Migratory connectivity then and now: a northward shift in breeding origins of a long-distance migratory bird wintering in the tropics.

Temporal variation in the connectivity of populations of migratory animals has not been widely documented, despite having important repercussions for population ecology and conservation. Because the long-distance movements of migratory animals link ecologically distinct and geographically distant areas of the world, changes in the abundance and migratory patterns of species may reflect differential drivers of demographic trends acting over various spatial scales. Using stable hydrogen isotope analyses (δ2H) of feathers from historical museum specimens and contemporary samples obtained in the field, we provide evidence for an approximately 600 km northward shift over 45 years in the breeding origin of a species of songbird of major conservation concern (blackpoll warbler, Setophaga striata) wintering in the foothills of the eastern Andes of Colombia. Our finding mirrors predictions of range shifts for boreal-breeding species under warming climate scenarios and habitat loss in the temperate zone, and underscores likely drivers of widespread declines in populations of migratory birds. Our work also highlights the value of natural history collections to document the effects of global change on biodiversity.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

ATF5 regulates ß-cell survival during stress.

The stress response and cell survival are necessary for normal pancreatic ß-cell function, glucose homeostasis, and prevention of diabetes. The homeodomain transcription factor and human diabetes gene pancreas/duodenum homeobox protein 1 (Pdx1) regulates ß-cell survival and endoplasmic reticulum stress susceptibility, in part through direct regulation of activating transcription factor 4 (Atf4). Here we show that Atf5, a close but less-studied relative of Atf4, is also a target of Pdx1 and is critical for ß-cell survival under stress conditions. Pdx1 deficiency led to decreased Atf5 transcript, and primary islet ChIP-sequencing localized PDX1 to the Atf5 promoter, implicating Atf5 as a PDX1 target. Atf5 expression was stress inducible and enriched in ß cells. Importantly, Atf5 deficiency decreased survival under stress conditions. Loss-of-function and chromatin occupancy experiments positioned Atf5 downstream of and parallel to Atf4 in the regulation of eIF4E-binding protein 1 (4ebp1), a mammalian target of rapamycin (mTOR) pathway component that inhibits protein translation. Accordingly, Atf5 deficiency attenuated stress suppression of global translation, likely enhancing the susceptibility of ß cells to stress-induced apoptosis. Thus, we identify ATF5 as a member of the transcriptional network governing pancreatic ß-cell survival during stress.

Origin and cross-century dynamics of an avian hybrid zone.

BACKGROUND: Characterizations of the dynamics of hybrid zones in space and time can give insights about traits and processes important in population divergence and speciation. We characterized a hybrid zone between tanagers in the genus Ramphocelus (Aves, Thraupidae) located in southwestern Colombia. We evaluated whether this hybrid zone originated as a result of secondary contact or of primary differentiation, and described its dynamics across time using spatial analyses of molecular, morphological, and coloration data in combination with paleodistribution modeling. RESULTS: Models of potential historical distributions based on climatic data and genetic signatures of demographic expansion suggested that the hybrid zone likely originated following secondary contact between populations that expanded their ranges out of isolated areas in the Quaternary. Concordant patterns of variation in phenotypic characters across the hybrid zone and its narrow extent are suggestive of a tension zone, maintained by a balance between dispersal and selection against hybrids. Estimates of phenotypic cline parameters obtained using specimens collected over nearly a century revealed that, in recent decades, the zone appears to have moved to the east and to higher elevations, and may have become narrower. Genetic variation was not clearly structured along the hybrid zone, but comparisons between historical and contemporary specimens suggested that temporal changes in its genetic makeup may also have occurred. CONCLUSIONS: Our data suggest that the hybrid zone likey resulted from secondary contact between populations. The observed changes in the hybrid zone may be a result of sexual selection, asymmetric gene flow, or environmental change.

RNA-binding protein PCBP2 regulates pancreatic ß cell function and adaptation to glucose.

Glucose plays a key role in shaping pancreatic ß cell function. Thus, deciphering the mechanisms by which this nutrient stimulates ß cells holds therapeutic promise for combating ß cell failure in type 2 diabetes (T2D). ß Cells respond to hyperglycemia in part by rewiring their mRNA metabolism, yet the mechanisms governing these changes remain poorly understood. Here, we identify a requirement for the RNA-binding protein PCBP2 in maintaining ß cell function basally and during sustained hyperglycemic challenge. PCBP2 was induced in primary mouse islets incubated with elevated glucose and was required to adapt insulin secretion. Transcriptomic analysis of primary Pcbp2-deficient ß cells revealed impacts on basal and glucose-regulated mRNAs encoding core components of the insulin secretory pathway. Accordingly, Pcbp2-deficient ß cells exhibited defects in calcium flux, insulin granule ultrastructure and exocytosis, and the amplification pathway of insulin secretion. Further, PCBP2 was induced by glucose in primary human islets, was downregulated in islets from T2D donors, and impacted genes commonly altered in islets from donors with T2D and linked to single-nucleotide polymorphisms associated with T2D. Thus, these findings establish a paradigm for PCBP2 in governing basal and glucose-adaptive gene programs critical for shaping the functional state of ß cells.

DNA Methylation-Based Assessment of Cell Composition in Human Pancreas and Islets.

Assessment of pancreas cell type composition is crucial to the understanding of the genesis of diabetes. Current approaches use immunodetection of protein markers, for example, insulin as a marker of ß-cells. A major limitation of these methods is that protein content varies in physiological and pathological conditions, complicating the extrapolation to actual cell number. Here, we demonstrate the use of cell type-specific DNA methylation markers for determining the fraction of specific cell types in human islet and pancreas specimens. We identified genomic loci that are uniquely demethylated in specific pancreatic cell types and applied targeted PCR to assess the methylation status of these loci in tissue samples, enabling inference of cell type composition. In islet preparations, normalization of insulin secretion to ß-cell DNA revealed similar ß-cell function in pre-type 1 diabetes (T1D), T1D, and type 2 diabetes (T2D), which was significantly lower than in donors without diabetes. In histological pancreas specimens from recent-onset T1D, this assay showed ß-cell fraction within the normal range, suggesting a significant contribution of ß-cell dysfunction. In T2D pancreata, we observed increased α-cell fraction and normal ß-cell fraction. Methylation-based analysis provides an accurate molecular alternative to immune detection of cell types in the human pancreas, with utility in the interpretation of insulin secretion assays and the assessment of pancreas cell composition in health and disease.

Characterization of Composite Freeze-Dried Aerogels with Simulant Lunar Regolith for Space Applications.

Recently, the goal of space exploration has shifted from the incognito of the solar system to the Moon. Concepts like human permanence on the Moon and thermal protective structures made with ISRU (in situ resource utilization) of raw materials have started to be implemented. By limiting the need to launch supplies from the Earth, the paradigm of spaceflight is changed, privileging the vanguard of the utilisation of resources in situ. Still, the main challenges of surviving the radiation dose and the cryogenic temperatures of the lunar night remain. Recent studies have demonstrated how innovative composite materials can help reduce the temperature stress on exploration vehicles. This research presents the material properties of aerogel insulating materials combined with LHS (lunar highlands simulant) regolith obtained by freeze frying. Organic-based aerogels with different percentages of LHS have been analysed in terms of material, morphology, and thermal properties.

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns.

OBJECTIVE: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. METHODS: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. RESULTS: Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance. SIGNIFICANCE: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.

Acute frataxin knockdown in induced pluripotent stem cell-derived cardiomyocytes activates a type I interferon response.

Friedreich ataxia, the most common hereditary ataxia, is a neuro- and cardio-degenerative disorder caused, in most cases, by decreased expression of the mitochondrial protein frataxin. Cardiomyopathy is the leading cause of premature death. Frataxin functions in the biogenesis of iron-sulfur clusters, which are prosthetic groups that are found in proteins involved in many biological processes. To study the changes associated with decreased frataxin in human cardiomyocytes, we developed a novel isogenic model by acutely knocking down frataxin, post-differentiation, in cardiomyocytes derived from induced pluripotent stem cells (iPSCs). Transcriptome analysis of four biological replicates identified severe mitochondrial dysfunction and a type I interferon response as the pathways most affected by frataxin knockdown. We confirmed that, in iPSC-derived cardiomyocytes, loss of frataxin leads to mitochondrial dysfunction. The type I interferon response was activated in multiple cell types following acute frataxin knockdown and was caused, at least in part, by release of mitochondrial DNA into the cytosol, activating the cGAS-STING sensor pathway.

U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment.

The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn. The approach is a pixel-wise regression model based on convolutional neural networks. To minimize the dependency on the conditions in which the images are taken, the effect of two image normalization techniques is also studied. Results indicate that the model performs satisfactorily on average, presenting low mean absolute and squared errors and high structural similarity index. While no significant differences are found when changing the normalization of the images, the performance is affected by their quality. This suggests that changes in the intensity of the images do not alter the relevant information about the wound, whereas changes in brightness, contrast and sharpness do.

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

α Cell dysfunction in islets from nondiabetic, glutamic acid decarboxylase autoantibody-positive individuals.

BACKGROUNDMultiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single GADA+ state may represent an early stage of T1D.METHODSHere, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic GADA+ and T1D donors.RESULTSSimilar to the few remaining ß cells in the T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, α cell glucagon secretion was dysregulated in both GADA+ and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of GADA+ α cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor ß (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in GADA+ donor islets.CONCLUSIONWe found that α cell dysfunction was present during the early stages of islet autoimmunity at a time when ß cell mass was still normal, raising important questions about the role of early α cell dysfunction in the progression of T1D.FUNDINGThis work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593).

Neonatal GLP1R activation limits adult adiposity by durably altering hypothalamic architecture.

OBJECTIVE: Adult obesity risk is influenced by alterations to fetal and neonatal environments. Modifying neonatal gut or neurohormone signaling pathways can have negative metabolic consequences in adulthood. Here we characterize the effect of neonatal activation of glucagon like peptide-1 (GLP-1) receptor (GLP1R) signaling on adult adiposity and metabolism. METHODS: Wild type C57BL/6 mice were injected with 1 nmol/kg Exendin-4 (Ex-4), a GLP1R agonist, for 6 consecutive days after birth. Growth, body composition, serum analysis, energy expenditure, food intake, and brain and fat pad histology and gene expression were assessed at multiple time points through 42 weeks. Similar analyses were conducted in a Glp1r conditional allele crossed with a Sim1Cre deleter strain to produce Sim1Cre;Glp1rloxP/loxP mice and control littermates. RESULTS: Neonatal administration of Ex-4 reduced adult body weight and fat mass, increased energy expenditure, and conferred protection from diet-induced obesity in female mice. This was associated with induction of brown adipose genes and increased noradrenergic fiber density in parametrial white adipose tissue (WAT). We further observed durable alterations in orexigenic and anorexigenic projections to the paraventricular hypothalamic nucleus (PVH). Genetic deletion of Glp1r in the PVH by Sim1-Cre abrogated the impact of neonatal Ex-4 on adult body weight, WAT browning, and hypothalamic architecture. CONCLUSION: These observations suggest that the acute activation of GLP1R in neonates durably alters hypothalamic architecture to limit adult weight gain and adiposity, identifying GLP1R as a therapeutic target for obesity prevention.

Birds of Antioquia: Georeferenced database of specimens from the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA).

The department of Antioquia, Colombia, lies in the northwestern corner of South America and provides a biogeographical link among divergent faunas, including Caribbean, Andean, Pacific and Amazonian. Information about the distribution of biodiversity in this area is of relevance for academic, practical and social purposes. This data paper describes the dataset containing all bird specimens deposited in the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA). We curated all the information associated with the bird specimens, including the georeferences and taxonomy, and published the database through the Global Biodiversity Information Facility network. During this process we checked the species identification and existing georeferences and completed the information when possible. The collection holds 663 bird specimens collected between 1940 and 2011. Even though most specimens are from Antioquia (70%), the collection includes material from several other departments and one specimen from the United States. The collection holds specimens from three endemic and endangered species (Coeligena orina, Diglossa gloriossisima, and Hypopirrhus pyrohipogaster), and includes localities poorly represented in other collections. The information contained in the collection has been used for biodiversity modeling, conservation planning and management, and we expect to further facilitate these activities by making it publicly available.