bluuebunny/crossref_metadata_2025 huggingface dataset:
Tip-activated single-atom catalysis: CO oxidation on Au adatom on oxidized rutile TiO
2
surface
Authors: Adachi Yuuki , Brndiar Ján , Konôpka Martin , Turanský Robert , Zhu Qiang , Wen Huan Fei , Sugawara Yasuhiro , Kantorovich Lev , Štich Ivan , Li Yan Jun
Published: September 2023

Single-atom catalysis of carbon monoxide oxidation on metal-oxide surfaces is crucial for greenhouse recycling, automotive catalysis, and beyond, but reports of the atomic-scale mechanism are still scarce. Here, using scanning probe microscopy, we show that charging single gold atoms on oxidized rutile titanium dioxide surface, both positively and negatively, considerably promotes adsorption of carbon monoxide. No carbon monoxide adsorption is observed on neutral gold atoms. Two different carbon monoxide adsorption geometries on gold atoms are identified. We demonstrate full control over the redox state of adsorbed gold single atoms, carbon monoxide adsorption geometry, and carbon monoxide adsorption/desorption by the atomic force microscopy tip. On charged gold atoms, we activate Eley-Rideal oxidation reaction between carbon monoxide and a neighboring oxygen adatom by the tip. Our results provide unprecedented insights into carbon monoxide adsorption and suggest that the gold dual activity for carbon monoxide oxidation after electron or hole attachment is also the key ingredient in photocatalysis under realistic conditions.
Programmable RNA-guided DNA endonucleases are widespread in eukaryotes and their viruses
Authors: Jiang Kaiyi , Lim Justin , Sgrizzi Samantha , Trinh Michael , Kayabolen Alisan , Yutin Natalya , Bao Weidong , Kato Kazuki , Koonin Eugene V. , Gootenberg Jonathan S. , Abudayyeh Omar O.
Published: September 2023

Programmable RNA-guided DNA nucleases perform numerous roles in prokaryotes, but the extent of their spread outside prokaryotes is unclear. Fanzors, the eukaryotic homolog of prokaryotic TnpB proteins, have been detected in genomes of eukaryotes and large viruses, but their activity and functions in eukaryotes remain unknown. Here, we characterize Fanzors as RNA-programmable DNA endonucleases, using biochemical and cellular evidence. We found diverse Fanzors that frequently associate with various eukaryotic transposases. Reconstruction of Fanzors evolution revealed multiple radiations of RuvC-containing TnpB homologs in eukaryotes. Fanzor genes captured introns and proteins acquired nuclear localization signals, indicating extensive, long-term adaptation to functioning in eukaryotic cells. Fanzor nucleases contain a rearranged catalytic site of the RuvC domain, similar to a distinct subset of TnpBs, and lack collateral cleavage activity. We demonstrate that Fanzors can be harnessed for genome editing in human cells, highlighting the potential of these widespread eukaryotic RNA-guided nucleases for biotechnology applications.
Targeting ryanodine receptor type 2 to mitigate chemotherapy-induced neurocognitive impairments in mice
Authors: Liu Yang , Reiken Steven , Dridi Haikel , Yuan Qi , Mohammad Khalid S. , Trivedi Trupti , Miotto Marco C. , Wedderburn-Pugh Kaylee , Sittenfeld Leah , Kerley Ynez , Meyer Jill A. , Peters Jonathan S. , Persohn Scott C. , Bedwell Amanda A. , Figueiredo Lucas L. , Suresh Sukanya , She Yun , Soni Rajesh Kumar , Territo Paul R. , Marks Andrew R. , Guise Theresa A.
Published: September 2023

Chemotherapy-induced cognitive dysfunction (chemobrain) is an important adverse sequela of chemotherapy. Chemobrain has been identified by the National Cancer Institute as a poorly understood problem for which current management or treatment strategies are limited or ineffective. Here, we show that chemotherapy treatment with doxorubicin (DOX) in a breast cancer mouse model induced protein kinase A (PKA) phosphorylation of the neuronal ryanodine receptor/calcium (Ca2+) channel type 2 (RyR2), RyR2 oxidation, RyR2 nitrosylation, RyR2 calstabin2 depletion, and subsequent RyR2 Ca2+leakiness. Chemotherapy was furthermore associated with abnormalities in brain glucose metabolism and neurocognitive dysfunction in breast cancer mice. RyR2 leakiness and cognitive dysfunction could be ameliorated by treatment with a small molecule Rycal drug (S107). Chemobrain was also found in noncancer mice treated with DOX or methotrexate and 5-fluorouracil and could be prevented by treatment with S107. Genetic ablation of the RyR2 PKA phosphorylation site (RyR2-S2808A) also prevented the development of chemobrain. Chemotherapy increased brain concentrations of the tumor necrosis factor–α and transforming growth factor–β signaling, suggesting that increased inflammatory signaling might contribute to oxidation-driven biochemical remodeling of RyR2. Proteomics and Gene Ontology analysis indicated that the signaling downstream of chemotherapy-induced leaky RyR2 was linked to the dysregulation of synaptic structure–associated proteins that are involved in neurotransmission. Together, our study points to neuronal Ca2+dyshomeostasis via leaky RyR2 channels as a potential mechanism contributing to chemobrain, warranting further translational studies.
The biofilm community resurfaces: new findings and post-pandemic progress
Authors: Greenwich Jennifer L. , Fleming Derek , Banin Ehud , Häussler Susanne , Kjellerup Birthe V. , Sauer Karin , Visick Karen L. , Fuqua Clay
Published: September 2023

The ninth American Society for Microbiology Conference on Biofilms was convened in-person on 13–17 November 2022 in Charlotte, NC. As the first of these conferences since prior to the start of the COVID-19 pandemic, the energy among the participants of the conference was clear, and the meeting was a tremendous success. The mixture of >330 oral and poster presentations resoundingly embodied the vitality of biofilm research across a wide range of topics and multiple scientific disciplines. Special activities, including a pre-conference symposium for early career researchers, further enhanced the attendee experience. As a general theme, the conference was deliberately structured to provide high levels of participation and engagement among early career scientists.
Biogeographical patterns of the soil fungal:bacterial ratio across France
Authors: Djemiel Christophe , Dequiedt Samuel , Bailly Arthur , Tripied Julie , Lelièvre Mélanie , Horrigue Walid , Jolivet Claudy , Bispo Antonio , Saby Nicolas , Valé Matthieu , Maron Pierre-Alain , Ranjard Lionel , Terrat Sébastien
Published: September 2023

Soils are one of the major reservoirs of biological diversity on our planet because they host a huge richness of microorganisms. The fungal:bacterial (F:B) ratio targets two major functional groups of organisms in soils and can improve our understanding of their importance and efficiency for soil functioning. To better decipher the variability of this ratio and rank the environmental parameters involved, we used the French Soil Quality Monitoring Network (RMQS)—one of the most extensive and a priori-free soil sampling surveys, based on a systematic 16 km × 16 km grid and including more than 2,100 samples. F:B ratios, measured by quantitative PCR targeting the 18S and 16S rDNA genes, turned out to be heterogenously distributed and spatially structured in geographical patterns across France. These distribution patterns differed from bacterial or fungal densities taken separately, supporting the hypothesis that the F:B ratio is not the mere addition of each density but rather results from the complex interactions of the two functional groups. The F:B ratios were mainly influenced by soil characteristics and land management. Among soil characteristics, the pH and, to a lesser extent, the organic carbon content and the carbon:nitrogen (C:N) ratio were the main drivers. These results improved our understanding of soil microbial communities, and from an operational point of view, they suggested that the F:B ratio should be a useful new bioindicator of soil status. The resulting dataset can be considered as a first step toward building up a robust repository essential to any bioindicator and aimed at guiding and helping decision making.

IMPORTANCE

In the face of human disturbances, microbial activity can be impacted and, e.g., can result in the release of large amounts of soil carbon into the atmosphere, with global impacts on temperature. Therefore, the development and the regular use of soil bioindicators are essential to (i) improve our knowledge of soil microbial communities and (ii) guide and help decision makers define suitable soil management strategies. Bacterial and fungal communities are key players in soil organic matter turnover, but with distinct physiological and ecological characteristics. The fungal:bacterial ratio targets these two major functional groups by investigating their presence and their equilibrium. The aim of our study is to characterize this ratio at a territorial scale and rank the environmental parameters involved so as to further develop a robust repository essential to the interpretation of any bioindicator of soil quality.
Metagenomic investigation reveals bacteriophage-mediated horizontal transfer of antibiotic resistance genes in microbial communities of an organic agricultural ecosystem
Authors: Zhang Yujie , Kitazumi Ai , Liao Yen-Te , de los Reyes Benildo G. , Wu Vivian C. H.
Published: September 2023

Agricultural microbiomes are major reservoirs of antibiotic resistance genes (ARGs), posing continuous risks to human health. To understand the role of bacteriophages as vehicles for the horizontal transfer of ARGs in the agricultural microbiome, we investigated the diversity of bacterial and viral microbiota from fecal and environmental samples on an organic farm. The profiles of the microbiome indicated the highest abundance of Bacteroidetes, Firmicutes, and Proteobacteria phyla in animal feces, with varying Actinobacteria and Spirochaetes abundance across farm animals. The most predominant composition in environmental samples was the phylum Proteobacteria. Compared to the microbiome profiles, the trends in virome indicated much broader diversity with more specific signatures between the fecal and environmental samples. Overall, viruses belonging to the order Caudovirales were the most prevalent across the agricultural samples. Additionally, the similarities within and between fecal and environmental components of the agricultural environment based on ARG-associated bacteria alone were much lower than those of total microbiome composition. However, there were significant similarities in the profiles of ARG-associated viruses across the fecal and environmental components. Moreover, the predictive models of phage-bacterial interactions on bipartite ARG transfer networks indicated that phages belonging to the order Caudovirales, particularly in the Siphoviridae family, contained diverse ARG types in different samples. Their interaction with various bacterial hosts further implied the important role of bacteriophages in ARG transmission across bacterial populations. Our findings provided a novel insight into the potential mechanisms of phage-mediated ARG transmission and their correlation with resistome evolution in natural agricultural environments.

IMPORTANCE

Antibiotic resistance has become a serious health concern worldwide. The potential impact of viruses, bacteriophages in particular, on spreading antibiotic resistance genes is still controversial due to the complexity of bacteriophage-bacterial interactions within diverse environments. In this study, we determined the microbiome profiles and the potential antibiotic resistance gene (ARG) transfer between bacterial and viral populations in different agricultural samples using a high-resolution analysis of the metagenomes. The results of this study provide compelling genetic evidence for ARG transfer through bacteriophage-bacteria interactions, revealing the inherent risks associated with bacteriophage-mediated ARG transfer across the agricultural microbiome.
2-Furoic acid associated with the infection of nematodes by
Dactylellina haptotyla
and its biocontrol potential on plant root-knot nematodes
Authors: Lei Hong-Mei , Wang Jun-Tao , Hu Qian-Yi , Li Chun-Qiang , Mo Ming-He , Zhang Ke-Qin , Li Guo-Hong , Zhao Pei-Ji
Published: September 2023

Dactylellina haptotyla
is a typical nematode-trapping fungus that has garnered the attention of many scholars for its highly effective lethal potential for nematodes. Secondary metabolites play an important role in
D. haptotyla
-nematode interactions, but which metabolites perform which function remains unclear. We report the metabolic functions based on high-quality, chromosome-level genome assembly of wild
D. haptotyla
YMF1.03409. The results indicate that a large variety of secondary metabolites and their biosynthetic genes were significantly upregulated during the nematode-trapping stage. In parallel, we identified that 2-furoic acid was specifically produced during nematode trapping by
D. haptotyla
YMF1.03409 and isolated it from fermentation production. 2-Furoic acid demonstrated strong nematicidal activity with an LD
50
value of 55.05 µg/mL against
Meloidogyne incognita
at 48 h. Furthermore, the pot experiment showed that the number of galls of tomato root was significantly reduced in the experimental group treated with 2-furoic acid. The considerable increase in the 2-furoic acid content during the infection process and its virulent nematicidal activity revealed an essential synergistic effect during the process of nematode-trapping fungal infection.

IMPORTANCE

Dactylellina haptotyla
have significant application potential in nematode biocontrol. In this study, we determined the chromosome-level genome sequence of
D. haptotyla
YMF1.03409 by long-read sequencing technology. Comparative genomic analysis identified a series of pathogenesis-related genes and revealed significant gene family contraction events during the evolution of
D. haptotyla
YMF1.03409. Combining transcriptomic and metabolomic data as well as
in vitro
activity test results, a compound with important application potential in nematode biocontrol, 2-furoic acid, was identified. Our result expanded the genetic resource of
D. haptotyla
and identified a previously unreported nematicidal small molecule, which provides new options for the development of plant biocontrol agents.
Abstracts from international emergency medicine journals
Authors: Production EMJ
Published: September 2023

Editor’s note: EMJ has partnered with the journals of multiple international emergency medicine societies to share from each a highlighted research study, as selected by their editors. This edition will feature an abstract from each publication.
An economic review of conservation tillage practices: select case studies from the eastern Prairies of Canada
Authors: Khakbazan M. , Carew R. , Crittenden S. , Mohr R.M. , Biswas D.K.
Published: September 2023

This study reviewed the literature on soil conservation practices and analyzed four case studies in different soil zones and diverse cropping systems of Manitoba (MB), western Canada, to show the potential impacts of no-till/reduced tillage practices on field crop economic performance. Primary and secondary data, such as tillage type, input cost, crop price, crop yield, and net revenue (NR), were used to assess the tillage practices in each of the case studies. Based on crop economic analysis, over 9 years (1998–2006) in southern Manitoba, cereal-based (spring wheat ( Triticum aestivum L.) and barley ( Hordeum vulgare L .)) cropping systems were more profitable under conservation tillage than conventional tillage practices, whereas the opposite was true for oilseed crops (e.g., canola, Brassica napus L.). In plot-scale studies at Portage, MB, low intensity tillage increased NR for soybean ( Glycine max L.) in 1 of 3 years when compared to high intensity tillage, and there appeared to be lower NRs for canola as tillage intensity increased. However, in studies near Brandon, MB, NRs for a 4-year wheat-pea ( Pisum sativum L .) rotation were lower in low disturbance seeding than high disturbance seeding systems for a clay loam soil but similar for a loam soil. While no single tillage system was found to consistently provide the highest NR based on these case studies, these findings provide insights into some of the factors behind decisions surrounding tillage management and the rationale for the continued use of a combination of conventional, reduced, and zero-till systems in the eastern Prairies of Canada.
Muon $$g-2$$ and W-mass in a framework of colored scalars: an LHC perspective
Authors: Chakrabarty Nabarun , Chakraborty Indrani , Ghosh Dilip Kumar , Saha Gourab
Published: September 2023

A color octet isodoublet can have esoteric origins and it complies with minimal flavour violation. In this study, we take a scenario where the well known Type-X Two-Higgs doublet model is augmented with a color octet isodoublet. We shed light on how such a setup can predict the recently observed value for the W-boson mass. The two-loop Barr-Zee contributions to muon $$g-2$$

g
-
2

stemming from the colored scalars are evaluated. It is subsequently found that the parameter space compatible with the observed muon $$g-2$$

g
-
2

gets relaxed w.r.t. what it is in the pure Type-X 2HDM by virtue of the contribution from the colored scalars. The extended parameter region therefore successfully accounts for both the W-mass and muon $$g-2$$

g
-
2

anomalies simultaneously. Finally, a collider signature leading to a $$\tau ^+ \tau ^- b \overline{b}$$

τ
+

τ
-

b

b
¯

final state is explored at the 14 TeV LHC using both cut-based and multivariate techniques. Such a signal can confirm the existence of both colorless as well colored scalars that are introduced by this framework.
Study on the RVB method for calculating the Hawking temperature of black holes
Authors: Xian Junlan , He Tangmei , Zhang Jingyi
Published: September 2023

In this work, we study the RVB method for calculating the Hawking temperature of different black holes and find that there is an undetermined integral constant in the temperature expression. We use this method to calculate the Hawking temperature of the black hole in Einstein gravity, and massive gravity, Einstein–Gauss–Bonnet gravity, Scalar–Tensor–Vector modified gravity and [Formula: see text] gravity, respectively. By comparing with the temperature obtained by the Hawking temperature formula, we find that regardless of the gravitational theory from which the black hole solution is obtained, after the black hole metric is reduced to two dimensions, if there is no first-order term of [Formula: see text] in [Formula: see text], the integral constant is 0. If there is a first-order term of [Formula: see text] in [Formula: see text], the integral constant is determined by the coefficient in front of the first-order term of [Formula: see text].
The quantum communication efficiency of the fractional anti-Jaynes–Cummings model
Authors: El-Hadidy E. G. , El Anouz K. , Metwally N.
Published: September 2023

The efficiency of the fractional state that is generated between a single atom and field by using anti-Jaynes–Cummings model (AJCM) is discussed. The fractional degree and the interaction parameters may be used as controllers to increase the efficiency of the fractional state in the context of quantum communication. It has been shown that, the quantum correlation, capacity, and the ability of the fractional state, increase suddenly/gradually at small/large fractional degree, respectively. At small fractional orders, the constant behavior for all these phenomena is displayed at a short interaction time. The fractional quantum state that is generated by the anti-Jaynes–Cummings model is more efficient than that generated by the well-known Jaynes–Cummings model (JCM), where it can be used to teleport an unknown two-qubit state with larger fidelity.
Content-Based Image Retrieval (CBIR): Using Combined Color and Texture Features (TriCLR and HistLBP)
Authors: Bosco P. John , Janakiraman S.
Published: September 2023

Content-Based Image Retrieval (CBIR) is a broad research field in the current digital world. This paper focuses on content-based image retrieval based on visual properties, consisting of high-level semantic information. The variation between low-level and high-level features is identified as a semantic gap. The semantic gap is the biggest problem in CBIR. The visual characteristics are extracted from low-level features such as color, texture and shape. The low-level feature increases CBIRs performance level. The paper mainly focuses on an image retrieval system called combined color (TriCLR) (RGB, YCbCr, and [Formula: see text]) with the histogram of texture features in LBP (HistLBP), which, is known as a hybrid of three colors (TriCLR) with Histogram of LBP (TriCLR and HistLBP). The study also discusses the hybrid method in light of low-level features. Finally, the hybrid approach uses the (TriCLR and HistLBP) algorithm, which provides a new solution to the CBIR system that is better than the existing methods.
Interaction Harvesting
Authors: Mamish John , Guo Amy , Cohen Thomas , Richey Julian , Zhang Yang , Hester Josiah
Published: September 2023

Whenever a user interacts with a device, mechanical work is performed to actuate the user interface elements; the resulting energy is typically wasted, dissipated as sound and heat. Previous work has shown that many devices can be powered entirely from this otherwise wasted user interface energy. For these devices, wires and batteries, along with the related hassles of replacement and charging, become unnecessary and onerous. So far, these works have been restricted to proof-of-concept demonstrations; a specific bespoke harvesting and sensing circuit is constructed for the application at hand. The challenge of harvesting energy while simultaneously sensing fine-grained input signals from diverse modalities makes prototyping new devices difficult. To fill this gap, we present a hardware toolkit which provides a common electrical interface for harvesting energy from user interface elements. This facilitates exploring the composability, utility, and breadth of enabled applications of interaction-powered smart devices. We design a set of "energy as input" harvesting circuits, a standard connective interface with 3D printed enclosures, and software libraries to enable the exploration of devices where the user action generates the energy needed to perform the device's primary function. This exploration culminated in a demonstration campaign where we prototype several exemplar popular toys and gadgets, including battery-free Bop-It--- a popular 90s rhythm game, an electronic Etch-a-sketch, a "Simon-Says"-style memory game, and a service rating device. We run exploratory user studies to understand how generativity, creativity, and composability are hampered or facilitated by these devices. These demonstrations, user study takeaways, and the toolkit itself provide a foundation for building interactive and user-focused gadgets whose usability is not affected by battery charge and whose service lifetime is not limited by battery wear.
SignRing
Authors: Li Jiyang , Huang Lin , Shah Siddharth , Jones Sean J. , Jin Yincheng , Wang Dingran , Russell Adam , Choi Seokmin , Gao Yang , Yuan Junsong , Jin Zhanpeng
Published: September 2023

Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives.
Cross-technology Communication between Visible Light and Battery-free RFIDs
Authors: Wang Ge , Han Lubing , Chang Yuance , Shi Yuting , Qian Chen , Zhao Cong , Ding Han , Xi Wei , Zhao Cui , Zhao Jizhong
Published: September 2023

The ubiquity of illumination facilities enables the versatile development of Visible Light Communication (VLC). VLC-based research achieved high-speed wireless access and decimeter-level indoor localization with complex equipment. However, it is still unclear whether the VLC is applicable for widely-used battery-free Internet-of-Things nodes, e.g., passive RFIDs. This paper proposes LightSign, the first cross-technology system that enables passive RFID tags to receive visible light messages. LightSign is compatible with commercial protocols, transparent to routine RFID communications, and invisible to human eyes. We propose a pseudo-timing instruction to achieve microsecond-level light switching to modulate the VLC message. To make it perceptible to passive RFIDs, we design an augmented RFID tag and prove its effectiveness theoretically and experimentally. With only one reply from an augmented tag, LightSign can decode 100-bit-long VLC messages. We evaluate LightSign in real industry environments and test its performance with two use cases. The results show that LightSign achieves up to 99.2% decoding accuracy in varying scenarios.
The Power of Speech in the Wild
Authors: Wang Weichen , Xu Weizhe , Chander Ayesha , Nepal Subigya , Buck Benjamin , Pakhomov Serguei , Cohen Trevor , Ben-Zeev Dror , Campbell Andrew
Published: September 2023

Mobile phone sensing is increasingly being used in clinical research studies to assess a variety of mental health conditions (e.g., depression, psychosis). However, in-the-wild speech analysis -- beyond conversation detecting -- is a missing component of these mobile sensing platforms and studies. We augment an existing mobile sensing platform with a daily voice diary to assess and predict the severity of auditory verbal hallucinations (i.e., hearing sounds or voices in the absence of any speaker), a condition that affects people with and without psychiatric or neurological diagnoses. We collect 4809 audio diaries from N=384 subjects over a one-month-long study period. We investigate the performance of various deep-learning architectures using different combinations of sensor behavioral streams (e.g., voice, sleep, mobility, phone usage, etc.) and show the discriminative power of solely using audio recordings of speech as well as automatically generated transcripts of the recordings; specifically, our deep learning model achieves a weighted f-1 score of 0.78 solely from daily voice diaries. Our results surprisingly indicate that a simple periodic voice diary combined with deep learning is sufficient enough of a signal to assess complex psychiatric symptoms (e.g., auditory verbal hallucinations) collected from people in the wild as they go about their daily lives.
Investigating Passive Haptic Learning of Piano Songs Using Three Tactile Sensations of Vibration, Stroking and Tapping
Authors: Fang Likun , Müller Timo , Pescara Erik , Fischer Nikola , Huang Yiran , Beigl Michael
Published: September 2023

Passive Haptic Learning (PHL) is a method by which users are able to learn motor skills without paying active attention. In past research, vibration is widely applied in PHL as the signal delivered on the participant's skin. The human somatosensory system provides not only discriminative input (the perception of pressure, vibration, slip, and texture, etc.) to the brain but also an affective input (sliding, tapping and stroking, etc.). The former is often described as being mediated by low-threshold mechanosensitive (LTM) units with rapidly conducting large myelinated (Aᵬ) afferents, while the latter is mediated by a class of LTM afferents called C-tactile afferents (CTs). We investigated whether different tactile sensations (tapping, light stroking, and vibration) influence the learning effect of PHL in this work. We built three wearable systems corresponding to the three sensations respectively. 17 participants were invited to learn to play three different note sequences passively via three different systems. The subjects were then tested on their remembered note sequences after each learning session. Our results indicate that the sensations of tapping or stroking are as effective as the vibration system in passive haptic learning of piano songs, providing viable alternatives to the vibration sensations that have been used so far. We also found that participants on average made up to 1.06 errors less when using affective inputs, namely tapping or stroking. As the first work exploring the differences in multiple types of tactile sensations in PHL, we offer our design to the readers and hope they may employ our works for further research of PHL.
Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction
Authors: Tabatabaie Mahan , He Suining , Shin Kang G.
Published: September 2023

Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both "spoken language" (e.g., human textual annotations) and "unspoken language" (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design.

To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken "languages", we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants.
I Know Your Intent
Authors: Xiao Jingyu , Zou Qingsong , Li Qing , Zhao Dan , Li Kang , Weng Zixuan , Li Ruoyu , Jiang Yong
Published: September 2023

With the booming of smart home market, intelligent Internet of Things (IoT) devices have been increasingly involved in home life. To improve the user experience of smart homes, some prior works have explored how to use machine learning for predicting interactions between users and devices. However, the existing solutions have inferior User Device Interaction (UDI) prediction accuracy, as they ignore three key factors: routine, intent and multi-level periodicity of human behaviors. In this paper, we present SmartUDI, a novel accurate UDI prediction approach for smart homes. First, we propose a Message-Passing-based Routine Extraction (MPRE) algorithm to mine routine behaviors, then the contrastive loss is applied to narrow representations among behaviors from the same routines and alienate representations among behaviors from different routines. Second, we propose an Intent-aware Capsule Graph Attention Network (ICGAT) to encode multiple intents of users while considering complex transitions between different behaviors. Third, we design a Cluster-based Historical Attention Mechanism (CHAM) to capture the multi-level periodicity by aggregating the current sequence and the semantically nearest historical sequence representations through the attention mechanism. SmartUDI can be seamlessly deployed on cloud infrastructures of IoT device vendors and edge nodes, enabling the delivery of personalized device service recommendations to users. Comprehensive experiments on four real-world datasets show that SmartUDI consistently outperforms the state-of-the-art baselines with more accurate and highly interpretable results.
DYPA
Authors: Zhong Shuhan , Song Sizhe , Tang Tianhao , Nie Fei , Zhou Xinrui , Zhao Yankun , Zhao Yizhe , Sin Kuen Fung , Chan S.-H. Gary
Published: September 2023

Identifying early a person with dyslexia, a learning disorder with reading and writing, is critical for effective treatment. As accredited specialists for clinical diagnosis of dyslexia are costly and undersupplied, we research and develop a computer-assisted approach to efficiently prescreen dyslexic Chinese children so that timely resources can be channelled to those at higher risk. Previous works in this area are mostly for English and other alphabetic languages, tailored narrowly for the reading disorder, or require costly specialized equipment. To overcome that, we present DYPA, a novel DYslexia Prescreening mobile Application for Chinese children. DYPA collects multimodal data from children through a set of specially designed interactive reading and writing tests in Chinese, and comprehensively analyzes their cognitive-linguistic skills with machine learning. To better account for the dyslexia-associated features in handwritten characters, DYPA employs a deep learning based multilevel Chinese handwriting analysis framework to extract features across the stroke, radical and character levels. We have implemented and installed DYPA in tablets, and our extensive trials with more than 200 pupils in Hong Kong validate its high predictive accuracy (81.14%), sensitivity (74.27%) and specificity (82.71%).
HMGAN
Authors: Chen Ling , Hu Rong , Wu Menghan , Zhou Xin
Published: September 2023

Wearable Human Activity Recognition (WHAR) is an important research field of ubiquitous and mobile computing. Deep WHAR models suffer from the overfitting problem caused by the lack of a large amount and variety of labeled data, which is usually addressed by generating data to enlarge the training set, i.e., Data Augmentation (DA). Generative Adversarial Networks (GANs) have shown their excellent data generation ability, and the generalization ability of a classification model can be improved by GAN-based DA. However, existing GANs cannot make full use of the important modality information and fail to balance modality details and global consistency, which cannot meet the requirements of deep multi-modal WHAR. In this paper, a hierarchical multi-modal GAN model (HMGAN) is proposed for WHAR. HMGAN consists of multiple modal generators, one hierarchical discriminator, and one auxiliary classifier. Multiple modal generators can learn the complex multi-modal data distributions of sensor data. Hierarchical discriminator can provide discrimination outputs for both low-level modal discrimination losses and high-level overall discrimination loss to draw a balance between modality details and global consistency. Experiments on five public WHAR datasets demonstrate that HMGAN achieves the state-of-the-art performance for WHAR, outperforming the best baseline by an average of 3.4%, 3.8%, and 3.5% in accuracy, macro F1 score, and weighted F1 score, respectively.
Uncovering Bias in Personal Informatics
Authors: Yfantidou Sofia , Sermpezis Pavlos , Vakali Athena , Baeza-Yates Ricardo
Published: September 2023

Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.
Fast Radio Map Construction with Domain Disentangled Learning for Wireless Localization
Authors: Jiang Weina , Shi Lin , Niu Qun , Liu Ning
Published: September 2023

The accuracy of wireless fingerprint-based indoor localization largely depends on the precision and density of radio maps. Although many research efforts have been devoted to incremental updating of radio maps, few consider the laborious initial construction of a new site. In this work, we propose an accurate and generalizable framework for efficient radio map construction, which takes advantage of readily-available fine-grained radio maps and constructs fine-grained radio maps of a new site with a small proportion of measurements in it. Specifically, we regard radio maps as domains and propose a Radio Map construction approach based on Domain Adaptation (RMDA). We first employ the domain disentanglement feature extractor to learn domain-invariant features for aligning the source domains (available radio maps) with the target domain (initial radio map) in the domain-invariant latent space. Furthermore, we propose a dynamic weighting strategy, which learns the relevancy of the source and target domain in the domain adaptation. Then, we extract the domain-specific features based on the site's floorplan and use them to constrain the super-resolution of the domain-invariant features. Experimental results demonstrate that RMDA constructs a fine-grained initial radio map of a target site efficiently with a limited number of measurements. Meanwhile, the localization accuracy of the refined radio map with RMDA significantly improved by about 41.35% after construction and is comparable with the dense surveyed radio map (the reduction is less than 8%).
InfoPrint
Authors: Jiang Weiwei , Wang Chaofan , Sarsenbayeva Zhanna , Irlitti Andrew , Wei Jing , Knibbe Jarrod , Dingler Tilman , Goncalves Jorge , Kostakos Vassilis
Published: September 2023

We present a fully-printable method to embed interactive information inside 3D printed objects. The information is invisible to the human eye and can be read using thermal imaging after temperature transfer through interaction with the objects. Prior methods either modify the surface appearance, require customized devices or not commonly used materials, or embed components that are not fully 3D printable. Such limitations restrict the design space for 3D prints, or cannot be readily applied to the already deployed 3D printing setups. In this paper, we present an information embedding technique using low-cost off-the-shelf dual extruder FDM (Fused Deposition Modeling) 3D printers, common materials (e.g., generic PLA), and a mobile thermal device (e.g., a thermal smartphone), by leveraging the thermal properties of common 3D print materials. In addition, we show our method can also be generalized to conventional near-infrared imaging scenarios. We evaluate our technique against multiple design and fabrication parameters and propose a design guideline for different use cases. Finally, we demonstrate various everyday applications enabled by our method, such as interactive thermal displays, user-activated augmented reality, automating thermal triggered events, and hidden tokens for social activities.
Spoofing Against Spoofing: Toward Caller ID Verification in Heterogeneous Telecommunication Systems
Authors: Wang Shen , Delavar Mahshid , Azad Muhammad Ajmal , Nabizadeh Farshad , Smith Steve , Hao Feng
Published: September 2023

Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission has mandated telecom providers in the U.S. to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this article, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-tone Multi-frequency, to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for Voice over Internet Protocol, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user’s phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to.
Intensionality and Propositionalism
Authors: Liefke Kristina
Published: September 2023

Propositionalism is the view that all intensional constructions (including nominal and clausal attitude reports) can be interpreted as relations to truth-evaluable propositional content. While propositionalism has long been silently assumed in semantics and the philosophy of language, it has only recently entered center stage in linguistic research. This article surveys the properties of intensional constructions, which require the introduction of fine-grained semantic values (intensions). It contrasts two ways of obtaining such values: through the introduction of either Russellian propositions or Frege-Church-style senses. The article identifies propositionalism with a specific variant of the Russellian strategy, reviews key arguments for propositionalism, and compares familiar varieties of propositionalism on the basis of instructive examples. It closes by discussing various challenges for propositionalism and suggesting a generalization of propositionalism that meets some of these challenges. Because of the association of propositions with semantic information, the article also addresses the more general question of whether all information content (including mental and pictorial content) is propositional.
Dynamic Risk Prediction of Treatment Discontinuation Using Patient-Reported Outcomes Data in the Phase III NSABP B-35 Trial
Authors: Calsavara Vinicius F. , Henry Norah L. , Hays Ron D. , Kim Sungjin , Luu Michael , Diniz Márcio A. , Gresham Gillian , Cecchini Reena S. , Yothers Greg , Ganz Patricia A. , Rogatko André , Tighiouart Mourad
Published: September 2023

Predicting an individual's risk of treatment discontinuation is critical for the implementation of precision chemoprevention. We developed partly conditional survival models to predict discontinuation of tamoxifen or anastrozole using patient-reported outcome (PRO) data from postmenopausal women with ductal carcinoma in situ enrolled in the NSABP B-35 clinical trial. In a secondary analysis of the NSABP B-35 clinical trial PRO data, we proposed two models for treatment discontinuation within each treatment arm (anastrozole or tamoxifen treated patients) using partly conditional Cox-type models with time-dependent covariates. A 70/30 split of the sample was used for the training and validation datasets. The predictive performance of the models was evaluated using calibration and discrimination measures based on the Brier score and AUC from time-dependent ROC curves. The predictive models stratified high-risk versus low-risk early discontinuation at a 6-month horizon. For anastrozole-treated patients, predictive factors included baseline body mass index (BMI) and longitudinal patient-reported symptoms such as insomnia, joint pain, hot flashes, headaches, gynecologic symptoms, and vaginal discharge, all collected up to 12 months [Brier score, 0.039; AUC, 0.76; 95% confidence interval (CI), 0.57–0.95]. As for tamoxifen-treated patients, predictive factors included baseline BMI, and time-dependent covariates: cognitive problems, feelings of happiness, calmness, weight problems, and pain (Brier score, 0.032; AUC, 0.78; 95% CI, 0.65–0.91). A real-time calculator based on these models was developed in Shiny to create a web-based application with a future goal to aid healthcare professionals in decision-making.

Prevention Relevance:

The dynamic prediction provided by partly conditional models offers valuable insights into the treatment discontinuation risks using PRO data collected over time from clinical trial participants. This tool may benefit healthcare professionals in identifying patients at high risk of premature treatment discontinuation and support interventions to prevent potential discontinuation.
The Anti-Reformist Stance in Investment Law
Authors: Heath J. Benton
Published: September 2023

This article articulates an ‘anti-reformist’ stance toward the reform of international investment law and dispute settlement. The mainstream debates over the future of investment law are generally ‘reformist,’ in the sense that they seek to maintain the system of special legal rights afforded to a discrete class of persons defined as ‘investors’. This system of special rights amplifies investors’ power vis-à-vis other constituencies and is premised on flimsy assumptions about the investor’s vulnerability that are not borne out either in theory or in practice. Drawing on recent work in other fields, this article sketches an anti-reformist approach that asserts these fundamental challenges, even as it remains engaged in contemporary reform debates. It also discusses the near-term implications of this approach for procedural and substantive reform of investment treaties, as well as the long-range goals of an anti-reformist stance.