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We aim to describe the relationships between climate variables and climate-sensitive diseases (CSDs) in semi-arid regions, highlighting the different main groups of CSDs and their climate patterns. This systematic review considered Medline, Science Direct, Scopus and Web of Science. The data collection period was August and September 2019 and included studies published between 2008 and 2019. This study followed a protocol based on the PRISMA statement. Data analysis was done in a qualitative way. The most of works were from Africa, Asia and Iran (71%), where temperature was the main climatic variable. Although the studies provide climatic conditions that are more favorable for the incidence of vector-borne and respiratory diseases, the influence of seasonal patterns on the onset, development and end of CSDs is still poorly understood, especially for gastrointestinal disorders. Moreover, little is known about the impact of droughts on CSDs. This review summarized the state of art of the relationship between climate and CSDs in semi-arid regions. Moreover, a research agenda was provided, which is fundamental for health policy development, priority setting and public health management.

Social economy has assumed a role of growing importance in these last decades, being seen by several entities and political agents as one of the pillars in a new sustainable economic model. Volunteer programs are multiplying, and greater coordination between the various involved actors is essential to guarantee that society's help reaches the people who need them. In this sense, a digital donation platform based exclusively on open source technologies has been proposed and developed, which simplifies the process of donation of goods to a set of causes created by social solidarity institutions. The application permits anonymous donations and allows the monitoring of the status of each donation by the donors. Additionally, usability was considered as a key element in the design of the application

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. We improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy, when we add Youden's $J$ statistic regularization term to the cross entropy loss. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to better results by helping advancing the optimization when cross entropy stalls. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of annotated images, some of which are poorly annotated., Submitted to ISBI 2020

We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions.

We present a weakly supervised deep learning method to perform instance segmentation of cells present in microscopy images. Annotation of biomedical images in the lab can be scarce, incomplete, and inaccurate. This is of concern when supervised learning is used for image analysis as the discriminative power of a learning model might be compromised in these situations. To overcome the curse of poor labeling, our method focuses on three aspects to improve learning: (i) we propose a loss function operating in three classes to facilitate separating adjacent cells and to drive the optimizer to properly classify underrepresented regions; (ii) a contour-aware weight map model is introduced to strengthen contour detection while improving the network generalization capacity; and (iii) we augment data by carefully modulating local intensities on edges shared by adjoining regions and to account for possibly weak signals on these edges. Generated probability maps are segmented using different methods, with the watershed based one generally offering the best solutions, specially in those regions where the prevalence of a single class is not clear. The combination of these contributions allows segmenting individual cells on challenging images. We demonstrate our methods in sparse and crowded cell images, showing improvements in the learning process for a fixed network architecture.

Acute organophosphate (OP) poisoning induces well-known signs of toxicosis related to acetylcholinesterase (AChE) inhibition. However, the relationship between acute OP poisoning and the onset of psychiatric disorders remains unclear. Thus, we investigated behavioural and biochemical consequences of acute exposure to the OP chlorpyrifos in male rats and also the effectiveness of the antidotes atropine and pralidoxime on reversing these changes. A sub-lethal dose of commercial chlorpyrifos (20 mg/kg, i.p.) elicited signs of acute toxicosis during the first hours after its injection in rats. Twenty-four hours after treatment, this single dose of chlorpyrifos induced a depressive-like behaviour in the rat forced swimming test without impairing locomotor activity. At this time (24 h), chlorpyrifos decreased plasma butyrylcholinesterase (BChE) activity and hippocampal, striatal and prefrontal cortical AChE activity in rats. The behavioural and biochemical consequences of acute chlorpyrifos poisoning do not seem to be long lasting, since 30 days later they were absent. We evaluated whether these behavioural and biochemical consequences of acute chlorpyrifos treatment would be reversed by the antidotes atropine (10 mg/kg i.p.) and/or pralidoxime (40 mg/kg; i.p.) given 1 h after poisoning. Pralidoxime partially reactivated the AChE activity in the prefrontal cortex, but not in the hippocampus and striatum. Atropine attenuated the depressive-like behaviour induced by chlorpyrifos in rats. Our results suggest that acute chlorpyrifos poisoning induces a transient depressive-like behaviour possible related to hippocampal AChE inhibition. They suggest that treatment with atropine and pralidoxime seems to be insufficient to counteract all the effects of OP acute poisoning, at least in rats.

The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to the huge amount of data generated and strong variations in image intensities. In this paper, we propose a new 3D segmentation approach which combines the discriminative power of convolutional neural networks (CNNs) for preprocessing and investigates the performance of three watershed-based postprocessing strategies (WS), which are well suited to segment object shapes, even when supplied with vague seed and boundary constraints. To leverage the full potential of the watershed algorithm, the multi-instance segmentation problem is initially interpreted as three-class semantic segmentation problem, which in turn is well-suited for the application of CNNs. Using manually annotated 3D confocal microscopy images of Arabidopsis thaliana, we show the superior performance of the proposed method compared to the state of the art., Comment: 5 pages, 4 figures, 1 table

Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil 2Center for Advanced Methods in Biological Image Analysis, California Institute of Technology, Pasadena, CA, USA 3Institute of Computing, University of Campinas, Campinas, SP, Brazil 4Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany 5 Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany 6Howard Hughes Medical Institute and Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.

Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm., Comment: 5 pages, 3 figures, 1 table

The aim of the present work was to investigate the wetting behaviour of biomedical grade Ti–6Al–4V alloy surfaces textured by a femtosecond laser treatment. The material was treated in ambient atmosphere using an Yb: KYW chirped-pulse-regenerative amplification laser with a wavelength of 1030 nm and a pulse duration of 500 fs. Four main types of surface textures were obtained depending on the processing parameters and laser treatment method. These textures consist of: (1) nanoscale laser-induced periodic surface structures (LIPSS); (2) nanopillars; (3) a bimodal roughness distribution texture formed of LIPSS overlapping microcolumns; (4) a complex texture formed of LIPSS overlapping microcolumns with a periodic variation of the columns size in the laser scanning direction. The wettability of the surfaces was evaluated by the sessile drop method using distilled-deionized (DD) water and Hank's balanced salt solution (HBSS) as testing liquids. The laser treated surfaces present a hydrophilic behaviour as well as a high affinity for the saline solution, with equilibrium contact angles in the ranges 24.1–76.2° for DD water and 8.4–61.8° for HBSS. The wetting behaviour is anisotropic, reflecting the anisotropy of the surface textures.

Aim: The aim of the present work was to investigate ultrafast laser surface texturing as a surface treatment of Ti-6Al-4V alloy dental and orthopedic implants to improve osteoblastic commitment of human mesenchymal stem cells (hMSCs). Materials & methods: Surface texturing was carried out by direct writing with an Yb:KYW chirped-pulse regenerative amplification laser system with a central wavelength of 1030 nm and a pulse duration of 500 fs. The surface topography and chemical composition were investigated by scanning electron microscopy and x-ray photoelectron spectroscopy, respectively. Three types of surface textures with potential interest to improve implant osseointegration can be produced by this method: laser-induced periodic surface structures (LIPSSs); nanopillars (NPs); and microcolumns covered with LIPSSs, forming a bimodal roughness distribution. The potential of the laser treatment in improving hMSC differentiation was assessed by in vitro study of hMSCs spreading, adhesion, elongation and differentiation using epifluorescence microscopy at different times after cell seeding, after specific stainings and immunostainings. Results: Cell area and focal adhesion area were lower on the laser-textured surfaces than on a polished reference surface. Obviously, the laser-textured surfaces have an impact on cell shape. Osteoblastic commitment was observed independently of the surface topography after 2 weeks of cell seeding. When the cells were cultured (after 4 weeks of seeding) in osteogenic medium, LIPSS- and NP- textured surfaces enhanced matrix mineralization and bone-like nodule formation as compared with polished and microcolumn-textured surfaces. Conclusion: The present work shows that surface nanotextures consisting of LIPSSs and NPs can, potentially, improve hMSC differentiation into an osteoblastic lineage.

Quantitative studies in plant developmental biology require monitoring and measuring the changes in cells and tissues as growth gives rise to intricate patterns. The success of these studies has been amplified by the combined strengths of two complementary techniques, namely live imaging and computational image analysis. Live imaging records time-lapse images showing the spatial-temporal progress of tissue growth with cells dividing and changing shape under controlled laboratory experiments. Image processing and analysis make sense of these data by providing computational ways to extract and interpret quantitative developmental information present in the acquired images. Manual labeling and qualitative interpretation of images are limited as they don't scale well to large data sets and cannot provide field measurements to feed into mathematical and computational models of growth and patterning. Computational analysis, when it can be made sufficiently accurate, is more efficient, complete, repeatable, and less biased. In this chapter, we present some guidelines for the acquisition and processing of images of sepals and the shoot apical meristem of Arabidopsis thaliana to serve as a basis for modeling. We discuss fluorescent markers and imaging using confocal laser scanning microscopy as well as present protocols for doing time-lapse live imaging and static imaging of living tissue. Image segmentation and tracking are discussed. Algorithms are presented and demonstrated together with low-level image processing methods that have proven to be essential in the detection of cell contours. We illustrate the application of these procedures in investigations aiming to unravel the mechanical and biochemical signaling mechanisms responsible for the coordinated growth and patterning in plants.

The emerging field of computational morphodynamics aims to understand the changes that occur in space and time during development by combining three technical strategies: live imaging to observe development as it happens; image processing and analysis to extract quantitative information; and computational modelling to express and test time-dependent hypotheses. The strength of the field comes from the iterative and combined use of these techniques, which has provided important insights into plant development.

We present methods for segmenting the sepal and shoot apical meristem of the Arabidopsis thaliana plant. We propose a mathematical morphology pipeline and a modified numerical scheme for the active contours without edges algorithm to extract the geometry and topology of plant cells imaged using confocal laser scanning microscopy. We demonstrate our methods in typical images used in the studies of cell endoreduplication and hormone transport and show that in practice they produce highly accurate results requiring little human intervention to cope with image aberrations.

Objective: To evaluate, using histological analysis, the systemic action and repair process of wounds produced on the back of rats and treated with red, infrared, or both lasers applied directly or indirectly to the wounds. Background Data: Skin tissue repair and wound healing are complex processes that involve a series of dynamic events. Many benefits are associated with biomodulation uisng laser therapy. Methods: Thirty-six male Wistar rats were divided into four groups: control (without laser), red laser (aluminium gallium indium phosphide (AlGaInP); lambda = 685 nm; phi = 0.0314 cm(2); CW; P = 30mW; D = 20 J, time of irradiation 667 sec), infrared laser (gallium-aluminum-arsenide (GaAlAs): lambda = 830 nm; phi = 0.0314 cm(2); CW; P = 50 mW; D = 20 J, time of irradiation 401 sec), and both lasers (infrared laser: GaAlAs; lambda = 830 nm; phi = 0.0314 cm(2); CW; P = 50 mW; D = 10 J, time of irradiation = 201 sec + red laser: AlGaInP; lambda = 685 nm; phi = 0.0314 cm(2); CW; P = 30 mW; D = 10 J, time of irradiation 334 sec; total dose 20 J). Three subgroups were formed according to observation time points. Three wounds were produced on the back of each animal. Only the wound closest to the head was irradiated in the experimental groups. For the evaluation of skin reaction and wound healing, three animals of each group were killed at 3, 5, and 7 days postoperatively. The irradiation protocol established 48-hour intervals between applications, with the first application immediately after the surgical procedure. Results: In the red and infrared laser group, healing was more advanced in the wound located furthest from the point of laser application. The most effective healing of a proximal wound was verified in the control group on the 7th postoperative day. Conclusion: The combined application of red and infrared lasers resulted in the most evident systemic effect on the repair of skin wounds produced in rats.

O objetivo principal deste artigo é interpretar as transformações no espaço urbano das vilas do ouro mineiras na passagem do século XVIII para o XIX, pensando particularmente as transformações na paisagem e na demografia, de forma articulada à dinâmica econômica da capitania. Destacam-se dois núcleos - Vila Rica (Ouro Preto) e São João del Rey -, mas transcendendo a análise para o conjuntos dos câmbios entre o urbano e o rural em Minas Gerais no período. Três são os principais grupos de fontes utilizados: relatos de viajantes, iconografia, e listagens ou mapas populacionais.This paper intends to contribute to the interpretation of the changes in the urban space of the gold mining villages in Minas Gerais at the end of 18th century and the beginning of 19th. It focuses particularly the transformations concerning landscapes, demography changes, and economic dynamics of the region. Two cities focused:Vila Rica (Ouro Preto) and São João del Rey, but the analysis tries to understand the exchanges between urban and rural spaces in Minas Gerais as a whole. The research is based especially on reports of foreign travelers, iconography, and demographic lists or maps.

Objetivos: Cuantificar los cambios en la alineacion sagital cervical en pacientes con escoliosis idiopatica del adolescente (EIA) sometidos a tratamiento quirurgico. Metodos: Estudio retrospectivo de los datos de analisis radiograficos. Se recogieron datos de 25 radiografias de pacientes con EIA, y se incluyeron 18 casos. La edad promedio fue de 15,2 anos (13 a 17), todos los sujetos eran mujeres, operados desde marzo de 2010 hasta octubre de 2015. En el periodo pre y postoperatorio, se midieron lordosis cervical (C2- C7), cifosis toracica (T5-T12) y lordosis lumbar (L1-S1). En las vistas anteroposteriores se analizaron las curvas de la escoliosis, medidas por el metodo de Cobb y clasificadas por la clasificacion de Lenke. Resultados: Fueron evaluados dieciocho pacientes adolescentes, con un seguimiento promedio de 31,3 meses. Se observo correlacion negativa (-0.613) entre la variacion de lordosis cervical pre y posquirurgica, es decir, cuando mayores los angulos cervicales obtenidos, como promedio, mayores fueron las reducciones. Por lo tanto, la correlacion se vuelve positiva en comparacion con el postoperatorio (0.579) . Conclusion: Se concluyo que la correccion de escoliosis idiopatica del adolescente no trajo cambios estadisticamente significativos en la columna cervical, con respecto a los valores de los angulos. Las curvas lordoticas cervicales con mayor valor angular mostraron una mayor variacion en el postoperatorio, resultando en un mejor equilibrio biomecanico.

By controlling processing parameters such as the average fluence, number of laser pulses and beam polarization direction, different types of multiscale surface textures were produced on Ti-6Al-4V surfaces by ultrafast laser processing. The samples were textured in ambient atmosphere using an Yb: KYW chirped-pulse-regenerative amplification laser with a wavelength of 1030 nm and pulse duration of 500 fs. The wetting of simulated biological fluids as well as the human mesenchymal stem cells (hMSCs) behavior were assessed. Three types of textured surfaces were tested, consisting of: (i) Laser-Induced Periodic Surface Structures-LIPSS; (ii) nanopillars-like structures; and (iii) LIPSS overlapped to microcolumns. The laser textured surfaces present hydrophilic behavior and high affinity for HBSS (Hank’s balanced salt solution). Cell spreading and adhesion strength is reduced by the laser nanotextures as compared to a polished control surface. Cytoskeleton stretching and stress fibers were clearly observed on LIPSS while significant filopodia formation was verified on nanopillars. There was no cell proliferation on the laser nanotextured surfaces. Ultrafast laser texturing of Ti-6Al-4V surfaces is an efficient technique for increasing surface wettability, and is potentially useful as a technique to control the behavior of hMSCs by changing the cytoskeleton shape, FAPs distribution and area, and proliferation.By controlling processing parameters such as the average fluence, number of laser pulses and beam polarization direction, different types of multiscale surface textures were produced on Ti-6Al-4V surfaces by ultrafast laser processing. The samples were textured in ambient atmosphere using an Yb: KYW chirped-pulse-regenerative amplification laser with a wavelength of 1030 nm and pulse duration of 500 fs. The wetting of simulated biological fluids as well as the human mesenchymal stem cells (hMSCs) behavior were assessed. Three types of textured surfaces were tested, consisting of: (i) Laser-Induced Periodic Surface Structures-LIPSS; (ii) nanopillars-like structures; and (iii) LIPSS overlapped to microcolumns. The laser textured surfaces present hydrophilic behavior and high affinity for HBSS (Hank’s balanced salt solution). Cell spreading and adhesion strength is reduced by the laser nanotextures as compared to a polished control surface. Cytoskeleton stretching and stress fibers were clearly observed on L...