Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of reinforcement learning and value-based decision-making frameworks in understanding food choice and eating behaviors

The obesogenic food environment includes easy access to highly-palatable, energy-dense, “ultra-processed” foods that are heavily marketed to consumers; therefore, it is critical to understand the neurocognitive processes the underlie overeating in response to environmental food-cues (e.g., food images, food branding/advertisements). Eating habits are learned through reinforcement, which is the process through which environmental food cues become valued and influence behavior. This process is supported by multiple behavioral control systems (e.g., Pavlovian, Habitual, Goal-Directed). Therefore, using neurocognitive frameworks for reinforcement learning and value-based decision-making can improve our understanding of food-choice and eating behaviors. Specifically, the role of reinforcement learning in eating behaviors was considered using the frameworks of (1) Sign-versus Goal-Tracking Phenotypes; (2) Model-Free versus Model-Based; and (3) the Utility or Value-Based Model. The sign-and goal-tracking phenotypes may contribute a mechanistic insight on the role of food-cue incentive salience in two prevailing models of overconsumption–the Extended Behavioral Susceptibility Theory and the Reactivity to Embedded Food Cues in Advertising Model. Similarly, the model-free versus model-based framework may contribute insight to the Extended Behavioral Susceptibility Theory and the Healthy Food Promotion Model. Finally, the value-based model provides a framework for understanding how all three learning systems are integrated to influence food choice. Together, these frameworks can provide mechanistic insight to existing models of food choice and overconsumption and may contribute to the development of future prevention and treatment efforts.
Forensic age estimation from human blood using age-related microRNAs and circular RNAs markers

Aging is a complicated process characterized by progressive and extensive changes in physiological homeostasis at the organismal, tissue, and cellular levels. In modern society, age estimation is essential in a large variety of legal rights and duties. Accumulating evidence suggests roles for microRNAs (miRNAs) and circular RNAs (circRNAs) in regulating numerous processes during aging. Here, we performed circRNA sequencing in two age groups and analyzed microarray data of 171 healthy subjects (17–104 years old) downloaded from Gene Expression Omnibus (GEO) and ArrayExpress databases with integrated bioinformatics methods. A total of 1,403 circular RNAs were differentially expressed between young and old groups, and 141 circular RNAs were expressed exclusively in elderly samples while 10 circular RNAs were expressed only in young subjects. Based on their expression pattern in these two groups, the circular RNAs were categorized into three classes: age-related expression between young and old, age-limited expression-young only, and age-limited expression-old only. Top five expressed circular RNAs among three classes and a total of 18 differentially expressed microRNAs screened from online databases were selected to validate using RT-qPCR tests. An independent set of 200 blood samples (20–80 years old) was used to develop age prediction models based on 15 age-related noncoding RNAs (11 microRNAs and 4 circular RNAs). Different machine learning algorithms for age prediction were applied, including regression tree, bagging, support vector regression (SVR), random forest regression (RFR), and XGBoost. Among them, random forest regression model performed best in both training set (mean absolute error = 3.68 years, r = 0.96) and testing set (MAE = 6.840 years, r = 0.77). Models using one single type of predictors, circular RNAs-only or microRNAs-only, result in bigger errors. Smaller prediction errors were shown in males than females when constructing models according to different-sex separately. Putative microRNA targets (430 genes) were enriched in the cellular senescence pathway and cell homeostasis and cell differentiation regulation, indirectly indicating that the microRNAs screened in our study were correlated with development and aging. This study demonstrates that the noncoding RNA aging clock has potential in predicting chronological age and will be an available biological marker in routine forensic investigation to predict the age of biological samples.
MsmR1, a global transcription factor, regulates polymyxin synthesis and carbohydrate metabolism in Paenibacillus polymyxa SC2

The multiple-sugar metabolism regulator (MsmR), a transcription factor belonging to the AraC/XylS family, participates in polysaccharide metabolism and virulence. However, the transcriptional regulatory mechanisms of MsmR1 in Paenibacillus polymyxa remain unclear. In this study, knocking out msmR1 was found to reduce polymyxin synthesis by the SC2-M1 strain. Chromatin immunoprecipitation assay with sequencing (ChIP-seq) revealed that most enriched pathway was that of carbohydrate metabolism. Additionally, electromobility shift assays (EMSA) confirmed the direct interaction between MsmR1 and the promoter regions of oppC3, sucA, sdr3, pepF, yycN, PPSC2_23180, pppL, and ydfp. MsmR1 stimulates polymyxin biosynthesis by directly binding to the promoter regions of oppC3 and sdr3, while also directly regulating sucA and influencing the citrate cycle (TCA cycle). In addition, MsmR1 directly activates pepF and was beneficial for spore and biofilm formation. These results indicated that MsmR1 could regulate carbohydrate and amino acid metabolism, and indirectly affect biological processes such as polymyxin synthesis, biofilm formation, and motility. Moreover, MsmR1 could be autoregulated. Hence, this study expand the current knowledge of MsmR1 and will be beneficial for the application of P. polymyxa SC2 in the biological control against the certain pathogens in pepper.
Early-life weight gain patterns of term small-for-gestational-age infants and the predictive ability for later childhood overweight/obesity: A prospective cohort study

Objectives We aimed to identify the weight gain patterns of small-for-gestational age (SGA) infants in early life and to explore the predictive value for later overweight/obesity in childhood. Methods We obtained data from a prospective cohort including term SGA infants born between January 2006 and November 2015 who received regular health care from birth to 5 years in West China Second University Hospital, Chengdu, China. A latent class growth analysis (LCGA) was applied to group children with similar growth trajectory patterns. Multiple logistic regression was performed to examine the association between weight gain patterns and later overweight/obesity. Results A total of 296 term SGA infants were finally included. Five weight gain trajectories were identified, including excessive rapid catch-up growth (ERCG) (class 1, 10.9%), rapid catch-up growth (RCG) (class 2, 17.9%), appropriate catch-up growth (ACG) (class 3, 53.0%), slow catch-up growth (SCG) (class 4, 13.4%) and almost no catch growth (NCG) (class 5, 4.8%). SGA infants in class 1 and class 2 had a higher BMI according to age- and sex-specific Z scores from 2–5 years of age. In addition, 25% of SGA infants in class 1 and 13.2% of SGA infants in class 2 were found to be overweight/obese at 2-5 years of age. After adjusting for confounders, we found that extremely rapid weight gain (class 1) in the first 2 years of life increased the risk of overweight/obesity by 2.1 times at 2 to 5 years of age (aOR=2.1, 95% CI: 1.3~4.8; P<0.05). Furthermore, the increment of ΔWAZ between 0 and 4 mo was prominently related to the risk of overweight/obesity at 2 to 5 years for term SGA infants (aOR=3.2, 95% CI: 1.7~8.1; P<0.001). A receiver operating characteristic (ROC) curve showed the area under curve (AUC) was 0.7, with a 95% confidence interval (CI) from 0.6 to 0.8 (P<0.001). Conclusions The extremely rapid weight gain pattern of term SGA infants in the first 2 years of life increased the risk of overweight/obesity at 2 to 5 years of age. It suggests monitoring weight gain across the infant period represents a first step towards primary prevention of childhood obesity.
Determinants Of Capital Structure – A Sector Specific Approach

To achieve the objective of wealth maximization, capital structure of the firm has momentous role. Apart from qualitative factors like economic condition, political & social status and management approach, quantitative factors like profitability, sales, depreciation and few more financial variables determine and influence the capital structure of the firm. This paper deals with the analysis of similarities and dissimilarities of determinants of capital structure among different sectors of Indian capital market using data from 2008 to 2018. OLS regression model is used to decompose the influence of explanatory variable on debt to equity ratio, which is dependent variable. The present paper is among very few research works focusing on different sectors individually, at least with reference to Indian capital market. The findings of paper confirm the presence of sector-specific determinants of capital structure in Indian capital market. The paper has its implication in the field of finance in terms of extending the understanding about variation of factors influencing capital structure of companies from different sectors
Gender De-Biasing in Speech Emotion Recognition

Machine learning can unintentionally encode and amplify negative bias and stereotypes present in humans, be they conscious or unconscious. This has led to high-profile cases where machine learning systems have been found to exhibit bias towards gender, race, and ethnicity, among other demographic categories. Negative bias can be encoded in these algorithms based on: the representation of different population categories in the training data; bias arising from manual human labeling of these data; as well as modeling types and optimisation approaches. In this paper we assess the effect of gender bias in speech emotion recognition and find that emotional activation model accuracy is consistently lower for female compared to male audio samples. Further, we demonstrate that a fairer and more consistent model accuracy can be achieved by applying a simple de-biasing training technique.
Regulatory Convergence in the Financial Periphery: How Interdependence Shapes Regulators’ Decisions

We examine the processes by which regulations prevailing in countries at the core of the global economy spread to countries outside this small group. We show how specific cross-border relationships between banks, regulators, and investors generate regulatory interdependence that drives the diffusion of international standards from the standard-setting countries at the core of the financial system to the financial periphery. We argue that regulatory decisions in the financial periphery are shaped by the prior choices of regulators in other countries, mediated through four specific cross-border relationships associated with banking globalization. We draw on a new dataset of Basel II adoption in over ninety jurisdictions in the financial periphery. Using spatial lag models we show that regulators’ decisions over the adoption of international standards are shaped by the choices of regulators to whom they are connected through the cross-border operations of individual banks, international professional networks, and competition for capital. Our analysis underscores the value of parsing out the relevant actor-level linkages that connect countries: while international considerations shape regulatory decisions, what matters is not the extent to which countries are connected to the global economy but rather the nature of these connections.
The Arabidopsis thylakoid chloride channel ClCe regulates ATP availability for light-harvesting complex II protein phosphorylation

Coping with changes in light intensity is challenging for plants, but well-designed mechanisms allow them to acclimate to most unpredicted situations. The thylakoid K+/H+ antiporter KEA3 and the voltage-dependent Cl− channel VCCN1 play important roles in light acclimation by fine-tuning electron transport and photoprotection. Good evidence exists that the thylakoid Cl− channel ClCe is involved in the regulation of photosynthesis and state transitions in conditions of low light. However, a detailed mechanistic understanding of this effect is lacking. Here we report that the ClCe loss-of-function in Arabidopsis thaliana results in lower levels of phosphorylated light-harvesting complex II (LHCII) proteins as well as lower levels of the photosystem I-LHCII complexes relative to wild type (WT) in low light conditions. The phosphorylation of the photosystem II core D1/D2 proteins was less affected either in low or high light conditions. In low light conditions, the steady-state levels of ATP synthase conductivity and of the total proton flux available for ATP synthesis were lower in ClCe loss-of-function mutants, but comparable to WT at standard and high light intensity. As a long-term acclimation strategy, expression of the ClCe gene was upregulated in WT plants grown in light-limiting conditions, but not in WT plants grown in standard light even when exposed for up to 8 h to low light. Taken together, these results suggest a role of ClCe in the regulation of the ATP synthase activity which under low light conditions impacts LHCII protein phosphorylation and state transitions.
Text-Based Rental Rate Predictions of Airbnb Listings

The validation of house price value remains a critical task for scientific research as well as for practitioners. The following paper investigates this challenge by integrating textual-based information contained in real estate descriptions. More specifically, we show different approaches surrounding how to integrate verbal descriptions from real estate advertisements in an automated valuation model. By using Airbnb listing data, we address the proposed methods against a traditional hedonic-based approach, where we show that a neural network-based prediction model—featuring only information from verbal descriptions—are able to outperform a traditional hedonic-based model estimated with physical attributes, such as bathrooms or/and bedrooms. We also draw attention to techniques that allow for interrelations between physical, locational, and qualitative, text-based attributes. The results strongly suggest the integration of textual information, specifically modelled in a 2-stage model architecture in which the first model (recurrent long short-term memory network) outputs a probability distribution over price classifications, which is then used along with quantitative measurements in a stacked feed-forward neural network.
Effective Intrusion Detection using Deeper Recurrent Neural Networks

Computer networks are susceptible to a variety of security threats. With the ever growing number of devices and people that are connecting to the network, it has become an utmost priority to defend these networks at a large scale. The constant changing nature of the networks and the increase in the type of attacks have made the traditional approaches to intrusion detection obsolete. In this paper, we are proposing a deeper recurrent neural network based approach for intrusion detection in large scale networks. The proposed model uses independent neurons in each layer to construct a deeper recurrent neural network. It helps in faster training and classification time as well as adaptability and scalability to dynamic environments. To evaluate our proposed model, CICIDS 2017 dataset was used to implement and compare against popular deep learning based approaches in network intrusion detection. The experiments have shown promising results that our proposed model can produce improved results over existing approaches.
Reliability of an Audit Tool to Measure Healthy Food Availability in Food Outlets across Baltimore City

ABSTRACT The objective of this study was to measure the reliability of an index to assess healthy food in food stores. Food stores across Baltimore City were assessed using the Healthy Food Availability Index-Brief (HFAI-B) tool and resulting HFAI-B score. Reliability was measured using the Pearson correlation for the HFAI-B score, and Cohen’s Kappa for other binary and categorical store parameters. All items included in the HFAI-B score had high inter-rater and test-retest reliability. The Pearson Correlation coefficient for HFAI-B scores between and within observers was very high. The HFAI-B is a reliable measure to assess the availability of healthy foods in food stores.
Neural Text-to-Speech Adaptation from Low Quality Public Recordings

Neural Text-to-Speech (TTS) synthesis is able to generate high-quality speech with natural prosody. However, these systems typically require a large amount of data, preferably recorded in a clean and noise-free environment. We focus on creating target voices from low quality public recordings and our ﬁndings show that even with a large amount of data from a speciﬁc speaker, it is challenging to train a speaker-dependent neural TTS model. In order to improve the voice quality, while simultaneously reducing the amount of data required, we introduce meta-learning to adapt the neural TTS front-end. We propose three approaches for multi-speaker systems: (1) a lookup-table-based system, (2) a speaker representation derived from the Personalized Hey Siri (PHS) system, and (3) a system with no speaker encoder. Re-sults show that: i) By using a signiﬁcantly smaller number of target voice recordings, the proposed system based on embed-dings trained from the PHS system can generate comparable quality and speaker similarity to the speaker-dependent model trained solely on the target voice. ii) Applying meta-learning to Tacotron can effectively learn a representation of an unseen speaker. iii) For low quality public recordings, the adaptation based on the multi-speaker corpus can generate a cleaner target voice in comparison with the speaker-dependent model.
Efeito dos antiinflamatórios tópicos acetato de prednisolona 1%, nepafenaco 0.1% e cetorolaco de trometamina 0.4% na manutenção da midríase intra-operatória em facectomias ; : estudo clnico aleatorizado

Introducao: Os Antiinflamatorios topicos sao utilizados comumente no tratamento da inflamacao ocular e do edema macular cistoide relacionado a cirurgia de catarata. Tem sido sugerido o uso de antiinflamatorios antes da cirurgia, para conseguir uma melhor midriase intraoperatoria. Foi relatado que quando midriase e maior do que 6 mm, a incidencia de ruptura da capsula posterior e reduzido pela metade. O objetivo deste estudo original foi comparar o efeito do uso pre-operatorio do acetato de prednisolona, do cetorolaco de trometamina, do nepafenaco e de um placebo, na manutencao da midriase intra-operatoria da cirurgia de catarata. Objetivo: Comparar o efeito do uso pre-operatorio dos antiinflamatorios topicos acetato de prednisolona 1%, nepafenaco 0.1% e cetorolaco de trometamina 0.4%, alem de um placebo, na manutencao da midriase intraoperatoria durante a cirurgia de catarata. Desenho: Ensaio clinico aleatorizado, mascarado, realizado em um unico centro. Metodos: E um estudo composto por 140 pacientes submetidos a cirurgia de facoemulsificacao de catarata. Os pacientes (35 voluntarios por grupo) foram aleatorizados para receber o placebo (carboximetilcelulose de sodio 0,5%), acetato de prednisolona 1%, cetorolaco de trometamina 0,4% e o nepafenaco 0,1%. Os colirios eram administrados 3 vezes ao dia e iniciados 48 horas antes da cirurgia. A medida do tamanho da pupila foi realizada pelo cirurgiao usando um compasso nos seguintes momentos: antes da seccao da cornea e ao final da cirurgia. O desfecho primario foi avaliar a eficacia de cada medicamento em inibir a miose intraoperatoria (pupilas > 6mm, no final da cirurgia). O desfecho secundario foi obter pupilas > 6mm no inicio da cirurgia. Resultados: O numero de pacientes no grupo da prednisolona (29/35), no grupo do nepafenaco (31/35) e no grupo do cetorolaco (30/35) com diâmetro pupilar > 6 mm foi maior do que no grupo placebo em relacao a manutencao da midriase intraoperatoria (19/35 - P =. 003). Nao houve diferenca estatistica entre o grupo da prednisolona, do nepafenaco e do cetorolaco na manutencao da midriase intraoperatoria (P =. 791). Nao houve complicacoes durante a cirurgia ou efeitos adversos relacionados com o uso pre-operatorio do colirio. Conclusao: O uso pre-operatorio da prednisolona, do cetorolaco e do nepafenaco foi eficaz na manutencao midriase intraoperatoria quando comparado com o placebo
Low computational cost speech synthesis based on deep neural networks using hidden semi-Markov model structures

We propose a method of changing the units of input features from states used conventionally to phonemes and moras to reduce the computational cost of deep neural networks (DNNs) with a hidden semi-Markov model structure for speech synthesis, which can model acoustic features and a temporal structure in a uniﬁed framework. Neural networks with very deep and wide structures have recently been applied successfully in the ﬁeld of speech synthesis. However, such models have very high computational cost, so they are not being applied on platforms with limited resources. To solve this problem, we increased the length of time of DNN input units. We used phoneme or mora units, which are longer than the state units used conventionally. Increasing the length in time of units of input features reduces the number of DNN forward propagations required for speech synthesis, reducing the computational cost. Since a mora in Japanese exhibits isochronism, the duration can be represented more appropriately than the phoneme units expressing conso-nants and vowels of different lengths with one neural network. Experimental results indicate that compared with speech synthesis based on a DNN with frame inputs, computational cost can be reduced by 97% without degrading the naturalness of the synthesized speech with the proposed method.
Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

The evolution of intelligent and data‐driven systems has pushed for the tectonic transition from ancient medication to human‐centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient‐centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state‐of‐the‐art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human‐centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future.