Specific Time Window Research Articles

A rare dance

The enormous success of The Last Dance, the sports documentary on Michael Jordan’s career, and especially his last season, is the result of a rare confluence of factors, each of which is a unique and rare phenomenon in the history of sport. Their combination has already turned the mini-series into a global media event of the kind that is usually reserved for live broadcasts of extraordinary events. A basketball player with unusual personal and professional abilities, supported by a highly polished and well-oiled marketing system; the specific window of time in which his star shone – the late 1990s, when the era of media commercialization and globalization flourished, yet before the emergence of social media and their typical critical discourse; the rise in sports documentaries in recent years; and encasing all of these is the time of the documentary’s broadcast, when sports life across the world ceased due to the coronavirus. The mini-series, which seemingly deals with a single season in the career of a single player in a single sport, is actually so much more. It is a composition reflecting much wider social, sports and media phenomena.

Treed distributed lag nonlinear models.

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.

Pilus Production in Acinetobacter baumannii Is Growth Phase Dependent and Essential for Natural Transformation.

Acinetobacter baumannii is a severe threat to human health as a frequently multidrug-resistant hospital-acquired pathogen. Part of the danger from this bacterium comes from its genome plasticity and ability to evolve quickly by taking up and recombining external DNA into its own genome in a process called natural competence for transformation. This mode of horizontal gene transfer is one of the major ways that bacteria can acquire new antimicrobial resistances and toxic traits. Because these processes in A. baumannii are not well studied, we herein characterized new aspects of natural transformability in this species that include the species' competence window. We uncovered a strong correlation with a growth phase-dependent synthesis of a type IV pilus (TFP), which constitutes the central part of competence-induced DNA uptake machinery. We used bacterial genetics and microscopy to demonstrate that the TFP is essential for the natural transformability and surface motility of A. baumannii, whereas pilus-unrelated proteins of the DNA uptake complex do not affect the motility phenotype. Furthermore, TFP biogenesis and assembly is subject to input from two regulatory systems that are homologous to Pseudomonas aeruginosa, namely, the PilSR two-component system and the Pil-Chp chemosensory system. We demonstrated that these systems affect not only the piliation status of cells but also their ability to take up DNA for transformation. Importantly, we report on discrepancies between TFP biogenesis and natural transformability within the same genus by comparing data for our work on A. baumannii to data reported for Acinetobacter baylyi, the latter of which served for decades as a model for natural competence.IMPORTANCE Rapid bacterial evolution has alarming negative impacts on animal and human health which can occur when pathogens acquire antimicrobial resistance traits. As a major cause of antibiotic-resistant opportunistic infections, A. baumannii is a high-priority health threat which has motivated renewed interest in studying how this pathogen acquires new, dangerous traits. In this study, we deciphered a specific time window in which these bacteria can acquire new DNA and correlated that with its ability to produce the external appendages that contribute to the DNA acquisition process. These cell appendages function doubly for motility on surfaces and for DNA uptake. Collectively, we showed that A. baumannii is similar in its TFP production to Pseudomonas aeruginosa, though it differs from the well-studied species A. baylyi.

A new LC-MS/MS technique for separation of gangliosides using a phenyl-hexyl column: Systematic separation according to sialic acid class and ceramide subclass

A LC-MS/MS technique separated the bovine and mouse brain gangliosides monosialotetrahexosylgangliosides (GM1), disialotetrahexosylgangliosides (GD1a), trisialotetrahexosylgangliosides (GT1b) and tetrasialotetrahexosylgangliosides (GQ1b) using a phenyl-hexyl HPLC column and employing a linear methanol gradient in water, which is 0.028% in ammonium hydroxide. The gangliosides were separated according to sialic acid class, and within a particular class, gangliosides having different ceramide carbon chain lengths were also separated. All gangliosides of a particular sialic acid class eluted in characteristic retention time windows in the order of GQ1b, (earliest), GT1b, GD1a, and GM1 (latest). Within each specific retention time window for a particular ganglioside class, gangliosides were separated in the order of increasing ceramide carbon chain length. The phenyl-hexyl column separation of gangliosides is advantageous over established hydrophilic interaction and conventional reversed-phase chromatography techniques, in that the former separates gangliosides according to sialic acid class but not ceramide composition and the latter distributes all the sialic acid ganglioside classes throughout the entire chromatogram. The mechanism of separation of the ganglioside sialic acid classes is proposed to be a π-electron repulsion of negatively-charged gangliosides by the column phenyl moiety.

Posterior insular activity contributes to the late laser-evoked potential component in EEG recordings

ObjectiveNociceptive activity in some brain areas has concordantly been reported in EEG source models, such as the anterior/mid-cingulate cortex and the parasylvian area. Whereas the posterior insula has been constantly reported to be active in intracortical and fMRI studies, non-invasive EEG and MEG recordings mostly failed to detect activity in this region. This study aimed to determine an appropriate inverse modeling approach in EEG recordings to model posterior insular activity, assuming the late LEP (laser evoked potential) time window to yield a better separation from other ongoing cortical activity. MethodsIn 12 healthy volunteers, nociceptive stimuli of three intensities were applied. LEP were recorded using 32-channel EEG recordings. Source analysis was performed in specific time windows defined in the grand-average dataset. Two distinct dipole-pairs located close to the operculo-insular area were compared. ResultsOur results show that posterior insular activity yields a substantial contribution to the latest part (positive component) of the LEP. ConclusionsEven though the initial insular activity onset is in the early LEP time window,modelingthe insular activity in the late LEP time window might result in better separation from other ongoing cortical activity. SignificanceModeling the late LEP activity might enable to distinguish posterior insular activity.

Studying partial hyperbolicity inside regimes of motion in Hamiltonian systems

A chaotic trajectory in weakly chaotic higher-dimensional Hamiltonian systems may locally present distinct regimes of motion, namely, chaotic, semiordered, or ordered. Such regimes, which are consequences of dynamical traps, are defined by the values of the Finite-Time Lyapunov Exponents (FTLEs) calculated during specific time windows. The Covariant Lyapunov Vectors (CLVs) contain the information about the local geometrical structure of the manifolds, and the distribution of the angles between them has been used to quantify deviations from hyperbolicity. In this work, we propose to study the deviation of partial hyperbolicity using the distribution of the local and mean angles during each of the mentioned regimes of motion. A system composed of two coupled standard maps and the Hénon–Heiles system are used as examples. Both are paradigmatic models to study the dynamics of mixed phase-space of conservative systems in discrete and continuous dynamical systems, respectively. Hyperbolic orthogonality is a general tendency in strong chaotic regimes. However, this is not true anymore for weakly chaotic systems and we must look separately at the regimes of motion. Furthermore, the distribution of angles between the manifolds in a given regime of motion allows us to obtain geometrical information about manifold structures in the tangent spaces. The description proposed here helps to explain important characteristics between invariant manifolds that occur inside the regimes of motion and furnishes a kind of visualization tool to perceive what happens in phase and tangent space of dynamical systems. This is crucial for higher-dimensional systems and to discuss distinct degrees of (non)hyperbolicity.

R&D alliance partner attributes and innovation performance: a fuzzy set qualitative comparative analysis

PurposeDrawing upon the importance of research and development (R&D) alliances in driving firm innovation performance, extant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance. Despite such analyzes, research has generally underestimated the configurations of partner attributes leading to firm innovation performance. This research gap is interesting to explore, as firms involved in R&D alliances usually face a combination of partner attributes. Moreover, gaining a better understanding of how R&D partner attributes tie into configurations is an issue that is attracting particular interest in coopetition research and alliance literature. This paper aims to obtain a better knowledge of this underrated, but important, aspect of alliances by exploring what configurations of R&D alliance partner attributes lead firms involved in R&D alliances to achieve high innovation performance. To tackle this question, first, this study reviews the extant literature on R&D alliances by relying on the knowledge-based view of alliances to identify the most impactful partner attributes on firms’ innovation performance. This paper then applies a fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of R&D alliance partner attributes that lead firms involved in R&D alliances to achieve high innovation performance.Design/methodology/approachThis study selects 27 R&D alliances formed worldwide in the telecom industry. This paper explores the multiple configurations of partner attributes of these alliances by conducting a fsQCA.FindingsThe findings of the fsQCA show that the two alternate configurations of partner attributes guided the firms involved in these alliances to achieve high innovation performance: a configuration with extensive partner technological relatedness and coopetition, but no experience; and a configuration with extensive partner experience and competition, but no technological relatedness.Research limitations/implicationsThe research highlights the importance of how partner attributes (i.e. partner technological relatedness, partner competitive overlap, partner experience and partner relative size) tie, with regard to the firms’ access to external knowledge and consequently to their willingness to achieve high innovation performance. Moreover, this paper reveals the beneficial effect of competition on the innovation performance of the firms involved in R&D alliances when some of the other knowledge-based partner attributes are considered. Despite these insights for alliance and coopetition literature, some limitations are to be noted. First, some of the partners’ attributes considered could be further disentangled into sub-partner attributes. Second, other indicators might be used to measure firms’ innovation performance. Third, as anticipated this study applies fsQCA to explore the combinatory effects of partner attributes in the specific context of R&D alliances in the telecom industry worldwide and in a specific time window. This condition may question the extensibility of the results to other industries and times.Practical implicationsThis study also bears two interesting implications for alliance managers. First, the paper suggests that R&D alliance managers need to be aware that potential alliance partners have multiple attributes leading to firm innovation performance. In this regard, partner competitive overlap is particularly important for gaining a better understanding of firm innovation performance. When looking for strategic partners, managers should try to ally with highly competitive enterprises so as to access their more innovative knowledge. Second, the results also highlight that this beneficial effect of coopetition in R&D alliances can be amplified in two ways. On the one hand, when the partners involved in the alliance have not yet developed experience in forming alliances. Partners without previous experience supply ideal stimuli to unlock more knowledge in the alliance because new approaches to access and develop knowledge in the alliance could be explored. On the other hand, this paper detects the situation when the allied partners are developing technologies and products in different areas. When partnering with firms coming from different technological areas, the knowledge diversity that can be leveraged in the alliances could drive alliance managers to generate synergies and economies of scope within the coopetitive alliance.Originality/valueExtant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance but has concurrently underestimated the configurations of partner attributes leading to firm innovation performance. Therefore, this paper differs from previous studies, as it provides an understanding of the specific configurations of R&D alliance partner attributes leading firms involved in R&D alliances to achieve high innovation performance.

Recent advances in treatment of cerebral ischemic stroke

Ischemic stroke occurs when there is an obstruction or collapse of a blood vessel in the head or neck, which causes a lack of oxygen to the brain. Due to the lack of oxygen, detrimental motor and sensory deficits transpire in the areas of the brain that are affected. When an individual suffers an ischemic stroke, there is a specific time window and treatment regimen that is used to manage symptoms of a stroke. The principal concerns with stroke treatments are that don’t fully recover motor and sensory function lost during a stroke occurrence. With the use of stem cells from several sources, they can be reprogrammed and transplanted into the brain to facilitate full recovery of motor and sensory function. The purpose of this review article is to investigate the precursors, diagnostic methods, treatment techniques and new stem cell treatment research associated with ischemic stroke in anticipation to cure cognitive, sensory and motor dysfunction.

Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification.

Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain-computer interface (BCI) applications. The effectiveness of CSP is determined by the quality of interception in a specific time window and frequency band. Although numerous algorithms have been designed to optimize CSP by splitting the EEG data with a sliding time window and dividing the frequency bands with a set of band-pass filters, simultaneously. However, they did not consider the drawbacks of the rapid increase in data volume and feature dimensions brought about by this method, which would reduce the classification accuracy and calculation efficiency of the model. Therefore, we propose an optimal channel-based sparse time-frequency blocks common spatial pattern (OCSB-CSP) feature extraction method to improve the model classification accuracy and computational efficiency. Comparative experiments on two public EEG datasets show that the proposed method can quickly select significant time-frequency blocks and improve classification performance. The average classification accuracies are higher than those of other winners' methods, providing a new idea for the improvement of BCI applications.

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

One of the most critical objectives in the healthcare system is maximising patient flows in the emergency care patient pathway. Patient emergency flow analysis indicates that the timetabling of a patient’s movement from one activity to another through the Emergency Department (ED) is critical for treating patients. The ED deals with the patient’s arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, and decisions to admit or discharge the patient. Any delayed activities in patient flow reduce the service level of healthcare. To address these challenges, this paper develops a stochastic ED Simulation-Optimisation approach by considering stochastic variables, such as patient interarrival times and treatment times, using statistical distributions. This type of distribution depends on two main elements: day shifts and patient categories. A hybrid evolutionary algorithm is integrated with the simulation to find a satisfactory solution for this stochastic optimisation problem in real time. Computational experiments show that the proposed approach can serve more patients in specific time windows or provide the same quality of the service with the use of fewer medical resources.

Top-Down Feedback Enables Flexible Coding Strategies in Olfactory Cortex

In chemical sensation, multiple models have been proposed to explain how odors are represented by patterns of neuronal activity in the olfactory cortex. One hypothesis is that the identity of combinations of active neurons within specific sniff-related time windows are critical for encoding information about odors. Another model is that patterns of neural activity evolve across time and it is this temporal structure that is essential for encoding odor information. Interestingly, we found that top-down feedback to the olfactory bulb dictates what information is transmitted to the olfactory cortex by switching between these two strategies. Using a detailed model of the early olfactory system, we demonstrate that feedback control of inhibitory granule cells in the main olfactory bulb influences the balance between excitatory and inhibitory synaptic currents in mitral cells, thereby restructuring the firing patterns of piriform cortical cells across time. This resulted in performance gains in both the accuracy and reaction time of odor discrimination tasks. These findings lead us to propose a new framework for early olfactory computation, one in which top-down feedback to the bulb flexibly controls the temporal structure of neural activity in olfactory cortex, allowing the early olfactory system to dynamically switch between two distinct models of coding

The PEDtracker: An Automatic Staging Approach for Drosophila melanogaster Larvae.

The post-embryonal development of arthropod species, including crustaceans and insects, is characterized by ecdysis or molting. This process defines growth stages and is controlled by a conserved neuroendocrine system. Each molting event is divided in several critical time points, such as pre-molt, molt, and post-molt, and leaves the animals in a temporarily highly vulnerable state while their cuticle is re-hardening. The molting events occur in an immediate ecdysis sequence within a specific time window during the development. Each sub-stage takes only a short amount of time, which is generally in the order of minutes. To find these relatively short behavioral events, one needs to follow the entire post-embryonal development over several days. As the manual detection of the ecdysis sequence is time consuming and error prone, we designed a monitoring system to facilitate the continuous observation of the post-embryonal development of the fruit fly Drosophila melanogaster. Under constant environmental conditions we are able to observe the life cycle from the embryonic state to the adult, which takes about 10 days in this species. Specific processing algorithms developed and implemented in Fiji and R allow us to determine unique behavioral events on an individual level—including egg hatching, ecdysis and pupation. In addition, we measured growth rates and activity patterns for individual larvae. Our newly created RPackage PEDtracker can predict critical developmental events and thus offers the possibility to perform automated screens that identify changes in various aspects of larval development. In conclusion, the PEDtracker system presented in this study represents the basis for automated real-time staging and analysis not only for the arthropod development.

The Need for an Evidence-Based Program in Sweden to Support Parents to Create Healthy Lifestyle Behaviors from the Start of Life-Parental Perceptions.

In Sweden, there have been no interventions promoting the development of healthy lifestyle behaviors starting in infancy. Thus this paper aims to: (i) investigate Swedish parents’ experiences regarding feeding of solid foods, screen time, and physical activity in healthy infants; (ii) investigate parents’ needs and perceptions regarding information/support provided in primary child healthcare (CHC) and (iii) explore whether a parenting program focused on child diet and active play would be relevant and utilized. Semi-structured interviews were conducted with 20 parents. These were recorded, transcribed verbatim and analyzed using thematic analysis. Seven themes emerged: Feeling excited to enter a new phase; Parents’ responsibility of doing it “right” can be stressful; Motivated to learn during specific time windows; Information to trust; The importance of social support from peers; Experiences of support received from CHC; and the Infant, Feeding, Activity, and Nutrition Trial (INFANT) for a Swedish context. Parents were excited to enter new phases in their child’s development. However, this came with worry they were doing things “right”, leading parents to want more information/support. Overall, Swedish parents were very positive about the prospects of engaging in a primary CHC delivered program to support them to promote healthy lifestyle behaviors from birth and felt this could complement the care currently provided.

Hippocampal neurons with stable excitatory connectivity become part of neuronal representations.

Experiences are represented in the brain by patterns of neuronal activity. Ensembles of neurons representing experience undergo activity-dependent plasticity and are important for learning and recall. They are thus considered cellular engrams of memory. Yet, the cellular events that bias neurons to become part of a neuronal representation are largely unknown. In rodents, turnover of structural connectivity has been proposed to underlie the turnover of neuronal representations and also to be a cellular mechanism defining the time duration for which memories are stored in the hippocampus. If these hypotheses are true, structural dynamics of connectivity should be involved in the formation of neuronal representations and concurrently important for learning and recall. To tackle these questions, we used deep-brain 2-photon (2P) time-lapse imaging in transgenic mice in which neurons expressing the Immediate Early Gene (IEG) Arc (activity-regulated cytoskeleton-associated protein) could be permanently labeled during a specific time window. This enabled us to investigate the dynamics of excitatory synaptic connectivity—using dendritic spines as proxies—of hippocampal CA1 (cornu ammonis 1) pyramidal neurons (PNs) becoming part of neuronal representations exploiting Arc as an indicator of being part of neuronal representations. We discovered that neurons that will prospectively express Arc have slower turnover of synaptic connectivity, thus suggesting that synaptic stability prior to experience can bias neurons to become part of representations or possibly engrams. We also found a negative correlation between stability of structural synaptic connectivity and the ability to recall features of a hippocampal-dependent memory, which suggests that faster structural turnover in hippocampal CA1 might be functional for memory.

Bidirectional Volatile Signatures of Metal–Oxide Memristors—Part I: Characterization

The multistate capabilities as well as the intrinsic integrating properties of memristors deem them suitable candidates for the realization of novel neuromorphic applications. To date, much of their prestige arises mostly from the versatility that is promised by the nonvolatile device families. However, memristors also exhibit volatile characteristics, which for as long as they remain unknown, will hinder their integration to large-scale applications. In this article, we present a comprehensive study for characterizing the relaxation dynamics of TiOx resistive RAM (RRAM) devices within a predefined volatility framework. These dynamics are tightly linked to the total energy of stimulation, and device relaxation can be accurately described using simple mathematical models. Moreover, we show that RRAM volatility is bidirectional and that relaxation time constants heavily depend on the level of invasiveness caused by programming stimulation. Our work further includes a demonstration of how volatility can be characterized within a specific time window. Moreover, our protocol can be altered to fit the specific needs of potential applications. We anticipate that the universality of our method can act as a stepping stone toward the understanding and modeling of volatile memristors across different technologies and materials, enabling the realization of a new family of time-related applications.

Machine Learning as a Flaring Storm Warning Machine: Was a Warning Machine for the 2017 September Solar Flaring Storm Possible?

Abstract Machine learning is now one of the methodologies of choice for flare forecasting, and supervised techniques, in both their traditional and deep versions, are becoming more frequently used for prediction in this area of space weather. Most studies assess the prediction effectiveness of machine-learning methods by computing confusion matrices, which are typically highly non-diagonal, particularly in applications concerning the forecasting of X-class flares. The present study suggests that the reliability of the outcomes of a supervised machine-learning method could be better assessed by using it as a warning machine, sounding binary alerts unrolled over time, and by comparing the number of alerts sounded by the machine in specific time windows with the number of events actually observed in those time windows. Indeed, when applied to the prediction of the events associated with the 2017 September solar storm, a hybrid LASSO algorithm was able to sound alerts every day a flare actually occurred; it also identified the corresponding flare class. In addition, the machine was able to predict with some accuracy a reliable proxy of the energy budget daily released by magnetic reconnection during the entire course of the storm. Finally, the analysis shows that the combination of sparsity-enhancing machine learning and feature ranking could allow the identification of the prominent role that the Ising energy played as an active region property in the forecasting process.

Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base stations called eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. Each eNodeB generates a large number of key performance indicators (KPIs). Hundreds of thousands of eNodeBs are typically deployed to cover a nation-wide service area. Operators need to handle hundreds of millions of KPIs to cover the areas. It is impractical to handle manually such a huge amount of KPI data, and automation of data processing is therefore desired. To improve network operation efficiency, a suitable machine learning technique is used to learn and classify individual eNodeBs into different states based on multiple performance metrics during a specific time window. However, an issue with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network that consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.

Maternal immune activation targeted to a window of parvalbumin interneuron development improves spatial working memory: Implications for autism

Maternal immune activation (MIA) increases risk for neuropsychiatric disorders such as autism spectrum disorder (ASD) in offspring later in life through unknown causal mechanisms. Growing evidence implicates parvalbumin-containing GABAergic interneurons as a key target in rodent MIA models. We targeted a specific neurodevelopmental window of parvalbumin interneurons in a mouse MIA model to examine effects on spatial working memory, a key domain in ASD that can manifest as either impairments or improvements both clinically and in animal models.Pregnant dams received three consecutive intraperitoneal injections of Polyinosinic:polycytidylic acid (poly(I:C), 5 mg/kg) at gestational days 13, 14 and 15. Spatial working memory was assessed in young adult offspring using touchscreen operant chambers and the Trial-Unique Non-matching to Location (TUNL) task. Anxiety, novelty seeking and short-term memory were assessed using Elevated Plus Maze (EPM) and Y-maze novelty preference tasks. Fluorescent immunohistochemistry was used to assess hippocampal parvalbumin cell density, intensity and co-expression with perineuronal nets. qPCR was used to assess the expression of putatively implicated gene pathways.MIA targeting a window of parvalbumin interneuron development increased spatial working memory performance on the TUNL touchscreen task which was not influenced by anxiety or novelty seeking behaviour. The model reduced fetal mRNA levels of Gad1 and adult hippocampal mRNA levels of Pvalb and the distribution of low intensity parvalbumin interneurons was altered.We speculate a specific timing window for parvalbumin interneuron development underpins the apparently paradoxical improved spatial working memory phenotype found both across several rodent models of autism and clinically in ASD.

How neuron types encode behavioral states

Neuroscience What is the contribution of molecularly defined cell types to neural coding of stimuli and states? Xu et al. aimed to evaluate neural representation of multiple behavioral states in the mouse paraventricular hypothalamus. To achieve this goal, they combined deep-brain two-photon imaging with post hoc validation of gene expression in the imaged cells. The behavioral states could be well predicted by the neural response of multiple neuronal clusters. Some clusters were broadly tuned and contributed strongly to the decoding of multiple behavioral states, whereas others were more specifically tuned to certain behaviors or specific time windows of a behavioral state. Science , this issue p. [eabb2494][1] [1]: /lookup/doi/10.1126/science.abb2494

Abstract A13: Linking cancer and vaccine registries in a retrospective cohort study: Risk of metastasis following perioperative vaccine use among cancer patients

Abstract Background: Surgical removal of solid tumors is a frequent treatment for cancer patients. Immunosuppression after surgery, which suppresses natural killer (NK) cells, increases the risk of metastasis (mets). Preclinical and animal studies have shown that perioperative vaccination can prevent postoperative NK cell dysfunction and may reduce the risk of mets. In the first epidemiologic study of its kind, we investigated this intriguing hypothesis in the Canadian province of Manitoba (MB). Methods: We linked the MB Cancer Registry, containing all cancer diagnoses and cancer-specific therapy in MB, with the MB Immunization Monitoring System and other administrative health databases covering >99% of the population. We identified all first cancer surgeries conducted during 1994-2014 in MB for an invasive, nonmetastatic, primary, solid cancer. We followed patients from their date of surgery to the earliest date of metastasis, 2nd primary cancer, 2nd cancer surgery, death, loss of coverage, or study end. We used competing-risk Cox models adjusted for age at surgery, gender, comorbidities, and cancer stage at diagnosis. Results: Out of 29,120 persons with a cancer surgery, 0.2% and 0.4% received a vaccine, mostly seasonal flu vaccines, 2-4 and 0-5 days before surgery respectively, and 21.8% developed metastasis during follow-up. Compared to those vaccinated 30 days before to 14 days after surgery outside of the specific time window, the hazard ratio of the association between vaccination 0-5 days before surgery and metastasis was 0.72 (0.32-1.61) for a 15-year follow-up, and the association was 0.44 (0.11-1.78) for vaccination 2-4 days before surgery. Conclusion: In this relatively small study, we found evidence for a strong association between vaccination just before cancer surgery and long-term occurrence of mets. Further studies of this associations in humans are warranted; they must be sufficiently large to investigate potentially narrow windows of vaccine effectiveness, and they could use biomarkers of mets as intermediate endpoints. Citation Format: Christiaan H. Righolt, Geng Zhang, Salaheddin M. Mahmud. Linking cancer and vaccine registries in a retrospective cohort study: Risk of metastasis following perioperative vaccine use among cancer patients [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A13.