REMAP:An online remote sensing application for land cover classification and monitoring

Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data. We have developed a free and open-access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high-quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem-based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives

Prenatal development is linked to bronchial reactivity: epidemiological and animal model evidence

Chronic cardiorespiratory disease is associated with low birthweight suggesting the importance of the developmental environment. Prenatal factors affecting fetal growth are believed important, but the underlying mechanisms are unknown. The influence of developmental programming on bronchial hyperreactivity is investigated in an animal model and evidence for comparable associations is sought in humans. Pregnant Wistar rats were fed either control or protein-restricted diets throughout pregnancy. Bronchoconstrictor responses were recorded from offspring bronchial segments. Morphometric analysis of paraffin-embedded lung sections was conducted. In a human mother-child cohort ultrasound measurements of fetal growth were related to bronchial hyperreactivity, measured at age six years using methacholine. Protein-restricted rats' offspring demonstrated greater bronchoconstriction than controls. Airway structure was not altered. Children with lesser abdominal circumference growth during 11-19 weeks' gestation had greater bronchial hyperreactivity than those with more rapid abdominal growth. Imbalanced maternal nutrition during pregnancy results in offspring bronchial hyperreactivity. Prenatal environmental influences might play a comparable role in humans

Phonon Density of States of LaFeAsO1-xFx

We have studied the phonon density of states (PDOS) in LaFeAsO1-xFx with inelastic neutron scattering methods. The PDOS of the parent compound(x=0) is very similar to the PDOS of samples optimally doped with fluorine to achieve the maximum Tc (x~0.1). Good agreement is found between the experimental PDOS and first-principle calculations with the exception of a small difference in Fe mode frequencies. The PDOS reported here is not consistent with conventional electron-phonon mediated superconductivity

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy

Objective. To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP).Approach. Our model provides for selection of preferred ID parameters (Ip) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopicIpand macroscopic RTP. Selection ofIpis demonstrated using published cell survival measurements for protons through argon, comparing results for nineteenIp:Nk,k= 2, 3, …, 10, the number of ionizations in clusters ofkor more per particle, andFk,k= 1, 2, …, 10, the number of clusters ofkor more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation.Main results. The preferredIpwereN4andF5for aerobic cells,N5andF7for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferredNk. Conversely, there was no significant difference forF5for aerobic cells andF7for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for theseIphad the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert thatIpexist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation.Significance. Our model provides a practical means to select preferredIpfrom radiobiological data, and to convertIpto the macroscopic cluster dose for particle RTP

Cerebellar c9RAN proteins associate with clinical and neuropathological characteristics of C9ORF72 repeat expansion carriers.

Clinical and neuropathological characteristics associated with G4C2 repeat expansions in chromosome 9 open reading frame 72 (C9ORF72), the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, are highly variable. To gain insight on the molecular basis for the heterogeneity among C9ORF72 mutation carriers, we evaluated associations between features of disease and levels of two abundantly expressed "c9RAN proteins" produced by repeat-associated non-ATG (RAN) translation of the expanded repeat. For these studies, we took a departure from traditional immunohistochemical approaches and instead employed immunoassays to quantitatively measure poly(GP) and poly(GA) levels in cerebellum, frontal cortex, motor cortex, and/or hippocampus from 55 C9ORF72 mutation carriers [12 patients with ALS, 24 with frontotemporal lobar degeneration (FTLD) and 19 with FTLD with motor neuron disease (FTLD-MND)]. We additionally investigated associations between levels of poly(GP) or poly(GA) and cognitive impairment in 15 C9ORF72 ALS patients for whom neuropsychological data were available. Among the neuroanatomical regions investigated, poly(GP) levels were highest in the cerebellum. In this same region, associations between poly(GP) and both neuropathological and clinical features were detected. Specifically, cerebellar poly(GP) levels were significantly lower in patients with ALS compared to patients with FTLD or FTLD-MND. Furthermore, cerebellar poly(GP) associated with cognitive score in our cohort of 15 patients. In the cerebellum, poly(GA) levels similarly trended lower in the ALS subgroup compared to FTLD or FTLD-MND subgroups, but no association between cerebellar poly(GA) and cognitive score was detected. Both cerebellar poly(GP) and poly(GA) associated with C9ORF72 variant 3 mRNA expression, but not variant 1 expression, repeat size, disease onset, or survival after onset. Overall, these data indicate that cerebellar abnormalities, as evidenced by poly(GP) accumulation, associate with neuropathological and clinical phenotypes, in particular cognitive impairment, of C9ORF72 mutation carriers

How manipulating task constraints in small-sided and conditioned games shapes emergence of individual and collective tactical behaviours in football: A systematic review

Background: Small-Sided and Conditioned Games are characterised by modifications of field dimensions, number of players, rules of the game, manipulations used to shape the key task constraints that performers need to satisfy in practice. Evidence has already demonstrated the importance of designing practice to enhance understanding of tactical behaviours in football, but there is a lack of information about how coaches can manipulate task constraints to support tactical learning. Objective: To investigate which task constraints have been most often manipulated in studies of SSCGs; and what impact each manipulation had on emerging tactical behaviours, technical–tactical actions, and positional relationships between players. Methods: PubMed, Web of Science, Scielo, and Academic Google databases were searched for relevant reports without time limits. The criteria adopted for inclusion were: a) studies performed with football players; b) studies that included SSCGs as an evaluation method; c) studies that investigated tactical behaviours in SSCGs; and d), articles in English and Portuguese. Results: The electronic database search included 24 articles in the review. Of these, five manipulated field dimensions, six manipulated number of players involved, five manipulated field dimensions and number of players, five used different scoring targets, two altered the number of players and scoring target, and one manipulated the number of players, field dimension, and scoring target. Conclusion: Among the task constraints analyzed in this systematic review, manipulation of number of players and playing field dimensions concomitantly occurred most frequentl

How manipulating task constraints in small-sided and conditioned games shapes emergence of individual and collective tactical behaviours in football: A systematic review

Background: Small-Sided and Conditioned Games are characterised by modifications of field dimensions, number of players, rules of the game, manipulations used to shape the key task constraints that performers need to satisfy in practice. Evidence has already demonstrated the importance of designing practice to enhance understanding of tactical behaviours in football, but there is a lack of information about how coaches can manipulate task constraints to support tactical learning. Objective: To investigate which task constraints have been most often manipulated in studies of SSCGs; and what impact each manipulation had on emerging tactical behaviours, technical–tactical actions, and positional relationships between players. Methods: PubMed, Web of Science, Scielo, and Academic Google databases were searched for relevant reports without time limits. The criteria adopted for inclusion were: a) studies performed with football players; b) studies that included SSCGs as an evaluation method; c) studies that investigated tactical behaviours in SSCGs; and d), articles in English and Portuguese. Results: The electronic database search included 24 articles in the review. Of these, five manipulated field dimensions, six manipulated number of players involved, five manipulated field dimensions and number of players, five used different scoring targets, two altered the number of players and scoring target, and one manipulated the number of players, field dimension, and scoring target. Conclusion: Among the task constraints analyzed in this systematic review, manipulation of number of players and playing field dimensions concomitantly occurred most frequentl

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956)

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation