Showing papers by "University of Patras published in 2020"

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.

Abstract: In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

Avelumab Maintenance Therapy for Advanced or Metastatic Urothelial Carcinoma

Abstract: Background Platinum-based chemotherapy is standard-of-care first-line treatment for advanced urothelial carcinoma. However, progression-free survival and overall survival are limited by ch...

Explainable AI: A Review of Machine Learning Interpretability Methods

Abstract: Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

Simulation in the design and operation of manufacturing systems: state of the art and new trends

Abstract: As the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manuf...

Effect of Colchicine vs Standard Care on Cardiac and Inflammatory Biomarkers and Clinical Outcomes in Patients Hospitalized With Coronavirus Disease 2019: The GRECCO-19 Randomized Clinical Trial.

Abstract: Importance Severe acute respiratory syndrome coronavirus 2 infection has evolved into a global pandemic Low-dose colchicine combines anti-inflammatory action with a favorable safety profile Objective To evaluate the effect of treatment with colchicine on cardiac and inflammatory biomarkers and clinical outcomes in patients hospitalized with coronavirus disease 2019 (COVID-19) Design, Setting, and Participants In this prospective, open-label, randomized clinical trial (the Greek Study in the Effects of Colchicine in COVID-19 Complications Prevention), 105 patients hospitalized with COVID-19 were randomized in a 1:1 allocation from April 3 to April 27, 2020, to either standard medical treatment or colchicine with standard medical treatment The study took place in 16 tertiary hospitals in Greece Intervention Colchicine administration (15-mg loading dose followed by 05 mg after 60 min and maintenance doses of 05 mg twice daily) with standard medical treatment for as long as 3 weeks Main Outcomes and Measures Primary end points were (1) maximum high-sensitivity cardiac troponin level; (2) time for C-reactive protein to reach more than 3 times the upper reference limit; and (3) time to deterioration by 2 points on a 7-grade clinical status scale, ranging from able to resume normal activities to death Secondary end points were (1) the percentage of participants requiring mechanical ventilation, (2) all-cause mortality, and (3) number, type, severity, and seriousness of adverse events The primary efficacy analysis was performed on an intention-to-treat basis Results A total of 105 patients were evaluated (61 [581%] men; median [interquartile range] age, 64 [54-76] years) with 50 (476%) randomized to the control group and 55 (524%) to the colchicine group Median (interquartile range) peak high-sensitivity cardiac troponin values were 00112 (00043-00093) ng/mL in the control group and 0008 (0004-00135) ng/mL in the colchicine group (P = 34) Median (interquartile range) maximum C-reactive protein levels were 45 (14-89) mg/dL vs 31 (08-98) mg/dL (P = 73), respectively The clinical primary end point rate was 140% in the control group (7 of 50 patients) and 18% in the colchicine group (1 of 55 patients) (odds ratio, 011; 95% CI, 001-096;P = 02) Mean (SD) event-free survival time was 186 (083) days the in the control group vs 207 (031) in the colchicine group (log rankP = 03) Adverse events were similar in the 2 groups, except for diarrhea, which was more frequent with colchicine group than the control group (25 patients [455%] vs 9 patients [180%];P = 003) Conclusions and Relevance In this randomized clinical trial, participants who received colchicine had statistically significantly improved time to clinical deterioration There were no significant differences in high-sensitivity cardiac troponin or C-reactive protein levels These findings should be interpreted with caution Trial Registration ClinicalTrialsgov Identifier:NCT04326790

Production and processing of graphene and related materials

Abstract: © 2020 The Author(s). We present an overview of the main techniques for production and processing of graphene and related materials (GRMs), as well as the key characterization procedures. We adopt a 'hands-on' approach, providing practical details and procedures as derived from literature as well as from the authors' experience, in order to enable the reader to reproduce the results. Section I is devoted to 'bottom up' approaches, whereby individual constituents are pieced together into more complex structures. We consider graphene nanoribbons (GNRs) produced either by solution processing or by on-surface synthesis in ultra high vacuum (UHV), as well carbon nanomembranes (CNM). Production of a variety of GNRs with tailored band gaps and edge shapes is now possible. CNMs can be tuned in terms of porosity, crystallinity and electronic behaviour. Section II covers 'top down' techniques. These rely on breaking down of a layered precursor, in the graphene case usually natural crystals like graphite or artificially synthesized materials, such as highly oriented pyrolythic graphite, monolayers or few layers (FL) flakes. The main focus of this section is on various exfoliation techniques in a liquid media, either intercalation or liquid phase exfoliation (LPE). The choice of precursor, exfoliation method, medium as well as the control of parameters such as time or temperature are crucial. A definite choice of parameters and conditions yields a particular material with specific properties that makes it more suitable for a targeted application. We cover protocols for the graphitic precursors to graphene oxide (GO). This is an important material for a range of applications in biomedicine, energy storage, nanocomposites, etc. Hummers' and modified Hummers' methods are used to make GO that subsequently can be reduced to obtain reduced graphene oxide (RGO) with a variety of strategies. GO flakes are also employed to prepare three-dimensional (3d) low density structures, such as sponges, foams, hydro- or aerogels. The assembly of flakes into 3d structures can provide improved mechanical properties. Aerogels with a highly open structure, with interconnected hierarchical pores, can enhance the accessibility to the whole surface area, as relevant for a number of applications, such as energy storage. The main recipes to yield graphite intercalation compounds (GICs) are also discussed. GICs are suitable precursors for covalent functionalization of graphene, but can also be used for the synthesis of uncharged graphene in solution. Degradation of the molecules intercalated in GICs can be triggered by high temperature treatment or microwave irradiation, creating a gas pressure surge in graphite and exfoliation. Electrochemical exfoliation by applying a voltage in an electrolyte to a graphite electrode can be tuned by varying precursors, electrolytes and potential. Graphite electrodes can be either negatively or positively intercalated to obtain GICs that are subsequently exfoliated. We also discuss the materials that can be amenable to exfoliation, by employing a theoretical data-mining approach. The exfoliation of LMs usually results in a heterogeneous dispersion of flakes with different lateral size and thickness. This is a critical bottleneck for applications, and hinders the full exploitation of GRMs produced by solution processing. The establishment of procedures to control the morphological properties of exfoliated GRMs, which also need to be industrially scalable, is one of the key needs. Section III deals with the processing of flakes. (Ultra)centrifugation techniques have thus far been the most investigated to sort GRMs following ultrasonication, shear mixing, ball milling, microfluidization, and wet-jet milling. It allows sorting by size and thickness. Inks formulated from GRM dispersions can be printed using a number of processes, from inkjet to screen printing. Each technique has specific rheological requirements, as well as geometrical constraints. The solvent choice is critical, not only for the GRM stability, but also in terms of optimizing printing on different substrates, such as glass, Si, plastic, paper, etc, all with different surface energies. Chemical modifications of such substrates is also a key step. Sections IV-VII are devoted to the growth of GRMs on various substrates and their processing after growth to place them on the surface of choice for specific applications. The substrate for graphene growth is a key determinant of the nature and quality of the resultant film. The lattice mismatch between graphene and substrate influences the resulting crystallinity. Growth on insulators, such as SiO2, typically results in films with small crystallites, whereas growth on the close-packed surfaces of metals yields highly crystalline films. Section IV outlines the growth of graphene on SiC substrates. This satisfies the requirements for electronic applications, with well-defined graphene-substrate interface, low trapped impurities and no need for transfer. It also allows graphene structures and devices to be measured directly on the growth substrate. The flatness of the substrate results in graphene with minimal strain and ripples on large areas, allowing spectroscopies and surface science to be performed. We also discuss the surface engineering by intercalation of the resulting graphene, its integration with Si-wafers and the production of nanostructures with the desired shape, with no need for patterning. Section V deals with chemical vapour deposition (CVD) onto various transition metals and on insulators. Growth on Ni results in graphitized polycrystalline films. While the thickness of these films can be optimized by controlling the deposition parameters, such as the type of hydrocarbon precursor and temperature, it is difficult to attain single layer graphene (SLG) across large areas, owing to the simultaneous nucleation/growth and solution/precipitation mechanisms. The differing characteristics of polycrystalline Ni films facilitate the growth of graphitic layers at different rates, resulting in regions with differing numbers of graphitic layers. High-quality films can be grown on Cu. Cu is available in a variety of shapes and forms, such as foils, bulks, foams, thin films on other materials and powders, making it attractive for industrial production of large area graphene films. The push to use CVD graphene in applications has also triggered a research line for the direct growth on insulators. The quality of the resulting films is lower than possible to date on metals, but enough, in terms of transmittance and resistivity, for many applications as described in section V. Transfer technologies are the focus of section VI. CVD synthesis of graphene on metals and bottom up molecular approaches require SLG to be transferred to the final target substrates. To have technological impact, the advances in production of high-quality large-area CVD graphene must be commensurate with those on transfer and placement on the final substrates. This is a prerequisite for most applications, such as touch panels, anticorrosion coatings, transparent electrodes and gas sensors etc. New strategies have improved the transferred graphene quality, making CVD graphene a feasible option for CMOS foundries. Methods based on complete etching of the metal substrate in suitable etchants, typically iron chloride, ammonium persulfate, or hydrogen chloride although reliable, are time- and resourceconsuming, with damage to graphene and production of metal and etchant residues. Electrochemical delamination in a low-concentration aqueous solution is an alternative. In this case metallic substrates can be reused. Dry transfer is less detrimental for the SLG quality, enabling a deterministic transfer. There is a large range of layered materials (LMs) beyond graphite. Only few of them have been already exfoliated and fully characterized. Section VII deals with the growth of some of these materials. Amongst them, h-BN, transition metal tri- and di-chalcogenides are of paramount importance. The growth of h-BN is at present considered essential for the development of graphene in (opto) electronic applications, as h-BN is ideal as capping layer or substrate. The interesting optical and electronic properties of TMDs also require the development of scalable methods for their production. Large scale growth using chemical/physical vapour deposition or thermal assisted conversion has been thus far limited to a small set, such as h-BN or some TMDs. Heterostructures could also be directly grown.

A CNN–LSTM model for gold price time-series forecasting

Abstract: Gold price volatilities have a significant impact on many financial activities of the world. The development of a reliable prediction model could offer insights in gold price fluctuations, behavior and dynamics and ultimately could provide the opportunity of gaining significant profits. In this work, we propose a new deep learning forecasting model for the accurate prediction of gold price and movement. The proposed model exploits the ability of convolutional layers for extracting useful knowledge and learning the internal representation of time-series data as well as the effectiveness of long short-term memory (LSTM) layers for identifying short-term and long-term dependencies. We conducted a series of experiments and evaluated the proposed model against state-of-the-art deep learning and machine learning models. The preliminary experimental analysis illustrated that the utilization of LSTM layers along with additional convolutional layers could provide a significant boost in increasing the forecasting performance.

Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review

Abstract: Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot technologies utilized in cultivation fields, which transform traditional farming practices into a new era of precision agriculture. In this paper, we perform a survey of the last research on IoT and UAV technology applied in agriculture. We describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming. Moreover, we present the role of UAV technology in smart agriculture, by analyzing the applications of UAVs in various scenarios, including irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. Furthermore, the utilization of UAV systems in complex agricultural environments is also analyzed. Our conclusion is that IoT and UAV are two of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.

Abstract: While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks. Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.

Wildfire management in Mediterranean-type regions: paradigm change needed

Abstract: During the last decades, climate and land use changes led to an increased prevalence of megafires in Mediterranean-type climate regions (MCRs). Here, we argue that current wildfire management policies in MCRs are destined to fail. Focused on fire suppression, these policies largely ignore ongoing climate warming and landscape-scale buildup of fuels. The result is a "firefighting trap" that contributes to ongoing fuel accumulation precluding suppression under extreme fire weather, and resulting in more severe and larger fires. We believe that a "business as usual" approach to wildfire in MCRs will not solve the fire problem, and recommend that policy and expenditures be rebalanced between suppression and mitigation of the negative impacts of fire. This requires a paradigm shift: policy effectiveness should not be primarily measured as a function of area burned (as it usually is), but rather as a function of avoided socio-ecological damage and loss.

Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures.

Abstract: Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

Recent aspects of the effects of zinc on human health.

Abstract: Zinc (Zn) is one of the most important essential nutrients of great public health significance. It is involved in numerous biological functions and it is considered as a multipurpose trace element, due to its capacity to bind to more than 300 enzymes and more than 2000 transcriptional factors. Its role in biochemical pathways and cellular functions, such as the response to oxidative stress, homeostasis, immune responses, DNA replication, DNA damage repair, cell cycle progression, apoptosis and aging is significant. Zn is required for the synthesis of protein and collagen, thus contributing to wound healing and a healthy skin. Metallothioneins are metal-binding proteins and they are potent scavengers of heavy metals, including Zn, and protect the organism against stress. Zn deficiency is observed almost in 17% of the global population and affects many organ systems, leading to dysfunction of both humoral and cell-mediated immunity, thus increasing the susceptibility to infection. This review gives a thorough insight into the most recent evidence on the association between Zn biochemistry and human pathologies, epigenetic processes, gut microbial composition, drug targets and nanomedicine.

Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: could nicotine be a therapeutic option?

Abstract: The effects of smoking on Corona Virus Disease 2019 (COVID-19) are currently unknown. The purpose of this study was to systematically examine the prevalence of current smoking among hospitalized patients with COVID-19 in China, considering the high-population smoking prevalence in China (26.6%). A systematic review of the literature (PubMed) was performed on April 1. Thirteen studies examining the clinical characteristics of hospitalized COVID-19 patients in China and presenting data on the smoking status were found. The pooled prevalence of current smoking from all studies was calculated by random-effect meta-analysis. To address the possibility that some smokers had quit shortly before hospitalization and were classified as former smokers on admission to the hospital, we performed a secondary analysis in which all former smokers were classified as current smokers. A total of 5960 patients were included in the studies identified. The current smoking prevalence ranged from 1.4% (95% CI 0.0-3.4%) to 12.6% (95% CI 10.6-14.6%). An unusually low prevalence of current smoking was observed from the pooled analysis (6.5%, 95% CI 4.9-8.2%) as compared to population smoking prevalence in China. The secondary analysis, classifying former smokers as current smokers, found a pooled estimate of 7.3% (95% CI 5.7-8.9%). In conclusion, an unexpectedly low prevalence of current smoking was observed among patients with COVID-19 in China, which was approximately 1/4th the population smoking prevalence. Although the generalized advice to quit smoking as a measure to reduce health risk remains valid, the findings, together with the well-established immunomodulatory effects of nicotine, suggest that pharmaceutical nicotine should be considered as a potential treatment option in COVID-19.

Editorial: Nicotine and SARS-CoV-2: COVID-19 may be a disease of the nicotinic cholinergic system.

Abstract: As of 20 April, almost 1.7 million people globally have been diagnosed with Corona Virus Disease 2019 (COVID-19), a pandemic that has evolved from the emergence of a new coronavirus strain, acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in China. More than 170,000 deaths have been reported, while there are certainly many more cases of milder disease that have not been diagnosed and officially confirmed due to limited testing capacity in most countries. The pandemic is a global emergency due to the rapid transmission of the disease and the potential to overwhelm the healthcare systems, and is expected to have considerable economic and health impacts. Contributing factors and their possible role in the relatively high infection, death rates between countries and origin have recently been studied [1,2]. This new outbreak has been additionally evaluated for current knowledge on coronaviruses based on a short history to epidemiology, pathogenesis, clinical manifestation of the disease, as well as treatment and prevention strategies [3]. The search for potential protective and therapeutic antiviral strategies is of particular and urgent concern [4]. While in most cases, especially in young people without any comorbidities, the disease is expected to be relatively mild, there is a substantial proportion of patients who develop complications and need intensive care-unit support and mechanical ventilation. In one case series of 1099 patients in China [5], 6.1 % of cases suffered from the primary composite end-point of admission to an intensive care unit, use of mechanical ventilation, or death. Patients with severe disease typically present with dyspnea and hypoxemia shortly after disease initiation, and may quickly progress to respiratory failure, acute respiratory distress syndrome (ARDS) and multi-organ failure [6]. Predictors of adverse outcomes include elevated levels of inflammatory markers and pro-inflammatory cytokines. A study of 150 COVID-19 cases reported that elevated levels of C-reactive protein (CRP), ferritin and IL-6 were associated with death [7]. IL-6, an important pro-inflammatory cytokine, was elevated in fatal cases of COVID-19 in another study of 191 patients [8]. Another study of 452 patients reported that those with severe disease showed lymphocytopenia, neutrophilia, low levels of monocytes, eosinophils and basophils, and elevated levels of infection-related biomarkers and inflammatory cytokines [9]. Pathological examination of a case in China revealed bilateral diffuse alveolar damage, desquamation of pneumocytes, hyaline membrane formation and interstitial mononuclear inflammatory infiltrates [10]. Flow cytometry of peripheral blood revealed reduced levels of CD4+ and CD8 + T cells, which however were hyper-activated, and elevated concentration of pro-inflammatory CCR6+ Th17 in CD4 + T cells. Such findings are hallmarks of ARDS and resemble features observed in SARS and Middle Eastern Respiratory Syndrome [11,12]. Systemic vasculitis was also observed [10]. Therefore, it seems that immune dysregulation may be implicated in the pathophysiology of severe COVID-19.

EMT Factors and Metabolic Pathways in Cancer.

Abstract: The epithelial-mesenchymal transition (EMT) represents a biological program during which epithelial cells lose their cell identity and acquire a mesenchymal phenotype. EMT is normally observed during organismal development, wound healing and tissue fibrosis. However, this process can be hijacked by cancer cells and is often associated with resistance to apoptosis, acquisition of tissue invasiveness, cancer stem cell characteristics, and cancer treatment resistance. It is becoming evident that EMT is a complex, multifactorial spectrum, often involving episodic, transient or partial events. Multiple factors have been causally implicated in EMT including transcription factors (e.g., SNAIL, TWIST, ZEB), epigenetic modifications, microRNAs (e.g., miR-200 family) and more recently, long non-coding RNAs. However, the relevance of metabolic pathways in EMT is only recently being recognized. Importantly, alterations in key metabolic pathways affect cancer development and progression. In this review, we report the roles of key EMT factors and describe their interactions and interconnectedness. We introduce metabolic pathways that are involved in EMT, including glycolysis, the TCA cycle, lipid and amino acid metabolism, and characterize the relationship between EMT factors and cancer metabolism. Finally, we present therapeutic opportunities involving EMT, with particular focus on cancer metabolic pathways.

Toward the 6G Network Era: Opportunities and Challenges

Abstract: The next generation of telecommunication networks will integrate the latest developments and emerging advancements in telecommunications connectivity infrastructures. In this article, we discuss the transformation and convergence of the fifth-generation (5G) mobile network and the internet of things technologies, toward the emergence of the smart sixth-generation (6G) networks which will employ AI to optimize and automate their operation.

2020 Roadmap on Carbon Materials for Energy Storage and Conversion.

Abstract: Carbon is a simple, stable and popular element with many allotropes. The carbon family members include carbon dots, carbon nanotubes, carbon fibers, graphene, graphite, graphdiyne and hard carbon, etc. They can be divided into different dimensions, and their structures can be open and porous. Moreover, it is very interesting to dope them with other elements (metal or non-metal) or hybridize them with other materials to form composites. The elemental and structural characteristics offer us to explore their applications in energy, environment, bioscience, medicine, electronics and others. Among them, energy storage and conversion are extremely attractive, as advances in this area may improve our life quality and environment. Some energy devices will be included herein, such as lithium-ion batteries, lithium sulfur batteries, sodium-ion batteries, potassium-ion batteries, dual ion batteries, electrochemical capacitors, and others. Additionally, carbon-based electrocatalysts are also studied in hydrogen evolution reaction and carbon dioxide reduction reaction. However, there are still many challenges in the design and preparation of electrode and electrocatalytic materials. The research related to carbon materials for energy storage and conversion is extremely active, and this has motivated us to contribute with a roadmap on 'Carbon Materials in Energy Storage and Conversion'.

Production of Sustainable and Biodegradable Polymers from Agricultural Waste

Abstract: Agro-wastes are derived from diverse sources including grape pomace, tomato pomace, pineapple, orange, and lemon peels, sugarcane bagasse, rice husks, wheat straw, and palm oil fibers, among other affordable and commonly available materials. The carbon-rich precursors are used in the production bio-based polymers through microbial, biopolymer blending, and chemical methods. The Food and Agriculture Organization (FAO) estimates that 20–30% of fruits and vegetables are discarded as waste during post-harvest handling. The development of bio-based polymers is essential, considering the scale of global environmental pollution that is directly linked to the production of synthetic plastics such as polypropylene (PP) and polyethylene (PET). Globally, 400 million tons of synthetic plastics are produced each year, and less than 9% are recycled. The optical, mechanical, and chemical properties such as ultraviolet (UV) absorbance, tensile strength, and water permeability are influenced by the synthetic route. The production of bio-based polymers from renewable sources and microbial synthesis are scalable, facile, and pose a minimal impact on the environment compared to chemical synthesis methods that rely on alkali and acid treatment or co-polymer blending. Despite the development of advanced synthetic methods and the application of biofilms in smart/intelligent food packaging, construction, exclusion nets, and medicine, commercial production is limited by cost, the economics of production, useful life, and biodegradation concerns, and the availability of adequate agro-wastes. New and cost-effective production techniques are critical to facilitate the commercial production of bio-based polymers and the replacement of synthetic polymers.

Current smoking, former smoking, and adverse outcome among hospitalized COVID-19 patients: a systematic review and meta-analysis

Abstract: Background:The purpose of this study was to examine the prevalence and effects of current smoking on adverse outcomes among hospitalized COVID-19 patientsMethods:A systematic review of the literat

Soluble urokinase plasminogen activator receptor (suPAR) as an early predictor of severe respiratory failure in patients with COVID-19 pneumonia.

Abstract: As of April 1, 2020, 885,689 cases of infections by the novel coronavirus SARS-CoV-2 (COVID-19) have been recorded worldwide; 44,217 of them have died (https:// www.worldometers.info/coronavirus). At the beginning of the illness, patients may experience low-degree fever or flu-like symptoms, but suddenly, severe respiratory failure (SRF) emerges [1]. Increased circulating levels of D-dimers [1, 2] suggest endothelial activation. Urokinase plasminogen activator receptor (uPAR) that is bound on the endothelium may be cleaved early during the disease course leading to an increase of its soluble counterpart, namely suPAR [3]. If this holds true, then suPAR may be used as an early predictor of the risk of SRF. The Hellenic Sepsis Study Group (HSSG, www.sepsis. gr) is collecting clinical information and serum samples within the first 24 h of admission from patients with infections and at least two signs of the systemic inflammatory response syndrome. Since March 1, 2020, 57 patients with community-acquired pneumonia and molecular documentation of SARS-CoV-2 in respiratory secretions were enrolled. Patients were followed up daily for 14 days; the development of SRF defined as PO2/FiO2 ratio less than 150 requiring mechanical ventilation (MV) or continuous positive airway pressure treatment (CPAP) was recorded. suPAR was measured by an enzyme immunoassay in duplicate (suPARnosticTM, ViroGates, Lyngby, Denmark); the lower detection limit was 1.1 ng/ml. Measurements were performed and reported by one technician who was blinded to clinical information. The study endpoint was the prognostic performance of suPAR admission levels for the development of SRF within 14 days. Measured levels were compared to those collected from 15 patients with COVID-19 from the emergency department (ED) of Rush University Medical Center. Thirty-four (59.6%) patients were male and 23 (40.1%) female; the mean ± SD age was 64.0 ± 10.3 years, and the Charlson’s comorbidity index was 2.70 ± 1.80. The mean ± SD admission total neutrophil count was 4414.1 ± 2526.5/ mm; the total lymphocyte count was 1149.1 ± 1131.4/ mm; the C-reactive protein was 73.1 ± 76.4mg/l. Admission levels of suPAR were significantly greater among patients who eventually developed SRF (Fig. 1a). Circulating levels of suPAR were of the same range as those of the US cohort (Fig. 1b). Receiver operator characteristics curve analysis identified levels ≥ 6 ng/ml as the best predictor for SRF. At that cutoff point, the sensitivity, specificity, positive predictive value, and negative predictive value for the prediction of SRF was 85.7%, 91.7%, 85.7%, and 91.7%, respectively. The time to SRF was much shorter among patients with suPAR ≥ 6 ng/ml (Fig. 1c). The only admission

Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing

Abstract: Digital Twin (DT) implementations can contribute to smart manufacturing by integrating the physical and the cyber space. Artificial Intelligence (AI) applications based on Machine Learning (ML) are...

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

Abstract: With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.