Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates.

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.

Widespread and increasing near-bottom hypoxia in the coastal ocean off the United States Pacific Northwest.

The 2021 summer upwelling season off the United States Pacific Northwest coast was unusually strong leading to widespread near-bottom, low-oxygen waters. During summer 2021, an unprecedented number of ship- and underwater glider-based measurements of dissolved oxygen were made in this region. Near-bottom hypoxia, that is dissolved oxygen less than 61 µmol kg-1 and harmful to marine animals, was observed over nearly half of the continental shelf inshore of the 200-m isobath, covering 15,500 square kilometers. A mid-shelf ribbon with near-bottom, dissolved oxygen less than 50 µmol kg-1 extended for 450 km off north-central Oregon and Washington. Spatial patterns in near-bottom oxygen are related to the continental shelf width and other features of the region. Maps of near-bottom oxygen since 1950 show a consistent trend toward lower oxygen levels over time. The fraction of near-bottom water inshore of the 200-m isobath that is hypoxic on average during the summer upwelling season increases over time from nearly absent (2%) in 1950-1980, to 24% in 2009-2018, compared with 56% during the anomalously strong upwelling conditions in 2021. Widespread and increasing near-bottom hypoxia is consistent with increased upwelling-favorable wind forcing under climate change.

An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heatwaves.

The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (Oncorhynchus tshawytscha) and southern resident killer whales (Orcinus orca). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 150-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.

BACKGROUND: Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. METHODS: We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. RESULTS: We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. CONCLUSIONS: Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Computational Advancements in Cancer Combination Therapy Prediction.

Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.

BACKGROUND: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. AIMS: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). METHODS AND RESULTS: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. CONCLUSIONS: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.

BACKGROUND: Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions. AIMS: Here we evaluated how well preclinical models recapitulate human cancer and non-diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue-specific genes. METHODS: By using publicly available gene expression profiles across multiple sources, we evaluated cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico. RESULTS: We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that glioblastoma (GBM) PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue-specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue-specific patterns are better preserved in GBM PDX models than in GBM cell lines. CONCLUSION: Future studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.

Background: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. Aims: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). Methods and results: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. Conclusions: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.

Background: Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. Methods: We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. Results: We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. Conclusions: Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

CINmetrics: an R package for analyzing copy number aberrations as a measure of chromosomal instability.

Genomic instability is an important hallmark of cancer and more recently has been identified in others like neurodegenrative diseases. Chromosomal instability, as a measure of genomic instability, has been used to characterize clinical and biological phenotypes associated with these diseases by measuring structural and numerical chromosomal alterations. There have been multiple chromosomal instability scores developed across many studies in the literature; however, these scores have not been compared because of the lack of a single tool available to calculate and facilitate these various metrics. Here, we provide an R package CINmetrics, that calculates six different chromosomal instability scores and allows direct comparison between them. We also demonstrate how these scores differ by applying CINmetrics to breast cancer data from The Cancer Genome Atlas (TCGA). The package is available on CRAN at https://cran.rproject.org/package=CINmetrics and on GitHub at https://github.com/lasseignelab/CINmetrics.

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed. METHODS: As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates' target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis. RESULTS: With this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo. CONCLUSION: Collectively, these results indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.

Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions. Here we evaluated how well preclinical models recapitulate human cancer and non-diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue-specific genes by using publicly available gene expression profiles across multiple sources. We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that GBM PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue-specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue-specific patterns are better preserved in GBM PDX models than in GBM cell lines. Future studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Pelagic calcifiers face increased mortality and habitat loss with warming and ocean acidification.

Global change is impacting the oceans in an unprecedented way, and multiple lines of evidence suggest that species distributions are changing in space and time. There is increasing evidence that multiple environmental stressors act together to constrain species habitat more than expected from warming alone. Here, we conducted a comprehensive study of how temperature and aragonite saturation state act together to limit Limacina helicina, globally distributed pteropods that are ecologically important pelagic calcifiers and an indicator species for ocean change. We co-validated three different approaches to evaluate the impact of ocean warming and acidification (OWA) on the survival and distribution of this species in the California Current Ecosystem. First, we used colocated physical, chemical, and biological data from three large-scale west coast cruises and regional time series; second, we conducted multifactorial experimental incubations to evaluate how OWA impacts pteropod survival; and third, we validated the relationships we found against global distributions of pteropods and carbonate chemistry. OWA experimental work revealed mortality increases under OWA, while regional habitat suitability indices and global distributions of L. helicina suggest that a multi-stressor framework is essential for understanding pteropod distributions. In California Current Ecosystem habitats, where pteropods are living close to their thermal maximum already, additional warming and acidification through unabated fossil fuel emissions (RCP 8.5) are expected to dramatically reduce habitat suitability.

Considerations and challenges for sex-aware drug repurposing.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021.

The Oregon continental shelf is embedded within the northern California Current System, a wind-driven, eastern boundary system that includes the equatorward flowing California Current and the poleward flowing California Undercurrent. During spring and summer months, equatorward winds drive the upwelling of cold, nutrient-rich, and oxygen-poor waters from depth onto the shelf, fueling a highly productive marine ecosystem that supports several valuable commercial fisheries. This data article describes a time series of hydrographic data collected on a biweekly to monthly schedule from March 1997 to July 2021 along the Newport Hydrographic Line (NHL; 44.652°N, 124.1 - 124.65°W) located west of Newport, Oregon. The NHL, with its 2-4 week sampling rate and inclusion of biological data such as zooplankton net tows, is the only long-term, high-frequency dataset of its kind for the California Current and as such is crucial to understanding the connectivity between changes in ocean-climate and ecosystem structure and function. Data were collected using Sea-Bird Scientific conductivity, temperature, depth (CTD) profilers with associated dissolved oxygen sensors at seven stations located between 1.9 and 46.3 km from shore. Water depths for the seven stations range from 30 to 296 m. Data collected during each cruise were processed using Sea-Bird Scientific's Seasoft software package. These CTD station data were gridded to a 0.01° x 1 dbar longitude - pressure grid using linear interpolation to create cross-shelf hydrographic sections of temperature, practical salinity, potential density, spiciness, and dissolved oxygen. From the gridded section data, seasonal climatologies were calculated for each variable at each location in the longitude - pressure section using harmonic analysis with a three-harmonic fit to the gridded transect observations. The station data, gridded transect data and monthly climatologies for all five variables are available via Zenodo at https://doi.org/10.5281/zenodo.5814071.

A role for GLUT3 in glioblastoma cell invasion that is not recapitulated by GLUT1.

The multifaceted roles of metabolism in invasion have been investigated across many cancers. The brain tumor glioblastoma (GBM) is a highly invasive and metabolically plastic tumor with an inevitable recurrence. The neuronal glucose transporter 3 (GLUT3) was previously reported to correlate with poor glioma patient survival and be upregulated in GBM cells to promote therapeutic resistance and survival under restricted glucose conditions. It has been suggested that the increased glucose uptake mediated by GLUT3 elevation promotes survival of circulating tumor cells to facilitate metastasis. Here we suggest a more direct role for GLUT3 in promoting invasion that is not dependent upon changes in cell survival or metabolism. Analysis of glioma datasets demonstrated that GLUT3, but not GLUT1, expression was elevated in invasive disease. In human xenograft derived GBM cells, GLUT3, but not GLUT1, elevation significantly increased invasion in transwell assays, but not growth or migration. Further, there were no changes in glycolytic metabolism that correlated with invasive phenotypes. We identified the GLUT3 C-terminus as mediating invasion: substituting the C-terminus of GLUT1 for that of GLUT3 reduced invasion. RNA-seq analysis indicated changes in extracellular matrix organization in GLUT3 overexpressing cells, including upregulation of osteopontin. Together, our data suggest a role for GLUT3 in increasing tumor cell invasion that is not recapitulated by GLUT1, is separate from its role in metabolism and survival as a glucose transporter, and is likely broadly applicable since GLUT3 expression correlates with metastasis in many solid tumors.

Recent Advances in the Acute Management of Intracerebral Hemorrhage.

Primary intracerebral hemorrhage (ICH) is a common, devastating disease that lacks an effective specific treatment. Mortality is high, functional outcomes are poor, and these have not substantially changed for decades. There is, therefore, considerable opportunity for advancement in the management of ICH. In recent years, a significant amount of research has begun to address this gap. This article is aimed at updating neurologists on the most clinically relevant contemporary research.

Phenological and distributional shifts in ichthyoplankton associated with recent warming in the northeast Pacific Ocean.

Understanding changes in the migratory and reproductive phenology of fish stocks in relation to climate change is critical for accurate ecosystem-based fisheries management. Relocation and changes in timing of reproduction can have dramatic effects upon the success of fish populations and throughout the food web. During anomalously warm conditions (1-4°C above normal) in the northeast Pacific Ocean during 2015-2016, we documented shifts in timing and spawning location of several pelagic fish stocks based on larval fish samples. Total larval concentrations in the northern California Current (NCC) during winter (January-March) 2015 and 2016 were the highest observed since annual collections first occurred in 1998, primarily due to increased abundances of Engraulis mordax (northern anchovy) and Sardinops sagax (Pacific sardine) larvae, which are normally summer spawning species in this region. Sardinops sagax and Merluccius productus (Pacific hake) exhibited an unprecedented early and northward spawning expansion during 2015-16. In addition, spawning duration was greatly increased for E. mordax, as the presence of larvae was observed throughout the majority of 2015-16, indicating prolonged and nearly continuous spawning of adults throughout the warm period. Larvae from all three of these species have never before been collected in the NCC as early in the year. In addition, other southern species were collected in the NCC during this period. This suggests that the spawning phenology and distribution of several ecologically and commercially important fish species dramatically and rapidly changed in response to the warming conditions occurring in 2014-2016, and could be an indication of future conditions under projected climate change. Changes in spawning timing and poleward migration of fish populations due to warmer ocean conditions or global climate change will negatively impact areas that were historically dependent on these fish, and change the food web structure of the areas that the fish move into with unforeseen consequences.

Recent Advances in the Acute Management of Intracerebral Hemorrhage.

Primary intracerebral hemorrhage (ICH) is a common, devastating disease that lacks an effective specific treatment. Mortality is high, functional outcomes are poor, and these have not substantially changed for decades. There is, therefore, considerable opportunity for advancement in the management of ICH. In recent years, a significant amount of research has begun to address this gap. This article is aimed at updating neurologists on the most clinically relevant contemporary research.

The pelagic ecosystem in the Northern California Current off Oregon during the 2014-2016 warm anomalies within the context of the past 20 years.

A warm anomaly in the upper ocean, colloquially named "the Blob," appeared in the Gulf of Alaska during the calm winter of 2013-2014, spread across the northern North Pacific (NP) Ocean, and shifted eastward and onto the Oregon shelf. At least 14 species of copepods occurred which had never been observed in shelf/slope waters off Oregon, some of which are known to have NP Gyre affinities, indicating that the source waters of the coastal "Blob" were likely of both offshore (from the west) and subtropical/tropical origin. The anomalously warm conditions were reduced during strong upwelling in spring 2015 but returned when upwelling weakened in July 2015 and transitioned to downwelling in fall 2015. The extended period of warm conditions resulted in prolonged effects on the ecosystem off central Oregon, lasting at least through 2016. Impacts to the lower trophic levels were unprecedented and include a novel plankton community composition resulting from increased copepod, diatom, and dinoflagellate species richness and increased abundance of dinoflagellates. Additionally, the multiyear warm anomalies were associated with reduced biomass of copepods and euphausiids, high abundance of larvaceans and doliolids (indictors of oligotrophic ocean conditions), and a toxic diatom bloom (Pseudo-nitzschia) throughout the California Current in 2015, thereby changing the composition of the food web that is relied upon by many commercially and ecologically important species.