Search

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.

Neural networks are modelling tools that are, in principle, able to capture the input–output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between ‘algorithm’ and ‘implementation’, which can provide insights into biological neural representations and their putative supporting architectures. [ABSTRACT FROM AUTHOR]

Enacting stringent climate policy has proven politically challenging, not least because of concentrated losses in fossil fuel-producing communities. "Just transition" strategies have been proposed to mitigate this distributional challenge. Yet, little is known about how such strategies affect voting behavior. Using a mixed-methods approach, we exploit a local climate policy in Spain—a "Just Transition Agreement" (JTA) to phase out coalmining, support affected workers, and invest in affected municipalities—which was negotiated by the incumbent Socialist Party (PSOE) government with affected unions and businesses shortly before a national election. A difference-in-differences study shows that PSOE's vote share in coalmining municipalities increased at the 2019 election relative to similar municipalities, implying that the JTA was electorally successful. Further statistical tests and elite interviews suggest that this electoral boost was driven by unions' support of the JTA. Our findings have implications for how parties can craft popular climate policy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For many governments, enacting green policies is a priority, but such policies often impose on citizens substantial and uneven costs. How does the introduction of green policies affect voting? We study this question in the context of a major ban on polluting cars introduced in Milan, which was strongly opposed by the populist right party Lega. Using several inferential strategies, we show that owners of banned vehicles—who incurred a median loss of €3,750—were significantly more likely to vote for Lega in the subsequent elections. Our analysis indicates that this electoral change did not stem from a broader shift against environmentalism, but rather from disaffection with the policy's uneven pocketbook implications. In line with this pattern, recipients of compensation from the local government were not more likely to switch to Lega. The findings highlight the central importance of distributive consequences in shaping the political ramifications of green policies. [ABSTRACT FROM AUTHOR]

Comparisons of carbon uptake estimates from bottom-up terrestrial biosphere models (TBMs) to top-down atmospheric inversions help assess how well we understand carbon dioxide (CO 2) exchange between the atmosphere and terrestrial biosphere. Previous comparisons have shown varying levels of agreement between bottom-up and top-down approaches, but they have almost exclusively focused on large, aggregated scales (e.g., global or continental), providing limited insights into reasons for the mismatches. Here we explore how consistency, defined as the spread in net ecosystem exchange (NEE) estimates within an ensemble of TBMs or inversions, varies with at finer spatial scales ranging from 1∘×1∘ to the continent of North America. We also evaluate how well consistency informs accuracy in overall NEE estimates by filtering models based on their agreement with the variability, magnitude, and seasonality in observed atmospheric CO 2 drawdowns or enhancements. We find that TBMs produce more consistent estimates of NEE for most regions and at most scales relative to inversions. Filtering models using atmospheric CO 2 metrics causes ensemble spread to decrease substantially for TBMs, but not for inversions. This suggests that ensemble spread is likely not a reliable measure of the uncertainty associated with the North American carbon balance at any spatial scale. Promisingly, applying atmospheric CO 2 metrics leads to a set of models with converging flux estimates across TBMs and inversions. Overall, we show that multiscale assessment of the agreement between bottom-up and top-down NEE estimates, aided by regional-scale observational constraints is a promising path towards identifying fine-scale sources of uncertainty and improving both ensemble consistency and accuracy. These findings help refine our understanding of biospheric carbon balance, particularly at scales relevant for informing regional carbon-climate feedbacks. [ABSTRACT FROM AUTHOR]

Gridded bottom-up inventories of CO 2 emissions are needed in global CO 2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO 2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1 ∘ × 0.1 ∘) monthly emissions fluxes of CO 2 fossil fuel (CO 2 ff, long cycle) and CO 2 biofuel (CO 2 bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO 2 ff (36.7 Gt) and CO 2 bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389 , last access: 15 May 2023; Urraca Valle et al., 2023). [ABSTRACT FROM AUTHOR]

Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia – a complex network believed to be involved in action selection – as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal's repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides. [ABSTRACT FROM AUTHOR]

Climate, technologies, and socio-economic changes will influence future building energy use in cities. However, current low-resolution regional and state-level analyses are insufficient to reliably assist city-level decision-making. Here we estimate mid-century hourly building energy consumption in 277 U.S. urban areas using a bottom-up approach. The projected future climate change results in heterogeneous changes in energy use intensity (EUI) among urban areas, particularly under higher warming scenarios, with on average 10.1–37.7% increases in the frequency of peak building electricity EUI but over 110% increases in some cities. For each 1 °C of warming, the mean city-scale space-conditioning EUI experiences an average increase/decrease of ~14%/ ~ 10% for space cooling/heating. Heterogeneous city-scale building source energy use changes are primarily driven by population and power sector changes, on average ranging from –9% to 40% with consistent south–north gradients under different scenarios. Across the scenarios considered here, the changes in city-scale building source energy use, when averaged over all urban areas, are as follows: –2.5% to –2.0% due to climate change, 7.3% to 52.2% due to population growth, and –17.1% to –8.9% due to power sector decarbonization. Our findings underscore the necessity of considering intercity heterogeneity when developing sustainable and resilient urban energy systems. This study quantifies mid-21st century hourly building energy use in 277 urban areas in the USA, revealing spatially and temporally heterogeneous changes influenced by future climate, population dynamics, and electric power sector decarbonization. [ABSTRACT FROM AUTHOR]

Recent applications of autonomous agents and robots have brought attention to crucial trust-related challenges associated with the current generation of artificial intelligence (AI) systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are ‘black boxes’, which renders their choices or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several approaches to eXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are sparse at this point in time. This paper reviews approaches on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents’ perceptual functions (e.g., senses, vision) and cognitive reasoning (e.g., beliefs, desires, intentions, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a road map for the possible realization of effective goal-driven explainable agents and robots. [ABSTRACT FROM AUTHOR]

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. Top down atmospheric measurements reveal a large difference between observed methane emissions and those reported from bottom up estimates of natural gas leakage for California. [ABSTRACT FROM AUTHOR]

Background: Although several brain regions and electrophysiological patterns have been related to sequence learning, less attention has been paid to the role that different neuromodulators play. Aims: Here we sought to investigate the role of substance P (SP) in sequence learning in an operant conditioning preparation, supported by a reinforcement learning model. Methods: Two experiments were performed to test the effects of an NK1 receptor (at which SP primarily acts) antagonist on learning and performing action sequences. In experiment 1, rats were trained to perform an action sequence until stable performance was achieved, and then, in phase 2, they were switched to perform the reverse sequence. In experiment 2, rats were trained to perform an action sequence, and in phase 2, they continued to do the same sequence. In both experiments in the first 3 days of phase 2, rats were injected with an NK1 receptor antagonist (L-733,060, i.p.) or with vehicle. Additionally, we developed a reinforcement learning model which allowed the in silico replication of our experimental tasks. Results: We found that administering an NK1 receptor antagonist weakened the stable retention of a well-learned sequence, allowing the faster acquisition of a new sequence, without impairing the continued performance of a crystallized sequence. Using our reinforcement learning model, we suggest that SP could be acting through the state value learning rate, modulating the effects of the reward prediction error. Conclusions: Our results suggest that SP could be involved in the consolidation of a sequence representation through a modulatory effect on the reward prediction error. [ABSTRACT FROM AUTHOR]

Software comments sometimes are not promptly updated in sync when the associated code is changed. The inconsistency between code and comments may mislead the developers and result in future bugs. Thus, studies concerning code-comment synchronization have become highly important, which aims to automatically synchronize comments with code changes. Existing code-comment synchronization approaches mainly contain two types, i.e., (1) deep learning-based (e.g., CUP), and (2) heuristic-based (e.g., HebCUP). The former constructs a neural machine translation-structured semantic model, which has a more generalized capability on synchronizing comments with software evolution and growth. However, the latter designs a series of rules for performing token-level replacements on old comments, which can generate the completely correct comments for the samples fully covered by their fine-designed heuristic rules. In this article, we propose a composite approach named CBS (i.e., Classifying Before Synchronizing) to further improve the code-comment synchronization performance, which combines the advantages of CUP and HebCUP with the assistance of inferred categories of Code-Comment Inconsistent (CCI) samples. Specifically, we firstly define two categories (i.e., heuristic-prone and non-heuristic-prone) for CCI samples and propose five features to assist category prediction. The samples whose comments can be correctly synchronized by HebCUP are heuristic-prone, while others are non-heuristic-prone. Then, CBS employs our proposed Multi-Subsets Ensemble Learning (MSEL) classification algorithm to alleviate the class imbalance problem and construct the category prediction model. Next, CBS uses the trained MSEL to predict the category of the new sample. If the predicted category is heuristic-prone, CBS employs HebCUP to conduct the code-comment synchronization for the sample, otherwise, CBS allocates CUP to handle it. Our extensive experiments demonstrate that CBS statistically significantly outperforms CUP and HebCUP, and obtains an average improvement of 23.47%, 22.84%, 3.04%, 3.04%, 1.64%, and 19.39% in terms of Accuracy, Recall@5, Average Edit Distance (AED), Relative Edit Distance (RED), BLEU-4, and Effective Synchronized Sample (ESS) ratio, respectively, which highlights that category prediction for CCI samples can boost the code-comment synchronization performance. [ABSTRACT FROM AUTHOR]

Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework. [ABSTRACT FROM AUTHOR]

Design-time evaluation is essential to build the initial software architecture to be deployed. However, experts’ assumptions made at design-time are unlikely to remain true indefinitely in systems that are characterized by scale, hyperconnectivity, dynamism, and uncertainty in operations (e.g. IoT). Therefore, experts’ design-time decisions can be challenged at run-time. A continuous architecture evaluation that systematically assesses and intertwines design-time and run-time decisions is thus necessary. This paper proposes the first proactive approach to continuous architecture evaluation of the system leveraging the support of simulation. The approach evaluates software architectures by not only tracking their performance over time, but also forecasting their likely future performance through machine learning of simulated instances of the architecture. This enables architects to make cost-effective informed decisions on potential changes to the architecture. We perform an IoT case study to show how machine learning on simulated instances of architecture can fundamentally guide the continuous evaluation process and influence the outcome of architecture decisions. A series of experiments is conducted to demonstrate the applicability and effectiveness of the approach. We also provide the architect with recommendations on how to best benefit from the approach through choice of learners and input parameters, grounded on experimentation and evidence. [ABSTRACT FROM AUTHOR]

Word-embedding models learned the word vectors by maximizing the likelihood of either predicting the context words when given the target word (e.g., word2vec:CBOW) or predicting the target word when given the context words (e.g., word2vec:Skipgram). Word-embedding models of lexical semantics were essentially a computational version of distributional semantics built upon the assumption that "difference of meaning correlates with difference of distribution" ([32]: 785). Network analysis, space particles, usage-based grammar, collocation, collostruction analysis, construction grammar We will closely investigate the network's properties at all three levels (i.e., microscopic, mesoscopic, and macroscopic) and will specifically contextualize their implications for the development of constructional semantics in relation to the emerging semantics of the usage-based grammar. Keywords: usage-based grammar; collocation; collostruction analysis; network analysis; space particles; construction grammar EN usage-based grammar collocation collostruction analysis network analysis space particles construction grammar 209 235 27 05/12/22 20220501 NES 220501 1 Introduction Meaning in language is a complex subject. [Extracted from the article]

Goal-directed behaviour is a fundamental means by which animals can flexibly solve the challenges posed by variable external and internal conditions. Recently, the processes and brain mechanisms underlying such behaviour have been extensively studied from behavioural, neuroscientific and computational perspectives. This research has highlighted the processes underlying goal-directed behaviour and associated brain systems including prefrontal cortex, basal ganglia and, in particular therein, the nucleus accumbens. This paper focusses on one particular process at the core of goal-directed behaviour: how motivational value is assigned to goals on the basis of internal states and environmental stimuli, and how this supports goal selection processes. Various biological and computational accounts have been given of this problem and of related multiple neural and behaviour phenomena, but we still lack an integrated hypothesis on the generation and use of value for goal selection. This paper proposes an hypothesis that aims to solve this problem and is based on this key elements: (a) amygdala and hippocampus establish the motivational value of stimuli and goals; (b) prefrontal cortex encodes various types of action outcomes; (c) nucleus accumbens integrates different sources of value, representing them in terms of a common currency with the aid of dopamine, and thereby plays a major role in selecting action outcomes within prefrontal cortex. The 'goals' pursued by the organism are the outcomes selected by these processes. The hypothesis is developed in the context of a critical review of relevant biological and computational literature which offer it support. The paper shows how the hypothesis has the potential to integrate existing interpretations of motivational value and goal selection. [ABSTRACT FROM AUTHOR]

Buildings contribute about 40% of greenhouse gas emissions in the USA. The estimation of urban building fossil fuel CO2 emissions with the fine spatial and temporal resolution is critical for climate change studies and developing optimal plans for reaching emission reduction goals. This study proposed a new method to map fossil fuel CO2 emissions at the building level and hourly scale. Specifically, a building energy use model was developed and used for modeling building energy consumption at the hourly scale through integrating building prototypes from the accessor's data, the number of floors from remote sensing data, floor area from footprint data, and energy use intensity from the Energy Plus model. Furthermore, the greenhouse gas equivalencies calculator was applied to convert calculated building energy consumption to greenhouse gas emissions at the building level and hourly scale. Research results indicate that the spatial distribution pattern of fossil fuel CO2 emissions is largely decided by building type, building height, and building density. In particular, the largest fossil fuel CO2 emissions are located in the center of Manhattan which is composed of large offices and hotels. In terms of temporal pattern, stable and similar monthly greenhouse gas emissions could be detected throughout four seasons, and two small emission peaks could be found in around July and December which are caused by high cooling electricity use and high gas heating consumption. The hourly emissions patterns also indicate the active relationship between fossil fuel CO2 emissions and daily human activities. [ABSTRACT FROM AUTHOR]

The article discusses the U.S. National Aeronautics and Space Administration (NASA) which plans to launch its carbon monitoring satellite on July 1, 2014 to map sources and sinks of greenhouse gas. It mentions 465 million U.S. dollars Orbiting Carbon Observatory-2 (OCO-2) satellite is designed to collect high-resolution measurements of the carbon dioxide in Earth's atmosphere. It presents the views of Kevin Gurney, biogeochemist, on the same, and NASA's other plans for OCO-3 in future.

In this paper, we have presented a method of combining the weight optimizations from ternary weight networks with a set of custom features inspired by the Viola–Jones Object Detection Framework to improve a network's performance. By replacing the first convolutional layer in a network with a function using a custom set of filters, we can reduce the number of operations required to compute the layer's outputs. These filters are composed of four rectangles, labeled as either positive or negative, within the filter space. When combined with an integral image, these filters allow for more efficient computations of feature values than a standard convolutional layer. When using our custom filters of shape 5 × 5, the layer only requires 64% of the operations that a standard convolutional layer need. This reduction only increases as the filter sizes get larger. Empirically, we have found that these layers' real‐world execution times behave as theoretically calculated and that their optimizations do not lead to significant drop inaccuracy. During testing, we evaluated our proposed optimization on two different data sets with two different network architectures. [ABSTRACT FROM AUTHOR]

The article reports on how Kevin Gurney, professor at Arizona State University (ASU), used crowdsourcing to identify power plants and emissions. He created an online game that uses contributions from the general public to pinpoint the locations of power plants and quantify the amount of carbon dioxide each emits into the atmosphere.

Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm. [ABSTRACT FROM AUTHOR]

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed,we cover the inference it affords, the computational means of building it, and its application in concept creation. [ABSTRACT FROM AUTHOR]

The article focuses on first step in that direction is to introduce attorneys and law students to the basis of artificial intelligence (AI ) system development and the ethical guidelines recently promulgated by the European Union Commission (EU). It mentions American Bar Association (ABA) study, attorneys reported saving time and increasing efficiency were advantages of adopting of AI systems in law firms. It also mentions federal rules governing their conduct or product development.

Many theorize that public opinion follows political elite on climate change skepticism, yet evidence of a causal link is lacking. I use a regression discontinuity design to establish the impact of the political party of a governor on constituents' global warming beliefs. I find that, relative to the election of a Democratic governor, the election of a Republican governor significantly decreases the probability of a Republican constituent believing in global warming by approximately 11–15 percentage points; there is no significant impact on Democratic constituents. I also find a negative effect of a Republican governor on belief in human-caused global warming that does not differ by constituent partisan affiliation. These results provide one explanation for the increased political polarization in global warming beliefs despite the scientific consensus. Belief formation often plays an important role in political economy models, so these findings also have implications for implementing climate change policy. [ABSTRACT FROM AUTHOR]

Modern armed conflicts and military strategies have undergone dramatic shifts as a result of new technologies, and the next generation of innovations will have profound consequences for how wars are fought, where they are fought, and who fights them. This, in turn, will inevitably have a pronounced influence on the development of the laws of war and the justice mechanisms mandated with enforcing those laws. Therefore, as new strategies and dynamics of war emerge related to the use of new technologies, war crimes investigators and prosecutors must adapt in order to meet the goals of establishing the truth, protecting the historical record, and holding individuals accountable for grave violations of international law. This article examines the characteristics of contemporary armed conflicts related to the use of new technologies and asks how this technological transformation of warfare will affect the ability of the global community to reach the goals of international justice. First, it examines the development of the use of technology-what technologies are used, how they are used, and what impact they have on armed conflicts and military affairs generally. Second, it identifies the complex legal issues arising from the use of new technologies and advocates for needed revisions to the definitions of crimes and modes of liability. Finally, it looks at the impact of the technological transformation of warfare on the fact-finding process for international criminal investigators and recommends a modified approach to evidence and the rules of procedure. In sum, this article takes a big picture approach to examining a current revolution based on the complex interplay of technology, law, and investigations in armed conflicts, and uses this understanding to chart a new way forward for the International Criminal Court. [ABSTRACT FROM AUTHOR]

For more than sixty years, "obviousness" has set the bar for patentability. Under this standard, if a hypothetical "person having ordinary skill in the art" would find an invention obvious in light of existing relevant information, then the invention cannot be patented. This skilled person is defined as a non-innovative worker with a limited knowledge-base. The more creative and informed the skilled person, the more likely an invention will be considered obvious. The standard has evolved since its introduction, and it is now on the verge of an evolutionary leap: Inventive machines are increasingly being used in research, and once the use of such machines becomes standard, the person skilled in the art should be a person using an inventive machine, or just an inventive machine. Unlike the skilled person, the inventive machine is capable of innovation and considering the entire universe of prior art. As inventive machines continue to improve, this will increasingly raise the bar to patentability, eventually rendering innovative activities obvious. The end of obviousness means the end of patents, at least as they are now. [ABSTRACT FROM AUTHOR]