In vitro pathogenicity of Northern Peru native bacteria on Phyllocnistis citrella Stainton (Gracillariidae: Phyllocnistinae), on predator insects (Hippodamia convergens and Chrysoperla externa), on Citrus aurantiifolia Swingle and white rats

citrella after 48 h (74.1% average mortality). Serratia sp. caused the highest mortality after 24 h in H. convergens (40%) and C. externa (30%), whereas the Lowest mortality rates were induced at 72 h by E. aerogenes on C. externa (3%) and by Pseudomonas sp. on H. convergens (10%). The bacteria did not affect neither C. aurantiifolia or the rats, which gained the same weight as control animals

Caracterización acústica de las agregaciones de krill (Euphausia superba) detectadas automáticamente en el Estrecho de Bransfield e Isla Elefante

In the present study, krill (Euphasia superba) aggregations identified in the Bransfield Strait and around of Elephant Island were characterized. Data were collected using a multifrequency SIMRAD EK80 echosounder during three austral summers: 2018, 2019 and 2020. For detection of krill agreggations, two frequencies (38 and 120 kHz) and an automated Echoview version 9 algorithm with the EchoviewR package in R were used. A total of 22,221 aggregations were detected. Acoustic descriptors were analyzed with Pearson's correlation. For the characterization of krill aggregations, principal component analysis (PCA) was applied, followed by hierarchical clustering. To determine temporal differences of clusters, an ANOVA was applied. In addition, krill aggregations were assigned to surface environmental variables to apply a generalized additive model (GAM). Using the first 3 dimensions of the PCA (which explained 81% of the total variability), three clusters were identified. The first cluster was characterized by krill aggregations having lower height (2 m), backscattering acoustic energy (7 m2 mn-2), and being located at a greater depth (81 m). The second cluster had the shallowest swarms (34 m), shortest length (75 m) and compactness (202). Finally, the third cluster had the largest swarms in length (849 m), volume (207,412 m3) and height (11 m); in addition of having greater acoustic energy (637 m2 mn-2), obliquity (6), compactness (2,436) and coefficient of variation (213). Spatially, cluster I was located with a greater presence around Elephant Island during 2018 and 2019, while for the same period, clusters I and II were located scattered throughout the study area but focused on the Bransfield Strait. By 2020, thermal anomalies of approximately +2 °C were presented and there was a dispersion of three clusters throughout the study area, where cluster I was located with a greater presence in the Bransfield Strait. Significant differences (p < 0.05) were found between the clusters by year. However, those differences were not so marked. Through a GAM, it was established that all variables for each cluster were significant (p < 0.05). Swarms were kept in average conditions of temperature (0.8 °C), salinity (34.14) and dissolved oxygen (8.16 ml l-1). On an interannual scale, it was observed that the characteristics of aggregations did not change.En el presente estudio se caracterizaron agregaciones de krill (Euphasia superba) identificadas en el Estrecho de Bransfield y los alrededores de la Isla Elefante. Los datos fueron recolectados con una ecosonda multifrecuencia SIMRAD EK80 durante tres veranos australes 2018, 2019 y 2020. Para la detección de agrega-ciones de krill se utilizaron dos frecuencias (38 y 120 kHz) y un algoritmo incluido en un programa destinado para el post procesamiento denominado Echoview versión 9, automatizado con el paquete EchoviewR en R. Se detectaron un total de 22.221 agregaciones. Los descriptores acústicos fueron analizados con la correlación de Pearson. Para la caracterización de agregaciones de krill se aplicó un análisis de componentes principales (PCA), seguidamente de un agrupamiento jerárquico. Para determinar las diferencias temporales de los clústeres fue aplicado un análisis de varianza ANOVA. Además, a las agregaciones de krill se le asignaron las variables ambientales superficiales para aplicarle un modelo generalizado aditivo (GAM). Utilizando las primeras 3 dimensiones del PCA (que explicaron el 81% de la variabilidad total) se identificaron tres clústeres. El primer clúster se caracterizó por tener agregaciones de krill con menor altura (2 m) y bajos valores en el coeficiente de retrodispersión acústica (7 m2 mn-2), y estar ubicado a mayor profundidad (81 m). El segundo clúster tuvo las agregaciones más someras (34 m), de menor longitud (75 m) y compacidad (202). Finalmente, el tercer clúster presentó agregaciones de mayor longitud (849 m), volumen (207.412 m3) y altura (11 m), además de tener elevados valores de retrodispersión acústica (637 m2 mn-2), oblicuidad (6), compacidad (2.436) y coeficiente de variación (213). Espacialmente, el clúster l se localizó con mayor presencia en los alrededores de la Isla Elefante durante el 2018 y 2019, mientras que para este mismo periodo los clústeres I y II se ubicaron dispersos en toda la zona de estudio, pero focalizados en el Estrecho de Bransfield. Para 2020 se presentaron anomalías térmicas de +2 °C aproximadamente y hubo una dispersión de los tres clústeres en toda la zona de estudio, donde se observó que el clúster I se localizó con mayor presencia en el Estrecho de Bransfield. Se encontraron diferencias significativas (p < 0,05) entre los clústeres por año. Sin embargo, dichas diferencias no fueron tan marcadas. Mediante un GAM, se estableció que todas las variables para cada clúster fueron significativas (p < 0,05). Las agregaciones se mantuvieron en condiciones promedio de temperatura (0,8 °C), salinidad (34,14) y oxígeno disuelto (8,16 ml l-1). A escala interanual, se observó que las características de las agrega-ciones no cambiaron

Characterization of Structures of Equivalent Tissue With a Pixel Detector

Research using hybrid pixel detectors in medical physics is on the rise. Timepix detectors have arrays of 256 × 256 pixels with a resolution of 55 μm. Here, and by using Timepix counts instead of Hounsfield units, we present a calibration curve of a Timepix detector analog to those used for CT calibration. Experimentation consisted of the characterization of electron density in 10 different kinds of tissue equivalent samples from a CIRS 062M phantom (lung, 3 kinds of bones, fat, breast, muscle, water and air). Radiation of the detector was performed using an orthodontic X-ray machine at 70 KeV and .06 second of tube current with a purpose-built aluminum collimator. Data acquisition was performed at 1 frame per second and taking 3 frames per phantom. We were able to find a curve whose behavior was similar to others already published. This will lead to the verification of the usage of Timepix for identification of different tissues in an organ

GC Insights: Lessons from participatory water quality research in the upper Santa River basin, Peru

Here we share four key lessons from an inter-disciplinary project (Nuestro Rio) that gathered community perspectives on local water quality in the Santa River basin (Peru) utilising a digital technological approach where we collected data via a novel photo elicitation app, supported by a field work campaign. The lessons explored in this article provide insights into challenges and opportunities for researchers considering developing technological tools for encouraging participation and engagement in marginalised communities

Whole-genome association analysis of treatment response in obsessive-compulsive disorder.

Up to 30% of patients with obsessive-compulsive disorder (OCD) exhibit an inadequate response to serotonin reuptake inhibitors (SRIs). To date, genetic predictors of OCD treatment response have not been systematically investigated using genome-wide association study (GWAS). To identify specific genetic variations potentially influencing SRI response, we conducted a GWAS study in 804 OCD patients with information on SRI response. SRI response was classified as 'response' (n=514) or 'non-response' (n=290), based on self-report. We used the more powerful Quasi-Likelihood Score Test (the MQLS test) to conduct a genome-wide association test correcting for relatedness, and then used an adjusted logistic model to evaluate the effect size of the variants in probands. The top single-nucleotide polymorphism (SNP) was rs17162912 (P=1.76 × 10(-8)), which is near the DISP1 gene on 1q41-q42, a microdeletion region implicated in neurological development. The other six SNPs showing suggestive evidence of association (P<10(-5)) were rs9303380, rs12437601, rs16988159, rs7676822, rs1911877 and rs723815. Among them, two SNPs in strong linkage disequilibrium, rs7676822 and rs1911877, located near the PCDH10 gene, gave P-values of 2.86 × 10(-6) and 8.41 × 10(-6), respectively. The other 35 variations with signals of potential significance (P<10(-4)) involve multiple genes expressed in the brain, including GRIN2B, PCDH10 and GPC6. Our enrichment analysis indicated suggestive roles of genes in the glutamatergic neurotransmission system (false discovery rate (FDR)=0.0097) and the serotonergic system (FDR=0.0213). Although the results presented may provide new insights into genetic mechanisms underlying treatment response in OCD, studies with larger sample sizes and detailed information on drug dosage and treatment duration are needed

Genome-wide association study in obsessive-compulsive disorder: results from the OCGAS.

Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive thoughts and urges and repetitive, intentional behaviors that cause significant distress and impair functioning. The OCD Collaborative Genetics Association Study (OCGAS) is comprised of comprehensively assessed OCD patients with an early age of OCD onset. After application of a stringent quality control protocol, a total of 1065 families (containing 1406 patients with OCD), combined with population-based samples (resulting in a total sample of 5061 individuals), were studied. An integrative analyses pipeline was utilized, involving association testing at single-nucleotide polymorphism (SNP) and gene levels (via a hybrid approach that allowed for combined analyses of the family- and population-based data). The smallest P-value was observed for a marker on chromosome 9 (near PTPRD, P=4.13 × 10(-)(7)). Pre-synaptic PTPRD promotes the differentiation of glutamatergic synapses and interacts with SLITRK3. Together, both proteins selectively regulate the development of inhibitory GABAergic synapses. Although no SNPs were identified as associated with OCD at genome-wide significance level, follow-up analyses of genome-wide association study (GWAS) signals from a previously published OCD study identified significant enrichment (P=0.0176). Secondary analyses of high-confidence interaction partners of DLGAP1 and GRIK2 (both showing evidence for association in our follow-up and the original GWAS study) revealed a trend of association (P=0.075) for a set of genes such as NEUROD6, SV2A, GRIA4, SLC1A2 and PTPRD. Analyses at the gene level revealed association of IQCK and C16orf88 (both P<1 × 10(-)(6), experiment-wide significant), as well as OFCC1 (P=6.29 × 10(-)(5)). The suggestive findings in this study await replication in larger samples

Lifetime prevalence, age of risk, and genetic relationships of comorbid psychiatric disorders in Tourette syndrome

IMPORTANCE: Tourette syndrome (TS) is characterized by high rates of psychiatric comorbidity; however, few studies have fully characterized these comorbidities. Furthermore, most studies have included relatively few participants

QuinuaSmartApp: A Real-Time Agriculture Precision IoT Cloud Platform to Crops Monitoring

IoT networks, cloud-based applications and the use of artificial intelligence models in precision agriculture present an important opportunity to increase production and optimize the use of water resources, which will allow the development of sustainable and responsible agriculture in the face of global food security. In order to provide real-time remote monitoring of quinoa crops, this article proposes and implements an integrated architecture based on sensor networks, drones with multispectral and Lidar cameras and cloud computing-based applications. The system has hardware and software applications that enable Quinoa crop monitoring during the different stages of its growth. Additionally, it comprises weather stations providing real-time data which permits actualising the predictive models that can be used for local climate change projections. The monitoring of the level of humidity in the crop field through soil stations feeds the training database based on machine learning that allows generating the projection of water demand, which allows more efficient and better-planned use of crop water. Additionally, it implements a service of warning messages, attended by experts who are connected to the system in order to provide technical recommendations to help deal with this issue in order to lessen the impact of pests and diseases in the field.2023-2