmin and Rfit had little effect on predictions of the effect of disturbance. Reproductive success increased with increased Lmin and with higher probability of reproductive success by length. Multiple seismic surveys were conducted in 2015 off the northeast coast of Sakhalin Island, with concomitant benthic prey surveys, photo-identification studies, and whale distribution sampling, thus providing a unique opportunity to compare output from SDP models with empirical observations. SDP model predictions of reproductive success and habitat use were similar with and without acoustic disturbance, and SDP predictions of reproductive success and large-scale habitat use were generally similar to values and trends in the data. However, empirical estimates of the proportion of pregnant females nearshore were much higher than SDP model predictions (a large effect, measured by Cohen's d) during the first week, and the SDP model overestimated whale density in the south and underestimated density around the mouth of Piltun Bay. Such differences in nearshore habitat use would not affect SDP predictions of reproductive success or survival under the current seismic air gun disturbance scenario.
Reducing microbial and human contamination in DNA extractions from ancient bones and teeth

Although great progress has been made in improving methods for generating DNA sequences from ancient biological samples, many, if not most, samples are still not amenable for analyses due to overwhelming contamination with microbial or modern human DNA. Here we explore different DNA decontamination procedures for ancient bones and teeth for use prior to DNA library preparation and high-throughput sequencing. Two procedures showed promising results: (i) the release of surface-bound DNA by phosphate buffer and (ii) the removal of DNA contamination by sodium hypochlorite treatment. Exposure to phosphate removes on average 64% of the microbial DNA from bone powder but only 37% of the endogenous DNA (from the organism under study), increasing the percentage of informative sequences by a factor of two on average. An average 4.6-fold increase, in one case reaching 24-fold, is achieved by sodium hypochlorite treatment, albeit at the expense of destroying 63% of the endogenous DNA preserved in the bone. While both pretreatment methods described here greatly reduce the cost of genome sequencing from ancient material due to efficient depletion of microbial DNA, we find that the removal of human DNA contamination remains a challenging problem.
Outward Movement of Targeting Ligands from a Built-In Reserve Pool in Nuclease-Resistant 3D Hierarchical DNA Nanocluster for in Vivo High-Precision Cancer Therapy

Nanostructures made entirely of DNAs display great potential as chemotherapeutic drug carriers but so far cannot achieve sufficient clinic therapy outcomes due to off-target toxicity. In this contribution, an aptamer-embedded hierarchical DNA nanocluster (Apt-eNC) is constructed as an intelligent carrier for cancer-targeted drug delivery. Specifically, Apt-eNC is designed to have a built-in reserve pool in the interior cavity from which aptamers may move outward to function as needed. When surface aptamers are degraded, ones in reserve pool can move outward to offer the compensation, thereby magically preserving tumor-targeting performance in vivo. Even if withstanding extensive aptamer depletion, Apt-eNC displays a 115-fold enhanced cell targeting compared with traditional counterparts and at least 60-fold improved tumor accumulation. Moreover, one Apt-eNC accommodates 5670 chemotherapeutic agents. As such, when systemically administrated into HeLa tumor-bearing BALB/c nude mouse model, drug-loaded Apt-eNC significantly inhibits tumor growth without systemic toxicity, holding great promise for high precision therapy.
End to End Segmentation of Canola Field Images Using Dilated U-Net

Semantic segmentation is used in many fields like agriculture, medical imaging, and autonomous driving. The paper proposes an end to end solution for efficient weeds and crop segmentation in field environment application. The crop/weeds segmented output is utilized to generate a decision map for variable rate fertilizer and herbicide application. Currently available models are memory expensive and do not have real time performance unless enough computational power is accessible in field. We use Maximum Likelihood Classification (MLC) and image processing techniques to label field images in three classes; background, crop, and weeds. This data is processed through our modified U-Net, which improves the semantic accuracy with reduced memory cost. We train our model with DICE loss and compare the results with state of the art. We achieve 89.12% mean Intersection Over Union (mIOU) with 86.11%, 82.99%, and 98.23% individual IOU for crop, weeds, and background, respectively. Our proposed model uses only 15M parameters which are 57M less than the state-of-the-art models with a compromise of 1% mIOU score.
I must go down to the seas again: Or, what happens when the sea comes to you? Murujuga rock art as an environmental indicator for Australia's north-west

The Dampier Archipelago (properly known as Murujuga) is a rich rock art province in north-western Australia which documents an arid-maritime cultural landscape. But this Archipelago of 42 islands has only existed since the mid-Holocene. When people started to engrave art here, the granophyre bedrock was part of an inland range, more than 160 km from the coast. The Pilbara archaeological record demonstrates human responses through over forty thousand years of environmental change. This paper discusses how rock art across Murujuga can give insights to changing dynamics of people in place as well as depicting major environmental change. A predictive model is developed to assist in understanding the social changes which have been wrought by sea-level rise and consequential environmental changes. (C) 2014 Elsevier Ltd and INQUA. All rights reserved.
From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.
Integrated clustering approach to developing technology for functional feature and engineering specification-based reference design retrieval

Engineering design is a complex activity, and is heavily reliant on the know-how of engineering designers. Hence, capturing, storing, and reusing design information, design intent, and underlining design knowledge to support design activities is a key issue in engineering knowledge management. To meet the demand for engineering designers regarding functional feature and engineering specification-based knowledge resources, this study proposes a novel scheme for functional feature and engineering specification-based reference design retrieval using an integrated clustering approach for providing engineering designers with easy access to relevant reference design and associated knowledge. The research objectives can be achieved by performing the following five tasks: (i) designing a functional feature and engineering specification-based reference design retrieval process, (ii) developing a functional feature and engineering specification representation, (iii) investigating and integrating ART1 (adaptive resonance theory 1) neural network, GA (genetic algorithm), and fuzzy ART (fuzzy adaptive resonance theory) clustering techniques, and (iv) implementing a functional feature and engineering specification-based reference design retrieval mechanism and experimenting with an example. The retrieval process involves three steps: functional feature and engineering specification-based query, similar design case search and retrieval, and similar design case ranking. The techniques involved include: (i) a binary code-based representation for functional feature and an EXPRESS language-based representation for engineering specification, (ii) ART1 neural network and genetic algorithm for functional feature-based similar design case clustering, (iii) fuzzy ART for engineering specification-based similar design ease clustering, (iv) similarity calculation for ranking similar design cases, and (v) a case-based representation for designed entities.
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles

The state-of-the-art decision and planning approaches for autonomous vehicles have moved away from manually designed systems, instead focusing on the utilisation of large-scale datasets of expert demonstration via Imitation Learning (IL). In this paper, we present a comprehensive review of IL approaches, primarily for the paradigm of end-to-end based systems in autonomous vehicles. We classify the literature into three distinct categories: 1) Behavioural Cloning (BC), 2) Direct Policy Learning (DPL) and 3) Inverse Reinforcement Learning (IRL). For each of these categories, the current state-of-the-art literature is comprehensively reviewed and summarised, with future directions of research identified to facilitate the development of imitation learning based systems for end-to-end autonomous vehicles. Due to the data-intensive nature of deep learning techniques, currently available datasets and simulators for end-to-end autonomous driving are also reviewed.
Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio-and neuro-vascularapplications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating group-wise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.
Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework

With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various traffic features and attack-related characteristics by experts, and even, private information might be required. However, due to the outdated features labeling and privacy protocols, the existing approaches may not fit with the characteristics of the changing network environment anymore. In this paper, we present a light-weight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR). Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information. In such a framework, our proposed algorithms are compared with other state-of-the-art methods using two public datasets. The experimental results show that our framework not only can outperform the state-of-the-art methods by averaging 13.49% on encrypted traffic classification's F1 score and by averaging 12.15% on intrusion detection's F1 score but also require much lesser storage resource requirement.
Preliminary Determination of Activation Products for a Varian Truebeam Linear Accelerator

Medical linear accelerators used to treat various forms of cancers are operated at a number of different energies. A by-product of the high-energy photons produced by accelerators is activation of components within the machine itself and its surrounding bunker. The activation products pose radiological and regulatory challenges during the operation of the accelerator as well as when it is time for final decommissioning. The Varian TrueBeam is a new state-of-the-art linear accelerator now operating in the Canadian market. There is currently limited information on the production of its activation products and the resulting impacts on operation and decommissioning. In this paper, activation products in the Varian TrueBeam accelerator are experimentally determined by performing gamma spectroscopy using a portable high purity germanium detector. A total of 10 isotopes are identified for the conditions tested, which include Na-24, Al-28, Mn-56, Ni-57, Cu-64, Cu-66, Br-82, Sb-122, Sb-124, W-187. The half-lives of these isotopes range from 2.3 min to 60.2 d. These preliminary results indicate that a decommissioning case similar to other radiotherapy accelerators can be made.
Uniform Embedding for Efficient JPEG Steganography

Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first-and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.
Structural basis for mouse receptor recognition by SARS-CoV-2 omicron variant

The sudden emergence and rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant has raised questions about its animal reservoir. Here, we investigated receptor recognition of the omicron's receptor-binding domain (RBD), focusing on four of its mutations (Q493R, Q498R, N501Y, and Y505H) surrounding two mutational hotspots. These mutations have variable effects on the RBD's affinity for human angiotensin-converting enzyme 2 (ACE2), but they all enhance the RBD's affinity for mouse ACE2. We further determined the crystal structure of omicron RBD complexed with mouse ACE2. The structure showed that all four mutations are viral adaptations to mouse ACE2: three of them (Q493R, Q498R, and Y505H) are uniquely adapted to mouse ACE2, whereas the other one (N501Y) is adapted to both human ACE2 and mouse ACE2. These data reveal that the omicron RBD was well adapted to mouse ACE2 before omicron started to infect humans, providing insight into the potential evolutionary origin of the omicron variant.
Effects of nanocapsules of poly-epsilon-caprolactone containing artemisinin on zebrafish early-life stages and adults

Artemisinin extracted from Artemisia annua L. plants has a range of properties that qualifies it to treat several diseases, such as malaria and cancer. However, it has short half-life, which requires making continuous use of it, which has motivated the association of artemisinin (ART) with polymeric nanoparticles to increase its therapeutic efficiency. However, the ecotoxicological safety of this association has been questioned, given the scarcity of studies in this area. Thus, in this work the toxicity of Poly (epsilon-Caprolactone) nanocapsules added with ART (ART-NANO) in zebrafish (Danio rerio), embryos and adults was studied. Different endpoints were analyzed in organisms exposed to ART-NANO, including those predictive of embryotoxicity and histopatoxicity. Embryotoxicity was analyzed based on Organization for Economic Co-operation and Development (OECD) test guideline (236) for fish embryo acute toxicity applied to zebrafish (Danio rerio) at 96 hpf under five nominal logarithmic concentrations (0.125 to 2.0 mg/L). Our results demonstrate, mainly, that fertilized eggs presented increased coagulation, lack of heart rate, vitelline sac displacement and lack of somite formation. On the other hand, adult individuals (exposed to the same concentrations and evaluated after 24 and 96 h of exposure) have shown increased pericarditis. Therefore, the treatment based on ART, poly (epsilon-caprolactone) nanocapsules and on their combination at different concentrations have shown toxic effects on zebrafish embryos and adult individuals. (C) 2020 Elsevier B.V. All rights reserved.
User-assisted image shadow removal

This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piecewise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods. (C) 2017 Elsevier B.V. All rights reserved.
luvHarris: A Practical Corner Detector for Event-Cameras

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only 'as-fast-as-possible', i.e., only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art; a necessity when using a high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.
Teaching application of watercolor light color technique

As a branch of watercolor painting, watercolor light color has been widely used in different fields. In the field of design, designers use the convenience, quickness, transparency and brilliance of watercolor to draw a design drawing. In the field of art creation, watercolor is usually the best choice for painters to go out to sketch and create large-scale drawings. In the field of art education, watercolor tools are easy to carry, low-cost and easy to operate, which can facilitate students' repeated practice and outside Sketching is helpful to cultivate students' sense of color and observation ability. Therefore, as a branch of art curriculum, watercolor light color has a wide range of uses and great practicability, which is worth exploring and studying.
Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.