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Abstract Several functions of autophagy associated with proliferation, differentiation, and migration of endothelial cells have been reported. Due to lack of models recapitulating angiogenic sprouting, functional heterogeneity of autophagy in endothelial cells along angiogenic sprouts remains elusive. Here, we apply an angiogenesis-on-a-chip to reconstruct 3D sprouts with clear endpoints. We perform single-cell RNA sequencing of sprouting endothelial cells from our chip to reveal high activation of autophagy in two endothelial cell populations- proliferating endothelial cells in sprout basements and stalk-like endothelial cells near sprout endpoints- and further the reciprocal expression pattern of autophagy-related genes between stalk- and tip-like endothelial cells near sprout endpoints, implying an association of autophagy with tip-stalk cell specification. Our results suggest a model describing spatially differential roles of autophagy: quality control of proliferating endothelial cells in sprout basements for sprout elongation and tip-stalk cell specification near sprout endpoints, which may change strategies for developing autophagy-based anti-angiogenic therapeutics.

Purpose: To maximize the therapeutic ratio, it is important to identify adverse prognostic features in men with prostate cancer, especially among those with intermediate risk disease, which represents a heterogeneous group. These men may benefit from treatment intensification. Prior studies have shown pretreatment mpMRI may predict biochemical failure in patients with intermediate and/or high-risk prostate cancer undergoing conventionally fractionated external beam radiation therapy and/or brachytherapy. This study aims to evaluate pretreatment mpMRI findings as a marker for outcome in patients undergoing stereotactic body radiation therapy (SBRT). Methods and Materials: We identified all patients treated at our institution with linear accelerator based SBRT to 3625 cGy in 5 fractions, with or without androgen deprivation therapy (ADT) from November 2015 to March 2021. All patients underwent pretreatment Magnetic Resonance Imaging (MRI). Posttreatment Prostate Specific Imaging (PSA) measurements were typically obtained 4 months after SBRT, followed by every 3 to 6 months thereafter. A 2 sample t test was used to compare preoperative mpMRI features with clinical outcomes. Results: One hundred twenty-three men were included in the study. Pretreatment MRI variables including median diameter of the largest intraprostatic lesion, median number of prostate lesions, and median maximal PI-RADS score, were each predictive of PSA nadir and time to PSA nadir (P < .0001). When separated by ADT treatment, this association remained for patients who were not treated with ADT (P < .001). In patients who received ADT, the pretreatment MRI variables were each significantly associated with time to PSA nadir (P < .01) but not with PSA nadir (P > 0.30). With a median follow-up time of 15.9 months (IQR: 8.5-23.3), only 3 patients (2.4%) experienced biochemical recurrence as defined by the Phoenix criteria. Conclusions: Our experience shows the significant ability of mpMRI for predicting PSA outcome in prostate cancer patients treated with SBRT with or without ADT. Since PSA nadir has been shown to correlate with biochemical failure, this information may help radiation oncologists better counsel their patients regarding outcome after SBRT and can help inform future studies regarding who may benefit from treatment intensification with, for example, ADT and/or boosts to dominant intraprostatic lesions.

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption, which is increasingly important. However, conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale, high-resolution, and fast forecasting due to their complexity and the difficulty in obtaining model parameters. This paper presents an artificial neural network (ANN) model to predict hourly heat demand on a national level, which replaces the traditional bottom-up model based on extensive building simulations and computation. The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection, making the model more realistic by removing non-essential inputs. The improved model can be trained using fewer meteorological data types and insufficient data, while accurately forecasting the hourly heat demand throughout the year within an acceptable error range. The model provides a framework to obtain accurate heat demand predictions for large-scale areas, which can be used as a reference for stakeholders, especially policymakers, to make informed decisions.

Preclinical evidence indicates the potential anti-tumor capabilities of cannabinoids in prostate cancer (PC). We undertook a cross-sectional study using National Survey on Drug Use and Health data from 2002 to 2020, involving 2503 participants in the USA. The independent variable was marijuana use status (current, former, never), while the dependent variable was self-reported PC (yes, no). Eleven other demographic variables were assessed as covariates. PC prevalence was lower among current marijuana users (46/145, 31.7%) and former users (323/1021, 31.6%) compared to non-users (534/1337, 39.9%, p < 0.001). PC prevalence was lower among users versus non-users in the elderly (≥65) (36.4% vs. 42.4%, p = 0.016) and non-Hispanic white subgroups (28.9% vs. 38.3%, p < 0.001). There were no significant PC prevalence differences between users and non-users in the younger population (50–64) or other race/ethnicity. In the multivariable analyses, former marijuana use was associated with lower PC compared to never using (odd ratio = 0.74, 95% CI 0.62–0.90, p = 0.001). Current use was also suggestive of reduced prevalence but was not statistically significant (odd ratio = 0.77, 95% CI 0.52–1.14, p = 0.198), possibly due to low sample size. Our findings from a large national survey provide additional data to link marijuana use with lower PC prevalence.

Abstract Hepatocellular carcinoma (HCC) is a highly invasive malignancy. Recently, GATOR1 (Gap Activity TOward Rags 1) complexes have been shown to play an important role in regulating tumor growth. NPRL2 is a critical component of the GATOR1 complex. Therefore, this study used NPRL2 knockdown to investigate how GATORC1 regulates the prognosis and development of HCC via the mammalian target of rapamycin (mTOR) and autophagy signaling pathways. We established HepG2 cells with NPRL2 knockdown using small interfering RNA (siRNA) and short hairpin RNA (shRNA) systems. The siRNA‐mediated and shRNA‐mediated NPRL2 down‐regulation significantly reduced the expression of NPRL2 and two other GATPOR1 complex components, NPRL3 and DEPDC5, in HepG2 cells; furthermore, the efficient down‐regulation of NPRL2 protein expression by both the shRNA and siRNA systems enhanced the proliferation, migration, and colony formation in vitro. Additionally, the NPRL2 down‐regulation significantly increased HCC growth in the subcutaneous and orthotopic xenograft mouse models. The NPRL2 down‐regulation increased the Rag GTPases and mTOR activation and inhibited autophagy in vitro and in vivo. Moreover, the NPRL2 level in the tumors was significantly associated with mortality, recurrence, the serum alpha fetoprotein level, the tumor size, the American Joint Committee on Cancer stage, and the Barcelona Clinic Liver Cancer stage. Low NPRL2, NPRL3, DEPDC5, and LC3, and high p62 and mTOR protein expression in the tumors was significantly associated with disease‐free survival and overall survival in 300 patients with HCC after surgical resection. Conclusion: The efficient down‐regulation of NPRL2 significantly increased HCC proliferation, migration, and colony formation in vitro, and increased HCC growth in vivo. Low NPRL2 protein expression in the tumors was closely correlated with poorer clinical outcomes in patients with HCC. These results provide a mechanistic understanding of HCC and aid the development of treatments for HCC.

To achieve net zero emissions by 2050, the world economy needs to be significantly decarbonized. Among all sectors, the decarbonization of heat is likely to incorporate a significant electronification using electric heat pumps, placing additional strain on electric grid infrastructure in countries like the UK. In this paper, we present an assessment of the impacts of heat electrification on the grid in the UK in 2050 when the net zero is reached. A high-resolution heat demand forecasting model is developed to estimate hourly heat demand at the national level and it is validated using published historical data. The validated model is then used to predict the hourly heat demand of the UK in 2050. The impact of heat electrification is then analysed based on several different scenarios. The results show that heat decarbonization through electrification will have a significant impact on the grid. In the case of extreme global warming, both hourly peak demand and annual total heat demand will drop by about 45% in 2050, compared with published data in 2010. In the case of 100% electrification with no global warming, the total annual electricity demand for heating is 109.4 TWh. The hourly peak demand of electricity will increase from 96.6 GW to 146.7 GW, which requires an increase of electricity generation capacity by around 50%. It is however economically infeasible and unviable to build such a huge new generation capacity that would only be used in winter when the peak heat demand peaks but will be switched off during summer. Hence, mitigation measures such as heat storage will play a vital role in balancing power generation and consumption, as well as reducing the required new generation capacity.

Detailed descriptions of behavior provide critical insight into the structure and function of nervous systems. In Drosophila larvae and many other systems, short behavioral experiments have been successful in characterizing rapid responses to a range of stimuli at the population level. However, the lack of long-term continuous observation makes it difficult to dissect comprehensive behavioral dynamics of individual animals and how behavior (and therefore the nervous system) develops over time. To allow for long-term continuous observations in individual fly larvae, we have engineered a robotic instrument that automatically tracks and transports larvae throughout an arena. The flexibility and reliability of its design enables controlled stimulus delivery and continuous measurement over developmental time scales, yielding an unprecedented level of detailed locomotion data. We utilize the new system’s capabilities to perform continuous observation of exploratory search behavior over a duration of 6 hr with and without a thermal gradient present, and in a single larva for over 30 hr. Long-term free-roaming behavior and analogous short-term experiments show similar dynamics that take place at the beginning of each experiment. Finally, characterization of larval thermotaxis in individuals reveals a bimodal distribution in navigation efficiency, identifying distinct phenotypes that are obfuscated when only analyzing population averages.

Abstract Microfluidic organ-on-a-chip technologies have enabled construction of biomimetic physiologically and pathologically relevant models. This paper describes an injection molded microfluidic platform that utilizes a novel sequential edge-guided patterning method based on spontaneous capillary flow to realize three-dimensional co-culture models and form an array of micro-vascularized tissues (28 per 1 × 2-inch slide format). The MicroVascular Injection-Molded Plastic Array 3D Culture (MV-IMPACT) platform is fabricated by injection molding, resulting in devices that are reliable and easy to use. By patterning hydrogels containing human umbilical endothelial cells and fibroblasts in close proximity and allowing them to form vasculogenic networks, an array of perfusable vascularized micro-tissues can be formed in a highly efficient manner. The high-throughput generation of angiogenic sprouts was quantified and their uniformity was characterized. Due to its compact design (half the size of a 96-well microtiter plate), it requires small amount of reagents and cells per device. In addition, the device design is compatible with a high content imaging machine such as Yokogawa CQ-1. Furthermore, we demonstrated the potential of our platform for high-throughput phenotypic screening by testing the effect of DAPT, a chemical known to affect angiogenesis. The MV-IMPACT represent a significant improvement over our previous PDMS-based devices in terms of molding 3D co-culture conditions at much higher throughput with added reliability and robustness in obtaining vascular micro-tissues and will provide a platform for developing applications in drug screening and development.

Background and introductionFederated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage analysis tasks such as lesion segmentation in multiple sclerosis (MS), due to variance in lesion characteristics imparted by different scanners and acquisition parameters.MethodsIn this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.ResultsThe proposed method has been validated on two FL MS segmentation scenarios using public and clinical datasets. Specifically, the case-wise and voxel-wise Dice score of the proposed method under the first public dataset is 65.20 and 74.30, respectively. On the second in-house dataset, the case-wise and voxel-wise Dice score is 53.66, and 62.31, respectively.Discussions and conclusionsThe Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by significantly outperforming other FL methods. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can achieve comparable performance to that from centralized training with all the raw data.

Battery energy storage system is one of the most important and effective means to achieve carbon neutrality, its large-scale applications put forward higher requirements for the battery operating safety. Accurate and real-time battery state estimation is an important function of battery management systems, and it provides critical information for ensuring safe and reliable operation of batteries. However, it is not an easy task to accurately estimate the battery internal state due to the complex operating conditions and the complicated electrochemical reactions inside the battery. This paper provided a systematic review of the state-of-the-art techniques on the state of charge (SOC) and state of health (SOH) estimation of batteries. The advantages and main challenges existing in practical applications of different techniques were analyzed and compared. Then, the future development needs for battery SOC and SOH estimation techniques were prospected, which provides references for further research in battery state estimation

Abstract This paper proposes a novel method for quantifying fault level in future grid scenarios with various penetrations of power electronics‐connected renewable energy sources. As it is known, the information regarding the fault level is critically important for designing protection schemes, different control loops, understanding voltage profile in the grid, etc. This method is focused on the steady‐state fault level calculation and it can be used to analyse future grid scenarios including uniform and non‐uniform penetration of power electronics‐based generation displacing all, or just specific conventional synchronous generation in the grid. Due to different possibilities for type, size, and location of power electronics‐based RES generation in future grid, it is required to analyse the unprecedented scale of scenarios. The proposed method for FLC enables us to assess the system fault level for large numbers of FG scenarios without a need for detailed system modelling and/or time‐domain simulations. The simulation results demonstrated the suitability of our proposed FLC method for various penetration levels of PE‐based RESs in the 2‐area and the IEEE 39‐bus test systems. The obtained results are compared with time‐domain simulations and the IEC 60909 standards performed in DIgSILENT PowerFactory, where the efficacy of the proposed methodology is demonstrated.

Abstract Background Epigallocatechin gallate (EGCG) acts as an antioxidant by preventing oxidative stress. The effect of EGCG on aluminum-induced testicular injury is not clear. Hence, the present study is planned to investigate the effect of EGCG on aluminum chloride (AlCl3)-induced changes in behavior, biochemical parameters, and spermatogenesis in male Sprague-Dawley rats. The rats were divided into six groups with six animals each. All the animals were administered with respective assigned treatment once daily for 28 days. The animals in groups I to VI were administered with drug vehicle, AlCl3, vitamin C, EGCG, vitamin C, and EGCG, respectively. The animals in groups V and VI were additionally challenged with AlCl3 (10 mg/kg) immediately after vitamin C and EGCG administration, respectively. Changes in behavior were measured on day 1, 14 and 28. At the end of the study, the blood sample was collected from all the animals, and the serum was separated and used for biochemical analysis. Later, the rats were subjected to bilateral orchiectomy; sperm was collected from the cauda epididymis for microscopic examination. Then, the animals were sacrificed, and the organs such as the brain, lungs, heart, liver, kidney, spleen, and testis were collected for organ weight analysis. Results The animal administered with AlCl3 showed a reduction in locomotor activity, grip strength, and escape latency time whereas vitamin C prevented the effect of AlCl3. But, EGCG did not show any significant changes in AlCl3-induced behavioral and biochemical changes. At the end of the study, vitamin C prevented AlCl3-induced behavioral and biochemical changes. The group of animals administered with AlCl3 showed a reduction in the number of spermatozoa whereas AlCl3 + vitamin C and AlCl3 + EGCG did not show any significant changes in the number of spermatozoa when compared to the control group. Conclusion EGCG prevented AlCl3-induced reduction in epididymal sperm count of male rats and did not show any significant effect on AlCl3-induced changes in behavior and biochemical parameters, whereas vitamin C had an ameliorative effect on AlCl3-induced changes in behavior, biochemical parameter, and spermatogenesis. Graphical abstract

BackgroundNeutrophils are involved in the initial host responses to pathogens. Neutrophils can activate T cell responses either independently or through indirect involvement of Dendritic cells (DCs). Recently we have demonstrated direct neutrophil-T cell interactions that initiate adaptive immune responses following immunization with live attenuated Leishmania donovani centrin deleted parasite vaccine (LdCen-/-). However, neutrophil-DC interactions in T cell priming in vaccine immunity in general are not known. In this study we evaluated the interaction between neutrophils and DCs during LdCen-/- infection and compared with wild type parasite (LdWT) both in vitro and in vivo.Methodology/findingsLdCen-/- parasite induced increased expression of CCL3 in neutrophils caused higher recruitment of DCs capable of inducing a strong proinflammatory response and elevated co-stimulatory molecule expression compared to LdWT infection. To further illustrate neutrophil-DCs interactions in vivo, we infected LYS-eGFP mice with red fluorescent LdWT/LdCen-/- parasites and sort selected DCs that engulfed the neutrophil containing parasites or DCs that acquired the parasites directly in the ear draining lymph nodes (dLN) 5d post infection. The DCs predominantly acquired the parasites by phagocytosing infected neutrophils. Specifically, DCs containing LdCen-/- parasitized neutrophils exhibited a proinflammatory phenotype, increased expression of costimulatory molecules and initiated higher CD4+T cell priming ex-vivo. Notably, potent DC activation occurred when LdCen-/- parasites were acquired indirectly via engulfment of parasitized neutrophils compared to direct engulfment of LdCen-/- parasites by DCs. Neutrophil depletion in LdCen-/- infected mice significantly abrogated expression of CCL3 resulting in decreased DC recruitment in ear dLN. This event led to poor CD4+Th1 cell priming ex vivo that correlated with attenuated Tbet expression in ear dLN derived CD4+ T cells in vivo.ConclusionsCollectively, LdCen-/- containing neutrophils phagocytized by DC markedly influence the phenotype and antigen presenting capacity of DCs early on and thus play an immune-regulatory role in shaping vaccine induced host protective response.

Background context: Hybrid cervical corpectomy/ACDF (HCC-ACDF) is commonly utilized to treat multilevel cervical myelopathy; however, the incidence and mechanisms of hardware failure remain largely uncharacterized. Purpose: We report our experience with this procedure with the goal of describing and better understanding post-operative failures. Methods: The records of 20 consecutive patients who underwent HCC-ACDF for multilevel CSM between June 2015 and December 2018 at this Hospital (blinded) were retrospectively reviewed. All patients were followed for at least 1 year after surgery and were therefore included in the study. Outcome measures include incidence of and reason for subsequent posterior cervical surgery, incidence of and reason for subsequent anterior cervical surgery, progressive symptomatic myelopathy, radiographic hardware failure, and net reduction of pre-operative kyphosis. Continuous variables are reported with means and standard deviations. Fisher’s exact test was used to compare outcomes of binary variables. Results: 20 patients (mean age 60) underwent anterior HCC-ACDF for 3-level CSM. Mean clinical follow up was 26 months (range: 12–56 months). Mean operative time was 205 min and mean blood loss was 105 mL. Radiographic fusion was achieved in 15 of 18 (83%) patients for whom adequate radiographic follow-up was available. HCC-ACDF resulted in an average restoration of 4 degrees of cervical lordosis (standard deviation: 7.3 degrees). One patient (5%) developed symptomatic hardware failure requiring additional surgery. One patient (5%) developed progressive myelopathy within 4 months of surgery. 2 others (10%) developed adjacent segment disease within 2 years of surgery. Three of 20 patients (15%) required subsequent posterior surgery. Conclusions: Rates of hardware failure after HCC-ACDF in our series compare favorably with reports of multilevel anterior corpectomy but are higher than those reported in previous series of HCC-ACDF. No patient characteristics were significantly associated with rates of surgical failure.

Recent advances in anticancer therapy have shown dramatic improvements in clinical outcomes, and adoptive cell therapy has emerged as a type of immunotherapy that can modulate immune responses by transferring engineered immune cells. However, a small percentage of responders and their toxicity remain as challenges. Three-dimensional (3D) in vitro models of the tumor microenvironment (TME) have the potential to provide a platform for assessing and predicting responses to therapy. This paper describes an in vitro 3D tumor model that incorporates clusters of colorectal cancer (CRC) cells around perfusable vascular networks to validate immune-cell-mediated cytotoxicity against cancer cells. The platform is based on an injection-molded 3D co-culture model and composed of 28 microwells where separate identical vascularized cancer models can be formed. It allows robust hydrogel patterning for 3D culture that enables high-throughput experimentation. The uniformity of the devices resulted in reproducible experiments that allowed 10× more experiments to be performed when compared to conventional polydimethylsiloxane (PDMS)-based microfluidic devices. To demonstrate its capability, primary natural killer (NK) cells were introduced into the vascularized tumor network, and their activities were monitored using live-cell imaging. Extravasation, migration, and cytotoxic activity against six types of CRC cell lines were tested and compared. The consensus molecular subtypes (CMS) of CRC with distinct immune responses resulted in the highest NK cell cytotoxicity against CMS1 cancer cells. These results show the potential of our vascularized tumor model for understanding various steps involved in the immune response for the assessment of adoptive cell therapy.

Due to the impact of occupants’ activities in buildings, the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term, which show seasonal variation and hourly variation, respectively. This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time. In order to solve this problem, this paper proposes two approaches for fitting and predicting the electricity demand of office buildings. The first proposed approach splits the electricity demand data into fixed time periods, containing working hours and non-working hours, to reduce the impact of occupants’ activities. After finding the most sensitive weather variable to non-working hour electricity demand, the building baseload and occupant activities can be predicted separately. The second proposed approach uses the artificial neural network (ANN) and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. In this approach, the power demand data is split into a narrower time range as no-occupancy hours, full-occupancy hours, and fuzzy hours between them, in which the occupancy rate is varying depending on the time and weather variables. The proposed approaches are verified by the real data from the University of Glasgow as a case study. The simulation results show that, compared with the traditional ANN method, both proposed approaches have less root-mean-square-error (RMSE) in predicting electricity demand. In addition, the proposed working and non-working hour based regression approach reduces the average RMSE by 35%, while the ANN with fuzzy hours based approach reduces the average RMSE by 42%, comparing with the traditional power demand prediction method. In addition, the second proposed approach can provide more information for building energy management, including the predicted baseload, peak load, and occupancy rate, without requiring additional building parameters.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a pandemic and has inflicted enormous damage on the lives of the people and economy of many countries worldwide. However, therapeutic agents against SARS-CoV-2 remain unclear. SARS-CoV-2 has a spike protein (S protein), and cleavage of the S protein is essential for viral entry. Nattokinase is produced by Bacillus subtilis var. natto and is beneficial to human health. In this study, we examined the effect of nattokinase on the S protein of SARS-CoV-2. When cell lysates transfected with S protein were incubated with nattokinase, the S protein was degraded in a dose- and time-dependent manner. Immunofluorescence analysis showed that S protein on the cell surface was degraded when nattokinase was added to the culture medium. Thus, our findings suggest that nattokinase exhibits potential for the inhibition of SARS-CoV-2 infection via S protein degradation.

Artificial neural network (ANN) has become an important method to model the nonlinear relationships between weather conditions, building characteristics and its heat demand. Due to the large amount of training data required for ANN training, data reduction and feature selection are important to simplify the training. However, in building heat demand prediction, many weather-related input variables contain duplicated features. This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features. The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus. The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20% training time compared with the traditional methods while maintaining the prediction accuracy. It indicates that the approach can be applied for analysing large number of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Background: Acetaminophen (paracetamol) is one of the most common medications used for management of pain in the world. There is lack of consensus about the mechanism of action, and concern about the possibility of adverse effects on reproductive health. Methods: We first established the metabolome profile that characterizes use of acetaminophen, and we subsequently trained and tested a model that identified metabolomic differences across samples from 455 individuals with and without acetaminophen use. We validated the findings in a European ancestry adult twin cohort of 1880 individuals (TwinsUK), and in a study of 1235 individuals of African American and Hispanic ancestry. We used genomics to elucidate the mechanisms targeted by acetaminophen. Findings: We identified a distinctive pattern of depletion of sulfated sex hormones with use of acetaminophen across all populations. We used a Mendelian randomization approach to characterize the role of Sulfotransferase Family 2A Member 1 (SULT2A1) as the site of the interaction. Although CYP3A7-CYP3A51P variants also modified levels of some sulfated sex hormones, only acetaminophen use phenocopied the effect of genetic variants of SULT2A1. Overall, acetaminophen use, age, gender and SULT2A1 and CYP3A7-CYP3A51P genetic variants are key determinants of variation in levels of sulfated sex hormones in blood. The effect of taking acetaminophen on sulfated sex hormones was roughly equivalent to the effect of 35 years of aging. Interpretation: These findings raise concerns of the impact of acetaminophen use on hormonal homeostasis. In addition, it modifies views on the mechanism of action of acetaminophen in pain management as sulfated sex hormones can function as neurosteroids and modify nociceptive thresholds.

The presence of distributed generation (DG) in a power distribution network can cause difficulties in managing the voltage, power flow and fault level as the network was designed for supplying passive loads. This study focuses on the control of a back-to-back (B2B) voltage source converter operated as a synchronverter and a soft open point (SOP) between adjacent feeders to mitigate the voltage fluctuation caused by DG and to increase the reliability of the supply without increasing the fault level. Better reliability is achieved as the B2B converter can continue to supply the loads when a loss of mains occurs on one side. The proposed control of the two DC–AC converters, which also needs to regulate the DC link voltage and adapts the synchronverter algorithm to the new application requirements, so that events will cause minimum disruption. Simulation shows that the B2B-synchronverter is capable of regulating the load voltage under normal mains connected condition, and also limiting the fault current and supplying the loads within its rating during islanding, providing seamless transition to the islanded mode.

The 2017 ELN risk stratification has been widely adopted, but some studies have suggested the outcomes are heterogenous within the ELN risk groups and may be affected by other co-existing genetic mutations. This study evaluated the impact of DNA methylation regulatory gene (TET2, IDH1/2, DNMT3A, SETBP1) mutations (DMRGM) evaluated by NGS in the outcome of AML patients in each ELN risk group. A total of 114 patients were analyzed with a median follow-up of 12 months. Overall, 30.7% (35/114) of patients had DMRGM. DMRGM status had no impact on CR rate in each ELN risk group. The OS, however, was significantly shorter in patients with DMRGM compared to those without DMRGM (median OS: 12 vs. 33 months, p = 0.0053). Multivariate analysis showed DMRGM status was an independent unfavorable factor for OS (HR: 2.704, 95% CI: 1.451–5.041, p = 0.0017). The adverse OS impact of DMRGM was only observed in the ELN favorable group (7 months vs. not reached, p = 0.0001), but not in the intermediate or adverse group. Among the favorable group with DMRGM (n = 16), DMRGM occurred predominantly in cases with mutated NPM1 (15/16, or 93.8%). Our results suggest that DMRGM adversely impact the outcomes of ELN favorable group patients, particularly those with mutated NPM1. Further studies are warranted to confirm our observations.

This paper presents the design and control of an advanced unidirectional DC/DC modular multilevel converter (MMC) that facilitate the integration of off-shore windfarms with the high-voltage direct current (HVDC) transmission system. The proposed converter deploys a single-phase MMC inverter coupled with series-connected rectifier modules through multiple medium frequency transformers. Unlike the conventional dq control method, which involves multiple transformations, this paper also proposes a simple control strategy that directly acts on the AC output of the MMC, under the stationary reference frame.

The creation of a suitable wide area monitoring system (WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure, efficient and sustainable for the foreseeable future. In Great Britain (GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring (VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.

ABSTRACT Toxin neutralizing antibodies represent the major mode of protective immunity against a number of toxin-mediated bacterial diseases, including anthrax; however, the cellular mechanisms that lead to optimal neutralizing antibody responses remain ill defined. Here we show that the cellular binding pathway of anthrax protective antigen (PA), the binding component of anthrax toxin, determines the toxin neutralizing antibody response to this antigen. PA, which binds cellular receptors and efficiently enters antigen-presenting cells by receptor-mediated endocytosis, was found to elicit robust anti-PA IgG and toxin neutralizing antibody responses. In contrast, a receptor binding-deficient mutant of PA, which does not bind receptors and only inefficiently enters antigen-presenting cells by macropinocytosis, elicited very poor antibody responses. A chimeric protein consisting of the receptor binding-deficient PA mutant tethered to the binding subunit of cholera toxin, which efficiently enters cells using the cholera toxin receptor rather than the PA receptor, elicited an anti-PA IgG antibody response similar to that elicited by wild-type PA; however, the chimeric protein elicited a poor toxin neutralizing antibody response. Taken together, our results demonstrate that the antigen capture pathway can dictate the magnitudes of the total IgG and toxin neutralizing antibody responses to PA as well as the ratio of the two responses. IMPORTANCE Neutralizing antibodies provide protection against a number of toxin-mediated bacterial diseases by inhibiting toxin action. Therefore, many bacterial vaccines are designed to induce a toxin neutralizing antibody response. We have used protective antigen (PA), the binding component of anthrax toxin, as a model antigen to investigate immune mechanisms important for the induction of robust toxin neutralizing antibody responses. We found that the pathway used by antigen-presenting cells to capture PA dictates the robustness of the neutralizing antibody response to this antigen. These results provide new insights into immune mechanisms that play an important role in the induction of toxin neutralizing antibody responses and may be useful in the design of new vaccines against toxin-mediated bacterial diseases.

In vivo, blood vessels constitutively experience mechanical stresses exerted by adjacent tissues and other structural elements. Vascular collapse, a structural failure of vascular tissues, may stem from any number of possible compressive forces ranging from injury to tumor growth and can promote inflammation. In particular, endothelial cells are continuously exposed to varying mechanical stimuli, internally and externally, resulting in blood vessel deformation and injury. This study proposed a method to model biomechanical-stimuli-induced blood vessel compression in vitro within a polydimethylsiloxane (PDMS) microfluidic 3D microvascular tissue culture platform with an integrated pneumatically actuated compression mechanism. 3D microvascular tissues were cultured within the device. Histological reactions to compressive forces were quantified and shown to be the following: live/dead assays indicated the presence of a microvascular dead zone within high-stress regions and reactive oxygen species (ROS) quantification exhibited a stress-dependent increase. Fluorescein isothiocyanate (FITC)-dextran flow assays showed that compressed vessels developed structural failures and increased leakiness; finite element analysis (FEA) corroborated the experimental data, indicating that the suggested model of vascular tissue deformation and stress distribution was conceptually sound. As such, this study provides a powerful and accessible in vitro method of modeling microphysiological reactions of microvascular tissues to compressive stress, paving the way for further studies into vascular failure as a result of external stress.

Heat demand of a district heating network needs to be accurately predicted and managed to reduce consumption and emissions. Detailed thermal parameters are essential for predictions using physics-based energy models, but they are not always available or sufficiently accurate. To reduce the simulation time in calibration and the dependency of accurate data of buildings, this paper develops a prediction approach using a building energy model whose parameters are calibrated by Bayesian-based calibration method to match the recorded data of monthly heat demand. In the proposed calibration approach, an emulator is established to evaluate the untested values of thermal parameters using Bayesian method, and then use the evaluation results to search for the most suitable parameters value. The designed approach greatly accelerates the calibration speed. The method is used to calibrate a single parameter and multiple parameters of the building thermal energy models for a district heating network. After it has been verified with measured data, the developed calibration method is used to calibrate parameters of building energy models. The output of the calibrated model can predict the hourly building heat demand in district heating networks.

Yarrowia lipolytica, an oleaginous yeast, is capable of accumulating significant cellular mass in lipid making it an important source of biosustainable hydrocarbon-based chemicals. In spite of a similar number of protein-coding genes to that in other Hemiascomycetes, the Y. lipolytica genome is almost double that of model yeasts. Despite its economic importance and several distinct strains in common use, an independent genome assembly exists for only one strain. We report here a de novo annotated assembly of the chromosomal genome of an industrially-relevant strain, W29/CLIB89, determined by hybrid next-generation sequencing. For the first time, each Y. lipolytica chromosome is represented by a single contig. The telomeric rDNA repeats were localized by Irys long-range genome mapping and one complete copy of the rDNA sequence is reported. Two large structural variants and retroelement differences with reference strain CLIB122 including a full-length, novel Ty3/Gypsy long terminal repeat (LTR) retrotransposon and multiple LTR-like sequences are described. Strikingly, several of these are adjacent to RNA polymerase III-transcribed genes, which are almost double in number in Y. lipolytica compared to other Hemiascomycetes. In addition to previously-reported dimeric RNA polymerase III-transcribed genes, tRNA pseudogenes were identified. Multiple full-length and truncated LINE elements are also present. Therefore, although identified transposons do not constitute a significant fraction of the Y. lipolytica genome, they could have played an active role in its evolution. Differences between the sequence of this strain and of the existing reference strain underscore the utility of an additional independent genome assembly for this economically important organism.

Brain-Computer Interfaces (BCIs) that convert brain-recorded neural signals into intended movement commands could eventually be combined with Functional Electrical Stimulation to allow individuals with Spinal Cord Injury to regain effective and intuitive control of their paralyzed limbs. To accelerate the development of such an approach, we developed a model of closed-loop BCI control of arm movements that (1) generates realistic arm movements (based on experimentally measured, visually-guided movements with real-time error correction), (2) simulates cortical neurons with firing properties consistent with literature reports, and (3) decodes intended movements from the noisy neural ensemble. With this model we explored (1) the relative utility of neurons tuned for different movement parameters (position, velocity, and goal) and (2) the utility of recording from larger numbers of neurons – critical issues for technology development and for determining appropriate brain areas for recording. We simulated arm movements that could be practically restored to individuals with severe paralysis, i.e., movements from an armrest to a volume in front of the person. Performance was evaluated by calculating the smallest movement endpoint target radius within which the decoded cursor position could dwell for one second. Our results show that goal, position, and velocity neurons all contribute to improve performance. However, velocity neurons enabled smaller targets to be reached in shorter amounts of time than goal or position neurons. Increasing the number of neurons also improved performance, although performance saturated at 30-50 neurons for most neuron types. Overall, our work presents a closed-loop BCI simulator that models error corrections and the firing properties of various movement-related neurons that can be easily modified to incorporate different neural properties. We anticipate that this kind of tool will be important for development of future BCIs.

We have shown that inhibition of mTOR in granulosa cells and ovarian follicles results in compromised granulosa proliferation and reduced follicle growth. Further analysis here using spontaneously immortalized rat granulosa cells has revealed that mTOR pathway activity is enhanced during M-phase of the cell cycle. mTOR specific phosphorylation of p70S6 kinase and 4E-BP, and expression of Raptor are all enhanced during M-phase. The predominant effect of mTOR inhibition by the specific inhibitor Rapamycin (RAP) was a dose-responsive arrest in the G1 cell cycle stage. The fraction of granulosa cells that continued to divide in the presence of RAP exhibited a dose-dependent increase in aberrant mitotic figures known as anaphase bridges. Strikingly, estradiol consistently decreased the incidence of aberrant mitotic figures. In mice treated with RAP, the mitotic index was reduced compared to controls, and a similar increase in aberrant mitotic events was noted. RAP injected during a superovulation regime resulted in a dose-dependent reduction in the numbers of eggs ovulated. Implications for the real-time regulation of follicle growth and dominance, including the consequences of increased numbers of aneuploid granulosa cells, are discussed.