Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex

Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two core (A1 and R, representing primary areas) and two belt (Slow and Fast, representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in core areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the Fast area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the Slow stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the Fast stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in Slow area and temporally in Fast area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments.
New Speakers in the Irish Context: Heritage Language Maintenance Among Multilingual Migrants in Dublin, Ireland

A migrant “boom” in Ireland in the 2000s led to a 143% increase in non-Irish nationals, marking a shift from the country's predominantly monocultural past, to a future that is multicultural, and by extension, multilingual. This report focuses on the Polish and Chinese communities in Dublin, Ireland, investigating individuals' and families' attitudes toward heritage language maintenance and transmission, and the strategies and practices they employed. Drawing on recorded semi-structured interviews with 45 migrants, a content analysis reveals that heritage language maintenance is of paramount importance for first, second and “Generation 1.5” migrants. Many parents send their children to weekend schools. Differences emerge, however, in the two communities' views of the utility and value of the heritage language. Polish migrants' discourse centers on the importance of the language for sustaining ties with extended family, particularly when return migration is a possibility; whereas Chinese migrants focus on the economic benefits of competence in Chinese, which is viewed as a passport to more attractive job opportunities in Asia. All participants report using their heritage language on a daily basis, and place strong emphasis on maintaining literacy, through print and digital media. Social media sites also enable migrants to communicate through their heritage language and maintain a sense of community, either through direct interaction or “passive monitoring” of others. This global inter-connectedness allows migrants to view their linguistic competence not as bilingualism per se, but as a repertoire of “mobile resources,” significantly widening the scope of what could be termed a “digital classroom. ”
Projected Changes to Extreme Runoff and Precipitation Events From a Downscaled Simulation Over the Western United States

Extreme precipitation and runoff events, which often impact natural and social systems more than mean changes, generally occur over regional scales. Future climate projections can be used to estimate how the hydrologic cycle may change, but the coarse resolution of global climate models (GCMs) (>1°) makes it difficult to evaluate regional changes, such as over a single watershed. To estimate changes in hydroclimatic variables at finer spatial resolutions, we dynamically downscale the Community Climate System Model version 4 (CCSM4) with the Weather Research and Forecasting (WRF) regional climate model over the western United States at 9 km spatial resolution. By running WRF at a higher spatial resolution, we estimate future climate conditions, including 99% event magnitude, over 17 watersheds: the Columbia, Lower Colorado, Upper Colorado, the Upper Missouri/Yellowstone, and 12 basins draining the western slope of the Sierra Nevada in California. Over each basin, we compare a historical period (1996–2005) with mid-century (2041–2050) and end-century (2091–2100). From the WRF/CCSM simulations, most basins are projected to have earlier peaks in springtime streamflow. The Columbia and the Lower Colorado watersheds are both expected to experience more extreme wet days, with the 99th percentile of daily precipitation estimated to increase by over 10%. For the Upper Colorado, however, the 99th percentile of daily runoff is projected to decrease by over 30%. Basins in the northern and central Sierra Nevada are projected to have substantial increases in extreme runoff, with doubling of high flow event magnitude possible for some basins. By end-century, the contribution of high-magnitude runoff (>90th percentile) to total runoff is projected to increase from 46 to 56%, when averaged across all 12 Sierra Nevada basins. Though only one realization from a single GCM, the downscaled simulation presented here shows interesting results regarding how extreme events may change; these results can be tested by downscaling other global models with WRF to create an ensemble of dynamically downscaled future projections.
Theory of Mind Impairments Highlighted With an Ecological Performance-Based Test Indicate Behavioral Executive Deficits in Traumatic Brain Injury

Background: In view of the recent literature, the negative impact of traumatic brain injury (TBI) on social cognition remains a debated issue. On one hand, a considerable number of studies reported significant impairments in emotion recognition, empathy, moral reasoning, social problem solving, and mentalizing or theory of mind (ToM) abilities in patients with TBI. On the other hand, the ecological validity of social cognition tasks is still a matter of concern and debate for clinicians and researchers. Objectives: The objectives of the present study were 2-fold: (1) to assess social cognition in TBI with an ecological performance-based test which focuses on ToM ability, and (2) to study the relationship between performances on this task and behavioral disorders. To this end, 47 patients with moderate to severe TBI in the chronic stage were assessed with a ToM task, the Movie for the Assessment of Social Cognition (MASC), a film displaying social interactions in natural settings and asking for an evaluation of the emotions, thoughts, and intentions of the characters. Behavioral disorders were assessed with the Behavioral Dysexecutive Syndrome Inventory (BDSI), a structured interview of an informant in assessing changes compared with previous behavior in 12 domains. Results: Patients were significantly less accurate in mental state attribution than a demographically matched group of 38 healthy control subjects. Significant others of patients also reported more behavioral executive problems than controls' relatives on most of the domains of the BDSI. In addition, social cognition performance in the MASC was significantly correlated with behavioral dysexecutive problems rated by proxies on the BDSI. Conclusions: This study is the first to find association between impairments in mentalizing abilities in the MASC and behavioral impairments in patients with TBI, confirming the added value of this ecological task and that the recognition of social signals is a key element for adequate behavioral functioning.
Validation of Subjective Well-Being Measures Using Item Response Theory

Background: Subjective well-being refers to the extent to which a person believes or feels that her life is going well. It is considered as one of the best available proxies for a broader, more canonical form of well-being. For over 30 years, one important distinction in the conceptualization of subjective well-being is the contrast between more affective evaluations of biological emotional reactions and more cognitive evaluations of one’s life in relation to a psychologically self-imposed ideal. More recently, researchers have suggested the addition of harmony in life, comprising behavioral evaluations of how one is doing in a social context. Since measures used to assess subjective well-being are self-reports, often validated only using Classical Test Theory, our aim was to focus on the psychometric properties of the measures using Item Response Theory. Method: A total of 1000 participants responded to the Positive Affect Negative Affect Schedule. At random, half of the participants answered to the Satisfaction with Life Scale or to the Harmony in life Scale. First, we evaluate and provide enough evidence of unidimensionality for each scale. Next, we conducted graded response models to validate the psychometric properties of the subjective well-being scales. Results: All scales showed varied frequency item distribution, high discrimination values (Alphas), and had different difficulty parameters (Beta) on each response options. For example, we identified items that respondents found difficult to endorse at the highest/lowest point of the scales (e.g., “Proud” for positive affect; item 5, “If I could live my life over, I would change almost nothing,” for life satisfaction; and item 3, “I am in harmony,” for harmony in life). In addition, all scales could cover a good portion of the range of subjective well-being (Theta): −2.50 to 2.30 for positive affect, −1.00 to 3.50 for negative affect, −2.40 to 2.50 for life satisfaction, and −2.40 to 2.50 for harmony in life. Importantly, for all scales, there were weak reliability for respondents with extreme latent scores of subjective well-being. Conclusion: The affective component, especially low levels of negative affect, were less accurately measured, while both the cognitive and social component were covered to an equal degree. There was less reliability for respondents with extreme latent scores of subjective well-being. Thus, to improve reliability at the level of the scale, at the item level and at the level of the response scale for each item, we point out specific items that need to be modified or added. Moreover, the data presented here can be used as normative data for each of the subjective well-being constructs.
DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.
Female Body Dissatisfaction and Attentional Bias to Body Images Evaluated Using Visual Search

One factor, believed to predict body dissatisfaction is an individual’s propensity to attend to certain classes of human body image stimuli relative to other classes. These attentional biases have been evaluated using a range of paradigms, including dot-probe, eye-tracking and free view visual search, which have yielded a range of – often contradictory – findings. This study is the first to employ a classic compound visual search task to investigate the relationship between body dissatisfaction and attentional biases to images of underweight and with-overweight female bodies. Seventy-one undergraduate females, varying their degree of body dissatisfaction and Body Mass Index (BMI), searched for a horizontal or vertical target line among tilted lines. A separate female body image was presented within close proximity to each line. On average, faster search times were obtained when the target line was paired with a uniquely underweight or with-overweight body relative to neutral (average weight only) trials indicating that body weight-related images can effectively guide search. This congruent search effect was stronger for individuals with high eating restraint (a behavioral manifestation of body image disturbance) when search involved a uniquely underweight body. By contrast, individuals with high BMIs searched for lines more rapidly when paired with with-overweight rather than underweight bodies, than did individuals with lower BMIs. For incongruent trials – in which a unique body was paired with a distractor rather than the target – search times were indistinguishable from neutral trials, indicating that the deviant bodies neither compulsorily “captured” attention nor reduced participants’ ability to disengage their attention from either underweight or with-overweight bodies. These results imply the existence of attentional strategies which reflect one’s current body and goal-directed eating behaviors.
Decreased Task-Related HRV Is Associated With Inhibitory Dysfunction Through Functional Inter-Region Connectivity of PFC in Major Depressive Disorder

The regulation of the autonomic nervous system (ANS) can improve cognitive function in major depressive disorders (MDD). Heart rate variability (HRV) derives from the dynamic control of the ANS and reflects the balance between the activities of the sympathetic and parasympathetic nervous systems by measuring tiny changes in adjacent heart beats. Task-related HRV may reflect the association between the flexibility of cognition and ANS function. The study was to investigate the neural mechanism of interactions between ANS and cognitive function in MDD with Magnetoencephalography (MEG) measurements. Participants included 20 MDD patients and 18 healthy controls (HCs). All participants were measured with a go/no-go task MEG. HRV indices, the standard deviation of the average normal-to-normal (NN) interval calculated over short periods (SDANN) and the square root of the mean squared differences of successive NN intervals (RMSSD), were derived from the raw MEG data. Results showed that MDD patients showed decreased SDANN and RMSSD. In MDD patients, both resting-state and task-related RMSSD were related to inhibitory and control dysfunction. In the go/no-go task, many areas in the prefrontal cortex (PFC) are responsible for an individual’s inhibitory function. A brain MEG functional connectivity analysis revealed that there were significant differences in four brain regions within the prefrontal cortex (PFC) between MDD patients and HCs. Task-related RMSSD in HCs were related to the functional connectivity between the left middle frontal gyrus and the anterior cingulate cortex (ACC), while in MDD patients, these values were not related to the above functional connectivity but were related to the functional connectivity between the left middle frontal gyrus and insula. However, the resting-state RMSSD value was not related to these significant difference functional connectivity networks in all participants. It concludes that the decreased task-related HRV is associated with inhibitory dysfunction through functional inter-region connectivity in the PFC in MDD, and the task-related HRV can be used as an index of the association between MDD and autonomic dysregulation.
Estimating Mangrove Tree Biomass and Carbon Content: A Comparison of Forest Inventory Techniques and Drone Imagery

Mangroves provide many ecosystem services including a considerable capacity to sequester and store large amounts of carbon, both in the sediment and in the above-ground biomass. Assessment of mangrove above-ground carbon stock relies on accurate measurement of tree biomass, which traditionally involves collecting direct measurements from trees and relating these to biomass using allometric relationships. We investigated the potential to predict tree biomass using measurements derived from unmanned aerial vehicle (UAV), or drone, imagery. This approach has the potential to dramatically reduce time-consuming fieldwork, providing greater spatial survey coverage and return for effort, and may enable data to be collected in otherwise hazardous or inaccessible areas. We imaged an Avicennia marina (grey mangrove) stand using an RGB camera mounted on a UAV. The imaged trees were subsequently felled, enabling physical measurements to be taken for traditional biomass estimation techniques, as well as direct measurements of biomass and tissue carbon content. UAV image-based tree height measurements were highly accurate (R2 = 0.98). However, the variables that could be measured from the UAV imagery (tree height and canopy area) were poor predictors of tree biomass. Using the physical measurement data, we identified that trunk diameter is a key predictor of A. marina biomass. Unfortunately, trunk diameter cannot be directly measured from the UAV imagery, but it can be predicted (with some error) using models that incorporate other UAV image-based measurements, such as tree height and canopy area. However, reliance on second-order estimates of trunk diameter leads to increased uncertainty in the subsequent predictions of A. marina biomass, compared to using physical measurements of trunk diameter taken directly from the trees. Our study demonstrates that there is potential to use UAV-based imagery to measure mangrove A. marina tree structural characteristics and biomass. Further refinement of the relationship between UAV image-based measurements and tree diameter is needed to reduce error in biomass predictions. UAV image-based estimates can be made far more quickly and over extensive areas when compared to traditional data collection techniques and, with improved accuracy through further model-calibration, have the potential to be a powerful tool for mangrove biomass and carbon storage estimation.
Outcome of Enhanced Recovery After Surgery (ERAS) for Colorectal Surgery in Early Elderly and Late Elderly Patients.

INTRODUCTION This study aimed to determine the outcome of enhanced recovery after surgery (ERAS) programme in elderly colorectal surgery patients. MATERIALS AND METHODS Details and surgical outcomes of elderly patients undergoing elective colectomy and/or proctectomy according to ERAS protocol from 2011 to 2017 were retrospectively reviewed. Patients were divided into 2 groups: early elderly (EE, n = 107) aged 65-74 years old and late elderly (LE, n = 74) aged ≥75 years old. RESULTS This study included 181 patients. The LE group had poorer baseline characteristics, but the operative details in both groups were comparable. Overall complication and severe complication rates were 28% and 3.3%, respectively. The LE group had a higher overall complication rate (38% vs 22%; P = 0.016) but comparable rate of severe complications (2.7% vs 3.7%; P = 1.00). Median postoperative stay was 4 days (interquartile range [IQR], 4-6 days) and it was not significantly different between both groups (5 days for LE vs 4 days for EE; P = 0.176). No difference was seen in time to gastrointestinal recovery and 30-day mortality or readmission between both groups. Overall compliance with ERAS protocol was 76% (IQR, 65-82%) and it did not vary significantly between the LE (71%) and EE (76%) groups (P = 0.301). However, the LE group had lower compliance with fluid management, nutrition therapy and use of multimodal analgesia. CONCLUSION ERAS is a safe and effective protocol that can be used in EE and LE colorectal surgery patients.
HPA-axis multilocus genetic profile score moderates the association between maternal prenatal perceived stress and offspring depression in early adulthood

Abstract Maternal stress during pregnancy can cause alterations to the fetal hypothalamus–pituitary–adrenal (HPA) axis, a phenomenon known as fetal programming that may have lasting effects on offspring outcomes, including depression. Evidence suggests that these effects may vary with respect to the offspring's genetic risk. Nonetheless, few studies have examined these effects into adulthood, when risk for depression onset is highest. The present study builds upon the extant literature by examining the interaction of maternal prenatal perceived stress (MPPS) and offspring HPA-axis polygenic risk to predict offspring depression in early adulthood. A total of 381 mother–child dyads participated in a prospective, longitudinal study that spanned from pregnancy until offspring were 20 years of age. Polygenic risk was defined by a multilocus genetic profile score (MGPS) that reflected the additive risk of three HPA-axis candidate genes. The results indicated that the interaction of MPPS and HPA-axis MGPS confers risk for offspring depression at age 20, in line with the differential susceptibility model. This interaction may be specific to prenatal stress, as maternal stress during early childhood did not interact with genetic risk to predict depression. These findings provide the first evidence that genetic variants that are associated with the HPA axis may act in a polygenic, additive fashion to moderate the association between fetal programming and adult depression.