Rosa Luisa Márquez (San Juan, Puerto Rico, 1947–)

2022 ◽  
pp. 116-120
Author(s):  
Paola S. Hernández ◽  
Analola Santana
Keyword(s):  
Puerto Rico ◽  
San Juan

scholarly journals Temporal and Spatial Blood Feeding Patterns of Urban Mosquitoes in the San Juan Metropolitan Area, Puerto Rico

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 129
Author(s):  
Matthew W. Hopken ◽  
Limarie J. Reyes-Torres ◽  
Nicole Scavo ◽  
Antoinette J. Piaggio ◽  
Zaid Abdo ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Aedes Aegypti ◽  
Metropolitan Area ◽  
Culex Quinquefasciatus ◽  
Urban Ecosystems ◽  
Life Cycles ◽  
Pathogen Transmission ◽  
San Juan ◽  
Vertebrate Species ◽  
Blood Meals

Urban ecosystems are a patchwork of habitats that host a broad diversity of animal species. Insects comprise a large portion of urban biodiversity which includes many pest species, including those that transmit pathogens. Mosquitoes (Diptera: Culicidae) inhabit urban environments and rely on sympatric vertebrate species to complete their life cycles, and in this process transmit pathogens to animals and humans. Given that mosquitoes feed upon vertebrates, they can also act as efficient samplers that facilitate detection of vertebrate species that utilize urban ecosystems. In this study, we analyzed DNA extracted from mosquito blood meals collected temporally in multiple neighborhoods of the San Juan Metropolitan Area, Puerto Rico to evaluate the presence of vertebrate fauna. DNA was collected from 604 individual mosquitoes that represented two common urban species, Culex quinquefasciatus (n = 586) and Aedes aegypti (n = 18). Culex quinquefasciatus fed on 17 avian taxa (81.2% of blood meals), seven mammalian taxa (17.9%), and one reptilian taxon (0.85%). Domestic chickens dominated these blood meals both temporally and spatially, and no statistically significant shift from birds to mammals was detected. Aedes aegypti blood meals were from a less diverse group, with two avian taxa (11.1%) and three mammalian taxa (88.9%) identified. The blood meals we identified provided a snapshot of the vertebrate community in the San Juan Metropolitan Area and have potential implications for vector-borne pathogen transmission.

scholarly journals Rainfall interception by six urban trees in San Juan, Puerto Rico

2018 ◽  
Vol 22 (1) ◽  
pp. 103-115 ◽  
Author(s):  
Christopher J. Nytch ◽  
Elvia J. Meléndez-Ackerman ◽  
María-Eglée Pérez ◽  
Jorge R. Ortiz-Zayas
Keyword(s):  
Puerto Rico ◽  
Urban Trees ◽  
San Juan ◽  
Rainfall Interception

A community-based test of a linguistic hypothesis

1996 ◽  
Vol 25 (1) ◽  
pp. 61-111 ◽  
Author(s):  
Richard Cameron
Keyword(s):  
Puerto Rico ◽  
Puerto Rican ◽  
San Juan ◽  
Community Based ◽  
Functional Compensation ◽  
Puerto Rican Spanish ◽  
Null Subject ◽  
Stratified Sample ◽  
Compensation Hypothesis

ABSTRACTThe Functional Compensation Hypothesis (Hochberg 1986a, b) interprets frequent expression of pronominal subjects as compensation for frequent deletion of agreement marking on finite verbs in Puerto Rican Spanish (PRS). Specifically, this applies to 2sg.túwhere variably deleted word-final -smarks agreement. If the hypothesis is correct, finite verbs with agreement deleted in speech should co-occur more frequently with pronominal subjects than finite verbs with agreement intact. Likewise, social dialects which frequently delete agreement should show higher rates of pronominal expression than social dialects which less frequently delete agreement. These auxiliary hypotheses are tested across a socially stratified sample of 62 speakers from San Juan. Functional compensation does show stylistic and social patterning in the category of Specifictú, not in that of Non-specifictú. However, Non-specifictúis the key to frequency differences between -s-deleting PRS and -s-conserving Madrid; hence the Functional Compensation Hypothesis should be discarded. (Functionalism, compensation, null subject, analogy, Spanish, Puerto Rico)

Infortunios que Alonso Ramirez, natural de la ciudad de San Juan de Puerto Rico, padecio

1987 ◽  
Vol 82 (1) ◽  
pp. 224
Author(s):  
C. R. Boxer ◽  
Carlos de Siguenza y Gongora ◽  
J. S. Cummins ◽  
Alan Soons
Keyword(s):  
Puerto Rico ◽  
San Juan

scholarly journals Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks

2018 ◽  
Vol 17 (1) ◽  
pp. 137-148
Author(s):  
Abdiel E. Laureano-Rosario ◽  
Andrew P. Duncan ◽  
Erin M. Symonds ◽  
Dragan A. Savic ◽  
Frank E. Muller-Karger
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Puerto Rico ◽  
Artificial Neural Networks ◽  
Dew Point ◽  
San Juan ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Artificial Neural ◽  
The U.S

Abstract Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

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