Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of Bacterial Community and Fermentation Quality in Leymus chinensis Silage Treated With Lactic Acid Bacteria and/or Water

This study aimed to reveal the bacterial community and fermentation quality of Leymus chinensis silage during the fermentation process. L. chinensis was harvested at the heading stage, and ensiled with lactic acid bacteria (LAB, L), water (W), or a combination of both (LW) in vacuum-sealed plastic bags. As a control silage, untreated L. chinensis silage was also assessed. The samples were taken at 0, 5, 15, 35, and 60 days after ensiling. The bacterial community structure was assessed by plate cultivation and Illumina sequencing, and the fermentation parameters were also analyzed. Fresh L. chinensis contained low moisture (509 g/kg) and LAB (3.64 log colony-forming units/g fresh weight). Control silage displayed higher pH and lower lactic acid (LA) than other treatments during ensilage (p < 0.05); moreover, LW-treatment had lower pH from 5 to 35 days and greater LA at 5 days than L- and W-treatments (p < 0.05). During the fermentation process, Lactobacillus in L- and LW-treatments was the most dominant bacterial genus (>97%), had higher abundance than that in control silage and W-treatment (p < 0.05), and correlated negatively with other main genera and pH, and positively with LA and acetic acid (p < 0.05). Moreover, Lactobacillus had considerable abundance in W-treatment from 5 to 15 days (81.38–85.86%). Enterobacteriaceae had the most abundance among bacteria in control silage during ensiling (49.31–69.34%), and in W-treatment from 35 to 60 days (47.49–54.15%). The L-, W-, and LW-treatments displayed the aggregated bacterial community at 5 and 15 days, with W-treatment diverging from L- and LW-treatments at 35 and 60 days. Overall, the low moisture and/or insufficient LAB in fresh L. chinensis led to Enterobacteriaceae dominating bacterial community and contributing to the high pH and low LA in control silage during the fermentation process. Applying L, W, or LW contributed to Lactobacillus succession, LA production, and pH reduction during early stage of fermentation; moreover, treating with L and LW displayed more efficiency. Lactobacillus dominated the entire ensilage process in L- and LW-treatments and the early stage of fermentation in W-treatment, and contributed to the satisfactory fermentation quality of L. chinensis silage. The L- and LW-treatments displayed a similar pattern of bacterial succession during ensiling.
A Framework for Generating High Spatiotemporal Resolution Land Surface Temperature in Heterogeneous Areas

Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas.
Profiling Antibody Response Patterns in COVID-19: Spike S1-Reactive IgA Signature in the Evolution of SARS-CoV-2 Infection

This contribution explores in a new statistical perspective the antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 141 coronavirus disease 2019 (COVID-19) patients exhibiting a broad range of clinical manifestations. This cohort accurately reflects the characteristics of the first wave of the SARS-CoV-2 pandemic in Italy. We determined the IgM, IgA, and IgG levels towards SARS-CoV-2 S1, S2, and NP antigens, evaluating their neutralizing activity and relationship with clinical signatures. Moreover, we longitudinally followed 72 patients up to 9 months postsymptoms onset to study the persistence of the levels of antibodies. Our results showed that the majority of COVID-19 patients developed an early virus-specific antibody response. The magnitude and the neutralizing properties of the response were heterogeneous regardless of the severity of the disease. Antibody levels dropped over time, even though spike reactive IgG and IgA were still detectable up to 9 months. Early baseline antibody levels were key drivers of the subsequent antibody production and the long-lasting protection against SARS-CoV-2. Importantly, we identified anti-S1 IgA as a good surrogate marker to predict the clinical course of COVID-19. Characterizing the antibody response after SARS-CoV-2 infection is relevant for the early clinical management of patients as soon as they are diagnosed and for implementing the current vaccination strategies.
Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.
Sex Role Reversal and High Frequency of Social Polyandry in the Pheasant-Tailed Jacana (Hydrophasianus chirurgus)

In a few species, males invest more than females in parental care while the females invest in mating competition and producing multiple broods for several mates. Species in the family Jacanidae are commonly used for studying this type of breeding system (called sex-role reversal), and previous studies found discrepancies and variation between species in the expected characteristics of reversed sex roles. Yet, a better understanding of sex role differences in breeding behavior in such species is crucial for disentangling possible evolutionary mechanisms leading to this peculiar breeding system. Sex-role reversal in the pheasant-tailed jacana Hydrophasianus chirurgus has been documented long time ago. Since the very early observation of this species, however, there was no attempt to provide a comprehensive and quantitative description of their breeding. This study aims to fill these knowledge gaps by investigating the sex role differences in the breeding behavior of pheasant-tailed jacanas, by observing and monitoring a breeding population in Taiwan. We focused on three main characteristics of sex-role reversal: (1) competition between females for access to males, such as agonistic and courtship behaviors, (2) polyandrous mating, and (3) male-only care. As expected, we found that females provide most of the territory defense toward conspecifics. Males also participated in agonistic behaviors, although less frequently than females. Furthermore, contrary to what was expected, we found that males spent more time than females on courtship behavior. Polyandrous females performed mating and laying sequentially with different mates but maintained the pair bonds simultaneously with multiple males. For the first time for the species, we could estimate that the average number of mates per female (i.e., degree of polyandry) was 2.4 and that at least 81.8% of the females in the population were polyandrous. Finally, our observations corroborated that brood care is predominantly provided by males, nevertheless females were also participating to some degree in brood attendance but never in direct care (i.e., brooding). This study highlights that some aspects of polyandrous breeding might deviate from stereotyped view on sex-role reversal, and stress the importance of further within species and comparative studies in order to fully understand the mechanisms leading to sex-role reversal.
A Review of the Potential of Virtual Walking Techniques for Gait Rehabilitation

Virtual reality (VR) technology has emerged as a promising tool for studying and rehabilitating gait disturbances in different cohorts of patients (such as Parkinson's disease, post-stroke, or other neurological disorders) as it allows patients to be engaged in an immersive and artificial environment, which can be designed to address the particular needs of each individual. This review demonstrates the state of the art in applications of virtual walking techniques and related technologies for gait therapy and rehabilitation of people with movement disorders makes recommendations for future research and discusses the use of VR in the clinic. However, the potential for using these techniques in gait rehabilitation is to provide a more personalized approach by simulate the experience of natural walking, while patients with neurological disorders are maintained localized in the real world. The goal of our work is to investigate how the human nervous system controls movement in health and neurodegenerative disease.
Reliability Evaluation of the Joint Observation of Cloud Top Height by FY-4A and HIMAWARI-8

It is well known that the measurement of cloud top height (CTH) is important, and a geostationary satellite is an important measurement method. However, it is difficult for a single geostationary satellite to observe the global CTH, so joint observation by multiple satellites is imperative. We used both active and passive sensors to evaluate the reliability of joint observation of geostationary satellites, which includes consistency and accuracy. We analyzed the error of CTH of FY-4A and HIMAWARI-8 and the consistency between the two satellites and conducted research on the problem of missing measurement (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has CTH data, but FY-4A/HIMAWARI-8 does not) of the two satellites. The results show that FY-4A and HIMAWARI-8 have good consistency and can be jointly observed, but the measurement of CTH of FY-4A and HIMAWARI-8 has large errors, and the error of FY-4A is greater than that of HIMAWIRI-8. The error of CTH is affected by the CTH, cloud optical thickness (COT) and cloud type, and the consistency between the two satellites is mainly affected by the cloud type. FY-4A and HIMAWARI-8 have the problem of missing measurement. The missing rate of HIMAWARI-8 is greater than that of FY-4A, and the missing rate is not affected by the CTH, COT and surface type. Therefore, although FY-4A and HIMAWARI-8 have good consistency, the error of CTH and the problem of missing measurement still limit the reliability of their joint observation.
Does Embodiment of Verbs Influence Predicate Metaphor Processing in a Second Language? Evidence From Picture Priming

Distinct from nominal metaphors, predicate metaphors entail metaphorical abstraction from concrete verbs, which generally involve more action and stronger motor simulation than nouns. It remains unclear whether and how the concrete, embodied aspects of verbs are connected with abstract, disembodied thinking in the brains of L2 learners. Since English predicate metaphors are unfamiliar to Chinese L2 learners, the study of embodiment effect on English predicate metaphor processing may provide new evidence for embodied cognition and categorization models that remain controversial, and offer practical insights into L2 metaphor processing and pedagogy. Hence, we aim to investigate whether the embodiment of verbs, via the activation of sensorimotor information, influences two groups of L2 learners during their comprehension of conventional and novel predicate metaphors. The results show a significant effect of embodiment: a stronger facilitation for novel predicate metaphors in both higher-level and lower-level groups, and a weaker facilitation for conventional predicate metaphors in the lower-level group. The findings demonstrate preliminary evidence for a graded effect of embodiment on predicate metaphors processing, modulated by L2 proficiency and metaphor novelty. The study supports a hybrid view of embodied cognition and reveals that sensorimotor aspects of verbs may be the intermediate entity involved in the indirect categorization.
Subjective Memory Complaints and Decision Making in Young and Older Adults: An Event-Related Potential Study

Subjective memory complaints (SMCs) may affect decision-making processes. This study aimed to investigate the neuronal correlates of feedback processing during a decision-making task in young and older adults with and without SMCs. Event-related potentials and behavioral performance during the Iowa gambling task were recorded in a total of 136 participants (65 young adults, 71 older adults). The participants were divided into two groups according to their SMCs (with SMCs: n = 60, without SMCs: n = 76). Feedback-related negativity (FRN) and P3 were analyzed in the feedback stage of the decision-making process. Older adults with SMCs scored worse in the ambiguity phase than older adults without SMCs. The FRN latency was longer for losses in older people with SMCs than in older people without SMCs in the first block. No significant differences between young and older adults with and without SMCs were observed in the other ERP measures. Compared to young adults, older adults showed delayed latency in the FRN component and reduced amplitudes and delayed latency in the P3 component. In conclusion, older people with SMCs present deficits in the decision-making process. These deficits are observed at the behavioral level, but also in neural mechanisms of early feedback processing of negative outcomes.
A Human-in-the-loop Framework to Construct Context-dependent Mathematical Formulations of Fairness

Despite the recent surge of interest in designing and guaranteeing mathematical formulations of fairness, virtually all existing notions of algorithmic fairness fail to be adaptable to the intricacies and nuances of the decision-making context at hand. We argue that capturing such factors is an inherently human task, as it requires knowledge of the social background in which machine learning tools impact real people's outcomes and a deep understanding of the ramifications of automated decisions for decision subjects and society. In this work, we present a framework to construct a context-dependent mathematical formulation of fairness utilizing people's judgment of fairness. We utilize the theoretical model of Heidari et al. (2019)---which shows that most existing formulations of algorithmic fairness are special cases of economic models of Equality of Opportunity (EOP)---and present a practical human-in-the-loop approach to pinpoint the fairness notion in the EOP family that best captures people's perception of fairness in the given context. To illustrate our framework, we run human-subject experiments designed to learn the parameters of Heidari et al.'s EOP model (including circumstance, desert, and utility) in a hypothetical recidivism decision-making scenario. Our work takes an initial step toward democratizing the formulation of fairness and utilizing human-judgment to tackle a fundamental shortcoming of automated decision-making systems: that the machine on its own is incapable of understanding and processing the human aspects and social context of its decisions.
A multiple testing framework for diagnostic accuracy studies with co‐primary endpoints

Major advances have been made regarding the utilization of machine learning techniques for disease diagnosis and prognosis based on complex and high‐dimensional data. Despite all justified enthusiasm, overoptimistic assessments of predictive performance are still common in this area. However, predictive models and medical devices based on such models should undergo a throughout evaluation before being implemented into clinical practice. In this work, we propose a multiple testing framework for (comparative) phase III diagnostic accuracy studies with sensitivity and specificity as co‐primary endpoints. Our approach challenges the frequent recommendation to strictly separate model selection and evaluation, that is, to only assess a single diagnostic model in the evaluation study. We show that our parametric simultaneous test procedure asymptotically allows strong control of the family‐wise error rate. A multiplicity correction is also available for point and interval estimates. Moreover, we demonstrate in an extensive simulation study that our multiple testing strategy on average leads to a better final diagnostic model and increased statistical power. To plan such studies, we propose a Bayesian approach to determine the optimal number of models to evaluate simultaneously. For this purpose, our algorithm optimizes the expected final model performance given previous (hold‐out) data from the model development phase. We conclude that an assessment of multiple promising diagnostic models in the same evaluation study has several advantages when suitable adjustments for multiple comparisons are employed.
Metastatic Pattern Discriminates Survival Benefit of Type of Surgery in Patients With De Novo Stage IV Breast Cancer Based on SEER Database

Background: The role of surgery and surgery type in de novo stage IV breast cancer (BC) is unclear. Methods: We carried out a retrospective cohort study that included the data of 4,108 individuals with de novo stage IV BC abstracted from SEER (Surveillance, Epidemiology, and End Results) data resource from 2010 to 2015. The patients were stratified into the non-surgery group, breast-conserving (BCS) surgery group, and mastectomy group. Inverse probability propensity score weighting (IPTW) was then used to balance clinicopathologic factors. Overall survival (OS), as well as the breast cancer-specific survival (BCSS), was assessed in the three groups using Kaplan–Meier analysis and COX model. Subgroups were stratified by metastatic sites for analysis. Results: Of the 4,108 patients, 48.5% received surgery and were stratified into the BCS group (574 cases) and mastectomy group (1,419 cases). After IPTW balance demographic and clinicopathologic factors, BCS and mastectomy groups had better OS (BCS group: HR, 0.61; 95% CI: 0.49–0.75; mastectomy group: HR, 0.7; 95% CI: 0.63–0.79) and BCSS (BCS group: HR, 0.6; 95% CI, 0.47–0.75; mastectomy group: HR, 0.71; 95% CI, 0.63–0.81) than the non-therapy group. Subgroup analyses revealed that BCS, rather than mastectomy, was linked to better OS (HR, 0.66; 95% CI: 0.48–0.91) and BCSS (HR, 0.63; 95% CI: 0.45–0.89) for patients with bone-only metastasis. For patients with viscera metastasis or bone+viscera metastases, BCS achieved similar OS (viscera metastasis: HR, 1.05; 95% CI: 0.74–1.48; bone+viscera metastases: HR, 1.01; 95% CI: 0.64–1.61) and BCSS (viscera metastasis: HR, 0.94; 95% CI: 0.64–1.38; bone+viscera metastases: HR, 1.06; 95% CI: 0.66–1.73) in contrast with mastectomy. Conclusions: Local surgery for patients with distant metastasis (DS) exhibited a remarkable survival advantage in contrast with non-operative management. BCS may have more survival benefits for patients with de novo stage IV BC with bone-only metastasis than other metastatic sites. Decisions on de novo stage IV BC primary surgery should be tailored to the metastatic pattern.
Developmental Factors That Predict Head Movement During Resting-State Functional Magnetic Resonance Imaging in 3–7-Year-Old Stuttering and Non-stuttering Children

Early childhood marks a period of dynamic neurocognitive development. Preschool-age coincides with the onset of many childhood disorders and is a developmental period that is frequently studied to determine markers of neurodevelopmental disorders. Magnetic resonance imaging (MRI) is often used to explore typical brain development and the neural bases of neurodevelopmental disorders. However, acquiring high-quality MRI data in young children is challenging. The enclosed space and loud sounds can trigger unease and cause excessive head movement. A better understanding of potential factors that predict successful MRI acquisition would increase chances of collecting useable data in children with and without neurodevelopmental disorders. We investigated whether age, sex, stuttering status, and childhood temperament as measured using the Child Behavioral Questionnaire, could predict movement extent during resting-state functional MRI (rs-fMRI) in 76 children aged 3–7 years, including 42 children who stutter (CWS). We found that age, sex, and temperament factors could predict motion during rs-fMRI scans. The CWS were not found to differ significantly from controls in temperament or head movement during scanning. Sex and age were significant predictors of movement. However, age was no longer a significant predictor when temperament, specifically effortful control, was considered. Controlling for age, boys with higher effortful control scores moved less during rs-fMRI procedures. Additionally, boys who showed higher negative affectivity showed a trend for greater movement. Considering temperament factors in addition to age and sex may help predict the success of acquiring useable rs-fMRI (and likely general brain MRI) data in young children in MR neuroimaging.