Respiration is a brain function on which our lives essentially depend. Control of respiration ensures that the frequency and depth of breathing adapt continuously to metabolic needs. In addition, the respiratory control network of the brain has to organize muscular synergies that integrate ventilation with posture and body movement. Finally, respiration is coupled to cardiovascular function and emotion. Here, we argue that the brain can handle this all by integrating a brainstem central pattern generator circuit in a larger network that also comprises the cerebellum. Although currently not generally recognized as a respiratory control center, the cerebellum is well known for its coordinating and modulating role in motor behavior, as well as for its role in the autonomic nervous system. In this review, we discuss the role of brain regions involved in the control of respiration, and their anatomical and functional interactions. We discuss how sensory feedback can result in adaptation of respiration, and how these mechanisms can be compromised by various neurological and psychological disorders. Finally, we demonstrate how the respiratory pattern generators are part of a larger and integrated network of respiratory brain regions.

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Update of doi: 10.1101/2022.07.19.500649.

Update of doi: 10.1101/2022.07.03.498570.

Chronic, or persistent pain affects more than 10% of adults in the general population. This makes it one of the major physical and mental health care problems. Although pain is an important acute warning signal that allows the organism to take action before tissue damage occurs, it can become persistent and its role as a warning signal thereby inadequate. Although per definition, pain can only be labeled as persistent after 3 months, the trajectory from acute to persistent pain is likely to be determined very early and might even start at the time of injury. The biopsychosocial model has revolutionized our understanding of chronic pain and paved the way for psychological treatments for persistent pain, which routinely outperform other forms of treatment. This suggests that psychological processes could also be important in shaping the very early trajectory from acute to persistent pain and that targeting these processes could prevent the development of persistent pain. In this review, we develop an integrative model and suggest novel interventions during early pain trajectories, based on predictions from this model.

Update of doi: 10.1101/2022.07.19.500579.

Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the 'cortico-hippocampal pattern separation' (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical-hippocampal interactions.

Plasmids enable the dissemination of antimicrobial resistance (AMR) in common Enterobacterales pathogens, representing a major public health challenge. However, the extent of plasmid sharing and evolution between Enterobacterales causing human infections and other niches remains unclear, including the emergence of resistance plasmids. Dense, unselected sampling is highly relevant to developing our understanding of plasmid epidemiology and designing appropriate interventions to limit the emergence and dissemination of plasmid-associated AMR. We established a geographically and temporally restricted collection of human bloodstream infection (BSI)-associated, livestock-associated (cattle, pig, poultry, and sheep faeces, farm soils) and wastewater treatment work (WwTW)-associated (influent, effluent, waterways upstream/downstream of effluent outlets) Enterobacterales. Isolates were collected between 2008-2020 from sites <60km apart in Oxfordshire, UK. Pangenome analysis of plasmid clusters revealed shared 'backbones', with phylogenies suggesting an intertwined ecology where well-conserved plasmid backbones carry diverse accessory functions, including AMR genes. Many plasmid 'backbones' were seen across species and niches, raising the possibility that plasmid movement between these followed by rapid accessory gene change could be relatively common. Overall, the signature of identical plasmid sharing is likely to be a highly transient one, implying that plasmid movement might be occurring at greater rates than previously estimated, raising a challenge for future genomic One Health studies.

Update of doi: 10.1101/2021.11.11.21266080.

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.

Comment on Elife. 2023 Feb 28;12:

Author information: (1)Department of Biochemistry & Molecular Biology, Howard University College of Medicine, Washington, District of Columbia, United States. (2)Division of Lung Diseases, National Institutes of Health (NIH), National Heart, Lung and Blood Institute (NHLBI), Bethesda, United States. (3)Department of Pediatrics, Division of Critical Care, Columbia University Vagelos College of Physicians and Surgeons and New York - Presbyterian Morgan Stanley Children's Hospital, New York, United States. (4)Division of Experimental Medicine, University of California, San Francisco, United States. (5)Department of Medicine, Joe R. and Teresa Lozano Long School of Medicine, University of Texas, San Antonio, United States. (6)Infectious Disease Division, Massachusetts General Hospital, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States. (7)Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, United States. (8)NIH RECOVER Research Initiative: Patient representative, New York, United States. (9)Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Standford, United States. (10)Division of Pulmonary, Allergy, Critical Care & Sleep Medicine, Department of Medicine, University of Arizona College of Medicine, Tucson, United States. (11)National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, United States. (12)Seattle Children's Hospital, Division of Pediatric Cardiology, Department of Pediatrics, University of Washington, Seattle, United States. (13)Public Health Research Institute and Department of Medicine, Rutgers New Jersey Medical School, Newark, United States. (14)Department of Biochemistry and Molecular Genetics, University of Illinois, College of Medicine, Chicago, United States. (15)Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, United States. (16)Department of Pediatrics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, United States.

Update of doi: 10.1101/2022.11.22.517476.

Update of doi: 10.1101/2022.08.31.506098.

Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.

His mosquito control project heading for failure, a field entomologist recalls how a chance encounter led to a Eureka moment.

In isolation from their peers, Photinus carolinus fireflies flash with no intrinsic period between successive bursts. Yet, when congregating into large mating swarms, these fireflies transition into predictability, synchronizing with their neighbors with a rhythmic periodicity. Here we propose a mechanism for emergence of synchrony and periodicity, and formulate the principle in a mathematical framework. Remarkably, with no fitting parameters, analytic predictions from this simple principle and framework agree strikingly well with data. Next, we add further sophistication to the framework using a computational approach featuring groups of random oscillators via integrate-and-fire interactions controlled by a tunable parameter. This agent-based framework of P. carolinus fireflies interacting in swarms of increasing density also shows quantitatively similar phenomenology and reduces to the analytic framework in the appropriate limit of the tunable coupling strength. We discuss our findings and note that the resulting dynamics follow the style of a decentralized follow-the-leader synchronization, where any of the randomly flashing individuals may take the role of the leader of any subsequent synchronized flash burst.