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Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore key considerations in selecting and interpreting loss and assessment metrics in the context of data imbalance, which arises when the classes have unequal proportions within the dataset or landscape being mapped. The challenges involved in calculating single, integrated measures that summarize classification success, especially for datasets with considerable data imbalance, have led to much confusion in the literature. This confusion arises from a range of issues, including a lack of clarity over the redundancy of some accuracy measures, the importance of calculating final accuracy from population-based statistics, the effects of class imbalance on accuracy statistics, and the differing roles of accuracy measures when used for training and final evaluation. In order to characterize classification success at the class level, users typically generate averages from the class-based measures. These averages are sometimes generated at the macro-level, by taking averages of the individual-class statistics, or at the micro-level, by aggregating values within a confusion matrix, and then, calculating the statistic. We show that the micro-averaged producer's accuracy (recall), user's accuracy (precision), and F1-score, as well as weighted macro-averaged statistics where the class prevalences are used as weights, are all equivalent to each other and to the overall accuracy, and thus, are redundant and should be avoided. Our experiment, using a variety of loss metrics for training, suggests that the choice of loss metric is not as complex as it might appear to be, despite the range of choices available, which include cross-entropy (CE), weighted CE, and micro- and macro-Dice. The highest, or close to highest, accuracies in our experiments were obtained by using CE loss for models trained with balanced data, and for models trained with imbalanced data, the highest accuracies were obtained by using weighted CE loss. We recommend that, since weighted CE loss used with balanced training is equivalent to CE, weighted CE loss is a good all-round choice. Although Dice loss is commonly suggested as an alternative to CE loss when classes are imbalanced, micro-averaged Dice is similar to overall accuracy, and thus, is particularly poor for training with imbalanced data. Furthermore, although macro-Dice resulted in models with high accuracy when the training used balanced data, when the training used imbalanced data, the accuracies were lower than for weighted CE. In summary, the significance of this paper lies in its provision of readers with an overview of accuracy and loss metric terminology, insight regarding the redundancy of some measures, and guidance regarding best practices. [ABSTRACT FROM AUTHOR]

Recent efforts to synchronise laboratory protocols for measuring luminescent solar concentrator (LSC) efficiencies, and to use consistent terminology, has been driven by a prior lack of consensus on both terminology and the reporting of experimental results. This analysis seeks to understand how terminology in the field of luminescent solar concentrators has been used in the past, and to recommend the most clear and consistent terminology that should be used for future publications. LSCs for electrical power generation, referred to as luminescent solar concentrator photovoltaics (LSC-PV), should be treated as integrated photovoltaic cells. Consequently, the power conversion efficiency (PCE) and external quantum efficiency (EQE L S C (λ)) should be reported, as they would be with any other photovoltaic device. The term "optical efficiency" has been defined inconsistently throughout the literature, with several different efficiency expressions, and thus it should be interpreted with great care. The highest values for the three most common LSC efficiency metrics have been examined in detail and their validity and significance have been assessed. Among the examined results, it has been assessed that the highest reliable results are a PCE of 7.1%, an optical power efficiency (η o p t) of 7.8% and an external photon efficiency (η e x t) of 6.8%. A significant number of highest efficiencies reported have used questionable calculation methods meaning those efficiency values should not be used as a comparison for current and future publications. Additionally, reporting optical power efficiency is not recommended unless optical power is relevant to the device application. • Recommended that power conversion efficiency is reported with J-V characteristics. • Reporting external quantum efficiency is valuable and is highly recommended. • Inconsistencies in LSC terminology and efficiency results have been identified. • Reporting optical power efficiency not recommended for LSC-PVs. [ABSTRACT FROM AUTHOR]

A lidar-derived canopy height profile (CHP), generated from canopy bulk density (CBD) estimates for a sequence of 1-m increments through the canopy, provides a physical measure of forest structure. Measurement of physical properties are intuitively understandable and thus facilitate the use of lidar for investigating hypotheses regarding avian resource use. We illustrate the use of lidar-derived physical measures to explore the hypothesis that golden eagles (Aquila chrysaetos) prefer an open understory, which potentially aids visual identification of prey. Two golden eagles fitted with GPS tracking devices overwintered in the New Jersey Pinelands National Preserve. We generated CHPs from discrete-return lidar data for an area occupied by the birds. We compared the CHPs of sites the birds occupied to the surrounding available habitat and found that the occupied sites were significantly lower in CBD from the ground up to 5 m for perched/stationary birds, and from the ground up to 8 m for birds in flight. These results could be used by forest resource managers for promoting golden eagle habitat through prescribed fire. In addition, these results demonstrate the power of lidar to generate physically and intuitively meaningful measures of forest structure. [ABSTRACT FROM AUTHOR]

This special issue, which is published in conjunction with the new Drones section of the I International Journal of Remote Sensing i ( I IJRS i ), gathers five key papers from the 6 SP th sp I Small Unmanned Aerial Systems for Environmental Research i ( I UAS4Enviro i ) conference. The capability of optical UAS data to generate fine spatial resolution imagery, as well as Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs), are among the benefits of UAS data compared to satellite and airborne data. Although publicly available LiDAR data from airborne sensors is often a common choice over relatively costly custom-acquired data from UAS-based LiDAR sensors, UAS-mounted instruments are generally preferable for generating the very high spatial resolution required for site-specific surveys. Although UAS-borne LiDAR remains relatively expensive compared to multispectral UAS remote sensing, UAS technology holds much potential for making LiDAR data available in areas where airborne data is prohibitively expensive, especially in less developed countries. [Extracted from the article]

• The Gabrg2+/Q390X knockin mice associated with DS had increased seizures and death. • Stiripentol alone was ineffective in suppressing seizures but increased survival. • Add-on of stiripentol to diazepam was beneficial in Gabrg2+/Q390X mice. • Stiripentol can be used as adjunct therapy in DS with GABRG2 deficiency. • The response to stiripentol may be different based on different genetic mutations. Anti-seizure drugs (ASDs) are widely used and known to increase inhibitory tone on neuro-circuits and reduce aberrant synchronous firing in epilepsy. Some ASDs act as agonist at the GABA A receptor. Stiripentol, known to increase GABA A receptor activity as well as the metabolites of GABA A receptor agonists, is often used in the treatment of an epileptic encephalopathy, Dravet syndrome (DS), which is caused by mutations mainly in SCN1A and in other genes such as GABRG 2. We have recently generated a Gabrg2+/Q390X knockin mouse model associated with DS in humans. The objective of the study was to explore the effects of stiripentol in DS with GABA A receptor functional deficiency because of the etiology heterogeneity in DS. Monotherapy (stiripentol or Diazepam) and polytherapy (stiripentol and diazepam) treatments were tested in Gabrg2+/Q390X mice challenged with pentylenetetrazol (PTZ) seizure induction in conjunction with video-monitoring synchronized electroencephalogram (EEG) recordings. A combination of stiripentol and diazepam greatly reduced seizure-related events in Gabrg2+/Q390X mice following PTZ administration and increased survival. However, the treatment of stiripentol alone was mostly ineffective in alleviating seizure-related events except that it reduced mortality in PTZ challenged Gabrg2+/Q390X mice. The study suggests that stiripentol could be only used as add-on therapy for DS with GABA A receptor functional deficiency, which is consistent with the most established clinical application of stiripentol. The study highlights the importance of mechanism-based precision treatment for DS considering the highly heterogeneous nature of etiology in DS and the fact that mutations in different genes give rise to the same clinical phenotype. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including peer-review report, strong empirical evidence and research journals.

Mountain shadows in optical satellite images complicate the mapping of glacial lakes. Due to the rugged topography in periglacial alpine regions, many glacial lakes, especially smaller lakes, are partially shaded by mountain shadows in remotely sensed images. Shadows not only reduce the accuracy of lake mapping but also make changes in lake area hard to detect. In this paper, the characteristics of mountain shadows in remotely sensed imagery are explored, and their spatial relationships with regards to glacial lakes are modelled. Building on the previously developed Glacial Lakes Iterative Local Mapping (GLILM) method, a new water mapping approach is presented. The new method utilizes log-transformed spectral data and a normalized difference water index, NDWIblue, for delineating the boundaries of lakes within shadowed regions. The application of this approach is explored within the context of mapping lakes across space and time using Landsat images in the glacially dominated Tianshan mountainous of Central Asia. The results demonstrate that glacial lakes, both in sunlit and in shaded areas, can be mapped reliably, and that the results are useful for lake change analysis studies. [ABSTRACT FROM AUTHOR]

Abstract For many millions of patients at secondary risk of coronary thrombosis pharmaceutical protection is supplied by dual anti-platelet therapy. Despite substantial therapeutic developments over the last decade recurrent thrombotic events occur, highlighting the need for further optimisation of therapies. Importantly, but often ignored, anti-platelet drugs interact with cyclic nucleotide systems in platelets and these are the same systems that mediate key endogenous pathways of platelet regulation, notably those dependent upon the vascular endothelium. The aim of this review is to highlight interactions between the anti-platelet drugs, aspirin and P2Y 12 receptor antagonists and endogenous pathways of platelet regulation at the level of cyclic nucleotides. These considerations are key to concepts such as anti-platelet drug resistance and individualized anti-platelet therapy which cannot be understood by study of platelets in isolation from the circulatory environment. We also explore novel and emerging therapies that focus on preserving haemostasis and how the concepts outlined in this review could be exploited therapeutically to improve anti-thrombotic efficacy whilst reducing bleeding risk. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user's and producer's accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user's and producer's accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable. [ABSTRACT FROM AUTHOR]

This study introduces a cost-effective and automated methodology to swiftly and accurately gauge disaster-induced land cover changes, harnessing satellite imagery and machine learning. Using open-source platforms like Google Earth Engine, the approach streamlines training sample generation and assesses diverse machine learning algorithms on pre- and post-disaster Landsat images. The innovation lies in its semi-automatic training sample generation methodology, reducing time and manual effort for accurate land cover mapping and addressing the challenge of crafting training samples in disaster settings. The methodology was tested on the 2010 Chilean earthquake and tsunami, revealing a 45% increase in sediment cover and a 15% reduction in water cover, underlining vast landscape alterations. SVM stood out for its consistent accuracy. To validate the methodological robustness and broaden its applicability, we replicated the procedures using diverse land cover collection, including ESA WorldCover 10 m and Dynamic World. This comprehensive validation encompassed varied catastrophic events, extending the methodology's relevance beyond its original scope. The consistent findings across different scenarios underscored the method's robustness and underscored its potential for broader utilization. This streamlined, cost-effective approach suits multiple land cover products, setting the stage for constant Earth feature monitoring. In light of escalating natural disasters, the synergy of machine learning and geospatial technology spotlighted here offers promising avenues for improved disaster preparedness and response. [ABSTRACT FROM AUTHOR]

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets. [ABSTRACT FROM AUTHOR]

The article introduces various reports published within the issue, including one about heat flux, one about slow-movement slope failure and another on remote sensing in land-use planning.

It has been established that febrile seizures and its extended syndromes like generalized epilepsy with febrile seizures (FS) plus (GEFS+) and Dravet syndrome have been associated with mutations especially in SCN1A and GABRG2 genes. In patients, the onset of FS is likely due to the combined effect of temperature and inflammation in genetically vulnerable individuals because fever is often associated with infection. Much effort has been spent to understand the mechanisms underlying fever induction of seizures. In addition to the role of cytokines in FS, previous studies in Scn1a +/− knockout mice, a model of Dravet syndrome, indicated that temperature elevation alone could result in seizure generation, and the effect of elevated temperature inducing seizures was age-dependent. Here, we report the thermal effect in a different mouse model of Dravet syndrome, the Gabrg2 +/Q390X knockin mouse. We demonstrated age-dependent dysregulated temperature control and that temperature elevation produced myoclonic jerks, generalized tonic clonic seizures (GTCSs) and heightened anxiety-like symptoms in Gabrg2 +/Q390X mice. The study indicated that regardless of other inflammatory factors, brief heat alone increased brain excitability and induced multiple types of seizures in Gabrg2 +/Q390X mice, suggesting that mutations like GABRG2(Q390X) may alter brain thermal regulation and precipitate seizures during temperature elevations. [ABSTRACT FROM AUTHOR]

The WorldView-3 (WV-3) sensor, launched in 2014, is the first highspatial resolution scanner to acquire imagery in the shortwave infrared (SWIR). A spectral ratio of the SWIR combined with the nearinfrared (NIR) can potentially provide an effective differentiation of wildfire burn severity. Previous high spatial resolution sensors were limited to data fromthe visible and NIR for mapping burn severity, for example using the normalized difference vegetation index (NDVI). Drawing on a study site in the Pine Barrens of New Jersey, USA, we investigate optimal processing methods for analysing WV-3 data, with a focus on the pre-fire minus post-fire differenced normalized burn ratio (dNBR). Although the imagery, originally acquired with a 3.7 m instantaneous field of view, was aggregated to 7.5 m pixels by DigitalGlobe due to current licensing constraints, a slight additional smoothing of the data was nevertheless found to help reduce noise in the multi-temporal dNBR imagery. The highest coefficient of determination (R2) of the regressions of dNBR with the field-based composite burn index was obtained with a dNBR ratio produced with the NIR1 and SWIR6 bands. Only a very small increase in R2 was found when dNBR was calculated using the average of NIR1 and NIR2 for the NIR bands, and SWIR5 to SWIR8 for the SWIR bands. dNBR calculated using SWIR1 as the NIR band produced notably lower R2 values than when either NIR1 or NIR2 were used. Differenced NDVI data was found to produce models with a much lower R2 than dNBR, emphasizing the importance of the shortwave infrared region formonitoring fire severity. High spatial resolution dNBR data fromWV-3 can potentially provide valuable information on finer details regarding burn severity patterns than can be obtained from Landsat 30 m data. [ABSTRACT FROM AUTHOR]

Olanzapine is an atypical antipsychotic drug reported to increase appetite and alter body weight. These side effects, while fairly equal between men andwomen at the clinical level, arefar more common amongjemale than male rats. The objective of the present study was to investigate another mode of olanzapine administration in order to yield a preclinical outcome comparable to clinical results. Olanzapine was placed in sweetened condensed milk to ensure complete consumption of the daily dose of the drug at a specified time, which simulates a human taking the drug on a typical once-a-day schedule. Male and female rats were chronically administered olanzapine over a 3-week period. Olanzapine had no influence on male rats; however, it did produce an increase in food and water intake for female rats. Additionally, regardless of the drug, female rats were more active than male rats. While the current study simulated a human taking the drug on a typical once-a-day schedule, the results failed to replicate the proper diet and likely plasma levels in humans; and provided further support for the administration of olanzapine in food, similar to the human diet, or water throughout each day to emulate the olanzapineinduced weight gain in humans. [ABSTRACT FROM AUTHOR]

Incorporating ancillary, non-spectral data may improve the separability of land use/land cover classes. This study investigates the use of multi-temporal digital terrain data combined with aerial National Agriculture Imagery Program imagery for differentiating mine-reclaimed grasslands from non-mining grasslands across a broad region (6085 km2). The terrain data were derived from historical digital hypsography and a recent light detection and ranging data set. A geographic object-based image analysis (GEOBIA) approach, combined with two machine learning algorithms, Random Forests and Support Vector Machines, was used because these methods facilitate the use of ancillary data in classification. The results suggest that mine-reclaimed grasslands can be mapped accurately, with user’s and producer’s accuracies above 80%, due to a distinctive topographic signature in comparison with other spectrally similar grasslands within this landscape. The use of multi-temporal digital elevation model data and pre-mining terrain data only generally provided statistically significant increased classification accuracy in comparison with post-mining terrain data. Elevation change data were of value, and terrain shape variables generally improved the classification. GEOBIA and machine learning algorithms were useful in exploiting these non-spectral data, as data gridded at variable cell sizes can be summarized at the scale of image objects, allowing complex interactions between predictor variables to be characterized. [ABSTRACT FROM PUBLISHER]

Summary Seizures are a known co-occurring symptom of Alzheimer's disease, and they can accelerate cognitive and neuropathological dysfunction. Sub-optimal vitamin C (ascorbic acid) deficiency, that is low levels that do not lead the sufferer to present with clinical signs of scurvy ( e.g. lethargy, hemorrhage, hyperkeratosis), are easily obtainable with insufficient dietary intake, and may contribute to the oxidative stress environment of both Alzheimer's disease and epilepsy. The purpose of this study was to test whether mice that have diminished brain ascorbic acid in addition to carrying human Alzheimer's disease mutations in the amyloid precursor protein (APP) and presenilin 1 (PSEN1) genes, had altered electrical activity in the brain (electroencephalography; EEG), and were more susceptible to pharmacologically induced seizures. Brain ascorbic acid was decreased in APP/PSEN1 mice by crossing them with sodium vitamin C transporter 2 (SVCT2) heterozygous knockout mice. These mice have an approximately 30% decrease in brain ascorbic acid due to lower levels of SVCT2 that supplies the brain with ASC. SVCT2+/−APP/PSEN1 mice had decreased ascorbic acid and increased oxidative stress in brain, increased mortality, faster seizure onset latency following treatment with kainic acid (10 mg/kg i.p.), and more ictal events following pentylenetetrazol (50 mg/kg i.p.) treatment. Furthermore, we report the entirely novel phenomenon that ascorbic acid deficiency alone increased the severity of kainic acid- and pentylenetetrazol-induced seizures. These data suggest that avoiding ascorbic acid deficiency may be particularly important in populations at increased risk for epilepsy and seizures, such as Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: The platelet fibrinogen receptor represents the final common pathway of platelet activation, and is formed from two glycoprotein (GP) subunits (GPIIb/IIIa). Carriage of the mutant PlA2 allele of GPIIIa has been shown to confer an increased risk of cardiovascular events, but published studies have disagreed as to the mechanism for this association. Objectives: To assess whether carriage of the PlA2 allele conforms to Mendelian patterns of expression and to identify whether carriage of the mutant allele modulates platelet function. Methods: Expression of the PlA2 allele was assessed in both healthy subjects (n=25) and patients with known coronary artery disease (n=90) through the development and validation of a liquid chromatography, tandem mass spectrometry (LC-MS/MS) assay. Platelet function was assessed in the patient cohort in response to multiple agonists, and these data were analysed in the context of the proteomic data. Results: Expression of the wild-type PlA1 allele and mutant PlA2 alleles was readily quantifiable and conformed to Mendelian patterns in both healthy and patient cohorts. Patients who were homozygous for the mutant PlA2 allele had an increased aggregatory response to adenosine diphosphate, collagen, adrenaline, ristocetin, thrombin receptor-activating peptide 6 and U46619, when assessed using agonist-concentration response curves. Conclusions: These findings support the hypothesis that carriage of the mutant PlA2 allele mediates an increased risk of cardiovascular events through the modulation of platelet reactivity. [ABSTRACT FROM AUTHOR]

Simultaneous protocols typically yield poorer stimulus equivalence outcomes than do other protocols commonly used in equivalence research. Two independent groups of three 3-member equivalence sets of stimuli were used in conditional discrimination procedures in two conditions, one using the standard simultaneous protocol and the other using a hybrid simultaneous training and simple-to-complex testing. Participants completed the two conditions in one long session in Experiment 1, but in separate sessions in Experiment 2. The same stimulus sets used in Experiment 1 were randomized for the two conditions in Experiment 2. Overall, accuracy was better with the hybrid than with the standard protocol in both experiments. The equivalence yield was also better under the hybrid than under the standard protocol in each experiment. The results suggest that the order of testing for emergent relations may account for the difficulty often encountered with the standard simultaneous protocol. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ultrasonic vocalizations (USVs) observed during intermittent swim stress (ISS). [•] USVs during ISS predict reduced immobility in forced swim test. [•] USVs during ISS predict proficiency in spatial learning. [•] USVs during ISS forecast resilience in two subsequent swim-motivated tasks. [ABSTRACT FROM AUTHOR]

Abstract: Various animal models of depression have been used to seek a greater understanding of stress-related disorders. However, there is still a great need for research in this area, as many unanswered questions remain. Therefore, we sought to employ a novel animal model of depression known as intermittent swim stress (ISS). In this model, the animal experiences 100 trials of cold water swim stress. ISS has already shown subsequent immobility in the forced swim test (FST), deficits in instrumental and spatial (spaced-trial procedure), and responsiveness to norepinephrine. We are now examining how this will translate in the Morris water maze for rats in a massed-learning trial procedure, and further assessing ISS sensitivity toward norepinephrine selective anti-depressant drugs. The results indicated no difference in cued learning when the platform was visible in the water maze, but a hidden platform task revealed poorer spatial learning for ISS-exposed rats versus controls. In terms of spatial memory, there was a notable ISS-induced deficit 1h after the learning trials, regardless of performance on the previous platform task. Interestingly, the administration of reboxetine interfered with the spatial learning and memory trials for both ISS and CC groups. As a result, ISS exposure compromised spatial learning and memory in the Morris water maze, and norepinephrine does not appear to be a mediator of this deficit. The results demonstrate a key difference in the effects of reboxetine in a massed- vs. spaced-learning trial procedure in the Morris water maze following ISS exposure. [Copyright &y& Elsevier]

Various animal models of depression have been used to seek a greater understanding of stress-related disorders. However, there is still a great need for novel research in this area, as many individuals suffering from depression are resistant to current treatment methods. Women have a higher rate of depression, highlighting the need to investigate mechanisms of sex differences. Therefore, we employed a new animal model to assess symptoms of depression, known as intermittent swim stress (ISS). In this model, the animal experiences 100 trials of cold water swim stress. ISS has already been shown to cause signs of behavioral depression in males, but has yet to be assessed in females. Following ISS exposure, we looked at sex differences in the Morris water maze and forced swim test. The results indicated a spatial learning effect only in the hidden platform task between male and female controls, and stressed and control males. A consistent spatial memory effect was only seen for males exposed to ISS. In the forced swim test, both sexes exposed to ISS exhibited greater immobility, and the same males and females also showed attenuated climbing and swimming, respectively. The sex differences could be due to different neural substrates for males and females. The goal of this study was to provide the first behavioral examination of sex differences following ISS exposure, so the stage of estrous cycle was not assessed for the females. This is a necessary future direction for subsequent experiments. The current article highlights the importance of sex differences in response to stress. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a common cause of morbidity and mortality. We have previously shown that TBI with a concurrent extra-cranial injury reliably leads to post-injury suppression of the innate immune system, but the impact of this injury on the adaptive immune system is unknown. We present data showing that combined injury reduced immune response as assayed in both blood and spleen samples and that these changes parallel apoptosis in the spleen. To assess the clinical relevance of these changes, we examined lungs for spontaneous bacterial colonization. For these studies, prepubescent (28 day old) rats were injured using a controlled cortical impact model and then 25% blood volume removal by arteriotomy, and injured animals were compared with sham injured animals. Blood and spleen samples at post-injury day 1 were incubated with or without immunostimulant and examined for IFN-γ production using an Eli-Spot assay. Spleen samples were also examined for apoptosis using Annexin V staining, and lungs were harvested and plated on blood agar to examine for spontaneous bacterial colonization. Stimulations of whole blood and spleen samples with phorbol 12-myristate 13-acetate/ionomycin (PMA/I) at post-injury day 1 were associated with significant decreases in IFN - γ-positive cells/million in injured animals. Stimulation of whole blood with either PMA/I or pokeweed mitogen led to reduced tumor necrosis factor alpha production. Spleen from injured animals showed a marked increase in apoptosis. Lung samples showed a 300% increase in colonies per plate in injured animals. These data suggest that the combined injury can lead to adaptive immunosuppression, and our findings further suggest a potential role for the spleen in altering leukocyte function following injury. [Display omitted] • A combined injury (brain injury and hemorrhage) in juvenile rats is presented. • Effects of this injury on the adaptive immune system in juvenile rats was studied. • Combined injury led to a reduced adaptive immune response in blood and spleen. • Splenocyte apoptosis in the white pulp was noted following combined injury. • Lungs from injured animals demonstrated increased bacterial colonization. [ABSTRACT FROM AUTHOR]

A small urban park in Yerevan, Armenia, was studied using biogechemical analysis of the tree canopy, field spectral reflectance measurements of tree leaves, simulated WorldView-2 multispectral data generated from the leaf spectra, and two summer images of real WorldView-2 data. The tree canopy of the park is dominated by two trees, Robinia pseudoacacia L. (locust) and Fraxinus excelsior L. (ash). The Highest values of lead, nickel, molybdenum, copper and zinc were found in leaves harvested from trees adjacent to the streets, whereas most of the lowest values for those metals were found in the interior of the park. A t-test of the field spectral measurements indicated that the green and red edge spectral reflectance of leaves from trees near the streets was significantly higher than that of leaves of trees in the interior (p < 0.05). However, in simulated WorldView-2 multispectral data, the street and interior leaves were only statistically separable in band 6 (Red Edge) raw data and hyperspherical direction cosine (HSDC) normalized band 6 data. HSDC-normalized band 6 digital numbers from real WorldView-2 data of 16 June and 9 August 2011 from trees adjacent to the streets were statistically higher than the interior locations for both dates. Maps of anomalously high HSDC-normalized band 6 values show a concentration on the park edges, suggesting vehicle pollution may indeed be the cause of the observed patterns. [ABSTRACT FROM AUTHOR]

The article discusses the use of high-speed shaking and fixed time point readings in standardised optical multichannel (optimul) platelet aggregometry. The authors provide an overview on how the optimul plates used for the of Tecan Sunrise kinetic plate reader can also be utilized with a high-speed thermoshaker. They also tackle the preparation of the optimul plates to determine the outcome which include the addition of platelet-rich plasma (PRP) into the appropriate wells of the plate.

Abstract: Intermittent swim stress (ISS) exposes a rat to cold water and the effects of the procedure produce detrimental results on activity measures 24h later. The ISS model can be used with the Morris water maze (MWM) to investigate the impact of stress on a spatial learning and memory task, known to involve the hippocampus. We investigated if the ISS model produced performance deficits in the MWM (experiments 1 and 2). We also investigated the role of norepinephrine by using an alpha-2 adrenergic agonist (i.e., clonidine) to exacerbate ISS-induced deficits (experiment 3), and using antidepressants (i.e., desipramine and reboxetine) that enhance the synaptic availability of norepinephrine to reduce ISS-induced deficits (experiments 4 and 5). Results indicated a main effect for stress in all experiments, with the exception of experiment 2, as ISS did induce performance deficits in the MWM. Clonidine enhanced ISS-induced deficits only in the learning trials, while desipramine and reboxetine reduced ISS-induced deficits in the learning trials. Additionally, only reboxetine reduced memory deficits in the MWM. These findings provide evidence that norepinephrine may act as a partial mediator of ISS-induced deficits in MWM performance. [Copyright &y& Elsevier]

Abstract: Platelets are essential to prevent blood loss and promote wound healing. Their activation comprises of several complex steps which are regulated by a range of mediators. Over the last few decades there has been intense interest in a group of gaseous mediators known as gasotransmitters; currently comprising nitric oxide (NO), carbon monoxide (CO) and hydrogen sulphide (H2S). Here we consider the action of gasotransmitters on platelet activity. NO is a well established platelet inhibitor which mediates its effects predominantly through activation of soluble guanylyl cyclase leading to a decrease in intraplatelet calcium. More recently CO has been identified as a gasotransmitter with inhibitory actions on platelets; CO acts through the same mechanism as NO but is less potent. The in vivo and platelet functions of the most recently identified gasotransmitter, H2S, are still the subject of investigations, but they appear generally inhibitory. Whilst there is evidence for the individual action of these mediators, it is also likely that combinations of these mediators are more relevant regulators of platelets. Furthermore, current evidence suggests that these mediators in combination alter the production of each other, and so modify the circulating levels of gasotransmitters. The use of gasotransmitters as therapeutic agents is also being explored for a range of indications. In conclusion, the importance of NO in the regulation of vascular tone and platelet activity has long been understood. Other gasotransmitters are now establishing themselves as mediators of vascular tone, and recent evidence suggests that these other gasotransmitters may also modulate platelet function. [Copyright &y& Elsevier]

Aspirin and P2Y12 antagonists are commonly used anti-platelet agents. Aspirin produces its effects through inhibition of thromboxane A2 (TXA2) production, while P2Y12 antagonists attenuate the secondary responses to ADP released by activated platelets. The anti-platelet effects of aspirin and a P2Y12 antagonist are often considered to be separately additive. However, there is evidence of an overlap in effects, in that a high level of P2Y12 receptor inhibition can blunt TXA2 receptor signalling in platelets and reduce platelet production of TXA2. Against this background, the addition of aspirin, particularly at higher doses, could cause significant reductions in the production of prostanoids in other tissues, e.g. prostaglandin I2 from the blood vessel wall. This review summarizes the data from clinical studies in which dose-dependent effects of aspirin on prostanoid production have been evaluated by both plasma and urinary measures. It also addresses the biology underlying the cardiovascular effects of aspirin and its influences upon prostanoid production throughout the body. The review then considers whether, in the presence of newer, more refined P2Y12 receptor antagonists, aspirin may offer less benefit than might have been predicted from earlier clinical trials using more variable P2Y12 antagonists. The possibility is reflected upon, that when combined with a high level of P2Y12 blockade the net effect of higher doses of aspirin could be removal of anti-thrombotic and vasodilating prostanoids and so a lessening of the anti-thrombotic effectiveness of the treatment. [ABSTRACT FROM AUTHOR]

Texture has been of great interest to remote sensing analysts for more than three decades. This paper is a review of texture approaches that are based on a moving window, or kernel, and that generate a summary measure of local spatial variation, which is assigned to the central pixel of the kernel. Texture methods are challenging to implement, partly because of the many parameters that need to be set prior to running a texture analysis. The list of parameters includes the texture order, metric, kernel size, and spectral band. For second-order metrics, additional parameters that need to be set include radiometric re-quantization, displacement, and angle. Although few general rules of thumb can be provided in selecting texture analysis parameters, understanding the conceptual role of these parameters helps illuminate the options available. In addition, future opportunities in object-oriented texture, adaptive texture measures, and multi-scale texture fusion offer the potential for addressing some of the inherent challenges in the application of texture in image analysis. [ABSTRACT FROM AUTHOR]

Simulated data were used to investigate the relationships between image properties and change detection accuracy in a systematic manner. The image properties examined were class separability, radiometric normalization and image spectral band-to-band correlation. The change detection methods evaluated were post-classification comparison, direct classification of multidate imagery, image differencing, principal component analysis, and change vector analysis. The simulated data experiments showed that the relative accuracy of the change detection methods varied with changes in image properties, thus confirming the hypothesis that caution should be used in generalizing from studies that use only a single image pair. In most cases, direct classification and post-classification comparison were the least sensitive to changes in the image properties of class separability, radiometric normalization error and band correlation. Furthermore, these methods generally produced the highest accuracy, or were amongst those with a high accuracy. PCA accuracy was highly variable; the use of four principal components consistently resulted in substantial decreased classification accuracy relative to using six components, or classification using the original six bands. The accuracy of image differencing also varied greatly in the experiments. Of the three methods that require radiometric normalization, image differencing was the method most affected by radiometric error, relative to change vector and classification methods, for classes that have moderate and low separability. For classes that are highly separable, image differencing was relatively unaffected by radiometric normalization error. CVA was found to be the most accurate method for classes with low separability and all but the largest radiometric errors. CVA accuracy tended to be the least affected by changes in the degree of band correlation in situations where the class means were moderately dispersed, or clustered near the diagonal. For all change detection methods, the classification accuracy increased as simulated band correlation increased, and direct classification methods consistently had the highest accuracy, while PCA generally had the lowest accuracy. [ABSTRACT FROM AUTHOR]

In this article, the author argues that there is little difference between the cardiovascular risks associated with non-steroid anti-inflammatory drugs (NSAIDs) and COX-2 selective drugs and says the belief that there is relies on the incorrect premise that a standard dose of a NSAID inhibits COX-2 less than COX-2 drugs do. Since NSAIDs and COX-2 drugs share a similar ability, a difference in the risk of thrombotic events it must be a result of dosage and not caused by the drugs themselves.

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region. [Copyright &y& Elsevier]

Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit the formation of prostanoids by the enzyme cyclooxygenase (COX). Work in the past 15 years has shown that COX exists in two forms: COX1, which is largely associated with physiological functions, and COX2, which is largely associated with pathological functions. Heated debate followed the introduction of selective COX2 inhibitors around 5 years ago: do these drugs offer any advantages over the traditional NSAIDs they were meant to replace, particularly in regard to gastrointestinal and cardiovascular side effects? Here we discuss the evidence and the latest recommendations for the use of selective inhibitors of COX2 as well as the traditional NSAIDs. [ABSTRACT FROM AUTHOR]

The beneficial actions of nonsteroidal anti-inflammatory drugs (NSAIDs) have been linked to their ability to inhibit inducible COX-2 at sites of inflammation, and their side effects (e.g., gastric damage) to inhibition of constitutive COX-1. Selective inhibitors of COX-2, such as celecoxib, etoricoxib, lumiracoxib, rofecoxib, and valdecoxib have been developed and the greatest recent growth in our knowledge in this area has been come from the clinical use of these compounds. Although clinical data indicate that COX-2 selectivity is associated with a reduction in severe gastrointestinal events, they also reveal there are roles for constitutive COX-2 within tissues such as the brain, kidney, pancreas, intestine, and blood vessels. We now better understand the roles of COX-1 and COX-2 in functions as disparate as the perception of pain and the progression of cancers. Clinical use of COX-2-selective compounds has ignited strong debates regarding potential side effects, most notably those within the cardiovascular system such as myocardial infarctions, strokes, and elevation in blood pressure. This review will discuss how the latest studies help us understand the roles of COX-1 and COX-2 and what clinically proven benefits the newer generation of COX-2-selective inhibitors offer. [ABSTRACT FROM AUTHOR]

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R2=68% (adj.)) within the test-site is observed. [Copyright &y& Elsevier]

Colony stimulating factors (CSF) promote leukocyte survival by reducing apoptotic cell death. However, their effects on non-leukocyte cell types are unclear. Reduced apoptosis in colon epithelial cells is thought to contribute to the initiation of cancer. Here, we report diminished spontaneous apoptosis of human colon epithelial HT-29 cells in the presence of macrophage-CSF or granulocyte macrophage-CSF. Moreover, reduced apoptosis induced by sulindac sulfide was also observed with macrophage-CSF. Granulocyte-CSF failed to modify spontaneous or sulindac sulfide induced apoptosis. It seems, therefore, that the action of CSFs on apoptosis is not confined to haematopoietic cells but may be extended to stromal cells. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of applications in the geospatial and remote sensing (RS) sciences, and consequently has been a focus of many recent studies. However, a review of accuracy assessment methods used in recently published RS DL studies, focusing on scene classification, object detection, semantic segmentation, and instance segmentation, indicates that RS DL papers appear to follow an accuracy assessment approach that diverges from that of traditional RS studies. Papers reporting on RS DL studies have largely abandoned traditional RS accuracy assessment terminology; they rarely reported a complete confusion matrix; and sampling designs and analysis protocols generally did not provide a population-based confusion matrix, in which the table entries are estimates of the probabilities of occurrence of the mapped landscape. These issues indicate the need for the RS community to develop guidance on best practices for accuracy assessment for CNN-based DL thematic mapping and object detection. As a first step in that process, we explore key issues, including the observation that accuracy assessments should not be biased by the CNN-based training and inference processes that rely on image chips. Furthermore, accuracy assessments should be consistent with prior recommendations and standards in the field, should support the estimation of a population confusion matrix, and should allow for assessment of model generalization. This paper draws from our review of the RS DL literature and the rich record of traditional remote sensing accuracy assessment research while considering the unique nature of CNN-based deep learning to propose accuracy assessment best practices that use appropriate sampling methods, training and validation data partitioning, assessment metrics, and reporting standards. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently developed image classification approach. With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of metrics that may be unfamiliar to the remote sensing (RS) community. To explore the differences between traditional RS and DL RS methods, we surveyed a random selection of 100 papers from the RS DL literature. The results show that RS DL studies have largely abandoned traditional RS accuracy assessment terminology, though some of the accuracy measures typically used in DL papers, most notably precision and recall, have direct equivalents in traditional RS terminology. Some of the DL accuracy terms have multiple names, or are equivalent to another measure. In our sample, DL studies only rarely reported a complete confusion matrix, and when they did so, it was even more rare that the confusion matrix estimated population properties. On the other hand, some DL studies are increasingly paying attention to the role of class prevalence in designing accuracy assessment approaches. DL studies that evaluate the decision boundary threshold over a range of values tend to use the precision-recall (P-R) curve, the associated area under the curve (AUC) measures of average precision (AP) and mean average precision (mAP), rather than the traditional receiver operating characteristic (ROC) curve and its AUC. DL studies are also notable for testing the generalization of their models on entirely new datasets, including data from new areas, new acquisition times, or even new sensors. [ABSTRACT FROM AUTHOR]

Beijing, China's capital city, has experienced decades of severe land subsidence due to the long-term overexploitation of groundwater. The implementation of the South-to-North Water Diversion Project (SNWDP) and artificial ecological restoration have significantly changed Beijing's hydro-ecological and geological environment in recent years, leading to a widespread rise in groundwater levels. However, whether the related land subsidence has slowed down or reversed under these measures has not yet been effectively monitored and quantitatively analyzed in terms of time and space. Accordingly, in this study, we proposed using an improved time-series deformation method, which combines persistent scatterers and distributed scatterers, to process Sentinel-1 images from 2015 to 2022 in the Beijing Plain region. We performed a geospatial analysis to gain a better understanding of how the new hydrological conditions changed the pattern of deformation on the Beijing Plain. The results indicated that our combined PS and DS method provided more measurements both in total quantity and spatial density than conventional PSI methods. The land subsidence in the Beijing Plain area has been effectively alleviated from a subsidence region of approximately 1377 km2 in 2015 to only approximately 78 km2 in 2022. Ecological restoration areas in the northeastern part of the Plain have even rebounded over this period, at a maximum of approximately 40 mm in 2022. The overall pattern of ground deformation (subsidence and uplift) is negatively correlated with changes in the groundwater table (decline and rise). Local deformation is controlled by the thickness of the compressible layer and an active fault. The year 2015, when anthropogenic water transfers were eliminated and ecological measures to recharge groundwater were implemented, was the crucial turning point of the change in the deformation trend in the subsidence history of Beijing. Our findings carry significance, not only for China, but also for other areas where large-scale groundwater extractions are causing severe ground subsidence or rebound. [ABSTRACT FROM AUTHOR]

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project. [ABSTRACT FROM AUTHOR]

1. In the normal blood vessel, the vascular endothelium regulates the tone of the underlying smooth muscle and the reactivity of blood elements, such as platelets and neutrophils, by the release of mediators, in particular nitric oxide (NO) and endothelin-1 (ET-1). 2. Nitric oxide is a potent vasodilator that also inhibits platelet and neutrophil aggregation and adhesion; ET-1 is the most potent mammalian vasoconstrictor peptide yet found. Recently, much research effort has focused on examining the interactions between these two important mediators. At a simple level, ET-1 acts on specific receptors on the endothelium to increase the release of NO, while NO depresses the production and/or release of ET-1 from endothelial cells. 3. While ET-1 appears to have a relatively small influence on the basal regulation of blood pressure, NO appears central. For example, inhibition of NO production in normotensive animals produces a marked elevation in blood pressure. 4. Conversely, numerous vascular disease states have been associated with elevations in the production and/or release of ET-1 and it has been implicated in the deleterious changes associated with ischaemia–reperfusion injury, subarachnoid haemorrhage and hypertension. In these conditions, NO production may also be increased by the induction of NO synthetic pathways within the vascular smooth muscle. Endothelin-1 may also be produced by the vascular smooth muscle under similar circumstances. 5. Therefore, in pathological states, a new balance between NO and ET-1 production may be central to changes in blood vessel reactivity, smooth muscle proliferation and blood coagulability. [ABSTRACT FROM AUTHOR]

Presents information on a study that reported data from human whole blood assay and a modified human whole blood assay for the association of nonsteroid anti-inflammatory drugs (NSAID) with inhibition of cyclo-oxygenase (COX)-2 with -1. Methodology of the study; Production of prostanoid; Inhibitor potencies of COX-2 and -2.

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user's and producer's accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated. [ABSTRACT FROM AUTHOR]

The article announces the recipients of various awards at the 60th annual meeting of the Southeastern Division of the Association of American Geographers held in West Palm Beach, Florida from November 20-22, 2005. It informs that the recipient of the Lifetime Achievement Awards are Neal Lineback and Chor-Pang Lo.

Presents the findings of a study comparing the potencies of cyclooxygenase (COX) inhibitors in the presence of different concentrations of protein in blood, synovial fluid and cerebrospinal fluid. Effect of extracellular protein on the potency of aspirin, celecoxib, indomethacin, lumiracoxib and melaxicam; Influence of extracellular protein on the potency of sodium salicylate against COX-1 and COX-2; Impact of differing protein concentrations on COX-1 as against COX-2 selectivity.

Presents a study on the role of cyclo-oxygenase in cellular mechanisms of acetaminophen. Drug used for safe and effective treatment of pain and fever; Circumstance in which acetaminophen had no effect on the expression of cyclo-oxygenase-2; Way in which the inhibitory effects of acetaminophen are fully reversed.

Peroxisome proliferator-activated receptor-γ (PPARγ) and inducible cyclooxygenase-2 (COX-2) are expressed in atherosclerotic lesions, particularly in the intimal monocytic and vascular smooth muscle cells. PPARγ ligands, which include endogenous COX products, induce apoptosis of vascular smooth muscle cells in culture. We therefore studied the interaction between PPARγ and inducible cyclo-oxygenase (COX-2), in rat aortic vascular smooth muscle cells (RASMCs). [ABSTRACT FROM AUTHOR]

Comments on an article offering evidence that some nonsteroidal anti-inflammatory drugs could inhibit the active transport of prostanoids from their generating cells, published in the August 5, 2003 issue of the 'Proceedings of the National Academy of Sciences of the United States of America.' Ways to influence prostanoid release from cells.

High-strength 7xxx Al-alloys are used in aerospace and are expected to be increasingly employed in the automotive sector for "lightweighting" but they can be sensitive to stress-corrosion cracking (SCC) depending on temper and loading conditions. Here, we performed double cantilever beam (DCB) crack growth tests and study at the nanoscale stress-corrosion cracks on two compositional variants of AA7140 from different regions of a thick-plate. We report on precipitate dissolution, matrix solute enrichment near the crack tips and a modified microstructure ahead of the crack. We relate our findings with the SCC behaviour on both samples and propose an array of active mechanisms. • DCB crack growth tests (70 °C and 85% RH) of two 7140 composition variants. • Both samples present identical behaviour in region I but dissimilar in region II. • Precipitate dissolution and matrix solute enrichment at and ahead of the crack. • Evidence of diffusional processes during SCC. • Active mechanisms are proposed and discussed. [ABSTRACT FROM AUTHOR]

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided. [ABSTRACT FROM AUTHOR]