Cardiovascular diseases remain the leading cause of mortality worldwide. Although new therapies are actively being developed and used for cardiovascular pathologies, these attempts have not significantly decreased mortality rates. Regenerative medicine has made enormous progress and set promising approaches over the past half-century. However, since autologous (donor-derived) vascular grafts are lacking, an alternative prosthesis must be constructed for cardiovascular disease patients. In vascular tissue manufacturing and regenerative medicine, scientists seek to improve this significant clinical challenge using bio-fabrication techniques combining additive manufacturing, biomaterials science, and advanced cellular biology. In the last few decades, many improvements and changes in various approaches have helped develop bioengineered concepts that reflect native blood vessels’ structure and function. However, numerous challenges must be overcome to clinically translate the next generation of tissue-engineered vascular transplants. This review provides update on the cell sources, scaffold essential for cardiovascular tissue engineering, and tissue engineering approaches as prospective options for curative therapy for blood vessel disease.

Introduction- The dynamics of SARS-CoV-2 shedding and replication in humans remain incompletely understood. Methods- We analyzed SARS-CoV-2 shedding from multiple sites in individuals with an acute COVID-19 infection by weekly sampling for five weeks in 98 immunocompetent and 25 immunosuppressed individuals. Samples and culture supernatants were tested by RT-PCR for SARS-CoV-2 to determine viral clearance rates and in vitro replication. Results- A total of 2447 clinical specimens were evaluated, including 557 nasopharyngeal swabs, 527 saliva samples, 464 urine specimens, 437 anal swabs and 462 blood samples. The SARS-CoV-2 genome sequences at each site were classified as belonging to the B.1.128 (ancestral strain) or Gamma lineage. SARS CoV-2 detection was highest in nasopharyngeal swabs regardless of the virus strain involved or the immune status of infected individuals. The duration of viral shedding varied between clinical specimens and individual patients. Prolonged shedding of potentially infectious virus varied from 10 days up to 191 days, and primarily occurred in immunosuppressed individuals. Virus was isolated in culture from 18 nasal swab or saliva samples collected 10 or more days after onset of disease. Conclusions-Our findings indicate that persistent SARS-CoV-2 shedding may occur in both competent or immunosuppressed individuals, at multiple clinical sites and in a minority of subjects is capable of in vitro replication.

For the replacement of lost contractile elements in the treatment of coronary heart disease, the most promising approaches to obtain cardiomyocytes by cardiac differentiation of pluripotent cells. The aim of this work is to develop a technology for the formation of a functional layer of cardiomyocytes differentiated from iPSCs, capable of generating rhythmic activity and synchronous contractions. To accelerate cardiomyocyte maturation, the renal subcapsular transplantation model was used in SCID mice. After explantation, the formation of cardiomyocyte contractile apparatus was assessed by fluorescence and electron microscopy, and calcium ion oscillation in the cytoplasm was assessed by visualization of the fluorescent calcium binding dye Fluo-8. It was shown that human iPSC-derived cardiomyocyte cell layers transplanted (for up to 6 weeks) under SCID mouse kidney fibrous capsules begin to form an ordered contractile apparatus and retain functional activity and the ability to oscillate calcium ion fluxes after explantation from the body.

Digital microfluidics (DMF) holds great potential for the alleviation of laboratory procedures in assisted reproductive technologies (ARTs). Electrowetting on dielectric (EWOD) provides dynamic culture conditions in vitro that may better mimic the natural embryo microenvironment. Thus far, EWOD microdevices have been proposed for in vitro gamete and embryo handling in mice and for analyzing the human embryo secretome. This article presents the development of the first microfluidic chip utilizing EWOD technology designed for the manipulation of bovine embryos in vitro. The prototype sustains the cell cycles of embryos manipulated individually on the chips during in vitro culture (IVC). Challenges related to the chip fabrication as well as to its application during bovine embryo IVC in accordance with the adapted on-chip protocol are thoroughly discussed, and future directions for DMF in ARTs are indicated.

Pairs of pyrrolysyl-tRNA synthetase (PylRS) and tRNAPyl from Methanosarcina mazei and Methanosarcina barkeri are widely used for site-specific incorporations of non-canonical amino acids into proteins (genetic code expansion). Previously, we achieved full productivity of cell-free protein synthesis for bulky non-canonical amino acids, including Ne-((((E)-cyclooct-2-en-1-yl)oxy)carbonyl)-L-lysine (TCO*Lys), by using Methanomethylophilus alvus PylRS with structure-based mutations in and around the amino acid binding pocket (first-layer and second-layer mutations, respectively). Recently, the PylRS•tRNAPyl pair from a methanogenic archaeon ISO4-G1 was used for genetic code expansion. In the present study, we determined the crystal structure of the methanogenic archaeon ISO4-G1 PylRS (ISO4-G1 PylRS) and compared it with those of structure-known PylRSs. Based on the ISO4-G1 PylRS structure, we attempted the site-specific incorporation of Ne-(p-ethynylbenzyloxycarbonyl)-L-lysine (pEtZLys) into proteins, but it was much less efficient than that of TCO*Lys with M. alvus PylRS mutants. Thus, the first-layer mutations (Y125A and M128L) of ISO4-G1 PylRS, with no additional second-layer mutations, increased the protein productivity with pEtZLys up to 578% of that with TCO*Lys, at high enzyme concentrations in the cell-free protein synthesis.

A key step in providing management/treatment options to men with suspected prostate cancer (PCa) is categorizing the risk for the presence of benign, low risk, intermediate risk, or high-risk disease. Our novel modality brings new evidence, based on the long-known hallmark characteristic of PCa – decreased Zinc (Zn), which is the most direct metabolic sign of malignancy and its aggressiveness. To date, this approach has not been adopted for clinical use for a number of reasons that are described in this article and which have been addressed by our approach: Zn has to be measured on fresh samples, prior to fixating in formalin, therefore samples have to be scanned during the biopsy session; as Zn depletion occurs in the glands, where the tumors develop, estimation of the glands’ levels in the scanned tissue along with their compactness, are essential for accurate diagnosis. Combined with the Zn depletion, this facilitates a reliable assessment of the disease aggressiveness. Data gathered in the clinical study described here indicate that in addition to improving the biopsy quality by real-time interactive guidance, a malignancy score can now be established for the entire prostate, allowing higher granularity personalized risk stratification and more decisive treatment decisions for all PCa patients.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Most pivotal clinical trials in advanced non-small cell lung cancer (NSCLC) have excluded patients with poor performance status (PS), and data on the efficacy and safety of pharmacotherapy have not been fully accumulated. For NSCLC patients with PS 2 and without druggable genetic alterations, monotherapy with cytotoxic agents or carboplatin-based combination therapy is usually administered based on the results of several randomized trials. However, the evidence of cytotoxic chemotherapy for patients with PS 2 is insufficient, with limited efficacy and toxicity concerns. Immune checkpoint inhibitors (ICIs) are a promising treatment for patients with PS 2 because of lower incidence of severe toxicity compared to cytotoxic chemotherapy. Meanwhile, several reports suggest that anti-PD-1 antibodies monotherapy is less effective for patients with PS 2, especially for those with PS 2 caused by disease burden. Although the combination therapy of nivolumab and ipilimumab is a promising treatment option, there is a divergence in efficacy data between clinical trials. The standard of care for advanced NSCLC with PS 2 has not been established, and future therapeutic strategies should take into account the heterogeneity of the PS 2 population.

Intratumoral heterogeneity can exist along multiple axes: Cancer Stem Cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple “attractors”. These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Dengue is a viral mosquito-borne disease that rapidly spreads in tropical and subtropical countries, including the Philippines. One of its most distinguishing characteristics is the ability of the Dengue Virus (DENV) to easily surpass the innate responses of the body, thus activating B cells of the adaptive immunity to produce virus-specific antibodies. Moreover, Dengvaxia® is the only licensed vaccine for DENV, but recent studies showed that seronegative individuals become prone to increased disease severity and hospitalization. Owing to this limitation of the dengue vaccine, this study determined and compared consensus and unique B cell epitopes among each DENV (1-4) Philippine isolate to identify potential areas of interest for future vaccine studies and therapeutic developments. An in silico-based epitope prediction of forty (40) DENV 1-4 strains, each serotype represented by ten (10) sequences from The National Center for Biotechnology Information (NCBI), was conducted using Kolaskar and Tongaonkar antigenicity, Emini surface accessibility, and Parker hydrophilicity prediction in Immune Epitope Database (IEDB). Results showed that five (5) epitopes were consensus for DENV-1 with no detected unique epitope, one (1) consensus epitope for DENV-2 with two (2) unique epitopes, one (1) consensus epitope for DENV-3 plus two (2) unique epitopes, and two (2) consensus epitopes and one (1) unique epitope for DENV-4. The findings of this study would contribute to determining potential vaccine and diagnostic marker candidates for further research studies and immunological applications against DENV (1-4) Philippine isolates.

The process of revitalising quiescent cells in order for them to proliferate plays a pivotal role in the repair of warn out tissues as well as for tissue homeostasis. This process is also crucial in the growth, development and well-being of higher multi-cellular organisms such as mammals. Deregulation of quiescent-proliferation transition is related to many diseases, in particular cancer. Recent studies have revealed that this process is regulated tightly by the Rb−E2F bistable switch mechanism. Based on experimental observations, in this study, we formulate a mathematical model to examine the effect of the growth factor concentration on the proliferation-quiescence transition in human cells. Working with a non-dimensionalised model, we prove positivity, boundedness and uniqueness of solutions. To understand model solution behaviour close to bifurcation points, we carry out bifurcation analysis, which is further verified by use of numerical bifurcation analysis, sensitivity analysis and numerical simulations. Indeed, bifurcation and numerical analysis of the model predicted a transition between stable, bistable and stable states, which are dependent on the growth factor concentration parameter (GF). The derived predictions confirm experimental observations.

To guide analysts to select the right tool and parameters in differential gene expression analysis of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline (https://github.com/interactivereport/scRNAseq_DE) for single cell analysis will enable scientists to better understand cell-type specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations of filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

Tri-dimensional (3D) cell aggregates or spheroids are considered to be closer to physiological conditions than traditional 2D cell culture. Mesenchymal stem cells (MSCs) assembling in spheroids have increased the survival of transplanted cells. The regulation of the biological processes that maintain crucial physiological reactions of MSCs is closely related to the functioning of ion channels. The pattern of expression, role and regulatory mechanisms of ion channels could be significantly different in 3D compared to 2D culture, and, thus, needed to be properly analyzed on the level of ionic currents. We developed a specific approach that allowed us to register, for the first time, endogenous ion channels in endometrial MSCs (eMSCs) assembled in spheroids. Particularly, using the single-channel patch-clamp technique, we have recorded the activity of ion channels and observed their functional interplay in mechanosensitive clusters. Our experimental protocol could be applied for identification and studying of ion channels in 3D cell cultures.

Conventional chemotherapy for colorectal cancer (CRC) gives only a small increase in patient survival, since it is often diagnosed in late stages, when tumor has disseminated to other organs. Besides, it is common to observe that malignant cells acquire tumor escape mechanisms which leads to therapy resistance. Considering these facts, the discovery of new molecules with therapeutic potential has become an invaluable tool in chemoprevention. In this context, we previously evaluated two hybrids (SAC-CAFA-MET and SAC-CAFA-PENT) which exhibited selective cytotoxicity against SW480, with better results than the conventional chemotherapeutic agent (5-fluorouracil; 5-FU). Here, we investigated a little deeper in the possible mechanism of these molecules to identify potential therapeutic alternatives for the treatment of CRC. Both compounds induced cell damage and reduced ROS formation. Further evaluations showed that SAC-CAFA-MET induces cell death independent from caspases and p53, but probably mediated by the negative regulation of the proapoptotic Bcl-2. In addition, the lack of activation of caspase 8 and the positive regulation of caspase 3 induced by SAC-CAFA-PENT suggest this compound acts through an apoptotic mechanism, probably initiated by intrinsic pathway. Besides, the down regulation of IL-6 by SAC-CAFA-PENT suggests it also induces a significant anti-inflammatory process. In addition, docking studies would suggest caspase-3 modulation as the primary mechanism by which hybrids elicits apoptosis in human colorectal adenocarcinoma SW480. Meanwhile, DFT calculations suggest that hybrids would produce effects in modulation of ROS in SW480 cells via hydrogen atom transfer pathway (HAT). Finally, both, SAC-CAFA-MET and SAC-CAFA-PENT displayed a favorable pharmacokinetic profile. The current work highlights the potential of the lead compounds SAC-CAFA-MET and SAC-CAFA-PENT as potential agents for colorectal cancer chemoprevention.

Chemically modified forms of tetraiodothyroacetic acid (tetrac), an L-thyroxine derivative, have been shown to exert their anticancer activity at plasma membrane integrin αvβ3 of tumor cells. Via a specific hormone receptor on the integrin, tetrac-based therapeutic agents modulate expression of genes relevant to cancer cell proliferation, survival and energy metabolism. P-bi-TAT, a novel bivalent tetrac-containing synthetic compound has anticancer activity in vitro and in vivo against Glioblastoma Multiforme (GBM) and other types of human cancers. In the current study, microarray analysis was carried out on a primary culture of human GBM cells exposed to P-bi-TAT (10-6 tetrac equivalent) for 24 h. P-bi-TAT significantly affected expression of a large panel of genes implicated in cancer cell stemness, growth, survival, and angiogenesis. Recent interest elsewhere in ATP synthase as a target in GBM cells caused us to focus attention on expression of genes involved in energy metabolism. Significantly downregulated transcripts included multiple energy metabolism-related genes: electron transport chain genes ATP5A1 (ATP synthase 1), ATP51, ATP5G2, COX6B1 (cytochrome c oxidase subunit 6B1), NDUFA8 (NADH dehydrogenase [ubiquinone] FA8), NDUFV2I and other NDUF genes. The NDUF and ATP genes are also relevant to control of oxidative phosphorylation and transcription. Qualitatively similar actions of P-bi-TAT on expression of energy metabolism-linked genes were also detected in established human GBM and pancreatic cancer cell lines. In conclusion, acting at αvβ3 integrin, P-bi-TAT caused downregulation in human cancer cells of expression of a large number of genes involved in electron transport and oxidative phosphorylation. These observations suggest that cell surface thyroid hormone receptors on αvβ3 regulate expression of genes relevant to tumor cell stemness and energy metabolism.

Sickle cell disorder (SCD) is a multisystem disease with heterogeneous phenotypes. Although all patients have the mutated haemoglobin (Hb) in the SS phenotype, the severity and frequency of complications are variable. When exposed to low oxygen tension, the Hb molecule becomes dense and, forms tactoids which, lead to the peculiar sickled shapes of the affected red blood cells, giving the disorder its name. This sickle cell morphology is responsible for the profound and widespread pathologies associated with this disorder, such as vaso-occlusive crisis, (VOC). How much of the clinical manifestation is due to sickled erythrocytes and what is due to the relative contributions of other elements in the blood, especially in the microcapillary circulation, is usually not visualized and quantified for each patient during clinical management. Here, we used a microfluidic microcirculation mimetic (MMM) which has 187 capillary-like constrictions to impose deformations on erythrocytes of SCD patients, visualizing and characterizing the morpho-rheological properties of the cells in normoxic, hypoxic (using of sodium meta-bisulfite) and in treatment conditions (using hydroxyurea). The MMM enabled a patient-specific quantification of shape descriptors (circularity and roundness) and transit time through the capillary constrictions, which are readouts for morphorheological properties implicated in VOC. Our results demonstrate the feasibility of microfluidics-based monitoring of individual patients for personalized care in the context of SCD complications such as VOC, even in re-source-challenged settings.

Substantial evidence indicates that bisphenol A (BPA), a ubiquitous environmental chemical used in the synthesis of polycarbonate plastics and epoxy resins, can impair brain development. Clinical and epidemiological studies exploring potential connections between BPA and neurodevelopmental disorders in humans have repeatedly identified correlations between early BPA exposure and developmental disorders, like attention deficit/hyperactivity disorder and autism spectrum disorder. Investigations using invertebrate and vertebrate animal models have revealed that developmental exposure to BPA can impair multiple aspects of neuronal development, including neural stem cell proliferation and differentiation, synapse formation, and synaptic plasticity—neuronal phenotypes that are thought to underpin the fundamental changes in behavior associated neurodevelopmental disorders. Consistent with BPA-associated neuronal phenotypes, behavioral analyses of BPA-treated animals have shown significant impacts on behavioral endophenotypes related to neurodevelopmental disorders, including altered locomotor activity, learning and memory deficits, and anxiety-like behavior. To contextualize the correlations between BPA and neurodevelopmental disorders in humans, this review summarizes current literature reporting on the developmental neurotoxicity of BPA in laboratory animals, with an emphasis on neuronal phenotypes, molecular mechanisms, and behavioral outcomes. The collective works described here predominantly support the notion that gestational exposure to BPA should be regarded as a risk factor for neurodevelopmental disorders.

At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

Cancer-associated fibroblasts (CAFs) in the tumor microenvironment perform glycolysis to produce energy, i.e., ATP. Since the origin of CAFs is unidentified, it is not determined whether the intracellular metabolism transitions from oxidative phosphorylation (OXPHOS) to glycolysis when normal tissue fibroblasts differentiate into CAFs. In this study, we established an experimental system and induced the in vitro differentiation of mesenchymal stem cells (MSCs) to CAFs. Additionally, we performed metabolomic and RNA-sequencing analyses before and after differentiation to investigate changes in the intracellular metabolism. Consequently, we discovered that OXPHOS, which was the primary intracellular metabolism in MSCs, was reprogrammed to glycolysis. In addition, we identified CAF-specific metabolites that were expressed during this reprogramming and determined their presence in the pancreatic tumor tissues of mouse models. Thus, we conclude that normal tissue fibroblasts that differentiate into CAFs undergo a metabolic reprogramming from OXPHOS to glycolysis. Moreover, we identified the CAF-specific metabolites expressed during metabolic reprogramming as potential future biomarkers for pancreatic cancer.

For many decades, X-ray diffraction (XRD) has been used for material characterization. With the recent development in material science understanding and technology, various new materials are being developed, which requires upgrading the existing analytical techniques such that intricate problems can be solved. Although, XRD is a well-established non-destructive technique, it still requires further improvements in its characterization capabilities, especially when dealing with complex mineral structures. The present review conducts comprehensive discussions on atomic crystal structure, XRD principle, its applications, uncertainty during XRD analysis, and required safety precautions, all in one place. It further discusses the future research directions, especially the use of artificial intelligence and machine learning tools for improving the effectiveness and accuracy of XRD technique for mineral characterization. It has been focused that how XRD patterns can be utilized for a thorough understanding of the crystalline structure, size, and orientation, dislocation density, phase identification, quantification, and transformation, information about lattice parameters, residual stress, and strain, and thermal expansion coefficient of materials. All these important discussions on XRD for mineral characterization are compiled in this short yet comprehensive review that would benefit specialists and engineers in the chemical, mining, iron, metallurgy, and steel industries.

Huge effort has been devoted to developing drugs targeting integrins over 30 years, because of the primary roles of integrins in the cell-matrix milieu. Five αv-containing integrins, in the 24 family members, have been a central target of fibrosis. Currently, a small molecule against αvβ1 is undergoing a clinical trial for NASH-associated fibrosis as a rare reagent aiming at fibrogenesis. Latent TGFβ activation, a distinct talent of αv-integrins, has been intriguing as therapeutic target. None of the αv-integrin inhibitors, however, has been in the clinical market. αv-integrins commonly recognize an Arg-Gly-Asp (RGD) sequence, and thus the pharmacophore of inhibitors for the 5-integrins is based on the same RGD structure. The RGD preference of the integrins, at the same time, dilutes ligand specificity, as the 5-integrins share ligands containing RGD sequence such as fibronectin. With the inherent little specificity in both drugs and targets, “disease specificity” has become less important for the inhibitors than blocking as many αv-integrins. In fact, an almighty inhibitor for αv-integrins, pan-αv, was in a clinical trial. On the contrary, approved integrin inhibitors are all specific to target integrins, which are expressed in cell-type specific manner: αIIbβ3 on platelets, α4β1, α4β7 and αLβ2 on leukocytes. Herein, “disease specific” integrins would serve as attractive targets. α8β1 and α11β1 are selectively expressed in hepatic stellate cells (HSCs) and distinctively induced upon culture activation. The exceptional specificity to activated HSCs reflects rather “pathology specific” nature of these new integrins. The monoclonal antibodies against α8β1 and α11β1 in preclinical examinations may illuminate the road to the first medical reagents.

Recent improvements within the fields of high-throughput screening and 3D tissue culture have provided the possibility of developing in vitro micro-tissue models that can be used to study diseases and screen potential new therapies. This paper reports a proof of concept study on the use of microvalve-based bioprinting to create laminar MSC-chondrocyte co-cultures as an in vitro model of autologous chondrocyte implantation (ACI), an established cellular therapy for osteoarthritis. Microvalve-based bioprinting uses microvalves to deposit cells suspended in a liquid in a consistent and repeatable manner. In this case MSCs and chondrocytes have been sequentially deposited into an insert based transwell system in order to create a laminar co-culture, with variations in the ratios of the cell types used to investigate the potential for MSCs to stimulate improved repair. Histological and indirect immunofluorescence staining revealed the formation of dense tissue structures within the chondrocyte and MSC-chondrocyte cell co-cultures, alongside the establishment of a proliferative region at the base of the tissue. No stimulatory or inhibitory effect in terms of ECM production was observed through the introduction of MSCs, although the potential for an immunomodulatory benefit remains. This proof-of-concept study therefore provides a novel method to enable the scalable production of therapeutically relevant micro-tissue models that can be used for in vitro research to optimise ACI procedures.

Breast cancer (BC) is the most common type of cancer in both men and women alike, but it is more prevalent in women. Natural compounds that can modulate the oncogenic process can be considered a significant anti-cancer agent for treating BC. These natural compounds are more effective than synthetic drugs, which have profound side effects on the normal cell and resistance to cancer cells. Genistein is a type of dietary phytoestrogen included in the flavonoid group with a similar structure of estrogen that might provide a strong alternative and complementary medicine to existing chemotherapeutics drugs. Several research studies demonstrated that it can target the estrogen receptor (ER), Human epidermal growth factor receptor-2 (HER2), and Breast cancer gene-1 (BRCA-1) in multiple BC cell lines, as well as sensitize cancer cell lines to this compound when used at an optimal inhibitory concentration. Genistein effectively showed anti-cancer activities through apoptosis induction, arresting cell cycle, inhibiting angiogenesis with metastasis, reducing inflammation, mammosphere formation, tumor growth, up-regulating tumor suppressor gene, and downregulating oncogene in suppressing cancer progression in vitro and animal model study. In addition, research studies have also suggested that these phytochemicals synergistically reverse the resistance mechanism of chemotherapeutic drugs, increasing the efficacy of some chemoinformatics drugs. Our review article aims to unbox and validate the molecular pharmacology in breast tissue, cell-specific anti-cancer mechanism with synergistic activity, and possible pharmacokinetic parameters of Genistein as a potential alternative therapeutic option for the treatment of BC.

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and showed to be effective for a small number of cancer patients. Thus, it is important to identify key genes, which can enhance the PD-1/PD-L1 response for promoting immunotherapy. Here, we used ssGSEA and unsupervised clustering analysis to identify three clusters to show different immune cell infiltration status, prognosis, and biological action. The cluster C showed a better survival rate, high immune cells infiltration, and immunotherapy effect enriched in a variety of immune active pathways, including T and B cell signal receptors. Besides, it showed more immune subtypes C2 and C3. Further, we used WGCNA analysis to confirm the cluster C correlated genes. The red module highly correlated with cluster C for 111 genes which were enriched in a variety of immune-related pathways. To pick candidate genes in SD/PD and CR/PR patients, we used the Least Absolute Shrinkage and SVM-RFE algorithms. In conclusion, our LASSO analysis and SVM-RFE based research identified targets with better prognosis, activated immune-related pathways, and better immunotherapy. The KLRC3 was identified as the key gene which can efficiently respond to immunotherapy with greater efficacy and better prognosis.

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

The investigation of membrane proteins, key constituents of cells, is hampered by the difficulty and complexity of their in vitro synthesis, of unpredictable yield. Cell-free synthesis is herein employed to unravel the impact of the expression construct on gene transcription and translation, without the complex regulatory mechanisms of cellular systems. Through the systematic design of plasmids in the immediacy of the start of the target gene, it was possible to identify translation initiation and the conformation of mRNA as the main factors governing the cell-free expression efficiency of the human voltage dependent anion channel (VDAC), a relevant membrane protein in drug-based therapy. A simple translation initiation model was developed to quantitatively assess the expression potential for the designed constructs. A scoring function is proposed that quantifies the feasibility of formation of the translation initiation complex through the ribosome-mRNA hybridization energy and the accessibility of the mRNA segment binding to the ribosome. The scoring function enables to optimize plasmid sequences and semi-quantitatively predict protein expression efficiencies. This scoring function is publicly available as webservice XenoExpressO at rna.tbi.univie.ac.at.

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.

A key feature of pulmonary arterial hypertension (PAH) is the hyperplastic proliferation exhibited by the vascular smooth muscle cells from patients (HPASMC). The growth inducers FOXM1 and PLK1 are highly upregulated in these cells. The mechanism by which these two proteins direct aberrant growth in these cells is not clear. Herein we identify cyclin dependent kinase 1 (CDK1) also termed cell division cycle protein 2 (CDC2), as having a primary role in promoting progress of the cell cycle leading to proliferation in HPASMC. HPASMC obtained from PAH patients and pulmonary arteries from Sugen/hypoxia rats were investigated for their expression of CDC2. Protein levels of CDC2 were much higher in PAH than in cells from normal donors. Knocking down FOXM1 or PLK1 protein expression with siRNA or pharmacological inhibitors lowered the cellular expression of CDC2 considerably. However, knockdown of CDC2 with siRNA or inhibiting its activity with RO-3306 did not reduce the protein expression of FOXM1 or PLK1. Expression of CDC2 and FOXM1 reached its maximum at G1/S, while PLK1 reached its maximum at G2/M phase of the cell cycle. The expression of other CDKs such as CDK2, CDK4, CDK6, CDK7 and CDK9 did not change in PAH HPASMC. Moreover, inhibition via Wee1 inhibitor adavosertib or siRNAs targeting Wee1, Myt1, CDC25A, CDC25B, or CDC25C led to dramatic decreases in CDC2 protein expression. Lastly, we found CDC2 expression at the RNA and protein level to be upregulated in pulmonary arteries during disease progression Sugen/hypoxia rats. In sum, our present results illustrate that the increased expression of FOXM1 and PLK1 in PAH leads directly to increased expression of CDC2 resulting in a potentiated growth hyperactivity of PASMC from patients with pulmonary hypertension. Our results further suggest that the regulation of CDC2, or associated regulatory proteins, will prove beneficial in the treatment of this disease.

The members of the IgLON superfamily of cell adhesion molecules facilitate fundamental cellular communication during brain development, maintain functional brain circuitry, and are associated with several neuropsychiatric disorders. Usage of alternative promoter-specific 1a and 1b mRNA isoforms in Lsamp, Opcml, Ntm and the single promoter of Negr1 in the mouse and human brain has been previously described. To determine the precise spatiotemporal expression dynamics of Lsamp, Opcml, Ntm isoforms and Negr1, in the developing brain, we generated isoform-specific RNA probes and carried out in situ hybridization in the developing (embryonic, E10.5, 13.5, 17; post natal, P0) and adult mouse brains. We show that promoter-specific expression of IgLONs is established early during pallial development (at E10.5), where it remains throughout its differentiation through adulthood. In the diencephalon, midbrain and hindbrain, strong expression patterns are initiated a few days later and begin fading after birth, being only faintly expressed during adulthood. Thus, the expression of specific IgLONs in the developing brain may provide the means for regionally specific functionality as well as for specific regional vulnerabilities. The current study will therefore improve the understanding of how IgLON genes are implicated in the development of neuropsychiatric disorders.

Abstract: Oncogenic RAS mutations drive more than half of human cancers, and RAS inhibition is the holy grail of oncology. Thirty years of relentless efforts and harsh disappointments have taught us about the intricacies of oncogenic RAS signalling that allow us to now get a pharmacological grip on this elusive protein. The inhibition of effector pathways, such as the RAF-MEK-ERK pathway, has largely proven disappointing. So far, most of these efforts were aimed at blocking the activation of ERK. Here, we discuss RAF dependent pathways that are regulated through RAF functions independent of catalytic activity and their potential role as targets to block oncogenic RAS signalling. We focus on the now well documented roles of RAF kinase independent functions in apoptosis, cell cycle progression and cell migration.

Cytokinesis is accomplished in higher plants by the phragmoplast, creating and conducting the cell plate, to separate daughter nuclei by a new cell wall. The microtubule-severing enzyme p60-katanin plays an important role in the centrifugal expansion and timely disappearance of phragmoplast microtubules. Consequently, aberrant structure and delayed expansion rate of the phragmoplast occur in p60-katanin mutants. Here, the consequences of p60-katanin malfunction in cell plate/daughter wall formation were investigated by transmission electron microscopy (TEM), while deviations in the chemical composition of cell plate/new cell wall were identified by immunolabeling and confocal microscopy, in root cells of the fra2 Arabidopsis thaliana mutant. It was found that, apart from defective phragmoplast microtubule organization, cell plates/new cell walls appeared also faulty in structure, being unevenly thick and perforated by large gaps. In addition, demethylesterified homogalacturonans were prematurely present in fra2 cell plates, while callose content was significantly lower than in the wild-type. Furthermore, KNOLLE syntaxin disappeared from newly formed cell walls in fra2 earlier than in the wild-type. Taken together, these observations indicate that delayed cytokinesis, due to faulty phragmoplast organization and expansion, results in a loss of synchronization between cell plate growth and its chemical maturation.

Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here we describe a ClearSee-based clearing protocol, which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining, and enables high quality 3D imaging of embryo morphology and expression of a fluorescent reporter with the cellular resolution.

Microcystins (MCs) are cyanobacterial toxins and potent inhibitors of protein phosphatases 1 (PP1) and 2A (PP2A), which are involved in plant cytoskeleton (microtubules and F-actin) organization. Therefore, studies on the toxicity of cyanobacterial products on plant cells have so far being focused on MCs. In this study, we investigated the effects of extracts from 16 (4 MC-producing and 12 non-MC-producing) cyanobacterial strains from several habitats, on various enzymes (PP1, trypsin, elastase), on the plant cytoskeleton and H2O2 levels in Oryza sativa (rice) root cells. Seedling roots were treated for various time periods (1, 12 and 24h) with aqueous cyanobacterial extracts and underwent either immunostaining for α-tubulin or staining of F-actin with fluorescent phalloidin. DCF-DA staining was performed for H2O2 imaging. The enzyme assays confirmed the bioactivity of the extracts of not only MC-rich (MC+), but also MC-devoid (MC-) extracts, which induced major time-dependent alterations on both components of the plant cytoskeleton. These findings suggest that a broad spectrum of bioactive cyanobacterial compounds, apart from MCs or other known cyanotoxins (such as cylindrospermopsin), can affect plants by disrupting the cytoskeleton.

Cancer immunotherapy reaches a remarkable achievement in various cancer types and brings new possibilities to improve cancer patients’ long-term survival. However, outcomes vary from case to case, and the present protocol benefits a small fraction of patients. One notable factor is the tumor microenvironment, especially the immune cell components, that may reflect the immune response's status quo on site. Thus, understanding the content of infiltrating immune cells in tumors is not only for research interesting but also a crucial subject toward precision medicine. We implement an algorithm for resolving relative proportions of twenty-one immune cell subclasses from a human tissue profiled transcriptome by microarray technology to reach the goal above. By selecting gene features and then adopting ?-Support Vector Regression, we can construct a deconvolution model and resolve the immune cell context. The excellent consistency between the estimated values and the correct immune-cell composition further demonstrates this approach provides a more natural alternative to revealing samples' immune cell content and reliable results like recent single-cell technologies. Based on this algorithm, the web-based deconvolution tool implemented named mySORT provides a user-friendly interface for estimating the immune cell content by uploading gene expression profiling. We also present comprehensive visualization 2D/3D plots in mySORT so that users can easily make a comparison between different samples. Finally, we synthesized pseudo-bulk expression data from single-cell transcriptomic datasets of 17 melanoma and 16 head and neck cancer patients. The deconvolution results of microarray-based data in the previous study and synthetic pseudo-bulk data all proved the excellent performance of mySORT. We believe that mySORT can help researchers in all fields easily understand complex immune microenvironment. The website of mySORT is freely accessible on https://symbiosis.iis.sinica.edu.tw/mySORT/.

Bone marrow-derived mesenchymal stem cells (BMSCs) have been considered a promising therapeutic approach to cardiovascular disease. This study intends to compare the effect of BMSCs through a standard active cardiac support device (ASD) and intravenous injection on global myocardial injury induced by isoproterenol. BMSCs were cultured in vitro, and the transplanted cells were labeled with a fluorescent dye CM-Dil. Isoproterenol (ISO) was injected into the rats; two weeks later, the labeled cells were transplanted into ISO-induced heart-injury rats through the tail vein or ASD device for five days. The rats were sacrificed on the first day, the third day, and the fifth day after transplantation to observe the distribution of cells in the myocardium by fluorescence microscopy. The hemodynamic indexes of the left ventricle were measured before sacrificing. H&E staining and Masson’s trichrome staining were used to evaluate the cardiac histopathology. In the ASD groups, after three days of transplantation, there were many BMSCs on the epicardial surface, and after five days of transplantation, BMSCs were widely distributed in the ventricular muscle. But in the intravenous injection group, there were no labeled-BMSCs distributed. In the ASD+BMSCs-three days treated group and ASD+BMSCs -five days-treated group, left ventricular systolic pressure (LVSP), the maximum rate of left ventricular pressure rise (+dP/dt), the maximum rate of left ventricular pressure decline (-dP/dt) increased compared with model group and intravenous injection group (P<0.05). By giving BMSCs through ASD device, cells can rapidly and widely distribute in the myocardium and significantly improve heart function.

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.

The C5-methylation of uracil to form 5-methyluracil (m⁵U) is a ubiquitous base modification of nucleic acids. Four enzyme families have converged to catalyze this methylation using different chemical solutions. Here, we investigate the evolution of 5-methyluracil synthase families in Mollicutes, a class of bacteria that has undergone extensive genome erosion. Many mollicutes have lost some of the m⁵U methyltransferases present in their common ancestor. Cases of duplication and subsequent shift of function are also described. For example, most members of the Spiroplasma subgroup, use the ancestral tetrahydrofolate-dependent TrmFO enzyme, to catalyze the formation of m⁵U54 in tRNA, while a TrmFO paralog (termed RlmFO) is responsible for m⁵U1939 formation in 23S RNA. RlmFO has replaced the S-adenosyl-l-methionine (SAM)-enzyme RlmD that adds the same modification in the ancestor and which is still present in mollicutes from the Hominis subgroup. Another paralog of this family, the TrmFO-like protein, has a yet unidentified function that differs from the TrmFO and RlmFO homologs. Despite having evolved towards minimal genomes, the mollicutes possess a repertoire of m⁵U modifying enzymes that is highly dynamic and has undergone horizontal transfer. This emphasizes the necessity for combining bioinformatics predictions with empirical testing and structural information to get a reliable functional annotation of these enzymes.

Given the high prevalence of cutaneous genus beta human papillomavirus (β-HPV) infections, it is important to understand how they are manipulating their host cells. This is particularly true for cellular responses to UV damage, since our skin is continually exposed to UV. The E6 protein from β-HPV (β-HPV E6) decreases the abundance of two essential UV-repair kinases (ATM and ATR). Since β-HPV E6 reduces their availability, the impact on downstream signaling events has been uncertain. We demonstrate that β-HPV E6 decreases ATM and ATR activation. This inhibition extended to XPA, an ATR target necessary for UV repair, lowering both its phosphorylation and accumulation. β-HPV E6 hinders POLη phosphorylation and foci formation, critical steps in translesion synthesis. ATM’s phosphorylation of BRCA1 is also attenuated by β-HPV E6. However, β-HPV E6’s hindrance of ATM/ATR signaling during UV-associated cell cycle arrest was incomplete. While there was less phosphorylation of immediate downstream targets (CHK1), events further down the cascade were not decreased. These observations are consistent with β-HPV infections making UV radiation more deleterious and support the proposed role of β-HPV in early stages of non-melanoma skin cancer development.

Gymnema sylvestre is a pharmacological plant which has a rich source of bioactive compounds specifically gymnemic acid (GA) and phenolic compounds (PC) that used for pharmaceutical industries. Sources for naturally occurring bioactive compounds are limited, due to geographical and seasonal variations; on the other hand, it is commercially in demand. Biosynthesis of G. sylvestre phytochemicals through in vitro culture often enhanced by elicitation. The use of cell suspension cultures (CSC) has interested serious attention on the production of essential phytochemicals. The current study is aimed at improving the contents of GA and PC in G. sylvestre CSC using the copper oxide nanoparticles (CuO NPs). Callus was obtained on MS medium with 2.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D), 0.1 mg/L kinetin (KIN), phytoagar (8.0 g/L), and sucrose (30 g/L). The above medium devoid of agar was used for the initiation of CSC. The CSC was treated with three levels of CuO NPs (1, 3 or 5 mg/L) to enhance the production of GA and PC. The greatest amount of GA (89.25 mg/g dry cell mass, DCM), total phenolic (245.10 mg/g), and flavonoid (4.57 mg/g) in CSC were achieved when G. sylvestre cells were treated for 48 h with 3 mg/L CuO NPs. Also, the biomedical potential (antioxidant, antidiabetic, anti-inflammatory, antibacterial, antifungal and anticancer activities) were also high in the CuO NPs (3 mg/L) treated CSC extracts of G. sylvestre. CuO NPs elicitation of CSC significantly increased production of GA (9-fold), and PC than non-elicited CSC in G. sylvestre.

The urban transportation network design problem involving exclusive bus lanes (XBLs) has been widely discussed and analyzed in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence can eliminate the impacts of slowly moving buses on the car traffic. This paper proposes a cell transmission model for separate car and bus traffic (CTM-SCB) in a network with some dedicated roadway segments reserved for buses. By encapsulating the CTM-SCB model, an XBL-based network design problem and an IBL-based network design problem are then formulated and solved, respectively, where the former model statically sets bus lanes while the later one allows a dynamic allocation of bus lanes. A synthetic freeway-arterial network and a real-world urban street network (where the latter was extracted from the Harbin South New Industrial City) are used as test networks for evaluating the proposed models and methods. The numerical results show that both XBLs and IBLs enjoy significant operational efficiency benefits compared to the situation of no protected bus lanes. However, we believe that the expected improvement from XBLs to IBLs need further tests and validations.

This paper reports a rational design of temperature-responsive nanofiber meshes with shape-memory effect. The meshes were fabricated by electrospinning a poly(ε-caprolactone) (PCL)-based polyurethane with different contents of soft and hard segments. The effects of PCL diol/hexamethylene diisocyanate (HDI)/1,4-butanediol (BD) molar ratio in terms of the contents of soft and hard segments on the shape-memory properties were investigated. Although the mechanical property improved with increasing hard segment ratio, optimal shape-memory properties were obtained with a PCL/HDI/BD molar ratio of 1:4:3. At a microscopic level, the original nanofibrous structure was easily deformed into a temporary shape, and recovered its original structure when the sample was reheated. A higher recovery rate (>89%) was achieved even when the mesh was deformed up to 400%. Finally, the nanofiber meshes were used to control the alignment of human mesenchymal stem cells (hMSCs). The hMSCs aligned well along the fiber orientation. The proposed nanofibrous meshes with the shape-memory effect have the potential to serve as in vitro platforms for the investigation of cell functions as well as implantable scaffolds for wound-healing applications.

Fluorescent carbon dots (C-dots) were synthesized by one pot hydrothermal treatment of garlic juice at 180 °C for 7h. The as-prepared C-dots with an average diameter of 2.13±0.71 nm can be well dispersed, and show absinthe fluorescence with a quantum yield of 8.9% in water. The C-dots were fully characterized by TEM, XPS, XRD, and FTIR, respectively. Additionally, the as-prepared C-dots reveal nearly nontoxic to SW480 cells and can be used for cell imaging. The water solution of C-dots show sensitivity to pH values and metal ions, and also have a high selectivity to the Hg²⁺ ion among other transition metal ions, and achieve a high sensitive detection with the detection limit of 0.45 μM. The fluorescent C-dots are expected to be useful for the evaluation of mercury ions in environmental water system.

In the last 20 years, an increasing number of studies have been reported on membrane active peptides, which exert their biological activity by interacting with the cell membrane either to disrupt it and lead to cell lysis or to translocate through it to deliver cargos into the cell and reach their target. These peptides are attractive alternatives to currently used pharmaceuticals. Antimicrobial peptides (AMPs) and peptides designed for drug and gene delivery currently in the drug pipeline suggest that these membrane active peptides will soon constitute a significant percentage of the drug market. Here, we focus on two most prominent classes of membrane active peptides; AMPs and cell-penetrating peptides (CPPs). AMPs are a group of membrane active peptides that disrupt the membrane integrity or inhibit the cellular functions of bacteria, virus and fungi. CPPs are another group of membrane active peptides that mainly function as cargo-carriers even though they may also show antimicrobial activity to some extent. Biophysical techniques to understand how they interact with the membrane have shed light on the peptide–membrane interaction at various levels of detail. Structural investigation of membrane active peptides in the presence of the membrane provides important clues on the effect of the membrane environment on peptide conformations. Advances in live imaging techniques have allowed examination of peptide action at a single cell or single molecule level. In addition to these experimental biophysical techniques, molecular dynamics simulations provided clues on the peptide-lipid interactions and dynamics of the cell entry process at atomic detail. In this review, we summarize the recent advances in experimental and computational investigation of membrane active peptides with particular emphasis on two amphipathic membrane active peptides, the AMP melittin and the CPP pVEC.

Initial attempts to develop monoclonal antibodies as therapeutics to resolve influenza infections focused mainly on searching for antibodies with the potential to neutralise the virus in vitro with classical haemagglutination inhibition and micro-neutralisation assays. This led to the identification of many antibodies that bind to the head domain of haemagglutinin (HA) which generally have potent neutralisation capabilities that block viral entry or viral membrane fusion. However, this class of antibodies has a narrow breadth of protection in that they are usually strain specific. This led to the emphasis on stalk targeting antibodies which are able to bind a broad range of viral targets that span across different influenza subtypes. Recently, a third class of antibodies targeting the vestigial esterase (VE) domain have been characterised. In this review, we describe the key features of neutralising VE targeting antibodies and compare them with head and stalk class antibodies.

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

A series of novel asymmetrical mono-carbonyl analogs of curcumin (AMACs) were synthesized and evaluated for cytotoxic activity using the brine shrimp lethality test (BSLT) and the methyl thiazolyl tetrazolium assay against Vero, HeLa, and MCF7 cell lines. The structures of the synthesized compounds were confirmed by Fourier transform infrared spectrophotometry (FTIR), ¹H-nuclear magnetic resonance (NMR), ¹³C-NMR, and mass spectral data. The results of the cytotoxicity evaluation showed that the synthesized compounds exhibited moderate to very high toxic activity in BSLT, requiring a concentration of 13.06–714.49 µg/mL to kill half the population. Most of the compound exhibited cytotoxic activity against HeLa cell lines, comparable to the activity of cisplatin with a concentration of the synthesized compounds required to inhibit 50% of the growth of the cell lines (IC₅₀) value of 40.65–95.55 µM, and most of the compounds tested against MCF7 cell lines exhibited moderate to very high cytotoxic activity (IC₅₀ value 7.86–35.88 µM). However, the selectivity index of the compounds was low, less than 1–1.96. Among the synthesized compounds, compound 1b showed the highest cytotoxicity and selectivity against MCF7 cell lines. Compound 1b could be considered for further development to obtain more active and selective chemotherapeutic agents against breast cancer.

Polyhydroxyalkanoates (PHA) are microbial biopolyesters utilized as “green plastics”. Their production under controlled conditions resorts to bioreactors operated in different modes. Because PHA biosynthesis constitutes a multiphase process, both feeding strategy and bioreactor operation mode need smart adaptation. Traditional PHA production setups based on batch, repeated batch, fed-batch or cyclic fed-batch processes are often limited in productivity, or display insufficient controllability of polyester composition. For highly diluted substrate streams like it is the case for (agro)industrial waste streams, fed-batch enhanced by cell recycling were recently reported as a viable tool to increase volumetric productivity. As emerging trend, continuous fermentation processes in single-, two-, and multi-stage setups are reported, which bring the kinetics of both microbial growth and PHA accumulation into agreement with process engineering, and allow tailoring PHA´s molecular structure. Moreover, we currently witness an increasing number of CO2-based PHA production processes using cyanobacteria; these light-driven processes resort to photobioreactors similar to those used for microalgae cultivation, and can be operated both discontinuously and continuously. This development goes in parallel to the emerging use of methane and syngas as an abundantly available gaseous substrates, which also calls for bioreactor systems with optimized gas transfer. The review sheds light on the challenges of diverse PHA production processes in different bioreactor types and operational regimes using miscellaneous microbial production strains such as extremophilic Archaea, chemoheterotrophic eubacteria and phototrophic cyanobacteria. Particular emphasize is dedicated to the limitations and promises of different bioreactor-strain combinations, and to efforts devoted to upscaling these processes to industrially relevant scales.

In this review, we show the unique potential of spleen as an optimal site for islet transplantation and a source of mesenchymal stem cells. Islet transplantation is a cellular replacement therapy to treat severe diabetes mellitus, but its clinical outcome is unsatisfactory at present. One factor in clinical success of this therapy is selection of the most appropriate transplantation site. The spleen has been studied for a long time as a candidate site for islet transplantation. Its advantages include physiological insulin drainage and regulation of immunity. Recently it has also been shown that the spleen contributes to the regeneration of transplanted islets. The efficacy of transplantation is not as high as that obtained with intraportal transplantation, which is the current representative method of clinical islet transplantation. Safer and more effective methods of islet transplantation need to be established before the spleen can be effectively used in the clinic. Spleen also has an interesting aspect as a mesenchymal stem cell reservoir. The splenic mesenchymal stem cells contribute to tissue repair in damaged tissue, and thus, the infusion can be a promising therapy for autoimmune diseases, including type 1 diabetes mellitus and Sjogren’s syndrome.

1) Background: Studies indicate that caffeic acid (CA), caffeic acid phenethyl ester (CAPE) are compounds with potent chemopreventive effects. Breast cancer is a common cancer among women worldwide. The study shows comparison of caffeic acid and its ester activity in the cells of breast cancer line MDA-MB-231; 2) Methods: The cells of MDA-MB-231 were treated by CA and CAPE with doses from 10 to 100 µM in time 24 h and 48 h. Cytotoxicity MTT test, apoptosis by Annexin V and cell cycle with Dead Cell Assay were performed; 3) Results: The cytotoxic activity was greater for CAPE comparing to CA, in both incubation time (same dosage). IC50 values for CAPE were 27.84 (24h) and 15.83 (48h) and >10000 (24h) and >1000 (48h) for CA. Polyphenols induced apoptosis, higher apoptotic effect observed for CAPE (dose dependent). CAPE induced cell cycle arrest in S phase (time and dose dependent). Dose dependent decline G0/G1 phase (48h) and elimination of phase G2/M (100 µM of CAPE). For CA, only after 48 hours, small effect of cell cycle at phase S (however dose dependent), and slight decline of phase G0/G1 and G2/M only for highest doses (50 and 100 µM); 4) Conclusions: Comparing CA and CAPE activity, on the MDA-MB-231, we clearly see better activity of CAPE, with the same dosage and experiment time.

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.

The epidermal growth factor receptor (EGFR) plays vital roles in cellular processes including cell proliferation, survival, motility and differentiation. Dysregulated activation of the receptor is often implicated in human cancers. EGFR is synthesized as a single-pass transmembrane protein, which consists of an extracellular ligand-binding domain and an intracellular kinase domain separated by a single transmembrane domain. The receptor is activated by a variety of polypeptide ligands such as epidermal growth factor and transforming growth factor α. It has long been thought that EGFR is activated by ligand-induced dimerization of the receptor monomer, which brings intracellular kinase domains into close proximity for trans-autophosphorylation. An increasing number of diverse studies, however, demonstrate that EGFR is present as a pre-formed, yet inactive, dimer prior to ligand binding. Furthermore, recent progress in structural studies has provided insight into conformational changes during the activation of a pre-formed EGFR dimer. Upon ligand binding to the extracellular domain of EGFR, its transmembrane domains rotate or twist parallel to the plane of the cell membrane, resulting in reorientation of the intracellular kinase domain dimer from a symmetric inactive configuration to an asymmetric active form (the “rotation model”). This model is also able to explain how oncogenic mutations activate the receptor in the absence of ligand without assuming that the mutations induce receptor dimerization. In this review, we discuss mechanisms underlying ligand-induced activation of the preformed EGFR dimer, as well as how oncogenic mutations constitutively activate the receptor dimer, based on the rotation model.

Waddington’s epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of “landscape” in the context of dynamical system theory represents a high-dimensional cell state space, in which each cell phenotype is considered as an “attractor” that is determined by interactions among multiple variables (molecular players), and is buffered against environmental fluctuations. Further, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes. Here, we discuss these phenotypic transitions in cancer from a dynamical systems perspective and invoke the concept of “cancer attractors” – hidden stable states of the underlying regulatory network that are not occupied by normal cells. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of drug/hormone resistance as examples, we illustrate how phenotypic plasticity in cancer cells enables them to acquire hybrid phenotypes (such as hybrid epithelial/mesenchymal and hybrid metabolic phenotypes) that tend to be more aggressive and notoriously resilient to drug/hormone treatment. Furthermore, we highlight multiple factors that may give rise to phenotypic plasticity in cancer cells, such as (a) multi-stability or oscillatory behaviors governed by underlying regulatory networks involved in cell-fate decisions in cancer cells, and (b) network rewiring due to conformational dynamics of intrinsically disordered proteins (IDPs) that are highly enriched in cancer cells. We conclude by discussing why a therapeutic approach that promotes ‘recanalization’, i.e. the exit from “cancer attractors” and re-entry into “normal attractors”, is more likely to succeed rather than a conventional approach that targets individual molecules/pathways.

Hidradenitis suppurativa and Langerhans cell histiocytosis are rare disorders that can reveal similar clinical manifestations, making differential diagnosis difficult. A definitive diagnosis requires biopsy and histological confirmation in suspicious cases. The complex pathophysiology of these diseases has still not been understood, therefore the treatment is still a challenge for physicians. It is seen with predilection in the intertriginous areas of the body, predominantly in the axillary region, rarely requiring a surgical procedure. However perineal, perianal, and gluteal HS usually necessitates some form of surgical treatment. Langerhans cell histiocytosis (histiocytosis X) is a very rare systemic disease manifested by clonal cell proliferation of histiocytic-dendritic nature, with pseudonodular manifestation that deeply infiltrates the hypodermis and ulcerates the epidermis on the surface. Langerhans cells are dendritic, antigen-presenting cells localized in the suprabasal regions of the squamous epithelium of the skin and mucous membranes, which help regulate the immune system. In this report, we present our experience with extensive gluteal, perineal, and perianal hidradenitis suppurativa and Langerhans cell histiocytosis cases, including our treatment methods and outcomes.

The use of chimeric antigen receptors (CAR-T cells) for the treatment of patients with malignant haematological diseases has become a well-established application, for conditions such as refractory or relapsed B cell acute lymphoblastic leukaemia (B-ALL), B cell lymphomas (BCL), and multiple myeloma (MM). Nearly 35,000 patients have received autologous CAR-T cells for the treatment of these conditions only in the USA. Since their approval by the Food and Drug Administration (FDA) in 2017, over 1200 clinical trials have been initiated globally and there are at least 10 different CAR-T cells with approval by different regulatory agencies around the globe. In the USA the Food and Drug Administration (FDA) has approved 6 commercial CAR-T cells that are widely distributed worldwide. At the time of this writing, several clinical trials have been performed in patients with solid tumours such as glioblastoma, renal and pancreatic cancer, as well as in patients with autoimmune conditions such as systemic lupus erythematosus (SLE), idiopathic inflammatory myositis (IIM), and systemic sclerosis (SS). There are also several studies showing the potential benefit of CAR-T cells for other non-malignant diseases such as asthma and even fungal infections. In this review, without pretending to cover all the current areas of treatments with CAR-T cells, we offer a brief summary of some of the most relevant aspects of the use of CAR-T cells for some of these conditions.

The United Nations' sustainable development goals have underscored the importance of enhancing supply chain sustainability while appropriately distributing orders. This study proposes a novel framework involving Z-number, game theory, indifference threshold-based attribute ratio analysis (ITARA), and combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations; enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are applied to assess the objective weight of criteria and the ranking of assessed order allocations, respectively. Afterwards, a case study of China’s company is illustrated the utility of the proposed framework and inform their decision-making process regarding the orders to be assigned to the assessed suppliers.

Digital transformation is revolutionizing inventory management practices within supply chains, offering unprecedented opportunities and challenges for businesses worldwide. This study explores the impact of digital technologies on inventory management, focusing on the adoption of IoT sensors, RFID tags, AI-driven analytics, and cloud-based systems. Through a qualitative research approach encompassing interviews with industry professionals and secondary data analysis, the study examines key themes including enhanced inventory visibility, improved accuracy, advanced demand forecasting, and streamlined supply chain collaboration. Findings reveal that digital technologies significantly enhance inventory visibility by providing real-time tracking and data integration capabilities. This facilitates accurate inventory monitoring and decision-making, reducing errors and optimizing inventory levels to meet fluctuating demand effectively. AI-driven analytics and machine learning models emerge as pivotal tools for predictive demand forecasting, enabling businesses to anticipate market trends and adjust inventory strategies accordingly. Additionally, cloud-based systems and electronic data interchange (EDI) foster improved communication and coordination among supply chain partners, enhancing overall operational efficiency. Despite these benefits, challenges such as system integration complexities, high implementation costs, data quality management, cybersecurity risks, and regulatory compliance issues are prevalent. Successful adoption of digital inventory management solutions requires strategic planning, investment in technology infrastructure, and organizational readiness to navigate these challenges effectively. This study contributes to the understanding of how digital transformation reshapes inventory management practices, offering insights for researchers and practitioners alike to leverage digital technologies for enhanced supply chain performance and competitive advantage

We present a novel benchmarking methodology for Large Language Models (LLMs) to evaluate their susceptibility to hallucinations, thereby determining their reliability for real-world applications involving greater responsibilities. This method, called Deception-Based Benchmarking, involves testing the model with a task that requires composing a short paragraph. Initially, the model performs under standard conditions. Then, it is required to begin with a misleading sentence. Based on these outputs, the model is assessed on three criteria: accuracy, susceptibility, and consistency. This approach can be integrated with existing benchmarks or applied to new ones, thus facilitating a comprehensive evaluation of models across multiple dimensions. It also encompasses various forms of hallucination. We applied this methodology to several small opensource models using a modified version of MMLU, DB-MMLU1 . Our findings indicate that most current models are not specifically designed to self-correct when the random sampling process leads them to produce inaccuracies. However, certain models, such as Solar-10.7B-Instruct, exhibit a reduced vulnerability to hallucination, as reflected by their susceptibility and consistency scores. These metrics are distinct from traditional benchmark scores. Our results align with TruthfulQA, a widely used benchmark for hallucination. Looking forward, DB-benchmarking can be readily applied to other benchmarks to monitor the advancement of LLMs.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Polymer-based (nano)composite foams, especially those having a microcellular structure, containing suitable conductive (nano)fillers, have been shown to act as good shielding materials, hence coming as promising to act as shields in electronic devices, limiting/avoiding electromagnetic interference (EMI) pollution. Nevertheless, due to their high (micro)structural complexity, related to their multiphase nature, there is still a lot of unawareness behind the shielding mechanisms acting in these materials, coming as a necessity to study their microstructure-cellular/porous structure-properties relations, especially in terms of their electrical conductivity and EMI shielding efficiency (EMI SE). To target and control the electrical conductivity through a controlled distribution/dispersion of conductive nanofillers, single or combined (nanohybrids), as there is a direct relation between electrical conductivity and EMI SE, as well as the main EMI shielding mechanisms working on the developed microcellular structure of the foams, which are expected to be absorption-dominated or absorption/multiple reflection-based, are two of the main objectives when combining polymer foaming with the addition of conductive nanofillers. The present review work considers the recent developments and trends on polymer-based foams containing conductive nanofillers, especially carbon-based such as carbon nanotubes or graphene-based materials, as well as similar porous structures created using trending technologies such as 3D printing, for EMI shielding applications. It is divided in different sections, starting with the strategy of microcellular foaming to develop polymer-based foams with enhanced EMI shielding characteristics, especially focusing on technologies and methods using supercritical CO2 (sCO2); continuing with the use of polymer foams as templates to synthesize carbon foams with improved EMI shielding performance for high use temperature applications; the recent strategy of combining different functional (nano)fillers to create hybrid nanofillers/nanohybrids to be used at lower amounts as conductive fillers in polymer foams; the control and selective distribution of the nanofillers in terms of favoring the formation of a more effective conductive network and hence promote the enhancement of the EMI SE; the very recent use of computational approaches to tailor the EMI shielding properties of composite foams; and the very trending possibility of creating cellular components with varied porous structures from these materials using 3D printing; to finish, some future perspectives are discussed.

Debates on climate change challenges around the world are moving in favour of nature-based solutions (NBS) as potential mitigation and adaptation options. Mangroves and associated blue carbon ecosystems are nature-based solutions against sea level rise and other coastal hazards. As such, protecting the existing mangrove forests and restoration of the degraded areas is integral in this aspect. However, conventional mangrove restoration approaches in high-energy areas result in poor performance due to the removal of seedlings by waves. Here, we assessed the efficacy of using modified Riley Encasement Methods in the restoration of eroding intertidal areas of Gazi Bay, Kenya. Vegetation and soil baseline data were collected in 49 square plots of 100m2; established along belt transects perpendicular to the waterline. The following mangrove vegetation data was collected; species composition, tree height (m), and stem diameter (cm); from which the importance value index (IV), basal area and standing density (stems/ha) were derived. Sediment cores were made in the center of each square plot for carbon and grain size analysis. Mangrove (Rhizophora mucronata) planting adopted a randomized complete block design (RCBD) in which the planting area was divided into three blocks (A, B, C). Within each block, treatments (bamboo and different-sized PVC pipes) were randomly assigned locations. Results show that seedlings grown within PVC encasements had high survival and growth rates, surpassing bamboo and control groups. This implies that PVC pipes offer better protection from external factors. These findings are critical in highlighting potential enhancements for the effectiveness of PVC pipes in mangrove restoration. They also reinforce on the need to consider the incorporation of PVC encasements as an alternative mangrove restoration technique in high-energy locations.

Reducing meat consumption is better for the environment. Unfortunately, commercial plant-based meat substitutes are often more expensive than meat and thus have not seen widespread adoption. This paper analyzes commercially-available spirulina, soy, pea, and brown rice protein isolates characteristics to provide data for non-meat protein cost reductions. Thermal and rheological properties, viscosity, density, and particle size distribution are analyzed for further study on alternative protein-based food processing. Differential scanning calorimetry analysis produced dry amorphous-shaped curves and paste curves with a more distinct endothermic peak. Extracted linear temperature ranges for processing in food production for spirulina was 70-90ºC; soy protein was 87-116ºC; pea protein was 67-77ºC; and brown rice protein was 87-97ºC. Viscosity analysis determined each protein material was shear-thinning and that viscosity increased with decreased water concentration, with rice being an exception to the latter trend. The obtained viscosity range for spirulina was 15,100-78,000cP; soy protein was 3,200-80,000cP; pea protein was 1,400-32,700cP; and brown rice protein was 600-3,500cP. The results indicate that extrusion is a viable method for further processing of the protein isolates as this technique has a large temperature operating range and variable screw speed. Data provided here can be used to make single or multi-component protein substitutes.

A small but growing body of literature suggests that outdoor experiences during childhood affect environment-related behaviors in adulthood. However, research on the magnitude of the effect (effect size) of outdoor experience on learners’ behaviors remains scarce. In this study, we explore the extent to which outdoor experiences associate with environmentally responsible behaviors. Our sample consisted of 143 ninth- and tenth-grade students living on a Greek island. The data were collected using a properly adjusted environmental literacy instrument. Two different methodological pathways, i.e., a quasi-experimental approach and correlation analysis, were used to analyse the data. A tentative variable representing the frequency and intensity of students’ experiential contact with nature was found to be the strongest available predictor of their self-reported pro-environmental behaviors. The findings of this study support the significance of outdoor, experiential learning during childhood in shaping individuals’ environmental behaviors.

Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

The possibility of deep radiochemical purification of Li2CO3 has been examined in the frame of the purification program of AMoRE collaboration. In this experiment, commercial Li2CO3 was converted into LiNO3. Co-precipitation with inorganic salt-based carriers followed by membrane filtration and sorption using MDM inorganic sorbent methods were tested for the removal of alkaline-earth and transition metals, potassium, magnesium, aluminum, uranium, thorium, and radium. The calcium molybdate-based carrier was the most efficient for removing Th, U, and K. Subsequently, the radium, calcium, and barium contaminations were removed with MDM sorbent. After the impurities’ removal, the final Li2CO3 product was synthesized with NH4HCO3 sludge. The separation factors were derived by means of ICP-MS and HPGe analyses of the initial material, intermediate and final products. The study showed the optimum conditions of co-precipitation and sorption to reach a high yield and radiopurity of lithium carbonate used for low radioactive background experiments.

Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Soybean are vulnerable to drought and temperature increases potentially induced by climate change. The purpose of this study was to determine if the use of cover crops (CC) can influence the gas exchange potential of glyphosate tolerant soybean when the vapor pressure deficit (Vpd) increases. This two years study was conducted in an open experimental field comprising Direct Seeding plots without CC (DS) or with CC (DSCC). Stomatal conductance (Gs) was measured five times on the same identified leaves following GBH application. These leaves were then collected in order to observe the stomata (size, density and stomatal index) with a scanning electron microscope and to perform measurements on foliar traits (venation density, width of midrib). The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CC promotes phenotypic change in soybean leaves (more elaborate venation and a higher stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in DSCC plots compared to DS ones along with similar Vpd values. This study shows that the benefits of using CC can be observed through the morphological development strategies of the crop plants and their higher tolerance to drought.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent based simulator will enable the policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify the vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM ) which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population, allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of Covid-19 are used to validate the proposed model. However, it is highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the expansion of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for static and dynamic scenarios, regardless of the number of stations. Moreover, the performance difference is amplified as the number of stations increases, with DQN and DDPG showing a 27% increase in throughput with 50 stations compared to BEB. Furthermore, DQN and DDPG presented similar performances.

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain whose management is particularly challenging and for which the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when relatively few randomized clinical trials did explore the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.

A Chinese superalloy GH4099 (~20 vol.% γ' phase), which can operate for long periods of time at temperatures of 1173-1273 K, was fabricated by electron beam melting (EBM). Argon gas atomized (AA) and plasma rotation electrode process (PREP) powders with the similar composition and size distribution were used as raw materials for comparison. Microstructure and mechanical properties of both the as-EBMed and post-treated alloy samples were investigated. The results show that the different powder characteristics resulted in the different build temperatures for AA and PREP samples, which were 1253 K and 1373 K, respectively. With increasing the building temperature, the EBM processing window shifted towards the higher scanning speed direction. Furthermore, intergranular cracking was observed for the as-fabricated PREP sample as a result of local enrichment of Si at grain boundaries. The cracks were completely eliminated by hot isostatic pressing (HIPing) and did not re-open during subsequent solution treatment and aging (STA). Fine spherical γ' phase precipitated uniformly after STA. The tensile strength of the HIP+STA samples was ~920 MPa in the building direction and ~850 MPa in the horizontal direction, comparable with that of the wrought alloy.

Place-based education (PBE) and active learning are effective strategies for geoscience education. However, traditional PBE via field trips requires significant resources, time, physical abilities, and expertise of teachers. We provide an alternative PBE experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location specific videos about Earth events to create a virtual PBE experience. Using Google Earth, GEGVL organizes place-based videos by locations and links pertinent non-place-based videos and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower division undergraduates show that the design logic of GEGVL is promising virtual PBE organizer for interesting students in and helping them learn about Earth sciences.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Plasmodium falciparum dihydroorotate dehydrogenase (PfDODH) is one of the enzymes currently explored in the treatment of malaria. Although there is currently no clinically approved drug targeting PfDODH, many of the compounds in clinical trials have [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine backbone structure. This study sought to design new compounds from the fragments of known experimental inhibitors of PfDODH. Nine experimental compounds retrieved from Drug Bank online were downloaded and broken into fragments using Schrodinger power shell; the fragments were recombined to generate new ligand structures using BREED algorithm. The new compounds were docked with PfDODH crystal structure, after which the compounds were filtered with extensive drug-likeness and toxicity parameters. A 2D-QSAR model was built using the multiple linear regression method and externally validated. The compounds electronic behaviours were studied using DFT calculations. Structural investigation of the six designed compounds, which had lower binding energies than the standard inhibitors, showed that five of them had [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine moieties and interacted with essential residues at the PfDODH binding site. In addition to their drug-like and pharmacokinetic properties, they also showed minimal toxicities. The externally validated 2D-QSAR model with R² and Q² values of 0.6852 and 0.6691, confirmed the inhibitory prowess of these compounds against PfDODH. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attack. The current study thus provides insight into the development of a new set of potent PfDODH inhibitors.

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.

In mountain communities like Sebei, Uganda, that are highly vulnerable to emerging and reemerging infectious diseases, community-based surveillance plays an important role in the monitoring of public health hazards. In this survey, we explored capacities of Village Health Teams (VHTs) in Sebei communities of Mount Elgon in undertaking surveillance tasks for emerging and reemerging infectious diseases in the context of a changing climate. We used participatory epidemiology techniques to elucidate VHTs’ perceptions on climate change and public health and assess their capacities in conducting surveillance for emerging and reemerging infectious diseases. Overall, VHTs perceived climate change to be occurring with wider impacts on public health. However, they have inadequate capacities in collecting sur-veillance data. The VHTs lack transport to navigate through their communities and have in-sufficient capacities in using mobile phones for sending alerts. They do not engage in reporting other hazards related with the environment, wildlife and domestic livestock that would ac-celerate infectious disease outbreaks. Records are not maintained for disease surveillance ac-tivities and the abilities of VHTs to analyze data are also limited. However, VHTs have access to platforms that can enable them to disseminate public health information. The VHTs thus need to be retooled to conduct their work effectively and efficiently through equipping them with adequate logistics and knowledge on collecting, storing, analyzing, and relaying data, which will improve infectious disease response and mitigation efforts.

With the continuous development of Artificial Intelligence technology and Internet of Things engineering, more and more driver-less vehicles have been developed and put into the industrial production. The birth of driver-less vehicles undoubtedly brings new vitality to a large amount of industries, particularly in transportation. For the mining industry, transportation is undoubtedly an extremely important link in the whole production process. If the driver-less vehicles can be applied to the underground mines, it can not only improve the production and transportation capacity of the whole mine, but also can reduce the occurrence of many mine safety accidents. ZigBee WSN technology can play a greater role in the narrow environment like underground mines according to the relevant literature, this concept paper just like a engineering project plan mainly tries to integrate the ZigBee WSN technology and the communication-based train control (CBTC) system to explore the possibility of the driver-less vehicles to be used in the underground mines, which aims to solve practical engineering problems for the engineering projects. As the mining engineers, we put forward the concept of this integrated system in this concept paper, but we need to continue to work hard for the future of the underground mines. This concept paper serves just as a guide to the Tossing out a brick to get a jade gem, has a few implications for the development of underground mine transportation.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently ‘reaction-prone’. The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic ‘hydrophobic ↔ polar’ mutations serve as a potential breakthrough.

Impact in the form of innovation and commercialisation is an essential component of publicly funded research projects. PHUSICOS, an H2020 Innovation Action project, aims at demonstrating the use of nature-based solutions for mitigating hydrometeorological hazards in rural and mountainous areas. The work program is built around key innovation actions, and each WP leader specifically responsible for nurturing innovation processes, maintaining market focus and ensuring relevance for the intended recipients of the project results. Key success criteria for PHUSICOS include up-scaling and mainstreaming of NBS to reach broader market access. An innovation strategy and supporting tools for implementing this within PHUSICS has been developed and key concepts forming the basis for this strategy are presented in this research note.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

Feature Selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. The problem of choosing a feature is to find the most important subset of the main attributes in a specific domain, and its main purpose is removing additional or unrelated features, and ultimately improving the accuracy of the classification algorithms. As a result, the problem of FS can be considered as an optimization problem, and use metaheuristic algorithms to solve it. In this paper, a new hybrid model combining whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of Opposition based Learning (OBL) which name is HWOAFPA. In our proposed method, using natural processes of WOA and FPA, we tried to solve the problem of optimization of FS; and on the other hand, we used an OBL method to ensure the convergence rate and accuracy of the proposed algorithm. In fact, in the proposed method, WOA create solutions in their search space using the prey siege and encircling process, bubble invasion and search for prey methods, and try to improve the solutions for the FS problem; along with this algorithm, FPA improves the solution of the FS problem with two global and local search processes in an opposite space with the solutions of the WOA. In fact, we used all of the possible solutions to the FS problem from both the solution search space and the opposite of solution search space. To evaluate the performance of the proposed algorithm, experiments were carried out in two steps. In the first stage, the experiments were performed on 10 FS datasets from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm in terms of spam e-mails detection. The results obtained from the first step showed that the proposed algorithm, performed on 10 UCI datasets, was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. Also, the results from the second step showed that the proposed algorithm which was run on the spam e-mail dataset, performed much more accurately than other similar algorithms in terms of accuracy of detecting spam e-mails.

Desalination accounts for 1% of the total global water consumption and is an energy-intensive process, with the majority of operational expenses attributed to energy consumption. Moreover, at present, a significant portion of the power comes from traditional fossil fuel-fired power plants and the greenhouse gas emissions associated with power production along with concentrated brine discharge from the process, pose a severe threat to the environment. Due to the dramatic impact of climate change, there is a major opportunity to develop sustainable desalination processes to combat the issues of brine discharge, greenhouse gas emissions along with a reduction in energy consumption per unit of freshwater produced. Nanotechnology can play a vital role to achieve specific energy consumption reduction as nanofluids application increases the overall heat transfer coefficient enabling the production of more water for the same size desalination plant. Furthermore, concentrated brine discharge harms the marine ecosystems, and hence, this problem must also be solved to support the objective of sustainable desalination. Several studies have been carried out in the past several years in the field of nanotechnology applications for desalination, brine treatment and the role of renewable energy in desalination. This paper aims to review the major advances in this field of nanotechnology for desalination. Furthermore, a hypothesis for developing an integrated solar thermal and nanofluid sustainable desalination system, based on the cyclic economy model is proposed.

The article introduces a new solution approach to grid-connected single-stage three-phase PV inverter whereby a dc-link voltage can successfully achieve the desired performance. As a single-stage, the variable output of perturb and observe maximum power point tracking (P&O based-MPPT) is fully utilized as dc-link voltage reference in inverter control scheme. As a result, it is challenging for the dc-link voltage to preserve energy balance by tracking its variable dc-link voltage reference under unpredictable environmental changes. To overcome this challenge, an adaptive reference proportional-integral (ARPI) controller, self-tuning, is designed and implemented to compel the dc-link voltage tracking its voltage reference to be equal as closely as possible; hence, the capability of the dc-link under abnormal events substantially guarantees the best energy balance and transient performance enhancement. To verify the validity of the method, simulation results for multiple events scenarios on 250 KW single-stage grid-connected PV systems show the effectiveness of the ARPI controller compared with PI controller.

In this paper, we present the design, manufacturing and characterization of a soft textile-based clutch (TBC) that switches between locking and unlocking of its linear displacement by exploiting vacuum stimulation. The applied vacuum locks the relative sliding motion between two elaborated textile webbings covered by an elastic silicone rubber bag. Based on different fabrication techniques, such as silicone casting on textile, melt embossing for direct fabrication of miniature patterns on textile and sewing, we developed three groups of TBC samples based on friction and interlocking principles and we compared their performance in blocking configuration. The clutch with interlocking mechanism presented the highest withstanding force (150 N) respect to the one (54 N) recorded for the friction-based clutch. The simple and compact structure of the proposed clutch, together with the intrinsic adaptability of fabric with other clothing and soft materials, make it a proper solution for applications in soft wearable robotics and generally as locking and variable stiffness solution for soft robotic applications.

The effects of heating rate before solution treatment and cooling rate after solution treatment on the morphological distribution and evolution of precipitation phase of nickel-based single crystal superalloy were studied. The dissolution, precipitation and growth of precipitation phase and matrix phase during heat treatment were analyzed by means of high power scanning electron microscopy. The results show that the morphology of precipitated phase has nothing to do with the distribution of precipitated phase and the heating rate in the heating process, but the cooling rate in the cooling process affects the shape, size and distribution of precipitated phase. The faster the cooling rate, the smaller the precipitated phase is, the more irregular the shape is, the smaller the equivalent edge length is, and the smaller the channel width of matrix phase is.

Due to low culturing costs and high seed protein contents, legumes represent the main global source of food protein. Pea (Pisum sativum L.) is one of the major economically important legume crops, impacting both animal feed and human nutrition. Therefore, the quality of pea seeds needs to be ensured in the context of sustainable crop production and nutritional efficiency. Obviously, changes in seed protein patterns might directly affect both of these aspects. Thus, here we address the pea seed proteome in more detail and provide, to the best of our knowledge, the most comprehensive annotation of the functions and intracellular localization of pea seed proteins. Accordingly, 1938 and 1989 non-redundant proteins were identified in yellow and green pea seeds, in total. Only 35 and 44 proteins, respectively, could be additionally identified after protamine sulfate precipitation (PSP) potentially indicating the high efficiency of our experimental workflow. In total 981 protein groups could be assigned to 34 functional classes, which were to a large extent differentially represented in yellow and green seeds. Closer analysis of these differences by processing of the data in KEGG and String databases revealed their possible relation to a higher metabolic status and reduced longevity of green seeds.

In this short review paper we have assembled the main characteristics of partial oxidation reactions (oxidative dehydrogenation and selective oxidation to olefins or oxygenates, as aldehydes and carboxylic acids and nitriles), as well as total oxidation, particularly for depollution, environmental issues and wastewater treatments. Both gas-solid and liquid-solid media have been considered with recent and representative examples within these fields. We have also discussed about their potential and prospective industrial applications. Particular attention has been brought to new raw materials stemming from biomass and to liquid-solid catalysts cases. This review paper also summarizes the progresses made in the use of unconventional activation methods for performing oxidation reactions, highlighting the synergy of these technologies with heterogeneous catalysis. Focus has been centered on usual catalysts activation methods but also on less usual ones, such as the use of ultrasounds, microwaves, grinding (mechanochemistry) and photo-activated processes, as well as their combined use.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

While the Internet of Things (IoT) technology has been widely recognized as the essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today's AC is the centralized authorization server, which can be the performance bottleneck or the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable an effective protection for devices, services and information in large scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registering, propagating and revocating of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI node) and more powerful computing devices (i.e., laptops), and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.