Abstract The “salty-gets-saltier, fresh-gets-fresher” paradigm predicts that an intensifying hydrological cycle should freshen the climatologically fresh Southern Ocean. Here we show the opposite: sea surface salinity increased at ~0.03 decade − 1 across 40–50°S during 2004–2024, most coherently in the Pacific and Atlantic sectors. We attribute this salinification to the poleward expansion of the southern subtropical gyres, which advects saline subtropical water into latitudes of steepest meridional salinity gradient. The Subtropical and Subantarctic Fronts, tracked via the 35 and 34 isohalines, migrate poleward at unequal rates (–0.46° and –0.18° decade −1 ), narrowing the frontal corridor and sharpening the cross-frontal gradient. A mixed-layer budget shows that horizontal advection dominates the salinity trend, roughly tripling the opposing contribution from surface freshwater flux. Forced by poleward-intensifying westerlies and a positive Southern Annular Mode trend, this circulation-driven salinification demonstrates that ocean dynamics can override freshwater forcing, cautioning against interpreting salinity trends as direct fingerprints of the hydrological cycle.

Historical profile observations characterize sound channel distributions across the North Atlantic Ocean and their relationships to water masses as well as support assessment of numerical ocean model performance. The focus of this study is sound channels above 500 m depth, below the sonic layer, and having a cutoff frequency of less than 200 Hz. Sound channels occur frequently around the Rockall Trough, the Reykjanes Ridge, the Labrador Current off the Grand Banks, and the Gulf Stream. Seasonal effects result in higher occurrences in spring and summer when the sonic layer is shallower. Water mass properties at sound channel axes exhibit lower salinity than at channel tops and bottoms on average, which implies cold, low salinity surface waters have been subducted into the water column. As ocean numerical model resolution is increased vertically and horizontally, model results are more consistent with observed sound channel occurrence rates as well as water mass properties. In addition to low salinity waters laterally interleaving with warmer, more saline waters of equal density, several ocean processes can move relatively cold low salinity surface waters to the subsurface including subduction from surface fronts along isopycnals and winter convection. Model deficiencies are considered relative to water mass distributions and possible physical processes.

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO2) in detecting term infants with brain injury. The study also examined whether quantitative rcSO2 features are associated with grade of hypoxic ischaemic encephalopathy (HIE). We analysed 58 term infants with HIE (>36 weeks of gestational age) enrolled in a prospective observational study. All newborn infants had a period of continuous rcSO2 monitoring and magnetic resonance imaging (MRI) assessment during the first week of life. rcSO2 Signals were pre-processed and quantitative features were extracted. Machine-learning and deep-learning models were developed to detect adverse outcome (brain injury on MRI or death in the first week) using the leave-one-out cross-validation approach and to assess the association between rcSO2 and HIE grade (modified Sarnat - at 1 h). The machine-learning model (rcSO2 excluding prolonged relative desaturations) significantly detected infant MRI outcome or death in the first week of life [area under the curve (AUC) = 0.73, confidence interval (CI) = 0.59-0.86, Matthew's correlation coefficient = 0.35]. In agreement, deep learning models detected adverse outcome with an AUC = 0.64, CI = 0.50-0.79. We also report a significant association between rcSO2 features and HIE grade using a machine learning approach (AUC = 0.81, CI = 0.73-0.90). We conclude that automated analysis of rcSO2 using machine learning methods in term infants with HIE was able to determine, with modest accuracy, infants with adverse outcome. De novo approaches to signal analysis of NIRS holds promise to aid clinical decision making in the future. KEY POINTS: Hypoxic-induced neonatal brain injury contributes to both short- and long-term functional deficits. Non-invasive continuous monitoring of brain oxygenation using near-infrared- spectroscopy offers a potential new insight to the development of serious injury. In this study, characteristics of the NIRS signal were summarised using either predefined features or data-driven feature extraction, both were combined with a machine learning approach to predict short-term brain injury. Using data from a cohort of term infants with hypoxic ischaemic encephalopathy, the present study illustrates that automated analysis of regional cerebral oxygen saturation rcSO2, using either machine learning or deep learning methods, was able to determine infants with adverse outcome.

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

The purpose of this study is to evaluate the association of Electrical Cardiometry (EC)-derived cardiac output indexed to weight (CO) and its changes during the first 48 h in relation to adverse short-term outcome in very preterm infants. In this prospective observational study of preterm infants < 32 weeks gestational age (GA), the combined adverse outcome was defined as mortality or abnormal cranial ultrasound (any grade intracranial hemorrhage (ICH) or periventricular leukomalacia) within the first 2 weeks postnatally. Logistic regression models were used to investigate the association between median CO and outcome and mixed-effects models for the time trajectory of CO. In the absence of device-specific thresholds for low or high CO, no thresholds were used in our analysis. Fifty-three infants (median (IQR) GA 29.0 (25.4–30.6) weeks, birthweight 1020 (745–1505) g) were included in the analysis. Median CO was 241 (197–275) mL/kg/min for the adverse outcome and 198 (175–227) mL/kg/min for normal outcome (odds ratio (OR) (95% confidence interval (95% CI)), 1.01 (1.00 to 1.03); p = 0.028). After adjustment for GA, the difference was not significant (adjusted OR (95% CI), 1.01 (0.99 to 1.02); p = 0.373). CO trajectory did not differ by outcome ( p = 0.352). A post hoc analysis revealed an association between CO time trajectory and ICH ≥ grade 2. Conclusions : EC-derived CO estimates within 48 h postnatally were not independently associated with brain injury (any grade) or mortality in the first 14 days of life. CO time trajectory was found to be associated with ICH ≥ grade 2. What is Known: • Bioreactance-derived cardiac output indexed to bodyweight (CO) in the transitional period has been associated with adverse short-term outcome in preterm infants. What is New: • Electrical Cardiometry (EC)-derived CO measurements in very preterm infants during the transitional period are not independently associated with adverse outcome (death or ultrasound detected brain damage) within 2 weeks postnatally. • In the first 48 h EC-derived CO increases over time and is higher in extremely preterm infants compared to very preterm and differs from previously reported bioreactance-derived CO values.

Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.

The mechanical and electrical design and control system firmware/software for the Tethered Ocean Profiler (TOP), are presented. TOP is designed to autonomously sample the evolving upper polar ocean water properties from 200 m depth to within 15 cm of the ice-ocean interface while drifting with the sea ice. In addition, by repeatedly traveling up to the ice-ocean interface, TOP is able to estimate the evolving draft of the floe supporting the instrument. To date, 7 TOP systems have been fielded in the Canada Basin sector of the Arctic. Engineering data and initial scientific findings based on those data are reported and plans for system enhancements discussed.

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.

Objective: The primary aim of this study is to develop and evaluate algorithms for neonatal EEG artefact detection. The secondary aim is to subsequently assess its application as a post-processing routine for automated EEG grading of background abnormalities in neonatal hypoxic-ischemic encephalopathy (HIE). Methods: A database of neonatal EEG with expertly annotated artefacts was used to train and validate machine learning models to automatically identify EEG epochs containing artefacts. Three approaches were developed and compared, specifically, a simple threshold-based digital signal processing (DSP) method, a machine learning method, and a deep learning method. The artefact detection classifier was subsequently assessed as a post-processing tool to assist in the application of automated EEG grading of HIE. A new deep learning model for grading the EEG was developed by training an existing network on a large, multi-centre dataset. The artefact detection algorithm was integrated into the grading algorithm through a post-processing routine. Results: Using a database containing 19 h of EEG from 51 patients with per-channel and per-second annotations of artefacts, a deep learning convolutional neural network solution achieved best performance for artefact detection with an area under the operating characteristic curve (AUC) of 0.84, compared to an AUC of 0.68 and 0.82 for a DSP method and a random-kernel ridge-classifier model, respectively. The automated EEG grading algorithm was trained and tested on 653 h of EEG from 181 patients, which achieved an accuracy of 82.8 % (95 % CI: 80.5 % to 85.2 %). The percentage of detected artefacts in the misclassified epochs was not statistically different (p = 0.568) compared to that of correctly classified epochs. Using artefact detection, a small number of epochs were removed from grading, resulting in a minor increase in accuracy for the EEG grading algorithm from 82.6 % to 83.6 %. Conclusion: Deep learning methods achieved highest classification performance for neonatal EEG artefact detection, although a ridge classifier using random kernels achieved comparable performance without significant parameter tuning or training time. The inclusion of artefact detection in automated EEG grading does not significantly improve accuracy in our curated dataset, but does allow for a quality measure to be presented alongside the automated EEG grades which may increase user confidence in its real-world application.

Background and aimsHeart rate variability (HRV) has previously been assessed as a biomarker for brain injury and prognosis in neonates. The aim of this cohort study was to use HRV to predict the electroencephalography (EEG) grade in neonatal hypoxic-ischaemic encephalopathy (HIE) within the first 12 h.MethodsWe included 120 infants with HIE recruited as part of two European multi-centre studies, with electrocardiography (ECG) and EEG monitoring performed before 12 h of age. HRV features and EEG background were assessed using the earliest 1 h epoch of ECG-EEG monitoring. HRV was expressed in time, frequency and complexity features. EEG background was graded from 0-normal, 1-mild, 2-moderate, 3-major abnormalities to 4-inactive. Clinical parameters known within 6 h of birth were collected (intrapartum complications, foetal distress, gestational age, mode of delivery, gender, birth weight, Apgar at 1 and 5, assisted ventilation at 10 min). Using logistic regression analysis, prediction models for EEG severity were developed for HRV features and clinical parameters, separately and combined. Multivariable model analysis included 101 infants without missing data.ResultsOf 120 infants included, 54 (45%) had normal-mild and 66 (55%) had moderate-severe EEG grade. The performance of HRV model was AUROC 0.837 (95% CI: 0.759–0.914) and clinical model was AUROC 0.836 (95% CI: 0.759–0.914). The HRV and clinical model combined had an AUROC of 0.895 (95% CI: 0.832–0.958). Therapeutic hypothermia and anti-seizure medication did not affect the model performance.ConclusionsEarly HRV and clinical information accurately predicted EEG grade in HIE within the first 12 h of birth. This might be beneficial when EEG monitoring is not available in the early postnatal period and for referral centres who may want some objective information on HIE severity.

To examine the impact of parent‐led massage on the sleep electroencephalogram (EEG) features of typically developing term‐born infants at 4 months.

The vertical structure of subinertial variability is examined using full-depth horizontal velocity and vertical isopycnal displacement observations derived from the Ocean Observatory Initiative (OOI). Vertical profiles on time scales between 100 h and 1 yr or longer are characterized through empirical orthogonal function decomposition and qualitatively compared with theoretical modal predictions for the cases of flat, sloping, and rough bathymetry. OOI observations were obtained from mooring clusters at four deep-ocean sites: Argentine Basin, Southern Ocean, Station Papa, and Irminger Sea. Because no single OOI mooring in these arrays provides temperature, salinity, and horizontal velocity information over the full water column, sensor observations from two or more moorings are combined. Depths greater than -150-300 m were sampled by McLane moored profilers; in three of the four cases, two profilers were utilized on the moorings. Because of instrument failures on the deployments examined here, only -2 yr of full-ocean-depth observations are available from three of the four sites and some 31 yr from the other. Results from the OOI "global" sites are contrasted with a parallel analysis of 3.5 yr of observations about the axis of the Gulf Stream where much of the subinertial variability is associated with stream meandering past the moorings. Looking across the observations, no universal vertical structure is found that characterizes the subinertial variability at the five sites examined; regional bathymetry, stratification, baroclinicity, nonlinearity, and the forcing (both local and remote) likely all play a role in shaping the vertical structure of the subinertial variability in individual ocean regions.

Objective: To determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns.Methods: We compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32-37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5-3 Hz; theta, 3-8 Hz; alpha, 8-15 Hz; beta, 15-30 Hz) and 3 higher-frequency bands (gamma1, 30-48 Hz; gamma2, 52-99 Hz; gamma3, 107-127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort.Results: We found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12-0.88) and 0.66 (0.36-0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups. High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set. Conclusions: This is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

Hintergrund Electrical Cardiometry (EC) ermöglicht eine kontinuierliche, nicht-invasive und objektive Überwachung des Herzzeitvolumens (CO) [1]. Zu den gemessenen Parametern gehört der CO (in mL/min/kg Körpergewicht). Es wurde gezeigt, dass der Verlauf des CO, gemessen mit einer vergleichbaren, aber anderen Technologie (Bioreactance) über die ersten beiden postnatalen Tage, mit einer Hirnschädigung oder NEC bei Frühgeborenen (FG, Geburtsgewicht < 1250g) assoziiert ist [2]. Bei FG mit ungünstigem Ausgang wurden niedrigere CO während des ersten, aber höhere während des zweiten Tages festgestellt. Diesbezüglich liegen keine Daten für EC vor.

The Arctic Ocean's Beaufort Gyre is a dominant feature of the Arctic system, a prominent indicator of climate change, and possibly a control factor for high-latitude climate. The state of knowledge of the wind-driven Beaufort Gyre is reviewed here, including its forcing, relationship to sea-ice cover, source waters, circulation, and energetics. Recent decades have seen pronounced change in all elements of the Beaufort Gyre system. Sea-ice losses have accompanied an intensification of the gyre circulation and increasing heat and freshwater content. Present understanding of these changes is evaluated, and time series of heat and freshwater content are updated to include the most recent observations.

Aim To describe early cerebral oxygenation (cSO(2)) and fractional tissue oxygen extraction (FTOE) values and their evolution over the first days of life in infants with all grades of hypoxic-ischaemic encephalopathy (HIE) and to determine whether cSO(2) and FTOE measured early (6 and 12 h) can predict short-term outcome. Methods Prospective, observational study of cerebral near-infrared spectroscopy (NIRS) in infants >36 weeks' gestation with HIE. Ten one-hour epochs of cSO(2) and FTOE were extracted for each infant over the first 84 h. Infants with moderate and severe HIE received therapeutic hypothermia (TH). Abnormal outcome was defined as abnormal magnetic resonance imaging (MRI) and/or death. Results Fifty-eight infants were included (28 mild, 24 moderate, 6 severe). Median gestational age was 39.9 weeks (IQR 38.1-40.7) and birthweight was 3.35 kgs (IQR 2.97-3.71). cSO(2) increased and FTOE decreased over the first 24 h in all grades of HIE. Compared to the moderate group, infants with mild HIE had significantly higher cSO(2) at 6 h (p = 0.003), 9 h (p = 0.009) and 12 h (p = 0.032) and lower FTOE at 6 h (p = 0.016) and 9 h (0.029). cSO(2) and FTOE at 6 and 12 h did not predict abnormal outcome. Conclusion Infants with mild HIE have higher cSO(2) and lower FTOE than those with moderate or severe HIE in the first 12 h of life. cSO(2) increased in all grades of HIE over the first 24 h regardless of TH status.

INTRODUCTION:The aim was to evaluate the agreement between cardiac output estimates obtained by electrical cardiometry (EC) and transthoracic echocardiography (TTE) in very preterm infants.METHODS:This is a single-center prospective observational study in infants born<32 weeks gestational age within 48 h of birth. Continuous EC was recorded and simultaneous TTE obtained on day 1 and day 2 of life. Blinded TTE measurements were performed within a 10 s timeframe using beat-to-beat EC data. The primary outcome was %error of left ventricular (LV) output in milliliters per kilogram per minute (cardiac index (CI)) obtained by TTE compared to LV-CI from EC. Secondary outcome parameters were bias, %bias, limits of agreement and include measures of right ventricular (RV) output and LV systolic time intervals.RESULTS:Analysis was performed for 34 infants (median (IQR) gestational age 29 + 0 (24 + 5 to 30 + 6) weeks + days, birthweight 960 (748 to 1,490) grams) including 44 pairwise LV output measurements on 24 participants (22 on day 1 and day 2). The %error was 54% for LV-CI (EC: 214 (38) mL/kg/min vs. TTE: 163 (47) mL/kg/min). The %error was 78% for RV-CI (EC: 213 (37) mL/kg/min vs. TTE: 241 (77) mL/kg/min). While only LV-CI values affected LV-CI bias, signal quality, heart rate, and RV-CI values affected RV-CI bias.CONCLUSION:EC is not interchangeable with TTE to estimate indices of LV or RV output in very preterm infants within the first 48 h postnatally. EC may not measure LV output distinctly in very preterm infants with intra- and extracardiac shunts.

DATA REPORT article Front. Mar. Sci., 30 June 2022Sec. Ocean Observation https://doi.org/10.3389/fmars.2022.910979