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Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs of multiple iCVIs to produce a streaming label of either “over”, “under”, or “correctly” partitioned. Experiments were conducted on generalized synthetic and real-world data sets to demonstrate the efficacy and application of this method. Results of 100% accuracy were achieved in labeling partition quality on real-world data sets including MNIST and FLIR ADAS, demonstrating that the Meta- iCVI is a powerful and efficient tool for classifying partition quality in a variety of conditions. Its introduction should empower new and more efficient streaming clustering techniques. Additionally, we believe this to be the first implementation of an ensemble iCVI metric and the first time iCVI validation performance has been evaluated on randomized sample presentation.

In sequential estimation methods often used in oceanic and general climate calculations of the state and of forecasts, observations act mathematically and statistically as source or sink terms in conservation equations for heat, salt, mass, and momentum. These artificial terms obscure the inference of the system's variability or secular changes. Furthermore, for the purposes of calculating changes in important functions of state variables such as total mass and energy or volumetric current transports, results of both filter and smoother-based estimates are sensitive to misrepresentation of a large variety of parameters, including initial conditions, prior uncertainty covariances, and systematic and random errors in observations. Here, toy models of a coupled mass–spring oscillator system and of a barotropic Rossby wave system are used to demonstrate many of the issues that arise from such misrepresentations. Results from Kalman filter estimates and those from finite interval smoothing are analyzed. In the filter (and prediction) problem, entry of data leads to violation of conservation and other invariant rules. A finite interval smoothing method restores the conservation rules, but uncertainties in all such estimation results remain. Convincing trend and other time-dependent determinations in “reanalysis-like” estimates require a full understanding of models, observations, and underlying error structures. Application of smoother-type methods that are designed for optimal reconstruction purposes alleviate some of the issues.

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds.

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.

Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible.

Abstract Background The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI. Findings Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain injury, biomarkers can enter the blood through pathways that do not require a breach in this barrier. A definitive accounting for the pathways that biomarkers follow from the brain to the blood after mTBI has not been performed. Although preliminary investigations of blood biomarkers kinetics after TBI are available, our current knowledge is incomplete and definitive studies are needed. Optimal sampling times for biomarkers after mTBI have not been established. Kinetic models of blood biomarkers can be informative, but more precise estimates of kinetic parameters are needed. Confounding factors for blood biomarker levels have been identified, but corrections for these factors are not routinely made. Little evidence has emerged to date to suggest that blood biomarker levels correlate with clinical measures of mTBI severity. The significance of elevated biomarker levels thirty or more days following mTBI is uncertain. Blood biomarkers have shown a modest but not definitive ability to distinguish concussed from non-concussed subjects, to detect sub-concussive hits to the head, and to predict recovery from mTBI. Blood biomarkers have performed best at distinguishing CT scan positive from CT scan negative subjects after mTBI.

The cytoskeletal protein tau is implicated in the pathogenesis of Alzheimer's disease which is characterized by intra-neuronal neurofibrillary tangles containing abnormally phosphorylated insoluble tau. Levels of soluble tau are elevated in the brain, the CSF, and the plasma of patients with Alzheimer's disease. To better understand the causes of these elevated levels of tau, we propose a three-compartment kinetic model (brain, CSF, and plasma). The model assumes that the synthesis of tau follows zero-order kinetics (uncorrelated with compartmental tau levels) and that the release, absorption, and clearance of tau is governed by first-order kinetics (linearly related to compartmental tau levels). Tau that is synthesized in the brain compartment can be released into the interstitial fluid, catabolized, or retained in neurofibrillary tangles. Tau released into the interstitial fluid can mix with the CSF and eventually drain to the plasma compartment. However, losses of tau in the drainage pathways may be significant. The kinetic model estimates half-life of tau in each compartment (552 h in the brain, 9.9 h in the CSF, and 10 h in the plasma). The kinetic model predicts that an increase in the neuronal tau synthesis rate or a decrease in tau catabolism rate best accounts for observed increases in tau levels in the brain, CSF, and plasma found in Alzheimer's disease. Furthermore, the model predicts that increases in brain half-life of tau in Alzheimer's disease should be attributed to decreased tau catabolism and not to increased tau synthesis. Most clearance of tau in the neuron occurs through catabolism rather than release to the CSF compartment. Additional experimental data would make ascertainment of the model parameters more precise.

Abstract Background Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks. Methods We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics. Results Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric. Conclusion Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances.

Validation is one of the most important aspects of clustering, particularly when the user is designing a trustworthy or explainable system. However, most clustering validation approaches require batch calculation. This is an important gap because of the value of clustering in real-time data streaming and other online learning applications. Therefore, interest has grown in providing online alternatives for validation. This paper extends the incremental cluster validity index (iCVI) family by presenting incremental versions of Calinski-Harabasz (iCH), Pakhira-Bandyopadhyay-Maulik (iPBM), WB index (iWB), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP), Representative Cross Entropy (irH), and Conn_Index (iConn_Index). This paper also provides a thorough comparative study of correct, under- and over-partitioning on the behavior of these iCVIs, the Partition Separation (PS) index as well as four recently introduced iCVIs: incremental Xie-Beni (iXB), incremental Davies-Bouldin (iDB), and incremental generalized Dunn's indices 43 and 53 (iGD43 and iGD53). Experiments were carried out using a framework that was designed to be as agnostic as possible to the clustering algorithms. The results on synthetic benchmark data sets showed that while evidence of most under-partitioning cases could be inferred from the behaviors of the majority of these iCVIs, over-partitioning was found to be a more challenging problem, detected by fewer of them. Interestingly, over-partitioning, rather then under-partitioning, was more prominently detected on the real-world data experiments within this study. The expansion of iCVIs provides significant novel opportunities for assessing and interpreting the results of unsupervised lifelong learning in real-time, wherein samples cannot be reprocessed due to memory and/or application constraints.

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of analysis to enhance the quality and the explainability of results. This methodology is applicable to other clinical data sets that exhibit significant heterogeneity as well as other diverse data science applications in biomedicine and elsewhere.

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI.

This paper presents a distributed optimal model reference adaptive control approach for solving containment control of heterogeneous multi-agent systems (MASs) with non-autonomous leaders. First, a fully distributed adaptive observer is designed to provide for each agent the desired reference trajectory by estimating the convex hull spanned by leaders. The distributed observer dynamics serves as a reference model for each follower to synchronize. The global communication graph information or the leader dynamics is not required to design the observer. In contrast to existing model reference adaptive controllers (MRAC) for single-agent systems and containment control solutions for MASs, the proposed MRAC approach imposes optimality and presents a distributed adaptive optimal solution to the containment control problem. To impose optimality, a performance function is defined based on the adaptive observers' states as well as the followers' local measurements. It is shown that considering non-autonomous leaders in this optimal control problem leads to solving inhomogeneous algebraic Riccati equations (AREs), instead of normal AREs in standard optimal control problems. To obviate the requirement of knowing the agents' dynamics, an off-policy reinforcement learning approach implemented on an actor-critic structure is utilized for solving the inhomogeneous ARE. A simulation example is conducted to illustrate the effectiveness of the presented method.

An earlier analysis of pore-water salinity (chlorinity) in two deep-sea cores, using terminal constraint methods of control theory, concluded that although a salinity amplification in the abyss was possible during the LGM, it was not required by the data. Here the same methodology is applied to δ18Ow in the upper 100 m of four deep-sea cores. An ice volume amplification to the isotopic ratio is, again, consistent with the data but not required by it. In particular, results are very sensitive, with conventional diffusion values, to the assumed initial conditions at −100 ky and a long list of noise (uncertainty) assumptions. If the calcite values of δ18O are fully reliable, then published enriched values of the ratio in seawater are necessary to preclude sub-freezing temperatures, but the seawater δ18O in pore fluids does not independently require the conclusion.

This paper presents the ECCO v4 non-linear inverse modeling framework and its baseline solution for the evolving ocean state over the period 1992–2011. Both components are publicly available and subjected to regular, automated regression tests. The modeling framework includes sets of global conformal grids, a global model setup, implementations of data constraints and control parameters, an interface to algorithmic differentiation, as well as a grid-independent, fully capable Matlab toolbox. The baseline ECCO v4 solution is a dynamically consistent ocean state estimate without unidentified sources of heat and buoyancy, which any interested user will be able to reproduce accurately. The solution is an acceptable fit to most data and has been found to be physically plausible in many respects, as documented here and in related publications. Users are being provided with capabilities to assess model–data misfits for themselves. The synergy between modeling and data synthesis is asserted through the joint presentation of the modeling framework and the state estimate. In particular, the inverse estimate of parameterized physics was instrumental in improving the fit to the observed hydrography, and becomes an integral part of the ocean model setup available for general use. More generally, a first assessment of the relative importance of external, parametric and structural model errors is presented. Parametric and external model uncertainties appear to be of comparable importance and dominate over structural model uncertainty. The results generally underline the importance of including turbulent transport parameters in the inverse problem.

Quantifying time-responses of the ocean to tracer input is important to the interpretation of paleodata from sediment cores – because surface-injected tracers do not instantaneously spread throughout the ocean. To obtain insights into the time response, a computationally efficient state-transition matrix method is demonstrated and used to compute successive states of passive tracer concentrations in the global ocean. Times to equilibrium exceed a thousand years for regions of the global ocean outside of the injection and convective areas and concentration gradients give time-lags from hundreds to thousands of years between the Atlantic and Pacific abyss, depending on the injection region and the nature of the boundary conditions employed. Equilibrium times can be much longer than radiocarbon ages – both because the latter are strongly biased towards the youngest fraction of fluid captured in a sample, and because they represent distinct physical properties. Use of different boundary conditions – concentration, or flux – produces varying response times, with the latter depending directly upon pulse duration. With pulses, the sometimes very different transient approach to equilibrium in various parts of the ocean generates event identification problems.

Background: The North American overdose crisis has continued at unprecedented rates with more than 100,000 overdose deaths occurring in the United States (US) in 2022. Overdose deaths have increasingly been polysubstance-involved, with novel substances (e.g., xylazine) complicating overdose risk and health outcomes. Understanding the effects of-and responses to-a changing drug supply among people who use drugs is critical to modifying harm reduction strategies to be more responsive to people's needs., Methods: This qualitative study draws on data collected from May to December 2022 in Rhode Island. Data include in-depth interviews with 50 people who use drugs and observational fieldwork in spaces frequented by participants (e.g., encampments, drop-in centers). Qualitative data were analyzed thematically drawing on concepts of situated rationality., Results: Participants described significant changes in the drug supply, with many attributing these transitions to COVID-19. Most participants characterized the local supply as "synthetic" with textures, color, and taste evolving. Notably, participants emphasized adverse outcomes related to available supplies, including during use (e.g., intense burning sensations) and post-consumption (e.g., heavy sedation, ongoing withdrawal, necrosis). Given the complex supply, participants highlighted the increased risk of overdose and shared how they altered their use practices to manage evolving health risks., Conclusion: Our results underscore how people who use drugs characterized the local drug supply, including perceived changes to supply contents. Implementing and scaling up harm reduction interventions that reduce risk and reinforce the agency of people who use drugs are urgently needed to effectively address the overdose crisis., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Before the coronavirus disease 2019 pandemic, federal law required in-person evaluation before buprenorphine initiation. Regulatory changes during the pandemic allow for buprenorphine initiation by audio-only or audiovisual telehealth. Little is known about treatment engagement after buprenorphine initiation conducted via audio-only telehealth., Methods: A retrospective cohort study of 94 individuals who received initial treatment through an audio-only encounter between April 2020 and February 2021 was performed. Participant demographics, substance use history, withdrawal symptoms, 30-day treatment engagement, and adverse outcomes were determined by an electronic chart and REDcap database review. Subsequent buprenorphine prescriptions filled within 30 days of the initial encounter were tracked through the Rhode Island Prescription Drug Monitoring Program., Results: Buprenorphine was prescribed for 94 individuals. Most (92 of 94 [97.9%]) filled their prescription within 30 days. Most had previously taken buprenorphine, including prescribed (42 of 92 [45.7%]) and nonprescribed (58 of 92 [63.0%]). Two thirds were in opioid withdrawal at the time of the call (61 of 92 [66.3%]) with a mean Subjective Opioid Withdrawal Scale of 26.8 (range, 4-57). Four individuals experienced precipitated withdrawal (4 of 94 [4.3%]), and 2 reported persistent withdrawal at their follow-up visit (2 of 94 [2.1%]). More than 70% filled a subsequent prescription for buprenorphine within 30 days of the end of their hotline prescription (65 of 92 [70.7%]), on average of 5.88 days (range, 0-28) after completion of their telehealth prescription., Conclusions: Expanding telehealth-delivered buprenorphine care has the potential to address treatment gaps and facilitate delivery of on-demand services during peak motivation. This evaluation of audio-only buprenorphine initiation found high rates of unobserved buprenorphine initiation and treatment continuation with low rates of complications., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 American Society of Addiction Medicine.)

Fluid flows generate an acoustic noise field. In principle, oceanic flows on varying time and length scales produce a sound field and its detectability is considered here. A fragile lower bound analysis is made of the acoustic signature, using the Lighthill theory, of a simple train of boundary vortices generated by baroclinic tidal flows. Subject to numerous assumptions, the accompanying sound should be detectable within the hum band of seismo-acoustic pressure fields, and more generally, across the entire oceanic spectrum-likely through wave number analyses of spatially coherent acoustic array data.

Expanding addiction treatment services in Rhode Island has never been more urgent. Today, we face colliding syndemics of COVID-19, preventable drug overdoses, and HIV, with another year of record overdoses. While the treatment of substance use disorder (SUD) is an essential component of general medical care, numerous barriers prevent broader treatment access for patients in Rhode Island. Buprenorphine and methadone therapy have restrictions that are not applied to other areas in medicine, including for more dangerous medications. In this piece, we highlight existing barriers to care, applaud current progress being made in our state, and provide recommendations for next steps to turn the tide of this deadly epidemic. We hope that these proposed changes will help develop a robust treatment landscape for all patients with SUD in Rhode Island.

Despite its proven efficacy, buprenorphine remains dramatically underutilized for management of opioid use disorder largely due to onerous barriers to treatment initiation. During the COVID-19 pandemic, many substance use disorder treatment facilities have reduced their hours and services, exacerbating existing barriers. To this end, the U.S. Drug Enforcement Administration and Substance Abuse Mental Health Services Administration adjusted their guidelines to allow for new buprenorphine prescriptions following audio-only telehealth encounters, no longer requiring an in-person evaluation prior to treatment initiation. Under this new guidance, we established a 24/7 telephone hotline to function as a "tele-bridge" clinic where people with opioid use disorder can be linked with a buprenorphine prescriber in real-time for OUD assessment and unobserved buprenorphine initiation with connection to follow-up if appropriate. Additionally, we developed an ED callback protocol to reach patients recently seen for opioid overdose and facilitate their entry into care if interested. In this commentary we describe our hotline and ED callback protocols, discuss theoretical and anecdotal benefits to this approach, and advocate for continuation of current regulatory changes post-COVID-19 to maintain expanded access to novel treatment approaches., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Multivisceral transplant (MVTx) and isolated intestinal transplant (ITx) are complex surgical procedures. The subsequent proinflammatory state in the immediate postoperative period makes interpretation of blood markers difficult., Method: We aimed to establish the course of various blood markers after MVTx/ITx, and to evaluate their use as diagnostic markers of complications. This was a single center prospective cohort. We analyzed blood markers collected preoperatively, on alternate days for the first postoperative week, and then weekly for 4 weeks. This study was in compliance with The Declaration of Helsinki., Results: Over a 16-month period (July 2017-October 2018), 20 subjects aged 2 to 67 years with a median age of 24.5 years received MVTx/ITx. Twelve recipients (60%) had an infection. Neutrophil lymphocyte count ratio (NLCR) was higher than established upper limits of normal, regardless of infection status. NLCR and white blood cell count were useful to identify infected MVTx/ITx recipients, with P values <.05 for 2 and 1 of 7 time points post transplant, respectively. Higher preoperative eosinophil% predicted future acute cellular rejection (P value .023)., Conclusions: This is the first study to extensively track the course of blood markers post MVTx/ITx and identified NLCR and white blood cell count as potential diagnostic blood markers of infection., (Copyright © 2021 Elsevier Inc. All rights reserved.)

My career spanned the revolution in understanding of the large-scale fluid ocean, as modern electronics produced vast new capabilities. I started in the days of almost purely mechanical instruments operated by seagoing scientists, ones not so different from those used more than a century earlier. Elegant theories existed of hypothetical steady-state oceans. Today, we understand that the ocean is a highly turbulent fluid, interacting over global scales, and it is now studied by large teams using spacecraft and diverse sets of self-contained in situ instrumentation. Mine was an accidental career: I was lucky to be in the right place at the right time.

While post-traumatic stress disorder (PTSD) can develop after exposure to severe traumatic events, data have shown that individuals with high sensation-seeking personality traits are less prone to developing PTSD. The current study used the rodent environmental enrichment preclinical model of sensation-seeking to determine if similar sensation seeking effects in animal models of PTSD-like behaviors were found. The study also attempted to determine whether environmental enrichment altered the effects of midazolam on these PTSD-like behaviors. Male Sprague-Dawley rats were received at postnatal day (PND) 21 and placed into either an enriched (EC), isolated (IC), or social (SC) condition. Beginning on PND 51, the animals underwent 3 fear conditioning trials where a tone was paired with a 2 s 0.7 mA footshock. Twenty-four hours later, rats were given 15-min i.p. pretreatments of 0, 0.5, or 1.5 mg/kg midazolam, before being placed into fear conditioning chambers for a test of expression of conditioned fear response in a novel context. Following fear conditioning, rodents were also tested in the elevated plus maze (EPM) and the forced swim task (FST) following pretreatments of midazolam. Results from fear conditioning indicated IC rats showed a significant decrease in freezing during acquisition compared to EC and SC rats. Also, during expression, IC rats had lower freezing following saline injections and 0.5 mg/kg midazolam but were equal in time freezing to EC and SC rats following 1.5 mg/kg midazolam. In the EPM there were no effects of midazolam and IC rats showed decreased time spent in the open arms compared to EC and SC rats. In FST, IC rats spent less time immobile and more time swimming compared to EC and SC rats. Overall, results suggest that the rodent environmental enrichment model of sensation-seeking seems to parallel the effects of sensation-seeking on likelihood of PTSD symptoms seen in humans., (Copyright © 2020 Elsevier Inc. All rights reserved.)

: During the COVID-19 pandemic, many addiction treatment and harm reduction organizations have had to reduce their hours and services for people with substance use disorders, placing these individuals at increased risk of death. In order to address restricted treatment access during COVID-19, guidance from the Substance Abuse Mental Health Services Administration, the US Drug Enforcement Administration, and the US Department of Health and Human Services has allowed for use of audio-only telehealth encounters for buprenorphine induction without requiring an in-person evaluation or video interface. This has enabled innovations in order to try to meet the needs of the most vulnerable among us during the current pandemic. In this new regulatory environment, we established the Rhode Island Buprenorphine Hotline, a phone hotline which functions as a "tele-bridge" clinic where people with moderate to severe opioid use disorder can be linked with a DATA 2000 waivered provider who can provide an initial assessment and, if appropriate, prescribe buprenorphine for unobserved induction and linkage to outpatient treatment. In this correspondence we briefly share our experience developing this common sense approach to addressing the complex problem of access to treatment only now permissible due to regulatory changes during COVID-19.

Background: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality., Results: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84-0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72-0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race., Conclusions: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a "Quasi-Sverdrupian" regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual "dominantly nonlinear" regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries.

Objective: Genetic variants in the transcription factor 7-like 2 (TCF7L2) gene have been described as the most noteworthy ones regarding the type 2 diabetes mellitus (T2DM) liability. This work is aimed to evaluate the association between rs12255372 and rs7903146 polymorphisms and T2DM in patients with cardiovascular disease (CAD) risk., Methods: A sample of six hundred and forty-seven patients that underwent the coronary angiography in a Cardiac Catheterization Lab was evaluated. The patients were investigated for the presence of T2DM and coronary stenosis. The TCF7L2 polymorphisms were genotyped by real-time PCR and the haplotype analysis was performed with the MLOCUS software. All genetic tests were carried out by considering the haplotype combinations in patients divided into three groups: 0 - carrying none disease risk allele, 1 - carrying one or two risk alleles and 2 - carrying three or four risk alleles., Results: No significant associations between TCF7L2 risk haplotypes and the presence of T2DM or CAD were detected., Conclusions: Our results indicate that the TCF7L2 rs12255372 and rs7903146 polymorphisms do not influence T2DM in Brazilian patients with the high risk for CAD. Therefore, we assume that these variants may only be relevant for a specific subgroup of T2DM patients or some particular human population.

Objectives: The aim of the present study was to increase the available evidence on how physical and psychiatric comorbidities influence the long-term outcome in bipolar I and II disorder., Methods: We examined the prevalence of comorbid physical (metabolic, cardiovascular, thyroid, and neurological) diseases and psychiatric (neurotic, stress-related, somatoform, and personality) disorders and their impact on the risk of relapse in bipolar disorder. A total of 284 consecutively admitted patients with ICD-10 bipolar I (n=161) and II (n=123) disorder were followed up naturalistically over a period of 4 years., Results: Globally, 22.0% patients had metabolic, 18.8% cardiovascular, 18.8% thyroid, and 7.6% neurological diseases; 15.5% had neurotic, stress-related, and somatoform disorders; 12.0% had personality disorders; and 52.9% had nicotine dependence. We did not find any effect of comorbid metabolic, cardiovascular or neurological diseases or psychiatric disorders on the relapse risk. However, the presence of thyroid diseases, and especially hypothyroidism, was associated with an increased risk of manic relapse in bipolar disorder I (thyroid disease: hazard ratio [HR]=2.7; P=.003; hypothyroidism: HR=3.7;, P<.001). Among patients with hypothyroidism, higher blood levels of baseline thyroid-stimulating hormone (bTSH) were also associated with an increased risk of manic relapse (HR=1.07 per milli-international units per liter; P=.011), whereas blood levels of free triiodothyronine (fT 3 ) or free thyroxine (fT 4 ) were not found to have an influence., Conclusions: Our data underline the negative long-term impact of thyroid diseases, and especially hypothyroidism with high blood levels of bTSH, on bipolar disorder with more manic episodes, and the importance of its detection and treatment., (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Aromatic prenyltransferases (aPTases) transfer prenyl moieties from isoprenoid donors to various aromatic acceptors, some of which have the rare property of extreme enzymatic promiscuity toward both a variety of prenyl donors and a large diversity of acceptors. In this study, we discovered a new aPTase, AtaPT, from Aspergillus terreus that exhibits unprecedented promiscuity toward diverse aromatic acceptors and prenyl donors and also yields products with a range of prenylation patterns. Systematic crystallographic studies revealed various discrete conformations for ligand binding with donor-dependent acceptor specificity and multiple binding sites within a spacious hydrophobic substrate-binding pocket. Further structure-guided mutagenesis of active sites at the substrate-binding pocket is responsible for altering the specificity and promiscuity toward substrates and the diversity of product prenylations. Our study reveals the molecular mechanism underlying the promiscuity of AtaPT and suggests an efficient protein engineering strategy to generate new prenylated derivatives in drug discovery applications.

Aspergillus terreus aromatic prenyltransferase (AtaPT) catalyzes predominantly C-monoprenylation of acylphloroglucinols in the presence of different prenyl diphosphates. With dimethylallyl diphosphate (DMAPP) as prenyl donor, gem-diprenylated products 1D3, 2D3, and 3D3 were also detected. High conversion of 1D1 to 1D3, 2D1 to 2D3, and 3D1 to 3D3 was demonstrated by incubation with AtaPT and DMAPP. The first example of gem-diprenylation by a member of the dimethylallyltryptophan synthase superfamily is provided.

Understanding the ocean requires determining and explaining global integrals and equivalent average values of temperature (heat), salinity (freshwater and salt content), sea level, energy, and other properties. Attempts to determine means, integrals, and climatologies have been hindered by thinly and poorly distributed historical observations in a system in which both signals and background noise are spatially very inhomogeneous, leading to potentially large temporal bias errors that must be corrected at the 1% level or better. With the exception of the upper ocean in the current altimetric-Argo era, no clear documentation exists on the best methods for estimating means and their changes for quantities such as heat and freshwater at the levels required for anthropogenic signals. Underestimates of trends are as likely as overestimates; for example, recent inferences that multidecadal oceanic heat uptake has been greatly underestimated are plausible. For new or augmented observing systems, calculating the accuracies and precisions of global, multidecadal sampling densities for the full water column is necessary to avoid the irrecoverable loss of scientifically essential information.

This paper investigates the average effects of firm-provided workplace health promotion measures on labour market outcomes of the firms' employees. Exploiting linked employer-employee panel data that consist of rich survey-based and administrative information on firms, workers and regions, we apply a flexible propensity score matching approach that controls for selection on observables and time-constant unobserved factors. While the effects of analysing sickness absenteeism appear to be rather limited, our results suggest that health circles/courses increase tenure and job stability across various age groups. A key finding is that health circles/courses strengthen the labour force attachment of elderly employees (51-60), implying potential cost savings for public transfer schemes such as unemployment insurance or early retirement schemes., (Copyright © 2015. Published by Elsevier B.V.)

Prenylated tryptophan-containing cyclic dipeptides are found in different fungi and serve as precursors for the biosynthesis of diverse biologically active secondary metabolites. They show distinct and usually higher biological and pharmacological activities than the respective non-prenylated dipeptides. Successful production of such compounds were achieved by a new approach based on the coexpression of ftmPS, a non-ribosomal peptide synthetase from Neosartorya fischeri, with three cyclic dipeptide prenyltransferase genes from different biosynthetic gene clusters in Aspergillus nidulans. The genes are expressed under the control of constitutive gpdA promoter and trpC terminator. Expression of ftmPS alone resulted in the formation of the expected cyclic dipeptide brevianamide F with a yield of up to 36.9 mg l(-1). Introducing the reverse C2-prenyltransferase gene cdpC2PT as well as the reverse C3-prenyltransferase gene cdpNPT into a ftmPS mutant yielded reversely C2- and C3-prenylated derivatives, respectively. Coexpression of ftmPS with the reverse C3-prenyltransferase gene cdpC3PT resulted in the formation of N1-regularly, C2-, and C3-reversely prenylated derivatives. The prenyl transfer reactions catalyzed by CdpC2PT, CdpNPT, and CdpC3PT observed in this study correspond well to those detected with purified proteins. The yields of the detected prenylated products were found to be up to 12.2 mg l(-1). The results presented in this study show the potential of synthetic biology for production of prenylated compounds.

During our search for novel prenyltransferases, a putative gene ATEG&#95;04218 from Aspergillus terreus raised our attention and was therefore amplified from strain DSM 1958 and expressed in Escherichia coli. Biochemical investigations with the purified recombinant protein and different aromatic substrates in the presence of dimethylallyl diphosphate revealed the acceptance of all the tested tryptophan-containing cyclic dipeptides. Structure elucidation of the main enzyme products by NMR and MS analyses confirmed the attachment of the prenyl moiety to C-7 of the indole ring, proving the identification of a cyclic dipeptide C7-prenyltransferase (CdpC7PT). For some substrates, reversely C3- or N1-prenylated derivatives were identified as minor products. In comparison to the known tryptophan-containing cyclic dipeptide C7-prenyltransferase CTrpPT from Aspergillus oryzae, CdpC7PT showed a much higher substrate flexibility. It also accepted cyclo-L-Tyr-L-Tyr as substrate and catalyzed an O-prenylation at the tyrosyl residue, providing the first example from the dimethylallyltryptophan synthase (DMATS) superfamily with an O-prenyltransferase activity towards dipeptides. Furthermore, products with both C7-prenyl at tryptophanyl and O-prenyl at tyrosyl residue were detected in the reaction mixture of cyclo-L-Trp-L-Tyr. Determination of the kinetic parameters proved that (S)-benzodiazepinedione consisting of a tryptophanyl and an anthranilyl moiety was accepted as the best substrate with a K M value of 204.1 μM and a turnover number of 0.125 s(-1). Cyclo-L-Tyr-L-Tyr was accepted with a K M value of 1,411.3 μM and a turnover number of 0.012 s(-1).

Acute abdominal pain in childhood is a frequent reason for a medical consultation. The main diseases that lead to the clinical situation of acute abdomen show a significant age dependency. It is reasonable to group such ailments into three age categories: (1) the neonatal and infant period, (2) toddlerhood to kindergarten and (3) school age children. The task of the pediatric radiological examination is the differential diagnostic correlation of symptoms to the respective diseases. In children ultrasound is the appropriate method of choice.

Using exceptionally rich linked administrative and survey information on German welfare recipients we investigate the health effects of transitions from welfare to employment and of assignments to welfare-to-work programmes. Applying semi-parametric propensity score matching estimators we find that employment substantially increases (mental) health. The positive effects are mainly driven by males and individuals with bad initial health conditions and are largest for males with poor health. In contrast, the effects of welfare-to-work programmes, including subsidised jobs, are ambiguous and statistically insignificant for most outcomes., (Copyright © 2010 John Wiley & Sons, Ltd.)

Purpose: Sonographic evaluation of the infant hip joint according to the method of Graf has proven to be an important pediatric investigative instrument. Our goal was to investigate quantitatively whether (and in what ways) the clinically relevant infant hip joint structures visualize differently when utilizing trapezoidal as opposed to linear transducers. Our approach was both theoretical via a mathematical model and practical with in-vivo measurements in neonates., Materials and Methods: In a prospective study: 1. theoretical and computed analyses were performed for both linear and trapezoidal transducers regarding their respective accuracy for demonstrating the anatomic geometry of the infant hip, assuming not only correctly centered transducer positioning but also cases with off-centered displacement in the cranial or caudal direction; 2. both hip joints in 97 infants were examined by experienced investigators with comparison of the results for parallel vs. trapezoidal transducers., Results: Theoretical mathematical error analysis reveals no intrinsic systemic deviations between trapezoidal vs. parallel transducers in US scanning of the infant hip and furthermore no inherent disadvantages in the trapezoidal technique. Even when off-center transducer alignments of 1.5 cm are employed in the mathematical models, there is no significant relative distortion of the required anatomic structures when comparing the characteristics of both transducers. The practical in-vivo data from our 97 neonates confirmed the theoretical considerations., Conclusion: No loss of accuracy or other negative factors are evident when trapezoidal transducers are used to visualize the infant hip joint in comparison with the customary parallel technique. There are no significantly measurable differences between the two approaches., (© Georg Thieme Verlag KG Stuttgart · New York.)

Paleophysical oceanography is the study of the behavior of the fluid ocean of the past, with a specific emphasis on its climate implications, leading to a focus on the general circulation. Even if the circulation is not of primary concern, heavy reliance on deep-sea cores for past climate information means that knowledge of the oceanic state when the sediments were laid down is a necessity. Like the modern problem, paleoceanography depends heavily on observations, and central difficulties lie with the very limited data types and coverage that are, and perhaps ever will be, available. An approximate separation can be made into static descriptors of the circulation (e.g., its water-mass properties and volumes) and the more difficult problem of determining transport rates of mass and other properties. Determination of the circulation of the Last Glacial Maximum is used to outline some of the main challenges to progress. Apart from sampling issues, major difficulties lie with physical interpretation of the proxies, transferring core depths to an accurate timescale (the "age-model problem"), and understanding the accuracy of time-stepping oceanic or coupled-climate models when run unconstrained by observations. Despite the existence of many plausible explanatory scenarios, few features of the paleocirculation in any period are yet known with certainty.

The 100,000-year timescale in the glacial/interglacial cycles of the late Pleistocene epoch (the past approximately 700,000 years) is commonly attributed to control by variations in the Earth's orbit. This hypothesis has inspired models that depend on the Earth's obliquity (approximately 40,000 yr; approximately 40 kyr), orbital eccentricity (approximately 100 kyr) and precessional (approximately 20 kyr) fluctuations, with the emphasis usually on eccentricity and precessional forcing. According to a contrasting hypothesis, the glacial cycles arise primarily because of random internal climate variability. Taking these two perspectives together, there are currently more than thirty different models of the seven late-Pleistocene glacial cycles. Here we present a statistical test of the orbital forcing hypothesis, focusing on the rapid deglaciation events known as terminations. According to our analysis, the null hypothesis that glacial terminations are independent of obliquity can be rejected at the 5% significance level, whereas the corresponding null hypotheses for eccentricity and precession cannot be rejected. The simplest inference consistent with the test results is that the ice sheets terminated every second or third obliquity cycle at times of high obliquity, similar to the original proposal by Milankovitch. We also present simple stochastic and deterministic models that describe the timing of the late-Pleistocene glacial terminations purely in terms of obliquity forcing.

Cystic fibrosis is the most common autosomal recessive metabolic disease in Europe with an incidence of 1:2500. The severity of the lung disease is the most important factor of morbidity and mortality in CF-patients. Because of the better diagnostic and therapeutic modalities life expectancy has increased significantly. The underlying pathology is a defect of chromosome 7, which encodes the regulation of the fluid balance across the cell membrane which effects chloride as well as sodium. The exocrine glands produce a viscous mucus which obstructs the airways and promotes infections. The result is the destruction of lung parenchyma. In daily routine, chest x-ray is still the most important radiological tool, although computed tomography depicts changes in morphology earlier and more exactly. Recent research studies show that MRI has-because of its additional functional options-interesting aspects for the future.