Optical motion capture is a mature contemporary technique for the acquisition of motion data, alas it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied for gap filling problem in motion capture sequences within FBM framework providing the representation for body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out, that for longer sequences simple linear feedforward neural networks can outperform the other, sophisticated architectures. We were also able to identify, that acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Fifth Generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising of device-to-device networks, macro-cells, and dissimilar categories of small-cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need and the lack of resources, the resource distribution problem desires to be focused capably to accomplish the traffic and to enhance network working. One of the utmost serious problems is to alleviate the jamming from the network in support of having a better QoS. However, there are limited review papers written on resource distribution, there is no particularize and organized review carry out in 5G resource allocation. Hence, this article covers and evaluates the argument using a classification of existing developing resource allocation schemes in 5G thoroughly by classifying the schemes to enhance the service quality. This survey comprises the discussion based on metrics used to evaluate the performance. It would also permit ahead beyond evidence on resource allocation methods in 5G and empowers the scholars to meet the present research areas to focus on.

CO₂ enhanced oil recovery (EOR) has been proven its capability to explore the unconventional tight oil reservoirs and potential for geological carbon storage. Meanwhile, the extremely low permeability pores exaggerate the difficulty CO₂ EOR and geological storage processing in the actual field. This paper initiates the ultrasonic-assisted approach to facilitate the oil-gas miscibility development and finally contribute to unlock more tight oils. First, the physical properties of crude oil with and without ultrasonic treatments were experimentally analysed through gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR) and viscometer. Second, the oil-gas minimum miscibility pressures (MMPs) were measured from the slim-tube test and the miscibility developments with and without ultrasonic treatments were interpreted from the mixing-cell method. Third, the nuclear-magnetic resonance (NMR) assisted coreflood tests were conducted to physically model the recovery process in porous media and directly obtain the recovery factor. Basically, the ultrasonic treatment (40KHz and 200W for 8 hours) was found to substantially change the oil properties, with viscosity (at 60°C) reduced from 4.1 to 2.8mPa·s, contents of resin and asphaltene decreased from 27.94% and 6.03% to 14.2% and 3.79%, respectively. The FTIR spectrum shows the unsaturated C-H bond, C-O bond and C≡C bond in macromolecules were broken from ultrasonic, which caused the macromolecules (e.g., resin and asphaltenes) to be decomposed into smaller carbon-number molecules. Accordingly, the MMP was determined to be reduced from 15.8 to 14.9MPa from the slim-tube test and the oil recovery factor increased by over 10%. This study reveals the mechanisms of ultrasonic-assisted CO₂ miscible EOR in producing tight oils.

This paper presents the evaluation of a fungal disease forecast model in vineyards for qualitative parameter analysis using the data from off the shelf sensors, i.e. temperature and air relative humidity, rain precipitation, and leaf wetness. The rules for the fungal disease models are digitalized as a decision support tool that serve as an indicator to farmers for the need of spraying of the chemical substances to ensure the best growing condition and suppress the level of parasites. The temperature and humidity contexts are used interchangeably in practice to detect the risk of the disease occurrence. By taking into account a number of influences on these parameters collected from the shelf sensors, new topics for research in the multidimensional field of precision agriculture emerge. In this study, the impact of the humidity is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each humidity parameter has it’s own threshold that triggers the second step of the disease modeling - risk index based on the temperature. The research showed that for humidity a low-cost relative humidity sensor can detect in average 14.61% risk values, a leaf wetness sensor an additional 3.99% risk cases, and finally, a precipitation sensor will detect only an additional 0.59% risk cases.

Several recent surges in COVID-19 cases due to newly emerging variant strains of SARS-CoV-2 with greater transmissibility have highlighted the virus’s capability to directly modulate spike-ACE2 interactions and promote immune evasion by sterically masking the immunogenic epitopes. Recently, there have also been reports of the bidirectional transfer of coronavirus between different animal species and humans. The ability of coronavirus to infect and adapt to a wide range of hosts can be attributed to new variants that modify the molecular recognition profile of the spike protein (S protein). The receptor-binding domain of the spike protein specifically interacts with key host receptor molecules present on the host cell membranes to gain entry into the host and begin the infection cycle. In this review, we discuss the molecular, structural, and functional diversity associated with the coronavirus receptors across their different phylogenetic lineages and its relevance to various symptomatology in the rapid human-to-human infection in COVID-19 patients, tropism, and zoonosis. Despite this seeming diversity of host receptors, there may be some common underlying mechanisms that influence the host range, virus transmissibility, and pathogenicity. Understanding these mechanisms may be crucial in not only controlling the ongoing pandemic but also help in stopping the resurgence of such virus threats in the future.

Growth benefits of high speed rail (HSR) for enterprises are not automatic, but require a buoyant local economy and a robust strategy. Against above background, main objectives of ex-ante case study have been to investigate local socio-economic and spatial impacts of proposed HSR project around Ahmedabad station area on Mumbai-Ahmedabad HSR corridor, analyze the opportunities and challenges of HSR development and draw policy implications. Qualitative research methodology has been used to elicit the perceptions and perspectives of randomly selected 43 small, medium and large enterprises. Data has been analyzed using descriptive statistics. The study reveals that HSR induced greater human interaction will activate local economy, improve business efficiency and generate employment. Frequent travels on business trips can influence enterprises decision to settle near HSR station for easy access and likely to promote realty development. The proposed HSR project should be developed in synergy with urban dynamics of cities in transition and metropolitan service cities. Poor public transport connectivity is likely to be a major challenge in improving HSR accessibility and enhancing overall impacts of HSR, which can be improved by developing an integrated urban transport system, for which cooperation of all stakeholders and coherence with firms’ strategies is essential.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

Purpose: To derive and provide analytic formulas for accommodative gain of presbyopia eyes. via sclera ablation and/or thermal shrinkage such that lens is reshaped an/or its positon is shifted. New mechanisms are also proposed. Study Design: To increased accommodation of presbyopia. Place and Duration of Study: New Taipei City, Taiwan, between June, 2021 and July, 2021. Methodology: Accommodation gain is calculated by a 4-component theory, in which the rate functions are derived by an effective eye model for the change of anterior curvature of the lens and its anterior shift. The measured data of accommodative response of the lens versus the lens curvature change and anterior shift are analyzed. The measured net change of the posterior vitreal zonules (PVZ) length and the space between the ciliary body and lens (CLS) during the accommodation are also analyzed. Results: The accommodative gain (AG) is mainly due the change of lens anterior curvature and its anterior shift. The AG per diopter change of the reshaped lens is 0.62 to 0.68 by our formulas, comparing to the measured average value M'=0.69. The efficacy of LASA (or AG) is proportional to the amount of scleral tissue removed (or shrinkaged), such that more space is produced for the change of PVZ and CLS from a UCS to AS for accommodation. Conclusion: The AG is proportional to the amount of scleral tissue removed (or shrinkaged), such that more space is produced for the change of PVZ and CLS from a UCS to AS for accommodation.

A major problem in the post-inversion geophysical interpretation is the extraction of geological information from inverted physical property models, which do not necessarily represent all underlying geological features. No matter how accurate the inversions are, each inverted physical property model is sensitive to limited aspects of subsurface geology and is insensitive to other geological features that are otherwise detectable with complementary physical property models. Therefore, specific parts of the geological model can be reconstructed from different physical property models. To show how this reconstruction works, we simulated a complex geological system that comprises an original layered earth model that has passed several geological deformations and alteration overprints. Linear combination of complex geological features comprised three physical property distributions: Electrical resistivity, induced polarization chargeability, and magnetic susceptibility models. This study proposes a multivariate feature extraction approach to extract information about the underlying geological features comprising the bulk physical properties. We evaluated our method in numerical simulations and compared three feature extraction algorithms to see the tolerance of each method to the geological artifacts and noises. We show that the fast-independent component analysis (fast-ICA) algorithm by negentropy maximization is a robust method in the geological feature extraction that can handle the added unknown geological noises. The post-inversion physical properties are also used to reconstruct the underlying geological sources. We show that the sharpness of the inverted images is an important constraint on the feature extraction process. Our method successfully separates geological features in multiple 3D physical property models. This methodology is reproducible for any number of lithologies and physical property combinations and can recover the latent geological features, including the background geological patterns from overprints of chemical alteration.

Spiritual intelligence had an impact on different biblical leaders, and in this text, we see some cases to serve as a sample (Joseph, Bezalel, and Daniel). In the Bible, this impact is demonstrated in innovations introduced by Joseph in Egypt, Bezalel the manager of the macro project of building in crisis time, Daniel the politician. It is the supreme intelligence and leaders are invited to make a shift from rationality to spirituality. The more leaders of organizations will use spiritual intelligence, the more leaders and followers will experience satisfaction, joy, accomplishment.

Type two diabetes mellitus (T2DM) represents a chronic condition with increasing prevalence worldwide among the older population. T2DM condition increases the risk of micro and macro-vascular complications as well as the risk of geriatric syndromes as falls, fractures and cognitive impairment. The management of T2DM in the older population represents a challenge for the cli-nician, and a Comprehensive Geriatric Assessment should always be prioritized, in order to tailor the glycate haemoglobin target according to functional and cognitive status comorbidities, life ex-pectancy and type of therapy. According to the most recent guidelines, older adults with T2DM should be cathegorized in three groups: healthy patients with good functional status, patients with complications and reduced functionality and patients at the end of life; for each group the target for the glycemic control is different, also according to the type of treatment drug. The therapeutic ap-proach should always begin with lifestyle changes; after that, several lines of therapies are available, with different mechanism of action and potential effect other than glucose level reduction. Partic-ular interest is growing around sodium-glucose cotransporter-2 inhibitors, due to their effect on the cardiovascular system. In this review we evaluate the therapeutic options available for the treat-ment of older diabetic patients, to ensure a correct treatment approach

Recent research has shown that the sensorimotor system plays a significant role in a variety of cognitive processes. In this paper, we will review recent studies performed in our lab (Body Action Language Lab, BALLAB) or in labs with which we collaborate, showing the involvement of the sensorimotor system at different levels. With the purpose of expounding on this aspect, we focus on studies that highlight two main characteristics of the involvement of the sensorimotor systems. First, we concentrate on the flexibility of sensorimotor grounding during interaction with objects. We report evidence showing how social context and current situations influence affordance activation. We then focus on the tactile and kinesthetic involvement in body-object interaction. Second, we illustrate flexible sensorimotor grounding in word use. We review studies showing that not only concrete words, like “bottle,” but also abstract words, like “freedom,” “thinking,” and “perhaps,” are grounded in the sensorimotor system. We report evidence showing that abstract words activate sensory modalities and involve the mouth effector more than concrete words due to their privileged relationship with language, both outer and inner speech. We discuss the activation of the mouth sensorimotor system in light of studies on adults (e.g., studies employing articulatory suppression), children (e.g., studies on the effects of pacifier use on word acquisition and processing), and infants (e.g. studies on emergence of new words). Finally, we pinpoint possible mechanisms at play in the acquisition and use of abstract concepts. We argue that with abstract concepts, we rely more on other people to learn or negotiate the meaning of words; we have called this mechanism social metacognition.Social metacognition is bidirectionally linked to our sensorimotor system. On the one hand, linguistic explanations constitute a primary source of grounding that may be re-enacted when retrieving a concept, for example through inner speech. On the other hand, it leads us to feel closer and be more synchronous in movement with others, who can help us understand the meaning of very complex words. Overall, we show that the sensorimotor system provides a grounding basis not only for objects and concrete words but also for more abstract and concrete ones. We conclude by arguing that future research should address and deepen two different and interrelated aspects concerning the involvement of the sensorimotor system during object and word processing. First, the sensorimotor system is flexibly modulated by the context, as studies on affordances reveal. Second, the sensorimotor system can be involved at different levels, and its role can be integrated and flanked by that of other systems, like the linguistic one, as studies on abstract concepts clearly show. We urge future research aimed at unravelling the role of the sensorimotor system in cognition to fully explore the complexity of this intricate-and sometimes slippery-relation.

This work analyzes the visibility and scientific impact of publications related to agricultural value chains. The incidence of bibliometric indicators allows for the interpretation of bibliographic information generated worldwide. Objective: The objective of this research is to analyze the published literature and bibliometric indicators on agricultural value chains. The Web of Science database was used to extract value chains data. The study analyzed articles published between 2010 and 2020. The keywords used are "agricultural value chains'' and articles from journals or studies related to the subject were selected for bibliometric analysis and methodological review. In the search for the keyword, a total of 4208 results were extracted, of which 1,669 records were considered for analysis. The bibliometric analysis of the data reveals that Wageningen University (55) has the highest number of publications, followed by Chinese Acad Sci (26). The author Klerkx L (9) has the highest number of records, followed by Hellin J (7). With respect to the countries with the greatest contributions on the subject are: the People's Republic of China, Germany, Italy, France and the United States. The study contributes to the analysis of bibliometrics and provides a methodological review of published journal articles on agricultural value chains. This bibliographic study presents the history of research development in agricultural value chains.

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%) and range of absolute error for ‘run down‘ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

The sustainable management of Land - Water - Energy - Food (LWEF) nexus requires an envi-ronmental characterization that allows the comparison of complex interlinkages between nexus resources and livelihoods. This complexity makes this characterization difficult coupled with limited study in quantifying sustainability of LWEF nexus and its linkage with livelihood. Therefore, the present study aimed to investigate the link between sustainable LWEF nexus and livelihoods. We used analytical hierarchy process and pairwise comparison matrix in combina-tion with weighting model. The result of composite LWEF nexus index was 0.083 representing, low sustainability. This could be linked with nexus resources consumption, use, and manage-ment. From the analysis of the weight of land, water, energy and food nexus resources, the highest weight was observed for food. The focus of on food production only shows no clear synergy on provisioning, supporting or regulating nexus resources to address livelihoods. The result further showed that LWEF nexus resources have strong correlation with livelihoods. This was evidenced by social (r>0.8, P<0.01), natural (r>0.3, P<0.05) and physical (r>0.6, P<0.01) liveli-hood indicators showed strong positive correlation with LWEF nexus resources. From this re-sults, it was observed that managing nexus resources not only provide a significant contribution to achieve sustainable LWEF nexus, but also be effective for enhancing livelihood through food security. This could be attained by strong evidence based policy to ensure sustainable use of nexus resources. The results provided by this study would serve as the foundation for future study, policy formulation and implementation.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

More than 700 thousand human deaths from mosquito bites are observed annually in the world. It is more than 2 times the number of annual murders in the world. In this regard, the invention of new more effective methods of protection against mosquitoes is necessary. In this article for the first time, comprehensive studies of mosquito neutralization using machine vision and a 1 W power laser are considered. Developed laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. We developed a program for mosquito tracking in real. The possibility of using deep neural networks, Haar cascades, machine learning for mosquito recognition was considered. We considered in detail the classification problems of mosquitoes in images. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle. Any harmful insects in the fields can be used as objects for control.

Black horehound (Ballota nigra L.) is one of the important medicinal plants, which is a rich source of health-promoting essential oils. Salinity stress affects plant development and alters the quality and quantity of plants extracts and their composition. This study was aimed to investigate the effect of salinity on morphological, physiological characteristics, and secondary metabolites of B. nigra under greenhouse, and in vitro culture conditions. The plants were treated with different concentrations of NaCl (25, 50, 75, 100 mM) and fresh and dry weight of leaf and stem were measured as well as morphological characteristics of the plant. Plant growth was reduced with the increased salinity concentrations. The results showed that all growth-related traits and SPAD were decreased both in vivo and in vitro. Additionally, increased salt concentration affected the cell membrane integrity. Total phenolics content of plants growing in the greenhouse, increased by 21% at 50 mM NaCl, but at higher stress levels (100 mM NaCl), the amounts were decreased significantly. Total flavonoids contents followed similar patterns, with a slight difference. In addition, the maximum and minimum total phenolics contents of plants growing under in vitro condition were observed at 50 mM NaCl and control treatments, respectively. Increasing the salt concentration significantly affected the total flavonoids content, and as a result, the highest amount was observed in 50 and 75 mM NaCl treatments. Antioxidant activity was also measured. Among the NaCl treatments, the highest DPPH scavenging activities (IC50) under greenhouse and in vitro conditions were detected at 50 mM and 25 mM concentrations, respectively. In general, based on the results, with increasing the salinity level to 75 mM, the activities of CAT and APX were significantly upregulated in both greenhouse and in vitro culture conditions. A correlation between total phenolics and flavonoids contents as well as antioxidant activity were obtained. With shifting salinity stress, the type and the amount of the identified essential oil compounds changed. Compounds such as styrene, tridecanol, germacrene-D, beta-Ionone, beta-bisabolene, and caryophyllene oxide increased compared to the controlled treatment.

The global COVID-19 pandemic claiming global spread continues to evolve, now to the verge of a third wave of outbreak possibly caused by the novel variants of concern of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2). The test positivity rate (TPR) and case fatal-ity rate (CFR) have increased steeply in the second wave of COVID-19 compared to the first. From the example of Kerala, a state in southern India, positivity increased from 1.33% at the peak of wave one in 10th June 2020 to 13.45% during 10th June 2021 in the second wave of pandemic. SARS-CoV-2 is an enveloped single-stranded RNA virus. Angiotensin-Converting Enzyme-2 (ACE-2) is a trans membrane surface protein present on multiple types of cells in the human body to which the viral spike protein attaches. Genetic variations in the SARS-CoV-2 and ACE2 receptor can affect the transmission, clinical manifestations, mortality and the efficacy of drugs and vaccines for COVID-19. Mutations are the primary cause of genetic variations. Given the high TPR and CFR, it is necessary to understand the variations of SARS-CoV-2 and cellular receptors of SARS-CoV-2 at the molecular level. In this review, we summarize the impact of genetic and ep-igenetic variations in determining COVID-19 pathogenesis and disease outcome.

The work addresses the problem of Power Quality (PQ) metrics (or indexes) suitable for DC grids, encompassing Low and Medium Voltage applications, including electric transports, all-electric ships and aircrafts, electric vehicles, distributed generation and microgrids, modern data centers, etc. The two main pillars on which such PQ indexes are discussed and built are: i) the physical justification, so the electric phenomena affecting DC grids and components (PV panels, fuel cells, capacitors, batteries, etc.), causing e.g. stress of materials, ageing, distortion, grid instability; ii) the existing standardization framework, pointing out desirable coverage and extension, similarity with AC grids standards, but also inconsistencies. The first point is made more clear and usable by a graphical overview of the discussed phenomena. On this basis PQ is interpreted beyond the usual low-frequency range, including thus supraharmonics and common-mode disturbance, and filling the gap with the Electromagnetic Compatibility domain. However, phenomena typical of EMC and electrical safety (such as various types of overvoltages and fast transients) are excluded. Suitable PQ indexes are then reviewed, suggesting integrations and modifications, to cover the relevant phenomena and technological progress, and to better follow the normative exigencies.

Modernist planners were given the opportunity to apply the idea of ​​the Modern Movement in the implementation of cities transforming after World War II, which was not possible before in long-term evolving structures. Usually, however, it was impossible to change everything: some urban and even architectural elements were necessary to be preserved. As a result, the elements of the ancient landscapes of these cities - historical objects and spaces - necessary for the continuity of evidence of history and identity have been preserved, sometimes through reconstruction. Finally, in some of these cities, both old and new elements contribute to the contemporary urban landscape. The aim of the research is to examine two key categories of urban structure in this perspective: 1. landmarks 2. urban network nodes. For this purpose: 1. the changes in the number and rank of the space with which the landmarks exhibition was carried out were compared; 2. the changes in the rank of nodes in the urban structure functioning both in the pre-war and in the present structure, as well as changes in their distribution and concentration areas were analyzed. The numbers of individual elements were balanced and the changes in the structure of their arrangement were analyzed with the use of a polygon grid. The analyzes were based on the case of a medium-scale European city (Białystok). Research shows that as a result of post-war reconstruction, the number of elements of the urban grid decreased, especially those of lower rank, the rank of others decreased.

The methods for constructing solutions to integro-differential equations of the Volterra type are considered. The equations are related to fractional conformable derivatives. Explicit solutions of homogeneous and inhomogeneous equations are constructed and a Cauchy-type problem is studied. It should be noted that the considered method is based on the construction of normalized systems of functions with respect to a differential operator of fractional order.

The article concerns a relatively small but important fragment of John Paul II's pastoral activity, namely his pilgrimages to Poland: the nature of these pilgrimages, their historical background and teaching - especially to the extent that went beyond the immediate and Polish context. The text is the result not only of reading papal statements, but also of personal observation of the political and social atmosphere. The first two parts contain a concise description of the Pope's eight pilgrimages to Poland (before and after the political transformation). The third highlights out the main universal themes raised by the Pope: the dignity of the person, truth and freedom. During consecutive pilgrimages to Poland, he deepened and expanded the themes of his teaching. This collection of his sermons and speeches to Poles constitutes a comprehensive (devoid of redundant repetitions and omissions) treatment of important social, philosophical and theological issues. John Paul II, when speaking to Poles and about Poles, did not lose his universal perspective; he appealed both to Christians and to all people of good will. Papal pilgrimages to Poland are a significant part of modern Polish history, and their message is a valuable intellectual and spiritual gift.

Teacher burnout has been revealed to be one of the most common negative consequence of the COVID-19 pandemic The purpose of this study was to identify distinct psychological resources and burnout risk profiles of teachers and to examine their association with Kolb’s Educator Roles and the professional experience. Methods: The survey data were collected from 330 preschool and primary school (84 males, Mage = 38.3, SD = 9.14) teachers using a convenience sampling method. Results: The two-step cluster analysis revealed two distinct profiles. The first profile, ‘High psychological resources, no burnout risk’, was characterised by absent symptoms of burnout and increased levels of well-being, self-control, and positive emotionality. The second profile, ‘Moderate psychological resources, mild burnout’, was associated with medium levels of well-being, self-control and positive emotionality accompanied by mild burnout. Our findings highlighted that cluster 1 had a significantly higher score for the Facilitator role and cluster 2 for the Expert and Coach roles. In addition, teachers with less professional experience were more likely to belong to cluster 1, taking into account their good skills on digital literacy. Conclusions: These findings help to provide new insights into the explanation of teacher burnout and the design of intervention programmes.

Residential load forecasting is one of the most important tasks of the overall supply management process in electrical grids, since it enables smart grid services such as demand response (DR). Hence, several approaches for accurate residential load forecasting have been proposed in the relevant literature. However, most of the existing methods focus on the forecasting performance and neglect other aspects of the problem like training time and model size (i.e. memory usage). In this paper, we introduce a new model for both short-term and day-ahead residential load forecasting. The model synthesizes an heterogeneous feature set, which is constituted by both automatically-selected lagged values from the load time series and manually-extracted temporal features. Then, the tree-based algorithm light gradient boosting machine (LGBM) is fed with the constructed feature set and used as a regression model. Finally, a data-lightweight strategy is used for retraining the proposed model, which leads to both high forecasting accuracy and low training times. The proposed model has been extensively evaluated on a large real-world residential load dataset. The experimental results indicate that the proposed model achieves both higher forecasting performance and lower training times and model sizes compared to state-of-the-art solutions.

Pharmacological treatment is not very effective for neuropathic pain (NP). A progressive decrease in the estimated effect of NP drugs has been reported, giving rise to an increase in the use of the multimodal analgesic approach. We performed a new, independent review to assess whether more evidence and of better-quality has become available since the last systematic review. We evaluated the efficacy, tolerability, and safety of double-blinded, randomized, controlled trials involving only adult participants and comparing combination therapy (CT: ≥ 2 drugs) to a placebo and/or at least one other comparator with an NP indication. The primary outcome assessed was the proportion of participants reporting ≥50% pain reductions from baseline. The secondary outcome assessed was the proportion of drop-outs due to treatment-emergent adverse events. After removing duplicates, 2323 citations were screened, with 164 articles assessed for eligibility, from which 16 were included for qualitative analysis. From the latter, only five lasted for at least 12 weeks and only six complied with the required data for complete analysis. CT has been adopted for years without robust evidence. Efforts have been made to achieve better-quality evidence, but the quality has not improved over the years. In this regard, guidelines for NP should attempt to make recommendations about CT research, prioritizing which combinations to analyze.

Background: Shared value creation in Base of the Pyramid (BoP) communities is a crucial process towards building sustainable societies. BoP communities in developing countries represent more than four billion people who live on low incomes with limited access to basic products and services. Current or emerging technologies offer promising solutions for organisations pursuing manufacturing opportunities in BoP communities. This study seeks to explore the literature on how BoP communities may become active participants in sustainably manufacturing products using micro-manufacturing factories. The research question posed is: What are the core concepts that need to be taken into consideration for creating shared value through micro-manufacturing factories in BoP communities? Method: A Systematised Literature review (SLR) was completed following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) method for data selection criteria and analysis. The SLR is used to explore the state of literature with regards to creating manufacturing shared value in BoP communities with the objective to identify study gaps and to explore shared value creation concepts. Results: Literature indicates BoP initiatives that have pursued to engage BoP communities through various innovation strategies. The findings of the review is organised under three strategic pillars: Capability building strategy, Implementation process, and Growth strategy. The capability building strategy defines the users’ intention to create shared value in BoP communities with Micro-manufacturing factories (MMF). It is followed by the implementation process which guides the users to create manufacturing shared value in BoP communities. This is followed by a growth strategy to scale for impact.

A project-based module on Sustainable Development Goal number 5, Gender Equality, was im-plemented on 5 different groups of Business English students consisting of a total number of 62 students in higher education. The main purpose of this project was to raise awareness of this goal by means of a flipped method in which students were required to carry out some research on specific areas of the aforementioned goal and work in teams to elaborate oral presentations. Once their findings were shared in class, students were expected to answer a written questionnaire of open-ended questions which were part of a qualitative analysis. Results of this survey showed that not only 90% of the students gained in depth knowledge of this goal, but also 85% had built a positive attitude to take initiative and 80% were optimistic about future gender equality. Finally, 70% of students suggested further social action to curb the problem of gender discrimination. On the whole, the flipped classroom method of learning combined with project-based group work have proven to be an effective way to raise awareness of this goal, create a more positive attitude, in-crease their willingness to take action as well as widening their English lexical resources.

Alterations in the carnitine shuttle system may be an indication of the presence of cancer. As such, in-depth analyses of this pathway in different malignant tumors could be important for the detec-tion and treatment of this disease. The current study aims to assess the profiles of carnitine and acylcarnitines in gliomas with respect to their grade, the presence of isocitrate dehydrogenase (IDH) mutations, and 1p/19q co-deletion. Brain tumors obtained from 19 patients were sampled on-site using solid-phase microextraction (SPME) immediately following excision. Analytes were desorbed and then analyzed via liquid chromatography-high resolution mass spectrometry. The results showed that SPME enabled the extraction of carnitine and 22 acylcarnitines. An analysis of the correlation factor revealed presence of two separate clusters: short-chain and long chain car-nitine esters. Slightly higher carnitine and acylcarnitine concentrations were observed in the higher malignancy tumor samples (high vs low grade), and in those samples with worse projected clinical outcomes (without vs with IDH mutation; without vs with 1p/19q co-deletion). Thus, the proposed chemical biopsy approach offers a simple solution for on-site sampling that enables sample preservation, thus supporting comprehensive multi-method analyses.

Ulcerative colitis (UC) is one of the two disorders known as inflammatory bowel diseases (IBD) along with Crohn’s disease (CD), with complex pathogenesis, requiring costly invasive investigations. Objective: to examine the most recent biomarkers proposed for UC diagnosis; to establish the strategy used to make the differential diagnosis between UC and CD relying on these biomarkers, also adding the benefit of finding new non-invasive tools in managing this condition. The search was performed in a single database (Web of Science) using the specific keywords „ulcerative colitis”, „biomarkers” and „diagnosis” for the last five years. Study eligibility criteria: clinical trials on adults and pediatric patients with ulcerative colitis compared with Crohn’s disease. Results: We selected 57 studies, randomized controlled trials (RCTs) and clinical case series (CCS), summarizing the latest most specific biomarkers in diagnosis of UC. Limitations: we considered RCTs and CCS from one database, limited to the search topics. Our findings indicate a important number of potential biomarkers with diagnostic value, which bring the advantage of a non-invasive method to approach this challenging disorder.

Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

This article explores the lack of unanimity regarding the nomenclature used to refer to the field of research that explores the intersection between art and science. A series of examples of nomenclatures and the context in which they are applied are listed and referenced. The diverse terminology reflects the heterogeneity, transdisciplinarity, and complexity of a research field in full expansion.

General relativity is a theory for gravitation based on Riemannian geometry, difficult to compatibilize with quantum mechanics. This is evident in relativistic problems in which quantum effects cannot be discarded. For example in quantum gravity, gravitation of zero-point energy or events close to a black hole singularity. Here, we set up a mathematical model to select general relativity geodesics according to compatibility with the uncertainty principle. To achieve this, we derived a geometric expression of the uncertainty principle (GeUP). This formulation identified proper space-time length with Planck length by a geodesic-derived scalar. GeUP imposed a minimum allowed value for the interval of proper space-time which depended on the particular space-time geometry. GeUP forced the introduction of a “zero-point” curvature perturbation over flat Minkowski space, caused exclusively by quantum uncertainty but not to gravitation. When applied to the Schwarzschild metric and choosing radial-dependent geodesics, our mathematical model identified a particle exclusion zone close to the singularity, similar to calculations by loop quantum gravity. For a 2 black hole merger, this exclusion zone was shown to have a radius that cannot go below a value proportional to the energy/mass of the incoming black hole multiplied by Planck length.

Usually, towed hydrophone arrays are instrumented with a set of compasses. Data from these sensors are utilized while beamforming the acoustic signal for target bearing estimation. However, elements of the hydrophone array mounted in the neighborhood of a compass can affect the Earth’s magnetic field detection. The effects depend upon the kind of elements present in the platform hosting the compass. If the disturbances are constant in time, they can be compensated for by means of a magnetic calibration. This process is commonly known as soft and hard iron compensation. In this paper, a solution is presented to carry out the magnetic calibration of a COTS (Commercial Off The Shelf) digital compass without unattainable sensor motion. This approach is particularly suited in applications where a physical rotation of the platform that hosts the sensor is unfeasible. In our case, the platform consists in an assembled and operational towed hydrophone array. A standard calibration process relies on physical rotation of the platform and thus on the use of the geomagnetic field as a reference during the compensation. As a variation on this approach, we provide to the sensor an artificial reference magnetic field to simulate the unfeasible physical rotation. We obtain this by using a tri-axial Helmholtz coil, which enables programmability of the reference magnetic field and assures the required field uniformity. In our work, the simulated geomagnetic field is characterized in terms of its uncertainty. The analysis indicates that our method and experimental set-up represent a suitably accurate approach for the soft and hard iron compensation of the compasses equipped in the hydrophone array under test.

In humankind’s endeavor to explore beyond our planet and travel further into space, we are now at the threshold of an era in which it is possible to move to and from low Earth orbit (LEO) with increasing ease and reduced cost. Through the International Space Station (ISS) U.S. National Laboratory, investigators from industry, academia, and government can easily access the unique LEO environment on the ISS to conduct research and development (R&D) activities in ways not possible on Earth. A key advantage of the LEO environment for life sciences research is the ability to conduct experiments in sustained microgravity conditions. The ability to conduct long-term research in microgravity enables opportunities for novel, fundamental studies in tissue engineering and regenerative medicine, including research on stem cell proliferation and differentiation, biofabrication, and disease modeling using microphysiological systems (MPS) that build on prior research using simulated microgravity conditions (Grimm, D., et al. 2018). Over the last decade, space-based research has demonstrated that microgravity informs our knowledge of fundamental biology and accelerates advancements in health care and medical technologies (International Space Station 2019). The benefits provided by conducting biomedical research in LEO may lead to breakthroughs not achievable on Earth. We are now at a transition point, in which nations are changing their approach to space-based R&D. The focus is shifting from government-funded fundamental science toward the expansion of privately funded R&D with terrestrial application and economic value that will drive a robust marketplace for innovation and manufacturing in LEO. Making this long-term transition requires public-private participation and near-term funding to support critical R&D to leverage the benefits of the LEO environment and de-risk space-based research. Studies conducted on the ISS over the past several years have indicated that one area with potential significant economic value and benefit to life on Earth is space-based biomanufacturing, or the use of biological and nonbiological materials to produce commercially relevant biomolecules and biomaterials for use in preclinical, clinical, and therapeutic applications. We must take advantage of the remaining lifetime of the ISS as a valuable LEO platform to demonstrate this economic value and Earth benefit. By facilitating access to the space station, the ISS National Lab is uniquely positioned to enable the R&D necessary to bridge the gap between the initial discovery phase of space-based biomedical research and the development of a sustainable, investment-worthy biomanufacturing market in LEO supported by future commercial platforms. Through a joint effort, the Center for the Advancement of Science in Space (CASIS), which manages the ISS National Lab, and the University of Pittsburgh’s McGowan Institute for Regenerative Medicine brought together thought leaders from around the U.S. for a Biomanufacturing in Space Symposium that consisted of a series of working sessions to review data from past space-based tissue engineering and regenerative medicine research, discuss relevant current space-based R&D in this area, and consider potential future markets to address the questions: What are the most promising opportunities to leverage the ISS to advance space-based biomanufacturing moving forward? What are the current gaps or barriers that, if overcome, could clear pathways toward private investment in LEO as a valued site for research, development, and production activity? And, most importantly: For which opportunities do the most compelling value propositions exist? The goal of the Biomanufacturing in Space Symposium was to help identify the specific areas in which government and industry investment would be most likely to stimulate advancements that overcome barriers. This would lead to a more investment-ready landscape for private interests to enter the market and fuel exponential growth. The symposium was meant to serve as the first step in developing a roadmap to a sustainable market for biomanufacturing in space. The symposium identified and prioritized multiple key R&D opportunities to advance space-based biomanufacturing. These opportunities fall in the areas of disease modeling, stem cells and stem-cell-derived products, and biofabrication. Additionally, symposium participants highlighted the critical need for additional data to help validate and de-risk these opportunities and concluded that approaches such as automation, artificial intelligence (AI), and machine learning will be needed to produce and capture the required data. Symposium participants also came to a consensus that public-private partnerships and funding will be needed to advance the opportunities toward a biomanufacturing marketplace in LEO. This paper will summarize the current state of the science and technology on the ISS and in the fields of tissue engineering and regenerative medicine; provide an overview of biomanufacturing R&D in space to date; review the goals of the Biomanufacturing in Space Symposium; highlight the key commercial opportunities and gaps identified during the symposium; provide information on potential market sizes; and briefly discuss the next steps in developing a roadmap to biomanufacturing in space.

Two sets of initial conditions are used in the investigation of capital return rate and carbon storage in boreal forests. Firstly, a growth model is applied in young stands as early as the inventory-based model is applicable. Secondly, the growth model is applied to observed wooded stands. Four sets of thinning schedules are investigated in either case. First, the capital return rate is aspired without any restriction. Second, the number of thinnings is restricted to at most one. Third, thinnings are restricted to the removal of only trees thicker than 237 mm. Fourth, commercial thinnings are omitted. The two sets of initial conditions yield similar results. The capital return rate is a weak function of rotation age, which results in variability in the optimal number of thinnings. Reducing the number of thinnings to one increases timber stock but induces a capital return rate deficiency. The deficiency per excess volume unit is smaller if the severity of any thinning is restricted by the removal of large trees only. Omission of thinnings best applies to spruce-dominated stands with stem count less than 2000/ha. Restricted thinning intensity applies to deciduous stands and dense pine stands. The albedo effect increases the benefits of restricted thinnings and increased clearcuttings instead of contradicting the carbon storage.

Increasing the metacognitive abilities of chemistry students is an indisputable output of the teaching and learning process today. Collaborative problem based learning is a learning method that has been tested and proven to be applied, especially in Western countries in increasing the metacognitive abilities of students, but it is still very minimal applied in Asian countries, including Indonesia. Thus, this study was conducted to explore previous studies that examined collaborative problem-based learning in improving students' metacognitive abilities. The research design used in this study is a Systematic Literature Review with the requirements of the inclusion of articles on collaborative problem-based learning in improving the metacognitive abilities of chemistry students, accredited national and international publications between 2010 and 2020, full text, journal articles, and open access. The results of the exploration that were carried out found 102 articles, then the title and abstract were read into 20 articles, and 4 articles were read in full, which fulfilled all the stipulated inclusion requirements. The results of the systematic literature review conducted in this study provide empirical evidence of literacy that problem based learning improves the metacognitive abilities of chemistry students. However, most of research conducted still uses various instruments, which are not standardized and validated.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) has become an unprecedented challenge to global public health. With the intensification of the COVID-19 epidemic, the development of vaccines and therapeutic drugs against the etiological agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are also widespread. To prove the effectiveness and safety of these preventive vaccines and therapeutic drugs, available animal models that faithfully recapitulate clinical hallmarks of COVID-19 are urgently needed. Currently, animal models including mice, golden hamsters, ferrets, nonhuman primates and other susceptible animals have been involved in the study of COVID-19. 92 vaccine candidates have entered clinical trials after the primary evaluation in animal models, of which inactivated vaccines, subunit vaccines, virus-vectored vaccines and mRNA vaccines are promising vaccine candidates. In this review, we summarize the landscape of animal models and advanced vaccines with efficacy range from about 50% to more than 95%. In addition, we point out future directions for animal models and vaccine development, aiming at providing valuable information and accelerating the breakthroughs confronting SARS-CoV-2.

Ultrafast chip nanocalorimetry opens up remarkable possibilities in materials science by allowing samples to be cooled and heated at extremely high rates. Due to heat transfer limitations, controlled ultrafast cooling and heating can only be achieved for tiny samples in calorimeters with a micron-thick membrane. Even if ultrafast heating can be controlled under quasi-adiabatic conditions, ultrafast controlled cooling can be performed if the calorimetric cell is located in a heat-conducting gas. It was found that the maximum possible cooling rate increases as 1/r0 with decreasing radius r0 of the hot zone of the membrane. The possibility of increasing the maximum cooling rate with decreasing r0 was successfully implemented in many experiments. In this regard, it is interesting to answer the question: what is the maximum possible cooling rate in such experiments if r0 tends to zero? Indeed, on submicron scales, the mean free path of gas molecules lmfp becomes comparable to r0, and the temperature jump that exists at the membrane/gas interface becomes significant. Considering the limitation associated with thermal resistance at the membrane/gas interface and considering the transfer of heat through the membrane, we show that the controlled cooling rate can reach billions of K/s, up to 1010 K/s.

African swine fever (ASF) is one of the most important and devastating viral diseases in wild boar and domestic pigs worldwide. In the absence of vaccines or treatment options, early clinical detection is key and requires sound knowledge of disease characteristics. To provide practitioners and state veterinarians with detailed information, the objective of the present study was to characterize the ASF virus (ASFV) isolate “Belgium 2018/1” in subadult and weaning domestic pigs. To this end, two animal trials were performed. Trial A included eight subadult domestic pigs and trial B five weaner pigs. In general, clinical signs and pathological lesions were in line with previous studies utilizing highly virulent ASF genotype II viruses. However, in trial A, four subadult domestic pigs survived and recovered pointing to an age dependent outcome. The long-term fate of those survivors remains under discussion and would need further investigations.

Emerging technologies have a potential future impact on the developments in higher education and teaching practices at the universities. The paper is based on the project “My future colleague robot” that aims to improve the competence of university teachers in the implementation of Emerging Technologies (ETs) in the teaching practices at the university. In this paper, we identified the strengths and weaknesses, opportunities and threats that are related to the adoption of two ETs, robotics and Artificial Intelligence (AI), in higher education. Additionally, we analyzed the perceptions of faculty about these ETs. The empirical data was collected using written essays from 18 university teachers and students. Deductive and inductive approaches with thematic analysis were used for the data analysis. The findings support the idea that previous experience related to ETs can support positive attitudes and the implementations of ET in university teaching. University teachers had optimistic expectations towards ETs accepting them as part of teaching practice development, while discussion about the negative effects of ETs was negligible.

The aim of the study was to gain deeper insights in the potential for polyclonal stimulation of PBMC from cows and pigs with banana lectin (BanLec) from Musa paradisiaca. BanLec induced a marked proliferative response in cow and pig PBMC. The response in pigs was even higher than to Concanavalin A. Molecular processes associated with respective responses were examined with differential proteome analyses. Discovery proteomic experiments was applied to BanLec stimulated PBMC and cellular and secretome responses were analyzed with label free LC-MS/MS. In PBMC, 3955 proteins were identified. After polyclonal stimulation with BanLec, 459 proteins showed significantly changed abundance in PBMC. In respective PBMC secretomes, 2867 proteins were identified with 231 differentially expressed candidates as reaction to BanLec stimulation. The transcription factor `E74 like ETS transcription factor 1 (ELF1)` was solely enriched in BanLec stimulated PBMC. BanLec induced secretion of several immune regulators, amongst them positive regulators of activated T cell proliferation and Jak-STAT signaling pathway. Top changed immune proteins were CD226, CD27, IFNG, IL18, IL2, CXCL10, LAT, ICOS, IL2RA, LAG3 and CD300C. BanLec stimulates PBMC of cows and pigs polyclonally and induces IL2 pathway and further proinflammatory cytokines. Proteomics data are available via ProteomeXchange with identifier PXD027505.

Background: Ovarian cancer (OC) represents the eighth most common cancer and the fifth leading cause of cancer-related deaths among the female population. In the advanced setting, chemotherapy represents the first-choice treatment, despite a high recurrence rate. In the last ten years, immunotherapy based on immune checkpoint inhibitors (ICIs) has profoundly modified the therapeutic scenario of many solid tumors. We sought to summarize the main findings regarding the clinical use of ICIs in the OC. Methods: We searched the PubMed, Embase, and Cochrane Databases, and conference abstracts from international congresses (such as ASCO, ESMO, SGO) for clinical trials, focusing on ICIs both as monotherapy and as combinations in the advanced OC. Results: 20 studies were selected, of which 16 were phase I or II and 4 phase III trials. ICIs targeting PD1 (nivolumab, pembrolizumab), PD-L1 (avelumab, atezolizumab, durvalumab), and CTLA4 (ipilimumab, tremelimumab) were employed. No significant survival improvement was achieved; conversely, early terminations due to futility or toxicity were recorded. Combinations with chemotherapy, anti-VEGF, and, overall, PARP-inhibitors seem feasible and enhance the response rate and survival, notwithstanding a worse safety profile. Conclusions: The identification of biomarkers with a predictive role for ICIs’ efficacy is mandatory. Moreover, genomic and immune profiling of the OC might lead to an improved treatments selection and design of tailored trials.

Most Acinetobacter baumannii strains are naturally competent. Although some information is available about factors that enhance or reduce the frequency of transformation of this bacterium, the regulatory elements and mechanisms are barely understood. In this article, we describe studies on the role of H-NS in the regulation of expression of genes related to natural competency and the ability to uptake foreign DNA. The expression levels of the natural transformation-related genes pilA, pilT, pilQ, comEA, comEC, comF, and drpA were significantly increased in a Δhns derivative of Acinetobacter baumannii A118. Complementation of the mutant with a recombinant plasmid harboring hns restored expression levels of six of these genes (pilT remained expressed at high levels) to those of the wild-type strain. The transformation frequency of the A. baumannii A118 Δhns strain was significantly higher than that of the wild-type. Similar, albeit not identical, effects occurred when hns was deleted from the hypervirulent A. baumannii AB5075 strain. Reduction of gene expression in a few cases was not as pronounced as to reach wild-type levels, and expression of comEA was enhanced further. In conclusion, the expression of all seven transformation-related genes was enhanced after deleting hns in A. baumannii A118 and AB5075, and these modifications are accompanied by an increase in the cells’ transformability. The results demonstrate a role of H-NS in A. baumannii’s natural competence.

The strictness of the result of an excavation, whether mechanical or by means of explosives, is naturally conditioned by the objective, and therefore by the type of technique applied to achieve it. To attain the best results in terms of rock breakage and respect of the final profile, it’s important to evaluate the excavation specific energy and its optimization. This research focuses on evaluating the effects of different techniques on the quality of final walls in open-pit and underground operations. Different geometries and configurations can be applied to both quarrying and tunnelling blasts. The research is aimed to push contour blasts to their limits, and the main aspects are discussed in order to improve the blast parameters in the daily practice.

This manuscript proposes a novel tangent-graph-based method that provides all distinctive topology paths, as a set of trajectories that can not be transformed into each other by gradual bending and stretching without colliding with obstacles. As the global optimal trajectory (limited by dynamic constraints) may not correspond to the shortest path (almost without dynamic constraints), it is important to provide all distinctive topology paths rather than just the globally shortest one. One of the application is provide initial paths for trajectory planning methods, such as time elastic band (TEB) and dynamic window approach (DWA). Considering that a mobile platform is always working in a dynamic changing environment and that update the whole graph is time consuming, a dynamic rectify method is proposed to update tangents according to local map provided by sensor reading. Finally, results under real grid maps are presented.

For gravitational wave (GW) detected neutron star mergers, one of the leading candidates for electromagnetic (EM) counterparts is the afterglow from an ultra-relativistic jet. Where this afterglow is observed, it will likely be viewed off-axis, such as the afterglow following GW170817/GRB 170817A. The temporal behaviour of an off-axis observed GRB afterglow can be used to reveal the lateral jet structure, and statistical model fits can put constraints on the various model free-parameters. Amongst these parameters is the inclination of the system to the line of sight. Along with the GW detection, the afterglow modelling provides the best constraint on the inclination to the line-of-sight and can improve the estimates of cosmological parameters e.g. the Hubble constant, from GW-EM events. However, modelling of the afterglow depends on the assumed jet structure and, often overlooked, the effects of lateral spreading. Here we show how the inclusion of lateral spreading in the afterglow models can affect the estimated inclination of GW-EM events.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

(1) Background: Antimicrobial resistance (AMR) requires urgent multidisciplinary solutions, and Pharmacovigilance (PV) has the potential to strengthen current antimicrobial stewardship (AMS) strategies. This study aimed to characterise AMR-relevant adverse drug reaction (ADR) reports submitted to The Netherlands Pharmacovigilance Centre (Lareb); (2) Methods: We carried out a descriptive analysis of ADR reports submitted to Lareb, coded with AMR-relevant MedDRA Preferred Terms (PTs).; (3) Results: Between 1998 and Jan 2019, 252 AMR-relevant ADR reports were submitted to Lareb. The most frequent antibiotics were tobramycin (n=89; 35%), colistin (n=30; 11,9%), ciprofloxacin (n=16; 6,35%), doxycycline (n=14; 5,5%) and aztreonam (n=12; 4,76%). The most frequently used PTs were drug ineffective (n=71; 28%), pathogen resistance (n=14; 5%) and drug resistance (n=13; 13%). A total of 119 reports (74%) suggested use-related issues. Watch antibiotics were in 54% of the reports and Reserve antibiotics were in 19%. In the Watch group, “Off label use” and “Product use in unapproved indication” were the most frequent PTs and majority of reports on Reserve antibiotics were coded as “Off label”. (4) Conclusions: Addressing AMR using the PV methods will provide an opportunity for PV expansion and could encourage further investment in both in AMS programs and PV systems.

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

Childhood obesity is a preventable multi-systemic disease that has become an epidemic. In this narrative review, we showed evidence, in the literature, of the affectation of body systems by obesity. Co-morbid conditions of obesity in the cardiovascular, nervous, dermatological, respiratory, endocrine, reproductive, musculoskeletal, gastrointestinal, and renal systems, and its effects on mental health are discussed. Obesity ultimately reduces the quality of life of children and is a leading cause of preventable deaths. Effective measures aimed at the prevention of obesity and the treatment of its co-morbid conditions should be taken.

In this paper, the notion of θ∗-weak contraction is introduced, which is utilized to prove some fixed point results. These results are helpful to give a positive response to certain open question raised by Kannan [Amer. Math. Monthly 76:1969] and Rhoades [Contemp. Math. 72:1988] on the existence of contractive definition which does not force the mapping to be continuous at the fixed point. Some illustrative examples are also given to support our results. As applications of our result, we investigate the existence and uniqueness of a solution of non-linear matrix equations and integral equations of Volterra type as well.

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

Haemodynamic indices are widely used in clinical practice for deciding on a particular type of treatment. Low quality of the CT data and tachycardia complicate interpretation of the measured or simulated values. In this work, we present a novel approach for evaluating resistances in terminal coronary arteries. Using 14 measurements from 10 patients, we show that this algorithm retains the accuracy of 1D haemodynamic simulations in less detailed (truncated) geometric models of coronary networks. We also apply the variable systole fraction model to study the effect of elevated heart rate on the values of FFR, CFR and iFR. We conclude that tachycardia may produce both overestimation or underestimation of coronary stenosis significance.

Cross-border e-commerce is an opportunity for micro, small and medium sized enterprise (MSMEs) in developing countries. Based on a resource-based approach, this research studied how to support resource lacking enterprises with export marketing strategy. It dealt with actual business cases of Mongolian entrepreneurs trying to export Mongolian products to Korean market. Multiple source data including interviews, internal documents, and group discussions, were matched with theories to come up with strategies and validated by supporting organizations in Mongolia and Korea. The research suggests that MSMEs should rely on third party digital platforms rather than setting up their own. For product strtategy of the marketing mix, sellecting competitive product categories and supporting them adjusting to foreign markets and quality assurance is needed. For price strategy, loccally high priced products should use a price penetration strategy with a lower price compared to competing foreign products. For place strategy, supporting organizations should partner with exporters for collective delivery. For promotion strategy, they need to support the capacity of MSMEs enabling them to use digital marketing tools effectivly. These strategies were validated and adopted by supporting organizations in Monglia and Korea.

Abstract: Oleuropein, the major compound of olive leaves, has been reported to exert numerous pharmacological properties, including anti-inflammatory, antidiabetic and anticancer. The purpose of this study is to evaluate, for the first time, the effect of oleuropein-rich leaf extracts (ORLE) in already-developed colon tumours colon tumours arising in an Apc (adenomatous polyposis coli) mutated PIRC rats (F344/NTac-Apcam1137). Here, we were able to investigate in parallel the anti-cancer effect of ORLE, both in vivo and in vitro, and its anti-inflammatory effect on macrophages, which represents a critical and abundant population in most solid tumours microenvironment. We found that in vivo ORLE treatment promoted apoptosis and attenuated iNOS activity both in colon tumours as in peritoneal macrophages of PIRC rats. We confirmed in vitro using primary RAW264.7 cells: ORLE reduced iNOS activity in parallel with COX-2 and pro-inflammatory cytokines, such as IL-1, IL-6 and TGF-. These findings suggest that ORLE possess a strong anti-inflammatory activity, which could be crucial for dampening the pro-tumourigenic activity elicited by a chronic inflammatory state generated by either tumour cells or tumour-associated macrophages.

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions of air pollutants, sensitivity experiments were implemented by random forest (RF) model, with the comparison of contributions of meteorology, road traffic, and emission sources between different periods. Besides, an emulator was operated to suggest an appropriate limit for control of transportation. The RF models showed different mechanisms for air pollutants. Within-city Migration index (WMI) was more important in the normal, pre-lockdown and post-pandemic model while Out-Migration Index (OMI) was emphasized in the lockdown model. In the COVID-19 lockdown period, 73.3% of the reduction can be attributed to the decreased road traffic, showing massive impact of road traffic on the air quality. In the post-pandemic period, meteorology controlled about 42.2% of the decrease and emissions from industry and household controlled 40.0% while road traffic only contributed to 17.8%. It was suggested that priority of restriction should be given to road traffic within the city. A limit of less than 40% on the control of the road traffic can get a better effect, especially for cities with severe traffic pollution.

The postnatal rodent spinal cord in-vitro is a useful model to investigate early pathophysiological changes after injury. While low dose nicotine (1µM) induces neuroprotection, how higher doses affect spinal networks is unknown. Using spinal preparations of postnatal wild-type Wistar rat and Wnt1Cre2:Rosa26Tom double-transgenic mouse, we studied the effect of nicotine (0.5-10µM) on locomotor networks in-vitro. Nicotine 10µM induced motoneuron depolarization, suppressed monosynaptic reflexes, and decreased fictive locomotion in rat spinal cord. Delayed fall in neuronal numbers (including motoneurons) of central and ventral regions emerged without loss of dorsal neurons. Conversely, nicotine (0.5-1µM) preserved neurons throughout the spinal cord and strongly activated the Wnt1 signaling pathway. High-dose nicotine enhanced expression of S100 and GFAP in astrocytes suggesting their response to stress. Excitotoxicity induced by kainate was contrasted by nicotine (10µM) in the dorsal area and persisted in central and ventral regions with no change basal Wnt signaling. When combining nicotine with kainate, the activation of Wnt1 was reduced compared to kainate/sham. The present results suggest that high dose nicotine was neurotoxic to central and ventral spinal neurons as the neuroprotective role of Wnt signaling became attenuated. This also corroborates the risk of cigarette smoking for the foetus/newborn since tobacco contains nicotine.

The paper interprets a comparison of two mostly used techniques of a field-oriented control for 6-phase electric drives, with their pros and cons, as well as their differences in construction and behaviour. Both of these approaches have been realized. Frequency and step responses analysis have been demonstrated with a 6-phase permanent magnet synchronous machine. Experimental results have been compared with simulations based on a mathematical model.

In the early 1980s it was well established that Leray solutions of the unforced Navier-Stokes equations in Rn decay in energy norm for large time. With the works of T. Miyakawa, M. Schonbek and others it is now known that the energy decay rate cannot in general be any faster than t^-(n+2)/4 and is typically much slower. In contrast, we show in this note that, given an arbitrary Leray solution u(.,t), the difference of any two Stokes approximations to the Navier-Stokes flow u(.,t) will always decay at least as fast as t^-(n+2)/4, no matter how slow the decay of || u(.,t) ||_L2 might happen to be.

Bioethanol has many environmental and practical benefits as a transportation fuel. It is one of the best alternatives to replace fossil fuels due to its liquid nature which is similar to petrol and diesel fuels traditionally used in transportation. In addition, bioethanol production technology has the capacity for negative carbon emissions which is vital for solving the current global warming dilemma. However, conventional bioethanol production takes place based on an inland site and relies on freshwater and edible crops (or land suitable for edible crop production) for production, which has led to the food vs fuel debate. Establishing a coastal marine biorefinery (CMB) system for bioethanol production that is based on coastal sites and relies on marine resources (seawater, marine biomass and marine yeast) could be the ultimate solution. In this paper, we aim to evaluate the environmental impact of using seawater for bioethanol production at coastal locations as a step towards the evaluation of a CMB system. Hence, a life cycle assessment for bioethanol production was conducted using the proposed scenario named Coastal-Seawater and compared to the conventional scenario, named Inland-Freshwater (IF). The impact of each scenario in relation to climate change, water depletion, land use and fossil depletion was studied for comparison. The coastal-seawater scenario demonstrated an improvement upon the conventional scenario in all the selected impact categories. In particular, the use of seawater in the process had a significant effect on water depletion showing an impact reduction of 31.2%. Furthermore, reductions are demonstrated in natural land transformation, climate change and fossil depletion of 5.5%, 3.5% and 4.2% respectively. This indicates the positive impact of using seawater and coastal locations for bioethanol production and encourages research to investigate the CMB system.

Hyperleptinemia potentiates the effects of many atherogenic factors, such as inflammation, platelet aggregation, migration, hypertrophy, proliferation of vascular smooth muscle cells, and endothelial cell dysfunction. The present study analysed the effects of long-term hyperleptinemia in an in vivo myocardial ischemia-reperfusion model to demonstrate whether the in vivo deleterious effect also affects cardiac structure and function. Rats by were subcutaneously administered leptin for 8 days to estimate the involvement of the JAK/STAT pathway. Data from 58 male Wistar rats were included in the final analysis. Myocardial infarction (MI) was modelled by the 30-minute ligation of the main left coronary artery followed by 120-minute reperfusion. Hemodynamic measurements, electrocardiography monitoring, echocardiography, myocardial infarct size and area at risk, blood biochemical parameters, leptin, IL-6, TNF-alpha, FGF-21, and cardiomyocyte morphology were measured. Statistical analyses were performed using IBM SPSS Statistics v.26. Seven-day hyperleptinemia in rats led to increased an blood pressure and heart rate, myocardial hypertrophy, impaired LV function, an increased frequency of ischemic arrhythmias, dyslipidaemia, systemic inflammation, and an increased size of induced myocardial infarction. The blockade of the JAK/STAT signalling pathway effectively reversed the negative effects of leptin, including increased blood pressure and total cholesterol.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

The electrification of rural areas of the planet has become one of the greatest challenges for sustainability. In fact, it would be the key to guaranteeing development for the poorest of the planet, but from which most of the raw material for the food market derives. The paradigm of centralized production is not applicable in these territories, because the distribution network would involve unjustifiable costs. For this reason, studies have multiplied to ensure the energy supply, especially electricity, of off-grid utilities, to guarantee energy autonomy while reducing the dependence on specialist assistance for the management of the system. In this work, a hybrid system (HRES) is proposed that combines the exploitation of solar energy with that of the wind through the use of static devices, in order to improve the system's availability and limit the cost of operation and maintenance.

Supplier selection (SC) continues to be a crucial component in manufacturing procurement. Using the PLCC advantages generates revenue and lowers costs more than might be anticipated. Supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. Past literature on this topic is limited. We try to close this gap in knowledge. In this study, we attempt to apply two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we are comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology Company; this cloud makes company decision–makers focus on low PLCC and select a better supplier in sustainable supply chains development.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic systemic inflammation causing progressive joint damage that can lead to lifelong disability. The pathogenesis of RA involves a complex network of various cytokines and cells that trigger synovial cell proliferation and cause damage to both cartilage and bone. Involvement of the cytokines TNF- and IL-6 is central to the pathogenesis of RA, but recent research has revealed that other cytokines such as IL-17, IL-23, GM-CSF, IL-33, and IL-2 also play a role. Clarification of RA pathology has led to the development of therapeutic agents such as biological disease-modifying anti-rheumatic drugs (DMARDs) and Janus kinase (JAK) inhibitors, and further details of the immunological background to RA are emerging. This review covers existing knowledge regarding the roles of cytokines, related immune cells and the immune system in RA, manipulation of which may offer the potential for even safer and more effective treatments in the future.

: Supplement the use of ergogenic aids in cyclist’s directly have been improved the body metabolism and hemodynamic factors that are micro supplement in chancing reactions on the body muscle mass and limb muscle. Mostly knowing that, muscle power development progressive fast glycolytic and short time oxidative systems reactions. Sport competition intervals, therefore, during periods has been used specific drinks supported to cyclists. But, be obtained during should be long race times. Athletes directly needed some drug and fluid intake to prevented from metabolic breakdown rapidly the dynamic physiologic performance factors. Beta-alanine supplementation can be direct muscle performance development affects the anaerobic metabolism and capacity. It should be de-termined how the cyclists will use the competitive and training period intervals can increase the cyclists specific sprint and endurance race performance. Science cyclist International Road doses will be created in which, intervals can random effectively the investigate. This study random a cohort studies is examined the effects of beta-alanine supplementation on aerobic and anaerobic power output in specific cyclists. Therefore, we have been databases PubMed, Scopus and Medline initial search 10 August 2020 were created prospective effect the quality of bias work concluded effect size (ES) 95% confidence interval (CI) were used in participant. Participations (N=66) have age range 25 to 38 of the using beta-alanine in training periods to endurance muscle performance, aerobic power, anaerobic power, and sprint time trials. As a result of beta-alanine improved an-aerobic and aerobic power output on 4-week time-dependent trial performance condition. Signifi-cant values are obtained level factor alpha <0.05 and p-value analysis pre-post interactive stand-ardization.

With increased participation in telework expected to continue, to support emerging hybrid work models in the aftermath of the Covid-19, it is important to consider the long-term impact this practice could have on sustainability outcomes. This paper describes a systematic review of 113 academic journal articles and identifies associations between telework and sustainability, explored by previous researchers. Those associations were categorized and discussed, based on their contributions to different United Nations Social Development Goals. Most of research was found to focus on countries classified as having a very high human development index status, and regions with a low, medium or high human development index, largely ignored. The SWOT matrix technique was used to illustrate the strengths and weaknesses identified in the current literature as well as threats and opportunities for future work. This can help to ensure policy coherence and that strategies to promote one outcome, such as economic productivity improvements, does not undermine another, such as improved health. Practical implications and potential research opportunities were identified across a range of SDG impact areas, including good health and well-being, gender equality, reduced inequality, climate mitigation, sustainable cities and resilient communities. On the whole, our impression is that increased rates of telework present an important opportunity to improve sustainability outcomes, however, it will be important that integrated and holistic policy is developed that mitigates key risks.

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can effectively improve the spectrum efficiency and energy efficiency of the wireless networks with extend coverage. An important design issue is to incentivize a relaying center user to participate in the cooperative process and achieve a win-win situation to both the BS and the center user. Some private information of the center users are hidden from the BS in the networks. We apply a contract theory-based incentive mechanism under such asymmetric information scenario to incentives center user to join the cooperative communication to maximize the BS profit utility and to guarantee the center user’s expect payoff. A match theory-based Gale-Shapley algorithm is proposed to obtain the optimal strategy with low computation complexity. Simulation results indicated the network performance of our proposed cooperative transmission is much better than the conventional NOMA transmission and the benefit utility of the BS with the stable match strategy is nearly close to the complete channel state information multi-users scenario while the center users get the satisfied expect payoffs.

Neural decoding from spiking activity is an essential tool for understanding the information encoded in population neurons, especially in applications like brain-computer interface (BCI). Various quantitative methods have been proposed and have shown superiorities under different scenarios respectively. From the machine learning perspective, the decoding task is to map the high-dimensional spatial &amp; temporal neuronal activity to the low-dimensional physical quantities (e.g., velocity, position). Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. Recently, the Transformer-based models are widely used in processing natural languages and images due to its superior performances in handling long-range and global dependencies. Hence, in this work we examine the potential applications of Transformers in neural decoding and introduce two Transformer-based models. Besides adapting the Transformer to neuronal data, we also propose a data augmentation method for overcoming the data shortage issue. We test our models on three experimental datasets and their performances are comparable to the previous state-of-the-art (SOTA) RNN-based methods. In addition, Transformer-based models show increased decoding performances when the input sequences are longer, while LSTM-based models deteriorate quickly. Our research suggests that Transformer-based models are important additions to the existing neural decoding solutions, especially for large datasets with long temporal dependencies.

The extracellular matrix (ECM) plays a key role as both structural scaffold and regulator of cell signal transduction in tissues. In times of ECM assembly and turnover, cells upregulate assembly of the ECM protein, fibronectin (FN). FN is assembled by cells into viscoelastic fibrils that can bind upward of 40 distinct growth factors and cytokines. These fibrils play a key role in assembling a provisional ECM during embryonic development and wound healing. Fibril assembly is also often upregulated during disease states, including cancer and fibrotic diseases. FN fibrils have unique mechanical properties, which allow them to alter mechanotransduction signals sensed and relayed by cells. Binding of soluble growth factors to FN fibrils alters signal transduction from these proteins, while binding of other ECM proteins, including collagens, elastins, and proteoglycans, to FN fibrils facilitates the maturation and tissue specificity of the ECM. In this review, we will discuss the assembly of FN fibrils from individual FN molecules; the composition, structure, and mechanics of FN fibrils; the interaction of FN fibrils with other ECM proteins and growth factors; the role of FN in transmitting mechanobiology signaling events; and approaches for studying the mechanics of FN fibrils.

One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

The Anthropocene has created a new cartography where various discursive levels are intertwined. It unites two fields of knowledge: geology and anthropology. In the 19th century, Romanticism challenged the separation between natural sciences and spirit sciences. With the Anthropocene a geological era is established, but with an epistemological dimension: environmental catastrophes are not a passive "object", they become an agent of social and political change. Images of the world (Weltbild) turn nature into an animated whole that challenges the dual vision: observer and observed. There is no nature without "observer", nor geology without anthropology. The Anthropocene modifies the foundations of our view of the world where we had excluded life. This is how concepts such as symbiogenesis, homeostasis, etc., which make visible and try to explain phenomena that are otherwise inexplicable. The Naturwissenschaft by J.W. Goethe is a point of support, with all these ideas that develop in the 20th century and anticipate the Anthropocene term of the 21th century. While the concepts of "belief" and "science" continue to be sharpened, rehabilitating "old quarrels" around anthropology, cosmology, theology, etc. The dignity of man is at stake.

At the plasma membrane, transmembrane receptors are at the interface between cells and their environment. They allow sensing and transduction of chemical and mechanical extracellular signals. The spatial distribution of receptors and the specific recruitment of receptor subunits to the cell membrane is crucial for the regulation of signaling and cell behavior. However, it is challenging to define what regulates such spatial patterns for receptor localization, as cell shapes are extremely diverse when cells are maintained in standard culture conditions. Bone morphogenic protein receptors (BMPRs) are serine-threonine kinases, which build heteromeric complexes of BMPRI and II. These are especially interesting targets for receptor distribution studies, since the signaling pathways triggered by BMPR-complexes depends on their dimerization mode. They might exist as pre-formed complexes, or assemble upon binding of BMP, triggering cell signaling which leads to differentiation or migration. In this work we analyzed BMPR receptor distributions in single cells grown on micropatterns, which allows not only to control cell shape, but also the distribution of intracellular organelles and protein assemblies. We developed a script called ComRed (Center Of Mass Receptor Distribution), which uses center of mass calculations to analyze the shift and spread of receptor distributions according to the different cell shapes. ComRed was tested by simulating changes in experimental data, showing that shift and spread of distributions can be reliably detected. Our ComRed-based analysis of BMPR-complexes indicates that receptor distribution depends on cell polarization. The absence of a coordinated internalization after addition of BMP suggests that a rapid and continual recycling of BMPRs occurs. Receptor complexes formation and localization in cells induced by BMP might yield insights into the local regulation of different signaling pathways.

Glutamate excitotoxicity is implicated in the pathogenesis of many disorders, including stroke, traumatic brain injury, and Alzheimer’s disease, for which central insulin resistance is a comorbid condition. Massive glutamate release primarily through ionotropic N-methyl-D-aspartate receptors (NMDARs) causes a sustained rise in [Ca2+]i, followed by mitochondrial depolarization and an increase in intracellular O2• (superoxide) production. Recently, we found that insulin protected neurons against excitotoxicity by diminishing the delayed calcium deregulation (DCD), However, a role of insulin in superoxide production in excitotoxicity still needs to be clarified. The present study is aimed to investigate the effects of insulin on glutamate-evoked superoxide generation and DCD using the fluorescent indicators dihydroethidium, MitoSOX Red, and Fura-FF in rats cultured cortical neurons. We found that insulin significantly diminished both the intracellular and mitochondrial superoxide production in neurons exposed to glutamate and there was a strong linear correlation between [Ca2+]i and intracellular superoxide. MK 801, an inhibitor of NMDAR-gated Ca2+ influx, completely abrogated the glutamate effects in both the presence and absence of insulin. In experiments on sister cultures, insulin diminishes neuronal death. Thus, collectively, data obtained suggest that insulin diminishes glutamate-induced superoxide production in neurons via fall of [Ca2+]i increased and thereby improves viability of neurons

Background: Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 have cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. Methods: In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during pregnancy was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n=749) and 15 (n=793) was measured using the Illumina HumanMethylation 450k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by meta-analysis effect estimates from Gruzieva et al. 2019 and standardizing the sums. Using mixed effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income to needs ratio, nonmartial birth status, and saliva cell type proportions. Results: Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with median income to needs ratio of 1.4. In the third trimester, mean PM2.5 exposure levels were 27.9 ug/m3/day (standard deviation: 7.0, 23.7% of observations exceeded safety standards) and PM10 were 15.0 ug/m3/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 g/m3/day) was associated with -0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: -0.0732, 0.0158, p=0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 g/m3/day) was associated with -0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: -0.3038, 0.0095, p=0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with -0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: -0.043, -0.005, p=0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. Conclusion: In utero PM10 associated DNA methylation differences persist until age 15 and can be detected in saliva. Benchmarking the persistence and cell type generalizability is critical for epigenetic exposure biomarker assessment.

Each year, millions of individuals suffer from a non-healing wound, abnormal scarring, or injuries accompanied by an infection. For these cases, scientists are searching for new therapeutic interventions, from which one of the most promising is the use of extracellular vesicles (EVs). Naturally, EV-based signalling takes part in all four wound healing phases: hemostasis, inflammation, proliferation and remodelling. Such an extensive involvement of EVs suggests exploiting their action to modulate the impaired healing phase. Furthermore, next to their natural wound healing capacity, EVs can be engineered for better defined pharmaceutical purposes, such as carrying specific cargo or targeting specific destinations by labelling them with certain surface proteins. This review aims to promote scientific awareness in basic and translational research of EVs by summarizing the current knowledge about their natural role in each stage of skin repair and the most recent findings in application areas, such as wound healing, skin regeneration and treatment of dermal diseases, including the stem cell-derived, plant-derived and engineered EVs.

In 1998, the Lisbon Universal Exhibition – Expo'98 – led to an urban regeneration process on Lisbon’s waterfront. Following other cities, this event was a pretext to replace a depressed area and to re-connect it with the river, through the creation of a set of new spaces for common use along the water. For them, it was promoted a public art program, which can be considered quite innovative in the Portuguese context, and that resulted in a monumentalisation of Lisbon’s eastern riverfront, later extended to other areas. Behind this framework, this article aims to debate the relations between public art and the dynamics of urban regeneration at the end of the 20th century. For that, it will analyse: 1) the Expo'98’s public art program, comparing its initial assumptions with the final results; 2) the impact of this program, through the identification of public art’s placements before (1974-1998) and after (1999-2009) the event. As a result, it is possible to find that the placement of public art reveals the spaces that were "conquered" to the port system, and a dialectic between functional/economic and leisure/symbolic values. It is concluded that public art had a significant role in the urban processes of the late 20th century, which is quite evident in a discourse that considers it as a qualifying factor of urban space and a mean of economic and social development.

Human Rhinovirus (HRV) is the most common cause of upper respiratory infections and exacerbations of asthma. In this work, we selected 14 peptides (6 from HRV A and 8 from HRV C) encompassing potential CD4 T cell epitopes. Peptides were selected for being highly conserved in HRV A and C serotypes and predicted to bind to multiple HLA II molecules. We found positive T cell recall responses by IFNγ-ELISPOT assays to 8 peptides, validating 7 of them (3 from HRV A and 4 from HRV C) as CD4 T cell epitopes through intracellular cytokine staining assays. Additionally, we verified their promiscuous binding to multiple HLA II molecules by quantitative binding assays. According to their experimental HLA II binding profile, the combination of all these 7 epitopes could be presented and recognized by > 95 % of the world population. We actually determined IFNγ responses to a pool encompassing these CD4 T cell epitopes by intracellular cytokine staining, finding positive responses in 29 out of 30 donors. The CD4 T cell epitopes identified in this study could be key to monitor HRV infections and to develop peptide-based vaccines against most HRV A and C serotypes.

Choline kinase inhibitors are an important class of cytotoxic compounds useful for the treatment of different forms of cancer since aberrant choline metabolism is a feature of neoplastic cells. Here we present the characterization and the structure activity relationship of a series of non-symmetrical choline kinase inhibitors characterized by a 3-aminophenol moiety, bound to 4-(dimethylamino)- or 4-(pyrrolidin-1-yl)pyridinium cationic heads through several linkers. These derivatives were evaluated both for their inhibitory activity on the enzyme and for their antiproliferative activity in a panel of six human tumor cell lines. The compounds with the best inhibitory results were those connected to the linker by the N-atom (4a-h) and these results are supported by docking studies. The compounds with the best antiproliferative results were those connected to the linker by the O-atom (3a-h). On the other hand, as was predictable in both families, the inhibitory effect on the enzyme is greater the shorter the length of the linker, while in tumor cells, lipophilicity and choline uptake inhibition could play a decisive role. Interestingly compounds 3c and 4f, selected for both their ability to inhibit the enzyme and good antiproliferative activity, are endowed with a low toxicity in non-tumoral cells (e.g human peripheral lymphocytes) respect to cancer cells. These compounds were also able to induce to induce apoptosis in Jurkat leukemic cells without causing significative variations of cell cycle. It is worth to mention that these derivatives, beside their inhibitory effect on choline kinase, displayed a modest ability to inhibit choline uptake thus suggesting that this mechanism may also contribute to the observed cytotoxicity.