Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Data Mining Applied To Horse Thermal Comfort

Thermal comfort plays a critical role in body temperature regulation. Heat-regulation mechanisms, such as changes in peripheral blood flow, are activated by thermal stress to maintain body homeostasis and it can results in a fluctuation of skin temperature. Although thermal comfort of horse has been studied, its relation with surface temperature is rarely seen in the literature. Therefore, the aim of this study was to verify the potential of data mining techniques in knowledge discovery by associating surface temperature with thermal comfort of horses. The decision tree model presented 74.0% of accuracy and all attributes of dataset were considered relevant for the classification problem. The results revealed the potential of data mining techniques to equine thermal comfort classification problems.422428Autio, E., Neste, R., Airaksinen, S., Heiskanen, M.-L., Measuring the heat loss in horses in different seasons by infrared thermography (2006) Journal of Applied Animal Welfare Science, 9 (3), pp. 211-221. , DOI 10.1207/s15327604jaws0903-3Batista, G.E.A.P.A., Prati, R.C., Monard, M.C., A study of the behavior of several methods for balancing machine learning training set (2003) SIGKDD Exploration, 6 (1), pp. 20-29Chapman, P., Clinton, J., Kerber, R., Khabaz, T., Reinartz, T., Shearer, C., Wirth, R., (2000) CRISP-DM 1.0: Step-by-step Data Mining Guide, , http://www.spss.ch/upload/1107356429_CrispDM1.0.pdf, Available at: Accessed 22 November 2011Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., SMOTE: Synthetic minority over-sampling technique (2002) Journal of Artificial Intelligence Research, 16, pp. 321-357. , http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume16/chawla02a.pdfCunningham, J.G., Termorregulação (2002) Tratado de Fisiologia Veterinária, , 3rd ed. São Paulo: Guanabara KooganFayyad, U., Stolorz, P., Data mining and KDD: Promise and challenges (1997) Future Generation Computer Systems, 13 (2-3), pp. 99-115. , PII S0167739X97000150Ferreira, V.M.O.S., Francisco, N.S., Belloni, M., Aguirre, G.M.Z., Caldara, F.R., Nääs, I.A., Garcia, R.G., Polycarpo, G.V., Infrared thermography applied to the evaluation of metabolic heat loss of chicks fed with different energy densities Brazilian J. of Poult. Sci., 13 (2), pp. 113-118Hodgson, D.R., Davies, R.E., McConaghy, F.F., Thermoregulation in the horse in response to exercise (1994) British Veterinary Journal, 150 (3), pp. 219-234Huang, P., Lin, P., Shangwei, Y., Xiao, M., Data Mining for seasonal influences in broiler breeding based on observational study (2011) Information Computing and Applic., 7030, pp. 25-32Japkowicz, N., Class imbalances: Are we focusing on the right issue? (2003) 2nd Workshop on Learning from Imbalanced Data Sets, pp. 17-23Jones, S., Horsback Riding in the Dog Days (2009) (S.L.): Animal Science e-news University of Arkansas, 2 (3), pp. 3-4. , The Cooperative Extension Divison, 7pKnížková, I., Kinc, P., Gürdil, G.A.K., Pinar, Y., Selvi, K.Ç., Applications of infrared thermography in animal production (2007) J. of Fac. of Agric., 22 (3), pp. 329-336Laurikkala, J., (2001) Improving Identification of Difficult Small Classes by Balancing Class Distribution, , http://sci2s.ugr.es/keel/pdf/algorithm/congreso/2001-Laurikkala-LNCS.pdf, Available at: Accessed 01 November 2011Marlin, D.J., Scott, C.M., Roberts, C.A., Casas, I., Holah, G., Schroter, R., Post exercise changes in compartmental body temperature accompanying intermittent cold water cooling in the hyperthermic horse (1998) Equine Vet. J., 30, pp. 28-34McCutcheon, L.J., Geor, R.J., Thermoregulation and exercise-associated heat stress (2008) Equine Exercise Physiology: The Science of Exercise in the Athletic Horse, pp. 382-386. , Hinchcliff, K.W.R. J. Geor, A. J. Kaneps. Philadelphia: ElsevierMutaf, S., Şeber Kahraman, N., Frat, M.Z., Surface wetting and its effect on body and surface temperatures of domestic laying hens at different thermal conditions (2008) Poult. Sci., 87, pp. 2441-2450Paludo, G.R., McManus, C., De Melo, R.Q., Cardoso, A.G., Da, S.M.F.P., Moreira, M., Fuck, B.H., Effect of Heat Stress and Exercise on Physiological Parameters of Horses of the Brazilian Army (2002) Revista Brasileira de Zootecnia, 31 (3), pp. 1130-1142Oliveira, L.A., Campei, J.E.G., Azevedo, D.M.M.R., Costa, A.P.R., Turco, S.H.N., Moura, J.W.S., Estudo de respostas fisiológicas de equinos sem raça definida e da raça quarto de milha às condições climáticas de Teresina, Piauí (2008) Ciencia Animal Brasileira, 9 (4), pp. 827-838Vale, M.M., Moura, D.J., Nääs, I.A., Pereira, D.F., Characterization of heat waves affecting mortality of broilers between 29 days and market age (2010) Brazilian J.of Poult.Sci., 12 (4), pp. 279-28

Data Mining As A Tool To Evaluate Thermal Comfort Of Horses

Thermal comfort is of great importance to preserve body temperature homeostasis during thermal stress conditions. Although thermal comfort of horses has been widely studied, research has not reported its relationship to surface temperature (TS). The aim of this study was to investigate the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. TS was measured using infrared thermographic image processing. Physiological and environmental variables were used to define the predicted class, which classified thermal comfort as "comfort" and "discomfort". The TS variables for the armpit, croup, breast and groin of horses and the predicted class were then submitted to a machine learning process. All dataset variables were considered relevant to the classification problem and the decision-tree model yielded an accuracy rate of 74.0%. The feature selection methods used to reduce computational cost and simplify predictive learning reduced the model accuracy to 70.1%; however the model became simpler with representative rules. For these selection methods and for the classification using all attributes, TS of armpit and breast had a higher rating power for predicting thermal comfort. The data mining techniques had discovered new variables relating to the thermal comfort of horses.281290FancomAutio, E., Neste, R., Airaksinen, S., Heiskanen, M., Measuring the heat loss in horses in different seasons by infrared thermography (2006) Journal of Applied Animal Welfare Science, 9, pp. 211-221Batista, G.H.A.P.A., Prati, R.C., Monard, M.C., A study of the behavior of several methods for balancing machine learning training data (2004) SIGK DD Explorations, 6, pp. 20-29Castanheira, M., Paiva, S.R., Louvandini, H., Landim, A., Fiorvanti, M.C.S., Paludo, G.R., Dallago, B.S., McManus, C., Multivariate analysis for characteristics of heat tolerance in horses in Brazil (2010) Tropical Animal Health and Production, 42, pp. 185-191Chapman, P., Clinton, J., Kerber, R., Khabaz, T., Reinartz, T., Shearer, C., Wirth, R., CRIS P-DM 1.0: Step-by-step data mining guide (2000) The CRIS P-DM Consortium, , http://www.spss.ch/upload/1107356429_CrispDM1.0.pdf, Available atChawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., SMOTE : Synthetic minority over-sampling technique (2002) Journal of Artificial Intelligence Research, 16, pp. 321-357Crivelenti, R.C., Coelho, R.M., Adami, S.F., Oliveira, S.R.M., Data mining to infer soil-landscape relationships in digital soil mapping (2009) Pesquisa Agropecuária Brasileira, 44, pp. 1707-1715. , Portuguese, with abstract in EnglishCunningham, J.G., (2002) Textbook F Veterinary Physiology, , Saunders/Elsevier, Philadelphia, PA, USAHan, J., Kamber, M., Pei, J., (2011) Data Mining: Concepts and Techniques, , Morgan Kaufmann Publishers, San Francisco, CA, USAHuang, C.-J., Yang, D.-X., Chuang, Y.-T., Application of wrapper approach and composite classifier to the stock trend prediction (2008) Expert Systems with Applications, 34, pp. 2870-2878Japkowicz, N., (2003) Class Imbalances: Are We Focusing on the Right Issue?, , http://www.site.uottawa.ca/~nat/Papers/papers.html, Accessed Oct. 16, 2012Jodkowska, E., Dudek, K., Przewozny, M., The maximum temperatures (Tmax) distribution on the body surface of sport horses (2011) Journal of Life Sciences, 5, pp. 291-297Jones, S., Horseback riding in the dog days (2009) Animal Science E-news University of Arkansas, 2 (3-4), p. 7. , http://www.aragriculture.org/news/animal_science_enews/2009/july2009.htm, The Cooperative Extension DivisonKohn, C.W., Hinchcliff, K.W., Physiological responses to the endurance test of a 3-dayevent during hot and cool weather (1995) Equine Veterinary Journal, 20, pp. 31-36Kohn, C.W., Hinchcliff, K.W., McKeever, K.H., Evaluation of washing with cold water to facilitate heat dissipation in horses exercised in hot, humid conditions (1999) American Journal of Veterinary Research, 60, pp. 299-305Lin, S.-W., Chen, S.-C., Parameter determination and feature selection for C4.5 algorithm using scatter search approach (2012) Software Computer, 16, pp. 63-75Lutu, P.E.N., Engelbrecht, A.P., A decision rule-based method for feature selection in predictive data mining (2010) Expert Systems with Applications, 37, pp. 602-609Marlin, D.J., Scott, C.M., Roberts, C.A., Casas, I., Holah, G., Schroter, R., Post exercise changes in compartmental body temperature accompanying intermittent cold water cooling in the hyperthermic horse (1998) Equine Veterinary Journal, 30, pp. 28-34McConaghy, F.F., Hodgson, D.R., Rose, R.J., Hales, J.R., Redistribution of cardiac output in response to heat exposure in the pony (1996) Equine Veterinary Journal Supplement, 22, pp. 42-46McCutcheon, L.J., Geor, R.J., Thermoregulation and exercise-associated heat stress (2008) Equine Exercise Physiology: The Science of Exercise in the Athletic Horse, pp. 382-396. , Hinchcliff, K.W. Geor, R.J. Kaneps, A.J. eds. Elsevier Health Sciences, Philadelphia, PA, USAMcKeever, K.H., Eaton, T.L., Geiser, S., Kearns, C.F., Lehnhard, R.A., Age related decreases I thermoregulation and cardiovascular function in horses (2010) Equine Veterinary Journal, 42, pp. 449-454Quinlan, J.R., (1993) C4.5: Programs for Machine Learning, , Morgan Kaufmann, San Francisco, CA, USASikora, M., Induction and pruning of classification rules for prediction of microseismic hazards in coal mines (2011) Expert Systems with Applications, 38, pp. 6748-6758Tattersall, G.J., Cadena, V., Insights into animal temperature adaptations revealed through thermal imaging (2010) The Imaging Science Journal, 58, pp. 261-268Tsang, S., Kao, B., Yip, K.Y., Ho, W., Lee, S.D., Decision tree for uncertain data (2011) IEEE Transactions on Knowledge and Data Engineering, 23, pp. 64-78Wang, T., Qin, Z., Jin, Z., Zhang, S., Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning (2010) The Journal of Systems and Software, 83, pp. 1137-114

Congenital Zika Virus Infection: Beyond Neonatal Microcephaly

Recent studies have reported an increase in the number of fetuses and neonates with microcephaly whose mothers were infected with the Zika virus (ZIKV) during pregnancy. To our knowledge, most reports to date have focused on select aspects of the maternal or fetal infection and fetal effects. OBJECTIVE To describe the prenatal evolution and perinatal outcomes of 11 neonates who had developmental abnormalities and neurological damage associated with ZIKV infection in Brazil. DESIGN, SETTING, AND PARTICIPANTS We observed 11 infants with congenital ZIKV infection from gestation to 6 months in the state of Paraíba, Brazil. Ten of 11 women included in this study presented with symptoms of ZIKV infection during the first half of pregnancy, and all 11 had laboratory evidence of the infection in several tissues by serology or polymerase chain reaction. Brain damage was confirmed through intrauterine ultrasonography and was complemented by magnetic resonance imaging. Histopathological analysis was performed on the placenta and brain tissue from infants who died. The ZIKV genome was investigated in several tissues and sequenced for further phylogenetic analysis. MAIN OUTCOMES AND MEASURES Description of the major lesions caused by ZIKV congenital infection. RESULTS Of the 11 infants, 7 (63.6%) were female, and the median (SD) maternal age at delivery was 25 (6) years. Three of 11 neonates died, giving a perinatal mortality rate of 27.3%. The median (SD) cephalic perimeter at birth was 31 (3) cm, a value lower than the limit to consider a microcephaly case. In all patients, neurological impairments were identified, including microcephaly, a reduction in cerebral volume, ventriculomegaly, cerebellar hypoplasia, lissencephaly with hydrocephalus, and fetal akinesia deformation sequence (ie, arthrogryposis). Results of limited testing for other causes of microcephaly, such as genetic disorders and viral and bacterial infections, were negative, and the ZIKV genome was found in both maternal and neonatal tissues (eg, amniotic fluid, cord blood, placenta, and brain). Phylogenetic analyses showed an intrahost virus variation with some polymorphisms in envelope genes associated with different tissues. CONCLUSIONS AND RELEVANCE Combined findings from clinical, laboratory, imaging, and pathological examinations provided a more complete picture of the severe damage and developmental abnormalities caused by ZIKV infection than has been previously reported. The term congenital Zika syndrome is preferable to refer to these cases, as microcephaly is just one of the clinical signs of this congenital malformation disorder.73121407141