Porous Semiconducting Polymer Nanoparticles as Intracellular Biophotonic Mediators to Modulate the Reactive Oxygen Species Balance.

The integration of nanotechnology with photoredox medicine has led to the emergence of biocompatible semiconducting polymer nanoparticles (SPNs) for the optical modulation of intracellular reactive oxygen species (ROS). However, the need for efficient photoactive materials capable of finely controlling the intracellular redox status with high spatial resolution at a nontoxic light density is still largely unmet. Herein, highly photoelectrochemically efficient photoactive polymer beads are developed. The photoactive material/electrolyte interfacial area is maximized by designing porous semiconducting polymer nanoparticles (PSPNs). PSPNs are synthesized by selective hydrolysis of the polyester segments of nanoparticles made of poly(3-hexylthiophene)-graft-poly(lactic acid) (P3HT-g-PLA). The photocurrent of PSPNs is 4.5-fold higher than that of nonporous P3HT-g-PLA-SPNs, and PSPNs efficiently reduce oxygen in an aqueous environment. PSPNs are internalized within endothelial cells and optically trigger ROS generation with a >1.3-fold concentration increase with regard to nonporous P3HT-SPNs, at a light density as low as a few milliwatts per square centimeter, fully compatible with in vivo, chronic applications.

High Polymer Molecular Weight Yields Solar Cells with Simultaneously Improved Performance and Thermal Stability.

Simple synthetic routes, high active layer thickness tolerance as well as stable organic solar cells are relentlessly pursued as key enabling traits for the upscaling of organic photovoltaics. Here, the potential to address these issues by tuning donor polymer molecular weight is investigated. Specifically, the focus is on PTQ10, a polymer with low synthetic complexity, with number average molecular weights of 2.4, 6.2, 16.8, 52.9, and 54.4 kDa, in combination with three different non-fullerene acceptors, namely Y6, Y12, and IDIC. Molecular weight, indeed, unlocks a threefold increase in power conversion efficiency for these blends. Importantly, efficiencies above 10% for blade coated devices with thicknesses between 200 and 350 nm for blends incorporating high molecular weight donor are shown. Spectroscopic, GIWAXS and charge carrier mobility data suggest that the strong photocurrent improvement with molecular weight is related to both, improved electronic transport and polymer contribution to exciton generation. Moreover, it is demonstrated that solar cells based on high molecular weight PTQ10 are more thermally stable due to a higher glass transition temperature, thus also improving device stability.

Understanding radiation-generated electronic traps in radiation dosimeters based on organic field-effect transistors.

Organic dosimeters offer unique advantages over traditional technologies, and they can be used to expand the capabilities of current radiation detection systems. In-depth knowledge of the mechanisms underlying the interaction between radiation and organic materials is essential for their widespread adoption. Here, we identified and quantitatively characterized the electronic traps generated during the operation of radiation dosimeters based on organic field-effect transistors. Spectral analysis of the trap density of states, along with optical and structural studies, revealed the origin of trap states as local structural disorder within the crystalline films. Our results provide new insights into the radiation-induced defects in organic dosimeters, and pave the way for the development of more efficient and reliable radiation detection devices.

Bimodal modulation of in vitro angiogenesis with photoactive polymer nanoparticles.

Angiogenesis is a fundamental process in biology, given the pivotal role played by blood vessels in providing oxygen and nutrients to tissues, thus ensuring cell survival. Moreover, it is critical in many life-threatening pathologies, like cancer and cardiovascular diseases. In this context, conventional treatments of pathological angiogenesis suffer from several limitations, including low bioavailability, limited spatial and temporal resolution, lack of specificity and possible side effects. Recently, innovative strategies have been explored to overcome these drawbacks based on the use of exogenous nano-sized materials and the treatment of the endothelial tissue with optical or electrical stimuli. Here, conjugated polymer-based nanoparticles are proposed as exogenous photo-actuators, thus combining the advantages offered by nanotechnology with those typical of optical stimulation. Light excitation can achieve high spatial and temporal resolution, while permitting minimal invasiveness. Interestingly, the possibility to either enhance (≈+30%) or reduce (up to -65%) the angiogenic capability of model endothelial cells is demonstrated, by employing different polymer beads, depending on the material type and the presence/absence of the light stimulus. In vitro results reported here represent a valuable proof of principle of the reliability and efficacy of the proposed approach and should be considered as a promising step towards a paradigm shift in therapeutic angiogenesis.

p-Type Functionalized Carbon Nanohorns and Nanotubes in Perovskite Solar Cells.

The incorporation of p-type functionalized carbon nanohorns (CNHs) in perovskite solar cells (PSCs) and their comparison with p-type functionalized single- and double-walled carbon nanotubes (SWCNTs and DWCNTs) are reported in this study for the first time. These p-type functionalized carbon nanomaterial (CNM) derivatives were successfully synthesized by [2 + 1] cycloaddition reaction with nitrenes formed from triphenylamine (TPA) and 9-phenyl carbazole (Cz)-based azides, yielding CNHs-TPA, CNHs-Cz, SWCNTs-Cz, SWCNTs-TPA, DWCNTs-TPA, and DWCNTs-Cz. These six novel CNMs were incorporated into the spiro-OMeTAD-based hole transport layer (HTL) to evaluate their impact on regular mesoporous PSCs. The photovoltaic results indicate that all p-type functionalized CNMs significantly improve the power conversion efficiency (PCE), mainly by enhancing the short-circuit current density (Jsc) and fill factor (FF). TPA-functionalized derivatives increased the PCE by 12-17% compared to the control device without CNMs, while Cz-functionalized derivatives resulted in a PCE increase of 4-8%. Devices prepared with p-type functionalized CNHs exhibited a slightly better PCE compared with those based on SWCNTs and DWCNTs derivatives. The increase in hole mobility of spiro-OMeTAD, additional p-type doping, better energy alignment with the perovskite layer, and enhanced morphology and contact interface play important roles in enhancing the performance of the device. Furthermore, the incorporation of p-type functionalized CNMs into the spiro-OMeTAD layer increased device stability by improving the hydrophobicity of the layer and enhancing the hole transport across the MAPI/spiro-OMeTAD interface. After 28 days under ambient conditions and darkness, TPA-functionalized CNMs maintained the performance of the device by over 90%, while Cz-functionalized CNMs preserved it between 75 and 85%.

clusterBMA: Bayesian model averaging for clustering.

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

Semiconducting Polymer Nanoporous Thin Films as a Tool to Regulate Intracellular ROS Balance in Endothelial Cells.

The design of soft and nanometer-scale photoelectrodes able to stimulate and promote the intracellular concentration of reactive oxygen species (ROS) is searched for redox medicine applications. In this work, we show semiconducting polymer porous thin films with an enhanced photoelectrochemical generation of ROS in human umbilical vein endothelial cells (HUVECs). To achieve that aim, we synthesized graft copolymers, made of poly(3-hexylthiophene) (P3HT) and degradable poly(lactic acid) (PLA) segments, P3HT-g-PLA. In a second step, the hydrolysis of sacrificial PLA leads to nanometer-scale porous P3HT thin films. The pore sizes in the nm regime (220-1200 nm) were controlled by the copolymer composition and the structural arrangement of the copolymers during the film formation, as determined by atomic force microscopy (AFM) and transmission electron microscopy (TEM). The porous P3HT thin films showed enhanced photofaradaic behavior, generating a higher concentration of ROS in comparison to non-porous P3HT films, as determined by scanning electrochemical microscopy (SECM) measurements. The exogenous ROS production was able to modulate the intracellular ROS concentration in HUVECs at non-toxic levels, thus affecting the physiological functions of cells. Results presented in this work provide an important step forward in the development of new tools for precise, on-demand, and non-invasive modulation of intracellular ROS species and may be potentially extended to many other physiological or pathological cell models.

A global perspective on the intrinsic dimensionality of COVID-19 data.

We develop a novel global perspective of the complexity of the relationships between three COVID-19 datasets, the standardised per-capita growth rate of COVID-19 cases and deaths, and the Oxford Coronavirus Government Response Tracker COVID-19 Stringency Index (CSI) which is a measure describing a country's stringency of lockdown policies. We use a state-of-the-art heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. Our findings suggest that these highly popular COVID-19 statistics may project onto two low-dimensional manifolds without significant information loss, suggesting that COVID-19 data dynamics are generated from a latent mechanism characterised by a few important variables. The low dimensionality imply a strong dependency among the standardised growth rates of cases and deaths per capita and the CSI for countries over 2020-2021. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. The results show how high-income countries are more prone to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from COVID-19. Finally, the temporal stratification of the dataset allows the examination of the intrinsic dimension at a more granular level throughout the pandemic.

Enhanced Organic Electrochemical Transistor Performance of Donor-Acceptor Conjugated Polymers Modified with Hybrid Glycol/Ionic Side Chains by Postpolymerization Modification.

Emergent bioelectronic technologies are underpinned by the organic electrochemical transistor (OECT), which employs an electrolyte medium to modulate the conductivity of its organic semiconductor channel. Here we utilize postpolymerization modification (PPM) on a conjugated polymer backbone to directly introduce glycolated or anionic side chains via fluoride displacement. The resulting polymers demonstrated increased volumetric capacitances, with subdued swelling, compared to their parent polymer in p-type enhancement mode OECTs. This increase in capacitance was attributed to their modified side chain configurations enabling cationic charge compensation for thin film electrochemical oxidation, as deduced from electrochemical quartz crystal microbalance measurements. An overall improvement in OECT performance was recorded for the hybrid glycol/ionic polymer compared to the parent, owing to its low swelling and bimodal crystalline orientation as imaged by grazing-incidence wide-angle X-ray scattering, enabling its high charge mobility at 1.02 cm2·V-1·s-1. Compromised device performance was recorded for the fully glycolated derivative compared to the parent, which was linked to its limited face-on stacking, which hindered OECT charge mobility at 0.26 cm2·V-1·s-1, despite its high capacitance. These results highlight the effectiveness of anionic side chain attachment by PPM as a means of increasing the volumetric capacitance of p-type conjugated polymers for OECTs, while retaining solid-state macromolecular properties that facilitate hole transport.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Attenuation of Seizures, Cognitive Deficits, and Brain Histopathology by Phytochemicals of Imperata cylindrica (L.) P. Beauv (Poaceae) in Acute and Chronic Mutant Drosophila melanogaster Epilepsy Models.

Imperata cylindrica is a globally distributed plant known for its antiepileptic attributes, but there is a scarcity of robust evidence for its efficacy. The study investigated neuroprotective attributes of Imperata cylindrica root extract on neuropathological features of epilepsy in a Drosophila melanogaster mutant model of epilepsy. It was conducted on 10-day-old (at the initiation of study) male post-eclosion bang-senseless paralytic Drosophila (parabss1) involved acute (1-3 h) and chronic (6-18 days) experiments; n = 50 flies per group (convulsions tests); n = 100 flies per group (learning/memory tests and histological examination). Administrations were done in 1 g standard fly food, per os. The mutant flies of study (parabss1) showed marked age-dependent progressive brain neurodegeneration and axonal degeneration, significant (P < 0.05) bang sensitivity and convulsions, and cognitive deficits due to up-regulation of the paralytic gene in our mutants. The neuropathological findings were significantly (P < 0.05) alleviated in dose and duration-dependent fashions to near normal/normal after acute and chronic treatment with extract similar to sodium valproate. Therefore, para is expressed in neurons of brain tissues in our mutant flies to bring about epilepsy phenotypes and behaviors of the current juvenile and old-adult mutant D. melanogaster models of epilepsy. The herb exerts neuroprotection by anticonvulsant and antiepileptogenic mechanisms in mutant D. melanogaster due to plant flavonoids, polyphenols, and chromones (1 and 2) which exert antioxidative and receptor or voltage-gated sodium ion channels' inhibitory properties, and thus causing reduced inflammation and apoptosis, increased tissue repair, and improved cell biology in the brain of mutant flies. The methanol root extract provides anticonvulsant and antiepileptogenic medicinal values which protect epileptic D. melanogaster. Therefore, the herb should be advanced for more experimental and clinical studies to confirm its efficacy in treating epilepsy.

Graph neural network-based anomaly detection for river network systems.

Background: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology.Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods: We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model's efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results: We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions: Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents.

INTRODUCTION: To better understand the relationships between neurophysiology, cognitive function and psychopathology risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health between such subgroups. METHODS: We developed a flexible and scaleable multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. For this preliminary cross-sectional study, EEG data was collected from 59 individuals in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. RESULTS: We identified 5 core clusters associated with distinct subtypes of resting state EEG frequency content. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. CONCLUSION: This method provides the potential to identify neurophysiological subgroups of adolescents in the general population based on resting state EEG, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

Stable and Solution-Processable Cumulenic sp-Carbon Wires: A New Paradigm for Organic Electronics.

Solution-processed, large-area, and flexible electronics largely relies on the excellent electronic properties of sp2 -hybridized carbon molecules, either in the form of π-conjugated small molecules and polymers or graphene and carbon nanotubes. Carbon with sp-hybridization, the foundation of the elusive allotrope carbyne, offers vast opportunities for functionalized molecules in the form of linear carbon atomic wires (CAWs), with intriguing and even superior predicted electronic properties. While CAWs represent a vibrant field of research, to date, they have only been applied sparingly to molecular devices. The recent observation of the field-effect in microcrystalline cumulenes suggests their potential applications in solution-processed thin-film transistors but concerns surrounding the stability and electronic performance have precluded developments in this direction. In the present study, ideal field-effect characteristics are demonstrated for solution-processed thin films of tetraphenyl[3]cumulene, the shortest semiconducting CAW. Films are deposited through a scalable, large-area, meniscus-coating technique, providing transistors with hole mobilities in excess of 0.1 cm2  V-1  s-1 , as well as promising operational stability under dark conditions. These results offer a solid foundation for the exploitation of a vast class of molecular semiconductors for organic electronics based on sp-hybridized carbon systems and create a previously unexplored paradigm.

Gold(III) Porphyrin Was Used as an Electron Acceptor for Efficient Organic Solar Cells.

The widespread use of nonfullerene-based electron-accepting materials has triggered a rapid increase in the performance of organic photovoltaic devices. However, the number of efficient acceptor compounds available is rather limited, which hinders the discovery of new, high-performing donor:acceptor combinations. Here, we present a new, efficient electron-accepting compound based on a hitherto unexplored family of well-known molecules: gold porphyrins. The electronic properties of our electron-accepting gold porphyrin, named VC10, were studied by UV-Vis spectroscopy and by cyclic voltammetry (CV) , revealing two intense optical absorption bands at 500-600 and 700-920 nm and an optical bandgap of 1.39 eV. Blending VC10 with PTB7-Th, a donor polymer, which gives rise to an absorption band at 550-780 nm complementary to that of VC10, enables the fabrication of organic solar cells (OSCs) featuring a power conversion efficiency of 9.24% and an energy loss of 0.52 eV. Hence, this work establishes a new approach in the search for efficient acceptor molecules for solar cells and new guidelines for future photovoltaic material design.

Semi-paracrystallinity in semi-conducting polymers.

Precise determination of structural organization of semi-conducting polymers is of paramount importance for the further development of these materials in organic electronic technologies. Yet, prior characterization of some of the best-performing materials for transistor and photovoltaic applications, which are based on polymers with rigid backbones, often resulted in conundrums in which X-ray scattering and microscopy yielded seemingly contradicting results. Here we solve the paradox by introducing a new structural model, i.e., semi-paracrystalline organization. The model establishes that the microstructure of these materials relies on a dense array of small paracrystalline domains embedded in a more disordered matrix. Thus, the overall structural order relies on two parameters: the novel concept of degree of paracrystallinity (i.e., paracrystalline volume/mass fraction, introduced here for the first time) and the lattice distortion parameter of paracrystalline domains (g-parameter from X-ray scattering). Structural parameters of the model are correlated with long-range charge carrier transport, revealing that charge transport in semi-paracrystalline materials is particularly sensitive to the interconnection of paracrystalline domains.

Y6 Organic Thin-Film Transistors with Electron Mobilities of 2.4 cm2 V-1 s-1 via Microstructural Tuning.

There is a growing demand to attain organic materials with high electron mobility, µe , as current reliable reported values are significantly lower than those exhibited by their hole mobility counterparts. Here, it is shown that a well-known nonfullerene-acceptor commonly used in organic solar cells, that is, BTP-4F (aka Y6), enables solution-processed organic thin-film transistors (OTFT) with a µe as high as 2.4 cm2  V-1  s-1 . This value is comparable to those of state-of-the-art n-type OTFTs, opening up a plethora of new possibilities for this class of materials in the field of organic electronics. Such efficient charge transport is linked to a readily achievable highly ordered crystalline phase, whose peculiar structural properties are thoroughly discussed. This work proves that structurally ordered nonfullerene acceptors can exhibit intrinsically high mobility and introduces a new approach in the quest of high µe organic materials, as well as new guidelines for future materials design.

Influence of Backbone Curvature on the Organic Electrochemical Transistor Performance of Glycolated Donor-Acceptor Conjugated Polymers.

Two new glycolated semiconducting polymers PgBT(F)2gT and PgBT(F)2gTT of differing backbone curvatures were designed and synthesised for application as p-type accumulation mode organic electrochemical transistor (OECT) materials. Both polymers demonstrated stable and reversible oxidation, accessible within the aqueous electrochemical window, to generate polaronic charge carriers. OECTs fabricated from PgBT(F)2gT featuring a curved backbone geometry attained a higher volumetric capacitance of 170 F cm-3 . However, PgBT(F)2gTT with a linear backbone displayed overall superior OECT performance with a normalised peak transconductance of 3.00×104  mS cm-1 , owing to its enhanced order, expediting the charge mobility to 0.931 cm2 V-1 s-1 .

Connecting virtual reality and ecology: a new tool to run seamless immersive experiments in R.

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences. We present R2VR, a package for implementing and performing VR experiments in R with the aim of easing the learning curve for applied scientists including ecologists. The package provides functions for rendering VR scenes on web browsers with A-Frame that can be viewed by multiple users on smartphones, laptops, and VR headsets. It also provides instructions on how to retrieve answers from an online database in R. Three published ecological case studies are used to illustrate the R2VR workflow, and show how to run a VR experiments and collect the resulting datasets. By tapping into the popularity of R among ecologists, the R2VR package creates new opportunities to address the complex challenges associated with conservation, improve scientific knowledge, and promote new ways to share better understanding of environmental issues. The package could also be used in other fields outside of ecology.