The present study provides an observation and documentation on the effect of static magnetic field on the growth of Triticum aestivum. The seeds are allowed to germinate and the seedlings are grown in an environment of the controlled static magnetic field while other physical factors such as temperature, light exposure, soil pH, etc. are held constant. Also, a comparison of influence on the average height between both the poles of the magnet is estimated and north pole oriented magnet is found to imply a significant difference. Finally some of the potential applications of the study in non-invasive protein silencing, GMO production, Vertical farming and Terraforming are briefly discussed.

Information molecules of DNA and RNA should obey principles of quantum mechanics where unitary operators in form of unitary matrices have key meanings. Unitary matrices are the basis of calculations in quantum computers. This article presents some author's results, which show that matrix forms of the representation of structured systems of molecular-genetic alphabets can be considered as sets of sparse unitary matrices related with phenomenologic features of the degeneracy of the genetic code. These sparse unitary matrices have orthogonal systems of functions in their rows and columns. A complementarity exists among some unitary genetic matrices in relation each other. Decompositions of numeric genetic matrices into sets of sparse unitary matrices are connected with the logical operation of modulo-2 addition used in quantum computers as well. Tensor (or Kronecker) families of unitary genetic matrices with their fractal-like properties are also considered. The described results are discussed in the frame of development of quantum-information approaches for modeling genetic systems.

Southern highbush blueberry plantations have been expanded into worldwide non-traditional growing areas with elite cultivars and improved horticultural practices. This article presents a comprehensive review of current production systems – alternatives to traditional open field production – such as production in protected environments, high-density plantings, evergreen production, and container-based production. We discuss the advantages and disadvantages of each system and compare their differences to the open field production. In addition, potential solutions have been provided for some of the disadvantages. We also highlight some of the gaps existing between academic studies and production in industry, providing a guide for future academic research. All these alternative systems have shown the potential to produce high yields with high quality berries. Alternative systems, compared to the field production, require higher establishment investments and thus create an entry barrier for new producers. Nevertheless, with their advantages, alternative productions have potential to be profitable.

(1) Background: the NGS based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization. (2) Methods: We implemented and validated the complete automation of the SOPHiA GENETICS’ CE-IVD Hereditary Cancer Solution™ (HCS) libraries preparation workflow on the Hamilton’s STARlet platform. (3) Results: We demonstrate that this automated workflow, used for more than 1000 samples achieved the same performances of manual setup in terms of coverages and reads uniformity, with extremely lower variability of reads mapping rate onto the regions of interest. (4) Conclusions: This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.

The Late Heavy Bombardment Period (4.1 to 3.8 billion years ago) of heightened impact cratering activity on young Earth is likely the driving force for the origin of life. During the Eoarchean, asteroids such as carbonaceous chondrites delivered the building blocks of life and water to early Earth. Asteroid collisions created innumerable hydrothermal crater lakes in the Eoarchean crust which inadvertently became the perfect cradle for prebiotic chemistry. These hydrothermal crater lakes were filled with cosmic water and the building blocks of life. forming a thick prebiotic soup. The unique combination of exogenous delivery of extraterrestrial building blocks of life, and the endogenous biosynthesis in hydrothermal impact crater lakes very likely gave rise to life. A new symbiotic model for the origin of life within the hydrothermal crater lakes is here proposed. In this scenario, life arose around four billion years ago through five hierarchical stages of increasing molecular complexity: cosmic, geologic, chemical, information, and biological. During the prebiotic synthesis, membranes first appeared in the hydrothermal crater lakes, followed by the simultaneous origin of RNA and protein molecules, creating the RNA/protein world. These proteins were noncoded protein enzymes that facilitated chemical reactions. RNA molecules formed in the hydrothermal crater basin by polymerization of the nucleotides on the montmorillonite mineral substrate. Similarly, the initial synthesis of abiotic protein enzymes was mediated by the condensation of amino acids on pyrite surfaces. The regular wet-dry cycles within the crater lakes assisted further concentration, condensation, and polymerization of the RNAs and proteins. Lipid membranes randomly encapsulated amino acids, RNA, and protein molecules from the prebiotic soup to initiate a molecular symbiosis inside the protocells, this led to the hierarchical emergence of several cell components. As the role of protein enzymes became essential for catalytic process in the RNA/protein world, Darwinian selection from noncoded to coded protein synthesis led to translation systems and the genetic code, heralding the information stage. In this stage, the biochemical pathways suggest the successive emergence of translation machineries such as tRNAs, aaRS, mRNAs, and of ribosomes for protein synthesis. The molecular attraction between tRNA and amino acid led to the emergence of translation machinery and the genetic code. tRNA is an ancient molecule that created mRNA for the purpose of storing amino acid information like a digital strip. Each mRNA strand became the storage device for genetic information that encoded the amino acid sequences in triplet nucleotides. As information became available in the digital languages of the codon within mRNA, biosynthesis became less random and more organized and directional. The original translation machinery was simpler before the emergence of the ribosome than that of today. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and adaptive theory. We believe that these three theories are not mutually exclusive, but are compatible with our coevolution model of translations machines and the genetic code. We suggest biosynthetic pathways as the origin of the translation machine that provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machinery: GNC code with four codons and four amino acids during interactions of pre-tRNA/pre-aaRS /pre-mRNA, SNS code consisting of 16 codons and 10 amino acids appeared during the tRNA/aaRS/mRNA interaction, and finally the universal genetic code evolved with the emergence of the tRNA/aaRS/mRNA/ribosome machine. The universal code consists of 64 codons and 20 amino acids, with a redundancy that minimizes errors in translation. To address the question of the origin of the biological information system in the RNA/protein world, we converted letter codons into numerical codons in the Universal Genetic Code Table. We developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa, gaining insight into how translation might have worked in the RNA/protein world. We simulated the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. We used AnyLogic software to simulate and visualize the evolution of the translation machines and the genetic code. We conclude that the emergence of the information age from the RNA/protein world was a watershed event in the origin of life about four billion years ago.

Forensic genetic genealogy, a technique leveraging new DNA capabilities and public genetic databases to identify suspects, raises specific considerations in a law enforcement context. Use of this technique requires consideration of its scientific and technical limitations, including the composition of current online datasets, and consideration of its scientific validity. Additionally, forensic genetic genealogy needs to be considered in the relevant legal context to determine the best way in which to make use of its potential to generate investigative leads while minimising its impact on individual privacy. This article presents these issues from an Australian perspective, with the observations and conclusions likely to be applicable to other jurisdictions.

Hearing impairment is an immensely diagnosed genetic cause, 5% of the total world population effects with different kind of congenital hearing loss (HL). In third-world countries or countries where consanguineous marriages are more common the frequency rate of genetic disorders are at its zenith. Approximately, the incidence of hearing afflictions is ostensibly 7-8:1000 individuals whereas it is estimated that about 466 million peoples suffer with significant HL, and of theses deaf cases 34 million are children’s up to March, 2020. Several genes and colossal numbers of pathogenic variants cause hearing impairment, which aided in next-generation with recessive, dominant or X-linked inheritance traits. This review highlights on syndromic and non-syndromic HL (SHL and NSHL), and categorized as conductive, sensorineural and mixed HL, which having autosomal dominant and recessive, and X-linked or mitochondrial mode of inheritance. Many hundred genes involved in HL are reported, and their mutation spectrum becomes very wide. Mapping of pathogenic genes in consanguinity family is facilitated to understand the disease history. Review presents the bases of HL and also focused on various genetic factors that cause deafness like the basics of genetic inheritance, and classic and well-characterized inherited factors of it. It also overviews the application of linkage analysis, SNPs genotyping and whole exome sequencing methods, in mapping and identification of new locus, causative genes and their variants in families inherited with HL. Conclusively, this review supports researchers in understanding the location of chromosome, the causative genes and specific locus which causing deafness in humans.

External quantum efficiency of industrial-grade green InGaN light-emitting diodes (LEDs) has been measured in a wide range of operating currents at various temperatures from 13 K to 300 K. Unlike blue LEDs, the efficiency as a function of current is found to have a multi-peak character, which could not be fitted by a simple ABC-model. This observation correlated with splitting of LED emission spectra into two peaks at certain currents. The characterization data are interpreted in terms of non-uniformity of the LED active region, which is tentatively attributed to extended defects like V-pits. We suggest a new approach to evaluation of temperature-dependent light extraction and internal quantum efficiencies taking into account the active region non-uniformity. As a result, the temperature dependence of light extraction and internal quantum efficiencies have been evaluated in the temperature range mentioned above and compared with those of blue LEDs.

This article presents answers to the questions on superresolution and structured illumination microscopy as raised in the editorial of a recent publication [K. Prakash et al. arXiv, 2102.13649, 2021]. The answers are based on my personal views on superresolution in light microscopy, supported by reasoning. Discussed are the definition of superresolution, Abbe’s resolution limit and the classification of superresolution methods into non-linear-, prior-knowledge- and near-field-based superresolution. A further focus is put on capabilities and technical aspects of present and future structured illumination microscopy (SIM) methods.

Over the past decade, the credit quality of loan portfolios across most countries in the world remained relatively stable until the financial crises hit the global economy in 2007–2008. In this paper I discuss, the relationship between non-performing loans and economic landscape. Since 2008, nonperforming loans have been an increasingly hot topic in the international scene, due to their important and rising volume and their impact on the economy as a whole, on the banking system and on its credit supply. Since then, average bank asset quality deteriorated sharply due to the global economic recession. Yet the deterioration of loan performance was very uneven across countries. I am interested in explaining these differences in bank asset quality across countries and over time. In this paper, I therefore study the empirical determinants of non-performing loan (NPL) ratios using a data set for EU countries covering the past decade. The paper assumes that the spatial organization of banking systems and the geographical distribution of comercial banks branches, ATMs and GDP growth are major factors influencing the effectiveness in credit system. The aim of this paper is to construct a continuous and quantifiable model, which will demonstrate a role of economic condition, technology, competition, policy, business climate in Financial Stability. Main hypotheses suggests, that GDP growth, interest rate, new business, FDI, ATMs and geographical distribution of branches have an influences on NPL (non-performing loans).

Omega-3 polyunsaturated fatty acids (ω3-PUFAs), including docosahexaenoic acid (DHA), have been shown to exert anticancer effects by inducing apoptotic cell death. However, the mechanism for DHA-induced cell death in lung cancer is not fully understood. Here, we show that DHA induces apoptosis in two human EGFR mutant non-small cell lung cancer (NSCLC) cell lines, and that DHA-induced cell death is accompanied by SIRT6 activation and attenuated Hedgehog (Hh) signaling. Knockdown of SIRT6 using siRNAs inhibited DHA-induced apoptosis, whereas SIRT6 overexpression increased apoptotic cell death. DHA-induced SIRT6 activation was associated with downregulation of Hh signaling, and knockdown of SIRT6 resulted in augmentation of Hh signaling. Pretreatment of NSCLC cells with a Smoothened agonist prevented DHA-induced decreases in the levels of Hh signaling proteins and increases in cleaved PARP levels. Moreover, endogenous production of ω3-PUFAs in PC9 cells via fat-1 expression resulted in elevated SIRT6 levels and reduced levels of Hh signaling molecules, including Gli, following DHA treatment. Overall, these results implicate that ω3-PUFAs induce apoptosis by downregulating Hh signaling via SIRT6 activation in human EGFR mutant NSCLC cells. These findings suggest that ω3-PUFAs potentially represent an effective therapy for the chemoprevention and treatment of NSCLC.

The present paper aims to increase knowlodge of the methods of resistance estimating of concrete in situ by means of non-destructive tests used to integrate the quantitative results from cylindrical specimens (core). The results of experimental investigations carried out on concrete conglomerate samples of a school building are shown. The experimental campaign then will be presented like a case study, conducted on a series of concrete beams and pillars of an existing building. The distructive tests on cores were conducted at the Civil Structures Laboratory of the Engineering Department of the University of Campania "Luigi Vanvitelli". The expression obtained through the calibration procedure of the values of non-destructive tests with those provided by the core drills allowed to estimate the average values of the compressive strength of the concrete. It is highlighted how this result was achieved with a very limited core number provided that they are extracted in selected points and that there was a proportionality link with the resistances obtained from non distructive tests.

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

This paper focuses on evaluating airline service quality from the perspective of passengers view. Until now a lot of researches has performed in airline service quality evaluation in the world but a little research has been conducted in Iran, yet. In this research a framework for measuring airline service quality in Iran is proposed. After reviewing airline service quality criteria, SSQAI model was selected because of its comprehensiveness in covering airline service quality dimensions. SSQAI questionnaire items were redesigned to adopt with Iranian airlines requirements and environmental circumstances in the Iran's economic and cultural context. This study includes fuzzy decision-making theory, considering the possible fuzzy subjective judgment of the evaluators during airline service quality evaluation. Fuzzy TOPSIS have been applied for ranking airlines service quality performances. Three major Iranian airlines which have the most passenger transfer volumes in domestic and foreign flights, were chosen for evaluation in this research. Results demonstrated Mahan airline has got the best service quality performance rank in gaining passengers' satisfaction with delivery of high quality services to its passengers, among the three major Iranian airlines. IranAir and Aseman airlines placed in the second and third rank, respectively, according to passenger's evaluation.Statistical analysis have been used in analyzing passenger responses. Due to abnormality of data, Non-parametric tests were applied. To demonstrate airline ranks in every criterion separately, Friedman test was performed. Variance analysis and Tukey test were applied to study the influence of increasing in age and educational level of passengers' on degree of their satisfaction from airline's service quality. Results showed that age has not significant relation with passenger satisfaction of airlines, however increasing in educational level demonstrated a negative impact on passengers' satisfaction from airline's service quality.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

Laws and regulations make powerful contribution in addressing multitudes of public health concerns. We examined the Public Health Act (PHA) in Malawi to understand its relevance to the ever-growing and changing threats posed by infectious and non-infectious diseases. The current Public Health Act of Malawi came into effect in 1948 to protect and preserve public health. The Act has undergone several amendments, the last one being in 1975. It draws much of its inspiration and standards from the 19th century British laws on insanitary housing, poor ventilation, and drainage. Such laws are silent on emerging major public health concerns including non-communicable diseases (NCDs) such as cardiovascular diseases and diabetes as well as road traffic injuries. This makes the Act outdated and ill equipped to address the 21st century public health concerns. Although supplementary legislation such as the HIV/AIDS Act and Mental Treatment Act have recently been enacted, they are yet to be consolidated into the Public Health Act. Consequently, existing policies and strategic plans that are meant to address gaps in public health and ensure coordinated effort lack support of laws and regulations. The Act also places great emphasis on mandatory vaccinations, quarantine and isolation against smallpox, a disease that has long been eradicated. Furthermore, although the Public Health Act outlines powers, duties and penalties, it fails to reinforce acceptable behaviour due to the insignificant penalties for noncompliance. There is a need for immediate and prompt revision and restructuring of the Public Health Act based on scientific evidence. Such laws require adequate consultation and interaction with key experts and stakeholders from a wide range of disciplines.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Forest community connections are crucial to ensure forest stewardship and sustainability. We explored the potential of mushrooming to enable such connections in contexts where these connections have been historically broken, alienating local people from forests. Taking the case of the recent devolution of a community forest (baldios) in central Portugal to the local population, we present a five-year pilot project to rework mycology from a mushroom-centered approach to a mushroom-in-baldios approach. Mushrooms were used as an entry-point to connect the forest ecology with the challenges of governance and community building. The devised activities provided an opportunity for people inside and outside the local community to adventure into the woods and find out more about their socio-ecological history, develop communal and convivial relationships and engage in the responsible gathering of wild mushrooms. However, the hosting of mushroomers to know, value and engage with the community forest recovery has constantly working against the enclosure of mushrooms to provide marketable forms of leisure. The outcome of these activities depends on the relationships established between mushrooms, mycologists, local administrators, commoners and poachers, all operating within a framework that favors the eradication of resources instead of long-term relationships that sustain places.

According to the operation conditions of time unlimited passive containment heat removal system (TUPAC), a separate effect experiment facility was established to investigate the heat transfer performance of steam condensation in presence of non-condensable gas. The effect of wall subcooling temperature, total pressure and mass fraction of the air on heat transfer process was analyzed. The heat transfer model was also developed. The results showed that the heat transfer coefficient decreased with the rising of subcooling temperature, the decreasing of the total pressure and air mass fraction. It was revealed that Dehbi’s correlation predicted the heat transfer coefficient conservatively, especially in the low pressure and low temperature region. The novel correlation was fitted by the data obtained in the following range: 0.20~0.45 MPa in pressure, 20% ~ 80% in mass fraction, 15°C ~ 45°C in temperature. The discrepancy of the correlation and experiment data was with ±20%.

All life on Earth uses three integrated molecular systems in which genetic information contained in DNA base sequences is transmitted to ribosomes by RNA and a genetic code, then translated into the amino acid sequences of structural and catalytic proteins. Therefore, the most important point for understanding the origin of life is to determine how such systems could emerge from random processes on the early Earth. In this review, two alternatives are compared: the RNA world hypothesis and the [GADV]-protein world hypothesis. [GADV] refers to four amino acids, Gly [G], Ala [A], Asp [D] and Val [V] that are conserved in the amino acid sequences of many common proteins. Here I will argue that the origins of the three primary processes required for life to begin can be better explained by the GADV hypothesis than the RNA world hypothesis. The GADV hypothesis also incorporates a conversion process by which random polymers can evolve into proteins with ordered sequences.

The remaining capacity can only be measured with offline method. This brings great challenge for the online prediction of Li-ion battery’s RUL. A novel online prediction method for Li-ion battery’s RUL was proposed, which is based on multiple health indicators (HIs) and can be derived from the batteries’ historical operation data. Firstly, four indirect HIs were built according to the battery’s operation current, voltage and temperature data respectively. On that basis, a generalized regression neural network (GRNN) was developed to estimate the battery’s remaining capacity, and the non-linear autoregressive approach (NAR) was utilized to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising were performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

Preconception lifestyle modification and reducing several known risk factors may have an influence on the future pregnancy outcome. The aim of the study was to analyse health behaviour and personal values, as well as to assess the relationship of these factors in women without children, in pregnant ones and in women who had already delivered babies. The questionnaire survey included Health Behaviour Inventory (HBI), Personal Value List (PVL) and the sociodemographic data and was conducted in 538 women. These women were divided into 3 groups: women who recently delivered (n = 235), pregnant women (n = 121) and childless women (n = 182). Pregnant women demonstrated a significantly higher level of declared health behaviours and also they rated higher subscales values "positive mental attitude" and "health practices" in comparison to women who recently delivered and to childless women. In all tested groups the highest rated personal value was "a successful family life", while the most appreciated symbol of happiness was "love and friendship". Our results suggest that the system of values and the perception of happiness symbols may influence women's health behaviours. Positioning “health” in the hierarchy of personal values as the most important one may facilitate introduction of healthy behaviours. This in turn could reduce several adverse pregnancy outcomes that are potentially modifiable with changing preconception health attitudes. Our results also identify several unanswered questions and highlights areas where new research is needed.

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

Bacterial infections occur when wound healing fails to reach the final stage of healing, usually hindered by the presence of different pathogens. Different topical antimicrobial agents are used to inhibit bacterial growth due to antibiotic failure in reaching the infected site accompanied very often by an increased drug resistance and other side effects. In this review, we focus on antimicrobial peptides (AMPs), especially those with a high potential of efficacy against multidrug-resistant and biofilm-forming bacteria and fungi present in wound infections. Currently, different AMPs undergo preclinical and clinical phase to combat infection-related diseases. AMP dendrimers (AMPDs) have been mentioned as potent microbial agents. Various AMP delivery strategies, such as polymers, scaffolds, films and wound dressings, organic and inorganic nanoparticles, to combat infection and modulate the healing rate have been discussed as well. New technologies such as CRISPR-Cas are taken into consideration as potential future tools for AMP delivery in skin therapy.

The burdens of malnutrition, protein and micronutrient deficiency, and obesity cause enormous costs to society. Crop nutritional quality has been compromised by the emphasis on edible yield and through the loss of biodiversity due to the introduction of high-yielding uniform cultivars. Heirloom crop cultivars are traditional cultivars grown for a long time (> 50 years), and whose heritage has been preserved by regional, ethnic or family groups. Heirlooms are recognized for their unique appearance, names, uses and historical significance. They are gaining in popularity because of their unique flavors and cultural significance to local cuisine, and their role in sustainable food production for small-scale farmers. As a contrast to modern cultivars, heirlooms may offer a welcome alternative in certain markets. Recently, market channels have emerged for heirloom cultivars in the form of farmer-breeder-chef collaborations and seed savers organizations. There is therefore urgent need to know more about the traits available in heirloom cultivars, particularly for productivity, stress tolerance, proximate composition, sensory quality and flavor. This information is scattered and the intention of this review is to document some of the unique characteristics of heirloom cultivars that may be channeled into breeding programs for developing locally adapted high value cultivars.

The rapid spread of the coronavirus disease 2019 (COVID-19) is a serious threat to public health systems globally and is subsequently, a cause of anxiety and panic within human society. Understanding the mechanisms and reducing the chances of having severe symptoms from COVID-19 will play an essential role in treating the disease, and become an urgent task to calm the panic. However, the COVID-19 test developed to identify virus carriers is unable to predict symptom development in individuals upon infection. Experiences from other plagues in human history and COVID-19 statistics suggest that genetic factors may determine the compliance with the virus, i.e., severe, mild, and asymptomatic. Here, a hypothesis is put forward based on the epidemiological characteristics and traits of COVID-19, and our gene expression analysis. It proposes that COVID-19 inactivation in the blood by blocking virus entry into other internal organs for reproduction through the blood circulation after lung cell invasion prevents severe symptoms. Additionally, we investigated a genetic connection between candidate genes and severe COVID-19 symptoms through the utilization of strategies combining hypothesis and data-driven approaches. A list of genes and important SNPs that require further investigation to aid the screening of individuals who may suffer severe illness if exposed to the virus is present. Those individuals should be intensively safeguarded and prioritised for treatment. Concurrently to further research on the COVID-19 pathogenesis, our results also offer a new research strategy for pandemic prevention and health maintenance.

An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric problem with a few core symptoms: weaknesses in social behavior, verbal impairments, repetitive behavior and restricted interests. Beyond the core symptoms, autism has strong association with other disorders such as intellectual disability, epilepsy, schizophrenia among many others. This paper outlines a theory of ASD with capacity to connect heterogeneous ‘core’ symptoms, medical and psychiatric comorbidities as well as other etiological theories of autism in a unifying cognitive framework rooted in neuroscience and genetics. Cognition is embedded into an ever-developing structure modified by experiences, including the outcomes of environment influencing behaviors. We introduce the hypothesis that autism is caused by deficits in component-based cognition and the internal learning reinforcing machinery. Specifically, we outline our Cartesian Factor forming autoencoder like model that supports cognition by breaking combinatorial explosion and discuss the cognitive and neural processes behind our model. The high dimensionality of sensory information poses serious problems, since the brain can handle only 7±2 relevant variables at a time making processes, such as the extraction and encoding of the relevant variables and their efficient manipulation critical. These processes are influenced by previous experiences and the internal reward system. In addition, large delays of distributed information processing should be counteracted by learned predictive models to synchronize sensory, proprioceptive, and cognitive signals and have timely and accurate model-based actions. Impairments in any of these aspects may disrupt learning and execution. Combinations of small impairments may allow the solving of low complexity tasks but may become visible if learned variables and the related metric are improper and imprecise, respectively, especially if their number is large. We claim that social interactions are amongst the most challenging cognitive tasks in terms of the number of variables involved. In turn, they are highly susceptible to combinations of small impairments. We consider impairments as the basic colors of autism, whereas the combinations of diverse impairments make the palette of autism. In turn, social processes can be spoiled in many ways and can lead to diverse comorbidities.

Since coffee has been shown to influence positively the metabolism of subjects with metabolic syndrome (MetS), we aimed to evaluate the short- and long-term effects of a coffee-based supplement on different features of diet-induced MetS. 24 Sprague Dawley rats were divided into control or nutraceuticals groups to receive a high-fat/high fructose diet with or without a mixture of caffeic acid (30 mg/day), trigonelline (20 mg/day), and cafestol (1 mg/day) for 12 weeks. An additional 11 rats were assigned to an acute crossover study. In the chronic experiment, nutraceuticals did not alter body weight or glycemic control but improved fed hyperinsulinemia and HOMA-IR, and plasma adiponectin levels. The impact of nutraceuticals on post-prandial glycemia tended to be more pronounced after acute administration than at the end of the chronic study. Circulating and intrahepatocellular alanine transaminase activity, assessed by hyperpolarized-¹³C NMR spectroscopy, were reduced by coffee nutraceuticals at endpoint. There was also a tendency towards lower liver triglyceride content and histological steatosis score in the intervention group. In conclusion, a mixture of coffee nutraceuticals improved insulin sensitivity and exhibited hepatoprotective effects in a rat model of MetS. Higher dosages with or without caffeine deserve to be studied in the future.

This article puts forward the bi-matrix games with crisp parametric payoffs based on interval value function approach. We conclude that the equilibrium solution of the game model can converted into optimal solutions of the pair of the non-linear optimization problem. Finally, experiment results show the efficiency of the model.

Water soluble polymers have gained an increasing interest in enhanced oil recovery (EOR) processes, especially as polymer flooding. Despite the fact that the flow of polymer in porous medium has been a research subject for many decades with numerous publications, there are still some research areas that need progress. The prediction of polymer injectivity remains elusive. Polymers with similar shear viscosity might have different in-situ rheological behaviors and may be exposed to different extent of mechanical degradation. Hence, determining polymer in-situ rheological behavior is of great significance for defining its utility. In this study, an investigation of rheological properties and mechanical degradation of different HPAM (partially hydrolyzed polyacrylamide) polymers was performed using Bentheimer sandstone outcrop cores. Results show that, HPAM in-situ rheology is different from bulk rheology measured in rheometer. Specifically, shear thickening behavior occurs at high rates, and near-Newtonian behavior is measured at low rates in porous media. This deviates strongly from measurements in the rheometer. Polymer molecular weight and concentration influence its viscoelasticity and subsequently its flow characteristics in porous media. Exposure to mechanical degradation by flow at high rate through porous media leads to significant reduction in shear thickening and thereby improved injectivity. More importantly, the degraded polymer maintained in-situ viscosity at low flow rates indicating that improved injectivity can be achieved without compromising viscosity at reservoir flow rates. This is explained by reduction in viscoelasticity. Mechanical degradation also leads to reduced residual resistance factor (RRF), especially for high polymer concentrations. For some of the polymer injections, successive degradation (increased degradation with transport length in porous media) was observed. The results presented here may be used to optimize polymer injectivity.

The Urban Heat Island (UHI) is one of the more serious consequences of urbanization resulting in impacts on thermal comfort levels, heat stress, and even mortality. For Municipal Beirut, implementation of “cool” surface materials and green spaces have been recommended to counterbalance the UHI. This paper builds on previous findings on the topic of non-constructible parcels within the district of Bachoura in Municipal Beirut and examines the possibility of implementing “cool” surface or paving materials and urban vegetation which can improve thermal conditions especially during the summer period and with the viewto project the positive findings of this case study to the entire Municipal Beirut area. A numerical analysis using ENVI-met 4.0 investigates the thermal performance of these non-constructibles further to implementation of high reflective surfaces and urban vegetation within a broad neighborhood scale in Bachoura. Results show reductions in ambient temperatures up to 1K on a summer day.. Within the framework of an integrated approach to planning, this form of urban acupuncture aims for substantial UHI reduction. Energy performance of buildings further to implementation of these mitigation measures is also recommended for future studies and to validate the findings in this paper.

In interepidemic periods, a sapronoses typically employs a variety of mechanisms for maintaining viability of its causative agent in terrestrial parasitic systems, associated with different adaptive strategies utilized by its populations to survive. Unlike spore-forming bacteria, causative agents of sapronoses use resistant cell forms, which is a viable but nonculturable (VBNC) state, and persistence. Implementation of these strategies is conditioned by effects of various stress factors of the habitat and is characterized by decreased metabolism, alteration of the morphology and physiology of bacterial cell, and cessation of its replication. It is important that the resistant forms of cells retain virulence and, as favorable conditions come, turn back into the active vegetative form again. The discovery of the genetic modules of bacterial toxin–antitoxin systems in recent years has made it possible to identify a number of complicated regulatory molecular mechanisms responsible for maintaining the pathogenic potential of resistant forms of causative agents of natural-focus sapronoses in interepidemic periods.

In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of vehicle semi-active energy regenerative suspension with electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and the mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using recursive least squares algorithm. On the basis of system principle, the nonlinear model of the semi-active energy regenerative suspension with EHA is built. Meanwhile, LQG control strategy of the system is designed. And then the influence of the main parameters of EHA on the damping performance and energy regenerative performance of suspension is analyzed. Finally, the main parameters of EHA actuator are optimized via genetic algorithm. The test results show that when sinusoidal is input at the frequency of 2Hz and the amplitude of 30mm, the spring mass acceleration RMS value of optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and energy regenerative power RMS value is increased by 40.51%, which means while meeting the requirements of certain vehicle ride comfort and driving safety, energy regenerative performance is improved significantly.

Acid lime [Citrus aurantifolia (Christm.) Swingle] is a fruit crop, enriched with high commercial value and is cultivated in 60 out of 75 districts representing all geographical landscapes of Nepal. Lack of high yielding cultivars is probably one of the main reason for its extremely reduced productivity which warrants a deep understanding of genetic diversity in existing germplasm. Hereby, we aim to access the genetic diversity of acid lime germplasm cultivated at 3-different ecological gradients of eastern Nepal employing PCR-based Inter-Simple Sequence Repeats markers (ISSR). Altogether, 21 polymorphic ISSR markers were used to assess the genetic diversity in 60 acid lime cultivars sampled from different geographical locations. Analysis of binary data matrix was performed on the basis of bands obtained, scoring of the data was done accordingly, and principal coordinate analysis and phenogram were constructed using different computer algorithms. ISSR profiling yielded 234 amplicons, of which 87.18% were found to be polymorphic. The number of amplified fragments ranged from 7-18 with amplicon size ranging from 250-3200 bp. The NTSYS based Cluster analysis using UPGMA algorithm taking Dice Similarity coefficient separated 60 accessions into 2-major and 3-minor clusters. The genetic diversity analysis revealed the highest for Terai and the lowest for High-hill zone. Cluster I comprised of accessions from High-hill and Mid-hill regions revealing the close genetic relationship, whereas cluster II comprised of accessions from all three agro-ecological zones and the exotic varieties. Furthermore, our results revealed the accessions harvested from different geographical gradients were not genetically distinct, but highest diversity was observed in Terai accessions in comparison to the regions belonging to the High and Mid-hills. Thus, our data indicate that the ISSR provides a better option for evaluating the genetic diversity of Nepalese Acid Lime cultivars and furnished significant information, assisting parental selection in current and future breeding programs and germplasm conservation which ultimately may help to provide a technological breakthrough for the farmers of the developing country like Nepal.

Small non-coding RNAs (sRNAs) are ubiquitously found in the three domains of life playing large-scale roles in gene regulation, transposable element silencing, and defense against foreign elements. While a substantial body of experimental work has been done to uncover function of sRNAs in Bacteria and Eukarya, the functional roles of sRNAs in Archaea are still poorly understood. Recently, high throughput studies using RNA-sequencing revealed that sRNAs are broadly expressed in the Archaea, comprising thousands of transcripts within the transcriptome during non-challenged and stressed conditions. Antisense sRNAs, which overlap a portion of a gene on the opposite strand (cis-acting), are the most abundantly expressed non-coding RNAs and they can be classified based on their binding patterns to mRNAs (3’ UTR, 5’ UTR, CDS-binding). These antisense sRNAs target many genes and pathways, suggesting extensive roles in gene regulation. Intergenic sRNAs are less abundantly expressed and their targets are difficult to find because of a lack of complete overlap between sRNAs and target mRNAs (trans-acting). While many sRNAs have been validated experimentally, a regulatory role has only been reported for very few of them. Further work is needed to elucidate sRNA-RNA binding mechanisms, the molecular determinants of sRNA-mediated regulation, whether protein components are involved, and how sRNAs integrate with complex regulatory networks.

The mitigation of NLOS (non-line-of-sight) propagation conditions is one of main challenges in wireless signals based indoor localization. When RFID localization technology is applied in applications, RSS fluctuates frequently due to the shade and multipath effect of RF signal, which could result in localization inaccuracy. In particularly, when tags carriers are walking in LOS (line-of-sight) and NLOS hybrid environment, great attenuation of RSS will happen, which would result in great location deviation. The paper proposes an IMU-assisted (Inertial Measurement Unit) RFID based indoor localization in LOS/NLOS hybrid environment. The proposed method includes three improvements over previous RSS based positioning methods: IMU aided RSS filtering, IMU aided LOS/NLOS distinguishing and IMU aided LOS/NLOS environment switching. Also, CRLB (Cramér-Rao Low Bound) is calculated to prove theoretically that indoor positioning accuracy for proposed method in LOS/NLOS mixed environment is higher than position precision of only use RSS information. Simulation and experiments are conducted to show that proposed method can reduce the mean positioning error to around 3 meters without site survey.

Water distribution networks are vital infrastructure, needed for providing consumers with sufficient water of appropriate quality. The cost of construction, operation, and maintenance of such networks is extremely large. The problem of optimization of a water distribution network is governed by the type of water distribution network and the size of pipelines placed in the distribution network. This problem of the optimal diameter allocation of pipes in a distribution network has been heavily researched over the past few decades. This study describes the development of a computer program, ‘Smart Optimization Program for Water Distribution Networks’ (SOP–WDN), which applies Genetic Algorithm to the problem of the least-cost design of water distribution networks. SOP–WDN demonstrates the application of an evolutionary optimization technique, Genetic Algorithm, linked with a hydraulic simulation solver EPANET, for the optimal design of water distribution networks. The developed program was applied to three benchmark water distribution network optimization problems and produced consistently good results. SOP–WDN can be utilized as a tool for guiding engineers during the design and rehabilitation of water distribution pipelines.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties in obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro-geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.

Human factors are the most relevant issues contributing to adverse events in obstetrics. Specific training of Crisis Resource Management (CRM) skills (i.e., problem solving and team management, resource allocation, awareness of environment, and dynamic decision-making) are now widespread and are often based on High Fidelity Simulation. In order to be used as a guideline in simulated scenarios, they need to be translated into specific and observable behavioral markers. To this purpose, we developed a set of observable behaviors related to the main elements of CRM in the delivery room. The observational tool was then adopted in a two-days seminar on obstetric hemorrhage where teams working in obstetric wards of six Italian hospitals took part to simulations. The tool was used as guide for the Io and as a peer-to-peer feedback. It was then rated for its usefulness in facilitating the reflection upon one’s own behavior, its ease of use, and its usefulness for the peer-to-peer feedback. The ratings were highly positive, around 4 in a 5-point scale. The CRM observational tool is therefore a useful, quick and easy solution to facilitate the debriefing, the peer-to-peer feedback and, most of all, the transfer of safe behavior from simulation to everyday practice.

The importance and near ubiquity of the Golden Ratio in disciplines like chemistry and biology is well known, but only recently has it come to light in areas pertaining to the Quantum domain. By using a modified tool-kit of hyper-complex numbers (known as Dimensional Gate Operators) and numerical analysis, we uncover a connection between hyper-dimensional objects, the Fibonacci sequence and that of quasiparticles and excitations. The results show that dimensionality increases in step with the Fibonacci sequence.

This publication delves into the intricate world of human communication, extending beyond the realm of spoken and written language to explore the profound influence of non-verbal communication. Focusing on body language—comprising gestures, facial expressions, postures, and movements—it reveals the pivotal role it plays in conveying emotions, intentions, and psychological states. The rapidly evolving fields of neuroscience and cognitive science are investigating the neural mechanisms and cognitive processes that underpin the interpretation and utilization of these non-verbal cues. Notably, the amygdala's involvement in processing emotional facial expressions has provided a neurological foundation for our emotional understanding in non-verbal communication. Concurrently, cognitive psychology sheds light on how perception, attention, and memory influence our interpretation of non-verbal cues and communication strategies.In our modern, technologically advancing society characterized by global interconnectivity and cultural diversity, the significance of non-verbal communication, especially in digital and virtual contexts, is paramount. The transformation of non-verbal expression into digital symbols like emoticons and emojis raises questions regarding its ability to maintain authenticity and emotional understanding. This publication endeavors to comprehensively explore the fundamental role of body language and non-verbal communication in contemporary society. By integrating insights from psychology, neuroscience, social sciences, and digital technologies, it aims to elucidate the underlying mechanisms guiding non-verbal communication. Additionally, it delves into the implications of non-verbal communication across diverse domains, including interpersonal communication, the workplace, politics, advertising, and the virtual world.The research presented herein strives to offer a thorough understanding of interpersonal dynamics, relationships, and social behavior, with the goal of promoting the development of more effective, conscious, and ethically guided communication strategies. Ultimately, this analysis aspires to inspire further interdisciplinary research and reflection, addressing emerging challenges and fully leveraging the potential of non-verbal communication in modern society. This, in turn, guides our path towards a communication landscape that is more inclusive, authentic, and empathetic.

Magnetic nanoparticles are met across many biological species ranging from magnetosensitive bacteria, fishes, bees, bats, rats, birds, to humans. They can be both of biogenetic origin and due to environmental contamination, being either in paramagnetic or ferromagnetic state. The energy of such naturally occurring single-domain magnetic nanoparticles can reach up to 10-20 room k_BT in the magnetic field of the Earth, which naturally led to supposition that they can serve as sensory elements in various animals. This work explores within a stochastic modeling framework a fascinating hypothesis of magnetosensitive ion channels with magnetic nanoparticles serving as sensory elements, especially, how realistic it is given a highly dissipative viscoelastic interior of living cells and typical sizes of nanoparticles possibly involved.

Biomimicry in additive manufacturing often refers to topology optimization and the use of lattice structures, due to the organic shape of the topology-optimized designs, and the lattices often looking similar to many light-weight structures found in nature such as trabecular bone, wood, sponges, coral, to name a few. Real biomimetic design however involves the use of design principles taken in some way from natural systems. In this work we use a methodology whereby high resolution 3D analysis of a natural material with desirable properties is “reverse-engineered” and the design tested for the purpose. This allows more accurate replication of the desired properties, and adaption of the design parameters to the material used for production (which usually differs from the biological material). One such example is the impact-protective natural design of the glyptodont body armour. In this paper we report on the production of body armour models in metal (Ti4Al4V) and analyze the resulting mechanical properties, assessing their potential for impact protective applications. This is the first biomimetic study using metal additive manufacturing to date.

Breast cancer is the most common malignancies in women and the second leading cause of cancer death in women. Triple negative breast cancer (TNBC) subtype is a breast cancer subset without ER, PR and HER2 expression, limiting treatment options and presenting a poorer survival rate. Thus, we investigated whether HDACi would be used as potential anti-cancer therapy on breast cancer cells. In this study, we found TNBC and HER2-enrich breast cancers are extremely sensitive to Panobinostat, Belinostat of HDACi via experiments of cell viability assay, apoptotic marker identification and flow cytometry measurement. On the other hand, we developed a bioluminescence based live cell non-invasive apoptosis detection sensor (IADS) detection system to evaluate the quantitative and kinetic analyses of apoptotic cell death by HDAC treatment on breast cancer cells. In addition, the use of HDACi may also be accompanied with chemotherapeutic agent such as doxorubicin to synergic drug sensitivity on TNBC cell (MDA-MB-231), but not in breast normal epithelia cells (MCF-10A), providing therapeutic benefits against breast tumor in clinic.

Radish is an annual herbaceous root crop, fruit, and oil crop plant belonging to the Cruciferae family. The important traits for radish breeding include high yield, early maturity, late bolting, pungency, cold-hardiness, drought resistance, heat tolerance, and soil adaptability. For successful radish production, need to the understand nature and behavior of the flower, and very important to identify the S haplotypes of parental lines to produce F₁ hybrids based on self-incompatibility to get rid of laborious hand emasculation in radish. In radish some desirable genes are not present within varieties. Therefore, further breeding programmes depend on inter-specific and intra-specific hybridization, which has a vital role in genomic studies and crop improvement by introducing desirable agronomic characters. It is essential to acquire detailed genetic information on chromosomes and information on inheritance. Genomics is now at the core of crop improvement, and radish crop is exploited to study the underlying differences in genotypes. But some monogenic characters are improved by genetic engineering. A three-decade span following the first documented instance of genetic engineering has witnessed its application's unprecedented growth. Researchers have successfully produced transgenic radishes with various agronomic characteristics over the last decade.

Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. Almost all cell types release EVs, thus they are naturally present in all body fluids. Among the several potential applications, EVs could be used as drug delivery vehicles in disease treatment, in immune therapy because of their immunomodulatory properties and in regenerative medicine. In addition to general markers, EVs are characterized by the presence of specific biomarkers (proteins, miRNAs) that allow the identification of their cell- or tissue-origin. For these features, they represent a potential powerful diagnostic tool to monitor state and progression of specific diseases. As regards, a large body of studies supports the idea that endothelial derived (EMPs) together with platelet-derived microparticles (PMPs) are deeply involved in the pathogenesis of diseases characterized by micro- and macrovascular damages, including diabetes. Existing literature suggests that the detection of circulating EMPs and PMPs and their specific miRNA profile may represent a very useful non-invasive signature to achieve informations about the onset of peculiar disease manifestations. In this Review, we discuss the possible utility of EVs in the early diagnosis of diabetes-associated microvascular complications, specifically related to kidney.

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date while meaningfully including African investigators, as greater inclusivity and enhanced research capacity affords enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages and challenges of studying the genetic architecture of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater diversity and heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

Below T_CO=157 K the quasi-one-dimensional charge-transfer salt (TMTTF)_2SbF_6 undergoes a pronounced phase transition to a charge-ordered ground state. We have explored the non-linear and photoconductive behavior as a function of applied voltage, laser pulse energy and temperature. Besides a decay of the photoconductive signal in a double exponential fashion in the millisecond range, we discover current oscillations in the kHz range induced by the application of short laser pulses. While the resonance frequencies do not depend on voltage or laser intensity and vary only slightly with temperature, the amplitude changes linearly with the laser intensity and voltage. The findings are discussed and compared to comparable phenomena in other low-dimensional electron systems.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

Medical institutions, where several patients are treated and medical workers engaged, are always exposed to secondary viral and bacterial infections. It is critical to prevent infection transmission by indirect as well as direct contact through air or splash. The infections of most diseases can be transmitted through the air. HEPA filters installed in air conditioning equipment are used to prevent infection transmission through air in medical institutions, but air circulation takes a long time in a large space. Virus and bacteria smaller than 0.3 μm cannot be removed by the HEPA filter; hence, those microbes remain alive throughout the air ventilation. A plasma sterilizer has the capability to provide environmental friendly sterilization by employing reactive oxide species and reactive nitrogen species at a low cost. We developed an excellent plasma sterilizer by using a non-thermal atmospheric-pressure biocompatible plasma (NBP). Ozone concentration in plasma sources has been derived by Kuhn et al. [1]. The diffusion coefficients inside (D₀) and outside (D₁) the plasma sterilizer have been calculated to be 0.0641 m² s^-1 and 0.717 m² s^-1, respectively. To sustain high O₃ concentrations over 121 ppm inside the plasma source and low O₃ concentrations below 0.05 ppm outside the sterilizer, it is necessary to keep O₃ concentrations at the exit of plasma sterilizer below 0.28 ppm. so that diffusion coefficient D₁ has been designed to be as large as 11 times of D₀.

With the development of data science, more and more machine learning technologies have been designed to solve complicated and challenging real-world tasks containing a large volume of data. And many significant real-world datasets contain data in the form of networks or graphs. Graph Neural Networks is one of the powerful machine learning tools, which could provide a perfect solution to processing a large amount of non-Euclidean data. And because most bio information data in bioinformatics is in the non-Euclidean domain, Graph Neural Networks could then directly be applied to solve problems in bioinformatics. Much research has been done in the field of GNN, and there are also some surveys related to GNN and its applications. However, there has been little research focusing on GNN in bioinformatics, and we think in the future we could better utilize GNN in the field of biology, so we would like to write a literature review to help take a comprehensive look at GNN and their applications in the field of bioinformatics. In this paper, we would first introduce SOTA models in Graph Neural Networks, and second, introduce their applications in bio information. And then we would provide future directions of Graph Neural Networks in bioinformatics.

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Past public health crises (e.g. tobacco, alcohol, opioids, cholera, HIV, lead, pollution, venereal disease, even COVID-19) have been met with interventions targeted both at the individual and all of society. While the healthcare community is very aware that the global pandemic of non-communicable diseases (NCD’s) has its origins in our Western ultraprocessed food diet, society has been slow to initiate any interventions other than public education, which has been ineffective, in part due to food industry interference. This article provides the rationale for such public health interventions, by compiling the evidence that added sugar, and by proxy the ultraprocessed food category, meets the four criteria set by the public health community as necessary and sufficient for regulation — addiction, toxicity, ubiquity, and externalities (how does your consumption affect me?). To its credit, some countries have recently heeded this science and have instituted sugar taxation policies to help ameliorate NCD’s within their borders. This article also supplies scientific counters to food industry talking points, in order to guide both scientists and policy makers in instituting further appropriate public health measures to quell this pandemic.

Background: As noninvasive prenatal screening usage grows in the general obstetrics setting, proper patient education on the screen’s benefits and limitations is needed. Objective: Describe the use of a technology platform designed for large-scale dissemination of noninvasive prenatal screening information and results. Study Design: The technology platform functioned as follows: Patients were emailed a link to an noninvasive prenatal screening general-education video upon laboratory receipt of a test requisition. Providers were then notified upon availability of patients’ results. If noninvasive prenatal screening results were negative, the patient was sent an automated email with instructions to access results through a secure portal where she could watch tailored informational videos, request “on-demand” or scheduled genetic counseling, or decline any further services. If genetic counseling was elected, a summary of the session was sent to the ordering provider and patient upon completion. If noninvasive prenatal screening results were positive, either the ordering provider or a board-certified genetic counselor contacted the patient directly to communicate test results and provide counseling. The number and type of results issued through the platform, the number and type of genetic counseling consultations completed, and factors associated with requesting laboratory-delivered genetic counseling were tracked and analyzed for a 39-month period. Results: Over the study period, 67,122 noninvasive prenatal screening results were issued through the platform, and 4,673 patients elected genetic counseling consultations; 95.2% (n=4,450) of consultations were for patients receiving negative results. Over 70% (n= 3,370) of consultations were on-demand rather than scheduled. Median consultation time was 14 minutes for positive results and six minutes for negative results. A positive screen, advanced maternal age, family history, previous history of a pregnancy with a chromosomal abnormality, and other high-risk pregnancy were associated with the greatest odds of electing laboratory-delivered genetic counseling. Conclusions: By combining web education, automated notifications, and genetic counseling, we implemented a service that effectively facilitates results disclosure for ordering providers. These data demonstrate the capability to deliver noninvasive prenatal screening results, education, and counseling—congruent with management guidelines—to a large population, which is imperative to quality care as uptake increases.

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

In this review, we discuss the insulin resistance (IR) and its development in the insulin target tissues that leads to diabetes. Also, we highlight the use of induced pluripotent stem cells (iPSCs) to understand the mechanisms underlying the development of IR. IR is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Although there are currently several mouse models for both IR and T2D that had provided a lot of information about the disease, these models cannot recapitulate all the aspects of this complex disease as seen in each individual. Patient-specific iPSCs can overcome the hurdles faced with the classical mouse models for studying IR. iPSC technology can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of the genome editing and iPSCs may provide important information about the inherited factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes.

Efficiently targeted cancer therapy without causing detrimental side effects is necessary for alleviating patient care and improving survival rates. This paper presents observations of morphological changes in H1299 human lung cancer cells following W-band MMW irradiation (75 – 105 GHz) at a non-thermal power density of 0.2 mW/cm², investigated over 14 days of subsequent physiological incubation following exposure. Microscopic analyses of physical parameters measured indicate MMW irradiation induces significant morphological changes characteristic of apoptosis and senescence. The Immediate short-term responses translate into long-term effects, retained over the duration of the experiment(s); reminiscent of the phenomenon of Accelerated Cellular Senescence (ACS) achieving terminal tumorigenic cell growth. Further, results were observed to be treatment-specific in energy (dose) dependent manner and were achieved without the use of chemotherapeutic agents, ionizing radiation or thermal ablation employed in conventional methods; thereby overcoming associated side effects. Adaptation of the experimental parameters of this study for clinical oncology concomitant with current developmental trends of non-invasive medical endoscopy alleviates MMW therapy as an effective treatment procedure for human non-small cell lung cancer (NSCLC).

Ploidy is a significant type of genetic variation, describing the number of chromosome sets per cell. Ploidy evolves in natural populations, clinical populations, and lab experiments, particularly in fungi. Despite a long history of theoretical work on this topic, predicting how ploidy will evolve has proven difficult, as it is often unclear why one ploidy state outperforms another. Here, we review what is known about contemporary ploidy evolution in diverse fungal species through the lens of population genetics. As with typical genetic variants, ploidy evolution depends on the rate that new ploidy states arise by mutation, natural selection on alternative ploidy states, and random genetic drift. However, ploidy variation also has unique impacts on evolution, with the potential to alter chromosomal stability, the rate and patterns of point mutation, and the nature of selection on all loci in the genome. We discuss how ploidy evolution depends on these general and unique factors and highlight areas where additional experimental evidence is required to comprehensively explain the ploidy transitions observed in the field and the lab.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Depending on human body conditions and environmental conditions, it is sometimes difficult to conduct subject experiments. In such cases, it is effective to use a thermal manikin. There are few studies that investigate the effect of the non-uniform and asymmetric outdoor thermal environment on the mean skin temperature. The purpose of this study is to clarify the influence of the non-uniform and asymmetric thermal radiation of short-wavelength solar radiation in an outdoor environment on the calculation of the mean skin temperature. The skin temperature of the front of the coronal surface, which was facing the sun and where the body received direct short-wavelength solar radiation, and the skin temperature of the rear of the coronal surface, which was in the shadow and did not receive direct short-wavelength solar radiation were respectively measured. The feet, upper arm, forearm, hand and lower leg, which are susceptible to short-wavelength solar radiation in a standing posture, had a noticeable difference in skin temperature between sites in the sun and in shade. The mean skin temperature of sites facing the sun was significantly higher than the mean skin temperature of those in the shade.

The Gilles de la Tourette syndrome (GTS) and Non-Coeliac Gluten Sensitivity (NCGS) may be associated. We analyse the efficacy of a gluten-free diet (GFD) in 29 patients with GTS (23 children; 6 adults) in a prospective pilot study. All of them followed a GFD for one year. The YGTSS, Y-BOCS/CY-BOCS and GTS-QOL questionnaires were compared before and after the GFD. 74% of children and 50% of adults were males, not significant (NS). At the beginning of the study, 69% of children and 100% of adults had associated OCD (NS). At baseline, the YGTSS scores were 55.0 ± 17.5 (children) and 55.8 ± 19.8 (adults) (NS), the Y-BOCS/CY-BOCS scores were 15.3 (SD = 12.3) (children) and 26.8 (9.2) (adults) (p = 0.043), and the GTS-QOL scores were 42.8 ± 18.5 (children) and 64 ± 7.9 (adults) (p = 0.000). NCGS was frequent in both groups, with headaches reported by 47.0% of children and 83.6% of adults (p = 0.001). After one year on a GFD there was a marked reduction in measures of tics (YGTSS) (p = 0.001), and the intensity and frequency of OCD (Y-BOCS/CY-BOCS) (p = 0.001), along with improved QOL (p = 0.001) in children and adults. In conclusion, a GFD maintained for one year in GTS patients led to a marked reduction in tics and OCD both in children and adults.

Characterization of genetic diversity, population structure and linkage disequilibrium is prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes were genotyped using DArTseq-Based SNP markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that covered by SNPs varied from 16236.36 kbp (LG8) to 67923.99 kbp (LG5), while LG4 showed higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6 and LG8 showed higher mean PIC value than average. Un-weighted Neighbor Joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and Discriminant Analysis of Principal Component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r²≥0.8, while 2961 pairs of markers showed complete LD (r²=1) and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggesting the presence of a high genetic diversity among studied chickpea genotypes. This study also demonstrated the efficiency of DArTseq-based SNP genotyping for large scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits such as seed yield, abiotic and biotic stresses and therefore can be efficiently used in breeding programs to improve chickpea.

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.

Predicting the growth response of seedlings from the environmental responses of photosynthesis and metabolism may be improved by considering the dynamics of non-structural carbohydrate, NSC, over a diurnal cycle. Attenuation of growth metabolism when NSC content is low could explain why some NSC is conserved through the night. A dynamic model, incorporating diurnal variation in NSC, was developed to simulate growth of seedlings hour-by-hour. I compared predictions of this model to published growth and NSC data for seedlings that varied according to temperature, light, day length, or CO₂. Prolonged-darkness experiments showed a temperature dependent upper limit on the respiration capacity. Respiration was attenuated as NSC was depleted. Furthermore, when NSC was high at dawn, inhibition of photosynthesis could attenuate the accumulation of NSC under low temperature, or high light, or high CO₂. These concepts were used to simulate plant metabolism and growth rates and diurnal variation of NSC in tomato seedlings under two light levels and various temperatures. Comparison of other results using the same model parameters showed the dynamic model could predict results for starch and starch-less plants, and when growth was affected by CO₂ enrichment and day length.

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

We find that the degeneracies and many peculiarities of the DNA genetic code may be described thanks to two closely related (fivefold symmetric) finite groups. The first group has signature $G=\mathbb{Z}_5 \rtimes H$ where $H=\mathbb{Z}_2 . S_4\cong 2O$ is isomorphic to the binary octahedral group $2O$ and $S_4$ is the symmetric group on four letters/bases. The second group has signature $G=\mathbb{Z}_5 \rtimes GL(2,3)$ and points out a threefold symmetry of base pairings. For those groups, the representations for the $22$ conjugacy classes of $G$ are in one-to-one correspondence with the multiplets encoding the proteinogenic amino acids. Additionally, most of the $22$ characters of $G$ attached to those representations are informationally complete. The biological meaning of these coincidences is discussed.

A plenty of theories on the origin of genetic codes have been proposed so far, yet all ignored the energetic driving force, its relation to the biochemical system, and most importantly, the missing “matchmaker” between proteins and nucleic acids. Here, a new hypothesis is proposed, according to which ATP is at the origin of the primordial genetic code by driving the coevolution of the genetic code with the pristine biochemical system. This hypothesis aims to show how the genetic code was produced e.g. by photochemical reactions in a protocell that derived from a lipid vesicle enclosing various life’s building blocks (e.g. nucleotides and peptides). At extant cell, ATP is the only energetic product of photosynthesis, and is at the energetic heart of the biochemical systems. ATP could energetically form and elongate chains of both polynucleotides and polypeptides, thus acting a “matchmaker” between these two bio-polymers and eventually mediating precellular biochemical innovation from energy transformation to informatization. ATP was not the only one that could drive the formation of polynucleotides and polypeptides, but favored by precellular selection. The protocell innovated a photosynthesis system to produce ATP efficiently and regularly with the aids of proteins and RNA/DNA. The completion of permanently recording the genetic information by DNA marked the dawn of cellular life operated by Darwinian evolution. The ATP hypothesis assumes or supports the photochemical origin of life, shedding light on the origins of both photosynthetic and biochemical systems, which remains largely unknown thus far. Based on ATP hypothesis, virus (like the new coronavirus) could not be the earliest life on Earth, as it has neither biochemical systems nor lipid bilayer membrane that provided relatively isolated environment for the development of protobiochemical reactions, although it owns the genetic code of a cellular life. Virus could not be a bridge between life and non-life, but is an anti-life substance, as it depletes cellular material for its own replication, and then spreads by destroying the host cells. It can be imagined that if cellular life are completely wiped out by the virus, the complete destruction of life on Earth would be inevitable.

This paper considers a non-mixture cure model for right censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Comparing with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose.

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter-receiver (T-R) sensor is designed with a small mean radius of 1 mm. Besides, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution , with a limit of 0.2 μm and selected as 0.25 mm. Experiments have been out on the voltage imaging of an aluminium sheet with 7 machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10 % for tilt angles up to 60 degrees.

This paper presents a novel micro-segmented genetic algorithm (μsGA) to identify the best solution for the locomotion of a quadruped robot designed on a rectangular ABS plastic platform. We compare our algorithm with three similar algorithms found in the specialized literature: a standard genetic algorithm (GA), a micro-genetic algorithm (μGA), and a micro artificial immune system (μAIS). The quadruped robot prototype guarantees the same conditions for each test. The platform was developed using 3D printing for the structure and can accommodate the mechanisms, sensors, servomechanisms as actuators. It also has an internal battery and a multicore embedded system (mES) to process and control the robot locomotion. This research proposes a μsGA that segments the individual into specific bytes. μGA techniques are applied to each segment to reduce the processing time; the same benefits as the GA are obtained, while the use of a computer and the high computational resources characteristic of the GA are avoided. This is the reason why some research in robot locomotion is limited to simulation. The results show that the performance of μsGA is better than the three other algorithms (GA, μGA and AIS). The processing time was reduced using a mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. This research solves the problem of continuous locomotion of a quadruped robot, and gives a feasible solution with real performance parameters using a μsGA bio-micro algorithm and a mES architecture.

Promoter is a small region of DNA sequence in response to various transcription factors, which initiates a particular gene expression. The promoter-engineered bio-sensor can activate or repress gene expression through transcription factor recognizing specific molecules, such as polyamine, sugars, lactams, amino acids, organic acids or redox molecule, however, the reported applications of promoter enhanced bio-sensor are not too much. This review paper highlights the strategies of construction of promoter-gene engineered bio-sensor with human and bacteria’s genetic promoter array for high-throughput screening (HTS) molecular drugs, study of membrane protein’s localization and nucleocytoplasmic shuttling mechanism of regulating factor, enzyme activity, detection of the toxicity of intermediate chemicals, and probing bacteria density to improve value-added product titer. These bio-sensors’s sensitivity and specificity can be further improved by proposed approaches of Mn²⁺ and Mg²⁺ added random Error-prone PCR and site-directed mutagenesis which is applied for construction of bacteria’s “mutant library”. It is expected to establish flexible HTS platform (Bio-sensor array) to large-scale screen transcription factor-acting drugs, reducing the toxicity of intermediate compounds, and constructing gene dynamic regulatory system in “push and pull” mode to effectively regulate the valuable medicinal product production. This proposed novel promoter-engineered biosensors aided synthetic genetic circuit construction will maximize the efficiency of bio-synthesis of medicinal compound, which will greatly promote the development of microbial metabolic engineering and biomedical science.

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating diaphragmatic function and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activities when the sensor is placed in the zone of apposition (ZOA). This system allows the direct measurements with only one ultrasound PZT5 piezo transducer. The system both generates pulsed ultrasound waves at 2.2 MHz and amplifies reflected echoes. According to the diaphragmatic motions, the respiratory signals of the proposed system is insensitive to human motion artifacts. Promising results were obtained from six subjects on six different tests with an average sensitivity and specificity of 84% and 93% of respiration detection, respectively. Measurements are referenced to a SPR-BTA commercial spirometer. In this study, we also evaluated inertial and photoplethysmography (PPG) sensors as other conventional methods in this area.

Ultraviolet light incident on organic material can initiate its spontaneous dissipative structuring into chromophores which can catalyze their own replication. This may have been the case for one of the most ancient of all chromophores dissipating the Archean UVC photon flux, the nucleic acids. Oligos of nucleic acids with affinity to particular amino acids which foment UVC photon dissipation would have been selected through non-equilibrium thermodynamic imperatives which favor entropy production. Indeed, we show here that those amino acids with characteristics most relevant to fomenting UVC photon dissipation are precisely those with greatest chemical affinity to their codons or anticodons. Entropy production could thus provide an explanation for the accumulation of information in nucleic acids relevant to the dissipation of the externally imposed thermodynamic potentials. The accumulation of information in this manner provides a link between evolution and entropy production.

Many dioecious plants have sex chromosomes that are cytologically heteromorphic, but about half of species lack cytological differences between males and females and are thus homomorphic. Very little is known about the size and content of the non-recombining sex-determining region (SDR) in these species. Here, we assess the size and content of the SDR of the diploid dioecious herb Mercurialis annua, which has homomorphic sex chromosomes and shows signatures of mild Y-chromosome degeneration. We used RNAseq to identify new Y-linked markers for M. annua. Twelve of 24 transcripts with male-specific and male-biased expression could only be PCR-amplified from males and are thus Y-linked. We found a further six Y-linked sequences that were present in males but not females using genome capture data from multiple populations. We used the Y-linked sequences to identify and sequence 17 sex-linked bacterial artificial chromosomes (BACs), which form 11 groups of non-overlapping sequence, covering a total sequence length of about 1.5 Mb. Content analysis of this region suggests it is enriched for repeats, has a low gene density and contains few candidate sex-determining genes. The BACs map to a subset of the sex-linked region of the genetic map, which is estimated to be at least 14.5 Mb. This is substantially larger than estimates for other dioecious plants with homomorphic sex chromosomes, especially given the small genome size of M. annua. Our data provide a rare, high-resolution view of the homomorphic Y chromosome of a dioecious plant.

Matiyasevich's theorem states that there is no algorithm to decide whether or not a given Diophantine equation has a solution in non-negative integers. Smorynski's theorem states that the set of all Diophantine equations which have at most finitely many solutions in non-negative integers is not recursively enumerable. We prove: (1) Smorynski's theorem easily follows from Matiyasevich's theorem, (2 ) Hilbert's Tenth Problem for Q has a negative solution if and only if the set of all Diophantine equations with a finite number of rational solutions is not recursively enumerable.

In the international scenario of agriculture, Brazil stands out as the main producer and consumer of common bean (Phaseolus vulgaris L.) The increase in the productive potential of the crop is mainly due to breeding programs. The objective of this study was to estimate genetic parameters, predict genotypic values with REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Prediction) and, based on these values, study the variability in common bean cultivars with carioca and black grain. Twenty three agromorphological descriptors were evaluated, among them grain yield. Deviance analysis detected significant differences between the cultivars in both groups. Selective accuracy (Ac) was considered high for most of the traits. Broad-sense heritability (h_g² ) ranged from 0.05 to 0.72, but it was low for the trait yield (YLD). In the carioca grain group, the h_g² values for the traits related to plant morphology were higher than in the black group. Nevertheless, the h_g² values in the black group were higher in relation to the pod and seed traits. The correlations for YLD were moderate but different in the two commercial groups studied. In the black group, variables related to the seed morphology were correlated with grain yield, and in the carioca group, traits related to seed quantity. Based on the groupings, variability among the cultivars was observed. Three distinct clusters were formed for the carioca group and four for the black group. Based on the predicted genetic values, genetic variability and the most adapted and stable cultivars were detected among the cultivars in the studied environments.

Computers and artificial intelligence affect every field of life nowadays. In medical image interpretation automatic decision making using algorithms are used increasingly in every sub-field and computer aided diagnosis (CAD) is one of the main tools available to medical science today. CAD systems are used as an augmented option for both the medical practitioner and the patients, with image analysis and interpretation being of primary importance. In particular, spatial relations are used in knowledge representation, and these relations can be used for effective medical image interpretation. In this paper, we put forth an algorithm for defining non-small cells lungs cancer (NSCLC) stages in lungs images interpretation using topological spatial relations. We show an application case study in event motion predictions for lung cancer staging scoring - tumor, nodes and metastasis (TNM) - with combined topological and directional relations.

In this paper, we established the existence of PC-mild solutions for non local fractional impulsive functional integro-differential equations with finite delay. The proofs are obtained by using the techniques of fixed point theorems, semi-group theory and generalized Bellman inequality. In this paper, we have used the distributed characteristic operators to define the mild solution of the system. Results obtained here improve and extend some known results.

Low back pain (LBP) is a leading cause of disability. It significantly impacts the patient’s quality of life, limits their daily living activities, and reduces work productivity. To reduce the burden of LBP, several pharmacological and non-pharmacological treatment options are available. This review summarizes the role of heat therapy in the management of LBP. First, we outline the common causes of LBP, then discuss the general mechanisms of heat therapy on (LB)P, and finally review the published evidence regarding the impact of heat therapy in patients with acute or chronic non-specific LBP. This review demonstrates that continuous, low-level heat therapy provides pain relief, improves muscular strength, and increases flexibility. Therefore, this effective, safe, easy-to-use, and cost-effective non-pharmacological pain relief option is still relevant in modern clinical practice.

Tidal swamp rice has long been cultivated by the local people of the South Kalimantan, Indonesia. This germplasm possess some important traits for adapted to a wide range of abiotic and biotic stresses. In this study, a total of sixteen cultivars of this germplasm, consisting of fifteen of the South Kalimantan Province and one of Sumatera Island, Indonesia (an outgroup) were analyzed, phylogenetically based on the chloroplast trnL-F and nuclear intergenic spacer region (IGS). The results showed that this germplasm has a relatively more extraordinary genetic diversity than other local rice germplasm. In a nucleotide level, this germplasm showed a genetic diversity of 0.61 for nuclear IGS and 0.58 for trnL-F. The phylogenetic reconstruction also exhibited that this germplasm has the unique illustration of phylogenetic trees, particularly for the combined sequence datasets. Thus, the results of our study would provide useful information for further understanding of evolutionary relationships of this germplasm and facilitate the efficient utilization of valuable genes for genetic improvement, particularly in the tidal swamp areas.

Background: Extremely rare progressive diseases like Sedaghatian-type Spondylometaphyseal Dysplasia (SSMD) can be neonatally lethal and therefore go undiagnosed or are difficult to treat. Recent sequencing efforts have linked this disease to mutations in GPX4, with consequences in the resulting enzyme, glutathione peroxidase 4. This offers potential diagnostic and therapeutic avenues for those suffering from this disease, though the steps toward these treatments is often convoluted, expensive, and time-consuming. Main body: The CureGPX4 organization was developed to promote awareness of GPX4-related diseases like SSMD, as well as support research that could lead to essential therapeutics for patients. We provide an overview of the 21 published SSMD cases and have compiled additional sequencing data for four previously unpublished individuals to illustrate the genetic component of SSMD, and the role of sequencing data in diagnosis. We outline in detail the steps CureGPX4 has taken to reach milestones of team creation, disease understanding, drug repurposing, and design of future studies. Conclusion: The primary aim of this review is to provide a roadmap for therapy development for rare, ultra-rare, and difficult to diagnose diseases, as well as increase awareness of the genetic component of SSMD. This work will offer a better understanding of GPx4-related diseases, and help guide researchers, clinicians, and patients interested in other rare diseases find a path towards treatments.

Mitochondria are classically termed as powerhouse of a mammalian cell. Most of the cellular chemical energy in the form of adenosine tri phosphate (ATP) is generated by mitochondria and dysregulation of mitochondrial functions thus can be potentially fatal of cellular homeostasis and health. Acute respiratory distress has been earlier linked to mitochondrial dysfunction. SARS-CoV-2 infection severity leads to acute respiratory distress syndrome (ARDS) and can be fatal. We tried to investigate possible connection between SARS-CoV-2, ARDS and mitochondria. Here, we report identification of SARS-CoV-2 non-structural proteins (particularly Nsp12 and 13) that have recognition sequence with respect to mitochondrial entry. We also report that these proteins can potentially shuttle between cytoplasm and mitochondria based on the localization signals and help in downstream maintenance of the virus. Their properties to use ATP for enzymatic activities may cause ATP scavenging allowing viral RNA functions in lieu of host cell health.

Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel impact-echo analysis system using online machine learning, which aims at achieving near-human performance for assessment of concrete structures. Current computerized impact-echo systems commonly employ lab-scale data to validate the models. In practice, however, the echo patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for echo characterization. More specifically, the proposed system can adaptively update itself to approaching human performance in impact-echo data interpretation. To this end, a two-stage framework has been introduced, including echo feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 echo instances from multiple inspection sites with each sample had been annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable echo pattern classification accuracy with high efficiency and low computation load.

Long non-coding RNAs (lncRNAs) have been found to be involved in many biological processes, including the regulation of cell differentiation, but a complete characterization of lncRNA is still lacking. Additionally, there is evidence that lncRNAs interact with ribosomes, raising questions about their functions in cells. Here, we used a developmentally staged protocol to induce cardiogenic commitment of hESCs and then investigated the differential association of lncRNAs with polysomes. Our results identified lncRNAs in both the ribosome-free and polysome-bound fractions during cardiogenesis and showed a very well-defined temporal lncRNA association with polysomes. Clustering of lncRNAs was performed according to the gene expression patterns during the five timepoints analyzed. In addition, differential lncRNA recruitment to polysomes was observed when comparing the differentially expressed lncRNAs in the ribosome-free and polysome-bound fractions or when calculating the polysome-bound vs ribosome-free ratio. The association of lncRNAs with polysomes could represent an additional cytoplasmic role of lncRNAs, e.g., in translational regulation of mRNA expression.

Zebrafish have been found to be the premier model organism in biological and biomedical research, specifically offering many advantages in developmental biology and genetics. This unique aquatic species has been found to have the capacity to regenerate their spinal cord after injury. However, the complete molecular and cellular mechanisms behind glial bridge formation in the central and peripheral nervous systems upon glial cell injury remains unclear. This review paper focuses on the molecular mechanisms and cellular processes that underlie spinal cord regeneration in four initial phases: proliferation and initial migration; migration and differentiation; glial bridge formation; and remodeling. We propose that within these four phases the cellular mechanisms that underlie spinal cord regeneration each express a terminating signal that aborts one step of the process and initiates the next. Specifically, future studies would be devoted to investigate transmitting signals in the spinal cord injury micro-environment in hope to contribute to the understanding of underlying cellular mechanisms by connecting each process of spinal cord regeneration in zebrafish.

Covid-19 pandemic lockdown has slow down the world economic system. The pandemic has cleared the roads, close factories and grounded planes causing severe economic challenges. The damaging impact of the pandemic amid lockdown has been a blessing in guise for the environment because of significant drop in pollution level as transport and industrial sectors shutdown. Transport and industrial sectors are major contributors to environmental degradation through various emissions as a result of fossil fuel consumption. Energy consumed by transport and industrial sectors will have to shift to viable, readily available, economically and environmentally friendly with no carbon build up post Covid-19 pandemic. Hydrogen energy remains the best alternative option technologies containing green house gas emission and pollutions of several forms. Hydrogen holds the potential to provide a clean, reliable, renewable and economical source of energy for meeting the growing and unending global energy needs post pandemic. The present paper explores the economic feasibility and potential of hydrogen to serve as a competitive fuel option post pandemic. In this paper, the role of hydrogen as an energy carrier hydrogen economy structure, potential of hydrogen economy, hydrogen production methods, hydrogen application and the economic and environmental importance of hydrogen as a viable fuel option post covid-19 pandemic were discussed. There will be a surge in demand and investment for hydrogen economy post Covid-19.

Artificial Intelligence is increasingly being used by social media platforms to tackle online hate speech. The sheer quantity of content, the speed at which is it developed and the enhanced pressure companies are facing by States to remove hate speech quickly from their platforms have led to a tricky situation. This commentary argues that automated mechanisms, which may have biased datasets and be unable to pick up on the nuances of language, should not be left unattended with hate speech as this can lead to issues of violating freedom of expression and the right to non-discrimination.

Atherosclerosis (ATH) and Coronary Artery Disease (CAD) are chronic inflammatory diseases with an important genetic background which derive from the cumulative effect of multiple common risk alleles, most of them located in genomic non-coding regions. These complex diseases behave as non-linear dynamical systems that show a high dependence on their initial conditions, so that long-term predictions of disease progression are unreliable. One likely possibility is that the non-linear nature of ATH could be dependent on non-linear correlations in the structure of the human genome. In this review we show how Chaos theory analysis highlighted genomic regions that shared specific structural constraints that could have a role in ATH progression. These regions were shown to be enriched in repetitive sequences of the Alu family, genomic parasites which colonized the human genome, which show a particular secondary structure and have been involved in the regulation of gene expression. We also review the impact of Alu elements on the mechanisms that regulate gene expression, especially highlighting the molecular mechanisms by which the Alu elements could alter the inflammatory homeostasis. We devise especial attention to their relationship with the lncRNA ANRIL, the strongest risk factor for ATH, their role as miRNA sponges, and their ability to interfere with the regulatory circuitry of the NF-kB response. We aim to characterize ATH as a non-linear dynamic system in which small initial alterations in the expression of a number of repetitive elements are somehow amplified to reach phenotypic significance.

This paper seeks to explain the rapid growth of the Islamic State of Iraq & Syria (ISIS) and approach the question of “what is” the Islamic State? The paper offers several contributions. First is the proposal of a dynamic hypothesis that ISIS is an emerging-state actor and differs notably from traditional non-state actors and insurgencies. The theory consists of both a causal loop diagram and key propositions. A detailed system dynamics simulation (E-SAM) was constructed to test the theory. The propositions of emerging-state actor theory are constructed as synthetic experiments within the simulation and confirm evidence of emerging-state actor behavior. E-SAM’s novelty is its combination of combat simulation with endogenous geospatial feedback, ethnographic behavior in choosing sides in conflict, and details internal simulation of key actor mechanisms such as financing, recruiting and governance. E-SAM can be loaded with scenarios to simulate non-state actors in different geospatial domains: ISIS in Libya, Boko Haram in Nigeria, Taliban in Afghanistan and even expatriated ISIS fighters returning to pursue new conflicts such as in Indonesia.

Many systems in nature and laboratories are far from equilibrium and exhibit significant fluc- tuations, invalidating the key assumptions of small fluctuations and short memory time in or near equilibrium. A full knowledge of Probability Distribution Functions (PDFs), especially time- dependent PDFs, becomes essential in understanding far-from-equilibrium processes. We consider a stochastic logistic model with multiplicative noise, which has gamma distributions as stationary PDFs. We numerically solve the transient relaxation problem, and show that as the strength of the stochastic noise increases the time-dependent PDFs increasingly deviate from gamma distributions. For sufficiently strong noise a transition occurs whereby the PDF never reaches a stationary state, but instead forms a peak that becomes ever more narrowly concentrated at the origin. The addition of an arbitrarily small amount of additive noise regularizes these solutions, and re-establishes the existence of stationary solutions. In addition to diagnostic quantities such as mean value, standard deviation, skewness and kurtosis, the transitions between different solutions are analyzed in terms of entropy and information length, the total number of statistically distinguishable states that a system passes through in time.

Sumudu transform of the Dixon elliptic function with non zero modulus a ≠ 0 for arbitrary powers sm^N(x,a) ; N ≥ 1 ; sm^N(x,a)cm(x,a) ; N ≥ 0 and sm^N(x,a)cm²(x,a) ; N ≥ 0 is given by product of Quasi C fractions. Next by assuming denominators of Quasi C fraction to 1 and hence applying Heliermann correspondance relating formal power series (Maclaurin series of Dixon elliptic functions) and regular C fraction, Hankel determinants are calculated and showed by taking a = 0 gives the Hankel determinants of regular C fraction. The derived results were back tracked to the Laplace transform of sm(x,a) ; cm(x,a) and sm(x,a)cm(x,a).

Intradermal injection of botulinum neurotoxin is a frequently performed procedure in aesthetic dermatology to improve facial skin tone, texture, fine wrinkles, and enlarged pores. In practice, botulinum neurotoxin type A is also used to reduce skin oiliness of the face. There is increasing evidence that acetylcholine plays specific roles in sebum production, suggesting that botulinum neurotoxin type A may reduce sebum production by interfering with cholinergic transmission between sebaceous glands and autonomic nerve terminals. Botulinum neurotoxins can also inhibit several pathogenetic components of acne development, suggesting that botulinum neurotoxins can be used as a safe and effective treatment modality for acne and other skin disorders related to the overactivity of sebaceous glands. This review aims to explore the current evidence behind the treatment of oily skin and acne with botulinum neurotoxin type A.

In this paper, a non-destructive test to diagnose wear of blade´s compressor of a gas turbine is reported. Gas turbine was operating in Campeche City, Mexico, in a very aggressive environment, where the entry of solid particles is unavoidable. The objective was to reduce cost of maintenance in this equipment. Analysis on a blade of gas turbine was performed, which was in operation on an offshore platform. Compressor blade was exposed to a severe damage by the impact of particles and environmental pollutants such as salts, sands and sulphurs. In first stage of this analysis, a visual inspection with a borescope was performed, which has the ability to illuminate dark internal areas, with a bright light for visual examination and/or make a photographic reproduction in semi-annual maintenance cycles. Images analysis was used to determine the typical failure modes. In a second stage, a tribological characterization was carried out. Chemical composition of the material of blades was obtained. Scanning electron microscopy (SEM) was used to measure roughness and evaluate degradation of surfaces of blades after 30,000 service hours. The points, where peak stresses were calculated, correspond to those places in witch corrosion and some irregular scratches similar to plowing action, was observed. These are the points in which failures take place. Results showed wear modes were originated by a severe stinging action. Also, large craters, similar to those observed in solid particle erosion, were developed by at normal impact. In the same way it could be found some localized areas with a witch corrosion and irregular scratches similar to plowing action, was observed. These are the points in which failures take place.

Objective: To recommend how physicians can best respond to families whose hopes for a miracle via divine intervention influence their medical decisions, like, for example, making them not want to withdraw ventilatory support in cases of poor neurologic prognosis because they are still hoping for God to intervene. Methods: Auto-ethnographic analysis of chaplaincy experience in this clinical context yields a nuanced 90-second, point-of-care spiritual intervention physicians can use to address the religious aspect of families who base medical decisions on their hopes for a miracle via divine intervention. Explanation of how spiritual intervention dovetails with existing physician communication protocol for responding to families hoping for a miracle. Results: Spiritual intervention for religious aspect of miracle-hoping families is integrated into existing physician communication protocol for responding to families hoping for a miracle with recommendations for utilization of existing communication technology when necessary. Conclusion: Properly addressing the religious dimension of families hoping for a miracle may be helpful for physicians interested in decreasing their own stress levels, improving outcomes for this clinical context, and ensuring that unintentional discrimination does not perpetuate racial disparities in end-of-life care.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.