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STUDY DESIGN: Clinical case series. OBJECTIVE: To describe the cause, treatment and outcome of 6 cases of perioperative spinal cord injury (SCI) in high-risk adult deformity surgery. SETTING: Adult spinal deformity patients were enrolled in the multi-center Scoli-RISK-1 cohort study. METHODS: A total of 272 patients who underwent complex adult deformity surgery were enrolled in the prospective, multi-center Scoli-RISK-1 cohort study. Clinical follow up data were available up to a maximum of 2 years after index surgery. Cases of perioperative SCI were identified and an extensive case review was performed. RESULTS: Six individuals with SCI were identified from the Scoli-RISK-1 database (2.2%). Two cases occurred intraoperatively and four cases occurred postoperatively. The first case was an incomplete SCI due to a direct intraoperative insult and was treated postoperatively with Riluzole. The second SCI case was caused by a compression injury due to overcorrection of the deformity. Three cases of incomplete SCI occurred; one case of postoperative hematoma, one case of proximal junctional kyphosis (PJK) and one case of adjacent segment disc herniation. All cases of post-operative incomplete SCI were managed with revision decompression and resulted in excellent clinical recovery. One case of incomplete SCI resulted from infection and PJK. The patients treatment was complicated by a delay in revision and the patient suffered persistent neurological deficits up to six weeks following the onset of SCI. CONCLUSION: Despite the low incidence in high-risk adult deformity surgeries, perioperative SCI can result in devastating consequences. Thus, appropriate postoperative care, follow up and timely management of SCI are essential.

STUDY DESIGN: Delayed diagnosis of degenerative cervical myelopathy (DCM) is likely due to a combination of its subtle symptoms, incomplete neurological assessments by clinicians and a lack of public and professional awareness. Diagnostic criteria for DCM will likely facilitate earlier referral for definitive management. OBJECTIVES: This systematic review aims to determine (i) the diagnostic accuracy of various clinical signs and (ii) the association between clinical signs and disease severity in DCM? METHODS: A search was performed to identify studies on adult patients that evaluated the diagnostic accuracy of a clinical sign used for diagnosing DCM. Studies were also included if they assessed the association between the presence of a clinical sign and disease severity. The QUADAS-2 tool was used to evaluate the risk of bias of individual studies. RESULTS: This review identified eleven studies that used a control group to evaluate the diagnostic accuracy of various signs. An additional 61 articles reported on the frequency of clinical signs in a cohort of DCM patients. The most sensitive clinical tests for diagnosing DCM were the Tromner and hyperreflexia, whereas the most specific tests were the Babinski, Tromner, clonus and inverted supinator sign. Five studies evaluated the association between the presence of various clinical signs and disease severity. There was no definite association between Hoffmann sign, Babinski sign or hyperreflexia and disease severity. CONCLUSION: The presence of clinical signs suggesting spinal cord compression should encourage health care professionals to pursue further investigation, such as neuroimaging to either confirm or refute a diagnosis of DCM.

STUDY DESIGN: Delayed diagnosis of degenerative cervical myelopathy (DCM) is associated with reduced quality of life and greater disability. Developing diagnostic criteria for DCM has been identified as a top research priority. OBJECTIVES: This scoping review aims to address the following questions: What is the diagnostic accuracy and frequency of clinical symptoms in patients with DCM? METHODS: A scoping review was conducted using a database of all primary DCM studies published between 2005 and 2020. Studies were included if they (i) assessed the diagnostic accuracy of a symptom using an appropriate control group or (ii) reported the frequency of a symptom in a cohort of DCM patients. RESULTS: This review identified three studies that discussed the diagnostic accuracy of various symptoms and included a control group. An additional 58 reported on the frequency of symptoms in a cohort of patients with DCM. The most frequent and sensitive symptoms in DCM include unspecified paresthesias (86%), hand numbness (82%) and hand paresthesias (79%). Neck and/or shoulder pain was present in 51% of patients with DCM, whereas a minority had back (19%) or lower extremity pain (10%). Bladder dysfunction was uncommon (38%) although more frequent than bowel (23%) and sexual impairment (4%). Gait impairment is also commonly seen in patients with DCM (72%). CONCLUSION: Patients with DCM present with many different symptoms, most commonly sensorimotor impairment of the upper extremities, pain, bladder dysfunction and gait disturbance. If patients present with a combination of these symptoms, further neuroimaging is indicated to confirm the diagnosis of DCM.

Paleontological reconstructions of plankton community structure during warm periods of the Cenozoic (last 66 million years) reveal that deep-dwelling 'twilight zone' (200-1000 m) plankton were less abundant and diverse, and lived much closer to the surface, than in colder, more recent climates. We suggest that this is a consequence of temperature's role in controlling the rate that sinking organic matter is broken down and metabolized by bacteria, a process that occurs faster at warmer temperatures. In a warmer ocean, a smaller fraction of organic matter reaches the ocean interior, affecting food supply and dissolved oxygen availability at depth. Using an Earth system model that has been evaluated against paleo observations, we illustrate how anthropogenic warming may impact future carbon cycling and twilight zone ecology. Our findings suggest that significant changes are already underway, and without strong emissions mitigation, widespread ecological disruption in the twilight zone is likely by 2100, with effects spanning millennia thereafter.

The end-Cretaceous bolide impact triggered the devastation of marine ecosystems. However, the specific kill mechanism(s) are still debated, and how primary production subsequently recovered remains elusive. We used marine plankton microfossils and eco-evolutionary modeling to determine strategies for survival and recovery, finding that widespread phagotrophy (prey ingestion) was fundamental to plankton surviving the impact and also for the subsequent reestablishment of primary production. Ecological selectivity points to extreme post-impact light inhibition as the principal kill mechanism, with the marine food chain temporarily reset to a bacteria-dominated state. Subsequently, in a sunlit ocean inhabited by only rare survivor grazers but abundant small prey, it was mixotrophic nutrition (autotrophy and heterotrophy) and increasing cell sizes that enabled the eventual reestablishment of marine food webs some 2 million years later.

Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

The assessment, diagnosis, operative and nonoperative management of degenerative cervical myelopathy (DCM) have evolved rapidly over the last 20 years. A clearer understanding of the pathobiology of DCM has led to attempts to develop objective measurements of the severity of myelopathy, including technology such as multiparametric magnetic resonance imaging, biomarkers, and ancillary clinical testing. New pharmacological treatments have the potential to alter the course of surgical outcomes, and greater innovation in surgical techniques have made surgery safer, more effective and less invasive. Future developments for the treatment of DCM will seek to improve the diagnostic accuracy of imaging, improve the objectivity of clinical assessment, and increase the use of surgical technology to ensure the best outcome is achieved for each individual patient.

The concentration of CO2 in the atmosphere is sensitive to changes in the depth at which sinking particulate organic matter is remineralized: often described as a change in the exponent "b" of the Martin curve. Sediment trap observations from deep and intermediate depths suggest there is a spatially heterogeneous pattern of b, particularly varying with latitude, but disagree over the exact spatial patterns. Here we use a biogeochemical model of the phosphorus cycle coupled with a steady-state representation of ocean circulation to explore the sensitivity of preformed phosphate and atmospheric CO2 to spatial variability in remineralization depths. A Latin hypercube sampling method is used to simultaneously vary the Martin curve independently within 15 different regions, as a basis for a regression-based analysis used to derive a quantitative measure of sensitivity. Approximately 30 % of the sensitivity of atmospheric CO2 to changes in remineralization depths is driven by changes in the subantarctic region (36 to 60° S) similar in magnitude to the Pacific basin despite the much smaller area and lower export production. Overall, the absolute magnitude of sensitivity is controlled by export production, but the relative spatial patterns in sensitivity are predominantly constrained by ocean circulation pathways. The high sensitivity in the subantarctic regions is driven by a combination of high export production and the high connectivity of these regions to regions important for the export of preformed nutrients such as the Southern Ocean and North Atlantic. Overall, regionally varying remineralization depths contribute to variability in CO2 of between around 5 and 15 ppm, relative to a global mean change in remineralization depth. Future changes in the environmental and ecological drivers of remineralization, such as temperature and ocean acidification, are expected to be most significant in the high latitudes where CO2 sensitivity to remineralization is also highest. The importance of ocean circulation pathways to the high sensitivity in subantarctic regions also has significance for past climates given the importance of circulation changes in the Southern Ocean.

Despite the important role of planktonic foraminifera in regulating the ocean carbonate production and their unrivalled value in reconstructing paleoenvironments, our knowledge on their ecology is limited. A variety of observational techniques such as plankton tows, sediment traps and experiments have contributed to our understanding of foraminifera ecology. But, fundamental questions around costs and benefits of calcification and the effect of nutrients, temperature and ecosystem structure on these organisms remain unanswered. To tackle these questions, we take a novel mechanistic approach to study planktonic foraminifera ecology based on trait theory. We develop a zero-dimensional (0-D) trait-based model to account for the biomass of prolocular (20 m) and adult (160 m) stages of non-spinose foraminifera species and investigate their potential interactions with phytoplankton and other zooplankton under different temperature and nutrient regimes. Building on the costs and benefits of calcification, we model two ecosystem structures to explore the effect of resource competition and temperature on planktonic foraminifera biomass. By constraining the model results with ocean biomass estimations of planktonic foraminifera, we estimate that the energetic cost of calcification could be about 10 50% and 10 40% for prolocular and adult stages respectively. Our result suggest that the shell provides protection against processes other than predation (e.g. pathogen invasion). We propose that the low standing biomass of planktonic foraminifera plays a key role in their survival from predation, along with their shell protection. Our model suggests a shift from temperature as a main regulator of foraminifera biomass in the early developmental stage to resource competition for adult biomass.

We present an extension to the carbon-centric Grid Enabled Integrated Earth system model (cGEnIE) that explicitly accounts for the growth and interaction of an arbitrary number of plankton species. The new package (ECOGEM) replaces the implicit, flux-based parameterisation of the plankton community currently employed, with explicitly resolved plankton populations and ecological dynamics. In ECOGEM, any number of plankton species, with ecophysiological traits (e.g. growth and grazing rates) assigned according to organism size and functional group (e.g. phytoplankton and zooplankton) can be incorporated at runtime. We illustrate the capability of the marine ecology enabled Earth system model (EcoGEnIE) by comparing results from one configuration of ECOGEM (with eight generic phytoplankton and zooplankton size classes) to climatological and seasonal observations. We find that the new ecological components of the model show reasonable agreement with both global-scale climatological and local-scale seasonal data. We also compare EcoGEnIE results to the existing biogeochemical incarnation of cGEnIE. We find that the resulting global-scale distributions of phosphate, iron, dissolved inorganic carbon, alkalinity, and oxygen are similar for both iterations of the model. A slight deterioration in some fields in EcoGEnIE (relative to the data) is observed, although we make no attempt to re-tune the overall marine cycling of carbon and nutrients here. The increased capabilities of EcoGEnIE in this regard will enable future exploration of the ecological community on much longer timescales than have previously been examined in global ocean ecosystem models and particularly for past climates and global biogeochemical cycles.

On geological time-scales, the production and degree of recycling of biogenic carbon in the marine realm and ultimately its removal to sediments, exerts a dominant control on atmospheric CO2 and hence variability in climate. This is a highly complex system involving a myriad of inter-connected biological, chemical, and physical processes. For this reason alone, linking observations, often highly abstracted in the form of proxies, to the primary processes involved and ultimately to explanatory hypotheses for specific geological events and transitions, is challenging. The past few decades have seen a progressive improvement in theoretical and process-based understanding of the various components that make up the marine carbon cycle and, hand-in-hand with this, the development of numerical model representations of the complete system. Models have also been designed and/or adapted with paleoclimate questions in mind and applied to quantitatively explore the role of the marine carbon cycle in both perturbations and long-term geologic evolutionary trends in global climate, and possible feedbacks between them. However, we must ask whether paleoclimate models incorporate sufficiently appropriate representations of the dynamics and sensitivities of the marine carbon cycle, and indeed, whether in the geological context, we really know what these dynamics are. Here we provide a comprehensive overview of how marine carbon cycling and the biological carbon pump is treated in available paleoclimate models, with the aim of critically evaluating their ability to help interpret past marine carbon cycle and climate dynamics. To this end, we first provide an overview of commonly used paleoclimate models and some of their associated paleo-applications, drawing from a wide range of global carbon cycle box models and Earth system Models of Intermediate Complexity (EMICs). Secondly, we review and evaluate the three dominant processes involved in the cycling of organic and inorganic carbon in the marine system and how they are represented in models, namely: biological productivity at the ocean surface, remineralisation/dissolution of particulate carbon within the water column, and the benthic-pelagic coupling at the seafloor. We generate and employ illustrative examples using the model GENIE to show how different parameterisations of water-column and sediment processes can lead to significantly different model projections. Our compilation reveals the prevalence of static parameterisations of marine carbon cycling among existing paleoclimate models, which are commonly empirically derived from present-day observations. Although such approaches tend to represent carbon transfer in the modern ocean well, they are potentially compromised in their ability to reflect the true degree of freedom and strength of feedbacks with respect to past climate events, particularly those characterised by environmental boundary conditions that differ fundamentally from today. Finally, we discuss the importance of using models of different complexities and how questions of model uncertainty may start to be addressed.

This study examined attachment processes of college student veterans and nonveterans and further examined how veteran status and attachment style directly and indirectly predict relationship functioning. Results indicated that student veterans were more often dismissing in their attachment style but less often preoccupied than nonveteran students. Veteran status moderated the association between attachment style and dyadic consensus. The contributions of attachment and communication processes to overall relationship adjustment differed for student veterans and nonveterans.