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The application of a performance-based fire engineering design has enabled the conservation of a heritage building in New Zealand. McDougall House is a two-story heritage building which suffered extensive damages during the Canterbury earthquakes in 2010 and 2011. The scope of the conservation plan included the preservation and restoration of the building external façade, the restoration of the ornamental plasterwork ceiling within the Ballroom, the reconstruction of all damaged internal lath and plaster linings, the adaptation of the fireplace, and the reconstruction of the damaged chimney. The fire engineering design adopted is the Verification Method C/VM2 with As Nearly As is Reasonably Practicable consideration for the heritage fabrics of the building. The combination of quantitative and qualitative analyses has demonstrated the building design compliance with the 10 design scenarios of C/VM2. These analyses include the assessment of minimum means of escape and fire protection provisions, the assessment of allowable unprotected areas in the external wall for horizontal fire spread, the assessment of firefighting provisions, the smoke and egress modelling of proposed design fires, and the benefit versus sacrifice analysis on the heritage fabrics. The application of C/VM2 results in an upgrade to the fire safety and fire protection systems of the building while also retaining and enhancing its heritage value.

The current surge in the deployment of web applications underscores the need to consider users’ individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform’s capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.

Summary: Animals need to sharpen their behavioral output in order to adapt to a variable environment. Hereby, light is one of the most pivotal environmental signals and thus behavioral plasticity in response to light can be observed in diurnal animals, including humans. Furthermore, light is the main entraining signal of the clock, yet immediate effects of light enhance or overwrite circadian output and thereby mask circadian behavior. In Drosophila, such masking effects are most evident as a lights-on response in two behavioral rhythms – the emergence of the adult insect from the pupa, called eclosion, and the diurnal rhythm of locomotor activity. Here, we show that the immediate effect of light on eclosion depends solely on R8 photoreceptors of the eyes. In contrast, the increase in activity by light at night is triggered by different cells and organs that seem to compensate for the loss of each other, potentially to ensure behavioral plasticity.

Introducción: La Tomografía Computarizada es una técnica de diagnóstico validada para el estudio previo de la morfología del sistema de conductos radiculares en endodoncia, por ser una técnica poco invasiva que entrega una imagen en tres dimensiones de alta resolución que se puede analizar en múltiples planos del espacio sin la sobreposición de estructuras anatómicas, permitiendo de este modo al especialista tomar mejores decisiones y establecer un adecuado tratamiento. Objetivo: describir el uso de la tomografía computarizada para la localización del conducto mv2 del primer molar superior. Método: el protocolo fue diseñado de acuerdo con los estándares Cochrane para revisiones bibliográficas. Los criterios de búsqueda cumplieron con las directrices Preferred Reporting Items for Systematic reviews and Meta-Analysis Protocols (PRISMA). Siendo esta una investigación de tipo bibliográfica, documental, exploratoria y no experimental, cualitativa. Resultados: dentro de los 210 dientes evaluados en el estudio de Souza, en los que 192 sí existía MV2 y fueron tratados endodónticamente, 156 presentaron lesión periapical, en un estudio analizaron 300 molares superiores, mencionan que 210 de ellos presentaron la configuración con la presencia de MV2, y se observó que 130 (61,67%) tenían el conducto MV2 obturado. Conclusiones: la presencia del canal MV2 en primeros molares superiores y de lesiones apicales en dientes con canal MV2 no obturado, en los estudios evaluados fue superior; comprobando así que la presencia de un canal MV2 no obstruido y la presencia de una lesión apical son variables mutuamente dependientes.

Extreme weather events, such as windstorms, hurricanes, and heat waves, exert a significant impact on global natural catastrophes and pose substantial challenges for weather forecasting systems. To enhance the accuracy and preparedness for extreme weather events, this study explores the potential of using expert crowdsourcing in storm forecasting research through the application of stigmergic collaboration. We present the development and implementation of an expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena (eCSAAP) system, designed to leverage the collective knowledge and experience of meteorological experts. Through a participatory co-creation process, we iteratively developed a web-based annotation tool capable of capturing multi-faceted insights from weather data and generating visualizations for expert crowdsourcing campaigns. In this context, this article investigates the intrinsic coordination among experts engaged in crowdsourcing tasks focused on the semantic annotation of extreme weather events. The study brings insights about the behavior of expert crowds by considering the cognitive biases and highlighting the impact of existing annotations on the quality of data gathered from the crowd and the collective knowledge generated. The insights regarding the crowdsourcing dynamics, particularly stigmergy, offer a promising starting point for utilizing stigmergic collaboration as an effective coordination mechanism for weather experts in crowdsourcing platforms but also in other domains requiring expertise-driven collective intelligence.

To evaluate a potentially valuable tool to study cancer progression and metastasis, we characterized a novel murine model composed of a parental oncogene-transformed embryonic fibroblast line and five cell lines isolated from progressively advanced tumors. Lines derived from distant metastases displayed significantly greater rates of motility, invasiveness, and extracellular acidification than lines derived from a primary tumor or local metastases. A comprehensive proteomic analysis of these cells showed numerous oncogenes to be upregulated and tumor suppressors to be downregulated in the advanced lines, and provided novel targets for future examination. The first cell line capable of extravasation displayed particularly high proteomic variation, which could provide insight into its epithelial to mesenchymal transition. The proteomic variation was less than that of an established human breast cancer model, indicating that the observed differences are more likely contributive to tumorigenesis. In total, we validated a novel cell model for the study of tumorigenesis, while providing a robust proteomic data set to guide future research.

BackgroundImplementing automated facial expression recognition on mobile devices could provide an accessible diagnostic and therapeutic tool for those who struggle to recognize facial expressions, including children with developmental behavioral conditions such as autism. Despite recent advances in facial expression classifiers for children, existing models are too computationally expensive for smartphone use. ObjectiveWe explored several state-of-the-art facial expression classifiers designed for mobile devices, used posttraining optimization techniques for both classification performance and efficiency on a Motorola Moto G6 phone, evaluated the importance of training our classifiers on children versus adults, and evaluated the models’ performance against different ethnic groups. MethodsWe collected images from 12 public data sets and used video frames crowdsourced from the GuessWhat app to train our classifiers. All images were annotated for 7 expressions: neutral, fear, happiness, sadness, surprise, anger, and disgust. We tested 3 copies for each of 5 different convolutional neural network architectures: MobileNetV3-Small 1.0x, MobileNetV2 1.0x, EfficientNetB0, MobileNetV3-Large 1.0x, and NASNetMobile. We trained the first copy on images of children, second copy on images of adults, and third copy on all data sets. We evaluated each model against the entire Child Affective Facial Expression (CAFE) set and by ethnicity. We performed weight pruning, weight clustering, and quantize-aware training when possible and profiled each model’s performance on the Moto G6. ResultsOur best model, a MobileNetV3-Large network pretrained on ImageNet, achieved 65.78% accuracy and 65.31% F1-score on the CAFE and a 90-millisecond inference latency on a Moto G6 phone when trained on all data. This accuracy is only 1.12% lower than the current state of the art for CAFE, a model with 13.91x more parameters that was unable to run on the Moto G6 due to its size, even when fully optimized. When trained solely on children, this model achieved 60.57% accuracy and 60.29% F1-score. When trained only on adults, the model received 53.36% accuracy and 53.10% F1-score. Although the MobileNetV3-Large trained on all data sets achieved nearly a 60% F1-score across all ethnicities, the data sets for South Asian and African American children achieved lower accuracy (as much as 11.56%) and F1-score (as much as 11.25%) than other groups. ConclusionsWith specialized design and optimization techniques, facial expression classifiers can become lightweight enough to run on mobile devices and achieve state-of-the-art performance. There is potentially a “data shift” phenomenon between facial expressions of children compared with adults; our classifiers performed much better when trained on children. Certain underrepresented ethnic groups (e.g., South Asian and African American) also perform significantly worse than groups such as European Caucasian despite similar data quality. Our models can be integrated into mobile health therapies to help diagnose autism spectrum disorder and provide targeted therapeutic treatment to children.

Upregulation of voltage-gated sodium channels (VGSCs) and Na+/K+-ATPase (sodium pumps) is common across most malignant carcinomas. Targeted osmotic lysis (TOL) is a developing technology in which the concomitant stimulation of VGSCs and pharmacological blockade of sodium pumps causes rapid selective osmotic lysis of carcinoma cells. This treatment of cervical carcinoma is evidence that TOL is a safe, well-tolerated and effective treatment for aggressive advanced carcinomas that has the potential to extend life without compromising its quality. TOL is likely to have broad application for the treatment of advanced-stage carcinomas.

BackgroundSelection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community. ObjectiveThe purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic. MethodsWe collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period. ResultsThere were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and –1.9% from the reported cumulative infection rate for the first and second survey periods, respectively. ConclusionsWe successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.

BackgroundA formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping. ObjectiveThis study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers. MethodsWe used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip. ResultsThe highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively. ConclusionsWe created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors.

The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance.

BackgroundAutomated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. ObjectiveWe designed a strategy to gamify the collection and labeling of child emotion–enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. MethodsWe leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion–centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. ResultsThe classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining “anger” and “disgust” into a single class. ConclusionsThis work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts.

Background. Targeted osmotic lysis (TOL) is a novel technology that involves concomitant stimulation of voltage-gated sodium channels (VGSCs) and the pharmacological blockade of Na+, K+-ATPase causing lysis of highly malignant cancer cells. Hypothesis/Objectives. TOL offers an option for treating advanced carcinomas in companion animals. Animals. Two cats and 2 dogs that presented to veterinary hospitals for evaluation and treatment of one of several forms of carcinoma. Methods. Digoxin was administered to achieve steady-state, therapeutic concentrations. The animals were then exposed to pulsed electric field stimulation. Pre- and posttreatment assessments of tumor size and quality of life were compared. The treatment frequency and survivability varied, based on the patient’s premorbid functioning and response to treatment. Results. Regardless of cancer type, TOL consistently increased survival beyond expected, often improving, but without compromising of quality of life. Conclusions and Clinical Importance. TOL warrants consideration as an option for managing advanced carcinomas.

Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area.

Voltage-gated sodium channels (VGSCs) are the target for many therapies. Variation in membrane potential occurs throughout the cell cycle, yet little attention has been devoted to the role of VGSCs and Na+,K+-ATPases. We hypothesized that in addition to doubling DNA and cell membrane in anticipation of cell division, there should be a doubling of VGSCs and Na+,K+-ATPase compared to non-dividing cells. We tested this hypothesis in eight immortalized cell lines by correlating immunocytofluorescent labeling of VGSCs or Na+,K+-ATPase with propidium iodide or DAPI fluorescence using flow cytometry and imaging. Cell surface expression of VGSCs during phases S through M was double that seen during phases G0–G1. By contrast, Na+,K+-ATPase expression increased only 1.5-fold. The increases were independent of baseline expression of channels or pumps. The variation in VGSC and Na+,K+-ATPase expression has implications for both our understanding of sodium’s role in controlling the cell cycle and variability of treatments targeted at these components of the Na+ handling system.

Olfactory information from Kenyon cells in the mushroom body and reward information from pPAM dopaminergic neurons is required for appetitive olfactory learning and memory. Here, the authors report evidence for a feedback circuit mechanism between Kenyon cells and pPAM neurons for reward memory that involves short neuropeptide F.

The conventional treatment of cancer has been based on the delivery of non-selective toxins and/or ionizing energy that affect both the cancer and normal tissues in the hope of destroying the offending disease before killing the patient. Unfortunately, resistance often develops to these treatments and patients experience severe, dose-limiting adverse effects that reduce treatment efficacy and compromise quality of life. Recent advances in our knowledge of the biology of tumor cells and their microenvironment, the recognition of surface proteins that are unique to specific cancers and essential to cell growth and survival and signaling pathways associate with invasion and metastasis have led to the development of targeted therapies that are able to identify specific cellular markers and more selectively deliver lethal treatment to the invading cancer thus improving efficacy and limiting adverse effects. In the context of targeted approaches to cancer therapy, we present targeted osmotic lysis as a novel and fundamentally different approach for treating advanced-stage carcinoma that exploits the conserved relationship between voltage-gated sodium channels and Na+, K+-ATPase and has the potential to increase survival without compromising quality of life in a broad spectrum of highly malignant forms of cancer.

ABSTRACT The objective of this study was to evaluate the effects of algae and cottonseed meal (CSM) supplementation on the fatty acid (FA) profile in the rumen fluid (RF) of steers fed a low-quality forage. Five Bos indicus crossbred steers, 187±7.5 kg liveweight (LW; mean±SD), were fed a low crude protein speargrass (Heteropogon contortus) hay as the basal diet alone or supplemented with either Spirulina platensis, Chlorella pyrenoidosa , Dunaliella salina , or CSM in Latin square design. The proportion of individual FA in the RF of steers varied in response to supplement, and these were most likely due to differences in the FA profile in supplements. Steers supplemented with Chlorella pyrenoidosa and CSM had a higher concentration of linoleic acid (C18:2n-6) in RF than unsupplemented steers or steers offered the other supplements, but there was no difference in the concentration in RF in steers supplemented with Chlorella pyrenoidosa and CSM. The concentration of linolenic acid (C18:3n-3) was higher in the RF of unsupplemented steers compared with supplemented steers. Steers receiving Chlorella pyrenoidosa supplementation showed an increase in total unsaturated FA in the RF compared with other supplemented and unsupplemented steers, which if transferred to meat, could have health related benefits to consumers. None of the supplements led to the formation of isomers known to inhibit fat synthesis.

Adhesion-type GPCRs (aGPCRs) participate in a vast range of physiological processes. Their frequent association with mechanosensitive functions suggests that processing of mechanical stimuli may be a common feature of this receptor family. Previously, we reported that the Drosophila aGPCR CIRL sensitizes sensory responses to gentle touch and sound by amplifying signal transduction in low-threshold mechanoreceptors (Scholz et al., 2017). Here, we show that Cirl is also expressed in high-threshold mechanical nociceptors where it adjusts nocifensive behaviour under physiological and pathological conditions. Optogenetic in vivo experiments indicate that CIRL lowers cAMP levels in both mechanosensory submodalities. However, contrasting its role in touch-sensitive neurons, CIRL dampens the response of nociceptors to mechanical stimulation. Consistent with this finding, rat nociceptors display decreased Cirl1 expression during allodynia. Thus, cAMP-downregulation by CIRL exerts opposing effects on low-threshold mechanosensors and high-threshold nociceptors. This intriguing bipolar action facilitates the separation of mechanosensory signals carrying different physiological information.

Honeybees (Apis mellifera) have fascinating navigational skills and learning capabilities in the field. To decipher the mechanisms underlying place learning in honeybees, we need paradigms to study place learning of individual honeybees under controlled laboratory conditions. Here, we present a novel visual place learning arena for honeybees which relies on high temperatures as aversive stimuli. Honeybees learn to locate a safe spot in an unpleasantly warm arena, relying on a visual panorama. Bees can solve this task at a temperature of 46°C, while at temperatures above 48°C bees die quickly. This new paradigm, which is based on pioneering work on Drosophila, allows us now to investigate thermal-visual place learning of individual honeybees in the laboratory, for example after controlled genetic knockout or pharmacological intervention.

Los fosfatos presentes en la Presa Multipropósito Chone han ocasionado problemas de eutrofización en la cuenca del río Grande que provoca afectaciones del tipo social, ambiental y económico para la zona. Una solución es el uso de tecnosoles que son suelos propios de lugar a los cuales se les adiciona elementos ajenos para mejorar su capacidad de remoción de un contaminante específico, sin embargo, este estudio se basó únicamente en la modificación del suelo de Chone mediante su calcinación y así simular un tecnosol capaz de remover fosfatos en soluciones acuosas mediante su calcinación, proceso que provoca un cambio en las propiedades físicas y químicas al tiempo de volatilizar los compuestos orgánicos presentes. Para esto, se realizó una disolución con concentración de 1.25 mg/L de fosfato monobásico de potasio, semejante a la concentración presente en el río. Las concentraciones del adsorbato contenidas en el agua ya filtrada a través del suelo modificado fueron medidas con espectrofotómetro en 4 sesiones continuas a intervalos de 2 horas, la máxima adsorción fue del 92% al cabo de 7 horas de filtrado; la cinética de la adsorción estuvo dada por una reacción de primer orden, además, se obtuvo un mejor ajuste con la isoterma de Langmuir que representó una adsorción en monocapa de fosfatos. Se modeló el comportamiento de la adsorción del fosfato en software libre con el que se estimó un tiempo de saturación del adsorbente de 14 horas. Como conclusiones del trabajo se tiene que los valores de adsorción de fosfatos son mayores con el suelo modificado (92%) que con el suelo natural (40.05%); la calcinación del suelo proporcionó una reactividad de los compuestos férricos presentes en el adsorbente con un incremento de su área superficial, intercambio catiónico y por ende una mayor adsorción del contaminante.

Animal behavior is, on the one hand, controlled by neuronal circuits that integrate external sensory stimuli and induce appropriate motor responses. On the other hand, stimulus-evoked or internally generated behavior can be influenced by motivational conditions, e.g., the metabolic state. Motivational states are determined by physiological parameters whose homeostatic imbalances are signaled to and processed within the brain, often mediated by modulatory peptides. Here, we investigate the regulation of appetitive and feeding behavior in the fruit fly, Drosophila melanogaster. We report that four neurons in the fly brain that release SIFamide are integral elements of a complex neuropeptide network that regulates feeding. We show that SIFamidergic cells integrate feeding stimulating (orexigenic) and feeding suppressant (anorexigenic) signals to appropriately sensitize sensory circuits, promote appetitive behavior, and enhance food intake. Our study advances the cellular dissection of evolutionarily conserved signaling pathways that convert peripheral metabolic signals into feeding-related behavior.