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Polar offshore environments are considered the last pristine soundscapes, but accelerating climate change and increasing human activity threaten their integrity. In order to assess the acoustic state of polar oceans, there is the need to investigate their soundscape characteristics more holistically. We apply a set of 14 ecoacoustic metrics (EAMs) to identify which metrics are best suited to reflect the characteristics of disturbed and naturally intact polar offshore soundscapes. We used two soundscape datasets: (i) the Arctic eastern Fram Strait (FS), which is already impacted by anthropogenic noise, and (ii) the quasi-pristine Antarctic Weddell Sea (WS). Our results show that EAMs when applied in concert can be used to quantitatively assess soundscape variability, enabling the appraisal of marine soundscapes over broad spatiotemporal scales. The tested set of EAMs was able to show that the eastern FS, which is virtually free from sea ice, lacks seasonal soundscape dynamics and exhibits low acoustic complexity owing to year-round wind-mediated sounds and anthropogenic noise. By contrast, the WS exhibits pronounced seasonal soundscape dynamics with greater soundscape heterogeneity driven in large part by the vocal activity of marine mammal communities, whose composition in turn varies with the prevailing seasonal sea ice conditions.

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Octave equivalence describes the perception that two notes separated by a doubling in frequency have a similar quality. In humans, octave equivalence is important to both music and language learning and is found cross-culturally. Cross-species studies comparing human and non-human animals can help illuminate the necessary pre-conditions to developing octave equivalence. Here, we tested whether rats (Rattus norvegicus) perceive octave equivalence using a standardized cross-species paradigm. This allowed us to disentangle concurring hypotheses regarding the evolutionary roots of this phenomenon. One hypothesis is that octave equivalence is directly connected to vocal learning, but this hypothesis is only partially supported by data. According to another hypothesis, the harmonic structure of mammalian vocalizations may be more important. If rats perceive octave equivalence, this would support the importance of vocal harmonic structure. If rats do not perceive octave equivalence, this would suggest that octave equivalence evolved independently in several mammalian clades due to a more complex interplay of different factors such as-but not exclusively-the ability to vocally learn. Evidence from our study suggests that rats do perceive octave equivalence, thereby suggesting that the harmonic vocal structure found in mammals may be a key pre-requisite for octave equivalence. Stage 1 approved protocol: the study reported here was originally accepted as a Registered Report and the study design was approved in Stage 1. We hereby confirm that the completed experiment(s) have been executed and analysed in the manner originally approved with any unforeseen changes in those approved methods and analyses clearly noted. The approved Stage 1 protocol can be found at: https://osf.io/gvf7c/?view_only=76dc1840f31c4f9ab59eb93cbadb98b7.

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Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with a hybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.

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Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with less attention to the frequency domain. In this paper, we propose a Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network (MFMSNet). Specifically, we utilize Octave convolutions instead of conventional convolutions to effectively separate high-frequency and low-frequency components while reducing computational complexity. Introducing the Multi-frequency Transformer block (MF-Trans) enables efficient interaction between high-frequency and low-frequency information, thereby capturing long-range dependencies. Additionally, we incorporate Multi-scale interactive fusion module (MSIF) to merge high-frequency feature maps of different sizes, enhancing the emphasis on tumor edges by integrating local contextual information. Experimental results demonstrate the superiority of our MFMSNet over seven state-of-the-art methods on two publicly available breast ultrasound datasets and one thyroid ultrasound dataset. In the evaluation of MFMSNet, tests were conducted on the BUSI, BUI, and DDTI datasets, comprising 130 images (BUSI), 47 images (BUI), and 128 images (DDTI) in the respective test sets. Employing a five-fold cross-validation approach, the obtained dice coefficients are as follows: 83.42 % (BUSI), 90.79 % (BUI), and 79.96 % (DDTI). The code is available at https://github.com/wrc990616/MFMSNet.

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Responses to complaints about low-frequency noise and infrasound at workplaces have not been extensively documented in the literature. The National Institute for Occupational Safety and Health evaluated low-frequency noise, infrasound, and health symptoms among employees of an organization providing services to homeless persons. The organization's campus was evacuated after two loud noise and vibration incidents related to methane flare on an adjacent landfill. Employees were interviewed about health symptoms, perceptions of noise, and how the incidents were handled. Available medical records were reviewed. Sound level and noise frequency measurements taken in vacated campus buildings not during these incidents revealed overall levels across frequencies up to 100 hertz were 64 to 73 dB, well below those associated with adverse health effects. However, an unbalanced frequency spectrum could have contributed to the unusual sounds or vibrations reported before the first incident. Some symptoms predating the incidents are consistent with low-frequency noise exposure but are also common and nonspecific. Most interviewed employees (57%) reported being uncomfortable returning to work on the campus. Multiple factors such as noise characteristics, health effects, and employee perceptions need to be considered when assessing health concerns related to low-frequency noise and infrasound.

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Human listeners prefer octave intervals slightly above the exact 2:1 frequency ratio. To study the neural underpinnings of this subjective preference, called the octave enlargement phenomenon, we compared neural responses between exact, slightly enlarged, oversized, and compressed octaves (or their multiples). The first experiment (n = 20) focused on the N1 and P2 event-related potentials (ERPs) elicited in EEG 50-250 ms after the second tone onset during passive listening of one-octave intervals. In the second experiment (n = 20) applying four-octave intervals, musician participants actively rated the different octave types as 'low', 'good' and 'high'. The preferred slightly enlarged octave was individually determined prior to the second experiment. In both experiments, N1-P2 peak-to-peak amplitudes attenuated for the exact and slightly enlarged octave intervals compared with compressed and oversized intervals, suggesting overlapping neural representations of tones an octave (or its multiples) apart. While there were no differences between the N1-P2 amplitudes to the exact and preferred enlarged octaves, ERP amplitudes differed after 500 ms from onset of the second tone of the pair. In the multivariate pattern analysis (MVPA) of the second experiment, the different octave types were distinguishable (spatial classification across electroencephalography [EEG] channels) 200 ms after second tone onset. Temporal classification within channels suggested two separate discrimination processes peaking around 300 and 700 ms. These findings appear to be related to active listening, as no multivariate results were found in the first, passive listening experiment. The present results suggest that the subjectively preferred octave size is resolved at the late stages of auditory processing.

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Two notes separated by a doubling in frequency sound similar to humans. This "octave equivalence" is critical to perception and production of music and speech and occurs early in human development. Because it also occurs cross-culturally, a biological basis of octave equivalence has been hypothesized. Members of our team previousy suggested four human traits are at the root of this phenomenon: (1) vocal learning, (2) clear octave information in vocal harmonics, (3) differing vocal ranges, and (4) vocalizing together. Using cross-species studies, we can test how relevant these respective traits are, while controlling for enculturation effects and addressing questions of phylogeny. Common marmosets possess forms of three of the four traits, lacking differing vocal ranges. We tested 11 common marmosets by adapting an established head-turning paradigm, creating a parallel test to an important infant study. Unlike human infants, marmosets responded similarly to tones shifted by an octave or other intervals. Because previous studies with the same head-turning paradigm produced differential results to discernable acoustic stimuli in common marmosets, our results suggest that marmosets do not perceive octave equivalence. Our work suggests differing vocal ranges between adults and children and men and women and the way they are used in singing together may be critical to the development of octave equivalence. RESEARCH HIGHLIGHTS: A direct comparison of octave equivalence tests with common marmosets and human infants Marmosets show no octave equivalence Results emphasize the importance of differing vocal ranges between adults and infants.

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Absolute pitch (AP) is the rare ability to name any musical note without the use of a reference note. Given that genuine AP representations are based on the identification of isolated notes by their tone chroma, they are considered to be invariant to (1) surrounding tonal context, (2) changes in instrumental timbre, and (3) changes in octave register. However, there is considerable variability in the literature in terms of how AP is trained and tested along these dimensions, making recent claims about AP learning difficult to assess. Here, we examined the effect of tonal context on participant success with a single-note identification training paradigm, including how learning generalized to an untested instrument and octave. We found that participants were able to rapidly learn to distinguish C from other notes, with and without feedback and regardless of the tonal context in which C was presented. Participants were also able to partly generalize this skill to an untrained instrument. However, participants displayed the weakest generalization in recognizing C in a higher octave. The results indicate that participants were likely attending to pitch height in addition to pitch chroma - a conjecture that was supported by analyzing the pattern of response errors. These findings highlight the complex nature of note representation in AP, which requires note identification across contexts, going beyond the simple storage of a note fundamental. The importance of standardizing testing that spans both timbre and octave in assessing AP and further implications on past literature and future work are discussed.

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Communicable disease pandemic is a severe disease outbreak all over the countries and continents. Swine Flu, HIV/AIDS, corona virus disease-19 (COVID-19), etc., are some of the global pandemics in the world. The major cause of becoming pandemic is community transmission and lack of social distancing. Recently, COVID-19 is such a largest outbreak all over the world. This disease is a communicable disease which is spreading fastly due to community transmission, where the affected people in the community affect the heathy people in the community. Government is taking precautions by imposing social distancing in the countries or state to control the impact of COVID-19. Social distancing can reduce the community transmission of COVID-19 by reducing the number of infected persons in an area. This is performed by staying at home and maintaining social distance with people. It reduces the density of people in an area by which it is difficult for the virus to spread from one person to other. In this work, the community transmission is presented using simulations. It shows how an infected person affects the healthy persons in an area. Simulations also show how social distancing can control the spread of COVID-19. The simulation is performed in GNU Octave programming platform by considering number of infected persons and number of healthy persons as parameters. Results show that using the social distancing the number of infected persons can be reduced and heathy persons can be increased. Therefore, from the analysis it is concluded that social distancing will be a better solution of prevention from community transmission.

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The cardiovascular health of the people in urbanised cities is linked to traffic air, and noise pollution. This study investigated the cardiovascular health of people working in two microenvironments such as street (vendors) and workplace (office workers) whose blood pressure (BP) and heart rate (HR) might be affected due to regular exposure to PM2.5 and traffic noise. The PM2.5 and noise levels measurements, face-to-face questionnaire survey and health check-ups were carried out on working days from 10 A.M. to 8 P.M. in Jan-Dec 2019. The data was analysed by various statistical approaches in which the link between the traffic-borne PM2.5 and noise level at 1/3rd octave frequencies has been established with the participants' BP and HR considering the demographic, socio-contextual, habitual and annoyance perception factors. The median measure of PM2.5 and noise levels violated the WHO and NAAQS limits, i.e. 106.67 µg/m3 at street level and 33.33 µg/m3 at office indoor; and 71.35 dB (A) at the street and 65.78 dB (A) at office indoor. The results further showed that the workers working in traffic corridors had abnormally high BP and HR. The systolic BP, diastolic BP and HR values were higher than normal in male workers than female workers. The influence of low noise spectrum (50-630 Hz) was mostly observed. Therefore, the combined effect of PM2.5 > 50 µg/m3 and noise spectrum (63 and 100 Hz) > 30 dB (A) significantly affect office workers' health in traffic corridors. The hearing aids, breathing troubles in the traffic corridor and annoyance perception also influenced the BP and HR of the respondents. The results are indicative and might be helpful in urban environmental planning to improve the well-being of urban traffic corridor users.

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Parkinson's disease (PD) is a common neurodegenerative disease in the elderly population. PD is irreversible and its diagnosis mainly relies on clinical symptoms. Hence, its effective diagnosis is vital. PD has the related gene mutation called gene-related PD, which can be diagnosed not only in the specific PD patients, but also in the healthiest people without clinical symptoms of PD. Since mutations in PD-related genes can affect healthy people, and unaffected PD-related gene carriers can develop into PD patients, it is very necessary to distinguish gene-related PD diseases. The magnetic resonance imaging (MRI) has a lot of information about brain tissue, which can distinguish gene-related PD diseases. However, the limited amount of the gene-related cohort in PD is a challenge for further diagnosis. Therefore, we develop a joint learning framework called feature-based multi-branch octave convolution network (FMOCNN), which uses MRI data for gene-related cohort PD diagnosis. FMOCNN performs sample-feature selection to learn discriminative samples and features and contains a deep neural network to obtain high-level feature representation from various feature types. Specifically, we first train a cardinality constrained sample-feature selection (CCSFS) model to select informative samples and features. We then establish a multi-branch octave convolution neural network (MBOCNN) to jointly train multiple feature inputs. High/low-frequency learning in MBOCNN is exploited to reduce redundant feature information and enhance the feature expression ability. Our method is validated on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset. Experiments demonstrate that our method achieves promising classification performance and outperforms similar algorithms.

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BACKGROUND & AIMS: Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). The efficacy and safety of tofacitinib were demonstrated in a dose-ranging phase 2 induction trial, 3 phase 3 randomized, placebo-controlled trials (OCTAVE Induction 1 and 2; and OCTAVE Sustain), and an ongoing, open-label, long-term extension trial (OCTAVE Open) in patients with moderately to severely active UC. Here, we assessed short- and long-term efficacy and safety of extended induction (16 weeks) with tofacitinib 10 mg twice daily (BID) in patients who failed to respond to initial induction (8 weeks) treatment. METHODS: In patients who achieved a clinical response following extended induction (delayed responders), the efficacy and safety of tofacitinib were evaluated up to Month 36 of OCTAVE Open. RESULTS: 52.2% of patients who did not achieve clinical response to 8 weeks' treatment with tofacitinib 10 mg BID in the induction studies achieved a clinical response following extended induction (delayed responders). At Month 12 of OCTAVE Open, 70.3%, 56.8%, and 44.6% of delayed responders maintained clinical response and achieved endoscopic improvement and remission, respectively. Corresponding values at Month 36 were 56.1%, 52.0%, and 44.6%. The safety profile of the subsequent 8 weeks was similar to the initial 8 weeks. CONCLUSIONS: Overall, the majority of patients achieved a clinical response after 8 or 16 weeks' induction therapy with tofacitinib 10 mg BID. Tofacitinib 10 mg BID, administered as induction therapy for up to 16 weeks, had a comparable safety profile to 8 weeks' induction therapy. Most delayed responders at Month 36 were in remission. CLINICALTRIALS: gov: NCT00787202; NCT01465763; NCT01458951; and NCT01470612.

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We present PuPl (Pu pillometry P ipel iner), an Octave-compatible library of Matlab functions for processing pupillometry data with an easy-to-use graphical user interface (GUI). PuPl's preprocessing tools include blink correction, data smoothing, and gaze correction. PuPl can also define and sort trials, and segment data to isolate event-related pupil dilation responses. PuPl's flexible tabular export tools enable a wide variety of statistical analyses. Furthermore, PuPl can translate GUI interactions into a Matlab script, enabling easy creation and reconfiguration of reusable data processing pipelines. Finally, PuPl is designed to be extensible, and users can easily contribute functionality as best practices for pupillometry evolve. Here we demonstrate PuPl by replicating published results using publicly available data. PuPl can be downloaded from github.com/kinleyid/pupl .

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The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.

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The accurate prognosis of glioblastoma multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional generative adversarial network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.

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Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.

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Musicians say that the pitches of tones with a frequency ratio of 2:1 (one octave) have a distinctive affinity, even if the tones do not have common spectral components. It has been suggested, however, that this affinity judgment has no biological basis and originates instead from an acculturation process ‒ the learning of musical rules unrelated to auditory physiology. We measured, in young amateur musicians, the perceptual detectability of octave mistunings for tones presented alternately (melodic condition) or simultaneously (harmonic condition). In the melodic condition, mistuning was detectable only by means of explicit pitch comparisons. In the harmonic condition, listeners could use a different and more efficient perceptual cue: in the absence of mistuning, the tones fused into a single sound percept; mistunings decreased fusion. Performance was globally better in the harmonic condition, in line with the hypothesis that listeners used a fusion cue in this condition; this hypothesis was also supported by results showing that an illusory simultaneity of the tones was much less advantageous than a real simultaneity. In the two conditions, mistuning detection was generally better for octave compressions than for octave stretchings. This asymmetry varied across listeners, but crucially the listener-specific asymmetries observed in the two conditions were highly correlated. Thus, the perception of the melodic octave appeared to be closely linked to the phenomenon of harmonic fusion. As harmonic fusion is thought to be determined by biological factors rather than factors related to musical culture or training, we argue that octave pitch affinity also has, at least in part, a biological basis.

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Two-dimensional (vertical and horizontal) vibrations of a wedge-type probe upon food rupture were evaluated separately using two accelerometers placed perpendicular to a guide rod of a swing-arm device for texture evaluation of the flesh of three varieties of apples and three types of potato chips. Voltage signals from the accelerometers were filtered using a half-octave band-pass filter. The energy texture index (ETI), based on kinetic energy of the vertical or horizontal probe vibrations, was calculated over low to high frequency bands (no. 1-21). The spectra for the flesh of the three varieties of apples included two common peaks for vertical ETI at band no. 11 (1,120-1,600 Hz) and 19 (17,920-25,600 Hz) and horizontal ETI at band no. 1 (0-10 Hz) and 15 (4,480-6,400 Hz). The spectra for the three types of potato chips had a common ETI peak at band no. 11 (1,120-1,600 Hz) for horizontal ETI and at no. 15 (4,480-6,400 Hz) for vertical ETI. The three apple varieties gave rise to different intensities of vertical and horizontal ETIs while the two peaks were maintained. The thick potato chip type had higher vertical and horizontal ETIs than the thin and soft varieties in most bands; however, the thin type had the highest vertical ETIs only in lower bands (0-200 Hz). The soft type had the lowest vertical and horizontal ETI. The above results suggest that different food textures can be distinguished by two-dimensional vibration analyses of probe insertion into a food sample based on frequency bands.

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Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet+ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet+ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

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Studies have reported the benefits of music-listening in stress-reduction using musical pieces of specific scale or 'Raaga'. But the influence of lower-level musical properties (like tempo, octave, timbre, etc.) lack research backing. Carnatic music concerts use incremental modulations in tempo and octave (e.g.: 'Ragam-Tanam-Pallavi') to elevate the mood of audiences. Therefore, the current study aimed to examine the anxiolytic effect of this musical property. A randomised controlled cross-over study with 21 male undergraduate medical students was followed. 11 participants listened to 'Varying music' (VM: instrumental music with incremental variations in tempo and octave) and 10 listened to 'Stable music' (SM: instrumental music without such variations), thrice daily for 6 days, both clips recorded in Raaga-Kaapi and silence being the control intervention. Electroencephalography (EEG) and Electrocardiography (for heart rate variability or HRV) were done on all 6 days. Beck's Anxiety inventory and State-trait anxiety scale were administered on Day-1 and Day-6. A significant anxiety score reduction was seen only in VM. VM showed marked decrease in lower frequency EEG power in bilateral temporo-parieto-occipital regions compared to silence, whereas SM showed increase in higher frequencies. Relatively, VM showed more midline power reduction (i.e., lower default mode network or DMN activity) and SM showed greater left-dominant alpha/beta asymmetry (i.e., greater right brain activation). During both music interventions HRV remained stable, unlike silence intervention. We speculate that, gradual transition between lower-slower and higher-faster music portions of VM induces a 'controlled-mind wandering' state involving balanced switching between heightened mind wandering ('attention to self') and reduced mind wandering ('attention to music') states, respectively. Therefore, music-selection has remarkable influence on stress-management and warrants further research.

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