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Oncolytic viruses (OV) are part of a burgeoning field of investigational oncolytic therapy (OT), in which lytic viruses dissolve advanced tumors productively and specifically. One such OT is a Coxsackievirus A21 (CVA21) based OV that is currently under clinical evaluation. A tissue culture infectious dose (TCID50) assay was used for CVA21 potency release and stability testing in early clinical development. The titer measured in this method was an extrapolated value from cytopathic effect (CPE) observed during the serial dilution but doesn't represent direct viral killing of cells. Moreover, the assay was not deemed to be optimal to carry into late phase clinical development due to limitations in assay precision, turn-around time, and sample throughput. To address these points, we developed a plaque assay to measure viral plaque forming units to measure the potency value for drug substance (DS), drug product (DP) and virus seed (master and working) stocks. In this manuscript, we describe the steps taken to develop this plaque assay for the late-stage clinical development, which include the assay qualification, validation, and robustness protocols, and describe statistical methods for data analysis. Moreover, the method was validated for linearity, accuracy, precision, and specificity. Furthermore, the plaque assay quantifies OV infectivity with better precision (32% vs 58%), with higher sample throughput (22 samples/week vs 3 samples/week) and shorter assay turnaround time (4 days vs 7 days) than the TCID50 method. This assay development strategy can provide guidance for the development of robust cell-based potency methods for OVs and other infectious viral products.

Abstract Methane, a powerful greenhouse gas, has a short atmospheric lifetime ( ~ 12 years), so that emissions reductions will have a rapid impact on climate forcing. In megacities such as Los Angeles (LA), natural gas (NG) leakage is the primary atmospheric methane source. The magnitudes and trends of fugitive NG emissions are largely unknown and need to be quantified to verify compliance with emission reduction targets. Here we use atmospheric remote sensing data to show that, in contrast to the observed global increase in methane emissions, LA area emissions decreased during 2011-2020 at a mean rate of (–1.57 ± 0.41) %/yr. However, the NG utility calculations indicate a much larger negative emissions trend of −5.8 %/yr. The large difference between top-down and bottom-up trends reflects the uncertainties in estimating the achieved emissions reductions. Actions taken in LA can be a blueprint for COP28 and future efforts to reduce methane emissions.

The vertebrate basal ganglia play an important role in action selection—the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot’s motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a ‘behavioral trap’. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.

PURPOSEAround a third of people with cancer will die outside of their preferred place of death, with substantial variation occurring between and within countries in terms of place of death. Here, we examine place of death within the New Zealand cancer context, with specific focus on differences between Indigenous Māori and other ethnic groups.METHODSUsing national-level data, we identified all those who died in New Zealand between 2007 and 2018 of cancer (N = 107,373), stratified by ethnicity and cancer type, and linked these patients to national health and mortality records. We then described the crude and age-standardized proportions of cancer deaths by location separately by ethnic group, and conducted logistic regression to compare odds of death within a given location between ethnic groups.RESULTSAfter adjusting for age, sex, and deprivation, we found that Māori people with cancer are more likely to die in a private residence than Europeans (46% v 26%; odds ratio [OR] 2.45; 95% CI, 2.36 to 2.55), and also somewhat more likely to die in hospital (27% v 23%; OR 1.26; 95% CI, 1.21 to 1.32). Commensurately, Māori are less likely to die in either hospice inpatient unit (14% v 27%; OR 0.48; 95% CI, 0.45 to 0.51) or residential care (12% v 30%; OR 0.56; 95% CI, 0.52 to 0.59). Pacific patients generally follow the same pattern as Māori patients. These findings were largely repeated across cancer types, with some variation in the magnitude not overall pattern.CONCLUSIONIt remains unclear whether these differences reflect differences in preferences for place of death between ethnic groups, or whether they reflect differences in access to appropriate supportive care. Further research is required to examine these differences in greater detail.

Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.

Kevin Gurney, whose work on tracking [CO.sub.2] emissions has been groundbreaking, will receive Sigma Xi's 2010 Young Investigator Award. Gurney is an associate professor in the Department of Earth and [...]

Current bottom up estimates of 'CO2' emission fluxes are based on a mixture of direct and indirect flux estimates relying to varying degrees on regulatory or self-reported data. Hence, it is important to use additional, independent information to assess biases and lower the flux uncertainty. We explore the use of a self-organizing map (SOM) as a tool to use multi-species observations to partition fossil fuel 'CO2 '('CO2ff') emissions by economic source sector. We use the Indianapolis Flux experiment (INFLUX) multi-species observations to provide constraints on the types of relationships we can expect to see, and show from the observations and existing knowledge of likely sources for these species that relationships do exist but can be complex. An Observing System Simulation Experiment (OSSE) is then created to test, in a pseudodata framework, the abilities and limitations of using an SOM to accurately attribute atmospheric tracers to their source sector. These tests are conducted for a variety of emission scenarios, and make use of the corresponding high-resolution footprints for the pseudo-measurements. We show here that the attribution of sector-specific emissions to measured trace gases cannot be addressed by investigating the atmospheric trace gas measurements alone. We conclude that additional a priori information such as inventories of sector-specific trace gases are required to evaluate sector-level emissions using atmospheric methods, to overcome the challenge of the spatial overlap of nearly every predefined source sector. Our OSSE additionally allows us to demonstrate that increasing the (already high) data density cannot solve the co-localization problem.

We present measurements of CO mole fraction and CO stable isotopes (δ13CO and δC18O) in air during the winters of 2013–14 and 2014–15 at tall tower sampling sites in and around Indianapolis, USA. A tower located upwind of the city was used to quantitatively remove the background CO signal, allowing for the first unambiguous isotopic characterization of the urban CO source and yielding 13CO of –27.7 ± 0.5‰ VPDB and C18O of 17.7 ± 1.1‰ VSMOW for this source. We use the tower isotope measurements, results from a limited traffic study, as well as atmospheric reaction rates to examine contributions from different sources to the Indianapolis CO budget. Our results are consistent with earlier findings that traffic emissions are the dominant source, suggesting a contribution of 96% or more to the overall Indianapolis wintertime CO emissions. Our results are also consistent with the hypothesis that emissions from a small fraction of vehicles without functional catalytic systems dominate the Indianapolis CO budget.

The striatum is the primary input nucleus for the basal ganglia, and receives glutamatergic afferents from the cortex. Under the hypothesis that basal ganglia perform action selection, these cortical afferents encode potential “action requests.” Previous studies have suggested the striatum may utilize a mutually inhibitory network of medium spiny neurons (MSNs) to filter these requests so that only those of high salience are selected. However, the mechanisms enabling the striatum to perform clean, rapid switching between distinct actions that form part of a learned action sequence are still poorly understood. Substance P (SP) and enkephalin are neuropeptides co-released with GABA in MSNs preferentially expressing D1 or D2 dopamine receptors respectively. SP has a facilitatory effect on subsequent glutamatergic inputs to target MSNs, while enkephalin has an inhibitory effect. Blocking the action of SP in the striatum is also known to affect behavioral transitions. We constructed phenomenological models of the effects of SP and enkephalin, and integrated these into a hybrid model of basal ganglia comprising a spiking striatal microcircuit and rate–coded populations representing other major structures. We demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, where action semantics define a fixed structure, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering. We also showed that selective pruning of SP connections allowed context–sensitive inhibition of specific undesirable requests that otherwise interfered with selection of an action group. Our model suggests that the interaction of SP and enkephalin enhances the contrast between selection and rejection of action requests, and that patterned SP connectivity in the striatum allows the “chunking” of actions and improves selection of sequences. Efficient execution of action sequences may therefore result from a combination of ordered cortical inputs and patterned neuropeptide connectivity within striatum.

The study of action selection in humans can present challenges of task design since our actions are usually defined by many degrees of freedom and therefore occupy a large action-space. While saccadic eye-movement offers a more constrained paradigm for investigating action selection, the study of reach-and-grasp in upper limbs has often been defined by more complex scenarios, not easily interpretable in terms of such selection. Here we present a novel motor behaviour task which addresses this by limiting the action space to a single degree of freedom in which subjects have to track (using a stylus) a vertical coloured target line displayed on a tablet computer, whilst ignoring a similarly oriented distractor line in a different colour. We ran this task with 55 subjects and showed that, in agreement with previous studies, the presence of the distractor generally increases the movement latency and directional error rate. Further, we used two distractor conditions according to whether the distractor's location changes asynchronously or synchronously with the location of the target. We found that the asynchronous distractor yielded poorer performance than its synchronous counterpart, with significantly higher movement latencies and higher error rates. We interpret these results in an action selection framework with two actions (move left or right) and competing 'action requests' offered by the target and distractor. As such, the results provide insights into action selection performance in humans and supply data for directly constraining future computational models therein.

We present a novel neurally based model for estimating angular velocity (AV) in the bee brain, capable of quantitatively reproducing experimental observations of visual odometry and corridor-centering in free-flying honeybees, including previously unaccounted for manipulations of behaviour. The model is fitted using electrophysiological data, and tested using behavioural data. Based on our model we suggest that the AV response can be considered as an evolutionary extension to the optomotor response. The detector is tested behaviourally in silico with the corridor-centering paradigm, where bees navigate down a corridor with gratings (square wave or sinusoidal) on the walls. When combined with an existing flight control algorithm the detector reproduces the invariance of the average flight path to the spatial frequency and contrast of the gratings, including deviations from perfect centering behaviour as found in the real bee's behaviour. In addition, the summed response of the detector to a unit distance movement along the corridor is constant for a large range of grating spatial frequencies, demonstrating that the detector can be used as a visual odometer.

One of the most widely implemented models for multi-alternative decision-making is the multihypothesis sequential probability ratio test (MSPRT). It is asymptotically optimal, straightforward to implement, and has found application in modelling biological decision-making. However, the MSPRT is limited in application to discrete (‘trial-based’), non-time-varying scenarios. By contrast, real world situations will be continuous and entail stimulus non-stationarity. In these circumstances, decision-making mechanisms (like the MSPRT) which work by accumulating evidence, must be able to discard outdated evidence which becomes progressively irrelevant. To address this issue, we introduce a new decision mechanism by augmenting the MSPRT with a rectangular integration window and a transparent decision boundary. This allows selection and de-selection of options as their evidence changes dynamically. Performance was enhanced by adapting the window size to problem difficulty. Further, we present an alternative windowing method which exponentially decays evidence and does not significantly degrade performance, while greatly reducing the memory resources necessary. The methods presented have proven successful at allowing for the MSPRT algorithm to function in a non-stationary environment.

We present a behavioural task designed for the investigation of how novel instrumental actions are discovered and learnt. The task consists of free movement with a manipulandum, during which the full range of possible movements can be explored by the participant and recorded. A subset of these movements, the 'target', is set to trigger a reinforcing signal. The task is to discover what movements of the manipulandum evoke the reinforcement signal. Targets can be defined in spatial, temporal, or kinematic terms, can be a combination of these aspects, or can represent the concatenation of actions into a larger gesture. The task allows the study of how the specific elements of behaviour which cause the reinforcing signal are identified, refined and stored by the participant. The task provides a paradigm where the exploratory motive drives learning and as such we view it as in the tradition of Thorndike [1]. Most importantly it allows for repeated measures, since when a novel action is acquired the criterion for triggering reinforcement can be changed requiring a new action to be discovered. Here, we present data using both humans and rats as subjects, showing that our task is easily scalable in difficulty, adaptable across species, and produces a rich set of behavioural measures offering new and valuable insight into the action learning process.

A system's wiring constrains its dynamics, yet modelling of neural structures often overlooks the specific networks formed by their neurons. We developed an approach for constructing anatomically realistic networks and reconstructed the GABAergic microcircuit formed by the medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) of the adult rat striatum. We grew dendrite and axon models for these neurons and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. The MSN dendrite models predicted that half of all dendritic spines are within 100µm of the soma. The constructed networks predict distributions of gap junctions between FSI dendrites, synaptic contacts between MSNs, and synaptic inputs from FSIs to MSNs that are consistent with current estimates. The models predict that to achieve this, FSIs should be at most 1% of the striatal population. They also show that the striatum is sparsely connected: FSI-MSN and MSN-MSN contacts respectively form 7% and 1.7% of all possible connections. The models predict two striking network properties: the dominant GABAergic input to a MSN arises from neurons with somas at the edge of its dendritic field; and FSIs are inter-connected on two different spatial scales: locally by gap junctions and distally by synapses. We show that both properties influence striatal dynamics: the most potent inhibition of a MSN arises from a region of striatum at the edge of its dendritic field; and the combination of local gap junction and distal synaptic networks between FSIs sets a robust input-output regime for the MSN population. Our models thus intimately link striatal micro-anatomy to its dynamics, providing a biologically grounded platform for further study.

Loss of dopamine from the striatum can cause both profound motor deficits, as in Parkinsons's disease, and disrupt learning. Yet the effect of dopamine on striatal neurons remains a complex and controversial topic, and is in need of a comprehensive framework. We extend a reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tune our D1 and D2 receptor MSN models using data from a recent large-scale compartmental model. The new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. We analytically derive a full set of equilibrium points and their stability for the original and dopamine modulated forms of the MSN model. We find that the stability types are not changed by dopamine activation, and our models predict that the MSN is never bistable. Nonetheless, the MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors. Our models also predict a more pronounced membrane potential bimodality following D1 receptor activation. This work thus establishes reduced yet accurate dopamine-modulated models of MSNs, suitable for use in large-scale models of the striatum. More importantly, these provide a tractable framework for further study of dopamine's effects on computation by individual neurons.

BackgroundMany technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model--the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified.Methodology/principal findingsWe defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S>1--an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process.Conclusions/significanceWe have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.