Consumers' beef purchasing behavior across countries.

In 2022, the value of United States (US) beef and beef product exports was $11.7 billion, and the US was the world's largest beef producer and second-largest beef exporter by volume. Therefore, we conducted surveys to evaluate beef purchasing behavior among consumers in important and emerging US beef export markets, including Japan, the United Kingdom (UK), Germany, and Mexico. Results reveal differences in consumers' beef purchasing behavior across countries. Most Mexican consumers purchase beef two-to-three times a week, while consumers in other countries typically purchase it once a week. Using ordered probit models, we examined the factors associated with beef purchase frequency in each country. Japanese consumers who consider price to be an important factor when purchasing beef are less likely to purchase it frequently. German consumers, for whom brands are important when buying beef, are more likely to buy it frequently. British consumers, who consider hormone-free production to be important when purchasing beef, are less likely to buy it frequently. Mexican consumers, who consider grass-fed production to be an important factor when purchasing beef, are less likely to buy it frequently. Across all countries, individuals who purchase beef at supermarkets and from butchers are more likely to purchase it more often. Results also indicate that various consumer demographics are associated with beef purchase frequency across countries. The findings provide valuable insights for stakeholders regarding international consumer beef purchasing behavior.

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning.

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a source domain dataset where sufficient data is available, and then generalize models to target domains to learn from only limited data. However, the gap between the source and target domains greatly hampers the generalization and target-domain few-shot finetuning. To address this problem, we analyze the domain gap from the aspect of frequency-domain analysis. We find the domain gap could be reflected by the compositions of source-domain spectra, and the lack of compositions in the source datasets limits the generalization. Therefore, we aim to expand the coverage of spectra composition in the source datasets to help the source domain cover a larger range of possible target-domain information, to mitigate the domain gap. To achieve this goal, we propose the Spectral Decomposition and Transformation (SDT) method, which first randomly decomposes the spectrogram of the source datasets into orthogonal bases, and then randomly samples different coordinates in the space formed by these bases. We integrate the above process into a data augmentation module, and further design a two-stream network to handle augmented images and original images respectively. Experimental results show that our method achieves state-of-the-art performance in the CDFSL benchmark dataset.

Non-Motor Effects of Low-Frequency Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease: A Systematic Review.

INTRODUCTION: Deep brain stimulation of the subthalamic nucleus is an effective therapy for the motor symptoms of Parkinson's disease (PD). Typically, stimulation is applied at a high frequency (≥100 Hz) to alleviate motor symptoms. However, the effects on non-motor symptoms can be variable. Low-frequency oscillations are increasingly recognized as playing an important role in the non-motor functions of the subthalamic nucleus. Therefore, it has been hypothesized that low-frequency stimulation of the subthalamic nucleus (<100 Hz) may have a direct effect on these non-motor functions, thereby preferentially impacting non-motor symptoms of PD. Despite important therapeutic implications, the literature on this topic has not been summarized. METHOD: To understand the current state of the field, we performed a comprehensive systematic review of the literature assessing the non-motor effects of low-frequency stimulation of the subthalamic nucleus in PD. We performed a supplementary meta-analysis to assess the effects of low- versus high-frequency stimulation on verbal fluency outcomes. RESULTS: Our search returned 7,009 results, of which we screened 4,199 results. A total of 145 studies were further assessed for eligibility, and a total of 21 studies met our inclusion criteria, representing 297 patients. These studies were a mix of case reports and control trials. The four clinical outcomes measured were sleep, sensory perception, cognition, and mood. A supplementary meta-analysis of six studies investigating the impact of low-frequency stimulation on verbal fluency did not find any significant results when pooling across subgroups. CONCLUSION: LFS of the STN may have benefits on a range of cognitive and affective symptoms in PD. However, current studies in this space are heterogeneous, and the effect sizes are small. Factors that impact outcomes can be divided into stimulation and patient factors. Future work should consider the interactions between stimulation location and stimulation frequency as well as how these interact depending on the specific non-motor phenotype.

Generation of Soliton Frequency Combs in NEMS.

In this study, we present an innovative approach leveraging combination internal resonances within a NEMS platform to generate mechanical soliton frequency combs (FCs) spanning a broad spectrum. In the time domain, the FCs take the form of a periodic train of narrow pulses, a highly coveted phenomenon within the realm of nonlinear wave-matter interactions. Our method relies on an intricate interaction among multiple vibration modes of a bracket-nanocantilever enabled by the strong nonlinearity of the electrostatic field. Through numerical simulation and experimental validation, we demonstrate that by amplifying the motions of the NEMS with the external electrostatic forcing tuned to excite the superharmonic resonance of order-n of the fundamental mode and exploiting combination internal resonances, we can generate multiple stable localized mechanical wave packets with different lobe sizes embodying soliton states I and II. This represents a significant breakthrough with profound implications for quantum computing and metrology.

Feature reassembly and L1 preemption: Acquiring CLLD in L2 Italian and L2 Romanian.

This study investigates feature acquisition and feature reassembly associated with Clitic Left Dislocation (CLLD). The article compares the acquisition of CLLD in second language (L2) Italian to L2 Romanian to examine effects of first language (L1) transfer, construction frequency and the type of interface involved (external vs. internal interface) within the same syntactic construction. The results from an acceptability judgment task and a written elicitation task show that while English near-native speakers of Italian/Romanian acquired the L2 constraints on CLLD, which is [+anaphor] for Italian and [+specific] for Romanian, data from both Romanian L2 learners of Italian and Italian L2 learners of Romanian showed persistent L1 transfer effects. Target-like acquisition for these groups requires both grammatical expansion and retraction; Romanian CLLD requires the addition of an L1-unavailable [+specific] feature and the loss of a [+anaphor] feature, while Italian CLLD requires the addition of an L1-unavailable [+anaphor] and the loss of a [+specific] feature. The reported findings extend evidence in favour of the Feature Reassembly Hypothesis to the syntax-discourse interface, as reassembly of interpretational features associated with CLLD proved more difficult than feature acquisition. While learners at the near-native levels were able to broaden the contexts that allow a clitic in the L2 (grammatical expansion), L1 preemption difficulties were attested as well. This was the case regardless of the frequency of the relevant construction in the input and the type of L2 feature that needed to be added/removed.

EXPRESS: Influence of Rheological Modifications on Primary Network Chemical and Structural Cure Kinetics for an Interpenetrating Polymer Network Resin.

The development of non-contact in situ techniques for monitoring of cure kinetics has the potential to greatly improve both resin formulation and processing. We have recently shown that low-frequency Raman spectroscopy is a viable method for assessing resin structural cure kinetics and complements the traditional chemical conversion determined from the fingerprint region of the spectrum. In this work we further evaluate the relationship between the structural and chemical conversion by investigating two chemically identical yet rheologically different interpenetrating polymer network resin formulations. Rheological analysis demonstrates a relationship between structural conversion and storage modulus, which is not observed in the chemical conversion data. We show that one can produce master cure kinetics curves with comparable kinetic constants using both the chemical and structural conversion methodologies. Parametric analysis of the structural conversion, chemical conversion, and photorheological conversion was combined with a semi-empirical model for the storage shear modulus as a function of extent of cure.

The impact of frequency and power on the ultrasonic purification of aluminum alloy.

In this study, the variations in hydrogen content and oxide content in alloys under 0 W, 500 W, 1000 W, 1500 W, 2000 W, 2500 W, 3000 W, and at 20 kHz, 30 kHz, 40 kHz were investigated. Hydrogen content was assessed using porosity and computed tomography, while oxygen content in the alloy was measured using element analyzer and elemental scanning. Compared to other conditions, the melt had the lowest hydrogen and oxide contents at a frequency of 20 kHz and an ultrasonic power of 2500 W, with values of 0.099 cm3/100 g and 0.0015 %, respectively. Experimental observations also indicate that the variations in hydrogen content and oxide content in the alloy during ultrasonic treatment are almost similar. In most cases, lower hydrogen content corresponds to lower oxide content in the same alloy. This is because hydrogen bubbles and oxides become a single entity. At the same time, ultrasonic purification increases the tensile strength of the alloy to 200.1 MPa and the elongation rate to 0.72 %. This study primarily investigates the relationship between hydrogen bubbles and oxides in aluminum melt under different ultrasonic frequencies and power levels, providing significant reference for the purification of various fluids under ultrasonic fields.

High-frequency ultrasound-assisted Mohs micrographic surgery for the treatment of dermatofibrosarcoma protuberans.

BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a superficial sarcoma characterized by infiltrative growth with tentacle-like borders. Mohs micrographic surgery (MMS) is the preferred treatment option for DFSP. However, the imprecise boundary localization in MMS leads to an increased number of Mohs layers required and a longer surgery time. High-frequency ultrasound has excellent tissue recognition capability for DFSP, allowing for precise boundary marking. MATERIALS AND METHODS: In this study, we retrospectively analyzed 14 cases of DFSP treated with MMS using preoperative ultrasound localization and three-dimensional reconstruction at Xiangya Hospital over the past 5 years. We also reviewed previous studies on MMS for DFSP treatment. RESULTS: It was found that the average number of Mohs layers for patients after preoperative ultrasound localization was 1.57, ranging from 1 to 3, which was less than the previously reported 1.86 layers, ranging from 1 to 12. This effectively reduced the number of Mohs layers required. CONCLUSIONS: By utilizing preoperative high-frequency ultrasound to determine the boundaries and depth of DFSP, the number of Mohs layers can be effectively reduced, leading to less workload for pathological examination, shorter operation time, and reduced surgical risks for patients. Ultrasound imaging data can be used for three-dimensional reconstruction, enabling less experienced Mohs surgeons to have a visual understanding of the morphology and extent of infiltration of the lesions. This aids in developing optimal surgical plans, smoothing the learning curve, and promoting the wider adoption of MMS.

Meta-analytical evidence for frequency-dependent selection across the tree of life.

Explaining the maintenance of genetic variation in fitness-related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

The value of home-uroflowmetry in evaluation of voiding function in children with overactive bladder.

INTRODUCTION: Overactive bladder (OAB) in children is clinically common and seriously affects the physical and mental health of children. The voiding frequency (VF) is an important basis for the diagnosis of OAB. The emergence of home-uroflowmetry (HUF) has allowed the patients to record the VF while recording the uroflowmetry at home, and the voiding at home can show the real voiding situation. However, the use of HUF to assess OAB in children and its clinical significance has not been reported in the literature. Thus, this study investigate the value of HUF in evaluation of voiding function in children with OAB and survey the VF of healthy children in Mainland China. MATERIALS AND METHODS: From May 2021 to July 2023, 52 children with OAB aged 7-10 years, 48 age-matched volunteers (control group) accepted HUF. Daytime VF and nighttime VF, voided volume (VV) per time, 24-h voided volume (24h-VV), maximum flow rate (Qmax), voiding time (VT), and uroflow pattern were recorded and compute corrected maximum urine flow rate (cQmax). VF in 600 health pupils (7-10 years) from five primary schools in Henan Province China were selected for questionnaire survey by cross-sectional survey and multi-stage sampling methods. RESULTS: 52 children with OAB and 48 healthy children completed the available 48-h HUF recordings. 24-hour, daytime, and nighttime VF, and cQmax were higher in the OAB group than in the control group (P < 0.05). However, average VV, Qmax, and VT were lower in the OAB group than in the control group (P < 0.05). There was no significant difference in 24h-VV between two groups (P > 0.05). A total of 502 questionnaires qualified for statistical analysis, and the 24h-VF was 6.3 ± 0.95 times, daytime VF was 5.6 ± 0.89 times, and nighttime VF was 0.7 ± 0.59 times. There was no significant difference in the comparison of 24-h, daytime, and nighttime VF between boys and girls and in the comparison of VF by age (P > 0.05). Compared with the results of the questionnaire, the difference of VF in HUF control group was not statistically significant (P > 0.05). CONCLUSIONS: The VF in children is similar to that of adults and the HUF is a useful tool with the ability to more realistically record changes in voiding function in children with OAB.

Effect of electrical muscle stimulation on the improvement of deltoid muscle atrophy in a rat shoulder immobilization model.

Immobilization following trauma or surgery induces skeletal muscle atrophy, and improvement in the muscle atrophy is critical for successful clinical outcomes. The purpose of this study is to evaluate the effect of electrical muscle stimulation (EMS) on muscle atrophy. The study design is a controlled laboratory study. Eighty rats (56 to establish the deltoid muscle atrophy [DMA] model and 24 to evaluate the effect of EMS on the model) were used. DMA was induced by completely immobilizing the right shoulder of each rat by placing sutures between the scapula and humeral shaft, with the left shoulder as a control. After establishing the DMA model, rats were randomly assigned into three groups: low-frequency EMS (L-EMS, 10 Hz frequency), medium-frequency EMS (M-EMS, 50 Hz frequency), and control (eight rats per group). After 3 weeks, the deltoid muscles of each rat were harvested, alterations in gene expression and muscle cell size were evaluated, and immunohistochemical analysis was performed. DMA was most prominent 3 weeks after shoulder immobilization. Murf1 and Atrogin were significantly induced at the initial phase and gradually decreased at approximately 3 weeks; however, MyoD expressed an inverse relationship with Murf1 and Atrogin. IL6 expression was prominent at 1 week. The time point for the EMS effect evaluation was selected at 3 weeks, when the DMA was the most prominent with a change in relevant gene expression. The M-EMS group cell size was significantly larger than that of L-EMS and control group in both the immobilized and intact shoulders (all p < 0.05), without significant differences between the L-EMS and control groups. The M-EMS group showed significantly lower mRNA expressions of Murf1 and Atrogin and higher expressions of MyoD and Col1A1 than that of the control group (all p < 0.05). In immunohistochemical analysis, similar results were observed with lower Atrogin staining and higher MyoD and Col1A1 staining in the M-EMS group. DMA model was established by complete shoulder immobilization, with the most prominent muscle atrophy observed at 3 weeks. M-EMS improved DMA with changes in the expression of relevant genes. M-EMS might be a solution for strengthening atrophied skeletal muscles and facilitating rehabilitation after trauma or surgery.

Acousto-optic-based time domain electric field sensor for magnetic resonance imaging applications.

An acousto-optic (AO)-based electric field sensor is presented for time domain measurement under magnetic resonance imaging (MRI). A fully MR-compatible sensor is designed and fabricated using a phase-shifted fiber Bragg grating mechanically coupled to a piezoelectric transducer. Mechanical resonance of the piezoelectric transducer is matched to the operating frequencies of commonly used MRI systems to increase the sensitivity of the sensor. Sensitivity of the sensor is measured as 1.27 mV/V/m, with a minimum detectable electric field of 4.4 mV/m/√/Hz. Directivity of the sensor is measured with a 18 dB orthogonal component rejection. The dynamic range of the sensor is calculated as 117 dB/Hz, which allows the measurement of electric fields up to 3.2 kV/m. In MRI studies, the AO sensor was able detect local hot spots around a reference implant accurately with high signal-to-noise ratio. AO sensor exhibited similar or better performance when compared with commercially available MRI compatible electric field sensors. Furthermore, the small size of the sensor with the flexible fiber optic link could allow in situ measurements of electric fields during critical interventional procedures such as pacemaker lead or deep brain stimulator placement as an MRI dosimeter during diagnostic scans.

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

Validity and reproducibility of food group intakes in a self-administered food frequency questionnaire for genomic and omics research: the Tohoku Medical Megabank Project.

BACKGROUND: The Tohoku Medical Megabank Project (TMM) was established to realize personalized healthcare and medicine using genomic and omics data. This study evaluated the validity and reproducibility of food group intakes derived from a self-administered food frequency questionnaire (TMM-FFQ) that included the response option "constitutionally unable to eat/drink it" among community-dwelling Japanese adults. METHODS: Participants comprised 89 men and 124 women aged ≥20 years from Miyagi Prefecture. Participants completed weighed food records (WFRs) for 3 consecutive days per season as reference intake and FFQs in 2019 (FFQ1) and 2021 (FFQ3). Spearman's rank correlation coefficients (CCs) were calculated for correlations between food group intakes estimated from the 12-day WFR and FFQ3 (validity), and for correlations between those estimated from the FFQ1 and FFQ3 (reproducibility). Cross-classification according to quintiles using FFQ and WFR data was also performed. RESULTS: The percentage of participants who chose the "constitutionally unable to eat/drink it" option was non-negligible for some food groups. In the validity analysis, CCs were >0.40 for many food groups; the median across 21 food groups was 0.49 in men and 0.45 in women. The median percentages of cross-classification into exact plus adjacent quintiles were 73.0% in men and 66.9% in women. In the reproducibility analysis, CCs were >0.50 for many food groups; the median across 21 food groups was 0.60 in men and 0.51 in women. CONCLUSIONS: The validity of the TMM-FFQ compared with 12-day WFR and the reproducibility of the TMM-FFQ were reasonable for food groups in the TMM cohort studies.

Disentangling the roles of age and knowledge in early language acquisition: A fine-grained analysis of the vocabularies of infant and child language learners.

The words that children learn change over time in predictable ways. The first words that infants acquire are generally ones that are both frequent and highly imageable. Older infants also learn words that are more abstract and some that are less common. It is unclear whether this pattern is attributable to maturational factors (i.e., younger children lack sufficiently developed cognitive faculties needed to learn abstract words) or linguistic factors (i.e., younger children lack sufficient knowledge of their language to use grammatical or contextual cues needed to figure out the meaning of more abstract words). The present study explores this question by comparing vocabulary acquisition in 53 preschool-aged children (M = 51 months, range = 30-76 months) who were adopted from China and Eastern Europe after two and half years of age and 53 vocabulary-matched infant controls born and raised in English speaking families in North America (M = 24 months, range = 16-33 months). Vocabulary was assessed using the MB-CDI Words and Sentences form, word frequency was estimated from the CHILDES database, and imageability was measured using adult ratings of how easily words could be pictured mentally. Both groups were more likely to know words that were both highly frequent and imageable (resulting in an over-additive interaction). Knowledge of a word was also independently affected by the syntactic category that it belongs to. Adopted preschoolers' vocabulary was slightly less affected by imageability. These findings were replicated in a comparison with a larger sample of vocabulary-matched controls drawn from the MB-CDI norming study (M = 22 months, range = 16-30 months; 33 girls). These results suggest that the patterns of acquisition in children's early vocabulary are primarily driven by the accrual of linguistic knowledge, but that vocabulary may also be affected by differences in early life experiences or conceptual knowledge.

Exponential PID controller for effective load frequency regulation of electric power systems.

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller's parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively.

An interdisciplinary integrated specialized one-stop outpatient clinic for idiopathic intracranial hypertension-a comprehensive assessment of clinical outcome.

BACKGROUND AND PURPOSE: Management of idiopathic intracranial hypertension (IIH) is complex requiring multiple specialized disciplines. In practice, this creates considerable organizational and communicational challenges for healthcare professionals and patients. Thus, an interdisciplinary integrated outpatient clinic for IIH (comprising neurology, neuroophthalmology, neuroradiology, neurosurgery and endocrinology) was established with central coordination and a one-stop concept. Here, the aim was to evaluate the effects of this one-stop concept on objective clinical outcome. METHODS: In a retrospective cohort study, the one-stop era with integrated care (IC) (1 July 2021 to 31 December 2022) was compared to a reference group receiving standard care (SC) (1 July 2018 to 31 December 2019) regarding visual impairment/worsening and headache improvement/freedom 6 months after diagnosis. Multivariate binary logistic regression models were used to adjust for confounders. RESULTS: Baseline characteristics of the IC group (n = 85) and SC group (n = 81) were comparable (female 90.6% vs. 90.1%; mean age 33.6 vs. 32.8 years; median body mass index 31.8 vs. 33.0; median cerebrospinal fluid opening pressure 32 vs. 34 cmH2O; at diagnosis, visual impairment was present in 71.8% vs. 69.1% and chronic headache in 55.3% vs. 56.8% in IC vs. SC). IC was associated with a higher likelihood of achieving both headache improvement (odds ratio [OR] 2.24, 95% confidence interval [CI] 1.52-4.33, p < 0.001) and headache freedom (OR 1.75, 95% CI 1.11-3.09, p = 0.031). Regarding the risk of visual impairment and visual worsening IC was superior numerically but not statistically significantly (OR 0.87, 95% CI 0.69-1.16, p = 0.231, and OR 0.67, 95% CI 0.41-1.25, p = 0.354). CONCLUSIONS: Interdisciplinary integrated care of IIH is favourably associated with headache outcomes and potentially also visual outcomes.

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification.

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

Transport Pathways of Nitrate in Stormwater Runoff Inferred from High-Frequency Sampling and Stable Water Isotopes.

Storm events can mobilize nitrogen species from landscapes into streams, exacerbating eutrophication and threatening aquatic ecosystems as well as human health. However, the transport pathways and storm responses of different nitrogen forms remain elusive. We used high-frequency chemical and isotopic sampling to partition sources of stormwater runoff and determine transport pathways of multiple nitrogen forms in an agricultural catchment. Bayesian mixing modeling reveals shallow subsurface water as the dominant source of stormwater runoff, contributing 74% of the water flux and 72, 71, and 79% of total nitrogen (TN), total dissolved nitrogen (TDN), and nitrate (NO3-N), respectively. Groundwater, by contrast, contributed 11% of stormwater runoff and 21, 22, and 17% of TN, TDN, and NO3-N, respectively. The remaining 14% of stormwater runoff can be attributed to rainwater, which contains much less TN, TDN, and NO3-N. Surprisingly, during storm events, the dominant nitrogen form was NO3-N rather than dissolved organic nitrogen. Antecedent conditions and runoff characteristics have an important influence on nitrogen loads during storm events. Our results provide insight into hydrological mechanisms driving nitrogen transport during storm events and may help in developing catchment management practices for reducing nitrogen pollution in aquatic ecosystems.