Miracle-dz/UniFAR-FTUs huggingface dataset:
Song Popularity Prediction using Ordinal Classiﬁcation

Predicting a song’s success based on audio descriptors before its release is an important task in the music industry, which has been tackled in many ways. Most approaches utilize audio descriptors to predict a song’s success, typically captured by either chart positions or listening counts. The popularity prediction task is then either modeled as a regression task, where the popularity metric is precisely predicted, or as a classiﬁcation task by, e.g., transforming the popularity task to distinct classes such as hits and non-hits. However, this way of modeling the task neglects that most popularity measures form an ordinal scale. While classiﬁcation ignores the order, regression assumes that the data is in interval (or ratio) scale. Therefore, we propose to model the task of popularity prediction as an ordinal clas-siﬁcation task. Further, we propose an approach that utilizes the relative order of classes in an ordinal classiﬁcation setup to predict the popularity (class) of songs. Our presented approach requires a machine learning model able to predict the relative order of two pieces of music, and hence can ﬂexibly be applied using many types of predictors. Furthermore, we investigate how different ways of mapping the underlying popularity metrics to ordinal classes inﬂuence our model. We compare the proposed approach with regression as well as classiﬁcation models and show its robustness w.r. t. different numbers of ordinal classes and the distribution of the number of songs assigned to them. Additionally, we show that, for some prediction settings, our approach results in a better predictive performance than classical regression and classiﬁcation approaches, while it achieves similar predictive performance on other settings.
Forest Composition: More Altered by Future Climate Change than by Euro-American Settlement in Western New York and Pennsylvania?

The amount of forest compositional change that occurred due to Euro-American settlement over the past two centuries is compared with changes simulated to occur in the future under 2X and 3.5X atmospheric CO2 scenarios. The comparison employs data from presettlement land survey records, modern forest inventory data, and future predictions from niche-based species distribution models. Comparisons are made in four independent study areas in western Pennsylvania and New York. Forest compositional changes in the recent past, attributed largely to anthropogenic factors other than climate change, are intermediate in size to changes predicted to occur as the result of climate change under the 2X CO2 and 3.5X CO2 scenarios. Results are similar across the four study areas, and are robust to variations in data collection and compilation methods. These results disagree with previous pollen-based estimates that suggested a greater relative influence of a 2X CO2 climate change, but do indicate that a 3.5X CO2 climate change may cause greater changes in forest composition than has already occurred due to anthropogenic impacts.
Patient Experience Journal Patient

The patient-centered medical home (PCMH), an innovative primary care model that fosters a stronger, more personal patient-doctor relationship than traditional health care models, should be particularly well suited for the treatment of chronic conditions such as diabetes that require ongoing management by both patients and providers. Despite growing research on the effectiveness of PCMHs in diabetes care, relatively little attention has been given to diabetic patients’ experiences. This qualitative study examines diabetic patients’ experiences at one PCMH setting, using in interviews to understand patients’ perspectives of the shared power and responsibility between patient and provider in their diabetes care. Our results suggest that even when patients feel comfortable and cared for by the physician, they may choose to take a more passive role in discussions about their diabetes in the clinical encounter because 1) they may see diabetes as a secondary concern, or 2) they may be con over the physical domain of the illness and the patient’s responsibility over the lifestyle domain of the illness. Thus, in order to build a relationship that is characterized by shared powe physicians should not only strive to create an also need to be aware of patient’s preconceptions about the clinical encounter. encourage more active patient participation in the clinical encounter and support patients more effectively in their self management journey.
Signiﬁcant Wave Height Estimation from Joint CYGNSS DDMA and LES Observations

: The signiﬁcant wave height (SWH) of oceans is the main parameter in describing the sea state, which has been widely used in the establishment of ocean process models and the ﬁeld of navigation and transportation. However, traditional methods such as satellite radar altimeters and buoys cannot achieve SWH estimations with high spatial and temporal resolution. Recently, the spaceborne Global Navigation Satellite System reﬂectometry (GNSS-R) has provided an opportunity to estimate SWH with a rapid global coverage and high temporal resolution observations, particularly with the Cyclone Global Navigation Satellite System (CYGNSS) mission. In this paper, SWH was estimated using the polynomial function relationship between SWH from ERA5 and Delay-Doppler Map Average (DDMA) as well as Leading Edge Slope (LES) from CYGNSS data. Then, the SWH estimated from CYGNSS data was validated by ERA-Interim data, AVISO data, and buoy data. The results showed that the average correlation coefﬁcient of CYGNSS SWH was 0.945, and the average RMSE was 0.257 m when compared to the ERA-Interim SWH data. The RMSE was 0.423 m and the correlation coefﬁcient was 0.849 when compared with the AVISO SWH. The correlation coefﬁcient with the buoy data was 0.907, and the RMSE was 0.247 m. This method can provide suitable SWH estimation data for ocean dynamics research and ocean environment prediction.
Digital Commons @ East Tennessee State University Digital Commons @ East Tennessee State University

Coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving and is a serious public health threat worldwide. Timely and effective control of the pandemic is highly dependent on preventive approaches. Perception of risk is a major determinant of health behavior. The current study explores the association between actual risk and perceived risk for one ’ s self, family/friends and friends, and community. A questionnaire was administered to participants in Central Appalachia ( n = 102). The actual risk was based on the number of chronic conditions of the following conditions: hypertension, heart disease, cancer, diabetes, and chronic obstructive pulmonary disease. Participants were also queried about their perception of risk for COVID-19. Generalized Linear Models were used to independently evaluate the likelihood of perceived risk for one ’ s: self, family/friends, and community, based on actual risk. Actual risk for COVID-19 was signi ﬁ cantly associated with higher likelihood of higher perception of risk for one ’ s self ( b = 0.24; p = 0.04), but not with one ’ s family/friends ( b = 0.05; p = 0.68), or one ’ s community ( b = 0.14; p = 0.16). No health insurance was negatively associated with perception of risk for self ( b = − 0.59; p = 0.04) and family/friends ( b = − 0.92; p < 0.001). Male gender ( b = − 0.47; p = 0.01) was also negatively associated with perception of risk for family/friends. In conclusion, individuals ’ actual risk for COVID-19 is associated with their own perception of risk. This indicates that one ’ s perception of risk for COVID-19 is greater for their own health compared to their family/friends or the community. Therefore, monitoring and following up with chronic disease patients and addressing their lack of awareness of
The Protective Effect of Plant-Based Diets in Urbanizing India

As the nutrition transition unfolds, low-fat, fiber-rich traditional foods are displaced by the ‘meat - sweet’ diet with more animal protein, refined sugars and fats, and processed foods. Together with reduced physical activity, these dietary changes lead to increases in obesity and other non-communicable diseases. Yet in India, the dynamics of the nutrition transition confront the country ’ s long history of vegetarianism. In this context, it is unclear if plant-based diets have protective effects for health. This paper thus aims to (1) estimate the protective effect of vegetarianism against unhealthy BMI, disentangling this from household wealth and urban status; and (2) evaluate the extent to which this plant-based protective effect operates differentially by place of residence. Findings contribute to our understanding of how vegetarianism operates with respect to health outcomes in the non-Western context amidst intensifying pressures towards diet Westernization.
Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics

The time-delay problem, which is introduced by the response time of hardware for correction, is a critical and non-ignorable problem of adaptive optics (AO) systems. It will result in significant wavefront correction errors while turbulence changes severely or system responses slowly. Predictive AO is proposed to alleviate the time-delay problem for more accurate and stable corrections in the real time-varying atmosphere. However, the existing prediction approaches either lack the ability to extract non-linear temporal features, or overlook the authenticity of spatial features during prediction, leading to poor robustness in generalization. Here, we propose a mixed graph neural network (MGNN) for spatiotemporal wavefront prediction. The MGNN introduces the Zernike polynomial and takes its inherent covariance matrix as physical constraints. It takes advantage of conventional convolutional layers and graph convolutional layers for temporal feature catch and spatial feature analysis, respectively. In particular, the graph constraints from the covariance matrix and the weight learning of the transformation matrix promote the establishment of a realistic internal spatial pattern from limited data. Furthermore, its prediction accuracy and robustness to varying unknown turbulences, including the generalization from simulation to experiment, are all discussed and verified. In experimental verification, the MGNN trained with simulated data can achieve an approximate effect of that trained with real turbulence. By comparing it with two conventional methods, the demonstrated performance of the proposed method is superior to the conventional AO in terms of root mean square error (RMS). With the prediction of the MGNN, the mean and standard deviation of RMS in the conventional AO are reduced by 54.2% and 58.6% at most, respectively. The stable prediction performance makes it suitable for wavefront predictive correction in astronomical observation, laser communication, and microscopic imaging
Serotonin signals through postsynaptic G a q , Trio RhoGEF, and diacylglycerol to promote Caenorhabditis elegans egg-laying circuit activity and behavior

Activated G a q signals through phospholipase-C b and Trio, a Rho GTPase exchange factor (RhoGEF), but how these distinct effector pathways promote cellular responses to neurotransmitters like serotonin remains poorly understood. We used the egg-laying behavior circuit of Caenorhabditis elegans to determine whether phospholipase-C b and Trio mediate serotonin and G a q signaling through independent or related biochemical pathways. Our genetic rescue experiments suggest that phospholipase-C b functions in neurons while Trio Rho GTPase exchange factor functions in both neurons and the postsynaptic vulval muscles. While G a q , phospholipase-C b , and Trio Rho GTPase exchange factor mutants fail to lay eggs in response to serotonin, optogenetic stimulation of the serotonin-releasing HSN neurons restores egg laying only in phospholipase-C b mutants. Phospholipase-C b mutants showed vulval muscle Ca 2 þ transients while strong G a q and Trio Rho GTPase exchange factor mutants had little or no vulval muscle Ca 2 þ activity. Treatment with phorbol 12-myristate 13-acetate that mimics 1,2-diacylglycerol, a product of PIP 2 hydrolysis, rescued egg-laying circuit activity and behavior defects of G a q signaling mutants, suggesting both phospholipase-C and Rho signaling promote synaptic transmission and egg laying via modulation of 1,2-diacyl-glycerol levels. 1,2-Diacylglycerol activates effectors including UNC-13; however, we ﬁnd that phorbol esters, but not serotonin, stimulate egg laying in unc-13 and phospholipase-C b mutants. These results support a model where serotonin signaling through G a q , phospholi-pase-C b , and UNC-13 promotes neurotransmitter release, and that serotonin also signals through G a q , Trio Rho GTPase exchange factor, and an unidentiﬁed, phorbol 12-myristate 13-acetate-responsive effector to promote postsynaptic muscle excitability. Thus, the same neuromodulator serotonin can signal in distinct cells and effector pathways to coordinate activation of a motor behavior circuit.
Adaptation and Preliminary Validation of the Self-Compassion Scale in a Chilean Context

Background: Self-compassion as a concept has only recently been incorporated to the clinical and research fields, and there are evidence that it is an important factor in reducing symptoms and increasing psychological well-being. Methods: The purpose of this study was to adapt the Self-Compassion Scale (SCS) to Spanish and to gather validity evidence for the translated instrument. The study was conducted in three phases: In phase one, the scale was translated and linguistically adapted into Spanish under supervision of a bilingual panel of experts. During phase two, a pilot test of the instrument was conducted with a sample of 10 participants to determine if the instructions and questions were clear and understandable. After making pertinent changes, a battery of tests and an identification sheet were presented to a final sample consisting of 268 participants. Finally, a test-retest was performed 14 days later to assess internal consistency, along with a confirmatory factor analysis (CFA) aimed to determine construct validity. Results: The analysis produced a solution in four factors that explains 49.88% of the common variance. Cronbach’s α was an adequate levels of consistency (α = .89), and the four subscales achieved too adequate values (α = .809 for the first factor, α = .83 for the second, α = .71 for the third and α = .79 for four factor). Correlation between the SCS and the other scales exhibited a significant results. With Negative Affect (PANAS -) SCS has a significant negative correlation, whereas with Positive Affects (PANAS +), Mindfulness (FFMQ) and Self-Esteem (Rosenberg’s Self-Esteem Scale) has a significant positive correlation. Conclusion: This preliminary results showed that the Spanish Chilean version of the Self-Compassion Scale (SCS) is reliable and has internal validity.
Comparison of the Fracture Resistance of Root-Canal-Treated Premolars Obturated With Dissimilar Materials: An In Vitro Study

Introduction The utilization of endodontic sealers is of paramount importance in ensuring the sustained efficacy and resilience of endodontic treatment. The primary objective of the research was to appraise and contrast the fracture resistance (FR) of three distinct categories of endodontic sealing materials that are frequently employed in the context of endodontic therapy. Materials and methods This research used an in vitro experimental design. Sixty single-rooted human teeth indicated for extraction were utilized according to established protocols. These teeth were then arbitrarily divided into four piles, one for each of the four possible sealants. All specimens were put through a standardized thermal cycling procedure to simulate clinical conditions after the root canals were obturated. The subsequent step involved testing the FR of each group by utilizing a universal testing machine up until failure. Any statistically significant difference in FR among the three sealing materials was identified through appropriate statistical analysis. Results Group 1, which utilized a particular sealing material, exhibited the highest mean fracture resistance, measuring at an impressive 1198.33 ± 321.4 Newtons (N). A post hoc analysis was done to see the exact differences between each group and statistically significant differences between Groups 1 and 2 (p<0.05), Groups 1 and 4 (p<0.05), and Groups 3 and 4 (p<0.001) were observed. Conclusion The FR of Group 1 specimens were noticeably greater than those of Group 2 and Group 4 while the FR of Group 3 specimens was more than that of the Group 4 specimens. This study provides important insights into the fracture resistance of various endodontic sealing materials.
Influence of Tumor-Treating Fields in a Young Patient With Primary Spinal Glioblastoma Multiforme (GBM): A Case Report of a Rare Tumor

We present a 31-year-old female patient with primary glioblastoma multiforme (GBM) of the thoracic spine, diagnosed in approximately mid-2020. Her symptoms began several months prior with right foot paresthesia, which progressed to neuropathy ascending from her distal to proximal right lower extremity. Over several months, she developed lumbo-thoracic throbbing pain, which was dermatomal radiating anteriorly. Her pain worsened with activity. A thoracic spine MRI showed a focus of abnormal intradural intramedullary enhancement present from the T10-T11 disc level to the T12-L1 disc level, producing a large amount of edema within the cord. She underwent a gross total surgical resection. The patient had WHO Grade IV spinal GBM per histopathology. The patient received adjuvant concurrent radiation therapy and temozolomide chemotherapy. She continues with maintenance temozolomide along with the compassionate use of Novocure alternating electrical field therapy for the spine. She is being monitored closely by a multi-specialty team. At 32 months post-radiation therapy, her disease is stable with no evidence of progression. She has made significant improvements in her ambulation and symptoms. While GBM is most commonly intracranial, primary spinal GBM is relatively rare. Although established treatment guidelines exist for supratentorial GBM, treatment protocol choices for spinal GBM remain controversial but typically mirror those used for intracranial GBM and include surgery, radiation therapy, and chemotherapy. Alternating electrical field therapy, also known as tumor-treating fields (TTFields), is indicated for adjuvant treatment of intracranial GBM. While further studies of TTFields in spinal GBM are needed, TTFields appear to be a safe adjunct treatment for spinal GBM. Further studies are still needed aimed at finding an improved treatment for spinal GBM.
Estimation of QZSS diﬀerential code biases using QZSS/GPS combined observations from MGEX

As an important error source in Global Navigation Satellite System (GNSS) positioning and ionospheric modeling, the diﬀerential code biases (DCB) need to be estimated accurately, e.g., the regional Quasi-Zenith satellite system (QZSS). In this paper, the DCB of QZSS is estimated by adopting the global ionospheric modeling method based on QZSS/GPS combined observations from Multi-GNSS experiment (MGEX). The performance of QZSS satellite and receiver DCB is analyzed with observations from day of year (DOY) 275–364, 2018. Good agreement between our estimated QZSS satellite DCB and the products from DLR and CAS is obtained. The bias and root mean square (RMS) of DCB are mostly within ±0.3 ns. The day-to-day ﬂuctuation of the DCB time series is less than 0.5 ns with about 96% of the cases for all satellites. However, the receiver DCB is a little less stable than satellite DCB, and their standard deviations (STDs) are within 1.9 ns. The result shows that the stability of the receiver DCBs is not signiﬁcantly related to the types of receiver or antenna. (cid:1) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
Tectonic Controls on the Endowment of Neoarchean Cratons in Volcanic-Hosted Massive Sulfide Deposits: Evidence from Lead and Neodymium Isotopes

Review and analysis of initial lead isotope ratios from Archean volcanic-hosted massive sulfide (VHMS) and lode gold deposits and of neodymium isotopes from igneous rocks from the geologic provinces that host these deposits identifies systematic spatial and temporal patterns, both within and between the provinces. The Abitibi-Wawa subprovince of the Superior province is characterized by highly juvenile lead and neodymium. Most other Archean provinces, however, are characterized by more evolved isotopes, although domains within them can be characterized by juvenile isotope ratios. The analysis indicates that the endowment (measured as the quantity of metal contained in geologic resources per unit surface area) of VHMS and komatiite-associated nickel sulfide deposits is related to the isotopic character and, therefore, the tectonic history of provinces that host these deposits. Provinces with extensive juvenile crust have significantly higher endowment of VHMS deposits, possibly as a consequence of higher heat flow and extension-related faults. Provinces with evolved crust have higher endowment of komatiite-associated nickel sulfide deposits, possibly because such crust provided either a source of sulfur or a stable substrate for komatiite emplacement. In any case, initial radiogenic isotope ratios can be useful in predicting the endowment of Archean terranes for VHMS and komatiite-associated nickel sulfide deposits. Limited data suggest similar relationships may hold in younger terranes. Lead isotope data may provide constraints on supercraton reconstructions in the Archean as lead isotope systematics appear to be highly provincial. Similar isotope systematics between the Superior province and the Eastern Goldfields superterrane may indicate a linkage during the Neoarchean, consistent with the similar geologic and metallogenic histories of these provinces.
Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders from potentially healthy individuals. popEVE identifies 442 genes in a cohort of developmental disorder cases, including evidence of 119 novel genetic disorders without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable. Interactive web viewer and downloads available at v2.evemodel. org.