Geochronology, petrogenesis and tectonic implications of the Jurassic Namco–Renco ophiolites, Tibet

<div><p>The nature of the Namco–Renco ophiolites in the northern Lhasa subterrane is widely disputed. To investigate their formation age, petrogenesis, and tectonic setting, the harzburgites, basalts, and metagabbros of the Namco ophiolite and the harzburgites, lherzolites, gabbros, and diabasic dikes of the Renco ophiolite were selected for whole-rock geochemical and zircon U-Pb dating and <i>in situ</i> Lu-Hf isotopic analyses. The geochemical and geochronological data indicate that the Namco metagabbros were generated at 178.0 ± 2.9 Ma, along with the Namco–Renco peridotites formed in the initial stage of a continental margin basin; whereas the Renco gabbros were developed at 149.7 ± 1.6 Ma, along with the Renco diabasic dikes and Namco basalts formed later in a mature back-arc basin. The Namco–Renco ophiolites were derived from a depleted mantle source with involvement of minor older crustal materials. Combined with the regional geological background, the Namco–Renco ophiolites were likely formed mainly associated with the southward subduction of the Bangong–Nujiang oceanic lithosphere beneath the Lhasa terrane. This study provides new constraints on the formation ages of the Namco–Renco ophiolites and the tectonic evolution of the Namco–Renco Ocean.</p></div

Moringa oleifera Lam Seed Oil Augments Pentobarbital-Induced Sleeping Behaviors in Mice via GABAergic Systems

Moringa oleifera Lam. (MO), which is widely consumed as both food and herbal medicine in tropical and subtropical regions, has a wide spectrum of health benefits. Yet, whether the oil obtained from MO seeds could affect (improve) the sleep activity remains unclear. Herein, we used the locomotor activity, pentobarbital-induced sleeping, and pentetrazol-induced convulsions test to examine sedative-hypnotic effects (SHE) of MO oil (MOO) and explored the underlying mechanisms. Besides, the main components of MOO like oleic acid, β-Sitosterol, and Stigmasterol were also evaluated. The results showed that they possessed good SHE. Except for oleic acid and Stigmasterol, they could significantly elevate γ-amino butyric acid (GABA) and reduce glutamic acid (Glu) levels in the hypothalamus of mice. Moreover, SHE was blocked by picrotoxin, flumazenil, and bicuculline, except for oleic acid, which could not be antagonized by picrotoxin. Molecular mechanisms showed that MOO and β-Sitosterol significantly upregulated the amount of protein-level expression of Glu decarboxylase-65 (GAD65) and α1-subunit of GABAA receptors in the hypothalamus of mice, not affecting GAD67, γ2 subunits. These data indicated that MOO modulates sleep architectures via activation of the GABAA-ergic systems

Additional file 1 of Prohemocytes are the main cells infected by dengue virus in Aedes aegypti and Aedes albopictus

Additional file 1: Figure S1. Schematic representation of DENV2-EGFP. The EGFP gene was inserted into the DENV capsid (C) for production during viral replication

Additional file 2 of Prohemocytes are the main cells infected by dengue virus in Aedes aegypti and Aedes albopictus

Additional file 2: Figure S2. Hemocytes of Ae. aegypti can be infected with DENV. Adult female mosquitoes were injected with DENV2-EGFP, which is used as a fluorescent indicator for DENV infection. Hemocytes were collected from uninfected (left panel) and infected (right panel) mosquitoes, and their morphologies observed with 100× phase contrast (BF) and green channel (GFP) fluorescence microscopy, shown on the left and right of each pair of images, respectively. Hemocytes can be divided into three groups based on their observed morphology under fluorescent microscopy and Giemsa staining: prohemocytes, oenocytoids, and granulocytes

Geochemistry, geochronology, and petrogenesis of mid-Cretaceous Talabuco volcanic rocks, central Tibet: implications for the evolution of the Bangong Meso-Tethys

The widespread mid-Cretaceous igneous rocks in the northern margin of the Lhasa Block play an important role in understanding deep geologic processes, matter exchange at depth, and tectonic evolution of the Tibetan Plateau. In this paper, we report new zircon U-Pb ages, whole-rock major and trace element data, and Sr-Nd-Pb-Hf isotope data from the Talabuco andesites and basalts, which were dated at ~111 Ma. These rocks belong to the high-K calc-alkaline and shoshonite series, and show enrichment in terms of large-ion-lithophile elements (LILEs, e.g. Rb, U, and Th) and light rare earth elements (LREEs), but depletion of high-field-strength elements (HFSEs, e.g. Nb and Ti). The (87Sr/86Sr)i ratios of the Talabuco andesites range from 0.7043 to 0.7048, and the εNd(t) contents range from 0.68 to 4.33. The ratios of 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb are 18.6064–18.8993, 15.6233–15.6707, and 38.8634–39.1720, respectively. The 176Lu/177Hf and 176Hf/177Hf ratios of one sample range from 0.00081 to 0.00206 and 0.28280 to 0.28296, respectively. The εHf(t) values for this sample range from 3.4 to 9.1, and the two-stage model Hf age (TDM2) is 0.59–0.95 Ga. Combined with previous studies, the geochemical and isotopic data reveal that the parental magma of the Talabuco andesites was probably derived by partial melting of EM II-type sub-continental lithospheric mantle (SCLM). The Talabuco andesites are most likely generated by fractionation of mafic magma contaminated by subducted oceanic sediment and represent product of arc magmatism due to northward subduction of the Yarlung Zangbo Neo-Tethyan slab or southward subduction of the Bangong Meso-Tethyan slab.</p

Video5_An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.avi

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.</p

Video2_An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.mp4

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.</p

Video4_An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.avi

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.</p

Video1_An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.mp4

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.</p

Video6_An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.mp4

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO2 concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.</p