[First autochthonous case of spotted fever associated with the Reserva Ecológica Costanera Sur, Buenos Aires City]. / Primer caso autóctono de fiebre manchada asociado a la Reserva Ecológica Costanera Sur, Ciudad Autónoma de Buenos Aires.

The first autochthonous case of rickettsiosis is reported here. The case occurred in the Costanera Sur Ecological Reserve, a protected area of the City of Buenos Aires, in August 2022, where 4 species of ticks were found, namely Amblyomma aureolatum, Ixodes auritulus sensu lato, Rhipicephalus sanguineus sensu stricto and Amblyomma triste. The epidemiological, ecological, clinical and laboratory aspects that allowed timely diagnosis and appropriate treatment are also described.

Genetic Disruption of cyp21a2 Leads to Systemic Glucocorticoid Deficiency and Tissues Hyperplasia in the Teleost Fish Medaka (Oryzias latipes).

cytochrome P-450, 21-hydroxylase (cyp21a2), encodes an enzyme required for cortisol biosynthesis, and its mutations are the major genetic cause of congenital adrenal hyperplasia (CAH) in humans. Here, we have generated a null allele for the medaka cyp21a2 with a nine base-pair insertion which led to a truncated protein. We have observed a delay in hatching and a low survival rate in homozygous mutants. The interrenal gland (adrenal counterpart in teleosts) exhibits hyperplasia and the number of pomca-expressing cells in the pituitary increases in the homozygous mutant. A mass spectrometry-based analysis of whole larvae confirmed a lack of cortisol biosynthesis, while its corresponding precursors were significantly increased, indicating a systemic glucocorticoid deficiency in our mutant model. Furthermore, these phenotypes at the larval stage are rescued by cortisol. In addition, females showed complete sterility with accumulated follicles in the ovary while male homozygous mutants were fully fertile in the adult mutants. These results demonstrate that the mutant medaka recapitulates several aspects of cyp21a2-deficiency observed in humans, making it a valuable model for studying steroidogenesis in CAH.

Enhancing 3D human pose estimation with NIR single-pixel imaging and time-of-flight technology: a deep learning approach.

The extraction of 3D human pose and body shape details from a single monocular image is a significant challenge in computer vision. Traditional methods use RGB images, but these are constrained by varying lighting and occlusions. However, cutting-edge developments in imaging technologies have introduced new techniques such as single-pixel imaging (SPI) that can surmount these hurdles. In the near-infrared spectrum, SPI demonstrates impressive capabilities in capturing a 3D human pose. This wavelength can penetrate clothing and is less influenced by lighting variations than visible light, thus providing a reliable means to accurately capture body shape and pose data, even in difficult settings. In this work, we explore the use of an SPI camera operating in the NIR with time-of-flight (TOF) at bands 850-1550 nm as a solution to detect humans in nighttime environments. The proposed system uses the vision transformers (ViT) model to detect and extract the characteristic features of humans for integration over a 3D body model SMPL-X through 3D body shape regression using deep learning. To evaluate the efficacy of NIR-SPI 3D image reconstruction, we constructed a laboratory scenario that simulates nighttime conditions, enabling us to test the feasibility of employing NIR-SPI as a vision sensor in outdoor environments. By assessing the results obtained from this setup, we aim to demonstrate the potential of NIR-SPI as an effective tool to detect humans in nighttime scenarios and capture their accurate 3D body pose and shape.

A Study on Generative Models for Visual Recognition of Unknown Scenes Using a Textual Description.

In this study, we investigate the application of generative models to assist artificial agents, such as delivery drones or service robots, in visualising unfamiliar destinations solely based on textual descriptions. We explore the use of generative models, such as Stable Diffusion, and embedding representations, such as CLIP and VisualBERT, to compare generated images obtained from textual descriptions of target scenes with images of those scenes. Our research encompasses three key strategies: image generation, text generation, and text enhancement, the latter involving tools such as ChatGPT to create concise textual descriptions for evaluation. The findings of this study contribute to an understanding of the impact of combining generative tools with multi-modal embedding representations to enhance the artificial agent's ability to recognise unknown scenes. Consequently, we assert that this research holds broad applications, particularly in drone parcel delivery, where an aerial robot can employ text descriptions to identify a destination. Furthermore, this concept can also be applied to other service robots tasked with delivering to unfamiliar locations, relying exclusively on user-provided textual descriptions.

Deep-learning blurring correction of images obtained from NIR single-pixel imaging.

In challenging scenarios characterized by low-photon conditions or the presence of scattering effects caused by rain, fog, or smoke, conventional silicon-based cameras face limitations in capturing visible images. This often leads to reduced visibility and image contrast. However, using near-infrared (NIR) light within the range of 850-1550 nm offers the advantage of reduced scattering by microparticles, making it an attractive option for imaging in such conditions. Despite NIR's advantages, NIR cameras can be prohibitively expensive. To address this issue, we propose a vision system that leverages NIR active illumination single-pixel imaging (SPI) operating at 1550 nm combined with time of flight operating at 850 nm for 2D image reconstruction, specifically targeting rainy conditions. We incorporate diffusion models into the proposed system to enhance the quality of NIR-SPI images. By simulating various conditions of background illumination and droplet size in an outdoor laboratory scenario, we assess the feasibility of utilizing NIR-SPI as a vision sensor in challenging outdoor environments.

Latin American consensus recommendations on the risk of infections in people with multiple sclerosis treated with disease modifying drugs.

INTRODUCTION: The emergence of several therapeutic options in multiple sclerosis (MS), which significantly modify the immune system functioning, has led to the need for the consideration of additional factors, such as risk of infections, in the decision-making process. The aim of these consensus recommendations was to discuss and perform a practical guide to Latin American neurologists on the risk of infections at diagnosis, follow-up and prior to initiation of DMDs. METHODS: A panel of Latin American neurologists, experts in demyelinating diseases and dedicated to management and care of MS patients, gathered during 2021 and 2022 to make consensus recommendations on the risk of infections in PwMS treated with DMDs in Latin America. The RAND/UCLA methodology was developed to synthesize the scientific evidence and expert opinions on health care topics and was used for reaching a formal agreement. RESULTS: Recommendations were established based on relevant published evidence and expert opinion, focusing on: 1- baseline infection disease and vaccination status; 2- opportunistic infections; 3- progressive multifocal leukoencephalopathy; 4- genitourinary system infections; 5- respiratory tract infections; 6- digestive system infections, 7-others local infections and 8- COVID-19. CONCLUSION: The recommendations of this consensus seek to optimize the care, management and treatment of PwMS in Latin America. The standardized evidence-based care of pwMS infections will allow better outcomes.

Single-pixel imaging: An overview of different methods to be used for 3D space reconstruction in harsh environments.

Different imaging solutions have been proposed over the last few decades, aimed at three-dimensional (3D) space reconstruction and obstacle detection, either based on stereo-vision principles using active pixel sensors operating in the visible part of the spectra or based on active Near Infra-Red (NIR) illumination applying the time-of-flight principle, to mention just a few. If extremely low quantum efficiencies for NIR active illumination yielded by silicon-based detector solutions are considered together with the huge photon noise levels produced by the background illumination accompanied by Rayleigh scattering effects taking place in outdoor applications, the operating limitations of these systems under harsh weather conditions, especially if relatively low-power active illumination is used, are evident. If longer wavelengths for active illumination are applied to overcome these issues, indium gallium arsenide (InGaAs)-based photodetectors become the technology of choice, and for low-cost solutions, using a single InGaAs photodetector or an InGaAs line-sensor becomes a promising choice. In this case, the principles of Single-Pixel Imaging (SPI) and compressive sensing acquire a paramount importance. Thus, in this paper, we review and compare the different SPI developments reported. We cover a variety of SPI system architectures, modulation methods, pattern generation and reconstruction algorithms, embedded system approaches, and 2D/3D image reconstruction methods. In addition, we introduce a Near Infra-Red Single-Pixel Imaging (NIR-SPI) sensor aimed at detecting static and dynamic objects under outdoor conditions for unmanned aerial vehicle applications.

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera.

Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera's back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.

A Review on Auditory Perception for Unmanned Aerial Vehicles.

Although a significant amount of work has been carried out for visual perception in the context of unmanned aerial vehicles (UAVs), not so much has been done regarding auditory perception. The latter can complement the observation of the environment that surrounds a UAV by providing additional information that can be used to detect, classify, and localize audio sources of interest. Motivated by the usefulness of auditory perception for UAVs, we present a literature review that discusses the audio techniques and microphone configurations reported in the literature. A categorization of techniques is proposed based on the role a UAV plays in the auditory perception (is it the one being perceived or is it the perceiver?), as well as a set of objectives that are more popularly aimed to be accomplished in the current literature (detection, classification, and localization). This literature review aims to provide a concise landscape of the most relevant works on auditory perception in the context of UAVs to date and provides insights into future avenues of research as a guide to those who are beginning to work in this field.

DeepPilot: A CNN for Autonomous Drone Racing.

Autonomous Drone Racing (ADR) was first proposed in IROS 2016. It called for the development of an autonomous drone capable of beating a human in a drone race. After almost five years, several teams have proposed different solutions with a common pipeline: gate detection; drone localization; and stable flight control. Recently, Deep Learning (DL) has been used for gate detection and localization of the drone regarding the gate. However, recent competitions such as the Game of Drones, held at NeurIPS 2019, called for solutions where DL played a more significant role. Motivated by the latter, in this work, we propose a CNN approach called DeepPilot that takes camera images as input and predicts flight commands as output. These flight commands represent: the angular position of the drone's body frame in the roll and pitch angles, thus producing translation motion in those angles; rotational speed in the yaw angle; and vertical speed referred as altitude h. Values for these 4 flight commands, predicted by DeepPilot, are passed to the drone's inner controller, thus enabling the drone to navigate autonomously through the gates in the racetrack. For this, we assume that the next gate becomes visible immediately after the current gate has been crossed. We present evaluations in simulated racetrack environments where DeepPilot is run several times successfully to prove repeatability. In average, DeepPilot runs at 25 frames per second (fps). We also present a thorough evaluation of what we called a temporal approach, which consists of creating a mosaic image, with consecutive camera frames, that is passed as input to the DeepPilot. We argue that this helps to learn the drone's motion trend regarding the gate, thus acting as a local memory that leverages the prediction of the flight commands. Our results indicate that this purely DL-based artificial pilot is feasible to be used for the ADR challenge.

Clinical and epidemiological comparison of Rickettsia parkeri rickettsiosis, related to Amblyomma triste and Amblyomma tigrinum, in Argentina.

The aim of this work was to compare the epidemiological and clinical characteristics of Rickettsia parkeri rickettsiosis related to Amblyomma triste and Amblyomma tigrinum ecological regions in Argentina. We reviewed cases of R. parkeri rickettsiosis from 2007 to 2017 evaluated at Muñiz Hospital, directly or through referral. Univariate analysis was used to examine the association between different variables and the disease related by each vector species. The eighteen cases of R. parkeri rickettsiosis included had fever, inoculation eschar and all except one had rash. Regional differences in epidemiological variables were identified, depending on the vector. There was a significantly increased risk of exposure to A. tigrinum in peri-domestic areas (odd ratio 12, p = 0.02), whereas an increased risk of exposure to A. triste was evident in wildlife areas (odd ratio 12, p = 0.02). Seasonality of R. parkeri rickettsiosis differed based on its vector. Cases associated with A. triste occurred predominantly during spring and summer, whereas those associated with A. tigrinum occurred during fall, winter, and springtime. Exanthema was maculopapular (13/18), maculo-vesicular (3/18) or petechial (1/18). No clinical differences were identified depending on the vector.

Exantema asociado a COVID-19 / COVID-19 skin rash

A partir de marzo de 2020 se han empezado a describir lesiones cutáneas asociadas a COVID-19 que fueron agrupadas en patrones y se relacionaron con la severidad y la temporalidad de la enfermedad. Se presenta el caso de un paciente con COVID-19 leve y lesiones cutáneas que pueden atribuirse a la infección. Se realiza una revisión de las manifestaciones cutáneas asociadas a COVID-19 y la interpretación de los diagnósticos diferenciales que se pensaron en el paciente

Since March 2020, skin lesions associated with COVID-19 have begun to be described. These were grouped into patterns, and were related to the severity and temporality of the disease. A patient with mild COVID-19 and skin lesions that can be attributed to the infection is presented. A review of the cutaneous manifestations associated with COVID-19 and the interpretation of the differential diagnoses that were thought of in the patient is carried out.

A Monocular SLAM-based Controller for Multirotors with Sensor Faults under Ground Effect.

Multirotor micro air vehicles can operate in complex and confined environments that are otherwise inaccessible to larger drones. Operation in such environments results in airflow interactions between the propellers and proximate surfaces. The most common of these interactions is the ground effect. In addition to the increment in thrust efficiency, this effect disturbs the onboard sensors of the drone. In this paper, we present a fault-tolerant scheme for a multirotor with altitude sensor faults caused by the ground effect. We assume a hierarchical control structure for trajectory tracking. The structure consists of an external Proportional-Derivative controller and an internal Proportional-Integral controller. We consider that the sensor faults occur on the inner loop and counteract them in the outer loop. In a novel approach, we use a metric monocular Simultaneous Localization and Mapping algorithm for detecting internal faults. We design the fault diagnosis scheme as a logical process which depends on the weighted residual. Furthermore, we propose two control strategies for fault mitigation. The first combines the external PD controller and a function of the residual. The second treats the sensor fault as an actuator fault and compensates with a sliding mode action. In either case, we utilize onboard sensors only. Finally, we evaluate the effectiveness of the strategies in simulations and experiments.

On the Use of the AIRA-UAS Corpus to Evaluate Audio Processing Algorithms in Unmanned Aerial Systems.

Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the previously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented, which is a set of recordings produced by the ego-noise of a drone performing different aerial maneuvers and by other drones flying nearby. It was found that the recordings have a very low Signal-to-Noise Ratio (SNR), that the noise is dynamic depending of the drone's movements, and that their noise signatures are highly correlated. Three popular filtering techniques were evaluated in this work in terms of noise reduction and signature extraction, which are: Berouti's Non-Linear Noise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled Recursive Averaging. Although there was moderate success in noise reduction, no filter was able to keep intact the signature of the drone flying in parallel. These results are evidence of the challenge in audio processing over drones, implying that this is a field prime for further research.

High Level 3D Structure Extraction from a Single Image Using a CNN-Based Approach.

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds.

Discrepancias entre el diagnóstico Clínico y Anatomo-Patológico en el Hospital Escuela Universitario De Honduras / Discrepancies between the Clinical and Anatomy-Pathological diagnosis at the Hospital Escuela Universitario in Honduras

La calidad diagnóstica es el resultado de integrar el conocimiento médico y reconocimiento de los errores clínicos, se alcanza únicamente con la identificación de las causas de muerte; es la correlación clínico patológica la herramienta principal para dicha acción. El objetivo general de la investigación fue determinar la discrepancia clínico-patológica y su relación con otras variables en las autopsias realizadas en la institución. Se revisaron 159 protocolos de autopsia del período comprendido entre enero 2012 y junio 2016, elaborados por el Servicio de Patología del Hospital Escuela Universitario de Tegucigalpa, Honduras. Se excluyeron 36 por no cumplir los criterios de inclusión. Se utilizaron la CIE-10 y la clasificación de Goldman et al. para clasificar las patologías y establecer las discrepancias diagnósticas, respectivamente. El sexo predominante fue el femenino (2,96:1), la edad media fue de 38 años; prevalecieron los diagnósticos de embarazo/parto/puerperio y enfermedades infecciosas y parasitarias. Concluimos que en 46% de los casos existe discrepancia diagnóstica y la glomerulonefritis fue la principal causa de error, seguida de bronconeumonía. Se recomienda estandarizar el protocolo de autopsias y promover sesiones clínico-patológicas periódicas e integrales.

Diagnostic quality is the result of the integration of medical knowledge and recognition of clinical error, achieved only by identifying the cause of death; clinical pathological correlation is the primary tool for this action. The overall objective of this research was to determine clinical pathological discrepancy and its relationship with other variables within the autopsies performed at the institution. 159 autopsy protocols, elaborated by the Department of Pathology of Hospital Escuela Universitario in Tegucigalpa, Honduras, from January 2012 to June 2016, were reviewed. 36 were excluded for not meeting the inclusion criteria. ICD-10 and Goldman et al. modified by Battle criteria were used to classify diseases and establish diagnostic discrepancies, respectively. The majority of patients were female (2.96:1), the mean age was 38 years old; diagnoses of pregnancy/birth/puerperium and infectious and parasitic diseases prevailed. We conclude that diagnostic discrepancies exist in 46% of all cases and glomerulonephritis was the leading cause of error, followed by bronchopneumonia. It is recommended that autopsy protocols be standardized, and integrative clinical pathological sessions are promoted and integral.

[A pilot study in a community pharmacy to determine the efficiency and the effectiveness of statin prescriptions]. / Estudio piloto en una farmacia comunitaria sobre la eficiencia y la efectividad de la prescripción de estatinas.

BACKGROUND: The efficacy of statins to reduce LDL-cholesterol serum levels is high, but effectiveness is limited and costs are elevated. OBJECTIVE: The efficiency and effectiveness of prescriptions were analyzed in a pilot study in a community pharmacy. DESIGN: A cross-sectional study. LOCATION: Community pharmacy. Prescriptions from two Murcian Health Service Centers in Lorca, Murcia (Spain). PARTICIPANTS: A total of 141 patients and 32 general practitioners were included. The efficiency was analyzed in 141 and effectiveness in 110 patients. MAIN MEASUREMENTS: Socio-demographic characteristics and clinical history of patients and information about statin type and dosage were collected. Each patient was analyzed to determine the effectiveness of treatment according to cardiovascular risk and previous LDL-cholesterol level, and efficiency comparing the statin prescribed against other statins with equal pharmacological power. RESULTS: The most prescribed statin was atorvastatin (57.4%). Almost two-thirds (63.9%) of prescriptions were inefficient, and 17.3% were ineffective. In a bivariate analysis, patients with previous cardiovascular events (8/38; 21% vs 41/103; 39.8%. P=.040) and smokers (42/114; 36.8% vs 4/23; 17.4%, P=.047) were more likely to receive an inefficient prescription than patients with no cardiovascular events and non-smokers. In a multivariate analysis, smokers were more likely to receive an inefficient prescription than non-smokers (OR ajusted 3.76; 95% CI;1.03-0.77, P=.012). CONCLUSIONS: Most of the participants reached therapeutic objectives for LDL-Cholesterol levels, but more than half of the prescriptions were considered inefficient.

Ligand effects on the structure and magnetic properties of alternating copper(II) chains with 2,2'-bipyrimidine- and polymethyl-substituted pyrazolates as bridging ligands.

A novel series of heteroleptic copper(II) compounds of formulas {[Cu2(µ-H2O)(µ-pz)2(µ-bpm)(ClO4)(H2O)]ClO4·2H2O}n (1), {[Cu2(µ-H2O)(µ-3-Mepz)2(µ-bpm)](ClO4)2·2H2O}n (2), and {[Cu2(µ-OH)(µ-3,5-Me2pz)(µ-bpm)(H-3,5-Me2pz)2](ClO4)2}n (3) [bpm = 2,2'-bipyrimidine, Hpz = pyrazole, H-3-Mepz = 3-methylpyrazole, and H-3,5-Me2pz = 3,5-dimethylpyrazole] have been synthesized and structurally characterized by X-ray diffraction methods. The crystal structures of 1 and 2 consist of copper(II) chains with regular alternating bpm and bis(pyrazolate)(aqua) bridges, whereas that of 3 is made up of copper(II) chains with regular alternating bpm and (pyrazolate)(hydroxo) bridges. The copper centers are six- (1) or five-coordinate (2) in axially elongated, octahedral (1) or square-pyramidal (2) environments in 1 and 2, whereas they are five-coordinate in distorted trigonal-bipyramidal surroundings in 3. The values of the copper-copper separations across the bpm/pyrazolate bridges are 5.5442(7)/3.3131(6) (1), 5.538(1)/3.235(1) (2), and 5.7673(7)/3.3220(6) Å (3). The magnetic properties of 1-3 have been investigated in the temperature range of 25-300 K. The analysis of their magnetic susceptibility data through the isotropic Hamiltonian for an alternating antiferromagnetic copper(II) chain model [H = -J∑i=1-n/2 (S2i·S2i-1 + αS2i·S2i+1), with α = J'/J and Si = SCu = 1/2] reveals the presence of a strong to moderate antiferromagnetic coupling through the bis(pyrazolate)(aqua) [-J = 217 (1) and 215 cm(-1) (2)] and (pyrazolate)(hydroxo) bridges [-J = 153 cm(-1) (3)], respectively, whereas a strong to weak antiferromagnetic coupling occurs through the bis-bidentate bpm [-J' = 211 (1), 213 (2), and 44 cm(-1) (3)]. A simple orbital analysis of the magnetic exchange interaction within the bpm- and pyrazolate-bridged dicopper(II) fragments of 1-3 visualizes the σ-type pathways involving the (dx(2)-y(2)) (1 and 2) or d(z(2)) (3) magnetic orbitals on each metal ion, which account for the variation of the magnetic properties in these three novel examples of one-dimensional copper(II) compounds with regular alternating intrachain antiferromagnetic interactions.

A longitudinal study of temperament continuity through IBQ, TBAQ and CBQ.

The aim of this work was to study the continuity of temperament in a Spanish sample (n = 60), covering the developmental stages of infancy, toddlerhood and childhood. Temperamental dimensions showed, with few exceptions, as much homotypic as heterotypic continuity as was to be expected. At the level of latent superconstructs continuity, we found that Anger and Fear followed different developmental paths and showed continuity over all the periods evaluated. Positive Affect/Regulation superconstruct showed continuity from infancy to toddlerhood. From toddlerhood, Positive Affect/Regulation showed continuity with the superconstruct of Effortful Control but not with the superconstruct of Surgency/Extraversion. At an ipsative level, we found two groups of subjects, labeled 'nonexpressive/controlled' and 'noncontrolled/expressive'. Generally, these results confirm the stability of temperament in the periods analyzed and underline the importance of toddlerhood as a transitional period in the maturity of self-regulatory capabilities shown in childhood.

Studying cross-cultural differences in the development of infant temperament: People's Republic of China, the United States of America, and Spain.

Investigated early development of temperament across three cultures: People's Republic of China (PRC), United States of America (US), and Spain, utilizing a longitudinal design (assessments at 3, 6, and 9 months of age). Selection of these countries presented an opportunity to conduct Eastern-Western/Individualistic-Collectivistic comparisons. The greatest number of significant differences (i.e., involving more temperament dimensions) was anticipated for the US (Western/Individualistic) and PRC (Eastern/Collectivistic) comparisons. The US sample included 66, the PRC group 69, and the Spanish sample, 60 mothers, all of whom completed the Infant Behavior Questionnaire (IBQ) 3 times, when their infants were 3, 6, and 9 months of age. Results related to mean group differences were generally consistent with our hypotheses, demonstrating a greater number of significant differences for US versus PRC, with fewer differences observed for US and Spain. Analyses addressing developmental changes in temperament indicated patterns consistent with a priori expectations and cross-cultural differences.