bluuebunny/crossref_metadata_2025 huggingface dataset:
NIT2 dampens BRD1 phase separation and restrains oxidative phosphorylation to enhance chemosensitivity in gastric cancer
Authors: Wang Ziyang , Di Yuqin , Wen Xiangqiong , Liu Ye , Ye Lvlan , Zhang Xiang , Qin Jiale , Wang Youpeng , Chu Huiying , Li Guohui , Zhang Weijing , Wang Xiongjun , He Weiling
Published: November 2024

5-Fluorouracil (5-FU) chemoresistance contributes to poor therapeutic response and prognosis of gastric cancer (GC), for which effective strategies to overcome chemoresistance are limited. Here, using a CRISPR-Cas9 system, we identified that nitrilase family member 2 (NIT2) reverses chemoresistance independent of its metabolic function. Depletion or low expression of NIT2 led to 5-FU resistance in GC cell lines, patient-derived organoids, and xenografted tumors. Mechanistically, NIT2 interacted with bromodomain-containing protein 1 (BRD1) to inhibit HBO1-mediated acetylation of histone H3 at lysine-14 (H3K14ac) and RELA-targeted oxidative phosphorylation (OXPHOS) gene expression. Upon 5-FU stimulation, NIT2 phosphorylation by Src at Y49 promoted the dissociation of NIT2 from BRD1, followed by binding to E3 ligase CCNB1IP1, causing autophagic degradation of NIT2. Consequently, reduced NIT2 protein resulted in BRD1 forming phase separation and binding to histone H3, as well as increased RELA stability due to suppression of inhibitor of growth family member 4–mediated RELA ubiquitination. In addition, NIT2 expression negatively correlated with H3K14ac and OXPHOS and positively correlated with the chemotherapeutic responses and prognosis of patients with GC. Our findings reveal the moonlighting function of NIT2 in chemoresistance and underscore that OXPHOS blockade by metformin enhances 5-FU chemosensitivity upon NIT2 loss.
Disrupting the RNA polymerase II transcription cycle through CDK7 inhibition ameliorates inflammatory arthritis
Authors: Chen Xi , Shibu Gayathri , Sokolsky Baila A. , Soussana Tamar Nicole , Fisher Logan , Deochand Dinesh K. , Dacic Marija , Mantel Ian , Ramirez Daniel C. , Bell Richard D. , Zhang Tinghu , Donlin Laura T. , Goodman Susan M. , Gray Nathanael S. , Chinenov Yurii , Fisher Robert P. , Rogatsky Inez
Published: November 2024

Macrophages are key drivers of inflammation and tissue damage in autoimmune diseases including rheumatoid arthritis. The rate-limiting step for transcription of more than 70% of inducible genes in macrophages is RNA polymerase II (Pol II) promoter-proximal pause release; however, the specific role of Pol II early elongation control in inflammation, and whether it can be modulated therapeutically, is unknown. Genetic ablation of a pause-stabilizing negative elongation factor (NELF) in macrophages did not affect baseline Pol II occupancy but enhanced the transcriptional response of paused anti-inflammatory genes to lipopolysaccharide followed by secondary attenuation of inflammatory signaling in vitro and in the K/BxN serum transfer mouse model of arthritis. To pharmacologically disrupt the Pol II transcription cycle, we used two covalent inhibitors of the transcription factor II H-associated cyclin-dependent kinase 7 (CDK7), THZ1 and YKL-5-124. Both reduced Pol II pausing in murine and human macrophages, broadly suppressed induction of pro- but not anti-inflammatory genes, and rapidly reversed preestablished inflammatory macrophage polarization. In mice, CDK7 inhibition ameliorated both acute and chronic progressive inflammatory arthritis. Lastly, CDK7 inhibition down-regulated a pathogenic gene expression signature in synovial explants from patients with rheumatoid arthritis. We propose that interfering with Pol II early elongation by targeting CDK7 represents a therapeutic opportunity for rheumatoid arthritis and other inflammatory diseases.
Inactivation of deposited bioaerosols on food contact surfaces with UV-C light emitting diode devices
Authors: Sharma Aakash , Singh Amritpal , Pendyala Brahmaiah , Balamurugan Sampathkumar , Patras Ankit
Published: November 2024

The airborne transmission of infectious diseases and bioaerosol-induced cross-contamination pose significant challenges in the food, dairy, and pharma industries. This study evaluated the effectiveness of 279 nm UV-C LED irradiation for decontaminating bioaerosols, specifically containing microorganisms such as
Escherichia coli
(C3040- Kanamycin resistant),
Salmonella
Enteritidis (ATCC 4931), and
Pseudomonas fragi
(ATCC 4973), on food contact surfaces. Borosilicate glass, silicon rubber, and stainless steel (316L) surfaces were selected for experimentation for their usage in the food industry. A 50 µL cell suspension was aerosolized at 25 psi pressure using a 4-jet BLAM Nebulizer within a customized glass chamber and then deposited onto the surface of the coupons. The serial dilution approach was used for the microbial enumeration, followed by duplicate plating. With a low Root Mean Square Error (RMSE) and high
R
2
values, the biphasic kinetic model for UV-C inactivation curves of all three pathogens demonstrated the excellent goodness of fit parameters. At a UV-C dose of 6 mJ cm
−2
, glass surfaces showed the maximum microbial inactivation (i.e., 2.80, 3.81, and 3.56 log CFU/mL for
E. coli
,
Salmonella
, and
P. fragi
, respectively). Stainless steel and silicon rubber surfaces showed significant microbial inactivation, but log
10
reductions observed were consistently lower than glass surface. Our research indicates that UV-C LEDs (279 nm) can effectively disinfect bioaerosols on food contact surfaces.

IMPORTANCE

Food safety is a major public health concern, with contaminated food causing serious illnesses. UV-C light, used for germicidal action, is effective in disinfecting surfaces and is not subject to the same strict legal restrictions as chemical disinfectants, simplifying compliance with food safety regulations. In this study, we evaluated the efficacy of UV-C (279 nm) LED systems for inactivation of surface-deposited bioaerosols of kanamycin-resistant
Escherichia coli
(C3040),
Salmonella
Enteritidis (ATCC 4931), and
Pseudomonas fragi
(ATCC 4973). The research outcomes can be used to develop UV-based surface disinfection systems to minimize the risk of foodborne illnesses and enhance safety in high-traffic food preparation areas.
Insights into the role of N6-methyladenosine (m6A) in plant-virus interactions
Authors: Secco Nicola , Sheikh Arsheed H. , Hirt Heribert
Published: November 2024

N6-methyladenosine (m6A) is a common and dynamic epitranscriptomic modification in eukaryotic RNAs, affecting stability, splicing, translation, and degradation. Recent technological advancements have revealed the complex nature of m6A modifications, highlighting their importance in plant and animal species. The m6A modification is a reversible process, with “writers” depositing methylation, “erasers” demethylating it, and “reader” proteins recognizing m6A and executing various biological functions. Studying the relationship between m6A methylation and viral infection is crucial. Animal viruses, including retroviruses, RNA viruses, and DNA viruses, often employ the host’s m6A machinery to replicate or avoid immune responses. In plant viruses, host methyltransferases or demethylases can stabilize or degrade viral RNA, depending on the virus-host interaction. Additionally, viral infections can modify the host’s m6A machinery, impacting the viral life cycle. This review examines the role of m6A modifications in plant viral pathogenesis, focussing on RNA viruses infecting crops like alfalfa, turnip, wheat, rice, and potato. Understanding the role of m6A in virus-host interactions can aid in studying plant viral disease development and discovering novel antiviral targets for crop protection. In this review, we summarize current information on m6A in RNA biology, focussing on its function in viral infections and plant-virus interactions.
On the relationship between the uppermost mantle (≤220 km) seismic velocity, crustal thickness, and topography in Tibet
Authors: Rowley David B. , Liu Chujie , Grand Stephen P. , Liang Xiaofeng , Sandvol Eric
Published: November 2024

We propose that the mantle lithospheric density and crustal thickness are correlated in such a way as to produce a flat Tibetan Plateau. We observe that the mantle lithosphere is relatively uniform beneath the Himalaya and southern and central Tibet, despite a near doubling of crustal thickness relative to India. Farther north, cratonic mantle lithosphere disappears over large regions of north-central Tibet, giving rise to large lateral variations in uppermost mantle Vs anomalies (>12%) that are uncorrelated with changes in surface elevation but are closely related to changes in crustal thickness. This decoupling of surface topography from spatial variations in upper mantle seismic velocity, and assumed buoyancy, implies that Tibetan topography is controlled by a crust-mantle interaction that is able to maintain its near constant elevation. This crust-mantle interaction is likely driven by gravitational potential energy with a very weak crust. Magmatism, with ages of ca. 20 Ma to Present, spatially correlated with this region with no sub-Moho mantle lithosphere implies destabilization of mantle lithosphere in northern Tibet. Cratonic Indian underthrusting for the past 25 m.y. has also not led to significant topography in the plateau through time. The magmatism may have helped weaken the crust, allowing it to respond to changes in uppermost mantle buoyancy, resulting in a flat plateau.
Colloidal gold dispersions in orogenic gold from the Barberton Greenstone Belt, South Africa
Authors: La Cock Rutger A. , von der Heyden Bjorn P.
Published: November 2024

The mechanisms of gold grain growth, as a necessary precursor to gold hyper-accumulation and the formation of high-grade ore deposits, remain equivocal. This is particularly true for orogenic gold deposits where primary growth textures may be poorly preserved due to episodic fault re-activations during successive seismic events. A sample collected from the Fairview Mine in the Barberton Greenstone Belt (South Africa) and subjected to non-destructive SELFRAG sample preparation and subsequent scanning electron microscopy provides rare capture of images of millimeter-scale gold grains associated with dispersed spheroidal gold colloids and nanoparticles (<5 nm to >1 µm diameter). Although the ultimate provenance of these gold spheroids remains equivocal, textural evidence suggests a net addition of mass to the adjacent gold grains. We suggest that Ostwald ripening (a process of particle growth or coarsening by diffusion) and coalescence may contribute to gold grain growth in this sample.
A dendritic growth mechanism for producing oscillatory zoning in igneous zircon
Authors: Gillespie Jack , Klein Benjamin Z. , Moore Jo , Müntener Othmar , Baumgartner Lukas P.
Published: November 2024

The concentric oscillatory zoning pattern of igneous zircon is generally interpreted as evidence for core to rim growth during magmatic crystallization. This interpretation anchors a wide variety of qualitative and quantitative studies of zircon age, chemistry, and texture. We show, via detailed trace element mapping, that euhedral magmatic zircon with apparently typical cathodoluminescence oscillatory zoning instead displays evidence of dendritic growth. This observation challenges the unique interpretation of oscillatory zoning in zircon. Dendritic growth occurs under conditions of substantial undercooling, resulting in disequilibrium trace element concentrations in the zircon. Geochronological and geochemical analyses of zircon with this type of growth history require a different interpretative framework, with implications for time scales of magmatic and volcanic systems.
Supplemental Material: A Holocene history of high bluffs, strandplains, terraces, and dunes along the southeastern margin of Lake Superior (Michigan, USA) with reference to fluctuating lake levels
Authors: Fisher Timothy G. , al. et
Published: November 2024

Supplemental Text S1: OSL methodology with example data plots (Figs. S1–S3). Supplemental Text S2: Example calculations for lake-level reconstructions. Figure S4: Large-scale version of Figure 4. Figure S5: Large-scale version of Figure 11.
Guidance for clinical management of pathogenic variant carriers at elevated genetic risk for ALS/FTD
Authors: Benatar Michael , Heiman-Patterson Terry D , Cooper-Knock Johnathan , Brickman Daniel , Casaletto Kaitlin B , Goutman Stephen A , Vinceti Marco , Dratch Laynie , Arias Jalayne J , Swidler Jean , Turner Martin R , Shefner Jeremy , Westeneng Henk-Jan , van den Berg Leonard H , Al-Chalabi Ammar
Published: November 2024

There is a growing understanding of the presymptomatic stages of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) and nascent efforts aiming to prevent these devastating neurodegenerative diseases have emerged. This progress is attributable, in no small part, to the altruism of people living with pathogenic variants at elevated genetic risk for ALS/FTD via their willingness to participate in natural history studies and disease prevention trials. Increasingly, this community has also highlighted the urgent need to develop paradigms for providing appropriate clinical care for those at elevated risk for ALS and FTD. This manuscript summarises recommendations emanating from a multi-stakeholder Workshop (Malvern, Pennsylvania, 2023) that aimed to develop guidance for at-risk carriers and their treating physicians. Clinical care recommendations span genetic testing (including counselling and sociolegal implications); monitoring for the emergence of early motor, cognitive and behavioural signs of disease; and the use of Food and Drug Administration-approved small molecule drugs and gene-targeting therapies. Lifestyle recommendations focus on exercise, smoking, statin use, supplement use, caffeine intake and head trauma, as well as occupational and environmental exposures. While the evidence base to inform clinical and lifestyle recommendations is limited, this guidance document aims to appraise carriers and clinicians of the issues and best available evidence, and also to define the research agenda that could yield more evidence-informed guidelines.
Clinical study of a micro-implantable pulse generator for the treatment of peripheral neuropathic pain: 12-month results from the COMFORT-randomized controlled trial
Authors: Hatheway John , Hersel Alexander , Engle Mitchell , Gutierrez Genaro , Khemlani Vishal , Kapural Leonardo , Moore Gregory , Ajakwe Reginald , Trainor Drew , Hah Jennifer , Staats Peter S , Makous James , Heit Gary , Kottalgi Shilpa , Desai Mehul J
Published: November 2024

Background

There is paucity of data from randomized controlled trials supporting the use of peripheral nerve stimulation, a well-established therapy for the treatment of chronic pain. This study was undertaken, in part, to provide randomized controlled trial data in support of patient access to appropriate peripheral nerve stimulation therapy. The COMFORT study is the first large, postmarket, multicenter randomized controlled trials investigating the use of a Food and Drug Administration-cleared micro-implantable pulse generator (IPG) for treating chronic pain via peripheral nerve stimulation therapy.

Methods

Consented, eligible subjects were randomized to either the active arm, which received peripheral nerve stimulation and conventional medical management, or the control arm, which received conventional medical management alone and were allowed to cross over to the active arm, after 3 months. Pain and patient-reported outcomes were captured. Therapy responders were subjects who achieved at least a 50% reduction in pain scores compared with baseline. We are reporting the 12-month results of this 36-month study.

Results

At 12 months, the responder rate was 87% with a 69% average reduction in pain compared with baseline (7.5±1.2 to 2.3±1.7; p<0.001). Statistical significance was achieved for all patient-reported outcomes. There was an excellent safety profile with no serious adverse device effects or reports of pocket pain. A majority of subjects used unique programming options and found this device easy to use and comfortable to wear.

Conclusions

These 12-month results are consistent with previously reported 6-month outcomes from this study, showing durability of peripheral nerve stimulation treatment with the micro-IPG system; subjects realized sustained large reduction in pain and improvement in patient-reported outcomes following treatment with this micro-IPG system.

Trial registration number

NCT05287373.
NUMERICAL SIMULATION ON FLOW COUPLING IN A TRIPLE-FRACTURE NETWORK MODEL BASED ON FRACTAL GEOMETRY THEORY
Authors: ZHOU ZHOU , ZHU JINGYUN , WANG BIN , LIU GUANNAN , JI MING , LIU YANG , YU BOMING
Published: November 2024

This study presents numerical simulations on fluid flow under high stress and intense hydraulic activity and focuses on complex reservoirs with porous and fractured microstructures based on fractal geometry theory. A Triple-Fracture Network model, which includes pores, micro-fractures and macro-fractures, is categorized into fractal fracture networks and fractal pore systems based on fractal geometry theory. Using the Monte Carlo method, we construct macroscopic randomly distributed fractures to examine the impact of macro-fracture topological structures on flow dynamics. Fractal permeability models for micro-fracture and micro-pore systems are developed, incorporating variables such as fracture lengths and pore sizes. The proposed approach partitions porous media into macroscopically discrete fractures and microscopically characterized pores, utilizing fractal geometry theory to analyze hydro-mechanics coupling. The validity of the proposed model is confirmed through comparisons with analytical solutions and available models as well as the published field data. This results indicate that the spatial distribution of the effective stress and macroscopic fractures significantly affects the fractal dimensions of microscopic fractures and pores. It is found that the density of macro-fractures influences the fracture pressure, while the initial porosity and proportionality constant [Formula: see text] of micro-fractures impact the pressure distributions in the tested domains. It is found that the maximum length of micro-fractures correlates positively with permeability. This model introduces a novel perspective on random macro-fractures and fluid coupling in microstructures, enhancing the understanding of macro- and micro-hydrodynamics interactions.
ANALYSIS ON MULTIPLICATIVE k-ATANGANA–BALEANU FRACTIONAL INTEGRALS WITH APPLICATION TO VARIOUS MERCER-TYPE INEQUALITIES
Authors: LONG YUN , DU TINGSONG
Published: November 2024

We innovatively introduce a new group of integral operators called multiplicative [Formula: see text]-Atangana–Baleanu fractional integrals. Following the path of this research, we discuss their [Formula: see text]integrability, continuity, boundedness together with linearity, and successfully construct two Hermite–Hadamard–Mercer inequalities employing this multiplicative fractional integrals, focusing on endpoint- and midpoint-type, respectively. Subsequently, we further propose three identities, involving trapezoid-Mercer-, midpoint-Mercer-, and parameterized-Mercer-type, and combining the function [Formula: see text] is multiplicatively convex or, for some fixed [Formula: see text], the function [Formula: see text] is convex, and we formulate a series of corresponding fractional Mercer-type inequalities. Intended to enhance the readers’ deeper understanding of the results, we offer two instances accompanied by graphical representations, facilitating the validity of the inequalities gained in the current paper. Finally, some applications in multiplicative differential equation, the quadrature formula and special means are presented as well.
The Landsberg curvature of the class of spherically symmetric Finsler metrics
Authors: Khani-Moghaddam Tahereh , Rafie-Rad Mehdi , Tayebi Akbar
Published: November 2024

In this paper, we study the Landsberg curvature of the class of spherically symmetric Finsler metrics. We find the necessary and sufficient condition under which a spherically symmetric Finsler metric of dimension [Formula: see text] has relatively isotropic Landsberg curvature. This yields an extension of Mo–Zhou and Elgendi’s results that proved for the spherically symmetric Finsler metric with vanishing Landsberg curvature. Finally, we characterize spherically symmetric Finsler metrics with vanishing stretch curvature.
Music Emotion Classification System Based on Quantum Feature Extraction and Its Effectiveness Evaluation
Authors: Li Shuqi
Published: November 2024

Strong emotions can be expressed and evoked via music. However, accurately recognizing the emotions in music using computational models is extremely challenging. When the music portions convey various complex emotions, the problem’s difficulty can rise significantly. In this work, a systematic strategy that blends multiple cutting-edge techniques is used to focus on the emotional classification of music. The Kaggle-sourced music emotion dataset provides a wide range of audio samples illustrating various emotional expressions in music. Spectral subtraction as a noise reduction approach is used to improve the quality of the analysis. This efficiently reduced unwanted background noise and improved the audio signals’ clarity. A Quantum Convolutional Neural Network (QCNN) is used for feature extraction, taking advantage of its special capacity to extract complex patterns and characteristics from the music data that are essential for emotion recognition. To provide accurate predictions, the training procedure is optimized and finally utilized Flexible Runge Kutta Optimized Multilayer Perceptrons (FRKO-MLP) for emotion classification. The goal of this combination of advanced classification approaches, quantum feature extraction and noise reduction is to increase the robustness and accuracy of music emotion identification systems. Based on the chosen datasets, the results of the experimental study demonstrate that the suggested model greatly increases the efficiency and accuracy of emotion classification in music. The proposed FRKO-MLP attains 93.21% accuracy, 90.52% precision, 87.34% sensitivity and 89.72% F1-score. These findings show that the model is far better at identifying emotions than traditional methods.
Pioneering Cooperative Air-Ground Instant Delivery Using UAVs and Crowdsourced Couriers
Authors: Pan Yan , Gao Junhui , Duan Jiashun , Shi Juan , Guo Bin , Liang Yunji , Hu Yujiao
Published: November 2024

Instant delivery, which has a strict time limit, has become a fundamental service in modern cities. Increasing demands and labor shortages make efficient instant delivery more challenging. Recently, the industry has recruited Unmanned Aerial Vehicles (UAVs) and part-time couriers to address this critical issue. However, the inherent delivery ability of crowdsourced couriers and the limited battery capacity of UAVs make them hard to solely meet the rapidly increasing instant delivery demands. Therefore, this paper proposes the first air-ground cooperative UAV and Courier Delivery paradigm, called UCD, to develop an optimal courier recruitment plan and assignment of instant delivery tasks for UAVs and couriers. In the UCD paradigm, courier and UAV delivery models are introduced, and a recruitment plan that considers both delivery demand and courier availability is designed. With this foundation, a data-driven algorithm is proposed to optimize delivery task assignments, maximizing the flexibility of UAVs and the capacity of couriers. Comprehensive evaluations are conducted on multiple long-term real-world datasets to demonstrate the superior performance of UCD.
rTsfNet: A DNN Model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition
Authors: Enokibori Yu
Published: November 2024

Many deep learning (DL) models have been proposed for the IMU (inertial measurement unit) based HAR (human activity recognition) domain. However, combinations of manually designed time series features (TSFs) and traditional machine learning (ML) often continue to perform well. It is not rare that combinations among TSFs and DL show better performance than the DL-only approaches. Those facts mean that TSFs have the potential to outperform automatically generated features using deep neural networks (DNNs). However, TSFs have a problem: their performances are only good if appropriate 3D bases are selected. Fortunately, DL's strengths include capturing the features of input data and adaptively deriving parameters automatically. Thus, as a new DNN model for an IMU-based HAR, this paper proposes rTsfNet, a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction. rTsfNet automatically selects multiple 3D bases from which features should be derived by extracting 3D rotation parameters within the DNN. Then, TSFs are derived to achieve HAR results using multilayer perceptrons (MLPs). With this combination, rTsfNet showed higher performance than existing models under well-managed benchmark conditions and multiple datasets: UCI HAR, PAMAP2, Daphnet, and OPPORTUNITY, all of which target different activities.
Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare
Authors: Adler Daniel A. , Yang Yuewen , Viranda Thalia , Xu Xuhai , Mohr David C. , Van Meter Anna R. , Tartaglia Julia C. , Jacobson Nicholas C. , Wang Fei , Estrin Deborah , Choudhury Tanzeem
Published: November 2024

Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.
MagDesk: Interactive Tabletop Workspace Based on Passive Magnetic Tracking
Authors: Huang Kunpeng , Iravantchi Yasha , Chen Dongyao , Sample Alanson P.
Published: November 2024

Accurate and responsive 3D tracking enables interactive and context-aware workspaces, including mixed reality 3D interfaces and collaborative tangible interactions. However, limitations of current tracking mechanisms - line-of-sight occlusion, drifting errors, small working volumes, or instrumentation that requires maintenance - ultimately restrict their adoption. This paper introduces MagDesk, an interactive tabletop workspace capable of real-time 3D tracking of passive magnets embedded in objects. Using a sensing array of 112 low-cost magnetometers underneath a table, our custom-designed signal processing and localization engine enables simultaneous tracking of multiple magnets in 5 degrees of freedom with millimeter accuracy. MagDesk can continuously and robustly track magnets at a maximum height of 600 mm over a 1750 mm (L) × 950 mm (W) table, while achieving an average positional and orientational error of 2.49 mm and 0.72 ° near the table surface and 14.40 mm and 2.25° across the entire sensing range. To demonstrate MagDesk's object-tracking capabilities, this work presents a series of magnetic widgets for tangible interactions and explores two applications - a 3D drawing interface and augmented-reality tabletop games. By instrumenting a regular table surface, MagDesk presents a low-cost and accurate approach to 3D tracking passive objects for home and office environments.
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms
Authors: Sui Yueyuan , Zhao Minghui , Xia Junxi , Jiang Xiaofan , Xia Stephen
Published: November 2024

We propose TRAMBA, a hybrid transformer and Mamba architecture for acoustic and bone conduction speech enhancement, suitable for mobile and wearable platforms. Bone conduction speech enhancement has been impractical to adopt in mobile and wearable platforms for several reasons: (i) data collection is labor-intensive, resulting in scarcity; (ii) there exists a performance gap between state-of-art models with memory footprints of hundreds of MBs and methods better suited for resource-constrained systems. To adapt TRAMBA to vibration-based sensing modalities, we pre-train TRAMBA with audio speech datasets that are widely available. Then, users fine-tune with a small amount of bone conduction data. TRAMBA outperforms state-of-art GANs by up to 7.3% in Perceptual Evaluation of Speech Quality (PESQ) and 1.8% in Short-Time Objective Intelligibility (STOI), with an order of magnitude smaller memory footprint and an inference speed up of up to 465 times. We integrate TRAMBA into real systems and show that TRAMBA (i) improves battery life of wearables by up to 160% by requiring less data sampling and transmission; (ii) generates higher quality voice in noisy environments than over-the-air speech; (iii) requires a memory footprint of less than 20.0 MB.
PrISM-Q&A: Step-Aware Voice Assistant on a Smartwatch Enabled by Multimodal Procedure Tracking and Large Language Models
Authors: Arakawa Riku , Lehman Jill Fain , Goel Mayank
Published: November 2024

Voice assistants capable of answering user queries during various physical tasks have shown promise in guiding users through complex procedures. However, users often find it challenging to articulate their queries precisely, especially when unfamiliar with the specific terminologies required for machine-oriented tasks. We introduce PrISM-Q&A, a novel question-answering (Q&A) interaction termed step-aware Q&A, which enhances the functionality of voice assistants on smartwatches by incorporating Human Activity Recognition (HAR) and providing the system with user context. It continuously monitors user behavior during procedural tasks via audio and motion sensors on the watch and estimates which step the user is performing. When a question is posed, this contextual information is supplied to Large Language Models (LLMs) as part of the context used to generate a response, even in the case of inherently vague questions like "What should I do next with this?" Our studies confirmed that users preferred the convenience of our approach compared to existing voice assistants. Our real-time assistant represents the first Q&A system that provides contextually situated support during tasks without camera use, paving the way for the ubiquitous, intelligent assistant.
Self-supervised Learning for Accelerometer-based Human Activity Recognition: A Survey
Authors: Logacjov Aleksej
Published: November 2024

Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune strategy, achieving state-of-the-art performances across many research areas. The field of accelerometer-based human activity recognition (HAR) can benefit from SSL since unlabeled data can be collected cost-efficiently due to the ubiquitous nature of sensors embedded in smart devices, which is in contrast to labeled data, that require a costly annotation process. Motivated by the success of SSL and the lack of surveys on SSL for HAR, this survey comprehensively examines 52 SSL methods applied to HAR, and categorizes them into four SSL paradigms based on pre-training objectives. We discuss SSL strategies, evaluation protocols, and utilized datasets. We highlight limitations in current methodologies, including little large-scale pre-training, the absence of foundation models, as well as the scarcity of systematic domain shift experiments and domain knowledge utilization. Notably, the diversity in evaluation protocols across papers poses a considerable challenge when comparing methods. Future directions outlined in this survey include the development of an SSL framework for HAR to enable standardized benchmarking and large-scale pre-training, along with integrating domain knowledge to enhance model performance.
U-Flash: Improving Underwater Optical Communication by Scattering Effect
Authors: Chi Yang , Lin Chi , Xiong Jie , Sun Yu , Wang Lei , Wu Guowei , Fan Xin , Luo Zhongxuan
Published: November 2024

Underwater communication has been a focal point of communication research, driven by a multitude of applications including underwater facility maintenance and marine exploration. However, current systems often require expensive specialized equipment, rendering them less accessible. In this paper, we break the norm by pioneering the use of Commercial Off-The-Shelf (COTS) smartphones for underwater optical communication, leveraging their built-in flashlight and camera. Contrary to prevalent belief that scattering has a negative impact on communication, we discover that scattering in water can provide a positive gain for camera-based optical communication. Based on this insight, we present and implement U-Flash, a novel system encompassing a Fresnel lens plugin design to enhance light directionality and a tailored encoding-decoding scheme optimized for underwater environments. Comprehensive experiments demonstrate the effectiveness of U-Flash, achieving significant improvements in communication distance, reliability, and data rate. Specifically, the achievable communication distance is improved from 2.4 m to 7.6 m during daytime in the presence of strong ambient light interference and further extended to 9.8 m in low ambient light scenarios at night.
Are We in the Zone? Exploring the Features and Method of Detecting Simultaneous Flow Experiences Based on EEG Signals
Authors: Zhang Baiqiao , Li Xiangxian , Zhou Yunfan , Liu Juan , Zhou Chao , Liu Weiying , Bian Yulong
Published: November 2024

When executing interdependent personal tasks for the team's purpose, simultaneous individual flow (simultaneous flow) is the antecedent condition of achieving shared team flow. Detecting simultaneous flow helps better understanding the status of team members, which is thus important for optimizing multi-user interaction systems. However, there is currently a lack exploration on objective features and methods for detecting simultaneous flow. Based on brain mechanism of flow in teamwork and previous studies on electroencephalogram (EEG)-based individual flow detection, this study aims to explore the significant EEG features related to simultaneous flow, as well as effective detection methods based on EEG signals. First, a two-player simultaneous flow task is designed, based on which we construct the first multi-EEG signal dataset of simultaneous flow. Then, we explore the potential EEG signal features that may be related to individual and simultaneous flow and validate their effectiveness in simultaneous flow detection with various machine learning models. The results show that 1) the inter-brain synchrony features are relevant to simultaneous flow due to enhancing the models' performance in detecting different types of simultaneous flow; 2) the features from the frontal lobe area seem to be given priority attention when detecting simultaneous flows; 3) Random Forests performed best in binary classification while Neural Network and Deep Neural Network3 performed best in ternary classification.
SemiCMT: Contrastive Cross-Modal Knowledge Transfer for IoT Sensing with Semi-Paired Multi-Modal Signals
Authors: Chen Yatong , Hu Chenzhi , Kimura Tomoyoshi , Li Qinya , Liu Shengzhong , Wu Fan , Chen Guihai
Published: November 2024

This paper proposes a novel contrastive cross-modal knowledge transfer framework, SemiCMT, for multi-modal IoT sensing applications. It effectively transfers the feature extraction capability (also called knowledge) learned from a source modality (e.g., acoustic signals) with abundant unlabeled training data, to a target modality (e.g., seismic signals) that lacks enough training data, in a self-supervised manner with the help of only a small set of synchronized multi-modal pairs. The transferred model can be quickly finetuned to downstream target-modal tasks with only limited labels. The key design constitutes of three aspects: First, we factorize the latent embedding of each modality into shared and private components and perform knowledge transfer considering both the modality information commonality and gaps. Second, we enforce structural correlation constraints between the source modality and the target modality, to push the target modal embedding space symmetric to the source modal embedding space, with the anchoring of additional source-modal samples, which expands the existing modal-matching objective in current multi-modal contrastive frameworks. Finally, we conduct downstream task finetuning in the spherical space with a KNN classifier to better align with the structured modality embedding space. Extensive evaluations on five multimodal IoT datasets are performed to validate the effectiveness of SemiCMT in cross-modal knowledge transfer, including a new self-collected dataset using seismic and acoustic signals for office activity monitoring. SemiCMT consistently outperforms existing self-supervised and knowledge transfer approaches by up to 36.47% in the finetuned target-modal classification tasks. The code and the self-collected dataset will be released at https://github.com/SJTU-RTEAS/SemiCMT.
Activity Unmasks Chirality in Liquid-Crystalline Matter
Authors: Maitra Ananyo
Published: November 2024

Active matter theories naturally describe the mechanics of living systems. As biological matter is overwhelmingly chiral, an understanding of the implications of chirality for the mechanics and statistical mechanics of active materials is a priority. This article examines active, chiral materials from a liquid-crystal physicist's point of view, extracting general features of broken-symmetry-ordered phases of such systems without reference to microscopic details. Crucially, this demonstrates that activity allows chirality to affect the hydrodynamics of broken-symmetry phases in contrast to passive liquid crystals, in which chirality induces the formation of a range of spatially periodic structures whose large-scale mechanics have no signatures of broken parity symmetry. In active systems, chirality leads to the formation of phases that break time translation symmetry, which is impossible in equilibrium, and the existence of new kinds of elastic force densities in translation symmetry-broken states.
CD70: An Emerging Anticancer Target in Renal Cell Carcinoma and Beyond
Authors: Zang Peter D. , Angeles Arkhjamil , Pal Sumanta K.
Published: November 2024

CD70 is an emerging target for anticancer therapies. It is an ideal antigen target given its limited expression in normal physiologic tissues and propensity to be aberrantly expressed in a variety of malignancies, thus limiting off-target toxicities. It is also heavily involved in immune homeostasis, and disruption of this pathway can help overcome tumor-related immune cell exhaustion. Recent phase I/II trials using cellular therapies targeting CD70, such as chimeric antigen receptor-T cells, have shown promising effectiveness and safety in treating relapsed or refractory renal cell carcinoma. Noncellular therapies targeting CD70, such as antibody-drug conjugates, monoclonal antibodies, radionuclides, and cytokines, are currently under investigation, with early data showing encouraging results as well. Efforts are already underway to further improve and optimize CD70-based therapies.
Cell Growth and Division of Staphylococcus aureus
Authors: Pinho Mariana G. , Foster Simon J.
Published: November 2024

Bacterial cell growth and division require temporal and spatial coordination of multiple processes to ensure viability and morphogenesis. These mechanisms both determine and are determined by dynamic cellular structures and components, from within the cytoplasm to the cell envelope. The characteristic morphological changes during the cell cycle are largely driven by the architecture and mechanics of the cell wall. A constellation of proteins governs growth and division in Staphylococcus aureus, with counterparts also found in other organisms, alluding to underlying conserved mechanisms. Here, we review the status of knowledge regarding the cell cycle of this important pathogen and describe how this informs our understanding of the action of antibiotics and the specter of antimicrobial resistance.
Models and Rating Systems for Head-to-Head Competition
Authors: Glickman Mark E. , Jones Albyn C.
Published: November 2024

One of the most important tasks in sports analytics is the development of binary response models for head-to-head game outcomes to estimate team and player strength. We discuss commonly used probability models for game outcomes, including the Bradley–Terry and Thurstone–Mosteller models, as well as extensions to ties as a third outcome and to the inclusion of a home-field advantage. We consider dynamic extensions to these models to account for the evolution of competitor strengths over time. Full likelihood-based analyses of these time-varying models can be simplified into rating systems, such as the Elo and Glicko rating systems. We present other modern rating systems, including popular methods for online gaming, and novel systems that have been implemented for online chess and Go. The discussion of the analytic methods are accompanied by examples of where these approaches have been implemented for various gaming organizations, as well as a detailed application to National Basketball Association game outcomes.
DC and RF Performance Optimization of Source Pocket Designed Hybrid-Dielectric Vertical Nanowire Tunnel-FET: Low Power Perspective
Authors: Macherla Swaroop Kumar , Goel Ekta , Singh Ashish Kumar , Pandey Archana
Published: November 2024

This article addresses a new source pocket designed hybrid-dielectric vertical nanowire tunnel-FET (SP-HD-VNW-TFET). The existence of a source pocket at the source and channel boundary is shown such that the potential barrier at the tunnel-junction is minimized which causes ON current to rise. This article studied a comparison between a SP-HD-VNW-TFET device and source pocket vertical nanowire tunnel field effect transistor (SP-VNW_TFET). Using a hetero/hybrid-dielectric material boosts the electric field, resulting in higher tunneling current (1.72 × 10−6 A μm−1). The device has undergone detailed investigation of both DC and AC characteristics like On-current, Off-current, ION/IOFF, Subthreshold-swing, VT, gm, fT, GWB, and TFP. Source Pocket engineering and Hybrid dielectric inclusion increase device properties, including on-current and subthreshold swing (SS). The device’s electrical properties have been evaluated and compared using the Sentaurus TCAD Tool.
Neural Networks With Linear Adaptive Batch Normalization and Swarm Intelligence Calibration for Real‐Time Gaze Estimation on Smartphones
Authors: Zhu Gancheng , Li Yongkai , Zhang Shuai , Duan Xiaoting , Huang Zehao , Yao Zhaomin , Wang Rong , Wang Zhiguo
Published: November 2024

Eye tracking has emerged as a valuable tool for both research and clinical applications. However, traditional eye‐tracking systems are often bulky and expensive, limiting their widespread adoption in various fields. Smartphone eye tracking has become feasible with advanced deep learning and edge computing technologies. However, the field still faces practical challenges related to large‐scale datasets, model inference speed, and gaze estimation accuracy. The present study created a new dataset that contains over 3.2 million face images collected with recent phone models and presents a comprehensive smartphone eye‐tracking pipeline comprising a deep neural network framework (MGazeNet), a personalized model calibration method, and a heuristic gaze signal filter. The MGazeNet model introduced a linear adaptive batch normalization module to efficiently combine eye and face features, achieving the state‐of‐the‐art gaze estimation accuracy of 1.59 cm on the GazeCapture dataset and 1.48 cm on our custom dataset. In addition, an algorithm that utilizes multiverse optimization to optimize the hyperparameters of support vector regression (MVO–SVR) was proposed to improve eye‐tracking calibration accuracy with 13 or fewer ground‐truth gaze points, further improving gaze estimation accuracy to 0.89 cm. This integrated approach allows for eye tracking with accuracy comparable to that of research‐grade eye trackers, offering new application possibilities for smartphone eye tracking.
Characterization of In‐House Produced Mono‐ and Polyclonal Antibodies Against Soy for Component‐Resolved Allergen Analysis
Authors: Wiederstein Michael , Fuehrer Manuela , Lauter Kathrin , Baumgartner Sabine
Published: November 2024

In recent years, food allergies have emerged as a significant global health concern that affect an increasing percentage of the population. Despite being under investigation and discussed as a major or minor elicitor of food allergies, soybean remains a notable allergenic food source, affecting approximately 0.3%–3% of people worldwide, with a higher prevalence in Asian countries. Allergic reactions to soy can manifest as mild symptoms or progress to severe, life‐threatening conditions such as anaphylaxis. Consequently, there is an urgent need for the specific detection of soy allergens in food to mitigate the risk of inadvertent exposure and associated allergic reactions. State‐of‐the‐art methods for detecting food allergens include immunoassays utilizing either food source‐ or, ideally, allergen‐specific antibodies, along with mass spectrometry for detecting specific protein‐derived peptides. However, many commercially available immunoassays predominantly utilize polyclonal antibodies, which can confirm the presence of the entire food source but lack specificity for individual allergens. In response to this limitation, the aim is to develop specific monoclonal antibodies targeting individual soy allergens. These antibodies enable “component‐resolved analysis” of food samples, aligning with the emerging trend of component‐resolved diagnosis in allergy research. The generated monoclonal antibodies not only offer the ability to detect individual soy allergens in food and raw materials but also serve as valuable tools for quality and safety control of materials used in oral immunotherapy and allergy diagnosis. This improvement contributes significantly to aiding people with soy allergies and can be adapted for other food allergenic sources. This advancement represents a pivotal step toward enhancing the precision and comprehensiveness of allergen detection methodologies, addresses the specific challenges faced by individuals with soy allergies, and lays the foundation for broader applications to other food allergenic sources.