ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the well-established pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art algorithms along with ablation study and a demonstration video (1) are provided to evaluate the efficacy and gauge the merits of the proposed approach. (1) https://youtu.be/J8Uth26_7rQhttps://youtu.be/J8Uth26_7rQ
Pseudomonas aeruginosa Antivirulence Strategies: Targeting the Type III Secretion System

The Pseudomonas aeruginosa type III secretion system (T3SS) is a complex molecular machine that delivers toxic proteins from the bacterial cytoplasm directly into host cells. This apparatus spans the inner and outer membrane and employs a needle-like structure that penetrates through the eucaryotic cell membrane into the host cell cytosol. The expression of the P. aeruginosa T3SS is highly regulated by environmental signals including low calcium and host cell contact. P. aeruginosa strains with mutations in T3SS genes are less pathogenic, suggesting that the T3SS is a virulence mechanism. Given that P. aeruginosa is naturally antibiotic resistant and multidrug resistant isolates are rapidly emerging, new antibiotics to target P. aeruginosa are needed. Furthermore, even if new antibiotics were to be developed, the timeline between when an antibiotic is released and resistance development is relatively short. Therefore, the concept of targeting virulence factors has garnered attention. So-called "antivirulence" approaches do not kill the microbe but instead focus on rendering it harmless and therefore unable to cause damage. Since these therapies target a particular system or pathway, the normal microbiome is unlikely to be affected and there is less concern about the spread to other microbes. Finally, and most importantly, since any antivirulence drug does not kill the microbe, there should be less selective pressure to develop resistance to these inhibitors. The P. aeruginosa T3SS has been well studied due to its importance for pathogenesis in numerous human and animal infections. Thus, many P. aeruginosa T3SS inhibitors have been described as potential antivirulence therapeutics, some of which have progressed to clinical trials.
A review of enhanced municipal wastewater treatment through energy savings and carbon recovery to reduce discharge and CO2 footprint

Municipal wastewater treatment that mainly performed by conventional activated sludge (CAS) process faces the challenge of intensive aeration-associated energy consumption for oxidation of organics and ammonium, contributing to significant directly/indirectly greenhouse gas (GHG) emissions from energy use, which hinders the achievement of carbon neutral, the top priority mission in the coming decades to cope with the global climate change. Therefore, this article aimed to offer a comprehensive analysis of recently developed biological treatment processes with the focus on reducing discharge and CO2 footprint. The biotechnologies including "Zero Carbon", "Low Carbon", "Carbon Capture and Utilization" are discussed, it suggested that, by integrating these processes with energy-saving and carbon recovery, the challenges faced in current wastewater treatment plants can be overcome, and a carbon-neutral even be possible. Future research should investigate the integration of these methods and improve anammox contribution as well as minimize organics lost under different scales.
DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs

Convolutional layers dominate the computation and energy costs of Deep Neural Network (DNN) inference. Recent algorithmic works attempt to reduce these bottlenecks via compact DNN structures and model compression. Likewise, state-of-the-art accelerator designs leverage spatiotemporal characteristics of convolutional layers to reduce data movement overhead and improve throughput. Although both are independently effective at reducing latency and energy costs, combining these approaches does not guarantee cumulative improvements due to inefficient mapping. This inefficiency can be attributed to (1) inflexibility of underlying hardware and (2) inherent reduction of data-reuse opportunities of compact DNN structures. To address these issues, we propose a dynamically reshaping, high data-reuse PE array accelerator, namely DyNNamic. DyNNamic leverages kernel-wise filter decomposition to partition the convolution operation into two compact stages: Shared Kernels Convolution (SKC) and Weighted Accumulation (WA). Because both stages have vastly different dimensions, DyNNamic reshapes its PE array to effectively map the algorithm to the architecture. The architecture then exploits data-reuse opportunities created by the SKC stage, further reducing data movement with negligible overhead. We evaluate our approach on various representative networks and compare against state-of-the-art accelerators. On average, DyNNamic outperforms DianNao by 8.4x and 12.3x in terms of inference energy and latency, respectively.
Efficient cascading of multi-domain image Gaussian noise filters

Image denoising is a well explored but still an active research topic. The focus is usually on achieving higher numerical quality which is theoretically interesting, however, often the factor of computation cost is not considered. Our idea is to employ different image Gaussian noise filters to construct an effective image denoiser, where the deficiency of each filter is compensated with others, while a wide variation of quality versus speed can be achieved. We integrate filters using different cascaded forms and show that if two filters use uncorrelated features, their cascaded form provides a higher quality than each separately. We start with easy-to-implement filters employing pixel- and frequency-domain with different kernel size to construct a fast yet high-quality multi-domain denoiser. Then, we propose more complex denoisers by integrating our cascaded multi-domain denoiser to other state-of-the-art denoising methods. Simulations show that the quality of proposed multi-domain denoiser is significantly higher than its building-blocks. We also show that the proposed multi-domain denoiser can be integrated to state-of-the-art denoisers to from a more effective denoiser, while adding negligible complexity.
Reciprocal associations between affective decision-making and mental health in adolescence

Poor affective decision-making has been shown to associate cross-sectionally with poor mental health in clinical populations. However, evidence from general population samples is scarce. Moreover, whether decision-making is prospectively linked to mental health in youth in the general population and whether such associations are reciprocal have yet to be examined. The present study examined bidirectional associations between various aspects of affective decision-making and emotional and behavioural problems at ages 11 and 14 years in 13,366 members of the Millennium Cohort Study. Decision-making (delay aversion, deliberation time, quality of decision-making, risk adjustment, risk-taking) and emotional (emotional symptoms, peer problems) and behavioural (conduct problems, hyperactivity/inattention) problems were measured using the Cambridge Gambling Task and the Strengths and Difficulties Questionnaire, respectively. Results of cross-lagged panel models adjusted for confounding revealed a negative reciprocal association between hyperactivity and quality of decision-making but also positive reciprocal associations between conduct problems and delay aversion, and between peer problems and deliberation time. Emotional problems and peer problems predicted a decrease in risk-taking, conduct problems predicted an increase in risk-taking, and hyperactivity predicted an increase in delay aversion and deliberation time. Furthermore, hyperactivity and conduct problems predicted less risk adjustment, and risk adjustment predicted fewer peer problems. The results suggest that behavioural problems are prospectively linked to greater risk-taking and lower risk adjustment in adolescence. Moreover, adolescents with behavioural problems tend to make poorer decisions and be more delay-averse, but also poorer quality of decision-making and increased delay aversion are associated with more behavioural problems over time.
WAVE: designing a heuristics-based three-way breadth-first search on GPUs

Breadth-first search (BFS) is a building block for improving the performance of many iterative graph algorithms. In addition to conventional BFS (push), a novel method that traverses a graph in the reverse direction (pull) has emerged and gained popularity because of its enhanced processing performance. Several frameworks have recently used a hybrid approach known as direction-optimizing BFS, which utilizes both directions. However, these frameworks are mostly interested in optimizing the procedure in each direction, instead of designing sophisticated methods for determining the appropriate direction between push and pull at each iteration. Owing to the lack of in-depth discussion on this decision, state-of-the-art direction optimizing BFS algorithms cannot demonstrate their comprehensive performance despite improvements in the design of each direction because they select ineffective directions at each iteration. We identified that the current frameworks suffer from high computational overheads for their decisions and make decisions that are overfitted to several graph datasets used for tuning their direction selection process. Based on observations from state-of-the-art limitations, we designed a direction-optimizing method for BFS called WAVE. WAVE minimizes the computational overhead to near zero and makes more appropriate direction selection decisions than the state-of-the-art heuristics based on the characteristics extracted from the input graph dataset. In our experiments on 20 graph benchmarks, WAVE achieved speedups of up to 4.95x, 5.79x, 46.49x, and 149.67x over Enterprise, Gunrock, Tigr, and CuSha, respectively.
CRITERIA OF MUSEUM COLOUR AND LUMINOUS ENVIRONMENT CONCEPT SELECTION

The article formulates the criteria of selection of the colour and luminous concept in museums of different profiles with focus on visitors of exhibition halls. The amount of light and the quality of illumination solution are important parameters affecting preservation of pieces of art, perception of museum environment, and capability to research any exhibit. Showcase illumination requires not only meeting exhibit lighting standards, but also balancing showcase illumination with general hall lighting. If daylighting is present, the problem becomes more complicated: it is necessary to select illuminance and luminance levels, locations of showcases and exhibits in them. For demonstration and convenient perception of paintings, it is important to create a comfortable illumination solution, to fill the space with light, to select appropriate contrast. Cultural heritage of Russia is huge, and, naturally, no exhibition may consist of exhibits of the same light resistance category. Annual luminous exposure may help find a solution for lighting design in this case. Light and shadow are inseparable, that combination gives a perspective of volume and texture of a piece of art. But shadow may distort exhibit perception therefore it is necessary to work with it. Another complication and, at the same time, a limitless area for creativity is using and/or limiting daylighting. Lighting design depends on the goals and objectives of a project.
Time-frequency feature extraction for classification of episodic memory

This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art TF representations: the spectrogram, the Welch method, the classically estimated WVS, and the Morlet wavelet scalogram. First, we evaluate the MSE of each spectrum estimate with respect to the true WVS for simulated data, where it is shown that the LSP-inference MSE optimal estimator clearly outperforms other methods. Then, we use the different TF representations to extract the features which feed a neural network classifier and compare the classification accuracies for simulated datasets. Finally, we provide an example of real data application on EEG signals measured during a visual memory encoding task, where the classification accuracy is evaluated as in the simulation study. The results show consistent improvement in classification accuracy by using the features extracted from the proposed LSP-inference MSE optimal estimator, compared to the use of state-of-the-art methods, both for simulated datasets and for the real data example.
Synthesis of a Bifunctional Cross-Bridged Chelating Agent, Peptide Conjugation, and Comparison of 68 Ga Labeling and Complex Stability Characteristics with Established Chelators

[68 Ga]Ga3+ can be introduced into receptor-specific peptidic carriers via different chelators to obtain radiotracers for Positron Emission Tomography imaging and the chosen chelating agent considerably influences the in vivo pharmacokinetics of the corresponding radiopeptides. A chelator that should be a valuable alternative to established chelating agents for 68 Ga-radiolabeling of peptides would be a backbone-functionalized variant of the chelator CB-DO2A. Here, the bifunctional cross-bridged chelating agent CB-DO2A-GA was developed and compared to the established chelators DOTA, NODA-GA and DOTA-GA. For this purpose, CB-DO2A-GA(tBu)2 was introduced into the peptide Tyr3 -octreotate (TATE) and in direct comparison to the corresponding DOTA-, NODA-GA-, and DOTA-GA-modified TATE analogs, CB-DO2A-GA-TATE required harsher reaction conditions for 68 Ga-incorporation. Regarding the hydrophilicity profile of the resulting radiopeptides, a decrease in hydrophilicity from [68 Ga]Ga-DOTA-GA-TATE (logD(7.4) of -4.11±0.11) to [68 Ga]Ga-CB-DO2A-GA-TATE (-3.02±0.08) was observed. Assessing the stability against metabolic degradation and complex challenge, [68 Ga]Ga-CB-DO2A-GA demonstrated a very high kinetic inertness, exceeding that of [68 Ga]Ga-DOTA-GA. Therefore, CB-DO2A-GA is a valuable alternative to established chelating agents for 68 Ga-radiolabeling of peptides, especially when the formation of a very stable, positively charged 68 Ga-complex is pursued.
A Boosted Multi-Task Model for Pedestrian Detection With Occlusion Handling

Pedestrian detection is a challenging problem in computer vision, and has achieved impressive progress in recent years. However, the current state-of-the-art methods suffer from significant performance decline with increasing occlusion level of pedestrians. A common approach for occlusion handling is to train a set of occlusion-specific detectors and merge their results directly, but these detectors are trained independently and the relationship among them is ignored. In this paper, we consider pedestrian detection in different occlusion levels as different but related problems, and propose a boosted multi-task model to jointly consider their relatedness and differences. The proposed model adopts multi-task learning algorithm to map pedestrians in different occlusion levels to a common space, where all models corresponding to different occlusion levels are constrained to share a common set of features, and a boosted detector is then constructed to distinguish pedestrians from background. The proposed approach is evaluated on three challenging pedestrian detection data sets, including Caltech, TUD-Brussels, and INRIA, and achieves superior performances against state of the art in the literature on different occlusion-specific test sets.
Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images

We present a novel descriptor for the characterization of pulmonary nodules in computed tomography (CT) images. The descriptor encodes information on nodule morphology and has scale-invariant and rotation-invariant properties. Information on nodule morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a spectrum. A library of spectra is created and labeled via unsupervised clustering, obtaining a Bag-of-Frequencies, which is used to assign each spectra a label. The descriptor is obtained as the histogram of labels along all the spheres. Additional contributions are a technique to estimate the nodule size, based on the sampling strategy, as well as a technique to choose the most informative plane to cut a 2-D view of the nodule in the 3-D image. We evaluate the descriptor on several nodule morphology classification problems, namely discrimination of nodules versus vascular structures and characterization of spiculation. We validate the descriptor on data from European screening trials NELSON and DLCST and we compare it with state-of-the-art approaches for 3-D shape description in medical imaging and computer vision, namely SPHARM and 3-D SIFT, outperforming them in all the considered experiments.
Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion

The theory and techniques of compressed sensing (CS) have shown their potential as a breakthrough in accelerating k-space data acquisition for parallel magnetic resonance imaging (pMRI). However, the performance of CS reconstruction models in pMRI has not been fully maximized, and CS recovery guarantees for pMRI are largely absent. To improve reconstruction accuracy from parsimonious amounts of k-space data while maintaining flexibility, a new CS SENSitivity Encoding (SENSE) pMRI reconstruction framework promoting joint sparsity (JS) across channels (JS CS SENSE) is proposed in this paper. The recovery guarantee derived for the proposed JS CS SENSE model is demonstrated to be better than that of the conventional CS SENSE model and similar to that of the coil-by-coil CS model. The flexibility of the new model is better than the coil-by-coil CS model and the same as that of CS SENSE. For fast image reconstruction and fair comparisons, all the introduced CS-based constrained optimization problems are solved with split Bregman, variable splitting, and combined-variable splitting techniques. For the JS CS SENSE model in particular, these techniques lead to an efficient algorithm. Numerical experiments show that the reconstruction accuracy is significantly improved by JS CS SENSE compared with the conventional CS SENSE. In addition, an accurate residual-JS regularized sensitivity estimation model is also proposed and extended to calibration-less (CaL) JS CS SENSE. Numerical results show that CaL JS CS SENSE outperforms other state-of-the-art CS-based calibration-less methods in particular for reconstructing non-piecewise constant images.
Do functional and phylogenetic nestedness follow the same mechanisms as taxonomic nestedness? Evidence from amphibians in the largest archipelago of China

Nested subset pattern (nestedness) has been raised to explain the distribution of species on islands and habitat fragments for over 60 years. However, previous studies on nestedness focused on species richness and composition and overlooked the role of species traits and phylogeny in generating and explaining nestedness. To address this gap, we sampled amphibians on 37 land-bridge islands in the largest archipelago of China, the Zhoushan Archipelago, to explore nestedness as well as the underlying causal processes through three facets of diversity, that is, taxonomic, functional and phylogenetic diversity. The taxonomic nestedness was measured through organizing the species incidence matrix to achieve a maximum value, while the functional and phylogenetic nestedness were quantified by incorporating the similarity of species in terms of their ecological traits and phylogeny. We also obtained six island characteristics and seven species traits as predictors of nestedness. Amphibian metacommunities were significantly nested in these three facets of diversity. When relating different predictors to nestedness, island area, habitat diversity and species traits were highly correlated with taxonomic nestedness. Moreover, island area and habitat diversity significantly influenced functional and phylogenetic nestedness. Therefore, the results support the selective extinction and habitat nestedness hypotheses. Interestingly, although we did not observe significant influences of island isolation on taxonomic nestedness, functional and phylogenetic diversities were significantly higher than expected when matrices were ordered by increasing distance to mainland. The result suggests that there are more functionally and phylogenetically diverse species on less-isolated islands, reflecting a selective colonization process overlooked by the traditional analysis of taxonomic nestedness. Although the three facets of nestedness and underlying processes were largely congruent, we detected the distance-related functional and phylogenetic nestedness for amphibian assemblages. Therefore, we highlight that a framework that simultaneously considers taxonomic, functional and phylogenetic nestedness can contribute to a complementary understanding of nestedness processes. In addition, it also improves our ability to conserve insular biodiversity from different perspectives.
Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D

Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling of long-range genomic interactions. However, data from these technologies are prone to technical noise and biases that hinder downstream analysis. We develop a normalization approach, BandNorm, and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identification.