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Interval discrimination is of fundamental importance to many forms of sensory processing, including speech and music. Standard interval discrimination tasks require comparing two intervals separated in time, and thus include both working memory (WM) and timing components. Models of interval discrimination invoke separate circuits for the timing and WM components. Here we examine if, in principle, the same recurrent neural network can implement both. Using human psychophysics, we first explored the role of the WM component by varying the interstimulus delay. Consistent with previous studies, discrimination was significantly worse for a 250 ms delay, compared to 750 and 1500 ms delays, suggesting that the first interval is stably stored in WM for longer delays. We next successfully trained a recurrent neural network (RNN) on the task, demonstrating that the same network can implement both the timing and WM components. Many units in the RNN were tuned to specific intervals during the sensory epoch, and others encoded the first interval during the delay period. Overall, the encoding strategy was consistent with the notion of mixed selectivity. Units generally encoded more interval information during the sensory epoch than in the delay period, reflecting categorical encoding of short versus long in WM, rather than encoding of the specific interval. Our results demonstrate that, in contrast to standard models of interval discrimination that invoke a separate memory module, the same network can, in principle, solve the timing, WM, and comparison components of an interval discrimination task.

The ability to predict and prepare for near- and far-future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing, the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that, on the scale of seconds, timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represents in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Abnormal uterine bleeding is a common benign gynecological complaint and is also the most common symptom of endometrial cancer (EC). Although many microRNAs have been reported in endometrial carcinoma, most of them were identified from tumor tissues obtained at surgery or from cell lines cultured in laboratories. The objective of this study was to develop a method to detect EC-specific microRNA biomarkers from liquid biopsy samples to improve the early diagnosis of EC in women. Endometrial fluid samples were collected during patient-scheduled in-office visits or in the operating room prior to surgery using the same technique performed for saline infusion sonohysterography (SIS). The total RNA was extracted from the endometrial fluid specimens, followed by quantification, reverse transcription, and real-time PCR arrays. The study was conducted in two phases: exploratory phase I and validation phase II. In total, endometrial fluid samples from 82 patients were collected and processed, with 60 matched non-cancer versus endometrial carcinoma patients used in phase I and 22 in phase II. The 14 microRNA biomarkers, out of 84 miRNA candidates, with the greatest variation in expression from phase I, were selected to enter phase II validation and statistical analysis. Among them, three microRNAs had a consistent and substantial fold-change in upregulation (miR-429, miR-183-5p, and miR-146a-5p). Furthermore, four miRNAs (miR-378c, miR-4705, miR-1321, and miR-362-3p) were uniquely detected. This research elucidated the feasibility of the collection, quantification, and detection of miRNA from endometrial fluid with a minimally invasive procedure performed during a patient in-office visit. The screening of a larger set of clinical samples was necessary to validate these early detection biomarkers for endometrial cancer.

BackgroundOral health, an essential part of general health and well-being, is influenced by multiple factors, including oral hygiene habits and dietary factors. Dietary preferences are influenced by variation in taste perceptions and threshold tasting. Polymorphisms in specific genes for sweet and bitter taste receptors and bitter taste perception have been associated with dental caries. However, taste is complex with multiple receptors, each with multiple potential polymorphisms contributing to taste perception as well as social, cultural, and environmental influences. Additionally, these association studies have been conducted in restricted cohorts (e.g., children only). Furthermore, outcomes have been limited to dental caries and studies between taste perception and oral hygiene habits have not been completed.MethodsA cross-sectional online survey was conducted to investigate the relationships between bitter and sweet taste perception (liking and intensity of index food items), self-reported oral hygiene habits and oral health (n = 518).ResultsHigher mean intensity scores for bitter (16–21%) and sweet (ConclusionsWhile there were numerous relationships identified between liking and perception of sweet and bitter and oral health outcomes, the magnitude and direction of associations varied by outcome. The direction of the associations cannot be inferred due to the cross-sectional nature of the study. The demonstrated relationships justify further future investigations, which could help better understand if taste liking and perception is impacted by oral hygiene and health, or vice versa. This could be important in understanding the causation and progression of oral health diseases or the development of novel therapeutics for oral health.

Neuromodulators such as dopamine have been shown to modulate short-term synaptic plasticity (STP). Here we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained RNNs that incorporated STP to produce complex motor trajectories—handwritten digits—with different temporal (speed) and spatial (size) scales. The learned dynamics underwent temporal and spatial scaling when higher synaptic release probabilities corresponded to higher speed/size. Neuromodulation of STP enhanced temporal or spatial generalization compared to weight modulation alone. The model accounted for the data of two experimental studies involving flexible sensorimotor timing. Our results address a long-standing debate regarding the role of dopamine in timing and predict novel mechanisms by which dopamine may slow down neural dynamics and thus slow “clock” speed.

Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that cortical circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established that four sets of weights ( W E←E , W E←I , W I←E , and W I←I ) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain can appropriately set the values of all four weight classes in an unsupervised manner to be in the inhibition-stabilized regime. We prove that standard homeostatic plasticity rules are generally unable to generate inhibition-stabilized dynamics and that their instability is caused by a signature property of inhibition-stabilized networks: the paradoxical effect. In contrast, we show that a family of “cross-homeostatic” rules overcome the paradoxical effect and robustly lead to the emergence of stable dynamics. This work provides a model of how—beginning from a silent network—self-sustained inhibition-stabilized dynamics can emerge from learning rules governing all four synaptic weight classes in an orchestrated manner.

Background Overweight, obesity, and associated comorbidities are a pressing global issue among children of all ages, particularly among low-income populations. Rapid weight gain (RWG) in the first 6 months of infancy contributes to childhood obesity. Suboptimal sleep-wake patterns and gut microbiota (GM) have also been associated with childhood obesity, but little is known about their influences on early infant RWG. Sleep may alter the GM and infant metabolism, and ultimately impact obesity; however, data on the interaction between sleep-wake patterns and GM development on infant growth are scarce. In this study, we aim to investigate associations of infant sleep-wake patterns and GM development with RWG at 6 months and weight gain at 12 months. We also aim to evaluate whether temporal interactions exist between infant sleep-wake patterns and GM, and if these relations influence RWG. Methods The Snuggle Bug/ Acurrucadito study is an observational, longitudinal study investigating whether 24-h, actigraphy-assessed, sleep-wake patterns and GM development are associated with RWG among infants in their first year. Based on the Ecological Model of Growth, we propose a novel conceptual framework to incorporate sleep-wake patterns and the GM as metabolic contributors for RWG in the context of maternal-infant interactions, and familial and socio-physical environments. In total, 192 mother-infant pairs will be recruited, and sleep-wake patterns and GM development assessed at 3 and 8 weeks, and 3, 6, 9, and 12 months postpartum. Covariates including maternal and child characteristics, family and environmental factors, feeding practices and dietary intake of infants and mothers, and stool-derived metabolome and exfoliome data will be assessed. The study will apply machine learning techniques combined with logistic time-varying effect models to capture infant growth and aid in elucidating the dynamic associations between study variables and RWG. Discussion Repeated, valid, and objective assessment at clinically and developmentally meaningful intervals will provide robust measures of longitudinal sleep, GM, and growth. Project findings will provide evidence for future interventions to prevent RWG in infancy and subsequent obesity. The work also may spur the development of evidence-based guidelines to address modifiable factors that influence sleep-wake and GM development and prevent childhood obesity.

This study aimed to investigate the effect of fluctuating female hormones during the menstrual cycle (MC) and oral contraceptive (OC) cycle on different measures of body composition. Twenty-two women with a natural MC and thirty women currently taking combined monophasic OC were assessed over three phases of the menstrual or oral contraceptive cycle. Body weight, skinfolds, bioelectric impedance analysis (BIA), ultrasound, dual-energy X-ray absorptiometry (DXA), and peripheral quantitative computed tomography (pQCT) measurements were performed to assess body composition. Urine specific gravity (USG) was measured as an indication of hydration, and serum oestradiol and progesterone were measured to confirm cycle phases. Five participants with a natural MC were excluded based on the hormone analysis. For the remaining participants, no significant changes over the MC and OC cycle were found for body weight, USG, skinfolds, BIA, ultrasound and pQCT measures. However, DXA body fat percentage and fat mass were lower in the late follicular phase compared to the mid-luteal phase of the MC, while for the OC cycle, DXA body fat percentage was higher and lean mass lower in the early hormone phase compared with the late hormone phase. Our findings suggest that assessment of body fat percentage through BIA and skinfolds may be performed without considering the MC or OC cycle. Body adiposity assessment via DXA, however, may be affected by female hormone fluctuations and therefore, it may be advisable to perform repeat testing using DXA during the same phase of the MC or OC cycle.

Background: Adult primary retroperitoneal sarcomas (RPSs) are a group of heterogeneous tumors with different histological subtypes. Comprehensive genomic profiling (CGP) analyses have recently provided significant insights into the biology of sarcomas by identifying genomic alterations (GAs) which could benefit from targeted therapies. Methods: RPS were evaluated by CGP using next-generation sequencing of up to 406 cancer-related genes. Tumor mutational burden (TMB) was determined on 0.83 to 1.14 mut/Mb of sequenced DNA. Finally, PD-L1 expression was determined. Results: Overall, 296 cases of primary RPS were analyzed. Liposarcoma (LPS) subtype had more GA/tumor than leiomyosarcoma (LMS) subtypes, with follicular dendritic cell sarcomas harboring the highest and synovial sarcomas the lowest. TP53 and Rb1 alterations were the highest in LMS, and CDK4/6 and MDM2 in LPS. However, both the TMB and targetable GA rates were low across subtypes. PD-L1 immunostaining was low positive in 21% and high positive in 5% of patients, respectively. Conclusions: CGP analysis revealed that potentially actionable genomic targets were rare in our cohort of RPS. Moreover, RPSs seem less likely to respond to immune checkpoint inhibitors based on putative biomarkers status. Nevertheless, genomic stratification according to histological subtypes led to description of GAs that can inform future clinical trials design.

Up-states are the best-studied example of an emergent neural dynamic regime. Computational models based on a single class of inhibitory neurons indicate that Up-states reflect bistable dynamical systems in which positive feedback is stabilized by strong inhibition and predict a paradoxical effect in which increased drive to inhibitory neurons results in decreased inhibitory activity. To date, however, computational models have not incorporated empirically defined properties of PV and SST neurons. Here we first, experimentally characterized the frequencycurrent (F-I) curves of pyramidal, PV, and SST neurons and confirmed a sharp difference between the threshold and slopes of PV and SST neurons. The empirically defined F-I curves were incorporated into a three-population computational model that simulated the empirically-derived firing rates of pyramidal, PV, and SST neurons. Simulations revealed that the intrinsic properties were sufficient to predict that PV neurons are primarily responsible for generating the nontrivial fixed points representing Up-states. Simulations and analytical methods demonstrated that while the paradoxical effect is not obligatory in a model with two classes of inhibitory neurons, it is present in most regimes. Finally, experimental tests validated predictions of the model that the Pyr↔PV inhibitory loop is stronger than the Pyr↔SST loop.SIGNIFICANCE STATEMENTMany cortical computations, such as working memory, rely on the local recurrent excitatory connections that define cortical circuit motifs. Up-states are among the simplest and best studied examples of neural dynamic regimes that rely on recurrent excitatory excitation. However, this positive feedback must be held in check by inhibition. To address the relative contribution of PV and SST neurons we characterized the intrinsic input-output differences between these classes of inhibitory neurons, and using experimental and theoretical methods show that the higher threshold and gain of PV leads to a dominant role in network stabilization.

Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases the task-irrelevant component influenced performance. RNN simulations revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioral findings. Over the course of training RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to- excitatory connections. Our results suggest that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

(1) Background: The aetiology of oral disease is multifactorial, involving genetic and environmental factors, including dietary ones. Bitter taste genetics may be related to oral health through dietary modulation or non-gustatory roles, including modulation of inflammation. Investigations of bitter taste and oral health associations to date have been restricted to specific polymorphisms, limited outcomes (caries), and age-groups (children), and links to inflammation remain to be elucidated. (2) Methods: A cross-sectional study (n = 65) investigated the correlations between bitter taste genotypes, oral health outcomes, and oral inflammation markers. Oral examinations were conducted, including saliva testing with evaluation of flow rate, pH, and buffering and antioxidant capacity (FRAP) and IL-1β, TNF-α, IL-6 levels. DNA was collected via buccal swabs and used to evaluate the presence of multiple bitter-taste receptor gene polymorphisms. (3) Results: The major allele for TAS2R4-rs2233998, TAS2R5-rs2227264, TAS2R50-rs1376251, and TAS2R9-rs3741845 was associated with a higher mean of unstimulated salivary flow rate, FRAP, TNF-α, IL-1β, and likelihood of filled teeth. Presence of the major allele for TAS2R4-rs2234001 and TAS2R9-rs3741845 was associated with lower means FRAP, TNF-α, IL-1β, DMFT index, and likelihood of missing teeth. (4) Conclusions: These findings suggest relationships between bitter-taste genotypes, oral health outcomes, and inflammatory markers. These findings justify the need for further studies that could help identify risk groups and develop novel agents for maintaining oral health.

SUMMARYChanges in the function of inhibitory interneurons (INs) during cortical development could contribute to the pathophysiology of neurodevelopmental disorders. Using all-optical in vivo approaches in postnatal mouse somatosensory cortex (S1), we find that parvalbumin (PV) IN precursors are hypoactive and decoupled from excitatory neurons in Fmr1−/− mice, a model of Fragile X Syndrome (FXS). This leads to a loss of PV-INs in both mice and humans with FXS. Increasing the activity of future PV-INs in neonatal Fmr1−/− mice restores PV density and ameliorates transcriptional dysregulation in S1, but not circuit dysfunction. Critically, administering a novel allosteric modulator of Kv3.1 channels after the S1 critical period does rescue circuit dynamics and tactile defensiveness. Symptoms in FXS and related disorders could be mitigated by targeting PV-INs.

The current national COVID-19 mortality rate for Black Americans is 2.1 times higher than that of Whites. In this commentary, we provide historical context on how structural racism undergirds multi-sector policies which contribute to racial health inequities such as those highlighted by the COVID-19 pandemic. We offer a concrete, actionable path forward to address structural racism and advance health equity for Black Americans through anti-racism, implicit bias, and cultural competency training; capacity building; community-based participatory research (CBPR) initiatives; validated metrics for longitudinal monitoring of efforts to address health disparities and the evaluation of those interventions; and advocacy for and empowerment of vulnerable communities. This necessitates a multi-pronged, coordinated approach led by clinicians; public health professionals; researchers; social scientists; policy-makers at all governmental levels; and local community leaders and stakeholders across the education, legal, social service, and economic sectors to proactively and systematically advance health equity for Black Americans across the USA.

ObjectivesPreterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment.MethodsThe ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N=847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and risk predictor/case management with pharmacological treatment (RP-MM, active). In the active arms, only subjects stratified as higher-risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects’ gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher’s exact test for neonatal morbidity/mortality (significance, pResultsThe model predicted improvements for all outcomes. RP-CM decreased neonatal and maternal hospital stay by 19% (p=0.029) and 8.5% (p=0.001), respectively; neonatal costs’ point estimate by 16% (p=0.098); and moderate-to-severe neonatal morbidity/mortality by 29% (p=0.025). RP-MM strengthened observed reductions and significance. Point estimates of benefit did not differ by race/ethnicity.ConclusionsModeled evaluation of a biomarker-based test-and-treat strategy in a diverse population predicts clinically and economically meaningful improvements in neonatal and maternal outcomes.Plain language summaryPreterm birth, defined as delivery before 37 weeks’ gestation, is the leading cause of illness and death in newborns. In the United States, more than 10% of infants is born prematurely, and this rate is substantially higher in lower-income, inner-city and Black populations. Prematurity associates with substantially increased risk of short- and long-term medical complications and can generate significant costs throughout the lives of affected children. Annual U.S. health care costs to manage short- and long-term prematurity complications are estimated to exceed $25 billion.Clinical interventions, including case management (increased patient outreach, education and specialist care), pharmacological treatment and their combination, can provide benefit to pregnancies at higher risk for preterm birth. Early and sensitive risk detection, however, remains a challenge.We have developed and validated a proteomic biomarker risk predictor for early identification of pregnancies at increased risk of preterm birth. The ACCORDANT study modeled treatments with real-world patient data from a racially and ethnically diverse U.S. population to compare the benefits of risk predictor testing plus clinical intervention for higher-risk pregnancies versus no testing and standard care. Measured outcomes included neonatal and maternal length of hospital stay, associated costs and neonatal morbidity and mortality. The model projected improved outcomes and reduced costs across all subjects, including ethnic and racial populations, when predicted higher-risk pregnancies were treated using case management with or without pharmacological treatment. The biomarker risk predictor shows high potential to be a clinically important component of risk stratification for pregnant women, leading to tangible gains in reducing the impact of preterm birth.