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The oceanic emission of dimethyl sulfide (DMS) plays a vital role in the Earth's climate system and constitutes a substantial source of uncertainty when evaluating aerosol radiative forcing. Currently, the widely used monthly climatology of sea surface DMS concentration falls short of meeting the requirement for accurately simulating DMS-derived aerosols with chemical transport models. Hence, there is an urgent need for a high-resolution, multi-year global sea surface DMS dataset. Here we develop an artificial neural network ensemble model that uses nine environmental factors as input features and captures the variability of the DMS concentration across different oceanic regions well. Subsequently, a global sea surface DMS concentration and flux dataset (1° × 1°) with daily resolution spanning from 1998 to 2017 is established. According to this dataset, the global annual average concentration was ∼ 1.71 nM, and the annual total emissions were ∼ 17.2 Tg S yr−1, with ∼ 60 % originating from the Southern Hemisphere. While overall seasonal variations are consistent with previous DMS climatologies, notable differences exist in regional-scale spatial distributions. The new dataset enables further investigations into daily and decadal variations. Throughout the period 1998–2017, the global annual average concentration exhibited a slight decrease, while total emissions showed no significant trend. The DMS flux from our dataset showed a stronger correlation with the observed atmospheric methanesulfonic acid concentration compared to those from previous monthly climatologies. Therefore, it can serve as an improved emission inventory of oceanic DMS and has the potential to enhance the simulation of DMS-derived aerosols and associated radiative effects. The new DMS gridded products are available at https://doi.org/10.5281/zenodo.11879900 (Zhou et al., 2024).

Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both “reprocessing” and “value-added data” with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or −0.17 to 0.78 or −0.06, respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at https://doi.org/10.5281/zenodo.8296768 (Yan et al., 2023).

A promising pollution control technology is cold plasma driven chemical processing. The plasma is a pulsed electric gas discharge inside a near atmospheric-pressure-temperature reactor. The system is energized by a continuous stream of very short high-voltage pulses. The exhaust gas to be treated flows through the reactor. The methods applied involve the development of robust cold plasma systems, industrial applications and measuring technologies. Tests of the systems were performed at many industrial sites and involved control of airborne VOC (volatile organic compound) and odor. Electrical, chemical and odor measuring data were collected with state-of-the-art methods. To explain the test data an approximate solution of global reaction kinetics of pulsed plasma chemistry was developed. It involves the Lambert function and, for convenience, a simple approximation of it. The latter shows that the amount of removal, in good approximation, is a function of a single variable. This variable is electric plasma power divided by gas flow divided by input concentration. In the results sections we show that in some cases up to 99% of volatile pollution can be removed at an acceptable energy requirement. In the final sections we look into future efficiency enhancements by implementation of (sub)nanosecond pulsed plasma and solid state high-voltage technology and by integration with catalyst technology.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).

Aptamers are short DNA or RNA sequences that adopt 3D structures and can bind to protein targets with high binding affinity and specificity. Aptamers exhibit excellent tissue penetration, are inexpensive to produce, and can be internalized by cells. Therefore, aptamers are attractive targeting ligands to direct the delivery of theranostic agents to the desired cells. Epithelial cell adhesion molecule (EpCAM) is a tumor-associated antigen that is aberrantly overexpressed on many epithelial-derived cancers, including on cholangiocarcinoma (CCA) cells. Its expression on treatment-resistant cancer stem cells, along with its abundance in the CCA tumor microenvironment, highlights the need to develop EpCAM-targeted therapies for CCA. Herein, an in silico approach was used to design and screen DNA aptamers capable of binding to the EpCAM monomer and homodimer. Two aptamers, PLD01 and PLD02, met the selection criteria and were validated in vitro. Both aptamers exhibited high affinity for EpCAM+ CCA cells, with negligible binding to EpCAM- leukemia cells. Modified versions of PLD01 and PLD02 were successfully incorporated into the membranes of milk-derived nanovesicles. PLD01-functionalized nanovesicles enabled EpCAM-targeted delivery of the therapeutic cargo to CCA cells. In summary, these EpCAM-targeting aptamers can be utilized to direct the delivery of theranostic agents to EpCAM-expressing cells.

The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.

Background:. Impaired natural killer (NK) cell-mediated antitumor responses contribute to the growth of liver tumors. Expression of a disintegrin and metalloprotease 9 (ADAM9) increases shedding of membrane-bound major histocompatibility complex class I chain-related protein A and results in evasion from NK cell-mediated cytolysis. ADAM9 is also involved in angiogenesis and tumor progression and is a target of miR-126-3p, a tumor suppressor that is downregulated and alters tumor cell behavior in the liver and other cancers. We evaluated the restoration of miR-126-3p and modulation of the miR-126-3p/ADAM9 axis as a therapeutic approach to simultaneously enhance NK cell-mediated cytolysis while targeting both tumor cells and their microenvironment. Methods:. Precursor miRNAs were loaded into milk-derived nanovesicles to generate therapeutic vesicles (therapeutic milk-derived nanovesicles) for the restoration of functional miR-126-3p in recipient cancer cells. Results:. Administration of therapeutic milk-derived nanovesicles increased miR-126-3p expression and reduced ADAM9 expression in target cells and was associated with an increase in membrane-bound major histocompatibility complex class I chain-related protein A. This enhanced NK cell cytolysis in adherent tumor cells and in multicellular tumor spheroids while also impairing angiogenesis and modulating macrophage chemotaxis. Moreover, IV administration of therapeutic milk-derived nanovesicles with adoptive transfer of NK cells reduced tumor burden in orthotopic hepatocellular cancer xenografts in mice. Conclusion:. A directed RNA therapeutic approach can mitigate NK cell immune evasion, reduce angiogenesis, and alter the tumor cell phenotype through the restoration of miR-126-3p in liver tumor cells. The pleiotropic effects elicited by this multi-targeted approach to modulate the local tumor microenvironment support its use for the treatment of liver cancer.

Abstract Introduction Melanoma surveillance photography (MSP) is a comprehensive surveillance method that comprises two- or three-dimensional total body photography with tagged digital dermoscopy, performed at prescribed intervals. It has the potential to reduce unnecessary biopsies and enhance early detection of melanoma, but it is not yet standard care for all high-risk patients in Australia. This protocol describes a randomised controlled trial (RCT) designed to evaluate the clinical impact and cost-effectiveness of using MSP for the surveillance of individuals at ultra-high or high risk of melanoma from a health system perspective. Methods and design This is a registry-based, unblinded, multi-site, parallel-arm RCT that will be conducted over 3 years. We aim to recruit 580 participants from three Australian states: Victoria, New South Wales and Queensland, via state cancer registries or direct referral from clinicians. Eligible participants within 24 months of a primary cutaneous melanoma diagnosis will be randomised 1:1 to receive either MSP in addition to their routine clinical surveillance (intervention group) or routine clinical surveillance without MSP (control group). Most participants will continue surveillance with their usual care provider, and the frequency of follow-up visits in both groups will depend on the stage of their primary melanoma and risk factors. The primary outcome measure of the study is the number of unnecessary biopsies (i.e. false positives, being cases where a lesion is biopsied due to suspected melanoma on clinical examination, either with or without MSP, but the resulting histopathology finding is negative for melanoma). Secondary outcomes include the evaluation of health economic outcomes, quality of life and patient acceptability. Two sub-studies will explore the benefit of MSP in high-risk patients prior to a melanoma diagnosis and the diagnostic performance of MSP in the teledermatology setting compared to the en face clinical setting. Discussion This trial will determine the clinical efficacy, cost-effectiveness and affordability of MSP to facilitate policy decision-making at the national and local levels, across primary and specialist care. Trial registration ClinicalTrials.gov NCT04385732 . Registered on May 13, 2020.

Abstract We had previously identified visual impairment increasing risk of incident dementia. While a bi-directional vision-cognition association has subsequently been proposed, no study has specifically examined the longitudinal association between dementia and incidence of clinically defined visual impairment. In this territory-wide community cohort study of 10,806 visually unimpaired older adults, we examined their visual acuity annually for 6 years and tested if dementia at baseline was independently associated with higher risk of incident visual impairment (LogMAR ≥ 0.50 in the better eye despite best correction, which is equivalent to moderate visual impairment according to the World Health Organization definition). By the end of Year 6, a total of 3151 (29.2%) participants developed visual impairment. However, we did not find baseline dementia associating with higher risk of incident visual impairment, after controlling for baseline visual acuity, cataract, glaucoma, diabetes, hypertension, hypercholesterolemia, heart diseases, stroke, Parkinson’s disease, depression, hearing and physical impairments, physical, intellectual and social activities, diet, smoking, age, sex, educational level, and socioeconomic status. Among different covariables, baseline visual acuity appears to be more important than dementia in contributing to the development of visual impairment. Our present findings highlight the need for re-evaluating whether dementia is indeed a risk factor for visual impairment.

The formation of economic yield and fruit quality of Roxburgh rose (Rosa roxburghii Tratt.) depends essentially on its source-sink interaction. Thus, a pruning experiment was conducted to assess the effects of source-sink regulation on photosynthetic physiology and fruit yield of Roxburgh rose, which was of great significance to production. Cutting off vegetative branches reduced physiological fruit loss and malformed fruits but increased single fruit quality and yield. Results revealed that the stomatal characteristics, the composition of mesophyll tissue, and photosynthesis of leaves on reproductive branches were significantly affected by the ratio of the vegetative and reproductive shoots. Our data indicated that the source-sink ratio could reflect the balance between vegetative growth and reproductive growth of the tree during the whole fruit period. Fruit tree pruning had guiding significance for improving the fruit yield of Roxburgh rose.

Abstract Background Active participation in intellectual leisure activities such as calligraphy helps prevent cognitive decline and dementia, but the underlying mechanisms are not fully understood. With disrupted functional connectivity (FC) of default mode network (DMN) associated with cognitive decline, we speculate that intellectual activities might optimize cognitive function through modulating FC of DMN. This two-arm single-blind randomized controlled trial aims to identify the effects of increasing practice of calligraphy on cognitive function and FC of DMN in people with subjective cognitive decline (SCD). Methods One hundred twelve community-living Chinese aged 55 to 75 years old with SCD but without mild cognitive impairment or dementia and with prior practice of calligraphy as defined by 1 h of calligraphy per week will be recruited through elderly social centres in Hong Kong and randomized into either control or intervention group. The control group will continue with their usual practice of calligraphy, whereas the intervention group will double their practice of calligraphy. Measurement of cognitive outcomes and neuroimaging on resting-state FC will be performed at baseline and in 6 months. Repeated measures analysis of variance will be used to assess cognitive and FC changes, with time being the within-group factor, control/intervention as the between-group measure, and important covariates (age, sex, educational and occupational attainment, health, and other lifestyle factors) controlled for. Discussion This study will shed light on the underlying neurocognitive mechanisms of how intellectual activities promotes cognitive maintenance. Our anticipated findings will provide evidence that reversing or slowing FC disruption by actively participating in intellectual activities is still possible for the at-risk individuals. Trial registration Chinese Clinical Trial Registry ChiCTR1900024433 . Registered on 11 July 2019.

Analyzing the diffusion kinetics behavior that leads to precipitates formation can assist in understanding the properties of Zr alloy materials. In this study, coarsening growth kinetics of precipitates in N36 zirconium alloy with Nb dominant are experimentally calculated during the isothermal and isochronal aging process (580 °C–700 °C, 0.5–30 h respectively). Microstructure observation and the second phase particles (SPPs) have been investigated and numerically extracted through several processes, including the use of an optical microscope (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), in addition to image software tools. Conclusions confirm that as the temperature or aging period increases, the SPPs begin to lose fine and linear distribution properties ranging from α-plate grain boundary to the random distributions. Moreover, the small particles dissolve, whereas the large ones grow, accurately pursuing the Ostwald ripening property. Besides, most SPPs are found to be in the form of Zr(Nb, Fe)2, and some of them are (Zr,Nb)2Fe. The activation energy of kinetic growth of precipitates has been experimentally calculated using Kahlwiet second-order kinetics and found to be 206 kJ/mol. Compared to other documented Zr-alloy series, we have noticed that the different types of SPPs and the Nb element content in most contribute greatly to the slow diffusion rate, explaining the higher value in the activation energy of the precipitates growth process.

Extracellular vesicles (EVs) show promise for targeted drug delivery but face production challenges with low yields. Cell-derived nanovesicles (CDNVs) made by reconstituting cell membranes could serve as EV substitutes. In this study, CDNVs were generated from mesenchymal stem cells by extrusion. Their proteomic composition, in vitro and in vivo toxicity, and capacity for loading RNA or proteins were assessed. Compared with EVs, CDNVs were produced at higher yields, were comprised of a broader range of proteins, and showed no detrimental effects on cell proliferation, DNA damage, or nitric oxide production in vitro or on developmental toxicity in vivo. CDNVs could be efficiently loaded with RNA and engineered to modify surface proteins. The feasibility of generating immunomodulatory CDNVs was demonstrated by preparing CDNVs with enhanced surface expression of PD1, which could bind to PD-L1 expressing tumor cells, enhance NK and T cell degranulation, and increase immune-mediated tumor cell death. These findings demonstrate the adaptability and therapeutic promise of CDNVs as promising substitutes for natural EVs that can be engineered to enhance immunomodulation.

We previously introduced a three-stage design and associated end-of-stage analyses for allergen immunotherapy (AIT) trials. End-of-stage differences alone may not provide a fuller picture of Stages 2 and 3 effects because they may depend upon stage-specific durations. Therefore, we introduce an additional trend analysis to evaluate the difference in progression curves of two groups over the entire stage. Results from such analysis are used to inform persistence of end-of-stage benefit and thus provide evidence for stagewise effects beyond the study periods. We jointly apply end-of-stage and trend analyses to support the enhanced three-stage design to determine treatment response over time and sustained response to AIT. A simulation study was performed to illustrate the statistical properties (bias and power) of trend analyses under varying statistical missing mechanisms and effect sizes. The extent of bias depended on the missing mechanism and magnitude. Powers were largely driven by effect and sample sizes as well as pre-specified success margins, particularly of relative trend. As an illustration, assuming relative treatment differences of 25-30%, stagewise dropout rate of 15%, and parallel outcome progressions, a sample size of 200 per group may achieve 97% power to demonstrate a treatment effect and 53% power to demonstrate a sustained effect post-treatment. Trend analysis supplements the end-of-stage analysis to enhance the statistical claims of stagewise effects. Inferential statistics support our proposed trend analysis for evaluating benefits of AIT over time and inform clinical understanding and decisions.

BackgroundThe COVID-19 pandemic has imposed a profound negative impact on the mental health and wellbeing of societies and individuals worldwide. Older adults may be more vulnerable to the mental health effects of the pandemic, either directly from the infection itself or indirectly through the preventive measures. However, the existing literature on mental health in the older age groups has not been consistent so far. The aim of this study was therefore to assess the prevalence of common mental disorders (CMD; including depression and anxiety disorders) given their association with dementia risk, and to further examine age-related differences between older (≥60 years old) and younger (18–59 years old) adult's psychological status during the COVID-19 pandemic.MethodThis was a secondary analysis of a cross-sectional survey-study conducted during the second wave of COVID-19 pandemic in Hong Kong. The survey was disseminated through different social media platforms to the general population and included sociodemographic questions, self-reported physical health, and previous encounter with SARS or COVID-19. CMD was the primary outcome and was assessed using the 6-item Kessler Scale. A total of 1030 adults fulfilled inclusion criteria.ResultsThe prevalence of CMD during the pandemic was 16.1%. Compared to younger adults, older adults were significantly less likely to have a CMD (unadjusted OR = 0.07, 95% CI = 0.02–0.30, p < 0.001), with 18.1% of younger adults having CMD compared to 1.6% in the older cohort. Age differences remained significant after controlling for sociodemographic factors, physical health, and previous encounter with SARS or COVID-19 (adjusted OR = 0.12, 95% CI = 0.02–0.57, p = 0.008).ConclusionCommon mental disorders are highly prevalent during the COVID-19 pandemic in Hong Kong, though older adults appeared to be less affected mentally. Present findings highlight the urgent need to implement measures and strategies to mitigate the mental health problems, with particular attention to the younger cohort. Given their association with higher dementia risk, early detection and treatment of depression and anxiety disorders will be of critical importance in providing some relief to the already pressurized dementia burden in the longer term.

Abstract We present a method for calculating event shapes in QCD based on correlation functions of conserved currents. The method has been previously applied to the maximally supersymmetric Yang-Mills theory, but we demonstrate that supersymmetry is not essential. As a proof of concept, we consider the simplest example of a charge-charge correlation at one loop (leading order). We compute the correlation function of four electromagnetic currents and explain in detail the steps needed to extract the event shape from it. The result is compared to the standard amplitude calculation. The explicit four-point correlation function may also be of interest for the CFT community.

By exploiting their biological functions, the use of biological nanoparticles such as extracellular vesicles can provide an efficient and effective approach for hepatic delivery of RNA‐based therapeutics for the treatment of liver cancers such as hepatocellular cancer (HCC). Targeting liver cancer stem cells (LCSC) within HCC provide an untapped opportunity to improve outcomes by enhancing therapeutic responses. Cells with tumor‐initiating capabilities such as LCSC can be identified by expression of markers such as epithelial cell adhesion molecule (EpCAM) on their cell surface. EpCAM is a target of Wnt/β‐catenin signaling, a fundamental pathway in stem‐cell growth. Moreover, mutations in the β‐catenin gene are frequently observed in HCC and can be associated with constitutive activation of the Wnt/β‐catenin pathway. However, targeting these pathways for the treatment of HCC has been challenging. Using RNA nanotechnology, we developed engineered biological nanoparticles capable of specific and effective delivery of RNA therapeutics targeting β‐catenin to LCSC. Extracellular vesicles isolated from milk were loaded with small interfering RNA to β‐catenin and decorated with RNA scaffolds to incorporate RNA aptamers capable of binding to EpCAM. Cellular uptake of these EpCAM‐targeting therapeutic milk‐derived nanovesicles in vitro resulted in loss of β‐catenin expression and decreased proliferation. The uptake and therapeutic efficacy of these engineered biological nanotherapeutics was demonstrated in vivo using tumor xenograft mouse models. Conclusion: β‐catenin can be targeted directly to control the proliferation of hepatic cancer stem cells using small interfering RNA delivered using target‐specific biological nanoparticles. Application of this RNA nanotechnology–based approach to engineer biological nanotherapeutics provides a platform for developing cell‐surface molecule–directed targeted therapeutics.

Mesenchymal stem cell (MSC) derived extracellular vesicles (EV) are emerging as acellular therapeutics for solid organ injury and as carriers for drug delivery. Graphene-based materials are novel two-dimensional crystal structure-based materials with unique characteristics of stiffness, strength and elasticity that are being explored for various structural and biological applications. We fabricated a biomaterial that would capture desirable properties of both graphene and stem cell derived EV. Metabolically engineered EV that express azide groups were cross-linked with alkyne-functionalized graphene oxide (GO) via a copper catalyzed alkyne-azide cycloaddition (CuAAC) reaction. The crosslinking between EV and GO was accomplished without the need for ligand expression on the metal. Scanning electron and fluorescence microscopy demonstrated excellent cross-linking between EV and GO. Biological effects were assessed by phagocytosis studies and cell viability studies. The uptake of GO or sonicated GO (sGO) resulted in a durable pro-inflammatory immune response. Cell studies further showed that crosslinked GO-EV scaffolds exhibited cell-type dependent cytotoxicity on liver cancer cells whereas there was minimal impact on healthy hepatocyte proliferation. In vitro, neither GO-EV nor sGO-EV induced DNA strand breaks. In vivo studies in zebrafish revealed gross developmental malformations but treatment-induced mortality was only seen with the highest doses of GO-EV and sGO-EV. With these advantages, this engineered biomaterial combining the versatility of graphene with the therapeutic effects of MSC-EV has potential for applications in tissue engineering and regenerative medicine.

The therapeutic activities elicited by mesenchymal stem cells (MSC) are in part mediated through paracrine action by the release of extracellular vesicles (EV) and secreted proteins. Collectively, these MSC-derived factors, referred to as the secretome product (SP), are intrinsically therapeutic and represent an attractive alternative to cell-based therapies. Herein, we developed a lyopreservation protocol to extend the shelf-life of the MSC-SP without compromising the structural or functional integrity of the vesicular components. The SP isolated from normoxia- and anoxia-exposed MSC elicited protective effects in an in vitro model of oxidative injury and the bioactivity was retained in the lyophilized samples. Three separate formulations of MSC-SP were isolated by tangential flow filtration using sucrose, trehalose, and mannitol as lyoprotectant agents. The MSC-SPs were lyophilized using a manifold protocol and the structural and functional integrity were assessed. The trehalose formulation of SP exhibited the highest EV and protein recovery after manifold-based lyophilization. To facilitate development as a therapeutic, a shelf lyophilization protocol was developed which markedly enhanced the recovery of EV and proteins. In conclusion, lyophilization represents an efficient method to preserve the structural and functional integrity of the MSC-SP and can be used to develop an off-the-shelf therapeutic.

Summary: TUC338 is an ultraconserved long non-coding RNA that contributes to transformed cell growth in hepatocellular carcinoma (HCC). Genomic regions of TUC338 occupancy were enriched in unique or known binding motifs homologous to the tumor suppressors Pax6 and p53. Genes involved in cell proliferation were enriched within a 9-kb range of TUC338-binding sites. TUC338 RNA-based purification was used to isolate chromatin for mass spectrometry, and the plasminogen activator inhibitor-1 RNA-binding protein (PAI-RBP1) was identified as a TUC338 RNA-binding partner. The PAI-RBP1 target gene plasminogen activator inhibitor-1 (PAI-1) itself could also be post-transcriptionally regulated by TUC338. Thus modulation of transformed cell growth by TUC338 may involve binding to PAI-RBP1 as well as to sequence-defined cis-binding sites to modulate gene expression. These findings suggest that ultraconserved RNAs such as TUC338 can function in a manner analogous to transcription factors to modulate cell proliferation and transformed cell growth in HCC. : Biomolecules; Molecular Genetics; Cancer Systems Biology Subject Areas: Biomolecules, Molecular Genetics, Cancer Systems Biology

Abstract Background High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. Results In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. Conclusions The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Circular ribonucleic acids are non-coding ribonucleic acids that can be identified from genome sequencing studies. Although they can be readily detected, their regulation and functional role in human diseases such as cancer are unknown. Using a systematic approach, we analyzed ribonucleic acid-sequencing data from a well-characterized cohort of intrahepatic cholangiocarcinoma to identify genetic pathways related to circular ribonucleic acids. Although the expression of most circular ribonucleic acids was similar in both the cancer and non-cancer tissues, expression of circ2174 was significantly increased in cancer tissues. Network analysis of co-related genes identified several pathways associated with circ2174, and common regulatory mediators between genes in these pathways and circ2174. Among these, alterations in several genes involved in interleukin-16 signaling responses such Lck, interleukin-16, and macrophage inflammatory protein-1-beta were the most prominent. Octamer transcription factor (Oct)-2 was identified as a signal transducer that was common to both circ2174 and interleukin-16. Circ2174 has sequence complementarity to miR149 which can target Oct-2. These data suggest a mechanism whereby circ2174 can act as a sponge to regulate the expression of miR149, and thereby modulate Oct-2 and interleukin-16 signaling pathways in cholangiocarcinoma.

A key challenge in the fabrication of ferromagnetically filled carbon nano-onions (CNOs) is the control of their thickness, dimensions and electric properties. Up to now literature works have mainly focused on the encapsulation of different types of ferromagnetic materials including α-Fe, Fe3C, Co, FeCo, FePd3 and others within CNOs. However, no report has yet shown a suitable method for controlling both the number of shells, diameter and electric properties of the produced CNOs. Here, we demonstrate an advanced chemical vapour deposition approach in which the use of small quantities of sulfur during the pyrolysis of ferrocene allows for the control of (i) the diameter of the CNOs, (ii) the number of shells and (iii) the electric properties. We demonstrate the morphological, structural, electric and magnetic properties of these new types of CNOs by using SEM, XRD, TEM, HRTEM, EIS and VSM techniques.