Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling

Microarray data analysis of gene expression levels in lactating cows treated with bovine somatotropin

Administration of bovine somatotropin (bST) to lactating cows results in an increase in milk production from 10 to 15%. While physiological mechanisms involved in bST administration are well known, there is limited knowledge about the mechanisms that regulate the bST action at genetic level. For this reason, a microarray experiment was conducted to identify differentially expressed genes when bST is given to milking cows. Sixteen high-density microarrays for cattle, each containing 18,263 gene spots, were used. RNA was extracted from the mammary tissue of four lactating Holstein cows, five and two days before, and one and six days after bST administration. A total of 1,251 and 1,167 differentially expressed genes were detected for mean and median expression intensities, respectively. Only the 115 genes which were identified by both mean and median intensities were taken into account. These genes were grouped into 8 clusters according to changes in expression through time points

Surgical therapy for atrial tachycardia in adults

AbstractEighteen adult patients with atrial tachycardia refractory to treatment with a mean of four drugs underwent attempted surgical cure. Atrial tachycardia originated in the right atrium in 17 patients and the left atrium in 1 patient. Tachycardia could be reproducibly induced and terminated by atrial extrastimuli or atrial pacing in 8 patients (44%). Resection of the arrhythmogenic area was performed in 16 patients (89%), and an isolation procedure was performed in 1 patient. In seven cases (39%), the area of isolation or excision included the sinoatrial node. One patient underwent His bundle section because the arrhythmogenic region was too close to the atrioventricular (AV) conduction system to enable resection.The mean duration of clinical follow-up was 56 ± 34 months. Clinical tachycardia recurred in five patients (28%), but in two patients it did not recur until >1 year after surgery. A permanent pacemaker was implanted in 3 (18%) of the 17 patients whose His-Purkinje system was left intact. One other patient had required permanent pacing before surgery. Only one of the seven patients undergoing sinoatrial node resection or isolation required permanent pacing for symptomatic bradycardia. Apart from the requirement for permanent pacing, no significant complications occurred.Surgical therapy for atrialtachycardia is a safe procedure, but the rate of cure appears to be less than that of supraventricular tachycardias associated with accessory AV connections. Excision or isolation of the sinoatrial node does not necessitate permanent pacing in most patients

Effect of Varying Levels of Fatty Acids from Palm Oil on Feed Intake and Milk Production in Holstein Cows

To determine the optimum feeding level of fatty acids of palm oil (PALM; Energizer RP10; 86.6% palmitic acid) on milk production, lactating cows (n = 18) were randomly assigned to a treatment sequence in replicated 4 x 4 Latin squares. Animals were assigned to squares by parity (3 multiparous and 1 primiparous squares with primiparous in the incomplete square). The 4 diets were designed to provide 0, 500, 1,000, and 1,500 g of PALM per day. Cows were fed individually with feed intake measured daily. Each period lasted 16 d with milk production and composition determined the final 2 d. Milk production, milk composition and feed intake data were analyzed using the MIXED procedure of SAS. Milk yields were 30.9, 34.0, 34.2, and 34.2 kg/ d (SEM = 1.9) for the 0, 500, 1,000, and 1,500 g levels, respectively. Milk yield was increased by the addition of PALM; however, there were no differences among the levels of PALM. Milk fat percentage was also increased from 3.44% for 0 g to 3.95% (SEM = 0.17) across all levels of PALM but there were no differences among the PALM treatments. Dry matter intakes were 23.3, 26.4, 24.7, and 23.8 kg/d (SEM = 1.4) for the 0, 500, 1,000 and 1,500 g levels, respectively. The addition of PALM increased milk yield and milk fat percentage, and no adverse effects on dry matter intake were observed

Conceptualization and Application of Arctic Tundra Landscape Evolution Using the Alaska Thermokarst Model

Thermokarst topography forms whenever ice-rich permafrost thaws and the ground subsides due to the volume loss when excess ice transitions to water. The Alaska Thermokarst Model (ATM) is a large-scale, state-and-transition model designed to simulate transitions between [non-]thermokarst landscape units, or cohorts. The ATM uses a frame-based methodology to track transitions and proportion of cohorts within a 1- km2 grid cell. In the arctic tundra environment, the ATM tracks thermokarst related transitions between wetland tundra, graminoid tundra, shrub tundra, and thermokarst lakes. The transition from one cohort to another due to thermokarst processes can take place if seasonal thaw of the ground reaches ice-rich soil layers either due to pulse disturbance events such as a large precipitation event, wildfire, or due to gradual active layer deepening that eventually reaches ice-rich soil. The protective layer is the distance between the ground surface and ice-rich soil. The protective layer buffers the ice-rich soils from energy processes that take place at the ground surface and is critical to determining how susceptible an area is to thermokarst degradation. The rate of terrain transition in our model is determined by the soil ice-content, the drainage efficiency (or ability of the landscape to store or transport water), and the probability of thermokarst initiation. Tundra types are allowed to transition from one type to another (i.e. a wetland tundra to a graminoid tundra) under favorable climatic conditions. In this study, we present our conceptualization and initial simulation results of the ATM for an 1792 km2 area on the Barrow Peninsula, Alaska. The area selected for simulation is located in a polygonal tundra landscape under varying degrees of thermokarst degradation. The goal of this modeling study is to simulate landscape evolution in response to thermokarst disturbance as a result of climate change.Alaska Climate Science Center, Arctic Landscape Conservation Cooperative, Western Alaska LCC, Northwest Boreal Landscape Conservation Cooperative, Next Generation Ecosystem Experiments: Arctic Systems Understandin

Conceptualization of Arctic Tundra Landscape Transitions Using the Alaska Thermokarst Model

Thermokarst topography forms whenever ice-rich permafrost thaws and the ground subsides due to the volume loss when excess ice transitions to water. The Alaska Thermokarst Model (ATM) is a large-scale, state-and-transition model designed to simulate landscape transitions between landscape units, or cohorts, due to thermokarst. The ATM uses a frame-based methodology to track transitions and proportion of cohorts within a 1-km2 grid cell. In the arctic tundra environment, the ATM tracks landscape transitions between non-polygonal ground (meadows), low center polygons, coalescent low center polygons, flat center polygons, high center polygons, ponds and lakes. The transition from one terrestrial landscape type to another can take place if the seasonal ground thaw penetrates underlying ice-rich soil layers either due to pulse disturbance events such as a large precipitation event, wildfire, or due to gradual active layer deepening. The protective layer is the distance between the ground surface and ice-rich soil. The protective layer buffers the ice-rich soils from energy processes that take place at the ground surface and is critical to determining how susceptible an area is to thermokarst degradation. The rate of terrain transition in our model is determined by the soil ice-content, the drainage efficiency (or ability of the landscape to store or transport water), and the probability of thermokarst initiation. Using parameterizations derived from small-scale numerical experiments, functional responses of landscape transitions will be developed and integrated into NGEE-Arctic climate-scale (CLM) modeling efforts.The Next-Generation Ecosystem Experiments (NGEE Arctic) project is supported by the Office of Biological and Environmental Research in the DOE Office of Science. Additional support is provided by the Alaska Climate Science Center, and the Arctic, Northwest Boreal, and Western Alaska Landscape Conservation Conservatives

Antibodies in the Diagnosis, Prognosis, and Prediction of Psychotic Disorders.

Blood-based biomarker discovery for psychotic disorders has yet to impact upon routine clinical practice. In physical disorders antibodies have established roles as diagnostic, prognostic and predictive (theranostic) biomarkers, particularly in disorders thought to have a substantial autoimmune or infective aetiology. Two approaches to antibody biomarker identification are distinguished: a top-down approach, in which antibodies to specific antigens are sought based on the known function of the antigen and its putative role in the disorder, and emerging bottom-up or omics approaches that are agnostic as to the significance of any one antigen, using high-throughput arrays to identify distinctive components of the antibody repertoire. Here we review the evidence for antibodies (to self-antigens as well as infectious organism and dietary antigens) as biomarkers of diagnosis, prognosis, and treatment response in psychotic disorders. Neuronal autoantibodies have current, and increasing, clinical utility in the diagnosis of organic or atypical psychosis syndromes. Antibodies to selected infectious agents show some promise in predicting cognitive impairment and possibly other symptom domains (eg, suicidality) within psychotic disorders. Finally, infectious antibodies and neuronal and other autoantibodies have recently emerged as potential biomarkers of response to anti-infective therapies, immunotherapies, or other novel therapeutic strategies in psychotic disorders, and have a clear role in stratifying patients for future clinical trials. As in nonpsychiatric disorders, combining biomarkers and large-scale use of bottom-up approaches to biomarker identification are likely to maximize the eventual clinical utility of antibody biomarkers in psychotic disorders

6-[(4-Hy­droxy­phen­yl)diazenyl]-1,10-phenanthrolin-1-ium chloride monohydrate

In the cation of the title mol­ecular salt, C18H13N4O+·Cl−·H2O, the dihedral angle between the mean planes of the 1,10-phenanthroline system and the phenol ring is 14.40 (19)°. The crystal packing is stabilized by O—H⋯O hydrogen bonds, weak N—H⋯Cl and O—H⋯Cl inter­molecular inter­actions and π—π stacking inter­actions [centroid–centroid distance = 3.6944 (13) and 3.9702 (12) Å