The current spreading novel coronavirus SARS-CoV-2 is highly infectious and pathogenic and has attracted global attention. Recent studies have found that SARS-CoV-2 and SARS-CoV share around 80% of homology and use the same cell entry receptor, ACE2. These inspired us to study other receptors of SARS-CoV, which may be used for SARS-CoV-2 binding as well. In this study, we screened the gene expression of three receptors (ACE2, DC-SIGN and L-SIGN) in four datasets of normal lung tissue from lung adenocarcinoma patients and two single-cell RNA sequencing datasets from normal lung and bronchial epithelial cells separately. No significant difference in gene expression of these three receptors were found between gender groups (male vs female). We found higher gene expression of DC-SIGN in elder with age>60 and higher gene expression of L-SIGN in Caucasian than Asian. Similar to ACE2, we observed significantly higher DC-SIGN gene expression in the lungs of smokers, especially former smokers. However, smokers upregulate ACE2 and DC-SIGN gene expression in different cell types. In the whole lung, ACE2 is actively expressed in remodeled Alveolar Type II cells of former smokers, while DC-SIGN is largely expressed in monocytes of former smokers and dendritic cells of current smokers. In bronchial epithelium, no obvious gene expression of DC-SIGN and L-SIGN was observed while ACE2 was found to be actively expressed in goblet cells of current smokers and club cells of non-smokers. In conclusion, our findings may indicate that smokers, especially former smokers, and people over 60 have higher risk and are more susceptible to SARS-CoV-2 infection. Also, this study provides hints on possible SARS-CoV-2 pathogenicity mechanisms in lung infection.

The recurrent coronavirus outbreaks in China (SARS-CoV and its relative, SARS-CoV-2) have raised speculations that perhaps Asians are somehow more susceptible to these coronaviruses. Here, we test this possibility based on an analysis of the lung-specific expression of ACE2, which encodes the known cell-entry receptor of both SARS-CoV and SARS-CoV-2. We show that ACE2 expression is not affected during tumorigenesis, supporting that the abundant transcriptomes in cancer genomic studies can be informatively used to study ACE2 expression among diverse individuals without cancer. We find that ACE2 expression in the lung increases with age, but is not associated with sex. Further, Asians do not differ from other populations for ACE2 expression and do not harbor unique genetic polymorphisms in the ACE2 locus. Thus, beyond illustrating an innovative method for assessing the potential impacts of demographic factors for non-cancer diseases from large-scale cancer sample datasets, our statistically robust findings emphasize that individuals of all races require the same level of personal protection against SARS-CoV-2.

Epidemiological data indicate that 5.8% of cancer deaths world-wide are attributable to alcohol consumption. The risk of cancer is higher in tissues in closest contact on ingestion of alcohol, such as the oral cavity, pharynx and esophagus. The risk of these cancers is increased even in people who have only one alcoholic drink per day. However, since ethanol is not mutagenic and the carcinogenic metabolite of ethanol (acetaldehyde) is mostly produced in the liver, it is not clear why alcohol use preferentially exerts a local carcinogenic effect. It is well known that ethanol causes cell death at the concentrations present in alcoholic beverages; however, this effect has been overlooked probably because dead cells cannot give rise to cancer. Here I discuss that the cytotoxic effect of ethanol on the cells lining the oral cavity, pharynx and esophagus activates the division of the stem cells located in deeper layers of the mucosa to replace the dead cells. Every time stem cells divide, they become exposed to unavoidable errors associated with cell division (e.g., mutations arising during DNA replication and chromosomal alterations occurring during mitosis) and also become highly vulnerable to the genotoxic activity of endogenous and exogenous DNA-damaging agents (e.g., reactive oxygen species, acetaldehyde and tobacco carcinogens). Alcohol consumption probably increases the risk of developing cancer of the oral cavity, pharynx and esophagus by promoting the accumulation of cell divisions in the stem cells that maintain these tissues in homeostasis. Because the cytotoxic activity of ethanol is concentration-dependent, the risk of these cancers will not only increase with increasing amounts of ethanol, but also with increasing concentrations; an ounce of whisky is probably more carcinogenic when taken undiluted than when taken mixed with non-alcoholic beverages. The local cytotoxic effect of ethanol can also explain the known synergistic effect of alcohol and tobacco use on the risk of these cancers. Understanding the mechanisms of carcinogenicity of alcohol is important to reinforce the epidemiological evidence and to raise public awareness of the strong link between alcohol consumption and cancer.

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

Almost 6% of cancers worldwide are attributable to alcohol consumption. Approximately half of them occur in tissues highly exposed to ethanol, such as the oral cavity, pharynx, upper larynx and esophagus. However, since ethanol is not mutagenic and the carcinogenic metabolite of ethanol (acetaldehyde) is mainly produced in the liver, it is unclear why alcohol consumption preferentially causes a local carcinogenic effect. We recently hypothesized that the cytotoxic activity of ethanol could explain the high risk of these cancers in alcohol users. Here we report that short-term exposures (2-3 seconds) to ethanol concentrations between 10% and 15% start to cause a marked cytotoxic effect on human epithelial keratinocytes in a concentration-dependent manner. After discussing new evidence that cancer is the end-result of the accumulation of cell divisions in stem cells, we explain why regular alcohol consumption imposes a high risk of cancer on these tissues. Briefly, the cytotoxicity of ethanol reduces the lifespan of the cells lining these tissues. The stem cells located in deeper layers need to divide more often than usual to renew the damaged epithelia. The accumulation of cell divisions in stem cells leads to the accumulation of cancer-promoting errors (e.g., mutations arising during DNA replication) that increase their risk of malignant transformation. Cell division also exposes the DNA of the stem cells to the genotoxic activity of acetaldehyde and tobacco carcinogens. We propose that choosing alcoholic beverages containing non-cytotoxic concentrations of ethanol, or diluting ethanol to non-cytotoxic concentrations, is a simple way to reduce the risk of cancer of the oral cavity, pharynx, larynx and esophagus in alcohol users. This preventive strategy may also abolish the known synergistic effect of alcohol drinking and tobacco smoking on the risk of these cancers.

Exploring the biological significance of mutations in SARS-CoV-2 coronavirus, causing the COVID–19 pandemic, has recently become an area of paramount interest for many researchers, who are pouring their tremendous efforts, in cracking the COVID–19 pandemic code. One of many such mutations that have occurred in the viral genome is V483A mutation, which is a part of the receptor-binding motif (RBM), present in the S1 domain of the spike protein. V483A mutant virus is becoming popular in North America with 36 cases so far, due to its frequent occurrences in recent days. In this review, we have assembled all information, currently available on V483A mutation, and have made a critical analysis based on the perspectives of many researchers all around the world. Comparison is made between the wild type and the V483A mutants to analyze certain factors like the type of interaction between the virus and host cell interface, binding affinity, stability, partition energy, hydrophobicity, occurrence rate, and transmissibility. Insilico dynamic analysis shows minimal alteration in the receptor-binding domain (RBD) of V483A mutant protein in free-state and no significant change of mutant tertiary structure of RBM upon binding to the ACE2 receptor. Comprehensive details about infectivity and evasion of the immune system by the virus are discussed. This information can in turn be of monumental importance in the field of vaccine and drug development because the mutants are becoming resistant to the vaccines and monoclonal antibodies.

The current COVID-19 pandemic is one of the most devastating events in recent history. The virus causes relatively minor damage to young, healthy populations, imposing life-threatening danger to the elderly and people with diseases of chronic inflammation. Therefore, if we could reduce the risk for vulnerable populations, it would make the COVID-19 pandemic more similar to other typical outbreaks. Children don’t suffer from COVID-19 as much as their grandparents and have a much higher melatonin level. Bats are nocturnal animals possessing high levels of melatonin, which may contribute to their high anti-viral resistance. Viruses induce an explosion of inflammatory cytokines and reactive oxygen species, and melatonin is the best natural antioxidant that is lost with age. The programmed cell death coronaviruses cause, which can result in significant lung damage, is also inhibited by melatonin. Coronavirus causes inflammation in the lungs which requires inflammasome activity. Melatonin blocks these inflammasomes. General immunity is impaired by anxiety and sleep deprivation. Melatonin improves sleep habits, reduces anxiety and stimulates immunity. Fibrosis may be the most dangerous complication after COVID-19. Melatonin is known to prevent fibrosis. Mechanical ventilation may be necessary but yet imposes risks due to oxidative stress, which can be reduced by melatonin. Thus, by using the safe over-the-counter drug melatonin, we may be immediately able to prevent the development of severe disease symptoms in coronavirus patients, reduce the severity of their symptoms, and/or reduce the immuno-pathology of coronavirus infection on patients’ health after the active phase of the infection is over.

Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in China towards the end of 2019, and has spread rapidly ever since. Previous studies showed that some virus could affect the reproductive system and cause long-term complications. Recent studies exploring the source of SARS-CoV-2 using genomic sequencing have revealed that SARS-CoV-2 enters the host cells via the angiotensin-converting enzyme II (ACE2), the receptor that recognizes SARS-CoV. To investigate the expression of ACE2 and to explore the potential risk of infection in the reproductive system, we performed a thorough bioinformatic analysis on data from public databases involving RNA expression, protein expression, and single-cell RNA expression studies. The analyzed data showed high levels of ACE2 mRNA and protein expression in the testis and spermatids and equal levels of ACE2 expression in the uterus and lung. Comprehensive single-cell analysis identified ACE2 expression in the lung, testis, spermatids, and uterus. In conclusion, this study revealed the potential risk associated with the SARS-CoV-2 infection in the reproductive system and predicted that long-term complications might have a significant impact on the prevention and management of COVID-19, the disease caused upon infection with SARS-CoV-2.

Background: CD169 has been found overexpressed in the blood of COVID-19 patients and identified as a biomarker in the early disease. We have analysed CD169 in blood cells of COVID-19 patients to assess its role as predictive marker of the disease. Methods : The ratio of the CD169 Median median Fluorescence fluorescence Intensity intensity of CD169 between monocytes and lymphocytes (CD169 RMFI ) was analysed by flow cytometry in blood samples of COVID-19 patients (COV) and healthy donors (HD ) and correlated with immunophenotyping, inflammatory markers, cytokines mRNA expression, pulmonary involvement and disease progression. Results: CD169 RMFI increased in COV but not in HD. CD169 RMFI correlated with T-cell differentiation and exhaustion markers as well as with B cells maturation and differentiation. In vitro stimulation of PBMCs of HD with SARS-CoV-2 Spike spike protein induced CD169 RMFI together with IL-6 and IL-10 gene expression. Likewise, CD169 RMFI correlated with blood cytokine mRNA levels, inflammatory markers, and pneumonia severity in patients which that had not received any treatment at sampling. Notably, in untreated patients, CD169 RMFI reflected the respiratory outcome during hospitalization. Conclusion : Considering the immunological role of CD169 and its involvement during the infection and the progression of COVID-19, it could be considered as an early biomarker to evaluate disease progression and clinical outcome.

The highest efficiency solar cell won in the efficiency race does not always give the most excellent annual energy yield in the real world solar condition that the spectrum is ever-changing. The study of the radiative coupling of the concentrator solar cells implied that the efficiency could increase by the recycle of the radiative recombination generated by the surplus current in upper junction. Such configuration is called by a super-multi-junction cell. We expanded the model in the concentrator solar cell to non-concentrating installation. It was shown that this super-multi-junction cell configuration was found robust and can keep the maximum potential efficiency (50 % in realistic spectrum fluctuation) up to 10 junctions. The super-multi-junction cell is also robust in the bandgap engineering of each junction. Therefore, the future multi-junction may not be needed to tune the bandgap for matching the standard solar spectrum, as well as relying upon artificial technologies like ELO (Epitaxial lift-off), wafer-bonding, mechanical-stacking, and reverse-growth, but merely uses up-right and lattice-matching growth technologies. We have two challenging techniques; one is the optical cap layer that may be the directional photon coupling layer in the application of the photonics technologies, and another is the high-quality epitaxial growth with almost 100 % of the radiative efficiency.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the current COVID-19 pandemic. Worldwide this disease has infected around 1.5 million individuals with a mortality rate ranging from 5 to 10%. It has also imposed extreme challenges on global health, economy, and social behavior. Due to the unavailability of therapeutics, several efforts are going on in the drug discovery to control the SARS-CoV-2 viral infection. The main protease (M^Pro) plays a critical role in viral replication and maturation, thus can serve as the primary drug target. To understand the structural evolution of M^Pro, we have performed phylogenetic and SSN analysis, that depicted divergence of Coronaviridae M^Pro in five clusters specific to viral hosts. This clustering was also corroborated with the comparison of M^Pro structures. Furthermore, it has been observed that backbone and binding site conformations are conserved despite variation in some of the residues. This conservation can be exploited to repurpose available viral protease inhibitors against SARS-CoV-2 M^Pro. In agreement with this, we performed screening of the custom-made library of ~7100 molecules including active ingredients present in the Ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 M^Pro as the primary target. We identified several natural molecules that strongly binds to SARS-CoV-2 M^Pro among which top seven molecules are d-Viniferin, Myricitrin, Taiwanhomoflavone A, Lactucopicrin 15-oxalate, Nympholide A, Biorobin and Phyllaemblicin B. Most of the predicted lead molecules are from Vitis vinifera, also reported for anti-tussive and/or antiviral activities. These molecules also showed strong binding with other main targets RdRp and hACE-2. We anticipate that our approach for identification of multi-target-directed ligand will provide new avenues for drug discovery against SARS-CoV-2 infection.

Prothrombotic thrombocytopathy mimicking heparin-induced thrombocytopenia has been observed in patients with severe COVID-19 and after immunisation with the Vaxzevria vaccine. Herein, we discuss the possible pathogenesis of this disorder focusing on the possible involvement of anti-platelet factor 4 (PFA) autoantibodies.

Many of the wide-ranging symptoms of COVID-19 have not been associated previously with a respiratory illness. A similarity between these atypical symptoms and the symptoms of malnutrition was observed. To investigate, a comparison of COVID-19 symptomatology with recognised states of malnutrition showed a significant relationship that was further explored in a review of the literature. This hypothesis links the atypical symptoms to the effects of increasing protein malnutrition. The rapid appearance of malnutrition symptoms at the beginning of the COVID-19 infection is concurrent with the rapid cell proliferation of the immune system. Although previous work has, in general, assumed that the body is capable of providing for the needs of the immune system during infection, heightened immune activity from the commencement of COVID-19 could require an early increase in energy yielding sources. Should insufficient availability of protein resources occur, this could quickly lead to malnutrition. Any continuing insufficiency, during the active phase of the virus, might result in widespread depletions in the tissues and the consequential malfunctioning of many organs. A new appraisal of the immune system requirements needed to counteract the impact of the novel virus, COVID-19, could show the potential benefit for earlier protein intervention in the disease process. This hypothesis could also have relevance for other life situations where a rapid cell proliferation might occur.

Infection by the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) results in the novel coronavirus disease COVID-19, which has posed a serious threat globally. Infection of SARS-CoV-2 during pregnancy is associated with complications like preterm labor and premature rupture of membranes; a proportion of neonates born to the infected mothers are also positive for the virus. During pregnancy, the placental barrier protects the fetus from pathogens and ensures healthy development. However, whether or not SARS-CoV-2 can infect the placenta is unknown. Herein, utilizing single-cell RNA-seq data, we report that the SARS-CoV-2 binding receptor ACE2 and the S protein priming protease TMPRSS2 are co-expressed by a subset of syncytiotrophoblasts (STB) in the first trimester and extra villous trophoblasts (EVT) in the second trimester human placenta. The ACE2- and TMPRSS2-positive (ACE2+TMPRSS2+) placental subsets express mRNA for proteins involved in viral budding and replication. These cells also express mRNA for proteins that interact with SARS-CoV-2 structural and non-structural proteins in the host cells. We also discovered unique signatures of genes in ACE2+TMPRSS2+ STBs and EVTs. The ACE2+TMPRSS2+ STBs are highly differentiated cells and express genes involved mitochondrial metabolism and glucose transport. The second trimester ACE2+TMPRSS2+ EVTs are enriched for markers of endovascular trophoblasts. Further, both these subtypes abundantly expressed genes in Toll like receptor pathway, the second trimester EVTs (but not first trimester STBs) are also enriched for component of the JAK-STAT pathway that drive inflammation. To conclude, herein we uncovered the cellular targets for SARS-CoV-2 entry and show that these cells can potentially drive viremia in the developing human placenta. Our results provide a basic framework towards understanding the paraphernalia involved in SARS-CoV-2 infections in pregnancy.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

Cellular agriculture is defined as the production of agricultural products from cell cultures rather than from whole plants or animals. With growing interest in cellular agriculture as a means to address the public health, environmental, and animal welfare challenges of animal agriculture, the concept of producing seafood from fish cell- and tissue-cultures is emerging as a means to address similar challenges with industrial aquaculture systems and marine capture. Cell-based seafood - as opposed to animal-based seafood - can combine developments in biomedical engineering with modern aquaculture techniques. Biomedical engineering developments such as closed-system bioreactor production of land animal cells create a basis for large scale production of marine animal cells. Aquaculture techniques such as genetic modification and closed system aquaculture have achieved marked gains in production that can pave the way for innovations in cell-based seafood production. Here, we present the current state of innovation relevant to the development of cell-based seafood across multiple species as well as specific opportunities and challenges that exist for advancing this science. The authors find that the physiological properties of fish cell- and tissue- culture may be uniquely suited to cultivation in vitro. These physiological properties, including hypoxia tolerance, high buffering capacity, and low-temperature growth conditions, make marine cell culture an attractive opportunity for scale production of cell-based seafood; perhaps even more so than mammalian and avian cell cultures for cell-based meats. This, coupled with the unique capabilities of crustacean tissue-friendly scaffolding such as chitosan, a common seafood waste product and mushroom derivative, presents great promise for cell-based seafood production via bioreactor cultivation. To become fully realized, cell-based seafood research will require more understanding of fish muscle culture and cultivation; more investigation into serum-free media formulations optimized for fish cell culture; and bioreactor designs tuned to the needs of fish cells for large scale production.

Abstract The COVID-19 pandemic is thought to began in Wuhan, China in December 2019. Mobility analysis identified East-Asia and Oceania countries to be highly-exposed to COVID-19 spread, consistent with the earliest spread occurring in these regions. However, here we show that while a strong positive correlation between case-numbers and exposure level could be seen early-on as expected, at later times the infection-level is found to be negatively correlated with exposure-level. Moreover, the infection level is positively correlated with the population size, which is puzzling since it has not reached the level necessary for population-size to affect infection-level through herd immunity. These issues are resolved if a low-virulence Corona-strain (LVS) began spreading earlier in China outside of Wuhan, and later globally, providing immunity from the later appearing high-virulence strain (HVS). Following its spread into Wuhan, cumulative mutations gave rise to the emergence of an HVS, known as SARS-CoV-2, starting the COVID-19 pandemic. We model the co-infection by an LVS and an HVS, and show that it can explain the evolution of the COVID-19 pandemic and the non-trivial dependence on the exposure level to China and the population-size in each country. We find that the LVS began its spread a few months before the onset of the HVS, and that its spread doubling-time is $\sim 1.59\pm 0.17$ times slower than the HVS. Although more slowly spreading, its earlier onset allowed the LVS to spread globally before the emergence of the HVS. In particular, in countries exposed earlier to the LVS and/or having smaller population-size, the LVS could achieve herd-immunity earlier, and quench the later-spread HVS at earlier stages. We find our two-parameter (the spread-rate and the initial onset time of the LVS) can accurately explain the current infection levels (R^2$\mathrm{=0.74}$; correlation p-value (p) of 5x10^-13$$). Furthermore, countries exposed early should have already achieved herd-immunity. We predict that in those countries cumulative infection levels could rise by no more than 2-3 times the current level through local-outbreaks, even in the absence of any containment measures. We suggest several tests and predictions to further verify the double-strain co-infection model and discuss the implications of identifying the LVS.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM_2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ_2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

Animals, with their complex and obligate multicellularity, evolved from microbial eukaryotes that were likely obligately or facultatively unicellular. The nature of the unicellular progenitors of animals has intrigued biologists since the late 19^th century, coinciding with the parallel spread of the cell theory and the theory of evolution. However, views on the ancestry of animals have been extremely varied. The huge diversity of single-celled organisms, the tremendous plasticity of animal cellular phenotypes, and the difficulties of organizing both into clear phylogenies in the pre-molecular era allowed a wide range of hypotheses to flourish, with nearly every major single-celled lineage, at one time or another, having been proposed as the precursor of animals. Most of these hypotheses never gained followers beyond their originator (such as the ideas that animals evolved directly from either bacteria, Volvox or fungi) and will not be discussed here. Three concepts, however, have been enduring and influential: (1) the amoeboid theory; (2) the flagellate theory; and the (3) the ciliate theory – to which a fourth category can now be added: (4) a mixed model, in which the ancestor was phenotypically plastic. We will discuss their origin, history, and current relevance.

Pathogenic Salmonella strains have a set of virulence factors allowing them to generate systemic infections and damage in a variety of hosts. Among these factors, bacterial proteins secreted by specialized systems are used to penetrate the host’s intestinal mucosa, through the invasion and destruction of specialized epithelial M cells in the intestine. On the other hand, numerous studies have demonstrated that humans, as well as experimental animal hosts, respond to Salmonella infection by activating both innate and adaptive immune responses. Here, through live cell imaging of S. Typhimurium infection of zebrafish larvae, we showed that besides the intestinal colonization, a deformed cloacae region and a concomitant accumulation of S. Typhimurium cells was observed upon bacterial infection. The swelling led to a persistent inflammation of infected larvae, although the infection was non-lethal. The in vivo inflammation process was confirmed by the co-localization of GFP-tagged S. Typhimurium with mCherry-tagged neutrophils at 72 hours post exposition. Our live-cell analyses suggest that Salmonella Typhimurium induce cloacitis-like symptoms in zebrafish larvae. Typhimurium induce cloacitis-like symptoms in zebrafish larvae.

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).

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

The “stem cells” discipline represents one of the most dynamic areas in biomedicine. While adult marine/aquatic invertebrate stem cell (MISC) biology is of prime research and medical interest, studies on stem cells from organisms outside the classical vertebrate (e.g., human, mouse, zebrafish) and invertebrate (e.g., Drosophila, Caenorhabditis) models have not been pursued vigorously. Marine/aquatic invertebrates constitute the largest biodiversity and the widest phylogenetic radiation on Earth, from morphologically simple organisms (e.g. sponges, cnidarians), to the more complex mollusks, crustaceans, echinoderms and protochordates. These organisms illustrate a kaleidoscope of MISC-types that participate in the production of a large number of novel bioactive-molecules, many of which are of significant potential interest for human health. MISCs further participate in aging and regeneration phenomena, including whole-body regeneration. For years, the European MISC-community has been highly fragmented and scarce ties were established with biomedical industries in attempts to harness MISCs for human welfare. Thus, it is important to: i) consolidate the fragmented European community working on MISCs; ii) promote and coordinate European research on MISC biology; iii) stimulate young researchers to embark on research in MISC-biology; iv) develop, validate, and network novel MISC tools and methodologies; v) establish the MISC discipline as a forefront interest of biomedical disciplines, including nanobiomedicine; vi) establish collaborations with industries to exploit MISCs as sources of bioactive molecules. In order to fill the recognised gaps, the EC-COST Action 16203 “MARISTEM”, has recently been launched. At its initial stage the consortium unites 26 scientists from EC countries, Cooperating countries and Near Neighbor Countries.

Mass transfer restrictions of scaffolds are currently hindering the development of three-dimensional (3D), clinically viable, and tissue engineered constructs. For this situation, a 3D poly(lactide-co-glycolide)/hydroxyapatite porous scaffold, which was much favorable for transfer of nutrients to and waste products from the cells in the pores, was developed in this study. The 3D scaffold had an innovative structure, including macropores with diameters of 300−450 μm for cell ingrowth and microchannels with diameters of 2−4 μm for nutrition and waste exchange. The mechanical strength in wet state was strong enough to offer the structural support. The typical structure was more beneficial for the attachment, proliferation, and differentiation of rabbit bone marrow mesenchymal stem cells (rBMSCs). The alkaline phosphatase (ALP) activity and calcium (Ca) deposition were evaluated on the differentiation of rBMSCs, and the results indicated that the microchannel structure was very favorable for differentiating rBMSCs into maturing osteoblasts. For repairing rabbit radius defects in vivo, there was rapid healing in the defects treated with the 3D porous scaffold with microchannels, where the bridging by a large bony callus was observed at 12 weeks post-surgery. Based on the results, the 3D porous scaffold with microchannels was a promising candidate for bone defect repair.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

We evaluated oleanolic acid (OA)-induced anti-cancer activity, apoptotic mechanism, cell cycle status, and MAPK kinase signaling in DU145 (prostate cancer), MCF-7 (breast cancer), and U87 (human glioblastoma) cells. The IC50 values for OA-induced cytotoxicity were 112.57 in DU145, 132.29 in MCF-7, and 163.60 in U87 cells, respectively. OA (at 100 µg/mL) increased the number of apoptotic cells to 27.0% in DU145, 27.0% in MCF-7, and 15.7% in U87 cells, when compared to control cells. This enhanced apoptosis was due to increases in p53, cytochrome c, and Bax expression. OA-treated DU145 cells were arrested in G2 because of the activation of p-ERK and p21, and the decrease in cyclin B1 and cyclin E expression. Furthermore, OA-treated MCF-7 cells were arrested in G1 owing to the activation of p-JNK, p-ERK, p21, and p27, and the decrease in p-AKT, cyclin E, and CDK2. U87 cells also exhibited G1 phase arrest caused by the increase in p-ERK, p-JNK, p21, and p27, and the decrease in CDK2. Thus, OA arrests the cell cycle in different phases, and increases apoptosis in cancer cells. These results suggest that OA may alter the expression of cell cycle regulatory proteins in a cancer type-dependent manner.

In this paper, modelling, simulation and verification of multi-agent manufacturing system with application of bio-inspired techniques are addressed. To this end, the new solution of abstract architecture for control and coordination decentralized systems - CODESA is suggested. Centralized architecture suffers from various problems, such as rigidity, scalability, low fault-tolerance or very limited flexibility, agility, energy efficiency and productivity. Prime is concrete application of CODESA in manufacturing domain. The undesirable characteristics of emergent behaviour are the problem to achieve optimization and impossibility to predict future states of the system. CODESA-Prime has been tested by simulations for automatic guided vehicle (AGV) systems guided by magnetic tape in Ella Software Platform.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

In the study of relativistic jets one of the key open questions is their interaction with the environment on the microscopic level. Here, we study the initial evolution of both electron–proton (e^- - p⁺) and electron–positron (e^±) relativistic jets containing helical magnetic fields, focusing on their interaction with an ambient plasma. We have performed simulations of "global" jets containing helical magnetic fields in order to examine how helical magnetic fields affect kinetic instabilities such as the Weibel instability, the kinetic Kelvin-Helmholtz instability (kKHI) and the Mushroom instability (MI). In our initial simulation study these kinetic instabilities are suppressed and new types of instabilities can grow. In the e^- - p⁺ jet simulation recollimation-like instability occurs and jet electrons are strongly perturbed. In the e^± jet simulation a recollimation-like instability occurs at early times followed by a kinetic instability and the general structure is similar to a simulation without helical magnetic field. Simulations using much larger systems are required in order to thoroughly follow the evolution of global jets containing helical magnetic fields.

Predicting electricity prices is a very important issue in modern society, because the associated decision process under uncertainty requires accurate forecasts for the economic agents involved. In this paper, we apply the decision tree extension of Random Forests to the prediction of electricity prices in Spain, but with the novelty of modeling prices jointly with demand, with the purpose of achieving greater accuracy than with univariate response Random Forests, particularly in price prediction, as well as understanding the effect of the input variables (lagged values of price and demand, current production levels of available energy sources) on the joint of the two outputs. The results are very encouraging, providing significant increase in price prediction accuracy. Also, interesting methodological challenges appear as far as the appropriate choice of the relative weights of price and demand in the joint modeling is concerned and a new procedure to provide the importance variable ranking is proposed. The partykit (package of R software) library allowing for multivariate Random Forests has been used.

The cell case temperature versus time profiles of a multistage fast charging technique (4C-1C-CV)/fast discharge (4C) in a 2.3 Ah cylindrical lithium-ion cell are analyzed using a 1D thermal model. Heat generation is dominated by the irreversible component associated to cell overpotential, although evidences of the reversible component are also observed, associated to the heat related to entropy from the electrode reactions. The final charging stages (i.e., 1C-CV) significantly reduce heat generation and cell temperature during charge, resulting in a thermally safe charging protocol. Cell heat capacity was determined from cell specific heats and cell materials thickness. The 1D model adjustment of the experimental data during the 2 min. resting period between discharge and charge allowed us to calculate both the time constant of the relaxation process and the cell thermal resistance. The obtained values of these thermal parameters used in the proposed model are almost equal to those found in the literature for the same cell model, which suggests that the proposed model is suitable for its implementation in thermal management systems.

It is known that knockdown of the mitochondrial 18 kDa translocator protein (TSPO) as well as TSPO ligands modulate various functions, including functions related to cancer. To study the ability of TSPO to regulate gene expression regarding such functions, we applied microarray analysis of gene expression to U118MG glioblastoma cells. Within 15 minutes, the classical TSPO ligand PK 11195 induced changes in expression of immediate early genes and transcription factors. These changes also included gene products that are part of the canonical pathway serving to modulate general gene expression. These changes are in accord with reverse transcriptase (RT) real-time -PCR. At the time points of 15, 30, 45, and 60 minutes, as well as 3 and 24 hours of PK 11195 exposure, the functions associated with the changes in gene expression in these glioblastoma cells covered well known TSPO functions. These functions included cell viability, proliferation, differentiation, adhesion, migration, tumorigenesis, and angiogenesis. This was corroborated microscopically for cell migration, cell accumulation, adhesion, and neuronal differentiation. Changes in gene expression at 24 hours of PK 11195 exposure were related to downregulation of tumorigenesis and upregulation of programmed cell death. In the vehicle treated as well as PK 11195 exposed cell cultures, our triple labeling showed intense TSPO labeling in the mitochondria but no TSPO signal in the cell nuclei. Thus, mitochondrial TSPO appears to be part of the mitochondria-to-nucleus signaling pathway for modulation of nuclear gene expression. The novel TSPO ligand 2-Cl-MGV-1 appeared to be very specific regarding modulation of gene expression of immediate early genes and transcription factors.

ACE2, the putative receptor for the novel coronavirus (2019-nCoV), played an important role in cell entry of 2019-nCoV. However, it is not yet clear what cell types within the human body express ACE2. Here, a systematic analysis was undertaken using published single cell datasets. In total, our study analyzed 229652 cells, from five different organs, derived from 88 donors. The top ACE2 expressing cells include proximal tubule cells in the kidney and enterocytes in the intestine. Other major ACE2 expressing cells in the kidney include podocytes, intercalated cells and endothelial cells. Our results offer a comprehensive atlas of ACE2 expression at the single cell level and unravel the enormous potential targets of 2019-nCoVinfection beyond the lung.

A problem with synthetic aperture radar (SAR) is that, due to the poor penetrating action of electromagnetic waves inside solid bodies, the capability to observe inside distributed targets is precluded. Under these conditions, imaging action is provided only on the surface of distributed targets. The present work describes an imaging method based on the analysis of micro-movements on the Khnum-Khufu Pyramid, which are usually generated by background seismic waves. The results obtained prove to be very promising, as high-resolution full 3D tomographic imaging of the pyramid's interior and subsurface was achieved. Khnum-Khufu becomes transparent like a crystal when observed in the micro-movement domain. Based on this novelty, we have completely reconstructed internal objects, observing and measuring structures that have never been discovered before. The experimental results are estimated by processing series of SAR images from the second-generation Italian COSMO-SkyMed satellite system, demonstrating the effectiveness of the proposed method.

In this paper, the technical aspects of a multi-lidar instrument, the long-range WindScanner system, will be presented accompanied by an overview of the results from several field campaigns. The long-range WindScanner system consists of three spatially separated coherent Doppler scanning lidars and a remote master computer that coordinates them. The lidars were carefully engineered to perform arbitrary and time controlled scanning trajectories. Their wireless coordination via the master computer allows achieving and maintaining lidars’ synchronization within ten milliseconds. As a whole, the long-range WindScanner system can measure an entire wind field by emitting and directing three laser beams to intersect, and then by moving the beam intersection over the points of interest. The long-range WindScanner system was developed to tackle the need for high-quality observations of wind fields from scales of modern wind turbine and wind farms. It has been in operation since 2013.

Cancer is a leading cause of death worldwide. It is theorized that underlying genetic and epigenetic changes enable cells to proliferate out of control by escaping regulatory mechanisms. The progression of cancer is associated with increased cell proliferation, metabolic modifications, resistance to apoptosis, genetic instability, induction of angiogenesis and augmented migratory capability. Recent developments in DNA and RNA analysis have made it possible to study these genetic changes systematically. These advances have enabled us to possess a deeper knowledge of the signaling pathways and involved processes. In-depth studies of the pathways involved in carcinogenesis have led to the identification of pathways that may be targeted for therapeutic purposes. In this review, we provide an overview of the relevant mechanisms and pathways involved in the development and progression of cancer.

Marine renewable energy is emerging as one of the fast-growing industry in the last decades, as modern society pushes for technologies that can convert energy contained from winds, waves, tides and stream flows. The implementation of renewable energy technologies impose high demands on both structural and environmental engineering, as the energy converters have to work under extreme conditions where parameters such as sea-bottom configuration, water transparency and depth, sea-states and prevailing winds are harsh. Constant monitoring of the marine environment is crucial in order to keep this sector reliable. Active acoustics is becoming a standard tool to collect multi-dimensional data from physical, geological and biological properties of the marine environment. The Div. of Electricity of Uppsala University have been developing an environmental monitoring platform based on sonar systems. This platform aims to monitor the installation, operation and decommissioning of marine renewable energy converters. The focus will be given the observations of behaviors of marine animals in vicinity of energy converters but also structural inspection and monitoring of MRETs. This paper describes how this multifunctional environmental monitoring platform come to existence from the design to the deployment phase.

Structure from Motion with Multi-View Stereo photogrammetry (SfM) is increasingly utilised in geoscience investigations as a cost-effective method of acquiring high resolution (sub-meter) topographic data, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via an Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via a handheld digital camera, ‘Ground’) SfM in modelling a hillslope gully system in dry-tropical savanna, and to assess the strengths and limitations of the approach at different scales. A UAV survey covered an entire hillslope gully system (0.715 km²), whereas a Ground survey covered a single gully within the broader system (650 m²). SfM topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate UAV SfM can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., 0.1 m resolution with ~ 0.5 – 1.3 m elevation error), while ground-based SfM is more capable of quantifying gully morphology (e.g., 0.01 m resolution with ~ 0.1 m elevation error). Key strengths of SfM for these applications include: the production of high resolution 3D topographic models and ortho-photo mosaics, low survey instrument costs (< $AUD 3,000); and rapid survey time (4 and 2 hours for UAV and Ground survey respectively). Current limitations of SfM include: difficulties in reconstructing vegetated surfaces; uncertainty as to optimal survey and processing designs; and high computational demands. Overall, this study has demonstrated great potential for SfM to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in tropical savanna landscapes and elsewhere.

The study of different forms of preconditioners for solving a system of nonlinear equations, by using Newton’s method, is presented. The preconditioners provide numerical stability and rapid convergence with reasonable computation cost, whenever chosen accurately. Different families of iterative methods can be constructed by using a different kind of preconditioners. The multi-step iterative method consists of a base method and multi-step part. The convergence order of base method is quadratic and each multi-step add an additive factor of one in the previously achieved convergence order. Hence the convergence of order of an m-step iterative method is m + 1. Numerical simulations confirm the claimed convergence order by calculating the computational order of convergence. Finally, the numerical results clearly show the benefit of preconditioning for solving system of nonlinear equations.

Nanocellulose is cellulose in the form of nanostructures, i.e. features not exceeding 100 nm at least in one dimension. These nanostructures include nanofibrils, e.g. in bacterial cellulose; nanofibers, e.g. in electrospun matrices; nanowhiskers and nanocrystals. These structures can be further assembled into bigger 2D and 3D nano-, micro- and macro-structures, such as nanoplatelets, membranes, films, microparticles and porous macroscopic matrices. There are four main sources of nanocellulose: bacteria (Gluonacetobacter), plants (trees, shrubs, herbs), algae (Cladophora) and animals (Tunicata). Nanocellulose has emerged for a wide range of industrial, technology and biomedical applications, e.g. for adsorption, ultrafiltration, packaging, conservation of historical artifacts, thermal insulation and fire retardation, energy extraction and storage, acoustics, sensorics, controlled drug delivery, and particularly for tissue engineering. Nanocellulose is promising for use in scaffolds for engineering of blood vessels, neural tissue, bone, cartilage, liver, adipose tissue, urethra and dura mater, for repairing connective tissue and congenital heart defects, and for constructing contact lenses and protective barriers. This review is focused on applications of nanocellulose in skin tissue engineering and wound healing as a scaffold for cell growth, for delivering cells into wounds, and as a material for advanced wound dressings coupled with drug delivery, transparency and sensorics. Potential cytotoxicity and immunogenicity of nanocellulose are also discussed.

Fossil fuels and carbon origin resources are affecting our environment. Therefore, alternative energy sources have to be established to co-produce energy along with fossil fuels and carbon origin resources until it is the right time to replace them. Microbial Fuel Cell (MFC) is a promising technology in the field of energy production. Compared to the conventional power sources it is more efficient and not controlled by the Carnot cycle. Its high efficiencies, low noise, and less pollutant output could make it revolutionize in the power generation industry with a shift from centrally located generating stations and long-distance transmission lines to dispersed power generation at load sites. In this review, several characteristics of the MFC technology will be highlighted. First, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Second, the focus is then shifted to elements responsible for the making MFC working with efficiency. Setup of the MFC system for every element and their assembly is then introduced, followed by an explanation of the working machinery principle. Finally, microbial fuel cell designs and types of main configurations used are presented along with scalability of the technology for the proper application.

Cell-penetrating peptides (CPPs), a group of small peptides capable of promoting the transport of molecular cargo across the plasma membrane, have become important tools in promoting the cellular uptake of exogenously delivered macromolecules. GV1001, a peptide derived from a reverse-transcriptase subunit of telomerase (hTERT) and developed as a vaccine against various cancers, reportedly has unexpected CPP properties. Unlike typical CPPs, such as the HIV-1 TAT peptide, GV1001 enabled the cytosolic delivery of macromolecules such as proteins, DNA and siRNA via extracellular heat shock protein 90 (eHSP90) and 70 (eHSP70) complexes. The eHSP-GV1001 interaction may have biological effects in addition to its cytosolic delivery function. GV1001 was originally designed as a MHC class II-binding cancer epitope, but its CPP properties may contribute to its strong anti-cancer immune response relative to other telomerase peptide-based vaccines. Cell signaling via eHSP-GV1001 binding may lead to unexpected biological effects, such as direct anticancer or antiviral effects. In this review, we focus on the CPP effects of GV1001 bound to eHSP90 and ehsp70.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Extremely low-frequency electromagnetic fields (ELF-EMFs) are non-ionizing radiations typically emitted by power lines, electrical wiring and electrical appliances. Epidemiological studies have repeatedly shown a positive association between ELF-EMFs and childhood leukemia. Exposures greater than 0.3-0.4 µT increase the risk by approximately 1.5-2 fold, and estimates indicate that up to 2% of childhood leukemia cases in Europe may be attributable to ELF-EMFs. However, it is considered unlikely that ELF-EMFs can cause cancer, because carcinogenesis requires the accumulation of DNA alterations and ELF-EMFs do not have enough energy to damage the DNA. Lack of biological plausibility is a barrier to accept the evidence of carcinogenicity in human studies and to take measures to protect pregnant women and children from ELF-EMFs. Recent evidence indicates that non-mutagenic agents can cause DNA alterations and increase the risk of cancer by promoting the accumulation of cell divisions in stem cells. Cell division generates DNA alterations (e.g., mutations arising during DNA replication), which occur even in the absence of DNA-damaging agents. Importantly, ELF-EMFs can trigger the division of stem cells; this effect is under development in the field of regenerative medicine. A possible mechanism by which ELF-EMFs induce the malignant transformation of hematopoietic stem cells is discussed.

The outbreak of the 2019 Novel Coronavirus (2019-nCoV) has rapidly spread from Wuhan, China to multiple countries, causing staggering number of infections and deaths. A systematic profiling of the immune vulnerability landscape of 2019-nCoV is lacking, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development. In this study, we predicted the potential of all the 2019-nCoV viral proteins to induce class I and II MHC presentation and form linear antibody epitopes. We showed that the enrichment for T cell and B cell epitopes is not uniform on the viral genome, with several focused regions that generate abundant epitopes and may be more targetable. We showed that genetic variations in 2019-nCoV, though fewer for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus, which should be considered in the development of therapies. We create an online database to broadly share our research outcome. Overall, we present an immunological resource for 2019-nCoV that could significantly promote both therapeutic development and mechanistic research.

Electric vehicles (EV) are getting more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace the internal combustion engine (ICE) vehicles in near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system can face huge instabilities with enough EV penetration; but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of smart grid. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emission from the transportation sector. However, there are some major obstacles for EVs to overcome before replacing the ICE vehicles totally. This paper is focused on reviewing all the useful data available on EV configurations, energy sources, motors, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

In this work commercial 18650 lithium-ion cells with LiMn₂O₄, LiFePO₄ and Li(Ni_0.33Mn_0.33Co_0.33)O₂ cathodes were exposed to external heating in an Accelerating Rate Calorimeter (es-ARC, THT Company) to investigate the thermal behavior under abuse conditions. New procedures for measuring external and internal pressure change of cells were developed. The external pressure was measured utilizing a gas-tight cylinder inside the calorimeter chamber in order to detect venting of the cells. For internal pressure measurements, a pressure line connected to a pressure transducer was directly inserted into the cell. During the thermal runaway experiments, three stages (low rate, medium rate and high rate reaction) have been observed. Both pressure and temperature change indicated different stages of exothermic reactions, which produced gases or/and heat. The onset temperature of thermal runaway was estimated according to temperature and pressure changes. Moreover, the different activation energies for the exothermic reactions could be derived from Arrhenius plots.

Arthropod-borne flaviviruses represent human pathogens of global medical importance, against which no effective small molecule-based antiviral therapy is currently available. Arbidol (umifenovir) is a broad spectrum antiviral compound approved in Russia and China for prophylaxis and treatment of influenza. This compound showed activity against numerous DNA and RNA viruses. Its mode of action is based predominantly on the impairment of critical steps of virus-cell interaction. Here we demonstrate that arbidol possesses a micromolar inhibition activity (EC50 values ranging from 10.57 ± 0.74 to 19.16 ± 0.29 µM) in Vero cells infected with Zika virus, West Nile virus, and tick-borne encephalitis virus, three medically important representatives of arthropod-borne flaviviruses. Interestingly, no antiviral effect of arbidol is observed in porcine stable kidney cells (PS), human neuroblastoma cells (UKF-NB-6), human hepatoma cells (Huh-7 cells) indicating that the antiviral effect of arbidol is strongly cell-type dependent. Arbidol presents a significant increasing in cytotoxicity profiles when tested in various cell lines in the order: Huh-7 < HBCA < PS < UKF-NB-6 < Vero with CC50 values ranging from 18.69 ± 0.1 to 89.72 ± 0.19 µM. Antiviral activity and acceptable cytotoxicity profiles suggest that arbidol could be a promising candidate for further investigation as a potential therapeutic agent in treating flaviviral infections.

Ovarian cancer (OC) is the most lethal gynecologic malignancy. Cannabis sativa is being used to treat different medical conditions. We sought to examine the effectiveness of combinations of cannabis compounds against OC. Cytotoxic activity was determined by XTT assay on HTB75 and HTB161 cell lines. Apoptosis and cell cycle were determined by fluorescence-activated cell sorting (FACS). Gene expression was determined by quantitative PCR. The two most active fractions, F5 and F7, from a high Δ9–tetrahydrocannabinol (THC) cannabis strain extract and their standard mix (SM) showed cytotoxic activity against OC cells. The most effective phytocannabinoid combination was THC+cannabichromene (CBC)+cannabigerol (CBG). F5, F7 and SM affected cell cycle, led to cell apoptosis and to a marked reduction in cell migration. Moreover, these fractions act in synergy with niraparib, and were ~50 fold more cytotoxic to OC cells than to normal keratenocytes. Niraparib+F7 treatment was effective on OC patient's cells. F7 and the niraparin+fraction (F5 and F7) treatments reduced Mitogen-Activated Protein Kinase 4 (MAPK4) gene expression; this reduction may act in synergy with the niraparib inhibition of Poly (ADP-ribose) polymerase 1 (PARP1) activity. Combinations of cannabis compounds and niraparib should be examined for efficacy in pre-clinical studies and clinical trials.

Lamin A, B and C, the nuclear intermediate-filament proteins, play a role in epigenetic regulation. While Lamin B is expressed in all nucleated cells studied, Lamin A/C are transcribed in most somatic cell types except mature B lymphocytes. Since Epstein-Barr virus (EBV), a human gammaherpesvirus, is associated with tumorigenic processes and is known to alter the epigenotype of its host cells, we studied the expression of the LMNA gene and its epigenetic marks in EBV-carrying human lymphoid cell lines. We observed a high lamin A/C mRNA and protein expression in EBV-immortalized lymphoblastoid cell lines (LCLs) and in group III Burkitt lymphoma (BL) lines where hypomethylated first exons were observed with activating histone marks. In most cell lines with low promoter activity a highly methylated first exon could be detected. Our data showed that methylation of the first exon of LMNA was associated with the downregulation of LMNA expression whereas euchromatic histone marks were enriched at active LMNA promoters in EBV-immortalized LCLs. These data suggest a role for viral latency products to activate LMNAp in EBV-infected latency type III B cells in vitro. Expression of lamin A/C may contribute to the establishment of activated B cell phenotype that needs further explorations.

Here, we present a staged approach for an innovative repurposing of a portable infant humidicrib into a live cell growth, observation, and imaging system. Furthmore, humidicrib can support different variations of “umbilical” bioreactors, and can be used to conduct electrophysiology experiments and in situ immunohistochemistry. Modifications incorporate a closed loop carbon dioxide (CO₂) concentration control system with umbilical CO₂ and heating support for tailored bioreactors. The repurposing cost is inexpensive and allows for the continued observation and imaging of cells. This prototype unit has been used to continuously observe and image live primary neurons for up to 21 days. This demonstrates the repurposed units’ suitability for use in tissue culture based research, particularly where modifications to microscopes are required or where sensitive manipulation outside of a standard incubator is needed.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

The evolving multiphase induction generators (MPIG) with more than three phases are receiving prominence in high power generation systems. This paper aims at the development of a comprehensive model of the wind turbine driven seven-phase induction generator (7PIG) along with necessary the power electronic converters and controller for grid interface. The dynamic model of the system is developed in Maltlab/Simulink. Synchronous reference frame phase-locked loop (SRFPLL) system is incorporated for grid synchronization. The modeling aspects are detailed and the system response is observed for various wind velocities. The effectiveness of seven phase induction generator is demonstrated with the fault tolerant capability and high output power with reduced phase current when compared to conventional 3-phase wind generation scheme. The response of the PLL is analyzed and the results are presented.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

Pseudopterosin, produced by the sea whip of the genus Antillogorgia, possesses a variety of promising biological activities including potent anti-inflammatory effects. However, few studies examined pseudopterosin in the treatment of cancer cells and, to our knowledge, the ability to inhibit triple negative breast cancer (TNBC) proliferation or invasion has not been explored. Thus, we evaluated the as yet unknown mechanism of action of pseudopterosin: Pseudopterosin was able to inhibit proliferation of TNBC. Interestingly, analyzing breast cancer cell proliferation after knocking down glucocorticoid receptor α (GRα) revealed that anti-proliferative effects of pseudopterosin were significantly inhibited when GRα expression was reduced. Furthermore, pseudopterosin inhibited invasion of MDA-MB-231 3D tumor spheroids embedded in an extracellular-like matrix. Remarkably, the knockdown of GRα in 3D tumor spheroids revealed increased ability of cells to invade the surrounding matrix. In a co-culture, encompassing peripheral blood mononuclear cells (PBMC) and MDA-MB-231 cells, production of interleukin 6 (IL-6) and interleukin 8 (IL-8) significantly increased compared to monoculture. Notably, pseudopterosin proved to block cytokine elevation, representing key players in tumor progression, in the co-culture. Thus, our results reveal pseudopterosin treatment as a potential novel approach in TNBC therapy.

The stromal vascular cell fraction (SVF) of visceral and subcutaneous adipose tissue (VAT and SAT) has increasingly come into focus in stem cell research, since these compartments represent a rich source of multipotent adipose-derived stem cells (ASCs). ASCs exhibit a self- renewal potential and differentiation capacity. Our aim was to study the different expression of embryonic stem cell markers NANOG, SOX2 and OCT3/4 and to evaluate if there exists a hierarchal role in this network in ASCs derived from both SAT and VAT. ASCs were isolated from SAT and VAT biopsies of 72 consenting patients (23 men, 47 women; age 45 ± 10; BMI between 25 and 30 range) undergoing elective open-abdominal surgery. Sphere-forming capability was evaluated by plating cells in low adhesion plastic. Stem cell markers CD90 and CD105 were analyzed by flow cytometry and stem cell transcription factors NANOG, SOX2 and OCT3/4 were detected by immunoblotting and Real-Time PCR. NANOG, SOX2 and OCT3/4 interplay was explored by gene silencing. ASCs from VAT and SAT confirmed their mesenchymal stem cell (MSC) phenotype expressing the specific MSC markers CD90, CD105, NANOG, SOX2 and OCT3/4. NANOG silencing induced a significant OCT 3/4 (70% ± 0.05) and SOX2 (75% ± 0.03) down-regulation whereas SOX2 silencing did not affect NANOG gene expression. Adipose tissue is an important source of MSC, and siRNA experiments endorse a hierarchical role of NANOG in the complex transcription network that regulates pluripotency and plasticity.

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. The use of multi-temporal data has allowed the overcoming of seasonal cultivation patterns and long-term visibility issues related to crop selection, large-scale irrigation and land use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 kms of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated.

It is proposed that the primary living cells (“probionts”) cannot emerge of organic substance simply by continuous chemical complication of prebiotic macromolecules and microsystems. The complication must be accompanied by the radical thermodynamic transformation (“jump”) of prebiotic microsystems that resulted in the acquired ability to extract free energy from the environment and export entropy. This transformation is called “the thermodynamic inversion” The inversion may occur by means of the efficient (intensified) response of the microsystems on the oscillations of physic-chemical parameters in hydrothermal environment. In this case the surplus available free energy within a microsystem, when combined with the informational modality, facilitates its conversion into a new microsystem—a living probiont. It is shown the schematic representation of an oscillating prebiotic microsystem that is transforming into a living probiont. A new kind of laboratory and computational experiments on prebiotic chemistry under oscillating conditions is offered to verify the inversion concept.

When swine flu vaccines and circulating influenza A virus (IAV) strains are poorly matched, vaccine-induced antibodies may not protect from infection. Highly conserved T cell epitopes may, however, have a disease-mitigating effect. The degree of T cell epitope conservation among circulating strains and vaccine strains can vary, which may also explain differences in vaccine efficacy. Here, we evaluate a previously developed conserved T cell epitope-based vaccine and determine the persistence of T cell epitope conservation over time. We used a pair-wise homology score to define conservation between the vaccine’s swine leukocyte antigen (SLA) class I and II-restricted epitopes and T cell epitopes found in 1,272 swine IAV strains sequenced between 2013 and 2017. Twenty-four of the 48 total T cell epitopes included in the epitope-based vaccine were highly conserved and found in >1,000 circulating swine IAV strains over the five-year period. In contrast, commercial swine IAV vaccines developed in 2013 exhibited declining conservation with the circulating IAV strains over the same five-year period. Conserved T cell epitope vaccines may be useful adjunct for commercial swine flu vaccines and to improve protection against influenza when antibodies are not cross-reactive.

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Fluid flow in human body is generally known to influence a variety of cellular behaviors. Different nanoparticle properties as well as cell type, interaction with other cells and cellular environments also show significant effect on nanoparticle uptake and drug efficacy. The aim of this study was to evaluate the effect of shear stress on cellular behaviors of biocompatible and biodegradable nanoparticles to cancer cells (A549 cell lines) in a biomimetic microfluidic system. We prepared a gelatin-oleic conjugate (GOC) as an amphiphilic biomaterial to prepare self-assembled gelatin-oleic nanoparticles (GON). Coumarin-6 and paclitaxel were used as the fluorescence marker and model drug, respectively, and were loaded into GONs by incubation (C-GONs; PTX-GONs). Additionally, we evaluated the cellular uptake of fluorescence labeled C-GONs and the drug efficacy of PTX-GONs. The cellular uptake of C-GONs by A549 cells in the absence of shear stress revealed that the mean fluorescence intensity was slightly decreased compared to that in the presence of shear stress. The results also indicated that negatively charged PTX-GONs had a lower cancer killing effect under dynamic conditions than that under static conditions. It also suggested that fluidic shear stress did not significantly affect drug uptake and efficiency in case of PTX-GONs. The cellular interactions between nanoparticles and cells in drug delivery should be carefully examined according to the physicochemical properties of nanoparticles such as the type of materials, size and mainly surface charge in a biomimetic microfluidic condition.

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.

Vimentin is an intermediate filament protein that plays key roles in integration of cytoskeletal functions, and therefore in basic cellular processes such as cell division and migration. Consequently, vimentin has complex implications in pathophysiology. Vimentin is required for a proper immune response, but it can also act as an autoantigen in autoimmune diseases or as a damage signal. Although vimentin is a predominantly cytoplasmic protein, it can also appear at extracellular locations, either in a secreted form or at the surface of numerous cell types, often in relation to cell activation, inflammation, injury or senescence. Cell surface targeting of vimentin appears to associate with the occurrence of certain posttranslational modifications, such as phosphorylation and/or oxidative damage. At the cell surface, vimentin can act as a receptor for bacterial and viral pathogens. Indeed, vimentin has been shown to play important roles in virus attachment and entry of severe acute respiratory syndrome-related coronavirus (SARS-CoV), dengue and encephalitis viruses, among others. Moreover, the presence of vimentin in specific virus-targeted cells and its induction by proinflammatory cytokines and tissue damage contribute to its implication in viral infection. Here, we recapitulate some of the pathophysiological implications of vimentin, including the involvement of cell surface vimentin in interaction with pathogens, with a special focus on its role as a cellular receptor or co-receptor for viruses. In addition, we provide a perspective on approaches to target vimentin, including antibodies or chemical agents that could modulate these interactions to potentially interfere with viral pathogenesis, which could be useful when multi-target antiviral strategies are needed .

Background: Studies employing next-generation sequencing (NGS) show that the oral fungal community (mycobiome) is far more complex than hitherto thought. However, the role of the oral mycobiome in health and disease, including oral carcinogenesis, has not been explored. Objective: To characterize the mycobiome associated with oral squamous cell carcinoma (OSCC). Methods: Tissue biopsies [cases: 25 OSCC; controls: 27 intra-oral fibro-epithelial polyp (FEP)] were collected from oral and maxillofacial units in Sri Lanka. Total DNA was extracted and subjected to sequencing of the fungal ITS2 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a BLASTN-algorithm with UNITE’s named species sequences as reference. Downstream analyses were performed using QIIME and LEfSe. Results: 364 species representing 160 genera and 2 phyla (Ascomycota and Basidiomycota) were identified, with Candida and Malassezia making up 48% and 11% of the average mycobiome, respectively. However, only 5 species and 4 genera were detected in ≥50% of the samples. The species richness and diversity were significantly lower in OSCC. At the genus level, Candida, Hannaella and Gibberella were overrepresented in OSCC while Alternaria and Trametes were more abundant in FEP. Species-wise, C. albicans, C. etchellsii and Hannaella luteola-like species were enriched in OSCC while Malassezia restricta, Aspergillus tamarii, Alternaria alternate, Cladosporium halotolerans, and Hanseniaspora uvarum-like species were the most significantly abundant in FEP. Conclusions: A dysbiotic mycobiome dominated by C. albicans was found in association with OSCC. Whether this dysbiosis plays a role in oral carcinogenesis warrants further investigation.

Even though the current classic aerospace propulsion methods are greatly improved, some of the most modern methods have already been introduced, among which the propulsion with electric motors. Energy can be obtained from hydrogen burners, honeycomb burners to protect the burning of hydrogen that burns 10 times faster than conventional oil fuels or alcohols. Hydrogen storage is also done in honeycomb cell reservoirs. Getting solar photovoltaic power even during flight is not yet profitable or efficient, but it can be proposed in the future for lighter and/or spacecraft. A great desire is to dismantle water in hydrogen and oxygen directly on the aircraft, so the stored fuel is actually water and the one actually used is the hydrogen removed from the water. In this case, there is no need for hydrogen storage. And other modern methods of propulsion and energy acquisition are briefly presented in the paper.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are amongst the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this case study. Actual locations of dams constructed in the area were used to verify the results of both methods.

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Intracellular aggregates of the alpha-synuclein protein result in cell loss and dysfunction in Parkinson’s disease and atypical parkinsonism, such as multiple system atrophy and dementia with Lewy bodies. Each of these neurodegenerative conditions, known collectively as alpha-synucleinopathies, may be characterized by a different suite of molecular triggers that initiate pathogenesis. The mechanisms whereby alpha-synuclein aggregates in turn mediate cytotoxicity also remain to be fully elucidated. However, recent studies have implicated the cell-to-cell spread of alpha-synuclein as the major mode of disease propagation between brain regions during disease progression. Here, we review the current evidence for different modes of alpha-synuclein cellular release, movement and uptake, including exocytosis, exosomes, tunnelling nanotubes, glymphatic flow and endocytosis. A more detailed understanding of the major modes by which alpha-synuclein pathology spreads throughout the brain may provide new targets for therapies that halt the progression of disease.

Non-melanoma skin cancers (NMSCs) are the leading cause of skin cancer-related morbidity and mortality. Effective strategies are needed to control NMSC occurrence and progression. Non-toxic, plant-derived extracts have been shown to exert multiple anti-cancer effects. Graviola (Annona muricata), a tropical fruit-bearing plant, has been used in traditional medicine against multiple human diseases including cancer. The current study investigated the effects of graviola leaf and stem extract (GLSE) and its solvent-extracted fractions on two human NMSC cell lines, UW-BCC1 and A431. GLSE was found to: i) dose-dependently suppress UW-BCC1 and A431 cell growth, motility, wound closure, and clonogenicity; ii) induce G₀/G₁ cell cycle arrest by downregulating cyclin/cdk factors while upregulating cdk inhibitors, and (iv) induce apoptosis as evidenced by cleavage of caspases-3, -8 and PARP. Further, GLSE suppressed levels of activated hedgehog (Hh) pathway components Smo, Gli 1/2, and Shh while inducing SuFu. GLSE also decreased the expression of pro-apoptotic protein Bax while decreasing the expression of the anti-apoptotic protein Bcl-2. We determined that these activities were concentrated in an acetogenin/alkaloid-rich dichloromethane subfraction of GLSE. Our data identify graviola extracts and their constituents as promising sources for new chemopreventive and therapeutic agent(s) to be further developed for the control of NMSCs

Diabetes mellitus (DM) is one of the major risk factors for COVID-19 complications as it is one of the chronic immune-compromising conditions especially if patients have uncontrolled diabetes, poor HbA1c &/or irregular blood glucose levels. Diabetic patient’s mortality rates with COVID-19 are higher than cardiovascular or cancer patients. Recently Bacillus Calmette–Guérin (BCG) has shown successful results in reversing diabetes in both rats and clinical trials based on different mechanisms from aerobic glycolysis to Beta cells regeneration. BCG is a multi-face vaccine that has been used extensively in protection from TB and leprosy and has been repositioned for treatment of bladder cancer, diabetes & multiple sclerosis. Recently, the COVID-19 epidemiological study confirmed that universal BCG vaccination reduced morbidity and mortality in certain geographical areas. Countries without universal policies of BCG vaccination (Italy, Nederland, USA) have been more severely affected compared to countries with universal and long-standing BCG policies that have shown low numbers of reported COVID-19 cases. Some countries have started clinical trials that included a single dose BCG vaccine as prophylaxis from COVID-19 or an attempt to minimize its side effects. This proposed research aims to use BCG vaccine as a double-edged weapon countering both COVID-19 & diabetes, not only as protection but also as therapeutic vaccination. The work includes a case study of regenerated pancreatic beta cells based on improved C-peptide & PCPRI laboratory findings after BCG vaccination for a 9 years’ patient. The patient was re-vaccinated based on a negative tuberculin test & no scar at the site of injection of the 1st BCG vaccination at birth. Furthermore, the authors in the present article described a prospective BCG multi-dose clinical study in full details that they will apply in case of acceptance of their submitted grant & the ethical committee approval. The aim of the clinical study is to check if double dose BCG (4 weeks apart) will show a significant difference in the protection of health care professionals in Egypt. The authors suggest and invite the scientific community to take into consideration the concept of direct BCG re-vaccination (after 4 weeks) because of the reported gene expressions & exaggerated innate immunity consequently. As the diabetic MODY-5 patient (mutation of HNF1B, Val2Leu) was on low dose Riomet® while eliminating insulin gradually, a simple analytical method for metformin assay was recommended to ensure its concentration before use as it is not approved yet by the Egyptian QC labs.

Studies on the possible association between bacteria and oral squamous cell carcinoma (OSCC) remain inconclusive, largely due to methodological variations/limitations. The objective of this study was to characterize the species composition as well as functional attributes of the bacteriome associated with OSCC. DNA obtained from 20 fresh OSCC biopsies (cases) and 20 deep-epithelium swabs (matched control subjects) were sequenced for the V1-V3 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a prioritized BLASTN-algorithm. Downstream analyses were performed using QIIME, PICRUSt, and LEfSe. Fusobacterium nucleatum subsp. polymorphum was the most significantly overrepresented species in the tumors followed by Pseudomonas aeruginosa and Campylobacter sp. Oral taxon 44, while Streptococcus mitis, Rothia mucilaginosa and Haemophilus parainfluenzae were the most significantly abundant in the controls. Functionally, genes involved in bacterial mobility, flagellar assembly, bacterial chemotaxis and LPS synthesis were enriched in the tumors while those responsible for DNA repair and combination, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, ribosome biogenesis and glycolysis/gluconeogenesis were significantly associated with the controls. This is the first epidemiological evidence for association of F. nucleatum and P. aeruginosa with OSCC. Functionally, an “inflammatory bacteriome” is enriched in OSSC.

Tumor microenvironment including cancer-associated fibroblasts (CAF) has developed as an important target for understanding tumor progression, clinical prognosis and treatment responses of cancer. Cancer cells appear to transform normal fibroblasts (NF) into CAFs involving direct cell-cell communication and epigenetic regulations. This review summarizes the current understanding on miR involvement in cancer cell – tumor environment/stroma communication, transformation of NFs into CAFs, their involved targets and signaling pathways in these interactions; and clinical relevance of CAF-related miR expression profiles. There is evidence that miRs have very similar roles in activating hepatic (HSC) and pancreatic stellate cells (PSC) as part of precancerous fibrotic diseases. In summary, deregulated miRs affect various intracellular functional complexes, such as transcriptional factors, extracellular matrix, cytoskeleton, EMT/MET regulation, soluble factors, tyrosine kinase and G-protein signaling, apoptosis and cell cycle & differentiation, but also formation and composition of the extracellular microenvironment. These processes result in the clinical appearance of desmoplasia involving CAFs and fibrosis characterized by deregulated stellate cells. In addition, modulated release of soluble factors can act as (auto)activating feedback loop for transition of NFs into their pathological counterparts. Furthermore, epigenetic communication between CAFs and cancer cells may confer to cancer specific functional readouts and transition of NF into their pathological counterparts. MiR related epigenetic regulation with many similarities should be considered as key factor in development of cancer and fibrosis specific environment.

Abstract: The SARS-CoV-2 pandemic has significantly impacted world health and economic status. In response, much work has been undertaken to provide effective, safe vaccines, antibodies and antiviral drugs with which to address this pandemic. Treatment of a pandemic population presents multiple challenges in addition to the primary issue of drug efficacy and safety, such as large scale drug manufacture and distribution, drug stability, oral dosing and pharmacoeconomic considerations. Ideally, these factors must be addressed if new candidate drugs are to be advanced for treatment of large (pandemic) populations. Subsequently, new antivirals have reached the market but choices are few. According to the NIH Covid Treatment Guidelines, only three small molecule antiviral drugs are available to treat COVID-19 disease. As such, a significant part of the research towards discovery of new antiviral drugs has focused on screening and evaluation of ‘repurposed drugs’ or previously approved or clinical stage drugs. Yet, in spite of this increased research activity, one promising clinical stage candidate drug has received little attention regarding its potential as a monotherapy or component of combination therapy for treatment of COVID-19 disease. Tucaresol, with documented human safety and pharmacokinetic data, is an orally active, stable, small molecule amenable to large scale manufacture by a proprietary two-step synthesis developed by us. Tucaresol functions as a host-targeted antiviral by selective protection/reconstitution of CD4+ T helper cells as demonstrated in HIV patients and SIV macaques. In view of similarities between HIV and SARS-CoV-2, especially with respect to host CD4+ T helper cells, and the suitability of Tucaresol for facile treatment of pandemic populations, Tucaresol is presented herein for treatment of mild-to-moderate COVID-19 patients but may also be useful for treatment of advanced disease accompanied by lymphopenia.

The world faces two seemingly unrelated challenges—a shortfall in the STEM workforce and increasing antibiotic resistance among bacterial pathogens. We address these two challenges with Tiny Earth, an undergraduate research course that excites students about science and creates a pipeline for antibiotic discovery.

High pressure crystallization technique has been successfully used to prepare an elusive form II of a non-steroidal anti-inflammatory drug, Mefenamic acid. Single crystal of form II was grown at 0.3 GPa from an 4:1 methanol/ ethanol mixture as a solvent using Diamond Anvil Cell. Comparison of crystal structures show that the efficient packing of MA molecules in Form II results from the structural flexibility of MA acid molecules. Compression studies performed on the single crystal of form I resulted in 14 % decrease of unit cell volume up to 2.5 GPa. No phase transition was observed up to this pressure. A reconstructive phase transition is required to induce conformational changes in the structure, which is confirmed by crystallization at high pressure results.

Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback–Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

Sickle cell disease (SCD) is an inherited disorder of major health challenge in Nigeria. Micronutrients deficiencies often associated with the disorder may cause inflammation and abnormal metabolisms in the body. The copper-to-zinc ratio is a more important assessment than the concentrations of either of the metals in clinical practice. This study seeks to evaluate serum levels of c-reactive protein (CRP), copper, zinc and copper-to-zinc ratio and to correlate copper-to-zinc ratio with CRP in adult subjects with SCD. Serum copper, zinc, CRP and plasma fibrinogen were assayed in 100 confirmed SCD patients in steady clinical state and 100 age and sex matched subjects with normal haemoglobin. Serum copper and zinc were assayed by colorimetric method using reagents supplied by Centronic, Germany while CRP and fibrinogen were assayed using reagents supplied by Sigma (St. Louis, MO, USA) and Anogen (Ontario, Canada), respectively. The copper to zinc ratio was calculated from serum levels of copper and zinc. The measured parameters were compared between the groups using Students t-test and Pearson correlation coefficient was used to relate CRP with the other parameters. Serum copper, CRP, fibrinogen and copper-to-zinc ratio were significantly higher (p < 0.001) while zinc level was lower in SCD patients than controls. Serum CRP concentration correlated with copper (r = 0.10; p < 0.02), zinc (r = −0.199; p < 0.05) and Copper-to-zinc ratio (r = 0.312; p < 0.002) but the correlation between CRP and fibrinogen was not significant. Inflammatory condition may modulate copper and zinc homeostasis and copper-to-zinc ratio may be used as marker of nutritional deficiency and inflammation in SCD patients.

We present information theoretic search strategies for single and multi-robot teams to localize the source of biochemical contaminants in turbulent flows. The robots synthesize the information provided by sporadic and intermittent sensor readings to optimize their exploration strategy. By leveraging the spatio-temporal sensing capabilities of a mobile sensing network, our strategies result in control actions that maximize the information gained by the team while optimizing the time spent localizing the position of the biochemical source. By leveraging the team's ability to obtain simultaneous measurements at different locations, we show how a multi-robot team is able to speed up the search process resulting in a collaborative information theoretic search strategy. We validate our proposed strategies in both simulations and experiments.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

Electrostatic endothelial cell seeding has evolved as an exceptional technique to improve the efficiency of cell seeding in terms of frequency of attached cells and the amount of cell adhesion for the treatment of vascular diseases. In the recent times, both untreated and superhydrophilic thin film nitinol (TFN) have exhibited strong prospect as substrates for creation of small-diameter endovascular grafts due to their hallmark properties of superelasticity, ultra low-profile character, grown hemocompatible oxide layer with the presence of a uniform endothelial layer on the surface. The purpose of the current study is to understand the effects of endothelial cell seeding parameters (i.e., applied voltage, incubation time, substrate chemistry and cell suspension solution) to investigate the cell seeding phenomenon and to improve the cell adhesion and growth on the TFN surface under electrostatic transplantation. Both parallel plate and cylindrical capacitor models were used along with the Taguchi Design of Experiment (DOE) methods to design in vitro test parameters. A novel in vitro system for cylindrical capacitor model was created using a micro flow pump, micro incubation system, and silicone tubings. The augmented endothelialization on thin film nitinol was developed to determine the effect of cell seeding and deployed in a 6 Fr intravascular catheter setup. Cell viability along with morphology and proliferation of adhered cells were evaluated using fluorescent and scanning electron microscopy. Our results demonstrated that the maximum number of cells attached on STFN in the catheter was observed in 5V with the 2 hr exposure of in the cell culture medium (CCM) solution. The condition showed 5V voltage with 0.68×10-6 µC electrostatic charge and 5.11 V·mm-1 electric field. Our findings have first demonstrated that the electrostatic endothelialization on the superhydrophilic thin film nitinol endograft within the catheter prior to the endovascular procedure could enhance the biocompatibility for low-profile endovascular applications.

The influence of short term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength; tensile strength and impact strength in dry and wet conditions. After short term water exposure; the maximum microhardness; tensile; flexural and impact toughness values were observed at 0.1 wt% MLG. The microhardness increased up to 50.3%; tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples; the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at concentration of 0.1 wt% to produce considerable improvement in mechanical properties of produced hybrid nanocomposites.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

Multi-microgrid has many new characteristics, such as bi-directional power flows, flexible operation modes and variable fault currents with different control strategy of inverter interfaced distributed generations (IIDGs). All these featuring aspects pose challenges to multi-microgrid protection. In this paper, current and voltage characteristics of different feeders are analyzed when fault occurs in different positions of multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal fault of multi-microgrid is proposed, which takes the change of phase difference and amplitude of measured bus admittance as the criterion. This scheme with high sensitivity and reliability, has a simple principle and is easy to be adjusted. PSCAD/EMTDC is used in simulation analysis, and simulation results have verified the correctness and effectiveness of the protection scheme.

The abundant mechanical vibration energy in bridge road environment can be converted into electric energy by using the piezoelectric energy harvest technology, which could be an efficient way to provide energy required by the wireless sensor network in the bridge condition monitoring system. An autonomous energy harvesting system has been designed based on cantilever beams for sensing and acquiring the bridge vibration energy. After the analysis of the dynamic properties of the piezoelectric cantilever beam in the energy conversion, three kinds of interface circuits were compared through simulation and experimental results. It was shown that the VD interface circuit has less power loss. Furthermore, the proposed closed loop control method based on the VD circuit was simple, adaptive, and self-powered, which is suitable for the road energy harvesting application. Finally, the energy harvesting system based on VD circuit was realized with harvested power of around 0.8mW.

TTF1-NP(5,2′,4′-trihydroxy-6,7,5′-trimethoxyflavone nanoparticles), derived from the traditional Changbai Mountain medicinal plant Sorbaria sorbifolia (SS), has been showedits anti-cancer effect in various liver cancer cell types and tissues. The present study was designed to evaluate the antitumor mechanism of the TTF1-NP against HepG2 hepatoma cells and HepG2 cells-induced hepatocarcinoma (HCC) in nude mouse model. Here we demonstrated that TTF1-NP inhibits tube formation of HUVECs and HepG2 cell migration and invasion, and inhibits tumor growth in nude mice implanted with HepG2 cells through the downregulation of STAT3 protein and activation, along with VEGF, KDR, bFGF, MMP2 and MMP9 levels. We further revealed that TTF1-NP decreased the DNA-binding capacity of STAT3. Together our results provide a mechanism by which TTF1-NP suppresses cancer cell migration, invasion and angiogenesis through the action of STAT3 and suggests TTF1-NP as a potential therapy for hepatocellular cancer treatment.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the most likely model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild.

Sickle cell disease is characterized by stiff, “sickled” red blood cells that have difficulty moving through the bloodstream and do not efficiently carry oxygen. It is an inherited disease with severely limited treatment options, and is caused by a point mutation. Its prevalence in black and brown communities makes the already limited treatment options even less accessible. Base editing and prime editing are two relatively recent discoveries in the field of genome editing and were developed after the groundbreaking discovery of the CRISPR Cas9 system. While not fully tested, they hold a lot of promise in providing alternative treatment options for sickle cell disease. Both editing systems are able to install individual point mutations in the beta globin gene, which is where the sickle cell mutation occurs, and can thus cure sickle cell disease (in theory). In this paper we outline the mechanisms of CRISPR-Cas9 systems and base and prime editing, and provide insight into how to apply them to treat SCD. Further investigation should be done on specific editing systems and designs to use to ensure optimal treatment of SCD.

Direct laser writing three-dimensional nano-lithography is an established technique for manufacturing functional 3D micro- and nano-objects via non-linear absorption induced polymerization process. In this Chapter an underlying physical mechanisms taking place during nano-confined polymerization reaction, induced by tightly focused ultra-short laser pulses, are reviewed and discussed. The special attention is paid on the effects that directly impact structuring resolution and minimum achievable feature size. Analysis of possible photo-initiation mechanisms as contributing multi-photon absorption and avalanche ionization in pre-polymers under diverse exposure conditions (wavelength, pulse duration) is presented. Feasible structuring of pure (non-photosensitized) and functional nanoparticles doped polymer precursors is justified and benefits of such materials/structures for microoptics, photonics and cell scaffolds are highlighted. The influence of temperature effects (induced by writing process itself or determined by ambient conditions) on polymerization process, observed in different pre-polymers under diverse exposure regimes is outlined. The further adjustment of the structuring resolution is possible via precise control of light polarization and diffusion assisted radical quenching. The work is concluded with a brief outlook on future challenges and perspectives related to refinement of 3D ultra-fast laser lithography fabrication process in the means of application of diverse post-processing methods and research into novel photo-curable materials including inorganic ones.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by the lack of estrogen receptors (ER), progesterone receptors, and HER-2 receptors. Thus, TNBC tumours do not benefit from the current therapies targeting ER or HER-2. Therefore, there is an urgent need to develop novel treatment for this subtype of breast cancer. Marijuana is a common name given to Cannabis plants, a group of plants in the Cannabis genus of the Cannabaceae family. Cannabis plants are among the oldest cultivated crops, traced back at least 12,000 years and are well known for their multi-purpose usage, including medicinal purposes. The main active compounds extracted from Cannabis plants are 21-carbon-containing terpenophenolics, which are referred to as phytocannabinoids. Of these, the tetrahydrocannabinol (THC) group contains highly potent cannabinoids, including delta-9-tetrahydrocannabinol (∆9-THC) and delta-8-tetrahydrocannabinol (∆8-THC), which are the most abundant THCs and are largely responsible for psychological and physiological effects of marijuana. The use of Cannabis plants for medicinal purposes was first recorded in 2337 BC in China, where Cannabis plants were used to treat pains, rheumatism, and gout. Recently, several cannabinoids have been approved for a number of treatments, one of which is the treatment of nausea and vomiting caused by chemotherapy in cancer patients. Furthermore, increasing evidence shows that cannabinoids not only attenuate side effects due to cancer treatment, but might also potentially possess direct antitumor effects in several cancer types, including breast cancer. However, anti-tumour activity of marijuana has been variable in different studies and even promoted tumour growth in some cases. In addition, the mechanisms of cannabinoid action in cancer remain unclear. This review summarizes evidence about the mixed actions of cannabinoids in cancer in general and triple-negative breast cancer in particular.

Coronavirus disease (COVID-19) is a new discovered strain where WHO officially declares the disease as COVID-19 while the virus responsible for it called Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. The incubation period of this disease is between 14 days. Ordinary clinical symptoms that reported around the world include fever, cough, fatigue, diarrhoea and vomiting as well as asymptomatic for certain people. Infection is spread mainly through broad droplets. In early March 2020, WHO again has announced that COVID-19 is a pandemic with currently no specific treatment. The potential use of SARS-COV-2 proteome as a vaccine candidate by analysing through B-cell and T-cell antigenicity by using a immunoinformatics approach as a vaccine development early stage. In this study, we used consensus sequence for SARS-COV-2 proteome that was retrieved from NCBI database. VaxiJen 2.0 was mainly used to identify the antigenic property of SARS-COV-2 proteins. IEDB then used to analyse the B-cell epitope, the presence of T cell immunogenic epitope in SARS-COV-2 proteins was obtained by using compromise method of MHC class I and II tools that accessible respectively using ProPred-1 server and MHC II Binding Prediction in IEDB database. The best epitopes of B and T-cell epitopes were predicted with high antigencity and the information is disseminated through web-based database resource (https://covid-19.omicstutorials.com/epitopes/). This study will be useful to find a new epitope-based candidate for SARS-COV-2. However, further study needs to be done for the next stages of vaccine development.

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative, is to resort to weather-insensitive synthetic aperture radar (SAR) images. However, in many real-world applications, critical decisions are made based on some informative spectral features, such as water, vegetation or soil indices, which cannot be extracted from SAR images. In the absence of optical sources, these data must be estimated. The current practice is to perform linear interpolation between data available at temporally close time instants. In this work, we propose to estimate missing spectral features through data fusion and deep-learning. Several sources of information are taken into account - optical sequences, SAR sequences, DEM - so as to exploit both temporal and cross-sensor dependencies. Based on these data, and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May to November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baselines methods according to all performance indicators.

We evaluated the mono- and combination-therapy effects of valproic acid (VPA) and doxorubicin (DOX) in hepatocellular carcinoma (HCC) and identified a specific and efficient, synergistic anti-proliferative effect of the VPA and DOX combination in HCC cells, especially HepG2 cells; this effect was not apparent in MIHA cells, a normal hepatocyte cell line. The calculation of the coefficient of drug interaction confirmed the significant synergistic effect of the combination treatment. Concurrently, the synergistic apoptotic cell death caused by the VPA and DOX combination treatment was confirmed by Hoechst nuclear staining and western blot analysis of caspase-3 and poly (ADP-ribose) polymerase (PARP) activation. Co-treatment with VPA and DOX enhanced reactive oxygen species (ROS) generation and autophagy, which were clearly attenuated by ROS and autophagy inhibitors, respectively. Furthermore, as an indication of the mechanism underlying the synergistic effect, we observed that DOX internalization, which was induced in the VPA and DOX combination-treated group, occurred via by the caveolae-mediated endocytosis pathway. Taken together, our study uncovered the potential effect of the VPA and DOX combination treatment with regard to cell death, including induction of cellular ROS, autophagy, and the caveolae-mediated endocytosis pathway. Therefore, these results present novel implications in drug delivery research for the treatment of HCC.