ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Modern yeast development: finding the balance between tradition and innovation in contemporary winemaking

A key driver of quality in wines is the microbial population that undertakes fermentation of grape must. Winemakers can utilise both indigenous and purposefully inoculated yeasts to undertake alcoholic fermentation, imparting wines with aromas, flavours and palate structure and in many cases contributing to complexity and uniqueness. Importantly, having a toolbox of microbes helps winemakers make best use of the grapes they are presented with, and tackle fermentation difficulties with flexibility and efficiency. Each year the number of strains available commercially expands and more recently, includes strains of non-Saccharomyces, strains that have been improved using both classical and modern yeast technology and mixed cultures. Here we review what is available commercially, and what may be in the future, by exploring recent advances in fermentation relevant strain improvement technologies. We also report on the current use of microbes in the Australian wine industry, as reported by winemakers, as well as regulations around, and sentiment about the potential use of genetically modified organisms in the future.
A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1-29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP's variant); (iv) Chang's extent analysis method, which is used for FPCMs' weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature). (C) 2016 Elsevier Ltd. All rights reserved.
RNA m6A reader IGF2BP3 promotes metastasis of triple-negative breast cancer via SLIT2 repression

Triple-negative breast cancer (TNBC) is a group of fatal malignancies characterized by high metastatic capacity, the underlying mechanisms of which remain largely elusive. We have found here that insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) is highly expressed in TNBC and correlates clinically with distant metastasis-free survival of TNBC patients. IGF2BP3 promotes the migration and invasion capabilities of TNBC cells dependent upon cellular RNA N6-methyladenosine (m6A) modification. Mechanistically, IGF2BP3 binds to and destabilizes m6A-methylated mRNA of the extracellular matrix glycoprotein, SLIT2, impairs its downstream signaling via the cognate receptor ROBO1, and consequently triggers the activation of canonical PI3K/AKT and MEK/ERK pathways. The IGF2BP3/SLIT2 axis is critically involved in the regulation of TNBC metastasis in vivo. These findings shed light into the regulatory network of distant metastasis of breast cancer and provide rationale for targeting the m6A machinery in the treatment of TNBC.
Multiaxis Contour Control-the State of the Art

Contour control is an important task for many motion control applications. This paper reviews the state-of-the-art control methods in academic research for contour tracking, including individual axis tracking control, cross coupled control and its variants, and other contour control methods, such as neural networks and velocity field control. Different methods for contour error estimation are also reviewed. Areas for future multiaxis contour control research are discussed.
Recent developments in the creation of a single molecular sensing tool for ternary iron (III), chromium (III), aluminium (III) ionic species: A review

Rational design of a molecular sensing tool is an important topic in molecular recognition, signalling, and optoelectronics that has piqued the interest of chemists, biologists, and environmental scientists. Approximately 150 years have passed since the beginning of the fluorescent chemosensor sector. Due to the paramagnetic properties of Cr3+ and Al3+ , it is tough to prepare a photoluminescence plug-in detector. Most dye-based Al3+ sensors must be utilized in organic or mixed solvents for robust hydration of Al3+ in water. The sophisticated molecular design of sensors, conversely, allows for the detection of these metal ions in aqueous medium. The design of chemosensors using various fluorophores and their mechanisms of action have been thoroughly discussed. A literature survey covering the design of chemosensors and their mechanisms of action have been thoroughly discussed covering the period 2010-2022 and that was carried out including innovative and exemplary activities from numerous groups throughout the world that have significantly contributed to this sector. The most important advantages of these probes are their aqueous solubility and quick response with outstanding selectivity and sensitivity for temporal distribution with high fidelity of metals in living cells.
Increased Incidence Of Inhalation Burn Injury During The Covid-19 Pandemic: A National Database Study

The COVID-19 pandemic has forced many Americans to adapt their daily routines. In 2020, there was a significant increase in house fires according to the National Fire Prevention Association (NFPA). The objective of this study was to characterize the changes in suspected smoke inhalations during the first year of the pandemic in the National Emergency Medical Services Information System (NEMSIS). The NEMSIS database was queried for all EMS transports captured between 2017-2020. Differences in the incidences of suspected smoke inhalations and fire dispatches in 2020 were estimated using Poisson regression models. There was a 13.4% increase in the incidence of fire dispatches and a 15% increase in suspected smoke inhalations transported in 2020 compared to the previous 3 years. The IRR of both fire dispatches (1.271; 95% CI: 1.254-1.288; p<0.001) and suspected smoke inhalation (1.152; 95% CI: 1.070-1.241; p<0.001) was significantly elevated in 2020. The increases in fire dispatches and suspected smoke inhalations observed in the NEMSIS database are in concordance with other literature indicating the increase in fire incidence and morbidity observed during the pandemic. These results should inform fire prevention outreach efforts and resource allocation in burn centers in the event of future pandemic.
Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.
Minimizing the deleterious effects of endophytes in plant shoot tip cryopreservation

Plant cryopreservation technologies are used within gene banks for the long-term preservation of vegetatively propagated collections. Surface-sterilized plant tissues grown in the field, greenhouse/screenhouse, growth chamber, or in vitro are the source of shoot tips subjected to vitrification-based cryopreservation methods. Here, we describe the methods used to minimize microbial contamination during the tissue culture initiation process. We also discuss the occurrence and possible elimination of endophytes after extended in vitro culture and during recovery after liquid nitrogen exposure. We describe two case studies in which bacterial endophytes were observed in Citrus gene bank accessions during recovery after cryopreservation. These were identified using the MinION Oxford Nanopore system and Kirby-Bauer disc diffusion assays to examine the bacterial responses to antibiotic exposure. The methods used in this case study could be applied to identify endophytes to better target antimicrobial treatments of plant tissue collections.
Allergen Content and Protease Activity in Milk Feeds from Mothers of Preterm Infants

Rationale: There is little information regarding the allergen content of milk feeds in the preterm population. Previous studies have not performed a broad analysis of the allergenic peptide content and protease activity of milk feeds in this population. Methods: To evaluate feasibility, we initially performed mass spectrometry on 4 human milk (HM) samples (2 term and 2 preterm) from the Mommy's Milk Human Milk Biorepository (HMB) and analyzed the results against the University of Nebraska FASTA database and UniProt for a total of 2,211 protein sequences. We then further analyzed five samples from the Microbiome, Atopy, and Prematurity (MAP) study including peptidomic and protease activity analysis. Results: Each HMB sample had between 806 and 1,007 proteins, with 37-44 nonhuman proteins/sample encompassing 26 plant and animal species. In the preterm MAP samples, 784 digested nonhuman proteins were identified, 30 were nonbovine in origin. Proteins from 23 different species including aeroallergens, food, and contact allergens were identified. Protease activity was highest in HM samples without human milk fortifier and lowest in preterm formula. Conclusions: These findings represent the first preterm milk feed mass spectrometry and protease analysis with identification of known allergenic proteins to food, contact, and aeroallergens. These results raise questions of whether the composition of milk feeds in the neonatal intensive care unit impact the development of atopic disease in the preterm population and whether the complex interaction between allergens, proteases, and other HM components can serve to induce sensitization or tolerance to allergens in infants. Clinical Trial Registration Number: NCT04835935.
Survival of Patient With Hemorrhagic Meningitis Associated With Inhalation Anthrax

This report describes a 49-year-old male construction worker who acquired a Bacillus anthracis infection after working on a sheep farm. He experienced a severe respiratory infection, septic shock, and hemorrhagic meningoencephalitis with severe intracranial hypertension. After several weeks with multiple organ dysfunction syndrome, he responded favorably to antibiotic treatment. Three weeks into his hospitalization, an intracranial hemorrhage and cerebral edema led to an abrupt deterioration in his neurological status. A single dose of raxibacumab was added to his antimicrobial regimen on hospital day 27. His overall status, both clinical and radiographic, improved within a few days. He was discharged 2 months after admission and appears to have fully recovered.
Adaptive Denoising by Singular Value Decomposition

This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and at high noise levels it provides improvements in both objective and subjective quality of the denoised image when compared to the state-of-the-art methods.
Laser nano-manufacturing - State of the art and challenges

This paper provides an overview of advances in laser based nano-manufacturing technologies including surface nano-structure manufacturing, production of nano materials (nanoparticles, nanotubes and nanowires) and 3D nano-structures manufacture through multiple layer additive techniques and nano-joining/forming. Examples of practical applications of laser manufactured nano-structures, materials and components are given. A discussion on the challenges and outlooks in laser nano-manufacturing is presented. (C) 2011 CIRP.
Factors impacting educational outcomes for students with traumatic brain injury in BrainSTEPS

Purpose: To describe child pre-injury and injury factors impacting post-injury educational outcomes for students with traumatic brain injury (TBI) participating in a state-wide, school-based, school re-entry consultation program, BrainSTEPS in Pennsylvania.Method: Retrospective analysis of a BrainSTEPS annual follow-up survey.Result: A total of 296 parent surveys were completed. Analysis revealed a significant difference between levels of severity of TBI and current educational placement (p < 0.001), receipt of current therapy (p < 0.05) and need for additional consult (p < 0.05). Severity of TBI was not related to other examined educational outcome variables (i.e. school performance, current symptoms). History of TBI, symptoms and treatment were not found to be associated with educational outcomes.Conclusion: These results both support findings from previous studies, and extend previous work by highlighting ongoing needs, including continued, individualised support, of children who sustain a moderate-severe TBI during childhood, and are currently in the chronic stages of injury, with consideration of pre- and post- injury factors. Programs such as BrainSTEPS provide identification of educational needs and provide needed services and supports for children with TBI. Sensitive, validated measures are needed to further understand the role of pre-injury and injury factors on educational outcomes, particularly in programs like BrainSTEPS.
Optimised quantisation method for approximate nearest neighbour search

We propose optimised group quantisation (OGQ) for approximate nearest neighbour (ANN) search. Specifically, we construct a group of codebooks and select a group of codewords from the codebooks to approximate the original data such that small quantisation error is obtained. We also propose an effective learning algorithm for optimisation. The experiments show that OGQ can significantly outperform several state-of-the-art ANN methods.
Carbon dots modified/prepared by supramolecular host molecules and their potential applications: A review

Supramolecular host molecules are used as tools in the design of multifunctional nanoparticles for sensors, catalysts, biometric elements, etc. Combining with carbon dots (CDs) has excellent host-guest recognition properties and fluorescence characteristics, which can precisely capture and identify target analytes. Consequently, supramolecular host molecules-based CDs can significantly improve the detection performance of ions and molecules with different structures or intrinsic chemical properties. This currently responds to a wide range of analytes including metal cations, anions, organic compounds and other biomolecules, yielding fascinating achievements in the field of chemistry. Therefore, the present review summarizes outstanding supramolecular host molecules-based CDs reported in the past ten years. The focus is on elucidating the mechanisms, methodologies, advantages and disadvantages of modifying or preparing CDs with supramolecular host molecules. Current challenges encountered and outlooks are also be discussed.
Monkeypox treatment: Current evidence and future perspectives

As of September 11, 2022, 57 669 reports of monkeypox infection raised global concern. Previous vaccinia virus vaccination can protect from monkeypox. However, after smallpox eradication, immunization against that was stopped. Indeed, therapeutic options following the disease onset are of great value. This study aimed to review the available evidence on virology and treatment approaches for monkeypox and provide guidance for patient care and future studies. Since no randomized clinical trials were ever performed, we reviewed monkeypox animal model studies and clinical trials on the safety and pharmacokinetics of available medications. Brincidofovir and tecovirimat were the most studied medications that got approval for smallpox treatment according to the Animal Rule. Due to the conserved virology among Orthopoxviruses, available medications might also be effective against monkeypox. However, tecovirimat has the strongest evidence to be effective and safe for monkeypox treatment, and if there is a choice between the two drugs, tecovirimat has shown more promise so far. The risk of resistance should be considered in patients who failed to respond to tecovirimat. Hence, the target-based design of novel antivirals will enhance the availability and spectrum of effective anti-Orthopoxvirus agents.
A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

In recent years, sentiment analysis is becoming more and more important as the number of digital text resources increases in parallel with the development of information technology. Feature selection is a crucial sub-stage for the sentiment analysis as it can improve the overall predictive performance of a classifier while reducing the dimensionality of a problem. In this study, we propose a novel wrapper feature selection algorithm based on Iterated Greedy (IG) metaheuristic for sentiment classification. We also develop a selection procedure that is based on pre-calculated filter scores for the greedy construction part of the IG algorithm. A comprehensive experimental study is conducted on commonly-used sentiment analysis datasets to assess the performance of the proposed method. The computational results show that the proposed algorithm achieves 96.45% and 90.74% accuracy rates on average by using Multi-nomial Naive Bayes classifier for 9 public sentiment and 4 Amazon product reviews datasets, respectively. The results also reveal that our algorithm outperforms state-of-the-art results for the 9 public sentiment datasets. Moreover, the proposed algorithm produces highly competitive results with state-of-the-art feature selection algorithms for 4 Amazon datasets. (C) 2020 Elsevier Ltd. All rights reserved.
An Adaptive Multiobjective Approach to Evolving ART Architectures

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).
Open-view human action recognition based on linear discriminant analysis

In the last decades, action recognition task has evolved from single view recording to unconstrained environment. Recently, multi-view action recognition has become a hot topic in computer vision. However, we notice that only a few works have focused on the open-view action recognition, which is a common problem in the real world. Open-view action recognition focus on doing action recognition in unseen view without using any information from it. To address this issue, we firstly introduce a novel multi-view surveillance action dataset and benchmark several state-of-the-art algorithms. From the results, we observe that the performance of the state-of-the-art algorithms would drop a lot under open-view constraints. Then, we propose a novel open-view action recognition method based on the linear discriminant analysis. This method can learn a common space for action samples under different view by using their category information, which can achieve a good result in open-view action recognition.
Can Aikido Help With the Comprehension of Physics? A First Step Towards the Design of Intelligent Psychomotor Systems for STEAM Kinesthetic Learning Scenarios

Educational stakeholders are promoting the development of educational approaches that go beyond classroom and engage learners in acquiring science, technology, engineering and mathematical (STEM) concepts with the support of arts (known as STEAM). Taking into account the recent advances in wearable technology to sense human motion, psychomotor intelligent tutoring systems (where students' motion interactions are collected with sensors and used to provide personalized vibrotactile feedback to support the learning process) can be designed to teach STEM concepts in a kinesthetic way. In order to investigate the feasibility of this approach, in this paper we have carried out an empirical study with high school students to determine if watching specific techniques of the defense martial art Aikido (which makes use of different types of rectilinear and curvilinear motion) can be used to learn some concepts of physics. Nonetheless, other martial arts could be used for similar purposes, but further studies are needed. Analyzing the outcomes of the 30 participants that took part in the study, we conclude that the proposed approach seems to have benefits in the learning of STEM concepts, and thus, the usage of martial arts deserves to be further investigated in STEAM kinesthetic learning scenarios, which can also consider the students' affective state.