bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of the gut microbiome and different phenotypes of COPD and asthma: a bidirectional Mendelian randomization study
Authors: Wang Zihan , Qu Jingge , Chang Chun , Sun Yongchang
Published: October 2024

Mounting evidence has revealed the association between gut microbiota and both chronic obstructive pulmonary disease (COPD) and asthma; however, the causal association between gut microbiota and specific disease phenotypes remains to be determined. This study employed bidirectional two-sample Mendelian randomization (MR) analyses to investigate the potential causal relationship between gut microbiota and these conditions. The research utilized genome-wide association study (GWAS) data from the MiBioGen consortium for gut microbiota and the integrative epidemiology unit (IEU) Open GWAS for these conditions. Four MR analysis methods were employed: the inverse variance weighted (IVW) test, MR-Egger, weighted median, and weighted mode methods. The IVW method results are considered the primary findings. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy analysis, and leave-one-out analysis, were used to enhance robustness. Our MR study identified eight gut microbiota taxa potentially associated with the risk of different types of COPD and asthma. These include two taxa for early-onset COPD:
Streptococcaceae
[odds ratio (OR) = 1.315, 95% confidence interval (CI) = 1.071–1.616,
P
= 0.009] and
Holdemanella
(OR = 1.199, 95% CI = 1.063–1.352,
P
= 0.003); three for later-onset COPD:
Acidaminococcaceae
(OR = 1.312, 95% CI = 1.098–1.567,
P
= 0.003),
Holdemania
(OR = 1.165, 95% CI = 1.039–1.305,
P
= 0.009), and
Marvinbryantia
(OR = 0.814, 95% CI = 0.697–0.951,
P
= 0.009); one for allergic asthma:
Butyricimonas
(OR = 0.794, 95% CI = 0.693–0.908,
P
= 0.001); and two for non-allergic asthma:
Clostridia
(OR = 1.255, 95% CI = 1.043–1.511,
P
= 0.016) and
Clostridiales
(OR = 1.256, 95% CI = 1.048–1.506,
P
= 0.014).

IMPORTANCE

Individuals with diverse phenotypes of chronic obstructive pulmonary disease (COPD) and asthma exhibit different responses to the conventional “one treatment fits all” approach. Recent research has revealed the significant role of the gut-lung axis in both COPD and asthma. However, the specific impact of gut microbiota on different subtypes of these conditions remains poorly understood. Our study has identified eight gut microbiota that may be associated with the risk of different types of COPD and asthma. These findings provide evidence suggesting a potential causal relationship between gut microbiota and various phenotypes of COPD and asthma. This offers a new perspective on the origins of different disease phenotypes and points toward future exploration of phenotype-specific and personalized therapies.
The Holleford structure: Insights into the pre- and post-impact geology of the Frontenac Terrane, Canada
Authors: McGregor Maree , Spray John G. , Dence Michael R. , McFarlane Christopher R.M. , van Soest Matthijs C.
Published: October 2024

A multi-chronometric investigation has been conducted on the ~2.0-km-diameter Holleford impact structure of Ontario, Canada. In situ triple quadrupole laser ablation–inductively coupled–mass spectrometry U-Pb and Rb-Sr geochronology, along with (U-Th)/He thermochronology, have been applied to multiple mineral phases from impact melt-bearing breccias and overlying sedimentary units. Impact-induced resetting is absent in all three isotopic systems. While Holleford’s small size resulted in insufficient heating to reset geochronometers during impact, our investigation has defined a more detailed pre- and post-impact geologic setting within the Precambrian basement and overlying Paleozoic lithologies. Target rock age determinations include those associated with the Proterozoic Frontenac Terrane. These are manifest as the Rigolet (1004 ± 5 Ma, U-Pb apatite dating) and Ottawan (1074 ± 12 Ma, Rb-Sr biotite dating) phases of the Grenville Orogeny. Pre-Grenville ages associated with the Penokean (1806 ± 46 Ma, apatite dating) and the Shawinigan (1184 ± 8 Ma, U-Pb titanite dating; 1134 ± 21 Ma, U-Pb zircon dating) orogenies are also retained. A Paleoproterozoic age records Superior Province tectonometamorphism (2421 ± 97 Ma, U-Pb zircon dating). Dated post-impact events include dolomitization associated with Silurian marine transgression (430 ± 14 Ma, U-Pb dolomite dating) and regional K-metasomatism of Mississippian age (346 ± 5 Ma, U-Pb titanite dating). Extremely young (U-Th)/He ages (27–3 Ma) were obtained from planar fractured and recrystallized apatite, which we interpret to be the result of impact-induced diffusion domain reduction. This, combined with low-temperature heating due to burial, significantly limited He retention. Stratigraphic constraints place the Holleford impact event at ca. 458 Ma, close to the Darriwilian–Sandbian boundary of the Ordovician. This age indicates that Holleford may be part of the Late Ordovician group of terrestrial impact craters related to L-chondrite parent breakup.
Supplemental Material: Substantial in-situ Ti isotope variations in rutile record source and fluid evolution of porphyry copper mineralization systems
Authors: Jiang Xiaojia , Chen Xin , al. et
Published: October 2024

Methodology and supplemental tables.
Long-term surgical outcomes and prognostic factors for advanced-stage retinopathy of prematurity after vitrectomy
Authors: Chang Yin-Hsi , Kang Eugene Yu-Chuan , Chen Kuan-Jen , Wang Nan-Kai , Liu Laura , Hwang Yih-Shiou , Lai Chi-Chun , Wu Wei-Chi
Published: October 2024

Background/aims

The aims of this study is to evaluate the anatomic, visual outcomes and associated prognostic factors in patients with advanced retinopathy of prematurity (ROP) following vitrectomy.

Methods

A retrospective cohort study of patients with ROP who underwent vitrectomy from 2005 to 2016 was conducted. All the patients had a follow-up period of at least 5 years. Univariate and multivariable logistic regression analyses were used to explore the factors related to unfavourable outcomes.

Results

In total, 81 eyes of 51 patients were included. The mean age at last follow-up was 10.2 years. The anatomic success rate was 96.3% (26/27) for stage 4A, 90.9% (20/22) for stage 4B and 31.3% (10/32) for stage 5 ROP (p<0.01). The mean logMAR best corrected visual acuity of the stage-4A eyes was the highest, followed by those of stage-4B and stage-5 eyes (0.8, 1.5 and 2.6 for stages 4A, 4B and 5, respectively; p<0.01). High myopia (≤ −5.0 D) was noted in 70.8% and 71.4% of stage-4A and stage-4B eyes, respectively. Cataract was the most common complication (25.9%), followed by corneal opacity (17.3%), strabismus (16.1%), and phthisis (16.1%). Stage of the disease was a poor prognostic factor in all vitrectomised eyes (p<0.01). Vitrectomy combined lensectomy was a significant predictor for poor anatomic outcomes for stage-4 eyes (p=0.03). Presence of plus disease was also a possible factor affecting the surgical outcomes.

Conclusion

The long-term surgical outcomes of the eyes with stage 4A and 4B ROP were favourable. Management of stage 5 ROP remained challenging.
Noisy knees - knee crepitus prevalence and association with structural pathology: a systematic review and meta-analysis
Authors: Couch Jamon L , King Matthew G , De Oliveira Silva Danilo , Whittaker Jackie L , Bruder Andrea M , Serighelli Fernanda , Kaplan Sean , Culvenor Adam G
Published: October 2024

Objective

Knee crepitus, the audible crackling or grinding noise during knee movement, can be experienced across the lifespan and create concern for underlying pathology. Our systematic review aims to provide a summary estimate of knee crepitus prevalence and its association with structural pathology among the general population and across knee conditions.

Design

Systematic review and meta-analysis.

Data sources

Medline, Embase, CENTRAL, Web of Science, SPORTDiscus and CINAHL.

Eligibility criteria

Studies evaluating knee crepitus prevalence.

Results

103 studies involving 36 439 participants (42 816 knees) were included. Based on very low certainty evidence, the pooled prevalence of knee crepitus in the general population was 41% (7609 knees; 95% CI 36% to 45%; I2=92.6%); in pain-free persons 36% (852 knees; 95% CI 23% to 50%; I2=91.9%), and in those with osteoarthritis (OA) 81% (18 821 knees; 95% CI 75% to 87%; I2=97.9%). Across other musculoskeletal knee conditions, the pooled prevalence of knee crepitus ranged from 35% (ligament injury; 2740 knees; 95% CI 27% to 44%; I2=95.6%) to 61% (cartilage pathology; 1445 knees; 95% CI 40% to 81%; I2=98.2%). There was low to very low certainty evidence of an association between knee crepitus and radiographic OA (OR 3.79, 95% CI 1.99 to 7.24; 1725 knees; I2=53.0%) and several OA-related features on magnetic resonance imaging (MRI).

Conclusion

In this review, knee crepitus was prevalent in the general population, pain-free persons, those with knee OA and other musculoskeletal knee conditions. Knee crepitus was associated with a more than threefold increased odds of radiographic OA diagnosis and several OA-related MRI features. The low to very low certainty of evidence informing our aggregated prevalence estimates and association outcomes suggest that results should be interpreted with caution.
Effects of freebase/protonated nicotine concentration, liquid composition and electrical power on throat hit in direct-to-lung vaping: theory and clinical measurements
Authors: El Hourani Mario , Lakkis Issam , Ammar Maram , AlGemayel Christina , Talih Soha , Golshahi Laleh , Hosseini Sana , Ashley David , Saleh Rawad , Eissenberg Thomas , Breland Alison , Shihadeh Alan
Published: October 2024

Background

For decades, the tobacco industry has engineered the sensory characteristics of combustible tobacco products including the degree of harshness experienced at the back of the throat. Commonly referred to as ‘throat hit’, this harshness derives from absorption of gas phase nicotine and other constituents by the sensory nerves. People who use tobacco products associate throat hit with the positive psychological effects of nicotine, making it a secondary reinforcer for smoking. On the other hand, high throat hit can make products aversive to naïve nicotine users and serves as a barrier to consumption of tobacco products.

Methods

We developed a first-principles theoretical model to predict nicotine absorption in the throat as a function of electronic nicotine delivery systems (ENDS) device power, liquid composition and puffing topography. The predicted nicotine absorption was compared with subjective throat harshness reported by human participants. We also simulated several ENDS use scenarios to identify the most important processes and factors that govern nicotine absorption in the throat.

Results

Across various ENDS configurations, we found that computed nicotine gas absorption in the throat was associated with subjective harshness scores (r=0.62; p<0.00001). We also found that liquid nicotine concentration, nicotine form, aerosol temperature and inhalation rate strongly influence nicotine absorption in the throat per unit of nicotine emitted by an ENDS.

Conclusions

Nicotine throat hit can be predicted and manipulated through ENDS device and liquid design variables. Regulating ENDS throat hit is feasible and may help reduce product appeal to nicotine-naïve individuals while maintaining acceptability for smokers.
Tachyonic effects on Kähler moduli stabilized inflaton potential in type-IIB/F theory
Authors: Let Abhijit , Ghosh Buddhadeb
Published: October 2024

We investigate the effects of inclusion of charged tachyonic open-string scalars in the perturbative and the non-perturbative Kähler moduli stabilizations in a geometry of three intersecting magnetized D7-brane stacks in type-IIB/F theory and also study the overall influence of this process on the inflaton potential, in a hybrid inflation scenario. We find that a tachyon lowers the minimum of the inflaton potential and assists to end the inflation. For simplicity, we have included one tachyon at a time in the present work and observe that this procedure preserves the features of slow-roll plateau of the potential. An interesting observation here is that the tachyonic part of the potential can be fine-tuned to get an almost zero minimum of the potential, thereby conforming to the small experimental value of the cosmological constant.
Impact of Q-balls formed by first-order phase transition on sterile neutrino dark matter
Authors: Ma Jiucheng , Jiang Siyu , Li Xiu-Fei
Published: October 2024

We explore the mechanism that can explain the production of lepton asymmetry and two types of sterile neutrino dark matter. The first type involves heavy sterile dark matter produced directly by the decay of Q-balls which are formed by first-order phase transition in the early universe; the second consists of keV sterile neutrino dark matter, produced resonantly with the aid of lepton asymmetry from Q-ball decay. Besides, gravitational waves from cosmic strings generated during the phase transition process could be detected at future interferometers.
Anatomy of vector-like bottom-quark models in the alignment limit of the 2-Higgs doublet model type-II
Authors: Arhrib A. , Benbrik R. , Boukidi M. , Moretti S.
Published: October 2024

Expanding upon our ongoing investigation of Vector-Like Quark (VLQ) phenomenology within a 2-Higgs Doublet Model (2HDM) framework, in this paper, we complement a previous one dedicated to Vector-Like Top-quarks (VLTs) by studying Vector-Like Bottom-quarks (VLBs), specifically focusing on their behavior in the alignment limit of a Type-II Yukawa structure. We examine the potential for detecting VLBs at the Large Hadron Collider (LHC) and analyze their decay signatures, encompassing both Standard Model (SM) processes and exotic decays. The objective is to differentiate among singlet, doublet, and triplet configurations of VLBs by identifying distinct decay patterns, thereby providing insights into the structure of Beyond the SM (BSM) physics.
Measurement of the absolute efficiency of the X-ARAPUCA photon detector for the DUNE Far Detector 1
Authors: Álvarez-Garrote R. , Brizzolari C. , Canto A. , Calvo E. , Cattadori C. M. , Cuesta C. , de la Torre Rojo A. , Gil-Botella I. , Gotti C. , Guffanti D. , Machado A. A. , Manthey Corchado S. , Martín I. , Massari C. , Meazza L. , Palomares C. , Pérez-Molina L. , Segreto E. , Terranova F. , Verdugo de Osa A. , Vieira de Souza H. , Warner D.
Published: October 2024

The DUNE far detector has been designed to detect photons and electrons generated by the charged products of the interaction of neutrinos with a massive liquid argon (LAr) target. The photon detection system (PDS) of the first DUNE far detector (FD1) is composed of 6000 photon detection units, named X-ARAPUCA. The detection of the prompt light pulse generated by the particle energy release in LAr will complement and boost the DUNE LAr Time Projection Chamber. It will improve the non-beam events tagging and enable at low energies the trigger and the calorimetry of the supernova neutrinos. The X-ARAPUCA is an assembly of several components. Its photon detection efficiency (PDE) depends on the design of the assembly, on the grade of the individual components and on their coupling. The X-ARAPUCA PDE is one of the leading parameters for the PDS sensitivity, that in turn determines the sensitivity of the DUNE for the detection of core-collapse supernova within the galaxy and for nucleon decay searches. In this work we present the final assessment of the absolute PDE of the FD1 X-ARAPUCA baseline design, measured in two laboratories with independent methods and setups. Preliminary results were reported in Palomares (JINST 18(02):C02064, https://doi.org/10.1088/1748-0221/18/02/C02064, 2023). One hundred sixty units of these X-ARAPUCA devices have been deployed in the NP04 facility at the CERN Neutrino Platform, the 1:20 scale FD1 prototype, and will be operated during the year 2024. The assessed value of the PDE is a key parameter both in the NP04 and in the DUNE analysis and reconstruction studies.
Gravitational orbital Hall effect of vortex light in Lense–Thirring metric
Authors: Qiu Wei-Si , Lian Dan-Dan , Zhang Peng-Ming
Published: October 2024

Vortex light, characterized by an intrinsic orbital angular momentum aligned with its propagation direction, is described through vortex electromagnetic waves. Similar to the gravitational spin Hall effect (SHE), vortex light is expected to exhibit intrinsic orbital angular momentum dependent trajectories and deviations from the null geodesic plane when propagating through a gravitational field, a phenomenon termed the gravitational orbital Hall effect (OHE). In this work, we model the vortex light as vortex Laguerre–Gaussian electromagnetic wave packets and analyze its motion by solving covariant Maxwell equations within the Lense–Thirring metric. Our findings reveal that the trajectory of vortex light with an intrinsic orbital angular momentum deviates from the null geodesic in two ways. It deviates both perpendicular to, and within, the null geodesic plane. This behavior contrasts with the gravitational SHE, where spin-polarized light primarily deviates perpendicular to the null geodesic plane. Moreover, the relationship between the deviation and intrinsic orbital angular momentum differs significantly from that between the deviation and spin. These results suggest a unique interaction between intrinsic orbital angular momentum and gravity, distinct from the spin-gravity coupling, indicating that the gravitational OHE of light might not be precisely predicted by merely substituting spin with intrinsic orbital angular momentum in the gravitational SHE of light.
Energetics of Buchdahl stars and the magnetic Penrose process
Authors: Shaymatov Sanjar , Dadhich Naresh , Tursunov Arman
Published: October 2024

Buchdahl star is the most compact object without an event horizon and is an excellent candidate for a black hole mimicker. Unlike black holes, rotating Buchdahl star can be over-extremal with respect to the black hole, sustaining a larger spin. We show that it can also develop an ergosphere above the threshold spin $$\beta > 1/\sqrt{2}$$

β
>
1
/

2

, which allows extraction of its rotational energy. Electromagnetic field around Buchdahl star is also expected to differ from that of black hole in both strength and topology. In this paper, we explore the energetics of Buchdahl star focusing on the magnetic Penrose process in the two magnetic field configurations, i.e., uniform and dipole. Below the threshold spin, Buchdahl star is expected to be quiet, while above the threshold it can be much more efficient than the black hole if a dipolar magnetic field is developed on its surface.
A Tumor-Immune System with Switching Threshold Depending on Tumor Cells and Its Change Rate
Authors: Peng Hengjie , Xiang Changcheng
Published: October 2024

Tumor prevention and control, taking into account the load of tumor cells or immune cells, are often described by Filippov systems, usually with a smooth function on the right-hand side describing the tumor immunity. However, these systems only consider the load of tumor cells or immune cells as a threshold function, which suggest that patients will only be treated if the load of tumor cells or immune cells exceeds a certain threshold. As a result, there may be an excessive growth of tumor cells or a rapid decrease in immune cells, which can lead to undesirable results. Considering this problem, we propose a weighted switching system using the load of tumor cells and its change rate as the threshold. Theoretical analysis and numerical simulation of the system are performed by changing the threshold value. We find that in the proposed nonlinear weighted threshold control method for the system, on the one hand, the system can produce rich dynamical behaviors, including many possible cases of bistability and sliding segments of the system, etc., and on the other hand, it is also very important for the prevention and control of tumor.
Model independent estimation of expansion rate from low and high redshift SNIa
Authors: Mohammadi Mohammad Hadi , Mehrabi Ahmad
Published: October 2024

Understanding the expansion rate history is a primary goal in cosmology. There are various ways to measure the expansion rate at different redshifts and measuring it through the luminosity distance of SNIa is widely used in cosmology. In this work, we split the latest dataset of SNIa (the Pantheon+ sample) into several redshift bins and compute the expansion rate through a non-parametric technique. To do this, we use the Gaussian process to obtain the expansion rate considering data in different redshift bins. For the sake of comparison, we also consider the [Formula: see text]CDM model and find the expansion history through an MCMC analysis. Our results indicate that the degeneracy between free parameters prevents a good constraint using the low redshifts data but this is not the case in the GP scenario. Moreover, we study how high redshift SNIa can affect the current expansion rate and observe that data at [Formula: see text] has almost no impact on the current expansion rate in both approaches.
NEW FRACTIONAL INTEGRAL INEQUALITIES FOR EXPONENTIALLY (h,m)-PREINVEX INTERVAL-VALUED FUNCTIONS
Authors: TAN YUN , ZHAO DAFANG , VALDÉS JUAN EDUARDO NÁPOLES , WANG XIAOLING
Published: October 2024

We introduce the concept of exponentially [Formula: see text]-preinvex interval-valued functions (EP-([Formula: see text])-IVFs) by using the pseudo-order relation, and derive some interesting properties. Based on such a definition, we present some new Hermite–Hadamard and Hermite–Hadamard–Fejér-type inequalities for [Formula: see text]-Riemann–Liouville fractional integrals. These inequalities not only generalize some existing research findings in the literature, but also provide a new direction for the further study of interval integral estimation. Additionally, graphical representations and numerical estimates of some examples are given to illustrate the validity of the main results.
AVERAGE GEODESIC DISTANCE OF SIERPIŃSKI NETWORKS ON TORUS
Authors: ZHAO ZIXUAN , XUE YUMEI , ZENG CHENG , PENG LULU
Published: October 2024

In this paper, we investigate the average geodesic distance on Sierpiński torus networks. We construct Sierpiński torus networks based on the classic Sierpiński carpet in an iterative way. By applying finite patterns on integrals, we deduce the exact value of the average geodesic distance of the Sierpiński torus. Furthermore, the asymptotic formula for the average geodesic distance of the corresponding networks can be obtained.
Synthesis of (oxo)chlorin dimers chelated with thallium(III)
Authors: Aravindu Kunche , Taniguchi Masahiko , Bocian David F. , Lindsey Jonathan S.
Published: October 2024

Two target dimers have been prepared for fundamental studies of hole/electron transfer. Metalation with thallium(III) enables clocking of the rate of hole/electron transfer between the two macrocycles. Each dimer contains a diphenylethyne linker joining two identical hydroporphyrins (chlorin or oxochlorin). The linker is substituted at the 4,4′-positions whereas each (oxo)chlorin is joined at the 10-position. Each (oxo)chlorin is equipped with a gem-dimethyl group at the 18-position to stabilize the hydroporphyrin chromophore toward adventitious dehydrogenation and a 3,5-di-tert-butyl group at the 5-position to achieve increased solubilization in organic media. The dimers parallel a prior set of diphenylethyne-linked (oxo)chlorin constructs containing zinc-free base, zinc-zinc, and copper-copper metalation states that have been examined in studies of electronic communication. The building block (oxo)chlorins for preparing the thallium-containing dimers have been prepared in quantities of 32–404 mg, a scale up to 14-fold larger than previously. Thallation of the free base (oxo)chlorin dimers was achieved with excess TlCl3⋅4H2O in CH2Cl2/CH3OH (3–4:1) upon overnight reaction at room temperature. The long-wavelength (Q[Formula: see text] absorption band of (oxo)chlorins lies between that of the zinc(II) and free base counterparts. Absorption spectral comparisons are provided of the thallium(III) and free base (oxo)chlorin monomers and dimers.
Detrital zircon tales between the Rodinia and Pangaea supercontinents; exploring connections between Avalonia, Cadomia and Central Asia
Authors: Collett Stephen
Published: October 2024

The Rodinia to Pangaea supercontinent cycle represents the most recent complete cycle in the geological record and thus informs models on the processes involved in supercontinent dispersal and assembly. The terranes and/or micro-continents of the Avalonian, Cadomian and Central Asian orogenic belts are witnesses to this complete cycle but their relationships to Rodinia, each other and an intermediate stage in this supercontinent cycle represented by the formation of a large (but not supercontinent) landmass, Gondwana, are at time ambiguous. One method for testing these relationships is detrital zircon geochronology; zircons are a highly resistant and easily datable mineral and thanks to rapid instrumental advances a huge number of detrital zircon datasets are now available. These data can provide a fingerprint whereby suspect terranes and micro-continental fragments can be linked to each other and their parental continent. Nonetheless, to develop coherent and testable geological hypotheses from these data it is necessary to integrate the data into a consistent framework. The presented work explores the possibilities and limitations of using detrital zircon data to better understand the position of the Avalonian, Cadomian and Central Asian terranes in Rodinia and their journey from there to Pangaea.
Sensor-Aware Data Imputation for Time-Series Machine Learning on Low-Power Wearable Devices
Authors: Hussein Dina , Belkhouja Taha , Bhat Ganapati , Doppa Jana
Published: October 2024

Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation is
aware
of sensor data observations. Experiments on seven diverse wearable sensor-based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.
SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive Thresholding
Authors: Tao Zekun , Wang Changjian , Tian Zhiliang , Xu Kele , Guo Yong , Li Shanshan , Bai Yanru , Xie Da
Published: October 2024

Document-level event extraction endeavors to automatically extract structural events from a given document. Many existing approaches focus on modeling entity interactions and decoding these interactions into events, assigning each entity as an event argument. However, these approaches encounter two primary limitations: they exclusively capture semantic dependencies to model entity interactions, overlooking the indication of the spatial distribution features of entities; they decode interactions imprecisely with a hard binary-classification boundary, potentially failing to calibrate micro differences in interactions. To overcome these limitations, we introduce a novel approach termed the
S
patiality-augmented
I
nteraction Model with
A
daptive
T
hresholding (SIAT). Our method addresses the first limitation by calculating the relative position encoding of entities to represent spatial interaction features. These features are then integrated with multi-granularity semantic interactions, enhancing the modeling of entity interactions for each entity pair. Furthermore, we introduce an adaptive event decoding mechanism, which establishes a more flexible decision boundary for different entity interactions. Additionally, an adaptive loss function for threshold learning is designed to further refine the model. Experimental results demonstrate that our proposed method achieves competitive performance compared to state-of-the-art methods on two public event extraction datasets while maintaining considerable training efficiency.
Early-Exit Deep Neural Network - A Comprehensive Survey
Authors: Rahmath P Haseena , Srivastava Vishal , Chaurasia Kuldeep , Pacheco Roberto G. , Couto Rodrigo S.
Published: October 2024

Deep neural networks (DNNs) typically have a single exit point that makes predictions by running the entire stack of neural layers. Since not all inputs require the same amount of computation to reach a confident prediction, recent research has focused on incorporating multiple “exits” into the conventional DNN architecture. Early-exit DNNs are multi-exit neural networks that attach many side branches to the conventional DNN, enabling inference to stop early at intermediate points. This approach offers several advantages, including speeding up the inference process, mitigating the vanishing gradients problems, reducing overfitting and overthinking tendencies. It also supports DNN partitioning across devices and is ideal for multi-tier computation platforms such as edge computing. This article decomposes the early-exit DNN architecture and reviews the recent advances in the field. The study explores its benefits, designs, training strategies, and adaptive inference mechanisms. Various design challenges, application scenarios, and future directions are also extensively discussed.
Contrastive Learning based Speech Spoofing Detection for Multimedia Security in Edge Intelligence
Authors: Sun Jiaqi , Deng Xianjun , liu Shenghao , Fan Xiaoxuan , Huang Yongling , He Yuanyuan , Wu Celimuge , Park Jong Hyuk
Published: October 2024

Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on the ASVspoof2019 dataset show that GEMINI reduces the Equal Error Rate and tandem Detection Cost Function by up to 96.75% and 96.35% in the physical access scenario, and by up to 86.62% and 87.71% in the logical access scenario compared to peer methods.
Ischemic Stroke Segmentation by Transformer and Convolutional Neural Network Using Few-Shot Learning
Authors: Alshehri Fatima , Muhammad Ghulam
Published: October 2024

Stroke is a major factor in causing disability and fatalities. Doctors use computerized tomography (CT) and magnetic resonance imaging (MRI) scans to assess the severity of a stroke. Automatic image segmentation can help doctors diagnose strokes more quickly and accurately, but it is challenging due to the variability of stroke lesions and the limited availability of labeled data. Deep learning is the cutting-edge technique of machine learning and artificial intelligence, which needs an extensive labeled dataset for effective training. Unfortunately, in the medical domain, the availability of labeled data is severely limited, posing a challenge for conventional deep- learning approaches. In this article, we introduce a system that utilizes deep learning in the form of fusing transformer-based and convolutional neural network (CNN)-based features and few-shot learning techniques to segment ischemic strokes in multimedia MRIs. To accomplish this, we employ two different methods. The first method involves parallel fusion, where we combine CNN-based and transformer-based features. The second method utilizes serial fusion, combining CNN-based and transformer models using few-shot learning. Through the integration of transformer and CNN models, we can extract both global and local features and enhance the system's performance. Moreover, we tackle the issue of limited labeled data by integrating few-shot learning techniques. Additionally, our system optimizes efficiency by selecting only the slices with lesions, disregarding unlesioned slices. The system under consideration is trained with the BraTS2020 dataset, evaluated on the ISLES 2015 dataset, and contrasted the performance with cutting-edge systems. The suggested system attains a dice coefficient score of 0.76, surpassing the scores of previous cutting-edge systems by a substantial margin.
D
e
ME
t
RIS: Counting (near)-Cliques by Crawling
Authors: Bera Suman K. , Choudhari Jayesh , Haddadan Shahrzad , Ahmadian Sara
Published: October 2024

We study the problem of approximately counting
cliques
and
near cliques
in a graph, where the access to the graph is only available through crawling its vertices. This model has been introduced recently to capture real-life scenarios in which the entire graph is too massive to be stored as a whole or be scanned entirely. Sampling vertices independently is non-trivial in this model, thus algorithms which rely on sampling often use a random walk. The goal is to provide an accurate estimate by seeing only a small portion of the graph. This model is known as the
random walk
model or the
neighborhood query
model.

We introduce D
e
ME
t
RIS: Dense Motif Estimation through Random Incident Sampling. This method provides a scalable algorithm for clique and near clique counting in the random walk model. We prove the correctness of our algorithm through rigorous mathematical analysis and extensive experiments. Both our theoretical results and our experiments show that D
e
ME
t
RIS obtains a high precision estimation by only crawling a sub-linear portion on vertices. Therefore, we demonstrate a significant improvement over previous known results.
Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss
Authors: Petrov Aleksandr Vladimirovich , Macdonald Craig
Published: October 2024

A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore, models trained with negative sampling tend to overestimate the probabilities of positive interactions – a phenomenon we call
overconfidence
. While the absolute values of the predicted scores/probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors’ explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. Through detailed experiments on three datasets, we show that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items. Finally, we show how addressing overconfidence can improve model calibration – the ability of a model to predict actual interaction probabilities accurately. By applying gBCE to the SASRec model on MovieLens-1M dataset, we reduce the models’ expected calibration error by 98.9% (from 0.966 to 0.01).
Automating Comment Generation for Smart Contract from Bytecode
Authors: Xiang Jianhang , Gao Zhipeng , Bao Lingfeng , Hu Xing , Chen Jiayuan , Xia Xin
Published: October 2024

Recently, smart contracts have played a vital role in automatic financial and business transactions. To help end users without programming background to better understand the logic of smart contracts, previous studies have proposed models for automatically translating smart contract source code into their corresponding code summaries. However, in practice, only 13% of smart contracts deployed on the Ethereum blockchain are associated with source code. The practical usage of these existing tools is significantly restricted. Considering that bytecode is always necessary when deploying smart contracts, in this article, we first introduce the task of automatically generating smart contract code summaries from bytecode. We propose a novel approach, named

Smart Contract Bytecode Translator (
SmartBT
)

for automatically translating smart contract bytecode into fine-grained natural language description directly. Two key challenges are posed for this task: structural code logic hidden in bytecode and the huge semantic gap between bytecode and natural language descriptions. To address the first challenge, we transform bytecode into
Control-Flow Graph (CFG)
to learn code structural and logic details. Regarding the second challenge, we introduce an information retrieval component to fetch similar comments for filling the semantic gap. Then, the structural input and semantic input are used to build an attentional sequence-to-sequence neural network model. The copy mechanism is employed to copy rare words directly from similar comments, and the coverage mechanism is employed to eliminate repetitive outputs. The automatic evaluation results show that
SmartBT
outperforms a set of baselines by a large margin, and the human evaluation results show the effectiveness and potential of
SmartBT
in producing meaningful and accurate comments for smart contract code from bytecode directly.
Diversity’s Double-Edged Sword: Analyzing Race’s Effect on Remote Pair Programming Interactions
Authors: Mason Shandler A. , Kuttal Sandeep Kaur
Published: October 2024

Remote pair programming is widely used in software development, but no research has examined how race affects these interactions between developers. We embarked on this study due to the historical underrepresentation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced same- and mixed-race pairs. Pairs collaborated on a programming task using the think-aloud method, followed by individual retrospective interviews. Our findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs. Mixed-race pairs excelled in task distribution, shared decision-making, and role-exchange but encountered communication challenges, discomfort, and anxiety, shedding light on the complexity of diversity dynamics. Our study emphasizes race’s impact on remote pair programming and underscores the need for diverse tools and methods to address racial disparities for collaboration.
Targeting Neuroplasticity in Substance Use Disorders: Implications for Therapeutics
Authors: Wolf Marina E.
Published: October 2024

The last two decades have witnessed substantial advances in identifying synaptic plasticity responsible for behavioral changes in animal models of substance use disorder. We have learned the most about cocaine-induced plasticity in the nucleus accumbens and its relationship to cocaine seeking, so that is the focus in this review. Synaptic plasticity pointing to potential therapeutic targets has been identified mainly using two drug self-administration models: extinction-reinstatement and abstinence models. A relationship between cocaine seeking and potentiated AMPAR transmission in nucleus accumbens is indicated by both models. In particular, an atypical subpopulation—Ca2+-permeable or CP-AMPARs—mediates cue-induced seeking that persists even after long periods of abstinence, modeling the persistent vulnerability to relapse that represents a major challenge in treating substance use disorder. We review strategies to reverse CP-AMPAR plasticity; strategies targeting other components of excitatory synapses, including dysregulated glutamate uptake and release; and behavioral interventions that can be augmented by harnessing synaptic plasticity.
Inhibitors of Intracellular RyR2 Calcium Release Channels as Therapeutic Agents in Arrhythmogenic Heart Diseases
Authors: Do Tri Q. , Knollmann Björn C.
Published: October 2024

Ryanodine receptor type 2 (RyR2) is the principal intracellular calcium release channel in the cardiac sarcoplasmic reticulum (SR). Pathological RyR2 hyperactivity generates arrhythmia risk in genetic and structural heart diseases. RYR2 gain-of-function mutations cause catecholaminergic polymorphic ventricular tachycardia. In structural heart diseases (i.e., heart failure), posttranslation modifications render RyR2 channels leaky, resulting in pathologic calcium release during diastole, contributing to arrhythmogenesis and contractile dysfunction. Hence, RyR2 represents a therapeutic target in arrhythmogenic heart diseases. We provide an overview of the structure and function of RyR2, and then review US Food and Drug Administration–approved and investigational RyR2 inhibitors. A therapeutic classification of RyR2 inhibitors is proposed based on their mechanism of action. Class I RyR2 inhibitors (e.g., flecainide) do not change SR calcium content and are primarily antiarrhythmic. Class II RyR2 inhibitors (e.g., dantrolene) increase SR calcium content, making them less effective as antiarrhythmics but preferable in conditions with reduced SR calcium content such as heart failure.
Electrical Conductivity of Binary, Ternary, Quaternary and Quinary Molten Salt Mixtures Based on NaCl-CaCl2
Authors: Sommerseth Camilla , Molvik Kent-Robert , Hillestad Helene , Nybrodahl Marthe , Gebarowski Wojciech , Kjos Ole S.
Published: October 2024

As intermittent energy sources like solar energy and wind power emerge, the need for energy storage becomes important, energy availability needs to be ensured also when the Sun is not shining, and the wind is not blowing. Energy storage can also be used for peak shaving purposes during periods of high demand. Energy storage solutions need to be inexpensive and reliable. Novel all-liquid batteries are considered one option for stationary energy storage and the Na-Zn battery is currently being investigated. During charging Na metal is formed on the negative electrode from a NaCl containing electrolyte and ZnCl2 is formed from a Zn pool on the positive electrode. The electrical conductivity of the molten salt is an important factor in the ohmic loss through the electrolyte. The composition of the electrolyte decides the electrical conductivity, and this conductivity also changes during the charge/discharge cycles of the battery as the electrolyte composition changes accordingly. Electrical conductivity has been measured on different compositions of NaCl-CaCl2, NaCl-CaCl2-LiCl, NaCl-CaCl2-BaCl2, NaCl-CaCl2-ZnCl2, NaCl-CaCl2-BaCl2-SrCl2, NaCl-CaCl2-BaCl2-ZnCl2 and NaCl-CaCl2-BaCl2-SrCl2-ZnCl2 molten salts in an in-house built apparatus. The smaller ions (Li and Na) give higher electrical conductivity, while the larger ions (Ba, Sr, and Zn) reduce the electrical conductivity.
Electrochemical Screening and DFT Analysis of Acetylacetonate Metal Complexes in Organic Solvents
Authors: Rivera Perez Alexia , Amjad Romina , Cedeno Guzman Maria , Constanza Cassandra , Juarez Yaisa , Miller Gwynneth , Cooper Courtney , Arnet Nicholas A.
Published: October 2024

Seven acetylacetonate (acac) metal complexes ranging from early transition metals to post-transition metals were examined by cyclic voltammetry in acetonitrile (MeCN), dichloromethane (DCM), tetrahydrofuran (THF), dimethyl sulfoxide (DMSO), and dimethylformamide (DMF). The electronic potential of any observed redox events is reported along with an analysis of the reversibility of those events across a range of scan rates. Group 8 compounds Fe(acac)3 Ru(acac)3 showed at least quasi-reversible reductions across all solvents while Ru(acac)3 also featured a reversible oxidation. The early and post-transition compounds VO(acac)2, Ga(acac)3 and In(acac)3 exhibited irreversible reductions, while TiO(acac)2 showed no redox activity within the examined potential ranges. Mn(acac)3 featured an oxidation that showed solvent-dependent reversibility, and a reduction that was irreversible in all examined solvents. DFT calculations indicated minimal solvent effects on the HOMO-LUMO gap for the majority of compounds, but a significant effect was observed for Ru(acac)3. This study serves as a valuable initial step for further examination of acetylacetonate metal complexes for applications as electrochemical internal standards, nanoparticle precursors, and electrocatalysts.