bluuebunny/crossref_metadata_2025 huggingface dataset:
Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain
Authors: Li Jixin , Ponnada Aditya , Wang Wei-Lin , Dunton Genevieve , Intille Stephen
Published: November 2024

Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection, but frequent interruptions with lengthy surveys on mobile devices can burden users, impacting compliance and data quality. We propose a method that reduces the length of each EMA question set measuring interrelated constructs, with only modest information loss. By estimating the potential information gain of each EMA question using question-answer prediction models, this method can prioritize the presentation of the most informative question in a question-by-question sequence and skip uninformative questions. We evaluated the proposed method by simulating question omission using four real-world datasets from three different EMA studies. When compared against the random question omission approach that skips 50% of the questions, our method reduces imputation errors by 15%-52%. In surveys with five answer options for each question, our method can reduce the mean survey length by 34%-56% with a real-time prediction accuracy of 72%-95% for the skipped questions. The proposed method may either allow more constructs to be surveyed without adding user burden or reduce response burden for more sustainable longitudinal EMA data collection.
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging with Neural Networks Based on Ballistocardiograms
Authors: Li Shuzhen , Chen Yuxin , Chen Xuesong , Gao Ruiyang , Zhang Yupeng , Yu Chao , Li Yunfei , Ye Ziyi , Huang Weijun , Yi Hongliang , Leng Yue , Wu Yi
Published: November 2024

Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medical sensors like EEG and ECG, can be effective but often present challenges such as unnatural user experience, complex deployment, and high costs. Ballistocardiography (BCG), a type of piezoelectric sensor signal, offers a non-invasive, user-friendly, and easily deployable alternative for long-term home monitoring. However, reliable BCG-based sleep staging is challenging due to the limited sleep monitoring data available for BCG. A restricted training dataset prevents the model from generalization across populations. Additionally, transferring to BCG faces difficulty ensuring model robustness when migrating from other data sources. To address these issues, we introduce SleepNetZero, a zero-shot learning based approach for sleep staging. To tackle the generalization challenge, we propose a series of BCG feature extraction methods that align BCG components with corresponding respiratory, cardiac, and movement channels in PSG. This allows models to be trained on large-scale PSG datasets that are diverse in population. For the migration challenge, we employ data augmentation techniques, significantly enhancing generalizability. We conducted extensive training and testing on large datasets (12393 records from 9637 different subjects), achieving an accuracy of 0.803 and a Cohen's Kappa of 0.718. ZeroSleepNet was also deployed in real prototype (monitoring pads) and tested in actual hospital settings (265 users), demonstrating an accuracy of 0.697 and a Cohen's Kappa of 0.589. To the best of our knowledge, this work represents the first known reliable BCG-based sleep staging effort and marks a significant step towards in-home health monitoring.
Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors
Authors: Chen Wenqiang , Cheng Jiaxuan , Wang Leyao , Zhao Wei , Matusik Wojciech
Published: November 2024

Visual Question-Answering, a technology that generates textual responses from an image and natural language question, has progressed significantly. Notably, it can aid in tracking and inquiring about daily activities, crucial in healthcare monitoring, especially for elderly patients or those with memory disabilities. However, video poses privacy concerns and has a limited field of view. This paper presents Sensor2Text, a model proficient in tracking daily activities and engaging in conversations using wearable sensors. The approach outlined here tackles several challenges, including low information density in wearable sensor data, insufficiency of single wearable sensors in human activities recognition, and model's limited capacity for Question-Answering and interactive conversations. To resolve these obstacles, transfer learning and student-teacher networks are utilized to leverage knowledge from visual-language models. Additionally, an encoder-decoder neural network model is devised to jointly process language and sensor data for conversational purposes. Furthermore, Large Language Models are also utilized to enable interactive capabilities. The model showcases the ability to identify human activities and engage in Q&A dialogues using various wearable sensor modalities. It performs comparably to or better than existing visual-language models in both captioning and conversational tasks. To our knowledge, this represents the first model capable of conversing about wearable sensor data, offering an innovative approach to daily activity tracking that addresses privacy and field-of-view limitations associated with current vision-based solutions.
Passive Haptic Rehearsal for Augmented Piano Learning in the Wild
Authors: Gemicioglu Tan , Hopper Elijah , Dwivedi Brahmi , Kulkarni Richa , Bhandarkar Asha , Rajan Priyanka , Eng Nathan , Ramanujam Adithya , Ramey Charles , Gilliland Scott M. , Mason Celeste , Seim Caitlyn , Starner Thad
Published: November 2024

Passive haptic learning (PHL) is a method for training motor skills via intensive repetition of haptic stimuli while a user is focused on other tasks. For the practical application of PHL to music education, we propose passive haptic rehearsal (PHR) where PHL is combined with deliberate active practice. We designed a piano teaching system that includes haptic gloves compatible with daily wear, a Casio keyboard with light-up keys, and an online learning portal that enables users to track performance, choose lessons, and connect with the gloves and keyboard. We conducted a longitudinal two-week study in the wild, where 36 participants with musical experience learned to play two piano songs with and without PHR. For 20 participants with complete and valid data, we found that PHR boosted the learning rate for the matching accuracy by 49.7% but did not have a significant effect on learning the notes' rhythm. Participants across all skill levels in the study require approximately two days less to reach mastery on the songs practiced when using PHR. We also confirmed that PHR boosts recall between active practice sessions. We hope that our results and system will enable the deployment of PHL beyond the laboratory.
Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information Seeking
Authors: Zhu Rongrong , Cheng Chi , Song Yunpeng , Cai ZhongMin
Published: November 2024

Extracting and collecting information during web exploration is an arduous process, demanding substantial cognitive and physical effort from users. Users must not only determine which content is worth capturing but also manually extract and save it, often disrupting the flow of their learning and exploration. To mitigate it, we propose AIbM (Attentive Interaction Behavior Modeling), which encodes and integrates multiple implicit interaction data to identify fine-grained helpful web content, providing an algorithmic foundation to facilitate automatic information extraction and collection. AIbM captures dynamic patterns of user interactions---specifically eye and mouse movements---by encoding these into images and time series. It further employs graph modeling to exploit relationships between a content block and its adjacent blocks. By incorporating graph attention networks, AIbM efficiently processes both intra-block and inter-block features, improving the identification of helpful web content. Experimental results demonstrate that AIbM achieves the highest F1 score of 82.80% in general helpful content identification, marking improvements of 10.86% over state-of-the-art models like GBT and at least 2.49% over individual modality inputs. These results underscore the potential of our approach to advance tools and systems designed for automated information extraction and collection to facilitate more effective exploratory search experiences.
Moat: Adaptive Inside/Outside Detection System for Smart Homes
Authors: Wang Chixiang , He Weijia , Pierson Timothy J. , Kotz David
Published: November 2024

Smart-home technology is now pervasive, demanding increased attention to the security of the devices and the privacy of the home's residents. To assist residents in making security and privacy decisions - e.g., whether to allow a new device to connect to the network, or whether to be alarmed when an unknown device is discovered - it helps to know whether the device is inside the home, or outside.

In this paper we present MOAT, a system that leverages Wi-Fi sniffers to analyze the physical properties of a device's wireless transmissions to infer whether that device is located inside or outside of a home. MOAT can adaptively self-update to accommodate changes in the home indoor environment to ensure robust long-term performance. Notably, MOAT does not require prior knowledge of the home's layout or cooperation from target devices, and is easy to install and configure.

We evaluated MOAT in four different homes with 21 diverse commercial smart devices and achieved an overall balanced accuracy rate of up to 95.6%. Our novel periodic adaptation technique allowed our approach to maintain high accuracy even after rearranging furniture in the home. MOAT is a practical and efficient first step for monitoring and managing devices in a smart home.
ActSonic: Recognizing Everyday Activities from Inaudible Acoustic Wave Around the Body
Authors: Mahmud Saif , Parikh Vineet , Liang Qikang , Li Ke , Zhang Ruidong , Ajit Ashwin , Gunda Vipin , Agarwal Devansh , Guimbretiere Francois , Zhang Cheng
Published: November 2024

We present ActSonic, an intelligent, low-power active acoustic sensing system integrated into eyeglasses that can recognize 27 different everyday activities (e.g., eating, drinking, toothbrushing) from inaudible acoustic waves around the body. It requires only a pair of miniature speakers and microphones mounted on each hinge of the eyeglasses to emit ultrasonic waves, creating an acoustic aura around the body. The acoustic signals are reflected based on the position and motion of various body parts, captured by the microphones, and analyzed by a customized self-supervised deep learning framework to infer the performed activities on a remote device such as a mobile phone or cloud server. ActSonic was evaluated in user studies with 19 participants across 19 households to track its efficacy in everyday activity recognition. Without requiring any training data from new users (leave-one-participant-out evaluation), ActSonic detected 27 activities, achieving an average F1-score of 86.6% in fully unconstrained scenarios and 93.4% in prompted settings at participants' homes.
UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
Authors: Bian Haoyu , Guo Bin , Liu Sicong , Ding Yasan , Gao Shanshan , Yu Zhiwen
Published: November 2024

Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user monitoring, potentially enabling more accurate and holistic data collection. However, in open and uncontrolled mobile environments, user movement and lighting introduce noises. Existing methods, such as curve-based or short-range deep learning recognition based on adjacent frames, strike the optimal balance between real-time performance and accuracy, especially under limited device resources. In this paper, we present UbiHR, a ubiquitous device-based heart rate sensing system. Key to UbiHR is a real-time long-range spatio-temporal model enabling noise-independent heart rate recognition and display on commodity mobile devices, along with a set of mechanisms for prompt and energy-efficient sampling and preprocessing. Diverse experiments and user studies involving four devices, four tasks, and 80 participants demonstrate UbiHR's superior performance, enhancing accuracy by up to 74.2% and reducing latency by 51.2%.
Contactless Arterial Blood Pressure Waveform Monitoring with mmWave Radar
Authors: Hu Qingyong , Zhang Qian , Lu Hao , Wu Shun , Zhou Yuxuan , Huang Qianyi , Chen Huangxun , Chen Ying-Cong , Zhao Ni
Published: November 2024

Arterial blood pressure waveform (ABPW) offers comprehensive insights into cardiovascular health compared to discrete blood pressure measurements. However, accurately estimating shapes and pressure values of ABPW points in a beat-to-beat manner poses significant challenges. Current ABPW monitoring methods require invasive procedures or continuous skin contact, which are inconvenient and unsatisfactory. Thus, we propose WaveBP, the first contactless ABPW monitoring system utilizing a commercial mmWave radar, driven by the understanding that cardiac information serves as an implicit bridge between mmWave signals and ABPW based on a hemodynamics analysis model. To preserve waveform details, we design a hybrid Transformer model called mmFormer, incorporated with spatially-informed shortcuts. mmFormer enables consistent sequence-to-sequence transformations while accommodating different levels of personalization efforts. To mitigate the inherent instability of mmWave signals, we develop a beamforming-based data augmentation approach that has been empirically and theoretically proven to enhance robustness with multiple spatial observations. Additionally, we introduce a cross-modality knowledge transfer framework to fuse knowledge from cardiac modalities (ECG/PPG) with vibrations captured in mmWave reflections, improving accuracy without requiring extra deployment overhead. Extensive evaluations conducted on 43 subjects using a leave-one-subject-out setup validate that WaveBP achieves a high waveform correlation of 0.903 and exhibits a low (mean±standard deviation) error of point-level measurements at (-0.14±7.48) mmHg, which could be further reduced by subject-specific specialization. WaveBP demonstrates remarkable performance under challenging scenarios and exhibits potential for detailed cardiac estimations, as evidenced by our case studies on relative cardiac output estimation and cardiac abnormality detection.
Characterizing Geometric Decoration Styles of the Ilkhanid Period Using Advanced Image Analysis
Authors: Mahmoudnejad Amirhossein , Andaroodi Elham
Published: November 2024

This study advances the understanding of architectural heritage by introducing a novel analytical approach for classifying geometric decorations. Through the integration of image clustering and statistical analysis, the research establishes a robust framework for examining the development of decorative styles in Ilkhanid architecture (1256–1335 CE), renowned for its vibrant and intricate designs. Focusing on the “shamsa,” a star-shaped geometric motif symbolizing the sun and frequently represented through intersecting lines, the study employs a four-stage process: (1) collecting and processing images of decorative elements; (2) developing and applying an unsupervised clustering algorithm to identify meaningful image groups; (3) creating a comprehensive metadata inventory for each image to support analysis; and (4) interpreting the clustered groups to reveal significant patterns and stylistic insights. Analyzing over 230 images from 34 Ilkhanid buildings using hierarchical clustering produced dendrograms illustrating relationships among the image groups. The metadata inventory validated the clusters by incorporating information on region, function, materials, and geometric patterns. This process identified 12 distinct groups of geometric patterns, demonstrating a range of complexity from simple designs to intricate knot motifs, or “gereh.” Ilkhanid decorations predominantly featured materials such as tile, brick, and plaster, with cyan, brick red, azure, and white being the primary colors. The findings present a comprehensive typology of architectural decorations, surpassing prior studies by analyzing broader features.
Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning
Authors: Sun Ying , Ji Yang , Zhu Hengshu , Zhuang Fuzhen , He Qing , Xiong Hui
Published: November 2024

Continuously learning new skills is essential for talents to gain a competitive advantage in the labor market. Despite extensive efforts on relevance- or preference-based skill recommendations, little attention has been given to the practical effects of job skills in the market. To bridge this gap, we propose an explainable personalized skill learning recommendation system that considers the long-term learning benefits and costs. Specifically, we model skill learning utilities based on salary and learning cost associated with job positions and propose a multi-objective deep reinforcement learning framework to model and maximize long-term utilities. Furthermore, we propose a Self-explaining Skill Recommendation Deep Q-network (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent’s long-term learning utility into contributions from each exemplar, offering a comprehensive and multi-factorial explanation across various skill learning options. To tackle the combinatorial complexity of the skill space, we develop an MCTS-based optimization-decoding iterative training procedure for explanation fidelity and human understandability. In this way, talents will receive a tailored roadmap of essential skills, complemented by exemplar-based explanations, to effectively plan their careers. Extensive experiments on a real-world dataset validate the effectiveness and explainability of our approach.
Certified Cost Bounds for Abstract Programs
Authors: Albert Elvira , Hähnle Reiner , Merayo Alicia , Steinhöfel Dominic
Published: November 2024

A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation or optimization. Static cost analysis derives the
precise
cost—or
upper
and
lower
bounds for it—of executing programs, as functions in terms of the program's input data size. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the
cost effect of program transformations
. This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.
Context-based Transfer Learning for Structuring Fault Localization and Program Repair Automation
Authors: Zhang Lehuan , Guo Shikai , Guo Yi , Li Hui , Chai Yu , Chen Rong , Li Xiaochen , Jiang He
Published: November 2024

Automated software debugging plays a crucial role in aiding software developers to swiftly identify and attempt to rectify faults, thereby significantly reducing developers’ workload. Previous researches have predominantly relied on simplistic semantic deep learning or statistical analysis methods to locate faulty statements in diverse projects. However, code repositories often consist of lengthy sequences with long-distance dependencies, posing challenges for accurately modeling fault localization using these methods. In addition, the lack of joint reasoning among various faults prevents existing models from deeply capturing fault information. To address these challenges, we propose a method named
CodeHealer
to achieve accurate fault localization and program repair. CodeHealer comprises three components: a Deep Semantic Information Extraction Component that effectively extracts deep semantic features from suspicious code statements using classifiers based on Joint-attention mechanisms; a Suspicious Statement Ranking Component that combines various fault localization features and employs multilayer perceptrons to derive multidimensional vectors of suspicion values; and a Fault Repair Component that, based on ranked suspicious statements generated by fault localization, adopts a top-down approach using multiple classifiers based on Co-teaching mechanisms to select repair templates and generate patches. The experimental results indicate that when applied to fault localization, CodeHealer outperforms the best baseline method with improvements of 11.4%, 2.7%, and 1.6% on Top-1/3/5 metrics, respectively. It also reduces the MFR and MAR by 9.8% and 2.1%, where lower values denote better fault localization effectiveness. Additionally, in automated software debugging, CodeHealer fixes an additional 6 faults compared to the current best method, totaling 53 faults repaired.
UIE-Based Relational Extraction Task for Mine Hoist Fault Data
Authors: Dang Xiaochao , Ding Guozhen , Dong Xiaohui , Li Fenfang , Gao Shiwei , Wang Yue
Published: November 2024

Information extraction is pivotal in natural language processing, where the goal is to convert unstructured text into structured information. A significant challenge in this domain is the diversity and specific needs of various processing tasks. Traditional approaches typically utilize separate frameworks for different information extraction tasks, such as named entity recognition and relationship extraction, which hampers their uniformity and scalability. In this study, this study introduce a Universal Information Extraction (UIE) framework combined with a cue learning strategy, significantly improving the efficiency and accuracy of extracting mine hoist fault data. Initially, domain-specific data is manually labeled to fine-tune the model, and the accuracy is further enhanced by constructing negative examples during this fine-tuning process. The model then focuses on faults using the Structured Extraction Language (SEL) and a schema-based prompt syntax, the Structural Schema Instructor (SSI), which targets and extracts key information from the fault data to meet specific domain requirements. Experimental results show that UIE substantially improves the processing efficiency and the F1 accuracy of the extracted mine hoist fault data, with the fine-tuned F1 score increasing from 23.59% to 92.51%.
Protein Captioning: Bridging the Gap between Protein Sequences and Natural Languages
Authors: Zhang Jianrong , Fan Hehe , Yang Yi
Published: November 2024

We introduce the multimodal task of
Protein Captioning
, which is an easy-to-understand and flexible way for protein analysis. Compared to specific protein recognition or classification tasks, such as enzyme reaction classification and gene ontology term prediction, protein captioning provides comprehensive textural descriptions for proteins, thus playing a key role in bridging the gap between protein sequences and natural languages. To address the problem, we propose a simple yet effective method, Protein-to-Text Generative Pre-trained Transformer (P2T-GPT), to fuse multimodal embeddings and translate the chain of amino acid residues in a protein to a sequence of natural language words,
i.e
., text. For the evaluation of protein captioning, we collect the ProteinCap dataset that contains 94,454 protein-text pairs. Experiments on ProteinCap demonstrate the effectiveness of the proposed P2T-GPT on protein captioning. For example, our method obtains improvements of 8.74, 10.03, and 11.05 in the BERTScore compared to the baseline model on ProteinCap-

\(\alpha,\beta,\gamma\)

, respectively. As minor contributions, first, P2T-GPT provides a way to connect protein science and Large Language Models (LLMs). By appending ChatGPT, our method can interact in a conversational way to answer questions given a protein. Second, we show that protein captioning can be treated as a pre-trained task that can benefit a range of downstream tasks, to a certain extent.
SILVIA: Automated Superword-Level Parallelism Exploitation via HLS-Specific LLVM Passes for Compute-Intensive FPGA Accelerators
Authors: Brignone Giovanni , Bosio Roberto , Ottati Fabrizio , Sansoè Claudio , Lavagno Luciano
Published: November 2024

High-level synthesis (HLS) aims at democratizing custom hardware acceleration with highly abstracted software-like descriptions. However, efficient accelerators still require substantial low-level hardware optimizations, defeating the HLS intent. In the context of field-programmable gate arrays, digital signal processors (DSPs) are a crucial resource that typically requires a significant optimization effort for its efficient utilization, especially when used for sub-word vectorization. This work proposes SILVIA, an open-source LLVM transformation pass that automatically identifies superword-level parallelism within an HLS design and exploits it by packing multiple operations, such as additions, multiplications, and multiply-and-adds, into a single DSP. SILVIA is integrated in the flow of the commercial AMD Vitis HLS tool and proves its effectiveness by packing multiple operations on the DSPs without any manual source-code modifications on several diverse state-of-the-art HLS designs such as convolutional neural networks and basic linear algebra subprograms accelerators, reducing the DSP utilization for additions by 70 % and for multiplications and multiply-and-adds by 50 % on average.
PSMA Radiotheranostics in Prostate Cancer: Principles, Practice, and Future Prospects
Authors: Szidonya Laszlo K. , Barwick Tara D. , Challapalli Amarnath , Naik Mitesh , Eccles Amy , Barbon Dennis , Mallak Nadine
Published: November 2024

Prostate-specific membrane antigen (PSMA) radioligand therapy is an evolving treatment modality for patients with metastatic prostate cancer, offering improved survival and quality of life through a targeted and personalized theranostic approach.
Review—Graphene Nanocomposites in Dentistry: A State-of-the-Art Review
Authors: Mahajan Priyanka , Khanna Virat , Singh Kamaljit , Alqarni Abdullah , Saeed Mohd
Published: November 2024

With advancements in the field of nanotechnology, various innovations and developments have been made in the field of dentistry during the past few years. The emergence of 2D nanomaterial, particularly graphene and its derivatives has further embellished the dental applications of nanomaterials due to its excellent electrical conductivity, mechanical strength, outrageous biocompatibility, modest cytotoxicity, cell differentiation and antimicrobial characteristics. The current review has been planned to highlight the significant contribution of graphene and its derivatives to the various aspects of dentistry. The structure, fabrication process and characterization techniques for graphene, a multifunctional nanomaterial, are discussed in general, followed by dental-based characteristics in particular. Graphene nanocomposites are of great value for restorative dentistry in developing nanofillers for filling the cavities created by infection as well as inhibits dentin demineralization. For bone tissue engineering, graphene plays a pivotal role in stem cell differentiation, scaffold development, and dental pulp regeneration. In the case of preventive dentistry, graphene and its derivatives are involved in the inhibition of biofilm accumulation, thus arresting dental carriers. Further, the challenges and prospects of graphene-derived materials in biomedical applications including dentistry have been accentuated. Various drawbacks linked with the usage of graphene in dental implications include its long-term stability in corrosive conditions, methodologies for expelling it from the body, long-term cytotoxicity, defects in graphene-derived dental implants as well as its precise insertion in the dental area. Introduction of advanced techniques viz., 3-dimensional printing, nanozymes and nanobots can further revolutionize the field of dentistry.
Human skeletal muscle possesses an epigenetic memory of high-intensity interval training
Authors: Pilotto Andrea M. , Turner Daniel C. , Mazzolari Raffaele , Crea Emanuela , Brocca Lorenza , Pellegrino Maria Antonietta , Miotti Danilo , Bottinelli Roberto , Sharples Adam P. , Porcelli Simone
Published: November 2024

Cells possess a “memory” such that adaptations can be more quickly regained when a previously encountered challenge is reintroduced. Exercise provides an excellent experimental model to explore the concept of cellular memory to physiologically relevant stressors in humans. This study highlights molecular mechanisms that contribute to muscle memory in response to high-intensity interval training in humans, showing retention of DNA methylation and gene expression profiles from earlier training into detraining and retraining.
Perinatal iron deficiency alters the cardiac proteome and mitochondrial function in neonatal offspring
Authors: Holody Claudia D. , Woodman Andrew G. , Nie Chunpeng , Liu Si Ning , Young Daniel , Wiedemeyer Alyssa , Carpenter Rowan , Noble Ronan M. N. , Graf Daniel , Dufour Antoine , Lemieux Hélène , Bourque Stephane L.
Published: November 2024

Iron deficiency (ID) is the most common nutritional deficiency worldwide and is highly prevalent among pregnant women and young children. ID causes changes in mitochondrial protein expression and function in neonatal hearts, which may contribute to functional impairments. Improving cardiac energy metabolism may represent a novel approach to improve short- and long-term outcomes in infants affected by ID, but sex of the neonate may be an important determinant of treatment efficacy.
Seismic Evaluation of Brick–Wood Structure Houses Based on Large‐Scale Shaking Table Tests
Authors: Wang Qiang , Wang Lanmin , Zheng Fang
Published: November 2024

Taking the typical brick–wood structure houses in northern China as the research object, two 1/2‐scale brick–wood structure house models with the same size and layout were tested using large‐scale shaking table test technology. Model 1 has seismic measures of ring beams, structural columns, wall tie bars, reinforced concrete lintels for doors and windows, and purlin bottom plates. Model 2 has the same structural layout, dimensions, and construction technical conditions as Model 1 but lacks the seismic measures of ring beams and structural columns as Model 1, and the other construction measures are the same. In the test, the Wenchuan record, EL‐Centro record, and Lanzhou artificial record were selected to simulate the horizontal seismic action on the models. Through the analysis and comparison of the seismic damage process and seismic response characteristics of the two models, the results showed that horizontal shear failure was the main failure mode of single‐story brick–wood houses, and the protruding gable wall parts of the crosswall were the weak points for seismic resistance. The seismic measures adopted in the test effectively improved the seismic performance of the houses, with Model 1 and Model 2 achieving the basic seismic fortification of intensity VIII and IX, respectively. The relevant research results presented in this paper clarify the seismic response law and seismic failure mechanism of brick–wood houses. Meanwhile, the corresponding seismic technical measures proposed based on these results have certain guiding significance for the construction of suburban or rural houses.
Study on Stress Deformation of Pipe Shed in Shallow Buried Tunnel Considering Micro‐Arch Effect
Authors: Rong Yao , Du Wanxu , Sun Yang , Xiong Fuyang , Liu Lin , Yu Sufan , Jie Gang
Published: November 2024

This study aims to investigate the influence of the tiny soil arch effect generated by the soil between the steel tubes in the ring direction of the tunnel overrun tube shed on the force deformation of the steel tubes during the working period. By assuming the arch effect axis between the steel tubes is circular and intercepting any three adjacent steel tubes on the tunnel contour line to establish a vertical coordinate system, the equation of the arch axis and the coordinates of the arch top were derived. Referring to the calculation theory of the peripheral rock pressure of the Taishaki tunnel, the peripheral rock pressure borne by the steel tubes of the pipe shed under the consideration of the micro‐arch effect was derived. Assuming the pipe shed acts as a semi‐infinite long beam on Pasternak’s two‐parameter elastic foundation, the pipe shed was divided into four sections axially based on the initial support application status and the tunneling position of the working surface. The controlling equations for each section were analyzed and solved. The theoretical deflection curves of the pipe shed were obtained using actual engineering construction parameters and were validated by monitoring the settlement values of the soil layer above the tunnel using multipoint displacement meters. The study shows that the initial support application status and the tunneling position of the working surface significantly affect the functioning mechanism of the pipe shed, causing it to play different roles at different stages. When the working surface advances to the target section, the pipe shed exerts a beam arch effect; when the working surface tunnels beyond the target section, the pipe shed exerts a combined beam effect. The deflection change trend of the pipe shed with the advancement of the working surface is characterized by rapidly increasing, rapidly decreasing, and slowly decreasing stages, with the maximum deflection change occurring above the working surface position. Considering the micro‐arch effect, the theoretical deflection changes are larger and closer to the actual engineering situation compared to the existing theory that does not consider the micro‐arch effect. The results provide theoretical references for the design and implementation of tunnel overrun pipe sheds, moving beyond mere engineering experience and validating new methods for monitoring the deformation of surrounding rock during tunnel construction, effectively ensuring construction safety during tunnel excavation.
Applying an Untargeted Metabolomic Strategy to Explore the Interventional Effect and Mechanism of NMN on Aging Model Mice
Authors: Lin Yuxian , Wu Yan , Zhu Yuanying , Luo Ting , Sun Zheng , Liu Xuecun , Yu Yingcong , Xu Hui
Published: November 2024

Background: Aging is usually accompanied by a significant decline in nicotinamide adenine dinucleotide (NAD+) level. Nicotinamide mononucleotide (NMN) is a crucial precursor molecule of NAD+ with a variety of bioactivities beneficial to health, and the present study just aimed to explore the antiaging effects of NMN and the mechanism of action by using a nontargeted metabolomic strategy.

Methods: An aging mouse model induced by D‐galactosamine (D‐gal) was established, which was followed by treatment with NMN (at doses of 100 to 500 mg/kg per day) via oral administration for 10 weeks. The physiological and biochemical changes involved in the aging process were closely monitored to investigate the interventional effects of NMN. Plasma samples were subjected to assay by UPLC‐Q‐Orbitrap HRMS to reveal the mechanism of action via systematic data mining and statistical analysis.

Results: D‐gal injection at a dose of 500 mg/kg per day for 7 weeks successfully induced the aging model in mice. Long‐term supplementation with NMN could significantly improve the antioxidant and immune function, boost lipid metabolism, and reduce aging‐related inflammatory response in a dose‐ and time‐dependent manner. The nontargeted metabolomic analysis further demonstrated that the interventional effect of NMN on aging mice may be closely related to the regulation of lipid and purine metabolism.

Conclusions: Long‐term NMN supplementation could display robust antiaging effects mainly by regulating lipid and purine metabolism, which thus deserves great attention for antiaging intervention.
Comprehensive Study on Thermal Characteristics of Lithium‐Ion Battery With Entropic Heat
Authors: Chun Heechan , Choi Hongseok , Jun Yongjoo , Lee Hoseong
Published: November 2024

Most battery thermal models have been validated at low C‐rates, often overlooking the influence of entropic heat (EH) owing to its relatively small magnitude. However, at high C‐rates, the exclusion of EH can lead to significant temperature deviations. In this context, this study investigated the influence of EH on battery temperature under various operating conditions. Experiments were conducted to determine the entropic coefficient (EC), and an electrochemical–thermal‐coupled battery model was developed and validated using experimental data under various discharge conditions. The results showed that the effect of EH on battery temperature increased as the C‐rate increased. With the consideration of EH, the battery shows up to 7 K temperature difference at the 3 C discharge rate with ambient temperature at 25°C. The effect of EH on battery temperature became prominent at low ambient temperatures. A temperature difference of 12.4 K was observed at the 3 C discharge rate with ambient temperature at −10°C. Additionally, using the developed battery thermal model, the temperature variations of 6, 8, and 10 C discharge were investigated. This model can estimate the battery thermal behavior over 60°C when the destruction of solid electrolyte interphase (SEI) layers begins. For practical implementation, the battery thermal model developed at the cell level was extended to the module level. With the reflection of EH, temperature differences of up to 1.7, 3.7, and 6.7 K are observed at 1, 2, and 3 C discharge rates, respectively.
A Study on Driving Load While Overtaking on Mountainous Two‐Lane Highways Based on Physiological Characteristics
Authors: Liu Tangzhi , Wang Guyi , Luo Wangxia , Liu Ruihang , Liu Xingliang , Xiang Tianjun , Liu Tong
Published: November 2024

The traffic environment of mountainous highways is more complex than that of nonmountainous highways, with higher driving loads, which increases the risk in overtaking. The changes in the driver’s pupils, eye gaze behavior, and heart rate can be used to evaluate the level of driving tension and safety. To analyze the driving load while overtaking on two‐lane highways in mountainous areas, an actual vehicle test was conducted. Twenty‐one drivers were divided into a skilled group and an unskilled group. The gaze time, gaze transfer characteristics, heart rate changes, and pupil area changes during the three stages of overtaking (intention, execution, and return) were compared and analyzed. The comprehensive evaluation of driving load during the overtaking process used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and Rank Sum Ratio (RSR) method. The results show that the two groups of drivers had the highest driving load during the overtaking execution stage and the lowest driving load during the intention stage. The driving load of overtaking on sections with poor‐sight distance was significantly higher than that on sections with good‐sight distance, and the risk in overtaking during the execution and return stages was highest on sections with poor‐sight distance. It is possible to reduce the driving load if the driver is familiar with the road conditions or has a rich driving experience. Compared to the unskilled group, the skilled group had lower driving loads at all stages of overtaking. The research results can provide a theoretical basis for optimizing traffic safety prevention and control technology on mountainous highways and for designing intelligent driving assistance.
Conventional and Nonconventional Therapies for COVID‐19 Management in Trinidad
Authors: Ismaila Mohammad Sani , Lall Kavita Ranjeeta , Sookram Kezia , Sundaram Venkatesan , Jones Kegan Romelle
Published: November 2024

This cross‐sectional study investigated nonconventional therapies for COVID‐19 in Trinidad, emphasizing the need for documentation supporting future pharmaceutical research. The survey, conducted from June 20 to July 19, 2022, garnered responses from 57 participants aged 18 and above, with 82.46% vaccinated. The majority (81%) utilized both conventional and nonconventional therapies, revealing insights for potential alternatives to traditional treatments. Conventional treatments, including antibiotics, Ivermectin, anti‐inflammatories, analgesics, bronchodilators, and cough/flu syrups, were frequently reported. Nonconventional therapies encompassed vitamins, minerals, supplements, and various plant and animal products. When participants used conventional therapies, either alone or in combination with nonconventional ones, 13.21% reported side effects. These included severe thirst, headache, nausea, drowsiness, and one case of weight gain. Conversely, those exclusively using nonconventional treatments reported no side effects. Encouragingly, nonconventional therapies demonstrated promising effects in managing COVID‐19, emphasizing the need for meticulous selection, research, and development of their bioactive compounds as potential alternatives to conventional therapies.
Multiomics Analysis Reveals Molecular Changes during Early Progression of Precancerous Lesions to Lung Adenocarcinoma in Never-Smokers
Authors: Chen Yun-Ching , Hsu Chia-Lang , Wang Hui-Min , Wu Shang-Gin , Chang Yih-Leong , Chen Jin-Shing , Wu Yu-Ching , Lin Yen-Ting , Yang Ching-Yao , Lin Mong-Wei , Lee Jang-Ming , Kuo Shuenn-Wen , Chen Ke-Cheng , Hsu Hsao-Hsun , Huang Pei-Ming , Huang Yen-Lin , Yu Chong-Jen , Pirooznia Mehdi , Huang Bevan E. , Yang Rob , Shih Jin-Yuan , Yang Pan-Chyr
Published: November 2024

Lung cancer is the most common cause of cancer mortality globally, and the prevalence of lung adenocarcinoma, the most common lung cancer subtype, has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in nonsmokers. In this study, we performed whole-exome sequencing and RNA sequencing of samples from 94 East Asian patients with precancerous lesions [25 with atypical adenomatous hyperplasia (AAH); 69 with adenocarcinoma in situ (AIS)] and 73 patients with early invasive lesions [minimally invasive adenocarcinoma (MIA)]. Cellular analysis revealed that the activities of endothelial and stromal cells could be used to categorize tumors into molecular subtypes within pathologically defined types of lesions. The subtypes were linked with the radiologically defined type of lesions and corresponded to immune cell infiltration throughout the early progression of lung adenocarcinoma. Spatial transcriptomic analysis revealed the distribution of epithelial cells, endothelial cells, fibroblasts, and plasma cells within MIA samples. Characterization of the molecular lesion subtypes identified positively selected mutational patterns and suggested that angiogenesis in the late-stage AIS type potentially contributes to tissue invasion of the MIA type. This study offers a resource that may help improve early diagnosis and patient prognosis, and the findings suggest possible approaches for early disease interception.

Significance: Integrative analysis of multiomics data revealed coordination between immune and nonimmune cells during early progression of precancerous lesions to lung adenocarcinomas and shed light on the molecular characteristics of clinically defined subtypes.
Cancer-Related Cognitive Impairment and the Potential of Dietary Interventions for the Prevention and Mitigation of Neurodegeneration
Authors: Crowder Sylvia L. , Gudenkauf Lisa M. , Hoogland Aasha I. , Han Hyo S. , Small Brent J. , Carson Tiffany L. , Parker Nathan H. , Booth-Jones Margaret , Jim Heather S.L.
Published: November 2024

Approximately 35% of long-term cancer survivors experience ongoing cancer-related cognitive impairment (CRCI). Yet, few efficacious interventions exist to prevent or ameliorate CRCI. The underlying biological processes driving CRCI are complex and are reported to include changes in brain structure and function, increased oxidative stress and inflammation, and alterations in gut microbiome composition. Some of the mechanisms promoting CRCI have the potential to be modified through behavioral changes, such as dietary changes. Compelling evidence from randomized controlled trials and observational research supports the positive impacts of the Mediterranean-DASH Intervention for Neurodegenerative Delay diet on cognition outside of the context of cancer, but studies investigating the Mediterranean-DASH Intervention for Neurodegenerative Delay diet as an intervention for people who experience CRCI are lacking. This review examines the current state of the science for cognitive outcomes of dietary interventions in aging populations and discusses future opportunities to adapt these interventions to cancer populations.
Patterns of Subsequent Cancer Incidence Over Time in Patients with Breast Cancer
Authors: Deng Zhengyi , Visvanathan Kala
Published: November 2024

Background:

Breast cancer survivors face a higher risk of subsequent primary cancers. This study investigated the patterns of subsequent cancer risk according to time since breast cancer diagnosis.

Methods:

Using data from the Surveillance, Epidemiology, and End Results Program (2000–2018), we identified a cohort of 771,681 breast cancer survivors. Standard incidence ratios (SIR) were calculated by comparing the observed with the expected number of subsequent cancers over different follow-up periods since breast cancer diagnosis. Analyses were conducted for multiple cancer types, stratified by hormone receptor status, treatment of the first breast cancer, age, and race/ethnicity.

Results:

Survivors experienced a 16% increased risk of subsequent cancer with the SIR continuing to increase with longer follow-up (SIR = 1.04, 1.22, and 1.31 for 12–59, 60–119, and ≥120 months). This trend was driven primarily by a subsequent breast cancer, particularly among women <50 years of age, those with initial hormone receptor–negative cancer, and racial/ethnic minorities. The patterns of subsequent non-breast cancer risk varied by type. An early-onset and sustained increased risk was observed for subsequent leukemia, thyroid cancer, soft-tissue sarcoma, melanoma, pancreatic cancer, and uterine cancer. A delayed increased risk was observed for subsequent esophageal, ovarian, oral cavity/pharyngeal, and lung cancers, whereas for small intestine, stomach, kidney, and colorectal cancers, there was a decrease after an initial increased risk.

Conclusions:

Patterns in subsequent cancer risk since breast cancer diagnosis differ by cancer type and characteristics of the first breast cancer.

Impact:

These findings can inform etiology and tailored approaches to screening and prevention of subsequent cancers.