Seasonal seminal quality variations and vitrification prospects in black flounder Paralichthys orbignyanus.

Long-term preservation of gametes has been identified as a tool to improve broodstock management and increase the number of juveniles produced by artificial fertilization. Paralichthys orbignyanus is an important commercial and recreational species distributed in marine and estuarine waters from Rio de Janeiro (Brazil) to the San Matías Gulf (Argentina). This work focused on studying the seminal quality of tank-reared P. orbignyanus, demonstrating that males are fluent year-round, with the highest yields at the early reproductive season. Fresh sperm exhibited good forward swimming, and samples could be refrigerated up to 48 h while retaining their motility after activation. The optimal conditions for P. orbignyanus sperm motility activation were established as 950 mOsmol/Kg and pH values between 7 and 7.9. Additionally, a well-defined protocol for semen vitrification was developed to assess the cryotolerance of this species' sperm. We successfully produced high-quality sperm samples, using two vitrification formulations containing trehalose and both z-1000 and x-1000 polymers, that can be used in a near-future in vitro embryo production program.

Mechanomyography-Based Metric Scale for Spasticity: A Pilot Descriptive Observational Study.

(1) Background: The Modified Ashworth Scale (MAS) is commonly used clinically to evaluate spasticity, but its qualitative nature introduces subjectivity. We propose a novel metric scale to quantitatively measure spasticity using mechanomyography (MMG) to mitigate these subjective effects. (2) Methods: The flexor and extensor muscles of knee and elbow joints were assessed with the Modified Ashworth Scale (MAS) during the acquisition of mechanomyography (MMG) data. The median absolute amplitude of the MMG signals was utilized as a key descriptor. An algorithm was developed to normalize the MMG signals to a universal gravitational (G) acceleration scale, aligning them with the limits and range of MAS. (3) Results: We evaluated 34 lower and upper limbs from 22 volunteers (average age 39.91 ± 13.77 years) of both genders. Polynomial regression provided the best fit (R2 = 0.987), with negligible differences (mean of 0.001 G) between the MAS and MMG. We established three numerical sets for the median, minimum, and maximum MMG(G) values corresponding to each MAS range, ensuring consistent alignment of the Modified Ashworth levels with our proposed scale. (4) Conclusions: Muscle spasticity can now be quantitatively and semi-automatically evaluated using our algorithm and instrumentation, enhancing the objectivity and reliability of spasticity assessments.

Optimization and Tradeoff Analysis for Multiple Configurations of Bio-Energy with Carbon Capture and Storage Systems in Brazilian Sugarcane Ethanol Sector.

Bio-energy systems with carbon capture and storage (BECCS) will be essential if countries are to meet the gas emission reduction targets established in the 2015 Paris Agreement. This study seeks to carry out a thermodynamic optimization and analysis of a BECCS technology for a typical Brazilian cogeneration plant. To maximize generated net electrical energy (MWe) and carbon dioxide CO2 capture (Mt/year), this study evaluated six cogeneration systems integrated with a chemical absorption process using MEA. A key performance indicator (gCO2/kWh) was also evaluated. The set of optimal solutions shows that the single regenerator configuration (REG1) resulted in more CO2 capture (51.9% of all CO2 emissions generated by the plant), penalized by 14.9% in the electrical plant's efficiency. On the other hand, the reheated configuration with three regenerators (Reheat3) was less power-penalized (7.41%) but had a lower CO2 capture rate (36.3%). Results showed that if the CO2 capture rates would be higher than 51.9%, the cogeneration system would reach a higher specific emission (gCO2/kWh) than the cogeneration base plant without a carbon capture system, which implies that low capture rates (<51%) in the CCS system guarantee an overall net reduction in greenhouse gas emissions in sugarcane plants for power and ethanol production.

Waste management optimization with NLP modeling and waste-to-energy in a circular economy.

This work presents a methodology integrating Non-Linear Programming (NLP) for multi-objective and multi-period optimization, addressing sustainable waste management and energy conversion challenges. It integrates waste-to-energy (WtE) technologies such as Anaerobic Digestion (AD), Incineration (Inc), Gasification (Gsf), and Pyrolysis (Py), and considers thermochemical, technical, economic, and environmental considerations through rigorous non-linear functions. Using Mexico City as a case study, the model develops waste management strategies that balance environmental and economic aims, considering social impacts. A trade-off solution is proposed to address the conflict between objectives. The economical optimal solution generates 1.79M$ with 954 tons of CO2 emissions while the environmental one generates 0.91M$ and reduces emissions by 54%, where 40% is due to gasification technology. Moreover, the environmentally optimal solution, with incineration and gasification generates 9500 MWh/day and 5960 MWh/day, respectively, demonstrates the capacity of the model to support sustainable energy strategies. Finally, this work presents an adaptable framework for sustainable waste management decision-making.

Cognitive Behavioural Therapy for insomnia decreases the discrepancy between objective and subjective measures of sleep.

INTRODUCTION: Individuals with insomnia disorder often exhibit differences between reported experiences of sleep and objectively measured sleep parameters; however, the implications of this subjective-objective sleep discrepancy during treatment remains unclear. OBJECTIVE: The aim of this study was to investigate the impact of cognitive behavioural therapy for insomnia (CBT-I) on the discrepancy between objective and subjective measures of sleep, and to assess whether changes in clinical variables such as depression, anxiety, fatigue, and beliefs about sleep, were related to changes in discrepancy. METHODS: Twenty-five participants with insomnia disorder were enrolled in group CBT-I. Sleep measures were continually sampled from baseline until 2 weeks post-treatment with both objective (i.e., actigraphy) and subjective (i.e., sleep diary) methods. RESULTS: The subjective-objective discrepancy significantly decreased from baseline early on in treatment (following the second session) and were maintained at post-treatment for sleep onset latency, wake after sleep onset (WASO) and sleep efficiency (SE). Total sleep time (TST) discrepancy and misperception decreased from baseline to post-treatment. Improvement in depression symptoms, fatigue symptoms, and negative beliefs about sleep were significantly correlated with the decrease in the discrepancy for WASO and SE. CONCLUSION: These findings suggest that CBT-I resolves the mismatch between objective and subjective sleep parameters early in treatment for adults with insomnia. Sleep misperception improved from underestimating to accurately estimating TST. Improvement of psychological symptoms were related to decrease in sleep discrepancies across treatment. Future research is needed to explore how feedback on objective and subjective sleep discrepancy may impact sleep perception across treatment with CBT-I.

asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy.

The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below.•The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process.•An archive-based update strategy was introduced to maintain diversity in the training set.•Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.

Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

Effect of three training programs on surgical performance in single-port laparoscopic surgery.

OBJECTIVE: The aim of this study was to evaluate the effect of three training methodologies on the acquisition of psychomotor skills for laparoendoscopic single-site surgery (LESS), using straight and articulating instruments. METHODS: A prospective study was conducted with subjects randomly divided into three groups, who performed a specific training for 12 days using three laparoscopic tasks in a laparoscopic simulator. Group-A trained in conventional laparoscopy setting using straight instruments and in LESS setting using both straight and articulating instruments. Group-B trained in LESS setting using straight and articulating instruments, whereas Group-C trained in LESS setting using articulating instruments. Participants' performance was recorded with a video-tracking system and evaluated with 12 motion analysis parameters (MAPs). RESULTS: All groups obtained significant differences in their performance in most of the MAPs. Group-C showed an improvement in nine MAPs, with a high level of technical competence. Group-A presented a marked improvement in bimanual dexterity skills. CONCLUSIONS: Training in LESS surgery using articulating laparoscopic instruments improves the quality of skills and allows smoother learning curves.

OBJETIVO: Evaluar el efecto de tres métodos de entrenamiento en la adquisición de habilidades psicomotrices para la cirugía laparoendoscópica por puerto único (LESS, laparoendoscopic single-site surgery) utilizando instrumental recto y articulado. MÉTODO: Se realizó un estudio prospectivo con sujetos divididos aleatoriamente en tres grupos, quienes realizaron un entrenamiento específico durante 12 días utilizando tres tareas laparoscópicas en un simulador laparoscópico. El grupo A entrenó en el entorno laparoscópico convencional con instrumentos rectos, y en el entorno LESS con instrumentos rectos y articulados. El grupo B entrenó en el entorno LESS con instrumentos rectos y articulados. El Grupo C entrenó en el entorno LESS con instrumentos articulados. El desempeño de los participantes se registró con un sistema de seguimiento en video y fue evaluado con 12 parámetros de análisis de movimiento (MAP, motion analysis parameters). RESULTADOS: Todos los grupos obtuvieron diferencias significativas en su desempeño para la mayoría de los MAP. El grupo C mostró una mejora en nueve MAP, con un alto nivel de competencia técnica. El grupo A mostró una marcada mejora en la habilidad de destreza bimanual. CONCLUSIONES: El entrenamiento en cirugía LESS con instrumentos articulados mejora la calidad de las habilidades adquiridas y permite curvas de aprendizaje más suaves.

People With Patellofemoral Pain Have Bilateral Deficits in Physical Performance Regardless of Pain Laterality.

CONTEXT: People with patellofemoral pain (PFP) may have lower performance during the forward step-down and single-leg hop with their painful (unilateral complaints) or most painful (bilateral complaints) limb when compared to pain-free controls. Yet, no study has investigated the appropriateness of using the pain-free/less painful limb as a reference standard in clinical practice or whether deficits might be present depending on the laterality of pain. OBJECTIVE: To compare performance scores and proportion of side-to-side limb symmetry during the forward step-down and single-leg hop tests among people with unilateral and bilateral PFP, and pain-free controls. DESIGN: Cross-sectional study. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: Fifty-two young adults (18-35 years old) with unilateral PFP, 72 with bilateral PFP, and 76 controls. MAIN OUTCOME MEASURE(S): Group-by-limb interactions on the performance during the step- down (repetitions) and hop test (distance [cm] normalized by the limb length) were investigated using a repeated-measures analysis of covariance controlling for sex. Pairwise comparisons were interpreted using effect sizes. A Chi-square test was used to compare the proportion of symmetry/asymmetry (cutoff point of ≥ 90% for symmetries indices) across groups and tests. RESULTS: Main effects for groups (small-to-medium effects) but not limbs indicated lower performance of both limbs of individuals with unilateral and bilateral PFP compared to controls during forward step-downs and single-leg hop tests. No significant differences for the proportion of symmetry/asymmetry were identified across groups (p ≥ 0.05), which further suggests an impaired physical performance of the contralateral limb. CONCLUSIONS: Our results indicate bilateral deficits in the physical performance of people with unilateral and bilateral PFP when compared to pain-free controls during the forward step-down and single-leg hop tests. Limb symmetries indices greater than 90% should be interpreted with caution, as they may overstate the physical performance by not assuming bilateral deficits.

Multi-channel and multi-harmonic analysis of Auditory Steady-State Response detection.

The Auditory Steady-State Response (ASSR) is a type of auditory evoked potential (AEP) generated in the auditory system that can be automatically detected by means of objective response detectors (ORDs). ASSRs are usually registered on the scalp using electroencephalography (EEG). ORD are univariate techniques, i.e. only uses one data channel. However, techniques involving more than one channel - multi-channel objective response detectors (MORDs) - have been showing higher detection rate (DR) when compared to ORD techniques. When ASSR is evoked by amplitude stimuli, the responses could be detected by analyzing the modulation frequencies and their harmonics. Despite this, ORD techniques are traditionally applied only in its first harmonic. This approach is known as one-sample test. The q-sample tests, however, considers harmonics beyond the first. Thus, this work proposes and evaluates the use of q-sample tests using a combination of multiple EEG channels and multiple harmonics of the stimulation frequencies and compare them with traditional one-sample tests. The database used consists of EEG channels from 24 volunteers with normal auditory threshold collected following a binaural stimulation protocol by amplitude modulated (AM) tone with modulating frequencies near 80 Hz. The best q-sample MORD result showed an increase in DR of 45.25% when compared with the best one-sample ORD test. Thus, it is recommended to use multiple channels and multiple harmonics, whenever available.

Cooling supply with a new type of evacuated solar collectors: a techno-economic optimization and analysis.

Renewable cooling via absorption chillers being supplied by various green heat technologies such as solar collectors has been widely studied in the literature, but it is still challenging to get positive economic outcomes from such systems due to the large expenses of solar thermal systems. This study offers the use of a new generation of solar collectors, so-called eccentric reflective solar collectors, for driving single-effect absorption chillers and thereby reducing the levelized cost of cooling. This article develops the most optimal design of this system (based on several different scenarios) using multi-objective optimization techniques and employs them for a case study in Brazil to assess its proficiency compared to conventional solar-driven cooling methods. For making the benchmarking analyses fair, the conventional system is also rigorously optimized in terms of design and operation features. The results show that the eccentric solar collector would enhance the cost-effectiveness by 29%. In addition, using optimally sized storage units would be necessary to get acceptable economic performance from the system, no matter which collector type is used. For the case study, at the optimal sizing and operating conditions, the levelized cost of cooling will be 124 USD/MWh and an emission level of 18.97 kgCO2/MWh.

Solar photovoltaic panel production in Mexico: A novel machine learning approach.

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10-4 m3/kWh) and the emissions generated (1.12 × 10-6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.

Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall.

Topic-based search systems retrieve items by contextualizing the information seeking process on a topic of interest to the user. A key issue in topic-based search of text resources is how to automatically generate multiple queries that reflect the topic of interest in such a way that precision, recall, and diversity are achieved. The problem of generating topic-based queries can be effectively addressed by Multi-Objective Evolutionary Algorithms, which have shown promising results. However, two common problems with such an approach are loss of diversity and low global recall when combining results from multiple queries. This work proposes a family of Multi-Objective Genetic Programming strategies based on objective functions that attempt to maximize precision and recall while minimizing the similarity among the retrieved results. To this end, we define three novel objective functions based on result set similarity and on the information theoretic notion of entropy. Extensive experiments allow us to conclude that while the proposed strategies significantly improve precision after a few generations, only some of them are able to maintain or improve global recall. A comparative analysis against previous strategies based on Multi-Objective Evolutionary Algorithms, indicates that the proposed approach is superior in terms of precision and global recall. Furthermore, when compared to query-term-selection methods based on existing state-of-the-art term-weighting schemes, the presented Multi-Objective Genetic Programming strategies demonstrate significantly higher levels of precision, recall, and F1-score, while maintaining competitive global recall. Finally, we identify the strengths and limitations of the strategies and conclude that the choice of objectives to be maximized or minimized should be guided by the application at hand.

Association between cardiovascular risk factors and cognitive impairment in adults aged 60 years or older from Chile: a cross-sectional study.

BACKGROUND: Few studies in Latin America have examined the association between cardiovascular risk factors and cognitive impairment (CI) in a nationally representative sample. Therefore, this study aimed to estimate the prevalence of CI in a nationally representative sample of adults aged 60 years or older from Chile and to investigate the association between cardiovascular risk factors and CI. METHODS: Data from the cross-sectional 2016-2017 National Health Survey of Chile, which included 2031 adults (63.7% women) was used. Body mass index, metabolic syndrome (blood pressure, triglycerides, fasting glucose or treatment for diabetics, waist circumference, and HDL cholesterol), risk of cardiovascular disease (history and measured variables, using the Framingham risk score), tobacco use, and physical activity were measured. CI was assessed using the Mini-Mental Status Examination (MMSE). RESULTS: Overall, the prevalence of CI was 12.2% at the national level. Significant differences in CI were observed by age, education level, risk of cardiovascular disease, and smoking. High risk of cardiovascular disease was associated with higher odds of CI (OR: 2.04; 95%CI: 1.20-3.45) compared to low risk. Smoking was significantly associated with a lower likelihood of CI (OR: 0.56; 95%CI: 0.36-0.87) compared to never smoking. Body mass index, metabolic syndrome, and physical activity were not associated with CI. CONCLUSIONS: This study provided additional support for previous findings on the relationship between cognitive decline and an elevated risk of cardiovascular disease. Worse CI was associated with the group with the highest risk of cardiovascular disease, and the presence of lifestyle factors, such as obesity and physical inactivity, exacerbate this relationship, but not being a current smoker.

Data, instance sets, and instances generator for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, and their bi-objective variants.

This article proposes a benchmark instance generator for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, and their bi-objective variants. The generator is developed in C++ and does not uses external libraries, being understandable, easy-to-read, and easy-to-use. Furthermore, the generator employs five parameters that makes possible to generate personalized benchmark instances for these problems. We also describe 640 benchmark instances that were previously used in computational experiments in the literature. Lastly, we include raw results obtained from computational experiments with the described benchmark instances. We hope that the data introduced in this article can foster the development and the evaluation of new algorithms for solving constrained minimum spanning tree problems.

The multidimensionality of social wellbeing: interactions from the individual to the collective level in large cities.

This paper responds to two questions-What dimensions and indicators are relevant to the construction of social wellbeing? How are the levels of wellbeing distributed in the municipalities of Mexico City? To answer these questions, we use data from the Wellbeing Survey (N = 2,871) that is representative of Mexico City and its municipalities. We employed two methods, DM-R distances, and Mamdani's Fuzzy Inference Method. The results show that all the proposed dimensions and indicators contributed to the building of multidimensional social wellbeing; in the case of some indicators (social security, built environment, and public insecurity) they contributed less. This suggests government interventions should be designed in order to improve the gaps in those areas. The evidence also indicates that community wellbeing is a relevant dimension when measuring social wellbeing in large cities, in addition to identifying areas of intervention for the development of more efficient and inclusive public policies.

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location.

To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

Constrained Dynamic Matrix Control under International Electrotechnical Commission Standard 61499 and the Open Platform Communications Unified Architecture.

This paper focuses on the implementation of a constrained Dynamic Matrix Control (DMC) approach within the level processes of the FESTO™ MPS-PA Compact Workstation plant in the context of the Industrial Internet of Things (IIoT) paradigm. The goal is to develop an industrial control application with decentralized logic that optimizes the operation of the plant while adhering to specific constraints. The implementation is carried out using the IEC-61499 standard and the OPC-UA protocol, enabling seamless communication between devices and systems. The authors utilize the 4diac-IDE and 4diac-FORTE as the development and runtime environments, respectively, to enable the execution of the control application on low-cost devices. The Beagle Bone Black (BBB) card is used for data acquisition and actuator control. Three types of constraints are considered: control increment (Δu(k)), output (ym(k)), and control (u(k)) constraints, to prevent unnecessary stress on the actuator and avoid damage to the plant. The QP algorithm is employed to optimize the objective function and address these constraints effectively. By integrating advanced control strategies into industrial processes in the IIoT paradigm and implementing them on low-cost devices, this paper demonstrates the feasibility and effectiveness of improving system performance, resource utilization, and overall productivity while considering system limitations and constraints.

National Health Examination Surveys: an essential piece of the health planning puzzle.

National health examination surveys (HESs) have been developed to provide important information that cannot be obtained from other sources. A HES combines information obtained by asking participants questions with biophysical measurements taken by trained field staff. They are observational studies with the highest external validity and make specific contributions to both population (public health) and individual health. Few countries have a track record of a regular wide-ranging HES, but these are the basis of many reports and scientific papers. Despite this, little evidence about HES usefulness and impact or the factors that influence HES effectiveness have been disseminated. This paper presents examples of HES contributions to society in both Europe and the Americas. We sought information by emailing a wide list of people involved in running or using national HESs across Europe and the Americas. We asked for examples of where examination data from their HES had been used in national or regional policymaking. We found multiple examples of HES data being used for agenda-setting, including by highlighting nutritional needs and identifying underdiagnosis and poor management of certain conditions. We also found many ways in which HES have been used to monitor the impact of policies and define population norms. HES data have also been used in policy formation and implementation. HES data are influential and powerful. There is need for global support, financing and networking to transfer capacities and innovation in both fieldwork and laboratory technology.

Objective burden, caregiver psychological distress, and patient religion and quality of life are associated with high-intensity burden of care among caregivers of advanced cancer patients in a Latino population.

OBJECTIVES: Family caregivers (FCs) of cancer patients experience burden of care. The aims of this study are to describe the caregiving phenomenon among FCs of advanced cancer patients in a Latino community and to identify caregiver and patient characteristics associated with high-intensity subjective caregiver burden. METHODS: In this cross-sectional study, advanced cancer patient-caregiver dyads assessed at a Palliative Care Unit in Santiago, Chile, enrolled in a longitudinal observational study were included. FCs completed questions to describe the caregiving phenomenon and surveys to assess burden of care, psychological distress, and perception of patients' symptoms; patients completed surveys to assess physical distress and quality of life (QOL). We explored associations between high-intensity subjective caregiver burden with caregiver and patient variables. RESULTS: Two hundred seven dyads were analyzed. FCs were on average 50 years old and 75% female. Thirty-two percent of FCs experienced high-intensity subjective burden of care. Eighty two percent of FCs took care of the patient daily and 31% took care of the patient alone. In univariate analysis, high-intensity caregiver burden was associated with caregiver depression (59% vs. 27%; p < 0.001), anxiety (86% vs. 67%; p = 0.003), caring for the patient alone (45% vs. 24%; p = 0.002), perception of patient symptom distress, patient religion, and worse patient QOL (mean [standard deviation] 58 [33] vs. 68 [27]; p = 0.03). In multivariate analysis, FC depression (OR [95% confidence interval] 3.07 [1.43-6.60]; p = 0.004), anxiety (3.02 [1.19-7.71]; p = 0.021), caring for the patient alone (2.69 [1.26-5.77]; p = 0.011), caregiver perception of patient's fatigue (1.26 [1.01-1.58]; p = 0.04), and patient's religion (3.90 [1.21-12.61]; p = 0.02) were independently associated with caregiver burden. SIGNIFICANCE OF RESULTS: FCs of advanced cancer patients in a Latino community frequently experience high-intensity burden of care and are exposed to measures of objective burden. High-intensity burden is associated with both caregiver and patient factors. Policies should aim to make interventions on patient-caregiver dyads to decrease caregiving burden among Latinos.