Temporal Acquisition Research Articles

RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax.

Cancer driver mutations often show distinct temporal acquisition patterns, but the biological basis for this, if any, remains unknown. RAS mutations occur invariably late in the course of acute myeloid leukaemia, upon progression or relapsed/refractory disease1-6. Here, by using human leukaemogenesis models, we first show that RAS mutations are obligatory late events that need to succeed earlier cooperating mutations. We provide the mechanistic explanation for this in a requirement for mutant RAS to specifically transform committed progenitors of the myelomonocytic lineage (granulocyte-monocyte progenitors) harbouring previously acquired driver mutations, showing that advanced leukaemic clones can originate from a different cell type in the haematopoietic hierarchy than ancestral clones. Furthermore, we demonstrate that RAS-mutant leukaemia stem cells (LSCs) give rise to monocytic disease, as observed frequently in patients with poor responses to treatment with the BCL2 inhibitor venetoclax. We show that this is because RAS-mutant LSCs, in contrast to RAS-wild-type LSCs, have altered BCL2 family gene expression and are resistant to venetoclax, driving clinical resistance and relapse with monocytic features. Our findings demonstrate that a specific genetic driver shapes the non-genetic cellular hierarchy of acute myeloid leukaemia by imposing a specific LSC target cell restriction and critically affects therapeutic outcomes in patients.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

A high‐performance asynchronous readout circuit with cascade function for a neural recording micro‐electrode array

AbstractThe data‐driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro‐electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large‐scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large‐scale MEA customization through cascading, and reduce the bandwidth of large‐scale MEA through spike compression. Firstly, based on in‐pixel spike detection, a row‐based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large‐scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation‐based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large‐scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.

Technical challenges and benefits of photon counting detector computed tomography

X-ray detector is the essential part of a computed tomography (CT) system that controls dose efficiency and image quality. All clinical CT scanners utilized scintillating detectors up until the first clinical photon-counting-detector (PCD) system was approved in 2021. These detectors do not record information about individual photons during the two-step detection process. PCDs, on the other hand, employ a single step process that transforms X-ray radiation straight into an electrical signal. This retains information on individual photons, allowing one to count the quantity of X-rays in various energy ranges. Better spatial resolution, reduced dosages of iodinated contrast material, enhanced iodine signal, enhanced radiation dose efficiency, and the lack of electronic noise are the main benefits of PCDs. PCDs with multiple energy thresholds are able to separate detected photons into two or more energy bins, allowing for the availability of energy-resolved data for each record. In the event of dual-source CT, this permits high pitch or high temporal resolution acquisitions in addition to high spatial resolution for tasks involving material quantification or classification. PCDCT imaging of anatomy, where excellent spatial resolution provides clinical benefit, is one of the most promising uses of the technology. Inner ear, bone, small blood artery, heart, and lung imaging are among them. Photon-counting CT will become the wave of the future for workhorse CT imaging systems. An overview of the PCDCT principle, possible clinical benefits, and limitations of conventional CT is provided in this review paper, along with potential future developments for this CT imaging technology.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

Remote Sensing of Forests in Bavaria: A Review

In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests.

Video Anomaly Detection in Crime Analysis using Deep learning Architecture- A survey

The importance of developing automated video surveillance systems for public safety and security, particularly in crime analysis, has witnessed significant growth in recent years. This survey delves into the current landscape of automated video surveillance systems, emphasizing advancements in crime analysis and exploring existing methodologies and technologies. The study underscores the significance of employing deep learning models in video analysis. Furthermore, the study suggests a deep learning architecture to address the challenges of the existing methods. The goal of the suggested approach is to help security and law enforcement organizations quickly react to any dangers by precisely identifying unusual occurrences or actions in video sequences. The DenseNet-121 architecture is used for efficient spatial and temporal data acquisition from the video frames. This architecture is characterized by a dense connection structure in which all levels get feature mappings from all layers before them. The characteristics of DenseNet-121 can help in the accurate identification of anomalies in video streams and differentiate between normal and abnormal actions. In addition, the study also delves into the topic of using a cell structure with varied sizes to effectively split video sequences. This allows for flexible analysis and can accommodate different sorts of abnormalities. Anomaly detection accuracy can be further improved by adding size, motion, and location information to prediction and measurement models. This study serves as a foundation for the future research that aims to develop a more robust and efficient automated video surveillance solutions.

Motion Complement and Temporal Multifocusing for Skeleton-Based Action Recognition

Modeling sequences with spatial-temporal graph convolutional networks has become a mainstream paradigm in skeleton-based action recognition. However, many existing methods adopt redundant or cluttered structures to mine the key action features, thus making it difficult to achieve a balanced or leading performance in accuracy and efficiency. In this paper, we propose a novel framework, referred to as Motion Complement and Temporal Multifocusing Network (MCTM-Net), to capture the relationships within skeleton sequences by means of an efficient decomposition of the spatiotemporal graph model. Specifically, for spatial modeling, we introduce a motion-related relational descriptor that extends the channel dimension so as to enhance the modeling of motion salient regions as a complement to the conventional physical adjacency relationships. An improved parameterized physical relationship model is also proposed to better fit the data characteristics. As for temporal modeling, we propose an efficient multi-focus temporal information acquisition strategy that aggregates the information from multiple temporal spans and adjacent regions. We conduct extensive experiments on multiple representative datasets, including NTU-RGB+D (60&120), Northwestern-UCLA, and UWA3D Multiview Activity II, to validate our innovations. The experimental results show the effectiveness of our method. The code will be available at https://github.com/cong-wu/MCMT-Net.

Detection of Coronary Artery Fistula between the LAD and the Great Cardiac Vein on Coronary CT Angiography.

Coronary artery fistulas (CAFs) are abnormal communications of coronary arteries whereby venous circuits bypass the normal capillaries within the myocardium. Coronary artery-to-cardiac vein fistula is the third most common type of CAF, accounting for 7% of cases. Electrocardiographic-gated cardiac computed tomographic (CT) angiography has emerged as a noninvasive alternative method of choice for diagnosis due to its high spatial and temporal resolution and short acquisition time. Herein, we aimed to present a left anterior descending coronary artery opened into the greater cardiac vein at the distal level, consistent with a coronary artery-to-cardiac vein fistula in a 77-year-old woman. Teaching point: Coronary artery CT angiography provides a detailed evaluation of the complex anatomy of coronary artery fistula without the need for invasive methods.

N/KRAS-Mutant AML LSCs Originate from Committed Myelomonocytic Progenitors and Drive Clinical Resistance to Venetoclax

Driver mutations in acute myeloid leukemia (AML) often exhibit distinct temporal acquisition patterns, but the biological basis for this, if any, remains unknown. RAS mutations occur invariably late in AML, upon progression or relapsed/refractory disease, in contrast to their function as early drivers in solid tumors. We developed synthetic leukemogenesis models in human induced pluripotent stem cell (iPSC)- and primary cord blood (CB)-derived human hematopoietic stem/progenitor cells (HSPCs) by introducing the prototypical NRAS G12D mutation alone or with other mutational combinations, using CRISPR, and engraftment of a transplantable leukemia into NSG mice as the readout. RAS mutations alone were not sufficient for leukemogenesis, but required specific cooperating mutations. Subsequently, by creating temporally controlled engineered models, we found that RAS mutations are obligatory late events that can only drive leukemic transformation when acquired after, but not before, cooperating mutations. We provide the mechanistic explanation for this in a requirement for mutant RAS to specifically transform committed granulocyte-monocyte progenitors (GMPs) harboring previously acquired driver mutations, through sorting and transplantation and multi-omics experiments. In contrast, acquisition of RAS mutations in earlier progenitors (common myeloid progenitors, CMP), with or without cooperating mutations, blocked the emergence of GMPs and abolished engraftment or gave rise to a myeloproliferative neoplasm. These results indicate that advanced RAS-mutant (MT) leukemic clones have a different cell type of origin from the ancestral clone. Next, using single-cell transcriptomics in cells isolated from xenografts of two AML-iPSC lines generated from an AML patient with a subclonal KRAS G12D mutation, one capturing the RAS WT major clone and one the KRAS G12D subclone, we show that KRAS G12D leukemia stem cells (LSCs) give rise to leukemia with a more monocytic immunophenotype, while the ancestral clone within the same patient generates more leukemic cells with primitive and neutrophil markers. These results were corroborated by Genotyping of Transcriptomes (GoT) in another AML patient with subclonal NRAS G12D mutation. Recent studies have linked monocytic AML and, independently, RAS pathway mutations to poor responses to Venetoclax (VEN)-containing regiments. In view of the causal link we found between RAS mutations and monocytic disease, we evaluated the associations between monocytic AML, RAS mutations and response to VEN. We reanalyzed data from a cohort of 118 older/unfit newly diagnosed AML patients treated on a prospective clinical trial with VEN and decitabine (DEC) (NCT03404193). All outcomes were comparable between monocytic and non-monocytic groups, including overall response rate, duration of response (DOR) and overall survival. On the other hand, patients with N/KRAS mutations had significantly shorter DOR. These results support the presence of N/KRAS mutations and not the monocytic stage as predictors of inferior responses to VEN regimens. To definitely test this, we treated defined cell types generated from RAS WT and RAS MT iPSC lines - CD34+ LSC-like and CD11b+/CD68+/CD14+ monocytic cells - with VEN. In agreement with previous studies, monocytes were resistant and RAS WT LSCs were sensitive to VEN. However, strikingly RAS MT LSCs were VEN-resistant. Consistent with these, single-cell transcriptomics in patient-derived AML-iPSC cells in vivo, GoT in primary AML cells and bulk transcriptomics in synthetic iPSC-AML cells showed downregulation of BCL2 and upregulation of MCL1 in the RAS MT LSCs. This study provides the first example of a mechanistic explanation of why the timing of mutational acquisition in AML is so strongly biased. It shows for the first time that interaction between an oncogenic driver and the non-genetic developmental hematopoietic hierarchy imposes a specific LSC cell-of-origin restriction and in turn shapes the hierarchical structure of the resulting AML and critically determines therapeutic outcomes in patients. Importantly, our results have direct implications for clinical practice, as they support that RAS MT LSCs drive clinical resistance to VEN and that treatment with VEN in patients with RAS mutations can accelerate disease progression by selection of RAS MT LSCs (Figure).

FIMAR: Fast incremental memory acquisition and restoration system for temporal-dimension forensic analysis

Recent advances in anti-forensic techniques, including encryption, obfuscation, and kernel-structure manipulations, make conventional storage, network, and memory forensics difficult. For example, the code of packed malware is encrypted on storage, network packets, and system RAM to hide its existence; it appears briefly in plain text on the system RAM during its execution phase. Memory forensics, therefore, has been a vital component of today's digital forensics. Conventional memory acquisition techniques, however, capture only the current state of a system RAM; if the timing of the acquisition is late, the evidence could be lost forever. Since the state-of-the-art anti-forensic attacks leave only the slightest evidence for the shortest time, finer temporal acquisition and analysis are needed. This paper presented a Fast Incremental Memory Acquisition and Restoration (FIMAR) system that enables us to acquire multiple memory snapshots (i.e., memory dumps) over time and to construct a timeline of past activities on a computer. A thin hypervisor and its Second Level Address Translation (SLAT) were used to track memory page changes and to obtain atomic memory snapshots. The hash calculation of 4 KiB chunks was used to detect updated parts of the system RAM to reduce the transfer size. We implemented the incremental memory acquisition algorithms using a thin hypervisor called BitVisor. Finally, we tested the developed FIMAR system to analyze BlueSky ransomware with anti-forensic logic. The developed FIMAR system improved the atomicity of memory snapshots using TLB shootdown and considering per-core Extended Page Tables (EPTs) compared to previous hypervisor-based memory acquisition systems.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping between data resolution, spatial and temporal coverage, acquisition costs, and high accuracy expectations. This article provides an overview of research work related to soil salinization mapping and monitoring using remote sensing. By synthesizing recent research and highlighting areas where further investigation is needed, this review helps to steer future efforts, provides insight for decision-making on environmental sustainability and soil resource management, and promotes interdisciplinary collaboration.

Clinical applications of photon counting detector CT.

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

FMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines

How functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows’ structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.

Computational Analysis Reveals the Temporal Acquisition of Pathway Alterations during the Evolution of Cancer

Cancer metastasis is the lethal developmental step in cancer, responsible for the majority of cancer deaths. To metastasise, cancer cells must acquire the ability to disseminate systemically and to escape an activated immune response. Here, we endeavoured to investigate if metastatic dissemination reflects acquisition of genomic traits that are selected for. We acquired mutation and copy number data from 8332 tumours representing 19 cancer types acquired from The Cancer Genome Atlas and the Hartwig Medical Foundation. A total of 827,344 non-synonymous mutations across 8332 tumour samples representing 19 cancer types were timed as early or late relative to copy number alterations, and potential driver events were annotated. We found that metastatic cancers had a significantly higher proportion of clonal mutations and a general enrichment of early mutations in p53 and RTK/KRAS pathways. However, while individual pathways demonstrated a clear time-separated preference for specific events, the relative timing did not vary between primary and metastatic cancers. These results indicate that the selective pressure that drives cancer development does not change dramatically between primary and metastatic cancer on a genomic level, and is mainly focused on alterations that increase proliferation.

Intrinsic Temporal Performance of the RF Receive Coil in Magnetic Resonance Imaging.

The functional magnetic resonance imaging (fMRI) at ultra-high field (UHF, [Formula: see text]) is a powerful temporal acquisition method which promises to capture neuronal activities at submillimeter scale. But high-spatial-resolution fMRI still remains difficult, as the nuisance temporal noise which also grows with the main magnetic field strength. For decades, mainstream solutions in reducing motion-induced temporal noise include motion-correction algorithms in image post-processing as well as MR acquisition schemes in RF pulse sequence designs, however hardware-related studies have been rarely reported over the RF receive coil. In this study, we have proposed the intrinsic temporal performance model, which is specifically used for measuring coil-related intrinsic temporal SNR (tSNR*), and the intrinsic sensitivity variability and thermal noise variability have been proposed as model parameters. The intrinsic temporal performance of single-channel loops and array coils were evaluated using numerical electromagnetic simulations, and phantom experiments were designed to investigate the intrinsic thermal noise variability. It was observed that the achievable intrinsic tSNR* can be greatly lowered by ~90% even with 2 mm translational motion in the normal direction, suggesting the effect of RF receive coils in producing temporal noise. The proposed model provides a new perspective in optimizing coil designs and array coil temporal combination methods, which may offer a feasible means in achieving submillimeter resolutions at UHF. Moreover, model parameters from the intrinsic temporal performance model can be directly calculated based on single MRI acquisition, offering a practical performance metric for manufactures and customers in quality and assurance checks of RF receive coil products.

Multiregion neuronal activity: the forest and the trees.

The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.

Processing and acquisition of temporality in L2 Mandarin Chinese: Effects of grammatical and lexical aspects.

This study investigated the second language (L2) processing and acquisition of Chinese temporality, specifically the interaction of grammatical and lexical aspects. An experimental group of 31 English-speaking learners of Chinese and a control group of 29 native speakers of Mandarin Chinese completed an online sentence-picture matching task and an offline translation task. Results from these experiments demonstrated the prototype effect: In aspectual development, perfective aspect started with telic verbs and progressive aspect started with activity verbs, in accordance with the Aspect Hypothesis, both for online processing and offline comprehension. The prototype effect of the grammatical aspect was evident for activity verbs but less so for accomplishment verbs in the L2 group across tasks, and this was explained through language-specific properties and L2 learners’ instructional input. In addition, L2 proficiency and working memory capacity were found to modulate these processes.

Visualization of frequent temporal patterns in single or two populations

Temporal knowledge discovery in clinical problems, is crucial to investigate problems in the data science era. Meaningful progress has been made computationally in the discovery of frequent temporal patterns, which may store potentially meaningful knowledge. However, for temporal knowledge discovery and acquisition, effective visualization is essential and still stores much room for contributions. While visualization of frequent temporal patterns was relatively under researched, it stores meaningful opportunities in facilitating usable ways to assist domain experts, or researchers, in exploring and acquiring temporal knowledge. In this paper, a novel approach for the visualization of an enumeration tree of frequent temporal patterns is introduced for, whether mined from a single population, or for the comparison of patterns that were discovered in two separate populations. While this approach is relevant to any sequence-based patterns, we demonstrate its use on the most complex scenario of time intervals related patterns (TIRPs). The interface enables users to browse an enumeration tree of frequent patterns, or search for specific patterns, as well as discover the most discriminating TIRPs among two populations. For that a novel visualization of the temporal patterns is introduced using a bubble chart, in which each bubble represents a temporal pattern, and the chart axes represent the various metrics of the patterns, such as their frequency, reoccurrence, and more, which provides a fast overview of the patterns as a whole, as well as access specific ones. We present a comprehensive and rigorous user study on two real-life datasets, demonstrating the usability advantages of the novel approaches.