We investigate experimentally the coalescence cascade process for a confined swarm of deformable bubbles immersed in a bidimensional vertical cell filled with water. For different gas volume fractions, air bubbles of size $D_0$ larger than the cell thickness are injected at the bottom of the cell. The bubbles swarms transformation is explored using high-speed visualizations. The time evolution of each bubble in the swarm is determined using a specifically developed algorithm, enabling bubble tracking and coalescence detection. We determine the evolution of the bubble size distribution downstream from the injection point, and show that the stages of the coalescence cascade are characterized by the diameter, $D_{V90}$, representative of the largest bubbles. The collision frequency of pairs of bubbles of sizes $D_k$ and $D_{k'}$, $h(D_k, D_{k'})$, and their coalescence efficiency, $\lambda$, are obtained from the experiments. The efficiency is nearly constant, independently of the bubble sizes and of the gas volume fraction. Concerning collision frequency, our results reveal the existence of two different coalescence regimes depending on the capability of the bubbles to deform. Models describing $h(D_k, D_{k'})$ for both regimes are provided. They take into account the specific response of the bubble pair, which depends on the reduced diameter $D_p = 2 D_k D_{k'} / (D_k + D_{k'})$, to the global swarm-induced agitation governed by $D_{V90}$ and the gas volume fraction. In the first regime, occurring for smaller $D_p$, bubbles are brought together by agitation and rapidly coalesce, while for sufficiently large $D_p$, both bubbles are able to deform and spend more time adapting mutually their shapes before coalescing.

The NOVA classification of food items has become increasingly popular and is being used in several observational studies as well as in nutritional guidelines and recommendations. We propose that there is a need for this classification and its use in the formulation of public health policies to be critically discussed and re-appraised. The terms “processing” and “ultra-processing,” which are crucial to the NOVA classification, are ill-defined, as no scientific, measurable, or precise reference parameters exist for them. Likewise, the theoretical grounds of the NOVA classification are unclear and inaccurate. Overall, the NOVA classification conflicts with the classic, evidence-based evaluation of foods based on composition and portion size because NOVA postulates that the food itself (or how much of it is eaten) are unimportant, but rather that dietary effects are due to how the food is produced. We contend that the NOVA system suffers from a lack of biological plausibility so the assertion that ultra-processed foods are intrinsically unhealthful is largely unproven, and needs further examination and elaboration.

Este artículo explora el concepto de “utopías realistas” del químico argentino Oscar Varsavsky. Mientras la mayoría de las interpretaciones del trabajo de Varsavsky recuperan sus aportaciones a los estudios de ciencia y tecnología en América Latina, aquí se argumenta que su idea de “estilo científico” forma parte de un marco teórico más amplio. De este modo, la discusión se enfoca en el uso que el autor dio a las ciencias nuevas y al enfoque de sistemas para adaptarlos al proyecto liberador de las utopías realizables. Inicialmente se problematiza el eclecticismo teórico de Varsavsky y su redefinición ideológica ante el fenómeno intelectual argentino de los años sesenta y setenta; después se discute su idea de utopías realizables e ingeniería utópica articulada a la noción de poder y cambio social; finalmente, se exponen los conceptos de racionalidad pueblocéntrica y estilos sociales.

Background: Brain-machine-interface research has utilized multichannel single neuron recordings to decode movement intention. However, the prefrontal cortex (PFC) contains mental representations of more abstract task and goal elements which may be utilized as important signals in a brain-machine-interface. We therefore utilized virtual reality to simulate a real-world task while recording from ensembles of primate PFC neurons. Methods: Two male rhesus macaques (macaca mulatta) were trained to navigate a virtual reality environment using a joystick and learn a context-object association rule. We implanted each monkey with two 96-channel Utah arrays (Blackrock Microsystems) in the lateral PFC (areas 9/46 and 8a) and simultaneously recorded from multiple single neurons. Results: A linear support-vector-machine decoded task elements (context, target location and chosen direction of movement) with significantly greater than chance accuracy. This information was decoded in a sequential manner as the primates made a rule-based decision, with context information appearing first, followed by target location, and chosen side. Conclusions: We found that different neuronal ensembles encode the elements needed for implementing the context rule, and that such ensembles are activated sequentially. Brain-machine-interface systems may benefit by integrating neural data from the PFC, providing salient goal-related information such as the content of the goal and its spatial location.

Background: Neuromuscular junction transmission impairment has been described in amyotrophic lateral sclerosis (ALS). The degree of repetitive nerve stimulation (RNS) decrement may correlate with disease activity if measurement is repeatable. We determined test-retest correlation of decremental response in patients with ALS. Methods: RNS (3 Hz) was assessed by blinded technician and clinician on median - abductor pollicis brevis (median-APB), ulnar - adductor digiti minimi (ulnar-ADM), and/or accessory - trapezius (accessory-TRAP) nerve-muscle pairs during two evaluations. Repeatability was assessed by measuring the strength of test-retest correlation using the Pearson correlation coefficient (r). Results: 24 patients were included. Decrement was measured for 16 patients on median-APB, 22 on ulnar-ADM, and 24 on accessory-TRAP. Repeated measures of decrement demonstrated strong test-retest correlation for median-APB (r = 0.82, p < 0.001) and accessory-TRAP (r = 0.87, p < 0.001). Correlation was poor in ulnar-ADM (r = −0.01, p = 0.949). Correlation was superior in median-APB muscles with higher Medical Research Council (MRC) grade strength (MRC ≥ 4, r = 0.89, p = 0.001 vs MRC < 4, r = 0.73, p = 0.062). Conclusions: Decremental response in patients with ALS has strong test-retest correlation in median-APB and accessory-TRAP, but not in ulnar-ADM.

OBJECTIVES/GOALS: Accumulating evidence supports the involvement of immune and inflammatory pathways in Bipolar Disorder (BD) pathophysiology. This pilot study aims to determine if altered peripheral IL-2, TNF-a, IL-4, IL-6, IL-10, IFN-y, IL-17A levels are associated with BD across mood episodes (euthymic, manic, depressive), and worsen neurocognitive function. METHODS/STUDY POPULATION: Twenty-eight participants (17 cases and 11 controls) were recruited. We assessed the clinical features and cytokine plasma levels of participants. Cytokines were measured using Flow Cytometry. All subjects were interviewed by a trained psychiatrist. Each participant was fasting before the blood sample was taken. Neuropsychological tests were used to measure verbal fluency, speed processing, working memory/attention, visuospatial skills, verbal learning, executive functions, and motor skills. Descriptive statistics were used to calculate the demographic characteristics of the sample. An independent-sample Kruskal-Wallis Test and Mann-Whitney Test were carried out using SPSS version 21. RESULTS/ANTICIPATED RESULTS: Serum biomarker concentration showed a decrease in levels of IL-4 (anti-inflammatory) in BD patients vs healthy controls (p < .05). There was a major concentration of IL-6 (pro-inflammatory) on bipolar patients vs controls (p = .003). When we analyze the results with the mood episodes, we found that patients with bipolar depression showed decreased levels of IL-4 (p = .046) and increase levels of IL-6 (p = .020) in comparison to the manic or euthymic episodes. In the neurocognitive tests, we found that the control participants had better performance in the working memory domain (p = .038) and also in the general performance (p = .036) in comparison to bipolar patients. We found also a positive significant correlation between IL-4 and verbal learning in the control sample (.829, p = .003). DISCUSSION/SIGNIFICANCE: The findings evidence a significant immune activation in bipolar patients, in particular during the depressive episode. Participants with BD have a decrease in the protective levels of IL-4 combined with high levels of IL-6 when compared to healthy controls. Worse neurocognitive functioning was found in bipolar patients.

OBJECTIVES/GOALS: The COVID-19 pandemic may have affected the relationship/experience of the mother-child dyad (Provenzi et al., 2021). Our objective is to identify the association of pandemic related maternal stress with child development. We will further evaluate the role of bonding, attachment, and trauma on this association. METHODS/STUDY POPULATION: We aim to recall a prospective cohort (n=200) of Latinx/Hispanic mothers from an ongoing study, power analysis will estimate minimum sample size (power=0.80 and alpha =0.05). Assessments of pandemic related maternal stress (PRMS) will be done with the COVID-19 and Perinatal Experiences Interview, perceived stress scale, and Parental Stress Index. Bonding, attachment, and trauma history will be assessed with psychological questionnaires and Childs behaviors with the Ackerman-CBCL questionnaire. Descriptive statistical analysis will be done. Correlations will identify associations and multivariate models will assess the role of parental bonding and effects of maternal attachment/trauma on associations to PRMS and child behavioral outcomes (controlling for confounding effects). RESULTS/ANTICIPATED RESULTS: First, we expect to find that mothers will report higher levels of stress (pandemic related, perceived, and parental) which will be associated with less bonding behaviors towards her child. Second, we expect that mothers levels of PRMS will be mediated by poorer bonding characteristics thus leading to negative child behavioral outcomes (i.e., poor regulation, crying spells, alterations in physiological patterns, and social-emotional developmental outcomes). Further mothers insecure attachment traits and trauma history will moderate perception of stress and negative child behavioral outcomes. DISCUSSION/SIGNIFICANCE: Results will describe stress in Latinx/Hispanics mothers during the pandemic and effects on child development. Identifying the role of maternal bonding/attachment will point to how this formative relationship has transformed during the pandemic, providing knowledge of mother-child resiliency.

Although it has been demonstrated that environmental changes within a year can affect the reproduction, survival, and growth of invasive species, these factors have rarely been incorporated into demographic analyses. Therefore, we applied multistate demographic models (based on capture–recapture animal methods accounting for imperfect detectability of individuals in natural conditions) to evaluate the effects of reproductive phenology and rainy season on the survival and transition/retrogression rates among stage categories of black elderberry (Sambucus nigra L.)—an invasive tree species widely distributed in temperate forests of Europe and America. In the Abies religiosa temperate forest, Mexico City, a multistate demographic model of S. nigra was built using bimonthly censuses during a year. We selected the best-fitting model according to Akaike’s information criterion adjusted for small sample sizes (AICc). We determined the response of reproductive phenology of S. nigra to the rainy season for 2 yr through repeatability and phenotypic plasticity indexes. Our results showed that the reproductive phenology of S. nigra has a low repeatability index and a high phenotypic plasticity index. We demonstrated that additive and interactive effects of reproductive phenology and rainy season promote changes in survival and transition/retrogression rates among stage categories. During the rainy season, the survival probability of seedlings and transition probability toward the adult category increased. Therefore, our study represents a significant contribution to the knowledge of the demographic dynamics of invasive species on an intra-annual scale.

This paper examines the effectiveness of forward guidance shocks in the US. We estimate a New Keynesian model with imperfect central bank credibility and heterogeneous expectations using Bayesian methods and survey data from the Survey of Professional Forecasters (SPF). The results provide important takeaways: (1) The estimated credibility of the Fed’s forward guidance announcements is relatively high, but anticipation effects are attenuated. Accordingly, output and inflation do not respond as favorably as in the fully credible counterfactual. (2) The so-called “forward guidance puzzle” arises partly from the unrealistically large responses of macroeconomic variables to forward guidance under perfect credibility and homogeneous fully informed rational expectations, assumptions which are found to be jointly inconsistent with the observed US data. (3) Imperfect credibility provides a plausible explanation for the empirical evidence of forecasting error predictability based on forecasting disagreement found in the SPF data. Thus, we show that accounting for imperfect credibility and forecasting disagreements is important to understand the formation of expectations and the transmission mechanism of forward guidance.

Background: Interictal epileptiform discharges (IEDs) are known as epilepsy biomarkers for seizure detection, and It is essential for clinicians to detect them from from physiological events with similar temporal frequency characteristics. Methods: We analyzed the SEEG recordings obtained from patients with medically-resistant epilepsy (MRE) implanted with DE at the Western University Hospital Epilepsy Unit. The data were cleaned, denoised, montaged and segmented based on the clinical annotations, such as sleep intervals and observed Ictals. For event detection, the signal waveform and its power were extracted symmetrically in non-overlapping intervals of 500 ms. Each waveform’s power across all detected spikes was computed and clustered based on their energy distributions. Results: The recordings included thirteen sessions of 24 hours of extracellular recordings from two patients, with 312 hours extracted from four hippocampus electrodes anterior and posterior hippocampus. Our results indicate IEDs carrying the most different characteristics in the bands [25-75] Hz; SWR, on the other hand, are distributed between [80-170] Hz. Conclusions: Our algorithm detected and successfully distinguished IED from SWRs based on their carrying energy during non-sleep periods. Also, the most powerful spectral features that they were distinguished from occur in [15-30] Hz and [75-90] Hz.

The burden of depression is increasing worldwide, specifically in older adults. Unhealthy dietary patterns may partly explain this phenomenon. In the Spanish PREDIMED-Plus study, we explored (1) the cross-sectional association between the adherence to the Prime Diet Quality Score (PDQS), an a priori-defined high-quality food pattern, and the prevalence of depressive symptoms at baseline (cross-sectional analysis) and (2) the prospective association of baseline PDQS with changes in depressive symptomatology after 2 years of follow-up. After exclusions, we assessed 6612 participants in the cross-sectional analysis and 5523 participants in the prospective analysis. An energy-adjusted high-quality dietary score (PDQS) was assessed using a validated FFQ. The cross-sectional association between PDQS and the prevalence of depression or presence of depressive symptoms and the prospective changes in depressive symptoms were evaluated through multivariable regression models (logistic and linear models and mixed linear-effects models). PDQS was inversely associated with depressive status in the cross-sectional analysis. Participants in the highest quintile of PDQS (Q5) showed a significantly reduced odds of depression prevalence as compared to participants in the lowest quartile of PDQS (Q1) (OR (95 %) CI = 0·82 (0·68, 0·98))). The baseline prevalence of depression decreased across PDQS quintiles (Pfor trend = 0·015). A statistically significant association between PDQS and changes in depressive symptoms after 2-years follow-up was found (β (95 %) CI = −0·67 z-score (–1·17, −0·18). A higher PDQS was cross-sectionally related to a lower depressive status. Nevertheless, the null finding in our prospective analysis raises the possibility of reverse causality. Further prospective investigation is required to ascertain the association between PDQS and changes in depressive symptoms along time.

We conducted a retrospective observational study in patients with laboratory-confirmed coronavirus disease (COVID-19) who received medical care in 688 COVID-19 ambulatory units and hospitals in Mexico City between 24 February 2020 and 24 December 2020, to study if the elderly seek medical care later than younger patients and their severity of symptoms at initial medical evaluation. Patients were categorised into eight groups (<20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79 and ≥80 years). Symptoms at initial evaluation were classified according to a previously validated classification into respiratory and non-respiratory symptoms. Comparisons between time from symptom onset to medical care for every age category were performed through variance analyses. Logistic regression models were applied to determine the risk of presenting symptoms of severity according to age, and mortality risk according to delays in medical care. In total, 286 020 patients were included (mean age: 42.8, s.d.: 16.8 years; 50.4% were women). Mean time from symptom onset to medical care was 4.04 (s.d.: 3.6) days and increased with older age categories (P < 0.0001). Mortality risk increased by 6.4% for each day of delay in medical care from symptom onset. The risk of presenting with the symptoms of severity was greater with increasing age categories. In conclusion, COVID-19 patients with increasing ages tend to seek medical care later, with higher rates of symptoms of severity at initial presentation in both ambulatory units and hospitals.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.