ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The complete chloroplast genome sequences of three lilies: genome structure, comparative genomic and phylogenetic analyses

We sequenced and analyzed the complete chloroplast genomes of Lilium amoenum, Lilium souliei, and Nomocharis forrestii in detail, including the first sequence and structural comparison of Nomocharis forrestii. We found that the lengths and nucleotide composition of the three chloroplast genes showed little variation. The chloroplast genomes of the three Lilium species contain 87 protein coding genes (PCGs), 38 tRNAs, and 8 rRNA genes. The only difference is that Nomocharis forrestii had an additional infA pseudogene. In the sequence analysis of the Lilium chloroplast genomes, 216 SSRs, 143 pairs of long repeats, 571 SNPs, and 202 indels were detected. In addition, we identified seven hypervariable regions that can be used as potential molecular markers and DNA barcodes of Lilium through complete sequence alignment. The phylogenetic tree was constructed from the three chloroplast genome sequences of Lilium obtained here and 40 chloroplast genome sequences from the NCBI database (including 35 Lilium species, 4 Fritillaria species, and one species of Smilax). The analysis showed that the species clustering of the genus Lilium essentially conformed to the classical morphological classification system of Comber, but differences in the classification of individual species remained. In our report, we support the reclassification of Lilium henryi and Lilium rosthorniiy in the genus Lilium. In general, this study not only provides genome data for three Lilium species, but also provides a comparative analysis of the Lilium chloroplast genomes. These advances will help to identify Lilium species, clarify the phylogenetic analysis of the Lilium genus, and help to solve and improve the disputes and deficiencies in the traditional morphological classification.
Piper longum L.: A comprehensive review on traditional uses, phytochemistry, pharmacology, and health-promoting activities

Piper longum (family Piperaceae), commonly known as "long-pepper" or "Pippali" grows as a perennial shrub or as an herbaceous vine. It is native to the Indo-Malaya region and widely distributed in the tropical and subtropical world including the Indian subcontinent, Sri Lanka, Middle-East, and America. The fruits are mostly used as culinary spice and preservatives and are also a potent remedy in various traditional medicinal systems against bronchitis, cough, cold, snakebite, and scorpion-sting and are also used as a contraceptive. Various bioactive-phytochemicals including alkaloids, flavonoids, esters, and steroids were identified from the plant extracts and essential oils from the roots and fruits were reported as antimicrobial, antiparasitic, anthelminthic, mosquito-larvicidal, antiinflammatory, analgesic, antioxidant, anticancer, neuro-pharmacological, antihyperglycaemic, hepato-protective, antihyperlipidaemic, antiangiogenic, immunomodulatory, antiarthritic, antiulcer, antiasthmatic, cardioprotective, and anti-snake-venom agents. Many of its pharmacological properties were attributed to its antioxidative and antiinflammatory effects and its ability to modulate a number of signalling pathways and enzymes. This review comprehensively encompasses information on habit, distribution, ethnobotany, phytochemistry, and pharmacology of P. longum in relation to its medicinal importance and health benefits to validate the traditional claims supported by specific scientific experiments. In addition, it also discusses the safety and toxicity studies, application of green synthesis and nanotechnology as well as clinical trials performed with the plant also elucidating research gaps and future perspectives of its multifaceted uses.
UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method

Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in the state-of-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.
Prehistoric Bird Watching in Southern Iberia? The Rock Art of Tajo de las Figuras Reconsidered

This paper studies the relationship between humans and birds in the recent prehistory of the Southern Iberian Peninsula. With its high number of bird, mammal, and anthropomorphic paintings, a small rock shelter -Tajo de las Figuras- provides an excellent case study to address this topic. The cave is situated in an ecosystem that, as we will argue, favoured human-bird interactions and enabled prehistoric groups to engage with a diverse and rich bird community at particular times of the year. Even though the recorded depictions can generally be integrated into the wider 'Schematic' style regime characterising the recent prehistory of the region, they exhibit some outstanding features including a highly distinct naturalism. This naturalism enables us to identify the represented birds, not only at the family but also at the species level. Our contribution describes these pictorial data and contextualises them with the ecology, archaeology, and archaeozoology of the area. We intend to show that the singularity of the image-corpus registered at Tajo de las Figuras mirrors the unique conditions of human-bird interactions at the time. We suggest that the significance of the images derives from the special location of the cave in the wider landscape encouraging early practices of bird watching.
A general framework for time-aware decision support systems

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations. (C) 2012 Elsevier Ltd. All rights reserved.
Risk Factors for Interstage Mortality Following the Norwood Procedure: Impact of Sociodemographic Factors

Interstage mortality remains significant for patients undergoing staged palliation for hypoplastic left heart syndrome and other related single right ventricle malformations (HLV). The purpose of this study was to identify factors related to demographics, socioeconomic position, and perioperative course associated with post-Norwood hospital discharge, pre-stage 2, interstage mortality (ISM). Medical record review was conducted for patients with HLV, born from 1/2000 to 7/2009 and discharged alive following the Norwood procedure. Sociodemographic and perioperative factors were reviewed. Patients were determined to have ISM if they died between Norwood procedure hospital discharge and stage 2 palliation. Univariable and multivariable logistic regressions were performed to identify risk factors associated with ISM. A total of 273 patients were included in the analysis; ISM occurred in 32 patients (12%). Multivariable analysis demonstrated that independent risk factors for interstage mortality included teen mothers [adjusted odds ratio (AOR) 6.6, 95% confidence interval (CI) 1.9-22.5], single adult caregivers (AOR 4.1, 95% CI 1.2-14.4), postoperative dysrhythmia (AOR 2.7, 95% CI 1.1-6.4), and longer ICU stay (AOR 2.7, 95% CI 1.2-6.1). Anatomic and surgical course variables were not associated with ISM in multivariable analysis. Patients with HLV are at increased risk of ISM if born to a teen mother, if they lived in a home with only one adult caregiver, suffered a postoperative dysrhythmia, or experienced a prolonged ICU stay. These risk factors are identifiable, and thus these infants may be targeted for interventions to reduce ISM.
SWIN transformer based contrastive self-supervised learning for animal detection and classification

The subdomain of computer vision applications is Image Classification which helps in categorizing the images. The advent of handheld devices and image sensors leads to the availability of a huge amount of data without labels. Hence, to categorize these images, a supervised learning algorithm won't be suitable as it requires labels. On the other hand, unsupervised learning uses clustering that also not useful as its accuracy is not reliable as the data are not labeled in advance. Self-Supervised Learning techniques can be used to overcome this problem. In this work, we present a novel Swin Transformer based Contrastive Self-Supervised Learning (Swin-TCSSL), where the paired sample is formed using the transformation of the given input image and this paired sample is passed to the Swin-T transformer which produces a feature vector. The maximum Mutual Information of these feature vectors is used to form robust clusters and these cluster labels get propagates to the Swin Transformer block until the appropriate clusters are obtained. It is then followed by contrastive learning and finally produces the classified output. The experimental results prove that the proposed system is invariant to occlusion, viewpoint variation, and illumination effects. The proposed Swin-TSSCL achieves state-of-the-art results in 5 benchmark datasets namely CIFAR-10, Snapshot Serengeti, Stanford dogs, Animals with attributes, and ImageNet dataset. As evident from the rigorous experiments, the proposed Swin-TCSSL has set a new global state-of-the-art with an average accuracy of 97.63%, which is comparatively higher than the state-of-the-art systems.
Pricing Theater Seats: The Value of Price Commitment and Monotone Discounting

We examine the value of price commitment in a non-profit organization using individual-level purchases over a series of concert performances. To decide on a pricing policy, the performing arts organization must be able to accurately measure when each ticket will be sold and what type of audience will purchase the tickets for each performance. We use a competing hazards framework to model the timing of ticket purchases when customer segments differ in their valuations and arrival times. We show that the customer purchase likelihoods change based on the prices observed earlier in the season. Hence, price commitment can aid in improving sales, revenues, and customer visits. In particular, we show that price commitment to a decreasing monotone discount policy can improve the revenues in the range 2.1%-6.7% per concert.
Martial Arts, Dancing and Sports dataset: A challenging stereo and multi-view dataset for 3D human pose estimation

Human pose estimation is one of the most popular research topics in the past two decades, especially with the introduction of human pose datasets for benchmark evaluation. These datasets usually capture simple daily life actions. Here, we introduce a new dataset, the Martial Arts, Dancing and Sports (MADS), which consists of challenging martial arts actions (Tai-chi and Karate), dancing actions (hip-hop and jazz), and sports actions (basketball, volleyball, football, rugby, tennis and badminton). Two martial art masters, two dancers and an athlete performed these actions while being recorded with either multiple cameras or a stereo depth camera. In the multi-view or single-view setting, we provide three color views for 2D image-based human pose estimation algorithms. For depth-based human pose estimation, we provide stereo-based depth images from a single view. All videos have corresponding synchronized and calibrated ground-truth poses, which were captured using a Motion Capture system. We provide initial baseline results on our dataset using a variety of tracking frameworks, including a generative tracker based on the annealing particle filter and robust likelihood function, a discriminative tracker using twin Gaussian processes [1], and hybrid trackers, such as Personalized Depth Tracker [2]. The results of our evaluation suggest that discriminative approaches perform better than generative approaches when there are enough representative training samples, and that the generative methods are more robust to diversity of poses, but can fail to track when the motion is too quick for the effective search range of the particle filter. The data and the accompanying code will be made available to the research community. (C) 2017 Elsevier B.V. All rights reserved.
Development of an α-synuclein fibril and oligomer specific tracer for diagnosis of Parkinson's disease, dementia with Lewy bodies and multiple system atrophy

The development of specific disease-associated PET tracers is one of the major challenges, the realization of which in neurodegenerative diseases would enable not only the efficiency of diagnosis but also support the development of disease-modifying therapeutics. Parkinson's disease (PD) is the most common neurodegenerative movement disorder and is characterized by neuronal fibrillary inclusions composed of aggregated α-synuclein (α-syn). However, these deposits are not only found in PD, but also in other related diseases such as multiple system atrophy (MSA) and dementia with Lewy bodies (DLB), which are grouped under the term synucleinopathies. In this study, we used NGS-guided phage display selection to identify short peptides that bind aggregated α-syn. By surface plasmon resonance (SPR)-based affinity screening, we identified the peptide SVLfib-5 that recognizes aggregated α-syn with high complex stability and sequence specificity. Further analysis SPR showed that SVLfib-5 is not only specific for aggregated α-syn, but in particular recognizes fibrillary and oligomeric structures. Moreover, fluorescence microscopy of human brain tissue sections from PD, MSA, and DLB patients with SVLfib-5 allowed specific recognition of α-syn and a clear discrimination between diseased and non-diseased samples. These findings provide the basis for the further development of an α-syn PET tracer for early diagnosis and monitoring of disease progression and therapy progress.
Microvesicle-inspired oxygen-delivering nanosystem potentiates radiotherapy-mediated modulation of tumor stroma and antitumor immunity

The efficacy of radiotherapy is greatly challenged by intense hypoxia, intricate stroma and suppressive immune microenvironments in tumors. Herein, we rationally designed a microvesicle-inspired oxygen-delivering polyfluorocarbon nanosystem loading DiIC18(5) and halofuginone (M-FDH) with prominent capacity of improving tumor oxygenation and intratumor distribution, synergizing radiation to disrupt tumor stroma and boost antitumor immunity for combinational cancer therapy. M-FDH produced a 10.98-fold enhancement of tumor oxygenation and caused efficient production of reactive oxygen species (ROS) upon radiation. M-FDH + X ray treatment resulted in notable DNA damages, over 90% elimination of cancer-associated fibroblasts (CAFs) and major components of extracellular matrix, significant enhancement of tumoricidal CD3+CD8+ T cells, and profound elimination of suppressive immune cells in 4T1 tumors. The therapeutic benefits of M-FDH + X ray on suppressing tumor growth were confirmed in two murine tumor models. Therefore, this study provides an encouraging microvesicle-inspired strategy to target cancer cells and CAFs in tumors and synergize radiotherapy for effective cancer treatment.
One-step backtracking for occlusion detection in real-time visual tracking

Occlusion is a challenging problem in real-time visual object tracking. Most state-of-the-art methods learn the inaccurate appearance of the target when it becomes occluded by other objects in the scene. To address this issue, a novel one-step backtracking (OB) tracker for occlusion detection is proposed, which backtracks to one previous frame and detects occlusion by comparing the tracking result with OB result in each frame. An adaptive learning model update scheme is further proposed by computing the peak-to-sidelobe ratio of the response maps to improve the tracking performance. Experiments on several benchmark sequences show that the proposed tracker outperforms state-of-the-art approaches and achieves real-time visual tracking.
M-estimators for robust multidimensional scaling employing l(2,1) norm regularization

Multidimensional Scaling (MDS) has been exploited to visualise the hidden structures among a set of entities in a reduced dimensional metric space. Here, we are interested in cases whenever the initial dissimilarity matrix is contaminated by outliers. It is well-known that the state-of-the-art algorithms for solving the MDS problem generate erroneous embeddings due to the distortion introduced by such outliers. To remedy this vulnerability, a unified framework for the solution of MDS problem is proposed, which resorts to half-quadratic optimization and employs potential functions of M-estimators in combination with 2,1 norm regularization. Two novel algorithms are derived. Their performance is assessed for various M-estimators against state-of-the-art MDS algorithms on four benchmark data sets. The numerical tests demonstrate that the proposed algorithms perform better than the competing alternatives. (C) 2017 Elsevier Ltd. All rights reserved.
Insulin Downregulates the Expression of ATP-binding Cassette Transporter A-I in Human Hepatoma Cell Line HepG2 in a FOXO1 and LXR Dependent Manner

ATP-binding cassette transporter A-I (ABCA1) is an ubiquitously expressed protein whose main function is the transmembrane transport of cholesterol and phospholipids. Synthesis of ABCA1 protein in liver is necessary for high-density lipoprotein (HDL) formation in mammals. Thus, the mechanism of ABCA1 gene expression regulation in hepatocytes are of critical importance. Recently, we have found the insulin-dependent downregulation of other key player in the HDL formation-apolipoprotein A-I gene (J. Cell. Biochem., 2017, 118:382-396). Nothing is known about the role of insulin in the regulation of ABCA1 gene. Here we show for the first time that insulin decreases the mRNA and protein levels of ABCA1 in human hepatoma cell line HepG2. PI3K, p38, MEK1/2, JNK and mTORC1 signaling pathways are involved in the insulin-mediated downregulation of human ABCA1 gene. Transcription factors LXRα, LXRβ, FOXO1 and NF-κB are important contributors to this process, while FOXA2 does not regulate ABCA1 gene expression. Insulin causes the decrease in FOXO1, LXRα and LXRβ binding to ABCA1 promoter, which is likely the cause of the decrease in the gene expression. Interestingly, the murine ABCA1 gene seems to be not regulated by insulin in hepatocytes (in vitro and in vivo). We suggest that the reason for this discrepancy is the difference in the 5'-regulatory regions of human and murine ABCA1 genes.
TGM2-mediated histone transglutamination is dictated by steric accessibility

Recent studies have identified serotonylation of glutamine-5 on histone H3 (H3Q5ser) as a novel posttranslational modification (PTM) associated with active transcription. While H3Q5ser is known to be installed by tissue transglutaminase 2 (TGM2), the substrate characteristics affecting deposition of the mark, at the level of both chromatin and individual nucleosomes, remain poorly understood. Here, we show that histone serotonylation is excluded from constitutive heterochromatic regions in mammalian cells. Biochemical studies reveal that the formation of higher-order chromatin structures associated with heterochromatin impose a steric barrier that is refractory to TGM2-mediated histone monoaminylation. A series of structure-activity relationship studies, including the use of DNA-barcoded nucleosome libraries, shows that steric hindrance also steers TGM2 activity at the nucleosome level, restricting monoaminylation to accessible sites within histone tails. Collectively, our data indicate that the activity of TGM2 on chromatin is dictated by substrate accessibility rather than by primary sequence determinants or by the existence of preexisting PTMs, as is the case for many other histone-modifying enzymes.
Generative Model With Dynamic Linear Flow

Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modeling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihood-based methods, however suffer from limited parallelizability. In this paper, we propose <italic>Dynamic Linear Flow (DLF)</italic>, a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-the-art performance on ImageNet $32\times 32$ and $64\times 64$ out of all flow-based methods. Additionally, DLF converges significantly faster than previous flow-based methods such as Glow.
Classifying Maqams of Qur'anic Recitations Using Deep Learning

The Holy Qur'an is among the most recited and memorized books in the world. For beautification of Qur'anic recitation, almost all reciters around the globe perform their recitations using a specific melody, known as maqam in Arabic. However, it is more difficult for students to learn this art compared to other techniques of Qur'anic recitation such as Tajwid due to limited resources. Technological advancement can be utilized for automatic classification of these melodies which can then be used by students for self-learning. Using state-of-the-art deep learning algorithms, this research focuses on the classification of the eight popular maqamat (plural of maqam). Various audio features including Mel-frequency cepstral coefficients, spectral, energy and chroma features are obtained for model training. Several deep learning architectures including CNN, LSTM, and deep ANN are trained to classify audio samples from one of the eight maqamat. An accuracy of 95.7% on the test set is obtained using a 5-layer deep ANN which was trained using 26 input features. To the best of our knowledge, this is the first ever work that addresses maqam classification of Holy Qur'an recitations. We also introduce the "Maqam-478" dataset that can be used for further improvements on this work.