A Quantitative Evaluation of Thin Slice Sampling for Parent-Infant Interactions

Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11-14 behavioural groups, with each group comprised of 3-13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matrices (probability of transitioning between behaviours, e.g., from looking at the infant to looking at a distraction) and stationary distributions (long-term proportion of time spent within behavioural states) for 15 thin slices of full, 5-min interactions. Measures drawn from the full sessions were compared to those from 1-, 2-, 3- and 4-min slices. We identified many instances where thin slice sampling (i.e., < 5 min) was an appropriate coding method, although we observed significant variation across different behaviours. We thereby used this information to provide detailed guidance to researchers regarding how long to code for each behaviour depending on their objectives.

Effects of Aerobic Exercise on Systemic Insulin Degludec Concentrations in People with Type 1 Diabetes

BACKGROUND

There is conflicting evidence on the effect of exercise on systemic insulin concentrations in adults with type 1 diabetes.

METHODS

This prospective single-arm study examined the effect of exercise on systemic insulin degludec (IDeg) concentrations. The study involved 15 male adults with type 1 diabetes (age 30.7 ± 8.0 years, HbA1c 6.9 ± 0.7%) on stable IDeg regimen. Blood samples were collected every 15 minutes at rest, during 60 minutes of cycling (66% VO₂max) and until 90 minutes after exercise termination. IDeg concentrations were quantified using high-resolution mass-spectrometry and analyzed applying generalized estimation equations.

RESULTS

Compared to baseline, systemic IDeg increased during exercise over time (P < .001), with the highest concentrations observed toward the end of the 60-minute exercise (17.9% and 17.6% above baseline after 45 minutes and 60 minutes, respectively). IDeg levels remained elevated until the end of the experiment (14% above baseline at 90 minutes after exercise termination, P < .001).

CONCLUSIONS

A single bout of aerobic exercise increases systemic IDeg exposure in adults on a stable basal IDeg regimen. This finding may have important implications for future hypoglycemia mitigation strategies around physical exercise in IDeg-treated patients.

From LTL to rLTL monitoring: improved monitorability through robust semantics

Runtime monitoring is commonly used to detect the violation of desired properties in safety critical cyber-physical systems by observing its executions. Bauer et al. introduced an influential framework for monitoring Linear Temporal Logic (LTL) properties based on a three-valued semantics for a finite execution: the formula is already satisfied by the given execution, it is already violated, or it is still undetermined, i.e., it can still be satisfied and violated by appropriate extensions of the given execution. However, a wide range of formulas are not monitorable under this approach, meaning that there are executions for which satisfaction and violation will always remain undetermined no matter how it is extended. In particular, Bauer et al. report that 44% of the formulas they consider in their experiments fall into this category. Recently, a robust semantics for LTL was introduced to capture different degrees by which a property can be violated. In this paper we introduce a robust semantics for finite strings and show its potential in monitoring: every formula considered by Bauer et al. is monitorable under our approach. Furthermore, we discuss which properties that come naturally in LTL monitoring-such as the realizability of all truth values-can be transferred to the robust setting. We show that LTL formulas with robust semantics can be monitored by deterministic automata, and provide tight bounds on the size of the constructed automaton. Lastly, we report on a prototype implementation and compare it to the LTL monitor of Bauer et al. on a sample of examples.

Body-Ordered Approximations of Atomic Properties

We show that the local density of states (LDOS) of a wide class of tight-binding models has a weak body-order expansion. Specifically, we prove that the resulting body-order expansion for analytic observables such as the electron density or the energy has an exponential rate of convergence both at finite Fermi-temperature as well as for insulators at zero Fermi-temperature. We discuss potential consequences of this observation for modelling the potential energy landscape, as well as for solving the electronic structure problem.

Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy

We postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the “effective” decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.

Evaluation of the SPARTACUS-Urban Radiation Model for Vertically Resolved Shortwave Radiation in Urban Areas

The heterogenous structure of urban environments impacts interactions with radiation, and the intensity of urban-atmosphere exchanges. Numerical weather prediction (NWP) often characterizes the urban structure with an infinite street canyon, which does not capture the three-dimensional urban morphology realistically. Here, the SPARTACUS (Speedy Algorithm for Radiative Transfer through Cloud Sides) approach to urban radiation (SPARTACUS-Urban), a multi-layer radiative transfer model designed to capture three-dimensional urban geometry for NWP, is evaluated with respect to the explicit Discrete Anisotropic Radiative Transfer (DART) model. Vertical profiles of shortwave fluxes and absorptions are evaluated across domains spanning regular arrays of cubes, to real cities (London and Indianapolis). The SPARTACUS-Urban model agrees well with the DART model (normalized bias and mean absolute errors < 5.5%) when its building distribution assumptions are fulfilled (i.e., buildings randomly distributed in the horizontal). For realistic geometry, including real-world building distributions and pitched roofs, SPARTACUS-Urban underestimates the effective albedo (< 6%) and ground absorption (< 16%), and overestimates wall-plus-roof absorption (< 15%), with errors increasing with solar zenith angle. Replacing the single-exponential fit of the distribution of building separations with a two-exponential function improves flux predictions for real-world geometry by up to half. Overall, SPARTACUS-Urban predicts shortwave fluxes accurately for a range of geometries (cf. DART). Comparison with the commonly used single-layer infinite street canyon approach finds SPARTACUS-Urban has an improved performance for randomly distributed and real-world geometries. This suggests using SPARTACUS-Urban would benefit weather and climate models with multi-layer urban energy balance models, as it allows more realistic urban form and vertically resolved absorption rates, without large increases in computational cost or data inputs.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10546-022-00706-9.

Detecting Validated Intracellular ROS Generation with 18F-dihydroethidine-Based PET

PURPOSE

To determine the sensitivity of the 18F-radiolabelled dihydroethidine analogue ([18F]DHE) to ROS in a validated ex vivo model of tissue oxidative stress.

PROCEDURES

The sensitivity of [18F]DHE to various ROS-generating systems was first established in vitro. Then, isolated rat hearts were perfused under constant flow, with contractile function monitored by intraventricular balloon. Cardiac uptake of infused [18F]DHE (50-150 kBq.min-1) was monitored by γ-detection, while ROS generation was invoked by menadione infusion (0, 10, or 50 μm), validated by parallel measures of cardiac oxidative stress.

RESULTS

[18F]DHE was most sensitive to oxidation by superoxide and hydroxyl radicals. Normalised [18F]DHE uptake was significantly greater in menadione-treated hearts (1.44 ± 0.27) versus control (0.81 ± 0.07) (p < 0.05, n = 4/group), associated with concomitant cardiac contractile dysfunction, glutathione depletion, and PKG1α dimerisation.

CONCLUSION

[18F]DHE reports on ROS in a validated model of oxidative stress where perfusion (and tracer delivery) is unlikely to impact its pharmacokinetics.

Diffusion in a disk with inclusion: Evaluating Green’s functions

We give exact Green’s functions in two space dimensions. We work in a scaled domain that is a circle of unit radius with a smaller circular “inclusion”, of radius a, removed, without restriction on the size or position of the inclusion. We consider the two cases where one of the two boundaries is absorbing and the other is reflecting. Given a particle with diffusivity D, in a circle with radius R, the mean time to reach the absorbing boundary is a function of the initial condition, given by the integral of Green’s function over the domain. We scale to a circle of unit radius, then transform to bipolar coordinates. We show the equivalence of two different series expansions, and obtain closed expressions that are not series expansions.

The effect of decellularisation on the real time mechanical fatigue of porcine aortic heart valve roots

Decellularised heart valve roots offer a promising option for heart valve replacement in young patients, having the potential to remodel and repair. Replacement heart valves have to undergo billions of opening and closing cycles throughout the patient’s lifetime. Therefore, understanding the effect of cyclic loading on decellularised heart valve roots is important prior to human implantation. The aim of this preliminary study was to investigate the influence of low concentration sodium dodecyl sulphate (SDS) decellularisation treatment on the in vitro real time mechanical fatigue of porcine aortic heart valve roots under physiological real time cyclic loading conditions. This required a specific real time in vitro method to be developed, since previous methods relied on accelerated testing, which is non-physiological, and not appropriate for valve replacement materials that exhibit time dependent characteristics. The effects of the real time fatigue on hydrodynamic function and mechanical properties of the heart valve roots were assessed. The mechanical fatigue of decellularised porcine aortic heart valve roots (n = 6) was assessed and compared to cellular porcine aortic heart valve roots (n = 6) in a modified Real time Wear Tester (RWT) at a physiological frequency and under cyclic pressure conditions for a maximum of 1.2 million cycles. Periodically, the heart valve roots were removed from the RWT to assess the influence of cyclic loading on valve competency (static leaflet closure). At the end of testing further hydrodynamic performance parameters were ascertained, along with determination of leaflet material properties. A real time mechanical fatigue assessment method was developed and applied; with two cellular and two decellularised porcine aortic leaflets in different heart valve roots showing tears in the belly region. The decellularised aortic heart valve roots exhibited comparative functionality to the cellular heart valve roots under in vitro static and pulsatile hydrodynamic conditions. However, the material properties of the decellularised aortic leaflets were significantly altered following cyclic fatigue assessment and showed increases in elastin and collagen phase slopes and ultimate tensile strength compared to the cellular porcine aortic leaflets in the circumferential direction. This preliminary study demonstrated that low concentration SDS decellularised porcine aortic heart valve roots can withstand physiological cyclic deformations up to 1.2 million cycles in a RWT whilst maintaining their overall hydrodynamic function and leaflet mechanical properties. This is the first full report of preclinical mechanical fatigue assessment of decellularised porcine aortic heart valve roots under physiological real time conditions.

Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.

Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning

Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.

Operator-norm homogenisation estimates for the system of Maxwell equations on periodic singular structures

For arbitrarily small values of ε > 0 , we formulate and analyse the Maxwell system of equations of electromagnetism on ε -periodic sets S ε ⊂ R 3 . Assuming that a family of Borel measures μ ε , such that supp ( μ ε ) = S ε , is obtained by ε -contraction of a fixed 1-periodic measure μ , and for right-hand sides f ε ∈ L 2 ( R 3 , d μ ε ) , we prove order-sharp norm-resolvent convergence estimates for the solutions of the system. Our analysis includes the case of periodic "singular structures", when μ is supported by lower-dimensional manifolds. The estimates are obtained by combining several new tools we develop for analysing the Floquet decomposition of an elliptic differential operator on functions from Sobolev spaces with respect to a periodic Borel measure. These tools include a generalisation of the classical Helmholtz decomposition for L 2 functions, an associated Poincaré-type inequality, uniform with respect to the parameter of the Floquet decomposition, and an appropriate asymptotic expansion inspired by the classical power series. Our technique does not involve any spectral analysis and does not rely on the existing approaches, such as Bloch wave homogenisation or the spectral germ method.

Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3 min

BACKGROUND

The application of cardiovascular magnetic resonance angiography (CMRA) for the assessment of thoracic aortic disease is often associated with prolonged and unpredictable acquisition times and residual motion artefacts. To overcome these limitations, we have integrated undersampled acquisition with image-based navigators and inline non-rigid motion correction to enable a free-breathing, contrast-free Cartesian CMRA framework for the visualization of the thoracic aorta in a short and predictable scan of 3 min.

METHODS

35 patients with thoracic aortic disease (36 ± 13y, 14 female) were prospectively enrolled in this single-center study. The proposed 3D T2-prepared balanced steady state free precession (bSSFP) sequence with image-based navigator (iNAV) was compared to the clinical 3D T2-prepared bSSFP with diaphragmatic-navigator gating (dNAV), in terms of image acquisition time. Three cardiologists blinded to iNAV vs. dNAV acquisition, recorded image quality scores across four aortic segments and their overall diagnostic confidence. Contrast ratio (CR) and relative standard deviation (RSD) of signal intensity (SI) in the corresponding segments were estimated. Co-axial aortic dimensions in six landmarks were measured by two readers to evaluate the agreement between the two methods, along with inter-observer and intra-observer agreement. Kolmogorov-Smirnov test, Mann-Whitney U (MWU), Bland-Altman analysis (BAA), intraclass correlation coefficient (ICC) were used for statistical analysis.

RESULTS

The scan time for the iNAV-based approach was significantly shorter (3.1 ± 0.5 min vs. 12.0 ± 3.0 min for dNAV, P = 0.005). Reconstruction was performed inline in 3.0 ± 0.3 min. Diagnostic confidence was similar for the proposed iNAV versus dNAV for all three reviewers (Reviewer 1: 3.9 ± 0.3 vs. 3.8 ± 0.4, P = 0.7; Reviewer 2: 4.0 ± 0.2 vs. 3.9 ± 0.3, P = 0.4; Reviewer 3: 3.8 ± 0.4 vs. 3.7 ± 0.6, P = 0.3). The proposed method yielded higher image quality scores in terms of artefacts from respiratory motion, and non-diagnostic images due to signal inhomogeneity were observed less frequently. While the dNAV approach outperformed the iNAV method in the CR assessment, the iNAV sequence showed improved signal homogeneity along the entire thoracic aorta [RSD SI 5.1 (4.4, 6.5) vs. 6.5 (4.6, 8.6), P = 0.002]. BAA showed a mean difference of < 0.05 cm across the 6 landmarks between the two datasets. ICC showed excellent inter- and intra-observer reproducibility.

CONCLUSIONS

Thoracic aortic iNAV-based CMRA with fast acquisition (~ 3 min) and inline reconstruction (3 min) is proposed, resulting in high diagnostic confidence and reproducible aortic measurements.

The history, state of the art and future prospects for oleaginous yeast research

Lipid-based biofuels, such as biodiesel and hydroprocessed esters, are a central part of the global initiative to reduce the environmental impact of the transport sector. The vast majority of production is currently from first-generation feedstocks, such as rapeseed oil, and waste cooking oils. However, the increased exploitation of soybean oil and palm oil has led to vast deforestation, smog emissions and heavily impacted on biodiversity in tropical regions. One promising alternative, potentially capable of meeting future demand sustainably, are oleaginous yeasts. Despite being known about for 143 years, there has been an increasing effort in the last decade to develop a viable industrial system, with currently around 100 research papers published annually. In the academic literature, approximately 160 native yeasts have been reported to produce over 20% of their dry weight in a glyceride-rich oil. The most intensively studied oleaginous yeast have been Cutaneotrichosporon oleaginosus (20% of publications), Rhodotorula toruloides (19%) and Yarrowia lipolytica (19%). Oleaginous yeasts have been primarily grown on single saccharides (60%), hydrolysates (26%) or glycerol (19%), and mainly on the mL scale (66%). Process development and genetic modification (7%) have been applied to alter yeast performance and the lipids, towards the production of biofuels (77%), food/supplements (24%), oleochemicals (19%) or animal feed (3%). Despite over a century of research and the recent application of advanced genetic engineering techniques, the industrial production of an economically viable commodity oil substitute remains elusive. This is mainly due to the estimated high production cost, however, over the course of the twenty-first century where climate change will drastically change global food supply networks and direct governmental action will likely be levied at more destructive crops, yeast lipids offer a flexible platform for localised, sustainable lipid production. Based on data from the large majority of oleaginous yeast academic publications, this review is a guide through the history of oleaginous yeast research, an assessment of the best growth and lipid production achieved to date, the various strategies employed towards industrial production and importantly, a critical discussion about what needs to be built on this huge body of work to make producing a yeast-derived, more sustainable, glyceride oil a commercial reality.

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization

Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This work makes a different assumption. It assumes that an overhead image of the workspace is available and utilizes that as a map for use for range-based sensor localization by a vehicle. Here, range-based sensors are radars and lidars. Our motivation is simple, off-the-shelf, publicly available overhead imagery such as Google satellite images can be a ubiquitous, cheap, and powerful tool for vehicle localization when a usable prior sensor map is unavailable, inconvenient, or expensive. The challenge to be addressed is that overhead images are clearly not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localization method that not only handles the modality difference, but is also cheap to train, learning in a self-supervised fashion without requiring metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations in cross-modality localization, achieving localization errors on-par with a prior supervised approach while requiring no pixel-wise aligned ground truth for supervision at training. We pay particular attention to the use of millimeter-wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting conditions, makes for a compelling and valuable use case.

Actinomadura graeca sp. nov.: A novel producer of the macrocyclic antibiotic zelkovamycin

As part of a screening programme for antibiotic-producing bacteria, a novel Actinomadura species was discovered from a soil sample collected in Santorini, Greece. Preliminary 16S rRNA gene sequence comparisons highlighted Actinomadura macra as the most similar characterised species. However, whole-genome sequencing revealed an average nucleotide identity (ANI) value of 89% with A. macra, the highest among related species. Further phenotypic and chemotaxonomic analyses confirmed that the isolate represents a previously uncharacterised species in the genus Actinomadura, for which the name Actinomadura graeca sp. nov. is proposed (type strain 32-07T). The G+C content of A. graeca 32-07 is 72.36%. The cell wall contains DL-diaminopimelic acid, intracellular sugars are glucose, ribose and galactose, the predominant menaquinone is MK-9(H6), the major cellular lipid is phosphatidylinositol and fatty acids consist mainly of hexadecanoic acid. No mycolic acid was detected. Furthermore, A. graeca 32-07 has been confirmed as a novel producer of the non-ribosomal peptide antibiotic zelkovamycin and we report herein a provisional description of the unique biosynthetic gene cluster.

Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data

BACKGROUND

Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection,L 0 L 1 penalisation andL 0 L 2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.

METHODS

Five large ( n 4000 ) genomic datasets were extracted from Gene Expression Omnibus. 'Gold-standard' regression models were trained on subspaces of these datasets ( n 4000 , p = 500 ). Penalised regression models were trained on small samples from these subspaces ( n ∈ { 25 , 75 , 150 } , p = 500 ) and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty 'preselection' according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation.

RESULTS

L 1 L 2 -penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models.L 0 L 2 -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions.L 0 L 2 also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics.

CONCLUSIONS

This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use ofL 0 L 2 penalisation for structural selection andL 1 L 2 penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

BACKGROUND

Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks.

RESULTS

Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation.

CONCLUSIONS

Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

Tris(pentafluorophenyl)borane-Catalyzed Carbenium Ion Generation and Autocatalytic Pyrazole Synthesis-A Computational and Experimental Study

In recent years, metal-free organic synthesis using triarylboranes as catalysts has become a prevalent research area. Herein we report a comprehensive computational and experimental study for the highly selective synthesis of N-substituted pyrazoles through the generation of carbenium species from the reaction between aryl esters and vinyl diazoacetates in the presence of catalytic tris(pentafluorophenyl)borane [B(C6 F5 )3 ]. DFT studies were undertaken to illuminate the reaction mechanism revealing that the in situ generation of a carbenium species acts as an autocatalyst to prompt the regiospecific formation of N-substituted pyrazoles in good to excellent yields (up to 81 %).

Numerical modelling of blood rheology and platelet activation through a stenosed left coronary artery bifurcation

Coronary bifurcations are prone to atherosclerotic plaque growth, experiencing regions of reduced wall shear stress (WSS) and increased platelet adhesion. This study compares effects across different rheological approaches on hemodynamics, combined with a shear stress exposure history model of platelets within a stenosed porcine bifurcation. Simulations used both single/multiphase blood models to determine which approach best predicts phenomena associated with atherosclerosis and atherothrombosis. A novel Lagrangian platelet tracking model was used to evaluate residence time and shear history of platelets indicating likely regions of thrombus formation. Results show a decrease in area of regions with pathologically low time-averaged WSS with the use of multiphase models, particularly in a stenotic bifurcation. Significant non-Newtonian effects were observed due to low-shear and varying hematocrit levels found on the outer walls of the bifurcation and distal to the stenosis. Platelet residence time increased 11% in the stenosed artery, with exposure times to low-shear sufficient for red blood cell aggregation (>1.5 s). increasing the risk of thrombosis. This shows stenotic artery hemodynamics are inherently non-Newtonian and multiphase, with variations in hematocrit (0.163-0.617) and elevated vorticity distal to stenosis (+15%) impairing the function of the endothelium via reduced time-averaged WSS regions, rheological properties and platelet activation/adhesion.

Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity

Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.

The Influence of Covid-19 on Perceived Health Effects of Wetland Parks in China

Wetland parks are designed to support urban ecological protection, flood control and human well-being. Existing research mainly focuses on their influence on ecology and economy. However, their influence on human well-being and health is rarely studied. In China, during the peak of the COVID-19 pandemic (Peak), people were very concerned about health, while at the same time wetland parks which are generally considered beneficial to health were closed. Thus, this study explores the public's perception of the health effects of visiting wetland parks and the impact of the pandemic on the perception. From March 5th to 8th, 2020, before the Peak in China was over, 1,400 respondents participated in a nationwide online survey. It was found that the perceived benefits from visiting wetland parks were higher in terms of mental health than in physical health. Also, the perceived health benefits of wetland parks after the Peak were slightly higher than before the pandemic. The results highlight that wildlife habitat services were considered to be the most important ecosystem services that promote the perceived health benefits. Interestingly, the perceived health benefits of wetland parks by health experts appear to be lower than in other groups, indicating that the health benefits of visiting wetland parks may be overestimated by lay-people or underestimated by health experts. The results provide empirical evidence for managing ecosystem services as delivered by these urban wetlands, in the context of COVID-19 or potential future pandemics, for promoting public health.

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

Halogen-Bonding Strapped Porphyrin BODIPY Rotaxanes for Dual Optical and Electrochemical Anion Sensing

Anion receptors employing two distinct sensory mechanisms are rare. Herein, we report the first examples of halogen-bonding porphyrin BODIPY [2]rotaxanes capable of both fluorescent and redox electrochemical sensing of anions. 1 H NMR, UV/visible and electrochemical studies revealed rotaxane axle triazole group coordination to the zinc(II) metalloporphyrin-containing macrocycle component, serves to preorganise the rotaxane binding cavity and dramatically enhances anion binding affinities. Mechanically bonded, integrated-axle BODIPY and macrocycle strapped metalloporphyrin motifs enable the anion recognition event to be sensed by the significant quenching of the BODIPY fluorophore and cathodic perturbations of the metalloporphyrin P/P+. redox couple.

Self-Propelling Hybrid Gels Incorporating an Active Self-Assembled, Low-Molecular-Weight Gelator

Hybrid gel beads based on combining a low-molecular-weight gelator (LMWG) with a polymer gelator (PG) demonstrate an enhanced ability to self-propel in water, with the LMWG playing an active role. Hybrid gel beads were loaded with ethanol and shown to move in water owing to the Marangoni effect changes in surface tension caused by the expulsion of ethanol - smaller beads move farther and faster than larger beads. Flat shapes of the hybrid gel were cut using a "stamp" - circles moved the furthest, whereas stars showed more rotation on their own axes. Comparing hybrid LMWG/PG gel beads with PG-only beads demonstrated that the LMWG speeds up the beads, enhancing the rate of self-propulsion. Self-assembly of the LMWG into a "solid-like" network prevents its leaching from the gel. The LMWG also retains its own unique function - specifically, remediating methylene blue pollutant dye from basic water as a result of noncovalent interactions. The mobile hybrid beads accumulate this dye more effectively than PG-only beads. Self-propelling gel beads have potential applications in removal/delivery of active agents in environmental or biological settings. The ability of self-assembling LMWGs to enhance mobility and control removal/delivery suggests that adding them to self-propelling systems can add significant value.

Ball-Milling-Enabled Reactivity of Manganese Metal*

Efforts to generate organomanganese reagents under ball-milling conditions have led to the serendipitous discovery that manganese metal can mediate the reductive dimerization of arylidene malonates. The newly uncovered process has been optimized and its mechanism explored using CV measurements, radical trapping experiments, EPR spectroscopy, and solution control reactions. This unique reactivity can also be translated to solution whereupon pre-milling of the manganese is required.

C-C Cross-Couplings from a Cyclometalated Au(III) C ∧ N Complex: Mechanistic Insights and Synthetic Developments

In recent years, the reactivity of gold complexes was shown to extend well beyond π-activation and to hold promises to achieve selective cross-couplings in several C-C and C-E (E=heteroatom) bond forming reactions. Here, with the aim of exploiting new organometallic species for cross-coupling reactions, we report on the Au(III)-mediated C(sp2 )-C(sp) occurring upon reaction of the cyclometalated complex [Au(CCH2 N)Cl2 ] (1, CCH2 N=2-benzylpyridine) with AgPhCC. The reaction progress has been monitored by NMR spectroscopy, demonstrating the involvement of a number of key intermediates, whose structures have been unambiguously ascertained through 1D and 2D NMR analyses (1 H, 13 C, 1 H-1 H COSY, 1 H-13 C HSQC and 1 H-13 C HMBC) as well as by HR-ESI-MS and X-ray diffraction studies. Furthermore, crystallographic studies have serendipitously resulted in the authentication of zwitterionic Au(I) complexes as side-products arising from cyclization of the coupling product in the coordination sphere of gold. The experimental work has been paralleled and complemented by DFT calculations of the reaction profiles, providing valuable insight into the structure and energetics of the key intermediates and transition states, as well as on the coordination sphere of gold along the whole process. Of note, the broader scope of the cross-coupling at the Au(III) CCH2 N centre has also been demonstrated studying the reaction of 1 with C(sp2 )-based nucleophiles, namely vinyl and heteroaryl tin and zinc reagents. These reactions stand as rare examples of C(sp2 )-C(sp2 ) cross-couplings at Au(III).

Closing the gap between 19F and 18F chemistry

Positron emission tomography (PET) has become an invaluable tool for drug discovery and diagnosis. The positron-emitting radionuclide fluorine-18 is frequently used in PET radiopharmaceuticals due to its advantageous characteristics; hence, methods streamlining access to 18F-labelled radiotracers can make a direct impact in medicine. For many years, access to 18F-labelled radiotracers was limited by the paucity of methodologies available, and the poor diversity of precursors amenable to 18F-incorporation. During the last two decades, 18F-radiochemistry has progressed at a fast pace with the appearance of numerous methodologies for late-stage 18F-incorporation onto complex molecules from a range of readily available precursors including those that do not require pre-functionalisation. Key to these advances is the inclusion of new activation modes to facilitate 18F-incorporation. Specifically, new advances in late-stage 19F-fluorination under transition metal catalysis, photoredox catalysis, and organocatalysis combined with the availability of novel 18F-labelled fluorination reagents have enabled the invention of novel processes for 18F-incorporation onto complex (bio)molecules. This review describes these major breakthroughs with a focus on methodologies for C-18F bond formation. This reinvigorated interest in 18F-radiochemistry that we have witnessed in recent years has made a direct impact on 19F-chemistry with many laboratories refocusing their efforts on the development of methods using nucleophilic fluoride instead of fluorination reagents derived from molecular fluorine gas.

Microplastics and anthropogenic fibre concentrations in lakes reflect surrounding land use

Pollution from microplastics and anthropogenic fibres threatens lakes, but we know little about what factors predict its accumulation. Lakes may be especially contaminated because of long water retention times and proximity to pollution sources. Here, we surveyed anthropogenic microparticles, i.e., microplastics and anthropogenic fibres, in surface waters of 67 European lakes spanning 30° of latitude and large environmental gradients. By collating data from >2,100 published net tows, we found that microparticle concentrations in our field survey were higher than previously reported in lakes and comparable to rivers and oceans. We then related microparticle concentrations in our field survey to surrounding land use, water chemistry, and plastic emissions to sites estimated from local hydrology, population density, and waste production. Microparticle concentrations in European lakes quadrupled as both estimated mismanaged waste inputs and wastewater treatment loads increased in catchments. Concentrations decreased by 2 and 5 times over the range of surrounding forest cover and potential in-lake biodegradation, respectively. As anthropogenic debris continues to pollute the environment, our data will help contextualise future work, and our models can inform control and remediation efforts.

Identifying chondrogenesis strategies for tissue engineering of articular cartilage

A key step in the tissue engineering of articular cartilage is the chondrogenic differentiation of mesenchymal stem cells (MSCs) into chondrocytes (native cartilage cells). Chondrogenesis is regulated by transforming growth factor-β (TGF-β), a short-lived cytokine whose effect is prolonged by storage in the extracellular matrix. Tissue engineering applications aim to maximise the yield of differentiated MSCs. Recent experiments involve seeding a hydrogel construct with a layer of MSCs lying below a layer of chondrocytes, stimulating the seeded cells in the construct from above with exogenous TGF-β and then culturing it in vitro. To investigate the efficacy of this strategy, we develop a mathematical model to describe the interactions between MSCs, chondrocytes and TGF-β. Using this model, we investigate the effect of varying the initial concentration of TGF-β, the initial densities of the MSCs and chondrocytes, and the relative depths of the two layers on the long-time composition of the tissue construct.