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Abstract Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metric to quantify the instantaneous rate of growth or decay of desynchronizing perturbations. We first use the transverse reactivity to design a coupling-efficient and energy-efficient synchronization strategy that involves varying the coupling strength dynamically according to the current state of the system. We find that our synchronization strategy is able to synchronize networks in both simulation and experiment over a significantly larger (often by orders of magnitude) range of coupling strengths than is possible when the coupling strength is constant. Then, we characterize the effects of network topology on the transient dynamics towards synchronization by introducing the concept of network syncreactivity: A network with a larger syncreactivity has a larger transverse reactivity at every point on the synchronization manifold, independent of the oscillator dynamics. We classify real-world examples of complex networks in terms of their syncreactivity.

Abstract Background Burn trauma is one of the major causes of morbidity and mortality worldwide. The standard management of burn wounds consists of early debridement, dressing changes, surgical management, and split-thickness skin autografts (STSGs). However, there are limitations for the standard management that inclines us to find alternative treatment approaches, such as innovative cell-based therapies. We aimed to systematically review the different aspects of cell-based treatment approaches for burn wounds in clinical trials. Methods A systematic search through PubMed, Medline, Embase, and Cochrane Library databases was carried out using a combination of keywords, including “Cell transplantation”, “Fibroblast”, “Keratinocyte”, “Melanocyte”, or “Stem Cell” with “Burn”, “Burn wound”, or “Burn injury”. Firstly, titles and abstracts of the studies existing in these databases until “February 2024” were screened. Then, the selected studies were read thoroughly, and considering the inclusion and exclusion criteria, final articles were included in this systematic review. Moreover, a manual search was performed through the reference lists of the included studies to minimize the risk of missing reports. Results Overall, 30 clinical trials with 970 patients were included in our study. Considering the type of cells, six studies used keratinocytes, nine used fibroblasts, eight used combined keratinocytes and fibroblasts, one study used combined keratinocytes and melanocytes, five used combined keratinocytes and fibroblasts and melanocytes, and one study used mesenchymal stem cells (MSCs). Evaluation of the preparation type in these studies showed that cultured method was used in 25 trials, and non-cultured method in 5 trials. Also, the graft type of 17 trials was allogeneic, and of 13 other trials was autologous. Conclusions Our study showed that employing cell-based therapies for the treatment of burn wounds have significant results in clinical studies and are promising approaches that can be considered as alternative treatments in many cases. However, choosing appropriate cell-based treatment for each burn wound is essential and depends on the situation of each patient.

Aim To determine the prevalence and characteristics of pulmonary embolism (PE) in patients presenting with haemoptysis. Additionally, we assessed the efficiency and failure rates of different clinical diagnostic algorithms for PE in this patient population. Methods We enrolled consecutive adult patients who presented to nine Italian emergency departments with haemoptysis as the primary complaint. PE diagnosis was ruled out in patients with a low pre-test probability in combination with a negative age-adjusted D-dimer (referred to as the “age-adjusted” D-dimer strategy), a negative computed tomography pulmonary angiography or when a clear alternative source of bleeding was identified, along with negative findings for venous thromboembolism during a 30-day follow-up. Results A total of 546 patients were included in the study. The prevalence of PE, including the 30-day follow-up, was 4.2% (95% CI 2.7–6.3%). The majority of these cases (78%) exhibited distal (segmental or subsegmental) emboli and there were no PE-related fatalities. The “age-adjusted” D-dimer strategy initially excluded PE in 24% of patients (95% CI 21–28%), with a failure rate of 0.8% (95% CI 0.0–4.1%). Retrospectively applied, the “clinical probability-adjusted” D-dimer strategies, specifically the YEARS and Pulmonary Embolism Graduated d-Dimer (PEGeD) algorithms, excluded PE in a significantly higher proportion (30% and 32%, respectively) compared with the “age-adjusted” D-dimer strategy (p

Abstract Scaling in insect wings is a complex phenomenon that seems pivotal in maintaining wing functionality. In this study, the relationship between wing size and the size, location, and shape of wing cells in dragonflies and damselflies (Odonata) is investigated, aiming to address the question of how these factors are interconnected. To this end, WingGram, the recently developed computer‐vision‐based software, is used to extract the geometric features of wing cells of 389 dragonflies and damselfly wings from 197 species and 16 families. It has been found that the cell length of the wings does not depend on the wing size. Despite the wide variation in wing length (8.42 to 56.5 mm) and cell length (0.1 to 8.5 mm), over 80% of the cells had a length ranging from 0.5 to 1.5 mm, which was previously identified as the critical crack length of the membrane of locust wings. An isometric scaling of cells is also observed with maximum size in each wing, which increased as the size increased. Smaller cells tended to be more circular than larger cells. The results have implications for bio‐mimetics, inspiring new materials and designs for artificial wings with potential applications in aerospace engineering and robotics.

The growing use of online social networks has intensified the focus on customer lifetime value (CLV), particularly how social conformity and influence shape purchasing decisions. Traditional CLV models have primarily captured positive interactions within networks but have largely ignored negative influences and the degree of customer susceptibility to conformity. These oversights limit the effectiveness of existing models when applied to real-world datasets. This paper introduces a novel model that enhances the estimation of CLV by incorporating both positive and negative interactions through sentiment analysis. The model leverages the CASINO algorithm to integrate social influence and conformity extracted from interpersonal interactions, building upon the foundational customer lifetime network value model. We validate our model using both semi-simulated and real-world data, comparing its performance with traditional approaches and illustrating its improved capacity to reflect complex social dynamics in CLV calculations.

Introduction: Keratoacanthoma (KA) and squamous cell carcinoma (SCC) are two cutaneous conditions with morphological resemblance, which can complicate the diagnosis in some cases. Using immunohistochemistry staining of biomarkers could be beneficial in resolving this obstacle. Objectives: we investigated a variety of biomarkers assessed in different studies in order to find the most important and helpful biomarkers for differentiation between SCC and lesions capable of spontaneous regression. Methods: PubMed medical literature and Google Scholar database were used to identify relevant literature up to 15 June 2022. The aim of our analyses was to determine the capability of biomarkers to distinguish SCC from lesions capable of spontaneous regression using calculated individual and pooled OR and 95% CI and I2 tests. Results: Six potential biomarkers are CD10 with pooled OR= 0.006 (95% CI 0.001 to 0.057) and I2=0%; COX-2 with pooled OR=0.089 (95% CI 0.029 to 0.269) and I2=17.1%; Elastic Fibers with pooled OR= 6.69 (95% CI 2.928 to 15.281) and I2=0%; IMP-3 with pooled OR=0.145 (95% CI 0.021 to 1.001) and I2=44.5%; P53 with pooled OR=0.371 (95% CI 0.188 to 0.733) and I2=55.9%; AT1R with OR=0.026 (95% CI 0.006 to 0.107). Conclusions: We suggest the utilization of the following IHC biomarkers for discrimination between lesions with spontaneous regression such as KA and SCC: CD10, COX-2, and Elastic Fibers.

Spinal cord injury (SCI) is a critical neurological condition that may impair motor, sensory, and autonomous functions. At the cellular level, inflammation, impairment of axonal regeneration, and neuronal death are responsible for SCI-related complications. Regarding the high mortality and morbidity rates associated with SCI, there is a need for effective treatment. Despite advances in SCI repair, an optimal treatment for complete recovery after SCI has not been found so far. Therefore, an effective strategy is needed to promote neuronal regeneration and repair after SCI. In recent years, regenerative treatments have become a potential option for achieving improved functional recovery after SCI by promoting the growth of new neurons, protecting surviving neurons, and preventing additional damage to the spinal cord. Transplantation of cells and cells-derived extracellular vesicles (EVs) can be effective for SCI recovery. However, there are some limitations and challenges related to cell-based strategies. Ethical concerns and limited efficacy due to the low survival rate, immune rejection, and tumor formation are limitations of cell-based therapies. Using EVs is a helpful strategy to overcome these limitations. It should be considered that short half-life, poor accumulation, rapid clearance, and difficulty in targeting specific tissues are limitations of EVs-based therapies. Hydrogel-encapsulated exosomes have overcome these limitations by enhancing the efficacy of exosomes through maintaining their bioactivity, protecting EVs from rapid clearance, and facilitating the sustained release of EVs at the target site. These hydrogel-encapsulated EVs can promote neuroregeneration through improving functional recovery, reducing inflammation, and enhancing neuronal regeneration after SCI. This review aims to provide an overview of the current research status, challenges, and future clinical opportunities of hydrogel-encapsulated EVs in the treatment of SCI.

Abstract Systems that synchronize in nature are intrinsically different from one another, with possibly large differences from system to system. While a vast part of the literature has investigated the emergence of network synchronization for the case of small parametric mismatches, we consider the general case that parameter mismatches may be large. We present a unified stability analysis that predicts why the range of stability of the synchronous solution either increases or decreases with parameter heterogeneity for a given network. We introduce a parametric approach, based on the definition of a curvature contribution function, which allows us to estimate the effect of mismatches on the stability of the synchronous solution in terms of contributions of pairs of eigenvalues of the Laplacian. For cases in which synchronization occurs in a bounded interval of a parameter, we study the effects of parameter heterogeneity on both transitions (asynchronous to synchronous and synchronous to asynchronous.).

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.

Background: Alterations in the kynurenine pathway (KP) metabolite can contribute to the pathogenesis and progression of many psychiatric and neurodegenerative illnesses, including Alzheimer disease (AD) and Huntington disease (HD), primarily through neuroinflammatory pathways and generating neurotoxic metabolites. Objectives: This systematic review highlights the evidence obtained by in vivo animal studies on alterations in KP metabolites and enzymes in AD and HD. Materials & Methods: We searched PubMed, Scopus, Web of Science, and EMBASE databases from the beginning of 2000 to January 2024 and included English-language in vivo articles comparing levels of KP metabolites or enzymes in rats or mice AD or HD models with controls. Results: A total of 19 studies, comprising 93 experimental and 95 control animals, were included. In AD models compared to controls, the following changes were reported: higher levels of tryptophan (TRP) in blood; higher kynurenine (KYN) levels in the cortex, hippocampus, hypothalamus, and prefrontal cortex; higher quinolinic acid (QUIN) levels in the hippocampus and cerebrum; higher indoleamine 2,3-dioxygenases levels in the cerebrum, prefrontal cortex, and hippocampus; and a higher KYN/TRP ratio in the hippocampus, cortex, and cerebellum. Reports on HD models compared to controls showed higher 3-hydroxykynurenine levels in the striatum and cortex and lowered TRP levels in the striatum. Conclusion: According to the primary outcomes, KP alterations may lead to the progression of AD and HD. These two diseases can also change the KP pathway factors. Here, we highlighted that changes in the KP metabolites and enzyme levels can help diagnose and treat these diseases.

Abstract Background Delayed neuropsychological sequelae (DNS) commonly occur after recovery from acute carbon monoxide (CO) poisoning. The preventive role and the indications for hyperbaric oxygen therapy in the acute setting are still controversial. Early identification of patients at risk in the Emergency Department might permit an improvement in quality of care. We conducted a retrospective study to identify predictive risk factors for DNS development in the Emergency Department. Methods We retrospectively considered all CO-poisoned patients admitted to the Emergency Department of Careggi University General Hospital (Florence, Italy) from 1992 to 2007. Patients were invited to participate in three follow-up visits at one, six and twelve months from hospital discharge. Clinical and biohumoral data were collected; univariate and multivariate analysis were performed to identify predictive risk factors for DNS. Results Three hundred forty seven patients were admitted to the Emergency Department for acute CO poisoning from 1992 to 2007; 141/347 patients participated in the follow-up visit at one month from hospital discharge. Thirty four/141 patients were diagnosed with DNS (24.1%). Five/34 patients previously diagnosed as having DNS presented to the follow-up visit at six months, reporting a complete recovery. The following variables (collected before or upon Emergency Department admission) were associated to DNS development at one month from hospital discharge in the univariate analysis: CO exposure duration >6 hours, a Glasgow Coma Scale (GCS) score Conclusions Our study identified several potential predictive risk factors for DNS. Treatment algorithms based on an appropriate risk-stratification of patients in the Emergency Department might reduce DNS incidence; however, more studies are needed. Adequate follow-up after hospital discharge, aimed at correct recognition of DNS, is also important.

The relevance of research is determined by the possibilities of measuring the potential magnetic field, which has self-similar (fractal) properties, as well as a practical tool for prospecting and exploration of iron ores. In the Esfordi area, this method was used by us for the first time to identify, separate and interpret geophysical (magnetic) anomalies. The main aim of this thematic and practical study is the qualitative interpretation of geophysical data, the application of new methods in the prospecting and exploration of mineral deposits, modeling the geological environment and forecasting new promising areas. Object: Esfordi region, Yazd province, Iran. Methods. To obtain additional information about the subsurface, magnetometric data were used with their interpretation by the RTP (reduction to the north magnetic pole) method. For modeling purposes, an artificial sample was made, consisting of a sphere, a cube and a cuboid, and it was found that the fractal method can be used to separate anomalies for unipolar models (cube and cuboid). Results. The results of the study were applied to the Esfordi region, where it was found that at a survey scale of 1:100000, there is a direct relationship between the fractal method and the 3D model, which can be used to locate iron ore mineralization.

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear dynamical systems with a large number of states and inputs. We attempt to reduce such problems into a set of independent OCPs of lower dimensions. Our decomposition is ‘exact’ in the sense that it preserves all the information about the original system and the objective function. Previous work in this area has focused on strategies that exploit symmetries of the underlying system and of the objective function. Here, instead, we implement the algebraic method of simultaneous block diagonalization of matrices (SBD), which we show provides advantages both in terms of the dimension of the subproblems that are obtained and of the computation time. We provide practical examples with networked systems that demonstrate the benefits of applying the SBD decomposition over the decomposition method based on group symmetries.

Assisted reproductive techniques as a new regenerative medicine approach have significantly contributed to solving infertility problems that affect approximately 15% of couples worldwide. However, the success rate of an in vitro fertilization (IVF) cycle remains only about 20%–30%, and 75% of these losses are due to implantation failure (the crucial rate-limiting step of gestation). Implantation failure and abnormal placenta formation are mainly caused by defective adhesion, invasion, and angiogenesis. Placental insufficiency endangers both the mother’s and the fetus’s health. Therefore, we suggested a novel treatment strategy to improve endometrial receptivity and implantation success rate. In this strategy, regulating mir-30d expression as an upstream transcriptomic modifier of the embryo implantation results in modified expression of the involved genes in embryonic adhesion, invasion, and angiogenesis and consequently impedes implantation failure. For this purpose, “scaffold/matrix attachment regions (S/MARs)” are employed as non-viral episomal vectors, transfecting into trophoblasts by exosome-liposome hybrid carriers. These vectors comprise CRISPR/dCas9 with a guide RNA to exclusively induce miR-30d gene expression in hypoxic stress conditions. In order to avoid concerns about the fetus’s genetic manipulation, our vector would be transfected specifically into the trophoblast layer of the blastocyst via binding to trophoblast Erb-B4 receptors without entering the inner cell mass. Additionally, S/MAR episomal vectors do not integrate with the original cell DNA. As an on/off regulatory switch, a hypoxia-sensitive promoter (HRE) is localized upstream of dCas9. The miR-30d expression increases before and during the implantation and placental insufficiency conditions and is extinguished after hypoxia elimination. This hypothesis emphasizes that improving the adhesion, invasion, and angiogenesis in the uterine microenvironment during pregnancy will result in increased implantation success and reduced placental insufficiency, as a new insight in translational medicine.

The gut microbiota undergoes significant alterations in response to viral infections, particularly the novel SARS-CoV-2. As impaired gut microbiota can trigger numerous neurological disorders, we suggest that the long-term neurological symptoms of COVID-19 may be related to intestinal microbiota disorders in these patients. Thus, we have gathered available information on how the virus can affect the microbiota of gastrointestinal systems, both in the acute and the recovery phase of the disease, and described several mechanisms through which this gut dysbiosis can lead to long-term neurological disorders, such as Guillain-Barre syndrome, chronic fatigue, psychiatric disorders such as depression and anxiety, and even neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. These mechanisms may be mediated by inflammatory cytokines, as well as certain chemicals such as gastrointestinal hormones (e.g., CCK), neurotransmitters (e.g., 5-HT), etc. (e.g., short-chain fatty acids), and the autonomic nervous system. In addition to the direct influences of the virus, repurposed medications used for COVID-19 patients can also play a role in gut dysbiosis. In conclusion, although there are many dark spots in our current knowledge of the mechanism of COVID-19-related gut-brain axis disturbance, based on available evidence, we can hypothesize that these two phenomena are more than just a coincidence and highly recommend large-scale epidemiologic studies in the future.

Despite growing interest in the studies on Zone of Proximal Development (ZPD), its operation in the forms of individualized and group-wide has been controversial. To cast some empirical light on the issue, this study was designed to study the applicability of the two scenarios of ZPD-based instructions to the writing accuracy of two levels of Iranian EFL learners (low vs. high scorers). To this end, 118 EFL learners identified as homogeneous based on the TOEFL ITP test were randomly assigned into two equal experimental groups which, respectively, received compatible ZPD-based instructions (i.e., individualized--I--and group-wide--GW) utilizing conference writing (as an individual scaffolding technique) and structured writing templates (as a whole-class scaffolding technique). The treatments were rendered to the groups in a random manner within homogeneous blocks. Before and after the treatments, three tests (two writing tests and one ZPD test) were administered to measure both groups' writing abilities (i.e., accuracy) and their ZPD levels. A ZPD test was also administered in the mid of treatments. The non-parametric analysis of covariance (ANCOVA) indicated the usefulness of I-ZPD-based instruction to the writing accuracy of the low-scorers and the uselessness of group-wide ZPD-based instruction to the writing accuracy of both the low and high scorers. The findings yield support to the use of ZPD-based instructions in TEFL writing and thereby affecting the learners' writing accuracy. The study; indeed, yield support to the feasibility of GW-ZPD measurement along with I-ZPD approach.

We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network and derive an essential model based on a ‘quotient network.’ We then compare the dynamics of the full network and that of the quotient network and obtain a modal decomposition of the error dynamics. We also provide a method to quantify the steady-state error and the maximum overshoot error. Two case studies are presented to illustrate application of our method.

The artificial neural network (ANN) was used to predict the modulus of rupture (MOR) of the laminated wood products adhered by melamine/urea formaldehyde (MUF) resin with different formaldehyde to melamine/urea molar ratios combined with different weight ratios of the protein adhesive resulting from the alkaline treatment (NaOH) of the soybean oil meal to MUF resin pressed at different temperatures according to the central composite design (CCD). After making the boards and performing the mechanical test to measure the MOR, based on experimental data, different statistics such as determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE) were determined, and then the suitable algorithm was selected to determine the estimated values. After comparing estimated values with the experimental values, the direct and interactive effects of the independent variables on MOR were determined. The results indicated that using suitable algorithms to train the ANN well, a very good estimate of the bending strength of the laminated wood products can be offered with the least error. In addition, based on the estimated and measured strengths and FTIR and TGA diagnostic analyses, it was found that the replacement of the MUF resin by the protein bio-based adhesive when using low F to M/U molar ratios, the MOR is maximized if a high range of temperature is used during the press.

Despite studies on the potential replacement of synthetic resins by bio-based adhesives such as proteins in recent years, there is still no reliable method for estimating the strength of wood products made using the combined parameters in the literature. This limitation is due to the nonlinear relationship between strength and the combined components. In the present research, the application of artificial intelligence techniques was studied to predict the bonding strength of glulam adhered by protein containing different ratios of MUF (melamine–urea–formaldehyde) resin with different F-to-U/M molar ratios at different press temperatures. For this purpose, the ANFIS artificial intelligence model was used as basic mode or combined with ant colony optimization (ACOR), particle swarm optimization (PSO), differential evaluation (DE) and genetic algorithms (GA) to develop an optimal trained model to predict the bonding strength of glulam based on experimental results. Comparison of the obtained results with the experimental results showed the ability of the above methods to estimate the bonding strength of glulam in a reliable manner. Although the basic ANFIS alone and in combination with other algorithms was not able to achieve an ideal performance prediction to estimate bonding strength, the combination of GA and ANFIS offered an excellent ability compared to the combination of other algorithms combined with ANFIS. Hence, the developed ANFIS-GA model is introduced as the best prediction technique to solve bonding strength problems of laminated products. In addition, using the developed optimal model, a precise attempt was made to show the nature of the parameters used to produce glulam and determine the optimum limit.

Background and Objectives. Acute aortic syndromes (AASs) are emergencies burdened by high morbidity and mortality. Guideline-recommended diagnostic workup is based on pre-test probability assessment (PPA) and d-dimer testing. However, the performance of PPA and d-dimer has never been studied in individuals with previous AAS (pAAS), which represent a challenging population. Materials and Methods. We analyzed a registry of patients with pAAS evaluated in two Emergency Departments (EDs) for suspected novel AAS (nAAS). Enrolment criteria were history of pAAS and the presence of truncal pain, syncope or perfusion deficit. All patients underwent advanced imaging. Clinical data were registered prospectively and PPA was performed by applying the aortic dissection detection (ADD) and an aorta simplified (AORTAs) score. Results. A total of 128 patients were enrolled, including 77 patients with previous Stanford type A aortic dissection and 45 patients with previous Stanford type B aortic dissection. The final diagnosis was nAAS in 40 (31%) patients. Clinical variables associated with nAAS were: aortic valve disease, thoracic aortic aneurysm, severe pain, sudden pain, ripping/tearing pain and hypotension/shock. ADD score ≥ 2 had a sensitivity of 65% and a specificity of 83% for nAAS; AORTAs score ≥ 2 had a sensitivity of 48% and a specificity of 88%. d-dimer (cutoff ≥ 500 ng/mL or age-adjusted cutoff) had a sensitivity of 97% and a specificity of 13%/14.7%, for diagnosis of nAAS. Patients that were candidates for guideline-compliant PPA/d-dimer integrated rule-out were: 5 (4.9%) with ADD ≤ 1/d-dimer and 8 (7.8%) with AORTAs ≤ 1/d-dimer < age-adjusted cutoff. None of them had a nAAS. Conclusions. Patients with pAAS evaluated in the ED for red-flag symptoms showed intermediate-to-high pre-test probability of nAAS. The ADD score had lower sensitivity and specificity than in unselected patients. d-dimer, alone and integrated with PPA, was highly sensitive for nAAS, but very unspecific. PPA/d-dimer integrated strategies are unlikely to significantly reduce the number of patients with pAAS undergoing advanced imaging.

Abstract COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.

Despite growing interest in the studies on Zone of Proximal Development (ZPD), its operation in the forms of individualized and group-wide has been controversial. To cast some empirical light on the issue, this study was designed to quantitatively and comparatively study the applicability of the two scenarios of ZPD-based instructions to the writing complexity of Iranian EFL learners in terms of learners' types (low vs. high scorers). To this end, 118 EFL learners identified as homogeneous based on the TOEFL ITP test were randomly assigned into two equal experimental groups (individualized ZPD- and group-wide ZPD-based groups) which respectively received compatible instructions utilizing conference writing (as an individual scaffolding technique) and low- and high-structured writing templates (as a whole-class scaffolding technique). The treatments were allocated to the groups in a random manner within homogeneous blocks. Prior to and after the treatments, three tests (two writing tests and one ZPD test) were administered to measure both groups’ writing complexity and their ZPD levels. A ZPD test was also administered in the mid of treatments. The non-parametric analysis of covariance (ANCOVA) revealed that the individualized-ZPD-based instruction is constructive to the writing complexity of the low scorers, while the group-wide ZPD-based instruction shows not much constructive to the writing complexity of the high scorers. The findings pointed to the use of ZPD-based instructions in TEFL writing in meaningful contexts and thereby showing impacts made on the writing complexity of learners. The study, indeed, yields support to the feasibility of group-wide ZPD measurement along with an individualized ZPD approach.

Background: The apoptotic route is mostly damaged in gastric cancer tumor cells. DNA methylation of promoter associated CpG islands inactivates tumor suppressor genes. The objective of the present study was to analyze the hypermethylation of death-associated protein kinase (DAPK) and Bcl-2-associated X protein (BAX) genes in individuals suffering from gastric cancer and undergoing chemotherapy. Methods: Genomic DNA was extracted from blood samples and the tissue fixed in the paraffin of 30 patients and normal individuals. Hypermethylation investigation of DAPK and BAX genes was conducted via methylation specific PCR technique, the outcomes of which were analyzed through electrophoresis and SPSS software version 20. Results: Methylation of both BAX and DAPK genes with a frequency of (28.3%, 21.7%) in blood and (23.3%, 23.3%) in tissue, respectively, had a significant relationship with gastric cancer (P˂0.01). A significant relationship was also observed between the methylation of BAX gene in tissue and tumor type (12, 35.3% and P˂0.01). No relationship was found between methylation and grade, stage, node, age, sex, and other pathologic and clinical data of the patients (P>0.05). There was a significant association between simultaneous methylation of DAPK and BAX genes in tumor and typical tissues with methylation a frequency of 40% and 95.83%, respectively (P˂ 0.01). Conclusion: Methylation of the BAX and DAPK genes can be used as a biomarker in bloodand an approach in the early detection of malignity and illness management. Methylation inhibitors with the potential for drug targeting of DAPK and BAX can further be employed in pharmacotherapy.

For improving aquafeed sustainability, insect meal is currently considered the most promising alternative to fishmeal. However, in this regard, more data are still necessary to avoid possible negative impacts on fish growth performance, metabolism, and welfare. The present study investigated the effects of increasing the inclusion of Hermetia illucens meal (0%, 17%, 33% and 50% of the feed, equating to 0%, 34%, 66% and 100% fishmeal replacement) on fish mortality, growth performance, intestine morphology, and gene expression of intestinal carriers. The results showed no adverse effects on fish mortality, feed intake and body weight and a positive effect on feed conversion ratio. Body weight gain was higher when 17% and 50% of Black soldier fly meals’ feed included (34% and 100% fishmeal replacement, respectively). Gut morphology was not affected by the dietary treatments except for the area of PAS-positive goblet cells that was higher in the treatment fed 33% of insect meal. The mRNA expression of intestinal epithelium functionality-specific marker genes, such as slc15a1 (alias pept1, alias slc15a1b), gata4 and nfkb1b, confirmed that the insect meal-based diets might replace fishmeal-based diets without negative effects. Overall, the results of the present study suggest that using Hermetia illucens larvae meal as a replacement for fishmeal in aquafeeds might help to enhance sustainability while assuring favorable fish growth performance and gut health.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs). Methods: Patients admitted to the ED of Careggi Hospital from April 7th–30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB). Results: Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days. Conclusions: Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Multiple linear regression (MLR), adaptive network-based fuzzy inference system–ant colony optimization algorithm hybrid (ANFIS-ACOR) and artificial neural network–multilayer perceptron (ANN-MLP) were tested to model the bending strength of Glulam (glue-laminated timber) manufactured with a plane tree (Platanus orientalis L.) wood layer adhered with different weight ratios (WR) of modified starch/urea formaldehyde (UF) adhesive containing different levels of nano-ZnO (NC) used at different levels of the press temperature (Tem) and time (Tim). According to X-ray diffraction (XRD) and stress–strain curves, some changes in the behavior of the product were seen. After selecting the best model through determining statistics such as the determination coefficient (R2) and root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE), the production process was optimized to obtain the highest modulus of rupture (MOR) using the Genetic Algorithm (GA) combined with MLP. It was determined that the MLP had the best accuracy in estimating the response. According to the MLP-GA hybrid, the optimum input values for obtaining the best response include: WR—49.1%, NC—3.385%, Tem—199.4 °C and Tim—19.974 min.

The purpose of the present article is to study the bending strength of glulam prepared by plane tree (Platanus Orientalis-L) wood layers adhered by UF resin with different formaldehyde to urea molar ratios containing the modified starch adhesive with different NaOCl concentrations. Artificial neural network (ANN) as a modern tool was used to predict this response, too. The multilayer perceptron (MLP) models were used to predict the modulus of rapture (MOR) and the statistics, including the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to validate the prediction. Combining the ANN and the genetic algorithm by using the multiple objective and nonlinear constraint functions, the optimum point was determined based on the experimental and estimated data, respectively. The characterization analysis, performed by FTIR and XRD, was used to describe the effect of the inputs on the output. The results indicated that the statistics obtained show excellent MOR predictions by the feed-forward neural network using Levenberg–Marquardt algorithms. The comparison of the optimal output of the actual values obtained by the genetic algorithm resulting from the multi-objective function and the optimal output of the values estimated by the nonlinear constraint function indicates a minimum difference between both functions.