Activity In Region Research Articles

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring.

Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers' ability to quantify species occurrence for effective conservation and management efforts. Automation of data processing is crucial, and machine learning algorithms can rapidly identify species using their sounds. Beaked whale acoustic events, often infrequent and ephemeral, can be missed when co-occurring with signals of more abundant, and acoustically active species that dominate acoustic recordings. Prior efforts on large-scale classification of beaked whale signals with deep neural networks (DNNs) have approached the class as one of many classes, including other odontocete species and anthropogenic signals. That approach tends to miss ephemeral events in favor of more common and dominant classes. Here, we describe a DNN method for improved classification of beaked whale species using an extensive dataset from the western North Atlantic. We demonstrate that by training a DNN to focus on the taxonomic family of beaked whales, ephemeral events were correctly and efficiently identified to species, even with few echolocation clicks. By retrieving ephemeral events, this method can support improved estimation of beaked whale occurrence in regions of high odontocete acoustic activity.

STARDUST: a pipeline for the unbiased analysis of astrocyte regional calcium dynamics.

Calcium imaging has become a popular way to probe astrocyte activity, but few analysis methods holistically capture discrete calcium signals that occur across the astrocyte domain. Here, we introduce STARDUST, a pipeline for the Spatio-Temporal Analysis of Regional Dynamics & Unbiased Sorting of Transients from fluorescence recordings of astrocytes, and provide step-by-step guidelines. STARDUST yields fluorescence time-series from data-defined regions of activity and performs systematic signal detection and feature extraction, enabling the in-depth and unbiased study of astrocyte calcium signals.

Impacts of Mineral Dust on Trace Element Concentrations (As, Cd, Cu, Ni and Pb) in Lichens and Soils at Lhù’ààn Mân’ (Yukon Territory, Canada)

AbstractDust is a mineral aerosol of the atmosphere that often contains trace elements such as As, Cd, and Pb. Lhù’ààn Mân’ (Kluane Lake), located in southwestern Yukon, is a region of frequent dust activity. In 2016, the lake level fell due to a dramatic decrease in inflow from glacier meltwater, and the delta of the lake became an important source of dust to surrounding ecosystems. To determine the impacts of dust deposition on vegetation and soil trace element concentrations and characteristics, we sampled the lichen Peltigera canina and soil layers at 57 sites along a deposition gradient located 1.4–33.6 km downwind from the principal dust source. Arsenic, Cd, Cu, Ni and Pb in lichens were negatively correlated with the distance away from the dust source, with the highest correlations in Ni and Pb (r2 = 0.50 and 0.48, respectively). Lichen and tree abundances were negatively impacted by dust deposition, suggesting that dust can affect ecosystem vegetation composition. Starting 8 km away from the dust source, the concentrations of As, Ni, and Pb decreased by more than 50% per km, while Cd and Cu concentrations decreased by more than 40% per km. Overall, within the sampled ecosystems, soil pH is 1.4 times higher in the first 8 km from the dust source while carbon content and nutrients are lower, which implies changes in nutrient availability and cycling in dust‐affected ecosystems.

Morphological characteristics and relationship with spreading rates in Mid-Ocean ridges

Mid-ocean Ridges is where plate spreads, serving as a window to explore the interior earth and its dynamics. This research investigates the morphological characteristics of mid-ocean ridges and their relationship with spreading rates, magma activity, and magma supply. The study reveals that the morphology of mid-ocean ridges is closely related to the stability and adequacy of magma supply. Mid-ocean ridges with rapid spreading exhibit relatively stable and uniform crustal thickness, thus developing axial high and smoother morphology. While those with slow and ultra-slow spreading have a cold thermal structure and thick brittle/plastic layers where fluctuations between segments are obvious and deeper axial valley shapes within segment. Additionally, the slope variation of mid-ocean ridges is associated with fluctuations in magma activity, with regions of higher magma activity often having steeper slopes. The study also finds a positive correlation between slope and axial depth, with faster spreading rates corresponding to greater axial depth.

A UK MW catalogue derived from coda envelopes

Summary The United Kingdom (UK) experiences low-to-moderate levels of seismicity; only 12 onshore earthquakes with local magnitude (ML) ≥ 4.0 have been recorded in the past 20 years. It is therefore difficult to estimate moment magnitude (MW) using conventional moment tensor inversion for the majority of UK seismicity, resulting in limited reliable estimates of MW. To address this, we calibrated coda envelopes at 16 broadband seismic stations distributed across the UK, to produce a MW catalogue for 100 events with MW≥2.13 that occurred since 2006. This was achieved using the open-source Coda Calibration Tool, which requires independent source parameter estimates for calibration. For 13 UK events between 2006 and 2022, we used spectral modelling to estimate apparent stress (0.32 to 1.74 MPa), and moment tensor inversion to estimate MW (3.35 to 4.52). These independent source parameters formed a subset of the inputs into the final calibration, which used seismic data from 33 events with coda-derived values of 2.57$\le $Mw$\le $4.49. The resultant coda calibration parameters were applied to 67 further events (MW≥2.13). The coda envelopes exhibit slow seismic coda decay across the UK, with significant energy up to 20 Hz, consistent with other regions of low tectonic activity. This MW catalogue, and the application of the calibration to future UK seismic events, will be useful for both assessing seismic hazard and event characterisation.

Localizing Stellar Activity on Low-mass Stars with the Transiting Exoplanet Survey Satellite

Magnetic reconnection is the underlying cause of stellar flares which are linked to regions of high magnetic activity, like star spots. To understand trends in stellar activity, we need to study how it is heightened by rapid rotation and deep convection in young low-mass stars. We analyze light curves of such stars observed by the Transiting Exoplanet Survey Satellite to investigate trends in flare timing with starspot modulation signals. Trends in flare properties and spot modulation can provide a means to “localize” which face of a star flares more frequently and better understand their association with active regions. We present an analysis of light curves from M and K dwarfs with no companions from five nearby and young moving groups spanning ages ∼20–150 Myr. We discuss a technique to analyze the distribution of flares and star spots and describe our results, which reveal a tentative correlation.

Optimal station placement based on grey wolf optimizer for regional target localization

The accuracy of target passive localization is influenced by the placement of signal receiving stations; therefore, many studies have been performed to optimize station placement. However, most of the present placement methods focus on the localization error of one target, and if the exact position of the target cannot be determined, but only the range of the target activity is known, how to study the localization station placement in a region is a problem that needs to be solved. This paper proposes a grey wolf optimization algorithm based on the regional target error model to solve the optimal station placement problem. Firstly, a regional target localization error model is established using the measured TDOA, and the overall error matrix within a region is derived. Then, by taking the trace of the error matrix as a criterion, the objective function is established to find the optimal location of the receiving station by grey wolf optimizer. The optimization parameters are also improved to increase the global search ability of the algorithm. Finally, the feasibility and reliability of the overall error model and the grey wolf algorithm proposed are verified by experiments from multiple perspectives. The station placement method proposed in this paper can effectively solve the localization problem of targets that are only known to be in a general activity region in advance, which is more realistic.

Relationship between radar reflectivity thresholds and very low frequency/low frequency total lightning for thunderstorm identification

AbstractTo improve our knowledge of the spatio‐temporal correspondence between radar reflectivity and VLF/LF total lightning data, this paper proposes indexes to quantify the horizontal distribution and temporal evolution characteristics between radar composite reflectivity images and total lightning density map, lightning clusters and radar cells with different radar reflectivity thresholds (30, 35, 40, 45 and 50 dBZ), respectively. It is found that the number of radar grid cells with radar reflectivity over 30 dBZ is ten times the number of lightning grid cells. At the identification stage, the lightning activity regions in a radar cell account for less than 30% using thresholds of 30, 35 and 40 dBZ, respectively, and the radar cell has more than one lightning cluster, which means that the mesoscale convective systems typically have more than one draft, and drafts are only small part of the convection. The majority of the centroid deviations is less than 10 km, indicating that there are some shifts between electrically active regions and convective regions. Results suggest that VLF/LF total lightning data are consistent with radar data and total lightning data can be used individually on a smaller spatio‐temporal scale than radar data.

Abstract PO2-01-01: Biophysical simulation using DCE-MRI to forecast response to NAT in HER2+ patients, with glucose characterization and orthogonal validation using FDG-PET

Abstract Background: Metabolic reprogramming and tumor angiogenesis are two tightly linked hallmarks of cancer. Regions of high metabolic activity within a tumor can become hypoxic or nutrient starved, eliciting a cascade of pro-angiogenic signals that can lead to increased tumor perfusion and in turn greater metabolic activity. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and 18Fluorodeoxyglucose (FDG) positron emission tomography (PET) are two imaging methods commonly used in the diagnosis of cancer; the former can be also used to identify the perfusion of the tumor microenvironment (TME) and the latter provides a coarse readout of glucose metabolic activity. Here, we are presented with a unique cohort of patients who underwent DCE-MRI, 18FDG-PET and 64Cu-DOTA-Traztuzumab offering potential to perform a pilot analysis. First, we characterize the performance of our perfusion model SimBioSys- Microvasculature (SBS-MV) through a stability analysis, as well as comparison with two PET modalities. Finally, we use this model in 3D simulations of individual HER2+ breast cancer patients to forecast responses to neoadjuvant chemotherapy (NAT). Methods: SBS-MV is a modified Tofts model for pharmacokinetic modeling of DCE-MRI data, utilizing a tissue segmentation model developed in-house to inform parameter fits and ensure that the derived parameters fall within acceptable values on a tissue-by-tissue level, in addition its function for standard fitting of DCE time-course data. We first evaluated the stability of the SBS-MV model by analyzing a separate cohort of 10 patients who underwent ultrafast DCE-MRI. Temporal undersampling was performed on the original high temporal resolution data to mimic low temporal resolution from standard-of-care (SoC) DCE studies. Both datasets were processed with SBS-MV model. Next, we analyzed data from a cohort of 18 patients, each of whom underwent a pre-NAT SoC DCE-MRI study, a mid-treatment FDG PET/CT study, and a mid-treatment 64Cu-DOTA-Trastuzumab/CT study during treatment. Tissue segmentation and SBS-MV fits were performed on the pre-NAT DCE MRI. Tightly cropped boxes encompassing the tumor in the 3D SBS-MV volumes as well as the PET volumes were created. Using these identified regions, all summary statistics were calculated and cross-correlated between imaging modalities. Results: SBS-MV was stable to SoC-mimicking temporally undersampled ultrafast DCE MRI series, demonstrating relatively little variability in the output parameters (a voxel wise median deviation of approximately 0.005 min-1 ). Further, we demonstrate that SBS-MV parameters (Ktrans, ve) are correlated with FDG SUVSA and 64Cu SUVSA. In addition, the glucose concentrations used in the biophysical simulations were correlated with FDG SUVSA, indicating that downstream applications of the SBS-MV models still carry this information about perfusion. Finally, using SBS-MV, our biophysical simulations of HER2-targeted therapy accurately predict patient response. In this small cohort, our biophysical simulation platform performed well, yielding predictive accuracy of 0.83, with sensitivity and specificity of 0.83 and 0.83, respectively. These performance metrics are in line with previously published reports. Conclusion: Our MV model is capable of extracting biologically meaningful perfusion parameters from standard clinical DCE MRI time series, providing the same benefits of a comprehensive kinetic analysis, without impacting current clinical workflow. This approach could be used in both the research and clinical settings, offering actionable information on what drives individual patient therapeutic response for a more personalized care. Citation Format: John Whitman, Vikram Adhikarla, Russell Rockne, Lusine Tumyan, Joanne Mortimer, Wei Huang, Joseph Peterson, Dorys Lopez-Ramos, John Cole. Biophysical simulation using DCE-MRI to forecast response to NAT in HER2+ patients, with glucose characterization and orthogonal validation using FDG-PET [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-01.

Climate and land use changes impacts on streamflow in the Brazilian Cerrado basin

This study evaluated the effects of climate change and land use (LULC) on the discharge regime of a watershed located in the Brazilian Cerrado. The SWAT model was calibrated and validated in three different periods: period 1 (LULC of 1990 with meteorological data from 1980 to 1995), period 2 (LULC of 2000 with meteorological data from 1996 to 2005), and period 3 (LULC of 2015 with meteorological data from 2006 to 2015). The statistical coefficients of performance NSE, PBIAS and R² and the flow duration curves for each period of analysis showed that the model is suitable for use in simulations on a monthly scale in the hydrographic basin of the Correntina River. The results show that variations in land use and land cover affect surface flow more intensely than changes in climate, and that the reduction in discharge over the 36 years of the study is due to changes in land use and land cover, in particular the use of water for agriculture. The results of this study show that it is necessary to consider the implications of climate change and land use for decision making, providing information to guide the future management of water resources in a region of intense agricultural activity and with conflicts over water use.

Screening and optimization of shark nanobodies against SARS-CoV-2 spike RBD

SARS-CoV-2 continues to threaten human health, antibody therapy is one way to control the infection. Because new SARS-CoV-2 mutations are constantly emerging, there is an urgent need to develop broadly neutralizing antibodies to block the viral entry into host cells. VNAR from sharks is the smallest natural antigen binding domain, with the advantages of small size, flexible paratopes, good stability, and low manufacturing cost. Here, we used recombinant SARS-CoV-2 Spike-RBD to immunize sharks and constructed a VNAR phage display library. VNAR R1C2, selected from the library, efficiently binds to the RBD domain and blocks the infection of ACE2-positive cells by pseudovirus. Next, homologous bivalent VNARs were constructed through the tandem fusion of two R1C2 units, which enhanced both the affinity and neutralizing activity of R1C2. R1C2 was predicted to bind to a relatively conserved region within the RBD. By introducing mutations at four key binding sites within the CDR3 and HV2 regions of R1C2, the affinity and neutralizing activity of R1C2 were significantly improved. Furthermore, R1C2 also exhibits an effective capacity of binding to the Omicron variants (BA.2 and XBB.1). Together, these results suggest that R1C2 could serve as a valuable candidate for preventing and treating SARS-CoV-2 infections.

Abstract LB422: Specific activation of the Epstein-Barr Virus BGLF4 locus via lowered DNA methylation in EBV-associated CNS lymphoproliferative disease

Abstract Introduction: Epstein-Barr virus positive (EBV+) central nervous system lymphoproliferative diseases (CNS-LPD) are aggressive clinical conditions with poor prognosis. We have previously reported use of a ganciclovir (GCV), zidovudine (AZT), rituximab and dexamethasone regimen (GARD) that induced complete and durable responses in patients with primary CNS post-transplant lymphoproliferative disease (PTLD). Responses were associated with detection of two GCV/AZT viral drug targets, the EBV protein kinases BGLF4 and BXLF1. These viral proteins are normally expressed in lytic phase EBV, and the molecular basis for expression in latently infected EBV+ CNS-LPD is unknown. Methods: RNA expression of EBV genes LMP1, BZLF1, BXLF1, and BGLF4 was measured in 12 CNS-LPD biopsies (n=8/12 DLBCL-type) via qRT-PCR. Mass spectrometry-based EpiTYPER assay was used to quantify DNA methylation in EBV gene promoters. Biopsies from 25 patients with systemic PTLD were used for comparison. The BGLF4 locus was investigated in detail using luciferase reporters and 5’RACE mapping of transcription start sites in 3 EBV-infected cell lines and 4 CNS-LPD biopsy samples. Overall EBV methylation was assessed in a screening assay covering 31 EBV loci. Mechanistic involvement of active demethylation via TET2 was studied in a HEK293 CRISPR-Cas9 gene knockout model under EBV (strain M81) infection. Results: Gene expression analysis of biopsy specimens showed expression of LMP1, BXLF1, and BGLF4, but not BZLF1. Expression of BXLF1 and BGLF4 was significantly higher in CNS-LPD when compared to systemic EBV+ PTLD (p=0.043 and p=0.00022, respectively). EpiTYPER data showed significantly decreased promoter methylation at BGLF4 (p=0.00013). Luciferase reporter analysis of the BGLF4 upstream sequence revealed 3 regions of promoter activity which were silenced by in vitro methylation of reporters. 5’RACE identified transcription start sites at all 3 BGLF4 promoters and detected their activity in EBV+ cell lines and CNS-LPD biopsies. By overlaying transcriptional activity, 5’RACE and DNA methylation data, we identified DNA methylation loss at single CpG dinucleotides correlated with BGLF4 expression (r2=0.73). These sites were specifically demethylated in CNS-LPD, while surrounding EBV methylation remained high. Knockout of TET2 followed by EBV infection revealed that active demethylation is required for demethylation at BGLF4 promoters. Conclusions: The molecular determinant for success of the GARD regimen in CNS-LPD has remained poorly understood, with immunohistochemical detection of BGLF4/BXLF1 as the only predictor of treatment response. Our results provide the molecular basis for expression of BGLF4 in CNS-LPD by showing decreased promotor DNA methylation. We identified specific BGLF4 methylation sites that hold promise as potential DNA biomarkers for EBV+ CNS-LPD. Citation Format: Christoph Weigel, Haley L. Klimaszewski, Selamawit Addissie, Sarah Schlotter, Fode Tounkara, James P. Dugan, Bradley M. Haverkos, Lynda Villagomez, Mark Lustberg, Pierluigi Porcu, Timothy Voorhees, Michael A. Caligiuri, Ginny Bumgardner, Christopher C. Oakes, Robert Baiocchi. Specific activation of the Epstein-Barr Virus BGLF4 locus via lowered DNA methylation in EBV-associated CNS lymphoproliferative disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB422.

Multi-parameters monitoring of geological disasters around the Taihang Mountains-Yanshan area, Hebei Province

The Hebei Taihang Mountains-Yanshan region marks North China’s natural boundary between plains and plateaus. The unique geological and geographical conditions have led to a hazardous environment with frequent geological events. Hence, conducting a comparative analysis of regional geological hazard drivers is necessary. A geological hazard driver analysis model using multiparameter preferential Geodetectors was developed, focusing on the Taihang Mountains-Yanshan region in Hebei. It was divided into zones: Taihang Mountains, Yanshan Mountains and Dam Plateau. The results showed significant differences in the explanatory power of geohazard drivers in various zones, varying with the softening of the natural geological environment and increased human activities. In the Taihang Mountains, the primary geohazard factor is average annual rainfall, whereas in the Yanshan Mountains, it is average annual rainfall and geomorphology. Interactions among factors reveal that annual average rainfall holds the highest explanatory power for geological hazard spatial differences, notably interacting with geomorphological, geological and human activity regions, yielding an escalating effect. The study findings can be crucial references for disaster prevention, mitigation, national land resource development, economic planning and the ‘six belts’ construction in Beijing, Tianjin and Hebei.

Study of the Intensity Distribution of the Green Line in the Inner Corona

The corona is a key region of solar atmospheric activity, and the source of solar-terrestrial space weather. Limited by observation, research on the plasma structure and magnetic field state of the lower coronal atmosphere is still very lacking, and there are few international studies on the brightness stratification of the lower coronal atmosphere in the visible light band. In this paper, the coronal green line (FeXIV5303 Å) observation data of Lijiang coronagraph YOGIS (Yunnan Observatories Green-line Imaging System) is used to analyze the bright structure and mid-coronal loops in the inner coronal region (1.03R⊙–1.25R⊙, where R⊙ represents the solar radius) effectively. By fitting the exponential decay of the intensity of the bright structures at the radial height of the sun and comparing these fitting results, it is found that the decay index of the static inner coronal loops obtained is around a fixed value. Then, the more obvious coronal loops are extracted, and by performing the same exponential fitting on the intensities of different heights, the obtained decay index is also relatively similar to that of the bright structure, which provides a reference for further study of the evolution of physical parameters in the corona.

Spectroscopic clarifications, topological insights, Hirshfeld interactions, and quantum chemical computational analysis of antibacterial agent benzimidazolium picrate

The distribution of density at the B3LYP level with a 6–311++G(d,p) basis set for Benzoimidazolium Picrate was presumably assessed to comprehend additional information regarding its molecular shape, harmonic emission, and complex kinetic spectra. Leveraging VEDA software, spectral investigations were conducted for each system which enabled us to gain both qualitative and quantitative assessments of the Raman and IR spectra. Simulations were additionally implemented for net Mulliken charge, Fukui inquiry, RDG review, and intact coupling orbital analyses. The likelihood of shifting charges in the molecule's structure is foretold by the FMO potential gap of 3.9719 eV. Conceptual and feasible computations were employed to evaluate the UV–vis optical properties of BP. The dimensions, design, charge concentration dispersion, and the chemical activity region of the components were established with the electrostatic potential layer (ESP). The morphological attributes of BP were further depicted via ELF and LOL charts. The hyperpolarizability ratings of the drug demonstrate an appropriate level of reliability, and found to be 24 times than urea. To identify the target molecule's appropriate bonding energy with amino acids, a docking virtual reality was conducted.

Gray matter volume associations in youth with ADHD features of inattention and hyperactivity/impulsivity.

Prior research has shown smaller cortical and subcortical gray matter volumes among individuals with attention-deficit/hyperactivity disorder (ADHD). However, neuroimaging studies often do not differentiate between inattention and hyperactivity/impulsivity, which are distinct core features of ADHD. The present study uses an approach to disentangle overlapping variance to examine the neurostructural heterogeneity of inattention and hyperactivity/impulsivity dimensions. We analyzed data from 10,692 9- to 10-year-old children from the Adolescent Brain Cognitive Development (ABCD) Study. Confirmatory factor analysis was used to derive factors representing inattentive and hyperactive/impulsive traits. We employed structural equation modeling to examine these factors' associations with gray matter volume while controlling for the shared variance between factors. Greater endorsement of inattentive traits was associated with smaller bilateral caudal anterior cingulate and left parahippocampal volumes. Greater endorsement of hyperactivity/impulsivity traits was associated with smaller bilateral caudate and left parahippocampal volumes. The results were similar when accounting for socioeconomic status, medication, and in-scanner motion. The magnitude of these findings increased when accounting for overall volume and intracranial volume, supporting a focal effect in our results. Inattentive and hyperactivity/impulsivity traits show common volume deficits in regions associated with visuospatial processing and memory while at the same time showing dissociable differences, with inattention showing differences in areas associated with attention and emotion regulation and hyperactivity/impulsivity associated with volume differences in motor activity regions. Uncovering such biological underpinnings within the broader disorder of ADHD allows us to refine our understanding of ADHD presentations.

Semi-supervised Active Learning for Video Action Detection

In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as un- labeled data along with informative sample selection for ac- tion detection. Video action detection requires spatio-temporal localization along with classification, which poses several challenges for both active learning (informative sample se- lection) as well as semi-supervised learning (pseudo label generation). First, we propose NoiseAug, a simple augmenta- tion strategy which effectively selects informative samples for video action detection. Next, we propose fft-attention, a novel technique based on high-pass filtering which enables effective utilization of pseudo label for SSL in video action detection by emphasizing on relevant activity region within a video. We evaluate the proposed approach on three different bench- mark datasets, UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness on video action detec- tion where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB- 21. Next, we also show its effectiveness on Youtube-VOS for video object segmentation demonstrating its generalization capability for other dense prediction tasks in videos.

Vulnerable brain regions in adolescent major depressive disorder: A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis.

Adolescent major depressive disorder (MDD) is a significant mental health concern that often leads to recurrent depression in adulthood. Resting-state functional magnetic resonance imaging (rs-fMRI) offers unique insights into the neural mechanisms underlying this condition. However, despite previous research, the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated. To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation (ALE) meta-analysis. We performed a comprehensive literature search through July 12, 2023, for studies investigating brain functional changes in adolescent MDD patients. We utilized regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) analyses. We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls (HCs) using ALE. Ten studies (369 adolescent MDD patients and 313 HCs) were included. Combining the ReHo and ALFF/fALFF data, the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs (voxel size: 648 mm3, P < 0.05), and no brain region exhibited increased activity. Based on the ALFF data, we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients (voxel size: 736 mm3, P < 0.05), with no regions exhibiting increased activity. Through ALE meta-analysis, we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients, increasing our understanding of the neuropathology of affected adolescents.

Physical, Chemical and Compaction Characteristics of Slightly Weathered Tephras of New Zealand

The North Island of New Zealand is a region of high volcanic activity, with significant eruptions over the past. Analogous to past events, future volcanic eruptions would produce a considerable volume of ash and granular soils, covering widespread areas and raising concerns for their disposal and storage. Such deposits, primarily airfall tephra, could be potentially used in geotechnical engineering applications such as foundations, roadway embankments and land reclamations. However, before their use as structural fills can be recommended, detailed laboratory investigations of their physical, chemical, compaction, and geotechnical engineering properties (strength, compressibility, collapsibility, liquefaction potential, etc.) must be conducted. Different tephra deposits can be products of different eruptions, so chemical composition analyses can be combined with the physical, compaction, and engineering properties to characterize such deposits. Accordingly, this paper provides useful insights from physical (grain size, specific gravity, and morphology), chemical (elemental and mineralogy using X-ray fluorescence and X-ray diffraction), and compaction tests (maximum dry density, optimum water content, and particle breakage) for eleven selected volcanic tephra samples sourced from the North Island of New Zealand in the Rotorua, Taupo, and Auckland regions.

Spatial correspondence of cortical activity measured with whole head fNIRS and fMRI: Toward clinical use within subject

Functional near infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) both measure the hemodynamic response, and so both imaging modalities are expected to have a strong correspondence in regions of cortex adjacent to the scalp. To assess whether fNIRS can be used clinically in a manner similar to fMRI, 22 healthy adult participants underwent same-day fMRI and whole-head fNIRS testing while they performed separate motor (finger tapping) and visual (flashing checkerboard) tasks. Analyses were conducted within and across subjects for each imaging approach, and regions of significant task-related activity were compared on the cortical surface. The spatial correspondence between fNIRS and fMRI detection of task-related activity was good in terms of true positive rate, with fNIRS overlap of up to 68 % of the fMRI for analyses across subjects (group analysis) and an average overlap of up to 47.25 % for individual analyses within subject. At the group level, the positive predictive value of fNIRS was 51 % relative to fMRI. The positive predictive value for within subject analyses was lower (41.5 %), reflecting the presence of significant fNIRS activity in regions without significant fMRI activity. This could reflect task-correlated sources of physiologic noise and/or differences in the sensitivity of fNIRS and fMRI measures to changes in separate (vs. combined) measures of oxy and de-oxyhemoglobin. The results suggest whole-head fNIRS as a noninvasive imaging modality with promising clinical utility for the functional assessment of brain activity in superficial regions of cortex physically adjacent to the skull.