Miracle-dz/UniFAR-FTUs huggingface dataset:
Compensation or preservation? Different roles of functional lateralization in speech perception in older non-musicians and musicians

Musical training can offset age-related decline of speech perception in noisy environments. However, whether functional compensation or functional preservation the older musicians adopt to counteract the adverse effects of aging is unclear yet, so do older non-musicians. Here, we employed the fundamental brain organization feature named functional lateralization, and calculated network-based lateralization indices (LIs) of resting-state functional connectivity (FC) in 23 older musicians (OM), 23 older non-musicians (ONM), and 24 young non-musicians (YNM). OM outperformed ONM and almost equalized YNM in speech-in-noise/speech tasks. In parallel, ONM exhibited reduced lateralization than YNM in LI of intrahemispheric FC (LI_intra) in cingulo-opercular network (CON) and interhemispheric heterotopic FC (LI_he) in language network (LAN). Moreover, OM showed higher neural alignment to YNM (i.e., similar lateralization pattern) than ONM in LI_intra in CON, LAN, frontoparietal network (FPN) and default mode network (DMN) and LI_he in DMN. These findings suggest that musical training contributes to the preservation of youth-like lateralization in older adults. Furthermore, stronger left-lateralized and lower alignment-to-young of LI_intra in somatomotor network (SMN) and dorsal attention network (DAN) and LI_he in DMN correlated with better speech performance in ONM. In contrast, stronger right-lateralized LI_intra in FPN and DAN and higher alignment-to-young of LI_he in LAN correlated with better performance in OM. Thus, functional preservation and compensation of lateralization may play different roles in speech perception in noise for the elderly with and without musical expertise, respectively. Our findings provide insight into successful aging theories from the unique perspective of functional lateralization and speech perception. Significance statement As a positive lifestyle which contributes to neural resource enrichment, musical training experience may mitigate age-related decline in speech perception in noise through both functional compensation and preservation. What is unknown is whether older musicians rely more on one of these mechanisms, and how is it different from older non-musicians. From a unique perspective of functional lateralization, we found that high-performing older musicians showed stronger preservation of youth-like lateralization with a more right-lateralized pattern whereas high-performing older non-musicians were associated with stronger scaffolding of compensatory networks with a more left-lateralized pattern. Our findings suggest that older musicians and non-musicians exhibit different coping strategies in terms of functional lateralization against aging, which would largely enrich aging theories and inspire training intervention.
Do glucocorticoids indicate stress? Meta-analysis reveals a tight association between glucocorticoids and metabolic rate

Glucocorticoid (GC) variation has long been thought to reflect variation in organismal ‘stress’, but associations between GCs and Darwinian fitness components are diverse in magnitude, direction, and highly context-dependent. This paradox reveals our poor understanding of the causes of GC variation, contrasting with the detailed knowledge of the functional consequences of GC variation. Amongst an array of effects in many physiological systems, GCs orchestrate energy availability to anticipate and recover from predictable and unpredictable environmental fluctuations and challenges. Although this is mechanistically well-known, the extent to which GC levels are quantitatively explained by energy metabolism is unresolved. We investigated this association through meta-analysis, selecting studies of endotherms in which (1) an experiment was performed that affected metabolic rate and (2) metabolic rate and GC-levels were measured simultaneously. We found that an increase in metabolic rate was associated with an increase in GC levels in 20 out of 21 studies (32 out of 35 effect sizes). More importantly, there was a strong positive correlation between the increases in metabolic rate and GCs (p=0.003). This pattern was similar in birds and mammals, and independent of the nature of the experimental treatment. We conclude that metabolic rate is a major driver of GC variation within individuals. Stressors often affect metabolic rate, leading us to question whether GC levels provide information on ‘stress’ beyond the stressor’s effect on metabolic rate.
Auditory hemispheric asymmetry as a specialization for actions and objects

What is the function of auditory hemispheric asymmetry? We propose that the identification of sound sources relies on two complementary and perceptually relevant acoustic invariants — actions and objects — that are processed asymmetrically and sufficient to model and categorize any sound. We observed that environmental sounds are an independent combination of purely temporal and spectral acoustic modulations. Behaviorally, discrimination of actions relied on temporal modulations, while discrimination of objects relied on spectral modulations. Functional magnetic resonance imaging data showed that actions and objects are respectively decoded in the left and right hemispheres, in bilateral superior temporal and left inferior frontal regions. This asymmetry reffects a generic differential processing — through differential neural sensitivity to temporal and spectral modulations present in all environmental sounds — that supports the efficient categorization of actions and objects. These results provide an ecologically valid framework of the functional role of auditory brain asymmetry.
Organohalide respiration by a Desulforhopalus-dominated community

Despite the fact that several potential organohalide-respiring bacteria (OHRB) were discovered in metagenome-assembled genomes (MAGs) in our previous study of marine sediments from Aarhus Bay, delineation of their roles and interactions are yet to be disentangled. Henceforth, obtaining corresponding pure cultures or more defined consortia would be highly instrumental for more detailed eco-physiological studies. To this end, we isolated a colony from an anaerobic slant tube culture inoculated with a stable PCE dehalogenating enrichment. Intriguingly, the derived culture exhibited debromination only, instead of PCE dechlorination, under sulfate-reducing conditions. The culture was capable of conserving energy for growth via debromination of 2,6-dibromophenol (2,6-DBP). Analysis of 16S rRNA gene sequence data extracted from shot gun metagenome sequences revealed that a strain belonging to Desulforhopalus was the predominant member of the consortium at a relative abundance of 29 %. Moreover, five bins (completeness > 85% and contamination < 3%) were assembled and all were identified as potentially new species (average nucleotide identity, ANI < 95%). Two bins from potential OHRB, bin.3 belonging to Desulfoplanes, and bin.4 belonging to Marinifilaceae, were found to encode reductive dehalogenase (RDase) genes, whereas bin.5 was found to contain a gene coding for thiolytic tetrachloro-p-hydroquinone (TPh-) RDase bearing 23.4 % identity to TPh-RDase of Sphingobium chlorophenolicum. The expression of all three RDase genes was strongly-induced after adding 2,6-DBP. Acetylene, a known inhibitor of different redox-active metalloenzymes, was found to inhibit methanogenesis as well as reductive dehalogenation without affecting gene expression, suggesting post-transcriptional inhibition. Phylogenomic analyses revealed the ecological importance of complementary roles of community members, including complete de novo vitamin B12 biosynthesis, which agreed with physiological data. Altogether, the findings presented here provided insight into the mutualism of the consortium and provided leads for synthetic OHR community optimization strategies for in situ bioremediation.
The transition from vision to language: distinct patterns of functional connectivity for sub-regions of the visual word form area

Reading entails transforming visual symbols to sound and meaning. This process depends on specialized circuitry in the visual cortex, the Visual Word Form Area (VWFA). Recent findings suggest that this word-selective cortex comprises at least two distinct subregions: the more posterior VWFA-1 is sensitive to visual features, while the more anterior VWFA-2 processes higher level language information. Here, we explore whether these two subregions exhibit different patterns of functional connectivity, and whether these patterns have relevance for reading development. We address these questions using two complementary datasets: Using the Natural Scenes Datasets (NSD; Allen et al, 2022) we identify word-selective responses in high-quality 7T individual adult data (N=8; 6 females), and investigate functional connectivity patterns of VWFA-1 and VWFA-2 at the individual level. We then turn to the Healthy Brain Network (HBN; Alexander et al., 2017) database to assess whether these patterns a) replicate in a large developmental sample (N=224; 98 females, age 5-21y), and b) are related to reading development. In both datasets, we find that VWFA-1 is more strongly correlated with bilateral visual regions including ventral occipitotemporal cortex and posterior parietal cortex. In contrast, VWFA-2 is more strongly correlated with language regions in the frontal and lateral parietal lobes, particularly bilateral inferior frontal gyrus (IFG). Critically, these patterns do not generalize to adjacent face-selective regions, suggesting a unique relationship between VWFA-2 and the frontal language network. While connectivity patterns increased with age, no correlations were observed between functional connectivity and reading ability. Together, our findings support the distinction between subregions of the VWFA, and portray the functional connectivity patterns of the reading circuitry as an intrinsic stable property of the brain.
Transcriptomics and Metabolomics Reveal Tomato Consumption Alters Hepatic Xenobiotic Metabolism and Induces Steroidal Alkaloid Metabolite Accumulation in Mice

Scope Tomato consumption is associated with many health benefits including lowered risk for developing certain cancers. It is hypothesized that tomato phytochemicals are transported to the liver and other tissues where they alter gene expression in ways that lead to favorable health outcomes. However, the effects of tomato consumption on mammalian liver gene expression and chemical profile are not well defined. Methods and results We hypothesized that tomato consumption would alter mouse liver transcriptomes and metabolomes compared to a control diet. C57BL/6 mice (n=11-12/group) were fed a macronutrient matched diet containing either 10% red tomato, 10% tangerine tomato, or no tomato powder for 6 weeks after weaning. RNA-Seq followed by gene set enrichment analyses indicated that tomato type and consumption, in general, altered expression of phase I and II xenobiotic metabolism genes. Untargeted metabolomics experiments revealed distinct clustering between control and tomato fed animals. Nineteen molecular formulas (representing 75 chemical features) were identified or tentatively identified as steroidal alkaloids and isomers of their phase I and II metabolites; many of which are reported for the first time in mammals. Conclusion These data together suggest tomato consumption may impart benefits partly through enhancing detoxification potential.
Schizophrenia-associated NRXN1 deletions induce developmental-timing- and cell-type-specific vulnerabilities in human brain organoids

De novo mutations and copy number deletions in NRXN1 (2p16.3) pose a significant risk for schizophrenia (SCZ). It is unclear how NRXN1 deletions impact cortical development in a cell type-specific manner and disease background modulates these phenotypes. Here, we leveraged human pluripotent stem cell-derived forebrain organoid models carrying NRXN1 heterozygous deletions in isogenic and SCZ patient genetic backgrounds and conducted single-cell transcriptomic analysis over the course of brain organoid development from 3 weeks to 3.5 months. Intriguingly, while both deletions similarly impacted molecular pathways associated with ubiquitin-proteasome system, alternative splicing, and synaptic signaling in maturing glutamatergic and GABAergic neurons, SCZ-NRXN1 deletions specifically perturbed developmental trajectories of early neural progenitors and accumulated disease-specific transcriptomic signatures. Using calcium imaging, we found that both deletions led to long-lasting changes in spontaneous and synchronous neuronal networks, implicating synaptic dysfunction. Our study reveals developmental-timing- and cell-type-dependent actions of NRXN1 deletions in unique genetic contexts.
Terahertz emission from a femtosecond laser focus in a two-color scheme

We critically revise the theory of terahertz emission from a plasma filament induced in a gas media by one or two focusd femtosecond laser pulses. We distinguish a radiation pressure force (RPF) from a ponderomotive force (PF), discuss conditions for one of these forces to be the dominating contribution to the terahertz emission, and also show that the angular distribution of the emitted power critically depends on which of the two forces dominates in a particular experiment. We show that the experimentally observed periodic dependence of the emitted terahertz power on the gas pressure reveals the dominating role of the RPF over the PF, whereas the angular diagram of the emission allows us to determine the predominant direction of the force. We also emphasize that the terahertz emission originated by a transient photocurrent exhibits a different dependency from the phase difference between the first and the second harmonics of the optic laser field, which generally enables the experimental detection of the prevailing mechanism of the terahertz emission from the plasma filament.
Influence of Irrigation Scheduling and Levels of Sulphur on Yield and Quality of Rabi Greengram [Vigna radiata (L.) Mills]

The field experiment was conducted during rabi season of the year 2010–11 on sandy loam soil of Regional Research Station, Anand Agricultural University, Anand to study the influence of irrigation scheduling and levels of sulphur on yield and quality of rabi greengram. Twelve treatment combinations comprised of four levels of irrigation viz., I1 (0.4 IW: CPE ratio), I2 (0.6 IW: CPE ratio), I3 (0.8 IW: CPE ratio) and I4 (critical growth stages) and three levels of sulphur viz., So (0 kg S ha−1), S1 (20 kg S ha−1) and S2 (40 kg S ha−1) with four replications. Seed and Straw yield of greengram was significantly affected due to levels of irrigation scheduling. Application of irrigation at 0.8 IW: CPE ratio produced significantly higher seed (1417 kg ha−1) and straw (2783 kg ha−1) yield. Application of sulphur @ 40 kg ha−1 recorded significantly higher seed yield (1323 kg ha−1). Maximum protein content in greengram seed observed with Irrigation scheduling at I3 (0.8 IW: CPE ratio) and with each increment in sulphur levels from 0 to 40 kg ha−1. Irrigation scheduling at 0.8 IW: CPE ratio and sulphur applied @ 40 kg ha−1 registered the highest net return of ‘ 66855 and ‘ 64600 ha−1 with maximum benefit cost ratio of 3.19 and 3.71, respectively.
A Review of Deep Learning and Machine Learning Approaches in COVID-19 Detection

COVID-19 resulted from SARS-CoV-2 and has disastrous effects on human. It has obliged researchers worldwide to exploit approaches for an accurate and reliable diagnostic tools for it early detection. However, the pandemic nature of this disease has made the traditional diagnostic methods ineffective. Artificial Intelligence (AI) researchers have come up with a number of promising algorithms to attain most effective and rapid classification system that can alternate the tedious and time consuming traditional diagnostic techniques. These algorithms used either Computed Tomography (CT) images, X-ray images or both for COVID-19 classification. The most recent exploited Deep Learning (DL) and Machine Learning (ML) approaches along with feature extraction techniques mostly on CT images are reviewed in this paper. The overall accuracy of these techniques ranged from 86.1% to 99.7% which indicate that they are applicable in COVID-19 detection. This paper will assist researchers in future development of these techniques for COVID-19 diagnosis.
Biometric-based Unique Identification for Bovine Animals — Comparative Study of Various Machine and Deep Learning Computer Vision Methods

Animal recognition and identification is an expanding area of inquiry in computer vision, feature extraction, cognitive science, and pattern recognition. In the context of zoonotic diseases, cattle recognition has become an unfolding research field in modern times for registration, distinctive identification, verification of livestock (cattle), controlling outbreaks of diseases, production management, vaccination, assignment of ownership, settlement of insurance claims, and traceability of livestock. Out of the existing noninvasive methods using computer vision, this study illustrates the fundamental implementation of a cattle biometric method to distinguish them using its muzzle (snout) point. Cattle muzzle point characteristics may be recognized in a manner how a human fingerprint can be recognized down to the tiniest of details, and how we can generate unique muzzle signatures to compare with each other. We have reviewed various methods like SIFT algorithm, LBP matcher approach, and image classification. Our study concludes that when the need is to distinctly identify between two cattle using muzzle images-based key points descriptor, then SIFT and LBP approaches are more suitable. For other scenarios, the image classification method gives better results.
A Review: Sentiment Analysis Methods and their use in Social Media Platforms

The method for recognizing positive, neutrality, or negativity sentiment in text is known as sentiment analysis. Sentimental analysis is mostly helpful for enhancing customer service, promoting goods and services, anticipating opinion polls, enhancing awareness of the signification of data security, and identifying how spectators feel about a certain sport. This paper examines the methodology employed in a review of sentimental analysis across several platforms, with a focus on social media, and discusses its applications. Social media networks have a lot of unprocessed data that users post as text and video. The utilization of sentimental analysis can transform the data into useful knowledge. The following reliable sources, including IEEE Xplore, Science Direct, and Springer, were utilized to carry out a systematic review of studies from 2016. 23 of the 50 articles that were originally and thoroughly reviewed have been chosen for the review process. The results demonstrate that most articles used lexicon- and machine-learning-based approaches to analyze text sentiment on social media platforms, with applications in industry, medical services, and safety.
Accuracy Criteria in Diffusion Routing

An analysis of Muskingum diffusion model, in which routing parameters are calculated based on channel and grid characteristics, is carried out. Numerical experiments are performed to throw additional light onto the criterion setting an upper limit to the space step if accuracy is to be preserved. Computational experience indicates that for very large values of the space step, there is a tendency for physically unrealistic negative outflows to occur. The results of the numerical experiments point to the condition C\d2 m \N ζ\N if accuracy is to be preserved, in which C\d2 is one of the routing coefficients and ζ\N is a real number. A value of ζ\N = 0.33 is recommended for practical applications. The spatial resolution criterion is expressed in terms of Courant and cell Reynolds numbers.
WCA: A New Efficient Nonlinear Adaptive Control Allocation for Planar Hexacopters

This paper presents an efficient control allocation (CA) strategy which significantly improves the flight performance of a planar hexacopter. This allocation maps the desired control vector composed of the thrust and torques in roll, pitch, and yaw axis, respectively, to propellers’ speed. This paper shows that a CA strategy based on the classical approach of pseudo-inverse matrix only exploits a limited range of the vehicle capabilities to generate thrust and moments. A novel approach is presented, which is based on a weighted pseudo-inverse matrix method, called WCA, capable of exploiting a much larger domain in the control vector. The three weights involved in WCA are adapted online according to nonlinear laws which are analytically derived by solving symbolically the equivalent constraint least-squares problem, thus removing the need for online-optimization calculations. This solution allows for very fast real-time operations, suitable for autopilots with limited computing resources. This paper provides 1) a detailed analysis of the limitations of the classical control allocation scheme, 2) the mathematical development of the WCA algorithm, 3) simulations and real experiments which show that this WCA strategy outperforms the classical CA approach in terms of a) capability to generate the maximum roll and pitch torques possible, without generating undesired yaw torques, b) prioritizing the generation of thrust over attitude torques, thus achieving better altitude tracking despite aggressive maneuvers or the presence of a payload, and c) better behavior in case of actuator saturation, faults or even failures.
The application of painting language in visual communication design

Visual communication design creates images, and the viewer gets the corresponding information by touching the images. In the process of conveying images, the generation, transfer and acquisition of vision are based on images. Also derived from visual research, clothing design and environmental art design, product design are all aimed at creating and producing goods, while visual communication design is mainly to transmit visual information, create graphics and transmit graphics. The application of painting language in visual communication design is a research field that studies how to use the artistic skills and elements in painting to create new forms. The main purpose of this study is to analyze how to apply painting skills to visual communication design, for example, using color, composition or texture as a communication tool. Although this technology has been used since ancient times (as evidenced by the works of artists such as Michelangelo), in the 20th century, it became more popular among painters such as Pablo Picasso who were famous for their experiments in color and shape.
Tailor-Made Nutrition Education for University Students through Data Science

This paper presents a study of daily food intake records with the help of educational technology. The aim was to analyze higher education students’ patterns of eating. The study took place in a private university in northern Mexico. Two phases were defined for this study. The first was to analyze a daily record of food consumption by 1,068 students. The second corresponded to a virtual reality supermarket where 3,189 students’ activities were registered from 2020 to 2022. Their food purchase decisions were studied based on each product’s nutritional information. Statistically significant differences were found in their eating habits in the variables of age, gender, and physical activity. The intake of sodium, sugar, saturated fats, and other fats consumption was also analyzed. This information is considered helpful for designing auxiliary educational material to promote well-being and food habit strategies for undergraduate students.