Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features

Parkinson’s disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world’s population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject’s voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network’s potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution’s space and time complexity.
Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.
Genome-wide Association Studies to Identify Quantitative Trait Loci and Positional Candidate Genes Affecting Meat Quality-related Traits in Pigs

Meat quality comprises a set of key traits such as pH, meat color, water-holding capacity, tenderness and marbling. These traits are complex because they are affected by multiple genetic and environmental factors. The aim of this study was to investigate the molecular ge-netic basis underlying nine meat quality-related traits in a Yorkshire pig population using a genome-wide association study (GWAS) and subsequent biological pathway analysis. In total, 45,926 single nucleotide polymorphism (SNP) markers from 543 pigs were selected for the GWAS after quality control. Data were analyzed using a genome-wide efficient mixed model association (GEMMA) method. This linear mixed model-based approach identified two quan - titative trait loci (QTLs) for meat color (b*) on chromosome 2 (SSC2) and one QTL for shear force on chromosome 8 (SSC8). These QTLs acted additively on the two phenotypes and explained 3.92%–4.57% of the phenotypic variance of the traits of interest. The genes encoding HAUS8 on SSC2 and an lncRNA on SSC8 were identified as positional candidate genes for these QTLs. The results of the biological pathway analysis revealed that positional candidate genes for meat color (b*) were enriched in pathways related to muscle development, muscle growth, intramuscular adipocyte differentiation, and lipid accumulation in muscle, whereas positional candidate genes for shear force were overrepresented in pathways related to cell growth, cell differentiation, and fatty acids synthesis. Further verification of these identified SNPs and genes in other independent populations could provide valuable information for understanding the variations in pork quality-related traits.
Management Updates on Prepartal Stress Effects on Transition Cow and Calf Health

The transition phase is thought to be the most critical period in high-producing dairy cows’ productive cycle. Maternal stresses during the peripartum period affect both dam and newborn calf health. The objective of this review article was to describe the role of prepartal metabolic and environmental stressors on postpartum-related disorders in both cows and newborn calves. The transition phase (21 d before to 21 d after calving) is considered the most critical period in dairy cattle life. Decreased dry matter intake coincides with increased nutrient demands due to the onset of lactation. It can lead to negative energy balance and tremendous metabolic challenges for high-producing dairy cows. During this time, insulin concentrations and peripheral tissue sensitivity decrease, leading to fat mobilization from adipose tissue. Such incidences would increase levels of non-esterified fatty acids and beta-hydroxybutyric acid in plasma. The success in the transition from the dry to lactating phase depends on how the cow could deal with such challenges. Failure to handle such metabolic alterations may predispose cattle to costly metabolic disorders such as ketosis, fatty liver, retained placenta, displacement abomasum, and infectious diseases, including metritis and mastitis in the postpartum period. The energy content of dairy cow diets in the dry-off (40 days before calving) or close-up (21 days before calving) periods may influence cow physiology and health in the peripartum period. The role of a transition period diet on cow health and productivity in the early or overall lactation period has been frequently investigated. However, the role of stressors such as nutritional deficiencies, heat stress, stocking density, and grouping in the late gestation period on the health and performance of cows and calves are much less addressed. Therefore, the present review delineates and reviews updates on the role of maternal stresses during the peripartum period on dam as well as newborn calf physiology and behavior.
Assessment of Elderly Type 2 Diabetic Patients who Self-adjust Their Insulin Regimen in Al-Qassim Region

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a multifactorial chronic disease. To avoid long-term and short-term complications, good glycemic control must be achieved. The majority of T2DM patients who require insulin therapy have their doses modified by their physicians; this procedure takes time and may not result in the optimal glycemic control. AIM: This study aims to ascertain whether elderly T2DM patients in Al-Qassim region modify their own insulin regimen and its effect on their glycemic control. METHODS: This cross-sectional study was conducted in Al-Qassim University Medical City and Diabetic Centers in Buraydah, Saudi Arabia. Data were collected using a questionnaire. RESULTS: A total of 384 participants were enrolled with a mean age of 70.2 ± 6.0 (61–94) years, majority (53.6%) of whom were females. About half (50.8%) were not educated and two-third (66.1%) giving insulin injections by themselves. Less than half (40.6%) of the participants changed their insulin dose, out of which 8.3% changed the dose by themselves. The mean hemoglobin A1C (HbA1c) level was 8.8 ± 1.9 with a range of 5.2–17. About 30.5% had recent hypoglycemic attacks, majority (47.9%) of whom had only one episode. Diabetic complications were seen among 127 participants with retinopathy as the most common (43.3%) complication. Hypoglycemic attacks and insulin dose adjusting were not found to be significantly associated (p = 0.476). The last HbA1c level was found to be significantly associated with adjusting insulin dose. CONCLUSION: Self-adjusting insulin dose was found to be rare. Moreover, HbA1c in patients who self-adjust their insulin dose was found to be significantly lower.
DE TRAVAIL-WORKING PAPERS W . P . 0925 The Ratio Bias Phenomenon : Fact or Artifact ?

The ratio bias––according to which individuals prefer to bet on probabilities expressed as a ratio of large numbers to normatively equivalent or superior probabilities expressed as a ratio of small numbers––has recently gained momentum, with researchers especially in health economics emphasizing the policy importance of the phenomenon. Although the bias has been replicated several times, some doubts remain about its economic significance. Our two experiments show that the bias disappears once order effects are excluded, and once salient and dominant incentives are provided. This holds true for both choice and valuation tasks. Also, adding context to the decision problem does not change this outcome. No ratio bias could be found in between-subject tests either, which leads us to the conclusion that the policy relevance of the phenomenon is doubtful at best.
H-Terminated Diamond MOSFETs on High-Quality Diamond Film Grown by MPCVD

Diamond-based transistors have been considered as one of the best choices due to the numerous advantages of diamond. However, difficulty in the growth and fabrication of diamond needs to be addressed. In this paper, high quality diamond film with an atomically flat surface was grown by microwave plasma chemical vapor deposition. High growth rate, as much as 7 μm/h, has been acquired without nitrogen doping, and the root mean square (RMS) of the surface roughness was reduced from 0.92 nm to 0.18 nm by using a pre-etched process. H-terminated diamond MOSFETs were fabricated on a high-quality epitaxial diamond layer, of which the saturated current density was enhanced. The hysteresis of the transfer curve and the shift of the threshold voltage were significantly reduced as well.
Geosphere-Biosphere Interactions and Climate: Index

Foreword Introduction L. O. Bengtsson and C. U. Hammer 1. The Antarctic ozone hole: a man-caused chemical instability in the stratosphere - what should we learn from it? P. J. Crutzen 2. Feedbacks and interactions between global change, atmospheric chemistry, and the biosphere M. O. Andraea 3. Atmospheric CO2 variations: response to natural and anthropogenic earth system forcings I. Fung 4. Modelling and evaluating terrestrial biospheric exchanges of water, carbon dioxide and oxygen in the global climate system M. Heimann 5. Carbon futures W. S. Broecker 6. Global climate change in the human perspective S. H. Schneider 7. Earth system models and the global biogeochemical cycles D. Schimel 8. The role of CO2, sea-level and vegetation during the Milankovitch-forced glacial-interglacial cycles A. Berger 9. Non-linearities in the earth system: the ocean's role T. F. Stocker 10. Simulations of the climate of the Holocene: perspectives gained with models of different complexity J. E. Kutzbach 11. Interactions of climate change and the terrestrial biosphere I. C. Prentice 12. The record of paleoclimatic change and its greenhouse implications W. R. Peltier 13. Long-term stability of earth's climate: the faint young sun problem revisited J. F. Kasting 14. Physical and chemical properties of the glacial ocean J. C. Duplessy 15. Ice core records and relevance for future climate variations J. Jouzel 16. Towards a new approach to climate impact studies W. Steffen 17. Future research objectives M. Heimann 18. Research objectives of the World Climate Research Programme H. Grassl Index.
Fast halftoning policy network with error feedback

Abstract. Deep learning-based halftoning methods have demonstrated their abilities in dithering images with desired blue-noise properties. However, tons of parameters and computations come along with the 2D convolutional neural networks (CNNs), which hinder their practical usage, particularly in the cost-sensitive printing imaging system equipped with low-end computing devices. We devise a fast halftoning policy network to alleviate the problem. Motivated by classic error diffusion algorithms, we introduce an error feedback mechanism to model the dependencies between halftone lines. Consequently, the problem collapses to 1D deep halftoning within each line, which simplifies the learning task. Furthermore, a very simple convolutional layer is applied to capture the texture details from the continuous-tone image. We find that the proposed halftoning model can stably converge under the reinforcement learning-based framework. Experiments show that our model achieves a 10.6× speedup with only 1.7% parameters compared to a previous 2D CNN-based work while maintaining similar halftone quality. We have also conducted experiments on a digital signal processor to demonstrate how the proposed model saves expensive off-chip memory accesses and improves processing throughput in real scenarios.
Molecular Mechanisms and Applications of Polyphenol-Protein Complexes with Antioxidant Properties: A Review

Proteins have been extensively studied for their outstanding functional properties, while polyphenols have been shown to possess biological activities such as antioxidant properties. There is increasing clarity about the enhanced functional properties as well as the potential application prospects for the polyphenol–protein complexes with antioxidant properties. It is both a means of protein modification to provide enhanced antioxidant capacity and a way to deliver or protect polyphenols from degradation. This review shows that polyphenol–protein complexes could be formed via non-covalent or covalent interactions. The methods to assess the complex’s antioxidant capacity, including scavenging free radicals and preventing lipid peroxidation, are summarized. The combination mode, the type of protein or polyphenol, and the external conditions will be the factors affecting the antioxidant properties of the complexes. There are several food systems that can benefit from the enhanced antioxidant properties of polyphenol–protein complexes, including emulsions, gels, packaging films, and bioactive substance delivery systems. Further validation of the cellular and in vivo safety of the complexes and further expansion of the types and sources of proteins and polyphenols for forming complexes are urgently needed to be addressed. The review will provide effective information for expanding applications of proteins and polyphenols in the food industry.
Proteomic and Mutant Analysis of Hydrogenase Maturation Protein Gene hypE in Symbiotic Nitrogen Fixation of Mesorhizobium huakuii

Hydrogenases catalyze the simple yet important redox reaction between protons and electrons and H2, thus mediating symbiotic interactions. The contribution of hydrogenase to this symbiosis and anti-oxidative damage was investigated using the M. huakuii hypE (encoding hydrogenase maturation protein) mutant. The hypE mutant grew a little faster than its parental 7653R and displayed decreased antioxidative capacity under H2O2-induced oxidative damage. Real-time quantitative PCR showed that hypE gene expression is significantly up-regulated in all the detected stages of nodule development. Although the hypE mutant can form nodules, the symbiotic ability was severely impaired, which led to an abnormal nodulation phenotype coupled to a 47% reduction in nitrogen fixation capacity. This phenotype was linked to the formation of smaller abnormal nodules containing disintegrating and prematurely senescent bacteroids. Proteomics analysis allowed a total of ninety differentially expressed proteins (fold change > 1.5 or <0.67, p < 0.05) to be identified. Of these proteins, 21 are related to stress response and virulence, 21 are involved in transporter activity, and 18 are involved in energy and nitrogen metabolism. Overall, the HypE protein is essential for symbiotic nitrogen fixation, playing independent roles in supplying energy and electrons, in bacterial detoxification, and in the control of bacteroid differentiation and senescence.
Operational Scenario Generation and Forecasting for Integrated Energy Systems

The integrated energy system (IES) is considered to be an efficient paradigm for low-carbon energy provision. The accurate modeling of inherent operational uncertainties in IES is of paramount importance for its optimal planning and energy management. This article develops a generation and forecasting approach of IES operational scenarios based on Wasserstein generative adversarial network (WGAN) for the characterization of IES operational uncertainties. The proposed solution can efficiently generate high-quality IES operational scenarios consisting of multivariate uncertain variables without explicit statistical assumptions. In addition, the well-trained WGAN is integrated into a constrained optimization problem to realize scenario forecasting in compliance with certain information (i.e., the past observation and point forecasting) for a specific coming period, and it is not restricted by the forecast look-ahead horizons. The proposed solution is assessed through a range of qualitative and quantitative evaluations, and the numerical results validate its effectiveness.
O R I G I Na L a Rt I C L E Loss of Sexual Recombination and Segregation Is Associated with Increased Diversification in Evening Primroses

The loss of sexual recombination and segregation in asexual organisms has been portrayed as an irreversible process that commits asexually reproducing lineages to reduced diversification. We test this hypothesis by estimating rates of speciation, extinction, and transition between sexuality and functional asexuality in the evening primroses. Specifically, we estimate these rates using the recently developed BiSSE (Binary State Speciation and Extinction) phylogenetic comparative method, which employs maximum likelihood and Bayesian techniques. We infer that net diversification rates (speciation minus extinction) in functionally asexual evening primrose lineages are roughly eight times faster than diversification rates in sexual lineages, largely due to higher speciation rates in asexual lineages. We further reject the hypothesis that a loss of recombination and segregation is irreversible because the transition rate from functional asexuality to sexuality is significantly greater than zero and in fact exceeded the reverse rate. These results provide the first empirical evidence in support of the alternative theoretical prediction that asexual populations should instead diversify more rapidly than sexual populations because they are free from the homogenizing effects of sexual recombination and segregation. Although asexual reproduction may often constrain adaptive evolution, our results show that the loss of recombination and segregation need not be an evolutionary dead end in terms of diversification of lineages. Sex is the dominant mode of reproduction among eukary-otic organisms, but why sex prevails despite ecological and evolutionary advantages associated with asexual reproduction is a paradox that has fueled over a century of research and shows that a breakdown in the two most important components of sex—recombination and segregation—leads to an accumulation These theoretical results are consistent with microbial microcosm experiments where asexual populations exhibited reduced rates of adaptive evolution compared to sexual populations (Colegrave 2002; Cooper 2007). The theoretical predictions are also supported by some phylogenetic analyses
Synthesis of Knowledge: Fire History and Climate Change

Efforts to quantify relationships between climate and wildfire in Alaska have not yet explored the role of higher-frequency meteorological conditions on individual wildfire ignition and growth. To address this gap, meteorological data for 665 large fires that burned across the Alaskan interior between 1980 and 2007 were assessed to determine the respective influence of higher-frequency weather and lower-frequency climate, in terms of both antecedent and post-ignition conditions on fire growth. Antecedent climate exhibited no discernable influence on eventual fire size. In contrast, fire size was sensitive to weather in the days to weeks following ignition, particularly the post-ignition timing of precipitation. Prolonged periods of warm and dry conditions coincident with blocking that persists for several weeks after ignition enabled growth of large wildfires, whereas the return of wetting precipitation generally within a week after ignition inhibited growth of smaller wildfires. These results suggest that daily weather data are a critical predictor of fire growth and large fire potential and encourage their use in fire management and
A inteligência artificial e o novo patamar da interação humano-máquina

O artigo apresenta alguns dos principais aspectos da revolução pela qual o campo da Inteligência Artificial (IA) está passando – especificamente no subcampo do processamento de linguagem natural – devido ao aprendizado de máquina. O objetivo é demonstrar como grandes modelos de linguagem – como aqueles que sustentam o ChatGPT – são passos decisivos não apenas na direção de uma IAs mais fortes, mas também para dentro e além de uma nova fronteira tecnológica: as máquinas estão cru- zando os portões que dão acesso ao mundo cultural humano. A competência semiótica para produzir e entender diversos tipos de signos (música, imagem, linguagem verbal, entre outros tipos) atribuída às máquinas coloca a interação humano-máquina para ope- rar em um novo nível. Neste artigo, propomos um esquema para analisar estes recentes avanços da IA dentro de um contexto mais amplo do desenvolvimento tecnológico de máquinas. A análises esquemática nos permite explorar, ao final deste breve ensaio, algumas importantes questões a respeito dos riscos e benefícios deste novo nível de interação humano-máquina.