Plasticity for the win: Flexible transcriptional response to host plant switches in the comma butterfly (Polygonia c-album).

Generalist plant-feeding insects are characterised by a broad host repertoire that can comprise several families or even different orders of plants. The genetic and physiological mechanisms underlying the use of such a wide host range are still not fully understood. Earlier studies indicate that the consumption of different host plants is associated with host-specific gene expression profiles. It remained, however, unclear if and how larvae can alter these profiles in the case of a changing host environment. Using the polyphagous comma butterfly (Polygonia c-album) we show that larvae can adjust their transcriptional profiles in response to a new host plant. The switch to some of the host plants, however, resulted in a larger transcriptional response and, thus, seems to be more challenging. At a physiological level, no correspondence for these patterns could be found in larval performance. This suggests that a high transcriptional but also phenotypic flexibility are essential for the use of a broad and diverse host range. We furthermore propose that host switch tests in the laboratory followed by transcriptomic investigations can be a valuable tool to examine not only plasticity in host use but also subtle and/or transient trade-offs in the evolution of host plant repertoires.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group in the Pediatric Population.

BACKGROUND: The fundamental gap obstructing forward progress of evidenced-based care in pediatric and neonatal disorders of consciousness (DoC) is the lack of defining consensus-based terminology to perform comparative research. This lack of shared nomenclature in pediatric DoC stems from the inherently recursive dilemma of the inability to reliably measure consciousness in the very young. However, recent advancements in validated clinical examinations and technologically sophisticated biomarkers of brain activity linked to future abilities are unlocking this previously formidable challenge to understanding the DoC in the developing brain. METHODS: To address this need, the first of its kind international convergence of an interdisciplinary team of pediatric DoC experts was organized by the Neurocritical Care Society's Curing Coma Campaign. The multidisciplinary panel of pediatric DoC experts proposed pediatric-tailored common data elements (CDEs) covering each of the CDE working groups including behavioral phenotyping, biospecimens, electrophysiology, family and goals of care, neuroimaging, outcome and endpoints, physiology and big Data, therapies, and pediatrics. RESULTS: We report the working groups' pediatric-focused DoC CDE recommendations and disseminate CDEs to be used in studies of pediatric patients with DoC. CONCLUSIONS: The CDEs recommended support the vision of progressing collaborative and successful internationally collaborative pediatric coma research.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data.

BACKGROUND: The implementation of multimodality monitoring in the clinical management of patients with disorders of consciousness (DoC) results in physiological measurements that can be collected in a continuous and regular fashion or even at waveform resolution. Such data are considered part of the "Big Data" available in intensive care units and are potentially suitable for health care-focused artificial intelligence research. Despite the richness in content of the physiological measurements, and the clinical implications shown by derived metrics based on those measurements, they have been largely neglected from previous attempts in harmonizing data collection and standardizing reporting of results as part of common data elements (CDEs) efforts. CDEs aim to provide a framework for unifying data in clinical research and help in implementing a systematic approach that can facilitate reliable comparison of results from clinical studies in DoC as well in international research collaborations. METHODS: To address this need, the Neurocritical Care Society's Curing Coma Campaign convened a multidisciplinary panel of DoC "Physiology and Big Data" experts to propose CDEs for data collection and reporting in this field. RESULTS: We report the recommendations of this CDE development panel and disseminate CDEs to be used in physiologic and big data studies of patients with DoC. CONCLUSIONS: These CDEs will support progress in the field of DoC physiologic and big data and facilitate international collaboration.

Colour moult phenology and camouflage mismatch in polymorphic populations of Arctic foxes.

Species that seasonally moult from brown to white to match snowy backgrounds become conspicuous and experience increased predation risk as snow cover duration declines. Long-term adaptation to camouflage mismatch in a changing climate might occur through phenotypic plasticity in colour moult phenology and or evolutionary shifts in moult rate or timing. Also, adaptation may include evolutionary shifts towards winter brown phenotypes that forgo the winter white moult. Most studies of these processes have occurred in winter white populations, with little attention to polymorphic populations with sympatric winter brown and winter white morphs. Here, we used remote camera traps to record moult phenology and mismatch in two polymorphic populations of Arctic foxes in Sweden over 2 years. We found that the colder, more northern population moulted earlier in the autumn and later in the spring. Next, foxes moulted earlier in the autumn and later in the spring during colder and snowier years. Finally, white foxes experienced relatively low camouflage mismatch while blue foxes were mismatched against snowy backgrounds most of the autumn through the spring. Because the brown-on-white mismatch imposes no evident costs, we predict that as snow duration decreases, increasing blue morph frequencies might help facilitate species persistence.

Challenges and Opportunities in Multimodal Monitoring and Data Analytics in Traumatic Brain Injury.

PURPOSE OF REVIEW: Increasingly sophisticated systems for monitoring the brain have led to an increase in the use of multimodality monitoring (MMM) to detect secondary brain injuries before irreversible damage occurs after brain trauma. This review examines the challenges and opportunities associated with MMM in this population. RECENT FINDINGS: Locally and internationally, the use of MMM varies. Practical challenges include difficulties with data acquisition, curation, and harmonization with other data sources limiting collaboration. However, efforts toward integration of MMM data, advancements in data science, and the availability of cloud-based infrastructures are now affording the opportunity for MMM to advance the care of patients with brain trauma. MMM provides data to guide the precision management of patients with traumatic brain injury in real time. While challenges exist, there are exciting opportunities for MMM to live up to this promise and to drive new insights into the physiology of the brain and beyond.

Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II: A Phase II Randomized Trial.

OBJECTIVES: A relationship between reduced brain tissue oxygenation and poor outcome following severe traumatic brain injury has been reported in observational studies. We designed a Phase II trial to assess whether a neurocritical care management protocol could improve brain tissue oxygenation levels in patients with severe traumatic brain injury and the feasibility of a Phase III efficacy study. DESIGN: Randomized prospective clinical trial. SETTING: Ten ICUs in the United States. PATIENTS: One hundred nineteen severe traumatic brain injury patients. INTERVENTIONS: Patients were randomized to treatment protocol based on intracranial pressure plus brain tissue oxygenation monitoring versus intracranial pressure monitoring alone. Brain tissue oxygenation data were recorded in the intracranial pressure -only group in blinded fashion. Tiered interventions in each arm were specified and impact on intracranial pressure and brain tissue oxygenation measured. Monitors were removed if values were normal for 48 hours consecutively, or after 5 days. Outcome was measured at 6 months using the Glasgow Outcome Scale-Extended. MEASUREMENTS AND MAIN RESULTS: A management protocol based on brain tissue oxygenation and intracranial pressure monitoring reduced the proportion of time with brain tissue hypoxia after severe traumatic brain injury (0.45 in intracranial pressure-only group and 0.16 in intracranial pressure plus brain tissue oxygenation group; p < 0.0001). Intracranial pressure control was similar in both groups. Safety and feasibility of the tiered treatment protocol were confirmed. There were no procedure-related complications. Treatment of secondary injury after severe traumatic brain injury based on brain tissue oxygenation and intracranial pressure values was consistent with reduced mortality and increased proportions of patients with good recovery compared with intracranial pressure-only management; however, the study was not powered for clinical efficacy. CONCLUSIONS: Management of severe traumatic brain injury informed by multimodal intracranial pressure and brain tissue oxygenation monitoring reduced brain tissue hypoxia with a trend toward lower mortality and more favorable outcomes than intracranial pressure-only treatment. A Phase III randomized trial to assess impact on neurologic outcome of intracranial pressure plus brain tissue oxygenation-directed treatment of severe traumatic brain injury is warranted.

Using Physiological Biomarkers to Optimize Management of TBI in Austere Environments.

INTRODUCTION: Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. METHODS: Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. RESULTS: The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. CONCLUSION: We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research.

Objective.The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.Approach.A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2µV or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.Main Results.Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.Significance.Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.

A Novel External Ventricular Drain Sensor to Improve Acquired Brain Injury Monitoring.

INTRODUCTION: The insufficiency of current methods to capture the context and environment of neurocritical care can negatively impact patient outcomes. Insertion of an external ventricular drain (EVD) into the ventricles to monitor intracranial pressure (ICP) is a common lifesaving procedure for acquired brain injury patients. Yet, nursing interventions that significantly affect the measured ICP value, such as changing the EVD stopcock position, are poorly documented. Environmental factors like light and noise levels are not monitored as standard of care despite worse outcomes in patients affiliated with sensory sensitivities. Capturing these missing data is an essential first step toward quantifying their effects. MATERIALS AND METHODS: Our entry point was the development of a stopcock position sensor (SPS) that attaches to the EVD stopcock and time-synchronously annotates the recorded ICP data with its position. A two-phase, prospective, nonrandomized observational study was conducted to evaluate the efficacy of the SPS. Phase I assessed the SPS using an ex vivo simulation of ICP management. Phase II involved human subjects with the SPS attached to the EVD stopcock while patients were managed per standard of care. RESULTS: The SPS accurately annotated the ICP data and identified that the EVD drained the cerebrospinal fluid for 94.52% of total patient monitoring time (16.98 h). For only 3.54% of the time, the stopcock directed the cerebrospinal fluid into the pressure transducer for accurate ICP measurement. For the remaining 1.94% of the time, the stopcock was positioned off: No cerebrospinal fluid drainage and no ICP monitoring. CONCLUSIONS: We successfully captured an important aspect of the ICP monitoring context, the EVD stopcock position, and time-synchronized it with the recorded physiology. Our system enables future investigations into the impact that a broad contextual data environment has on physiological measurements and acquired brain injury patient outcomes. In the future, we aim to capture additional contextual data sources and expand the scope to battlefield environments.

Medical Device Connectivity Challenges Outline the Technical Requirements and Standards For Promoting Big Data Research and Personalized Medicine in Neurocritical Care.

Brain injuries are complicated medical problems and their management requires data from disparate sources to extract actionable information. In neurocritical care, interoperability is lacking despite the perceived benefits. Several efforts have been underway, but none have been widely adopted, underscoring the difficulty of achieving this goal. We have identified the current pain points of data collection and integration based on the experience with two large multi-site clinical studies: Transforming Research And Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) in the United States and Collaborative European Neuro Trauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) in Europe. The variability of measurements across sites remains a barrier to uniform data collection. We found a need for annotation standards and for a standardized archive format for high-resolution data. Overall, the hidden cost for successful data collection was initially underestimated.Although the use of bedside data integration solutions, such as the Moberg's Component Neuromonitoring System (Moberg Research, Inc., Ambler, PA, USA) or ICM+ software (Cambridge Enterprise, Cambridge, UK), facilitated the homogenous collection of synchronized data, there remain issues that need to be addressed by the neurocritical care community. To this end, we have organized a Working Group on Neurocritical Care Informatics, whose next step is to create an overarching informatics framework that takes advantage of the collected information to answer scientific questions and to accelerate the translation of trial results to actions benefitting military medicine.