bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of an operon and its regulator required for autoaggregation in
Tetragenococcus halophilus
Authors: Endo Ryuhei , Hotta Shiori , Wakinaka Takura , Mogi Yoshinobu , Watanabe Jun
Published: November 2023

Bacterial autoaggregation is associated with self- and non-self-recognition, surface colonization, and biofilm formation. Numerous aggregation factors have been cloned for basic and applied purposes. Snowflake Forming Collagen Binding Aggregation Factor (SFCBAF) is one such aggregation factor isolated from a few lactic acid bacteria species; however, the regulatory mechanisms of SFCBAF remain poorly understood. Here, we report the
aggR
and
aggABC
genes associated with autoaggregation in the halophilic lactic acid bacterium
Tetragenococcus halophilus
. A draft genome sequence analysis of five aggregating strains and subsequent comparative genomic analysis revealed that all of them have
aggR
and
aggABC
genes, which are possibly involved in autoaggregation. SFCBAF, a sortase, and a hypothetical protein are encoded by
aggABC
, which constitutes the
agg
operon. To analyze the function of these genes, phenotype- and genotype-based mutant screening was carried out. Mutants of either
aggR
or
aggA
lack autoaggregation and collagen binding abilities, indicating that they are both essential for these phenotypes.
aggR
was considered a positive regulator for the
agg
operon because
aggR
encodes a helix-turn-helix protein and
aggR
mutation resulted in diminished
agg
operon expression. Our results provide genetic bases underlying the mechanisms of aggregation and its regulation in
T. halophilus
.

IMPORTANCE

Tetragenococcus halophilus
is a halophilic lactic acid bacterium generally used as a starter culture in fermenting soy and fish sauces. Aggregating strains can be useful in fermenting and obtaining clear soy sauce because cell clumps are trapped by the filter cake when the soy sauce mash is pressed. However, the genetic mechanisms of aggregation in
T. halophilus
are unknown. In this study, we identified genes encoding aggregation factor and its regulator. These findings may provide a foundation for developing improved
T. halophilus
starter cultures for soy sauce fermentation, leading to more efficient and consistent clear soy sauce production.
Angucyclinones rescue PhLOPS
A
antibiotic activity by inhibiting Cfr-dependent antibiotic resistance
Authors: Schaenzer Adam J. , Rodriguez Hernandez Annia , Tsai Kaitlyn , Hobson Christian , Fujimori Danica Galonić , Wright Gerard D.
Published: November 2023

Methylation of the ribosome is a prevalent strategy conferring resistance to antibiotics that inhibit protein synthesis. Methylation is catalyzed by several distinct enzymes, many encoded on mobile genetic elements widespread in bacteria. One enzyme that is increasingly common in Gram-positive pathogens is Cfr. This radical S-adenosyl-L-methionine rRNA methylase confers resistance to the antibiotics
ph
enicols,
l
incosamides,
o
xazolidinones,
p
leuromutilins, and
s
treptogramin
A
(PhLOPS
A
)
via methylation of A2503 of the 23S rRNA of the large ribosomal subunit. One strategy to safeguard the utility of these antibiotics is to identify inhibitors of Cfr-dependent methylation that may rescue antibiotic activity. We screened our in-house library of microbial natural product extracts against Cfr
+
Escherichia coli
and identified an extract from
Streptomyces
WAC01849 that potentiated the PhLOPS
A
antibiotic linezolid in a Cfr-dependent manner. We isolated the active angucyclinones 8-
O
-methyltetrangomycin (MTN) and 8-
O
-methyltetrangulol (MTL) from this extract. Both compounds selectively potentiated representatives of the PhLOPS
A
antibiotics in
Escherichia coli
and methicillin-resistant
Staphylococcus aureus
in a Cfr-dependent manner. Finally, we observed inhibition of Cfr-mediated ribosome methylation in cells treated with MTN and MTL. Our findings suggest that MTN and MTL interfere with Cfr-dependent methylation to protect the efficacy of the PhLOPS
A
antibiotics and offer a unique chemical scaffold that may be applied in developing clinically useful Cfr inhibitors.

IMPORTANCE

Cfr is an antibiotic resistance enzyme that inhibits five clinically important antibiotic classes, is genetically mobile, and has a minimal fitness cost, making Cfr a serious threat to antibiotic efficacy. The significance of our work is in discovering molecules that inhibit Cfr-dependent methylation of the ribosome, thus protecting the efficacy of the PhLOPS
A
antibiotics. These molecules are the first reported inhibitors of Cfr-mediated ribosome methylation and, as such, will guide the further discovery of chemical scaffolds against Cfr-mediated antibiotic resistance. Our work acts as a foundation for further development of molecules that safeguard the PhLOPS
A
antibiotics from Cfr.
Draft genome sequence of
Streptomyces murinus
strain SPC1 with antimicrobial potential against fungal pathogens of palms
Authors: Dhillon Braham , Chakrabarti Seemanti
Published: November 2023

Streptomyces murinus
strain SPC1, isolated from foxtail palm seeds, exhibited antimicrobial activity against fungal pathogens of palms. The assembled genome was 8.3 Mb, with 71.96% GC content, and contained 37 secondary metabolite clusters (SMCs). A complete SMC for antifungal metabolite pentamycin (fungichromin) biosynthesis was identified in SPC1 genome.
Insights into virulence: structure classification of the
Vibrio parahaemolyticus
RIMD mobilome
Authors: Kinch Lisa N. , Schaeffer R. Dustin , Zhang Jing , Cong Qian , Orth Kim , Grishin Nick
Published: November 2023

Vibrio parahaemolyticus
(
Vpar
) is a major cause of seafood-related gastroenteritis. A pandemic strain, RIMD2210633 (RIMD), has emerged from ancestors due to virulence mechanisms acquired through horizontal gene transfer (HGT). Mobile genetic elements play an essential role in RIMD pathogenicity, but they evolve rapidly and are challenging for sequence-based homology detection and functional inference. Guided by the Evolutionary Classification of protein Domains (ECOD), we utilized structure-assisted homology detection of high-quality AlphaFold models for RIMD proteins to augment their functional interpretation. This approach detected 7,107 domains from 3,970 RIMD proteins and classified them into the ECOD hierarchy, providing functional insights into 80% of the proteome. RIMD proteins originating from HGT were identified by comparison against 86 complete proteomes of other
Vpar
strains. The resulting RIMD mobilome is enriched with phage markers and bacterial defense domains. Evolutionary classification of known HGT proteins suggests they provide selective advantages against the human host, phage, and other stresses. Compared to a pre-pandemic strain, we found fast-evolving proteins with potential involvement in RIMD virulence and phage avoidance. These findings provide vital insights into bacterial evolution, pathogenic mechanisms, and adaptation strategies while offering testable hypotheses for experimental studies and an online resource for future investigation of the RIMD proteome.

IMPORTANCE

The pandemic
Vpar
strain RIMD causes seafood-borne illness worldwide. Previous comparative genomic studies have revealed pathogenicity islands in RIMD that contribute to the success of the strain in infection. However, not all virulence determinants have been identified, and many of the proteins encoded in known pathogenicity islands are of unknown function. Based on the EOCD database, we used evolution-based classification of structure models for the RIMD proteome to improve our functional understanding of virulence determinants acquired by the pandemic strain. We further identify and classify previously unknown mobile protein domains as well as fast evolving residue positions in structure models that contribute to virulence and adaptation with respect to a pre-pandemic strain. Our work highlights key contributions of phage in mediating seafood born illness, suggesting this strain balances its avoidance of phage predators with its successful colonization of human hosts.
Altered microbiota, antimicrobial resistance genes, and functional enzyme profiles in the rumen of yak calves fed with milk replacer
Authors: Zhuang Yimin , Guo Wei , Cui Kai , Tu Yan , Diao Qiyu , Zhang Naifeng , Bi Yanliang , Ma Tao
Published: November 2023

The use of milk replacer (MR) is a modern strategy to ensure the health and growth of neonatal ruminants. Some studies have confirmed that dietary factors have significant effects on ruminal antimicrobial resistance genes (ARGs), which, as a potential threat to livestock, have received more attention. However, the effect of different milk sources (milk or MR) on the rumen resistome of yaks is still largely unknown. Here, using metagenome technology, we investigated the occurrence of ruminal ARGs in yaks and their relationship with rumen bacteria in response to different milk sources. The metagenomic result showed that MR feeding significantly increased the abundance of
Prevotella
members compared to milk feeding. A total of 138 rumen resistome-conveying ARGs to 17 different classes of antimicrobials were detected, of which tetracycline resistance was the predominant. MR feeding also drove the changes in the rumen resistome, and the predicted bacterial origins of ARGs had complex and various manifestations. Moreover, enzymes of butyrate metabolism and carbohydrate-active enzymes encoding carbohydrate and cellulose degradation were increased. This study revealed that MR feeding increased the abundance of beneficial bacteria
Prevotella
members, changed the microbial resistome, and promoted the metabolic function of volatile fatty acid biosynthesis in the rumen of yaks, which could provide beneficial evidence for the regulation of the rumen microbiome and the intervention of antimicrobial resistance in ruminants by dietary or nutritional methods.

IMPORTANCE

Yaks, as ruminants inhabiting high-altitude environments, possess a distinct rumen microbiome and are resistant to extreme living conditions. This study investigated the microbiota, resistome, and functional gene profiles in the rumen of yaks fed milk or milk replacer (MR), providing insights into the regulation of the rumen microbiome and the intervention of antimicrobial resistance in yaks through dietary methods. The abundance of
Prevotella
members increased significantly in response to MR. Tetracycline resistance was the most predominant. The rumen of yaks contained multiple antimicrobial resistance genes (ARGs) originating from different bacteria, which could be driven by MR, and these ARGs displayed intricate and complex interactions. MR also induced changes in functional genes. The enzymes associated with fiber degradation and butyrate metabolism were activated and showed close correlations with
Prevotella
members and butyrate concentration. This study allows us to deeply understand the ruminal microbiome and ARGs of yaks and their relationship with rumen bacteria in response to different milk sources.
SalivaDirect: an alternative to a conventional RNA extraction protocol for molecular detection of SARS-CoV-2 in a clinical setting
Authors: Uddin Mohammad Khaja Mafij , Hossain Mohammad Enayet , Ami Jenifar Quaiyum , Hasan Rashedul , Hasan Md. Mahmudul , Islam Ashabul , Hasan Md. Jahid , Shaly Nusrat Jahan , Ahmed Shahriar , Samina Pushpita , Rahman Mohammed Ziaur , Rahman Mustafizur , Banu Sayera
Published: November 2023

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) continues to spread and evolve, giving rise to new surges in cases and deaths. Although nasopharyngeal swab (NPS) samples are the gold standard for diagnosing SARS-CoV2, NPS sampling is invasive and requires skilled staff, specialized transport medium, and nucleic acid extraction. The SalivaDirect method uses saliva samples and a simplified, flexible approach to overcome these drawbacks. In our study conducted at the COVID-19 Screening Unit of the Dhaka Hospital of icddr,b (International Centre for Diarrheal Disease Research, Bangladesh), the diagnostic performance of SalivaDirect and NPS was compared using paired samples. SARS-CoV2 was detected by performing RT-qPCR using RNA extracted from saliva samples with the SalivaDirect method and from the NPS with extracted RNA. A total of 200 participants were enrolled from February to March 2021, among whom 78 (39.0%) tested positive for SAR-CoV2 from at least one sample. Among the 78 participants testing positive, 65 (83.3%) tested positive in both specimens, eight (10.3%) tested positive only in NPS, and five (6.4%) tested positive only in saliva. SalivaDirect had a sensitivity of 89.0% and specificity of 96.1%, with NPS as the reference test. The sensitivity of NPS and SalivaDirect was 93.6% and 89.7% respectively, with a composite reference standard (where patients are defined as positive if tested positive in either method) as the reference. Our findings demonstrated that the SalivaDirect method can be used as an alternative to NPS in our clinical setting and also supports the use of SalivaDirect in other settings.

IMPORTANCE

Affordable and accessible tests for COVID-19 allow for timely disease treatment and pandemic management. SalivaDirect is a faster and easier method to implement than NPS sampling. Patients can self-collect saliva samples at home or in other non-clinical settings without the help of a healthcare professional. Sample processing in SalivaDirect is less complex and more adaptable than in conventional nucleic acid extraction methods. We found that SalivaDirect has good diagnostic performance and is ideal for large-scale testing in settings where supplies may be limited or trained healthcare professionals are unavailable.
Supplemental Material: Holocene gigascale rock avalanches in Vaigat strait, West Greenland—Implications for geohazard
Authors: Svennevig Kristian , al. et
Published: November 2023

Detailed data description and methodology.  
MDSCs-derived GPR84 induces CD8+T-cell senescence via p53 activation to suppress the antitumor response
Authors: Liu Jinyan , Liu Jiayin , Qin Guohui , Li Jiahui , Fu Ziyi , Li Jieyao , Li Miaomiao , Guo Caijuan , Zhao Ming , Zhang Zhen , Li Feng , Zhao Xuan , Wang Liping , Zhang Yi
Published: November 2023

Backgrounds

G-protein-coupled receptor 84 (GPR84) marks a subset of myeloid-derived suppressor cells (MDSCs) with stronger immunosuppression in the tumor microenvironment. Yet, how GPR84 endowed the stronger inhibition of MDSCs to CD8+T cells function is not well established. In this study, we aimed to identify the underlying mechanism behind the immunosuppression of CD8+T cells by GPR84+MDSCs.

Methods

The role and underlying mechanism that MDSCs or exosomes (Exo) regulates the function of CD8+T cells were investigated using immunofluorescence, fluorescence activating cell sorter (FACS), quantitative real-time PCR, western blot, ELISA, Confocal, RNA-sequencing (RNA-seq), etc. In vivo efficacy and mechanistic studies were conducted with wild type, GPR84 and p53 knockout C57/BL6 mice.

Results

Here, we showed that the transfer of GPR84 from MDSCs to CD8+T cells via the Exo attenuated the antitumor response. This inhibitory effect was also observed in GPR84-overexpressed CD8+T cells, whereas depleting GPR84 elevated CD8+T cells proliferation and function in vitro and in vivo. RNA-seq analysis of CD8+T cells demonstrated the activation of the p53 signaling pathway in CD8+T cells treated with GPR84+MDSCs culture medium. While knockout p53 did not induce senescence in CD8+T cells treated with GPR84+MDSCs. The per cent of GPR84+CD8+T cells work as a negative indicator for patients’ prognosis and response to chemotherapy.

Conclusions

These data demonstrated that the transfer of GPR84 from MDSCs to CD8+T cells induces T-cell senescence via the p53 signaling pathway, which could explain the strong immunosuppression of GPR84 endowed to MDSCs.
Assessing injury risk in male and female Royal Navy recruits: does the Functional Movement Screen provide understanding to inform effective injury mitigation?
Authors: Gibbs J. , Power C N T , Böhning D , Warner M , Downie S , Allsopp A , Stokes M , Fallowfield J L
Published: November 2023

Introduction

Musculoskeletal injuries (MSKIs) are common during military and other occupational physical training programmes, and employers have a duty of care to mitigate this injury risk. MSKIs account for a high number of working days lost during initial military training, contribute to training attrition and impact training costs. Poorer movement quality may be associated with increased MSKI risk.

Methods

The present study evaluated the relationship between the Functional Movement Screen (FMS) Score, as a measure of movement quality, and injury risk in Royal Navy (RN) recruits. A cohort of 957 recruits was assessed using the FMS prior to the 10-week phase I training programme. Injury occurrence, time, type and severity were recorded prospectively during the training period.

Results

Total FMS Score was associated with injury risk (p≤0.001), where recruits scoring ≥13 were 2.6 times more likely to sustain an injury during training. However, FMS Score accounted for only 10% of the variance in injury risk (R2=0.1). Sex was the only additional variable to significantly affect the regression model. Mean FMS Scores for men (14.6±2.3) and women (14.4±2.4) were similar, but injury occurrence in women was 1.7 times greater than in men. Examining the influence of individual FMS movement tests on injury prediction did not improve the model, where those movements that significantly contributed to injury prediction only accounted for a small amount of the variance (R2=0.01).

Conclusion

There was a weak relationship between FMS and injury risk in RN recruits. Evidence is provided that FMS score alone would not be appropriate to use as an injury prediction tool in military recruits.
Complete infinitesimal prolongation of the Riemann–Liouville and Caputo derivatives
Authors: Costa Felix S. , Soares Junior C. A. , Frederico Gastão S. F. , Sousa J. Vanterler da C. , Jarosz S.
Published: November 2023

This paper presents the infinitesimal prolongation to Riemann–Liouville and Caputo fractional derivatives without the restrictive lower limit fixed in the integrals, when applicated to the transformation group. The properties are presented, and the examples are illustrated along with the symmetry to fractional derivative criteria.
Gibbs states and their classical limit
Authors: van de Ven Christiaan J. F.
Published: November 2023

A continuous bundle of [Formula: see text]-algebras provides a rigorous framework to study the thermodynamic limit of quantum theories. If the bundle admits the additional structure of a strict deformation quantization (in the sense of Rieffel) one is allowed to study the classical limit of the quantum system, i.e. a mathematical formalism that examines the convergence of algebraic quantum states to probability measures on phase space (typically a Poisson or symplectic manifold). In this manner, we first prove the existence of the classical limit of Gibbs states illustrated with a class of Schrödinger operators in the regime where Planck’s constant [Formula: see text] appearing in front of the Laplacian approaches zero. We additionally show that the ensuing limit corresponds to the unique probability measure satisfying the so-called classical or static KMS-condition. Subsequently, we conduct a similar study on the free energy of mean-field quantum spin systems in the regime of large particles, and discuss the existence of the classical limit of the relevant Gibbs states. Finally, a short section is devoted to single site quantum spin systems in the large spin limit.
Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks
Authors: Zhao Ruibin , Xie Zhiwei , Zhuang Yipeng , L. H. Yu Philip
Published: November 2023

Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as “golden” benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each ‘ground-truth’ description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases.
IoT Ecosystem Security via Distributed Ledger Technology (Blockchain versus IOTA): A Bibliometric Analysis Research
Authors: Singh Jaspreet , Singh Gurpreet , Gupta Deepali , Rani Shalli , Srivastava Gautam
Published: November 2023

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems.
Identification of Hardware Trojan in Gate-Level Netlist
Authors: Mondal Anindan , Ghosh Archisman , Karmakar Shubrojyoti , Mahalat Mahabub Hasan , Roy Suchismita , Sen Bibhash
Published: November 2023

Hardware Trojans (HT) are tiny, malicious circuits intentionally designed by an adversary. The existing works found in the literature on gate-level netlists are mainly based on supervised classification with few attempts at unsupervised clustering. However, the over-reliance on pre-defined structural features used in these supervised classification methods makes them vulnerable to the new Trojan attacks, whereas most unsupervised methods ignore this feature completely. This work presents an unsupervised approach for HT net detection based on the structural features required for small rare-event triggered HTs irrespective of the payload. The proposed work uses k-means clustering on these features to reduce the search space. A new metric based on combinational controllability is applied next to detect the possible trigger net. Experimental results of fifteen selected Trust-HUB benchmarks show the capability of the proposed technique against different types of HT triggers. Results show that the proposed approach reduces the search space massively (up to 99%) while running within a reasonable time frame.
Design, Simulation and Comparative Analysis of Two Stage Operational Amplifier Based on CNTFETs Using Indirect Feedback Frequency Compensation
Authors: Islam Mir Bintul , Nizamuddin M. , Islam S. S.
Published: November 2023

In the course of this study, an efficient implementation of a high gain and low power two-stage operational amplifier using indirect feedback frequency compensation based on CNTFETs. HSPICE software was used to develop and simulate CNTFETs. The op-amps were designed using 0.9[Formula: see text]V input supply voltage. The proposed structures were formed either using CNTFETs only known as pure CNTFET-IFFC-2SOA or hybrid structures consisting a mix of both CNTFETs and conventional CMOS named as PCNTFET-NMOS-IFFC-2SOA and NCNTFET-PMOS-2SOA. The comparative investigation revealed that CNT-based structures performed significantly better than the traditional CMOS-based devices. In the instance of CNTFET-IFFC-2SOA, there was a considerable improvement in DC gain, power dissipation, phase margin, and CMRR. The DC Gain increased by 140.92%, the CMRR increased by 32.94%, and the phase margin increased by 4.9%. Furthermore, at the 32[Formula: see text]nm technology node, a GNRFET-based structure was designed and compared to conventional CMOS and pure CNTFET-based IFFC-2SOA. It was discovered that the DC gain, phase margin, and power dissipation obtained by the CNTFET-based structure were superior to both. The DC Gain of CNTFET-IFFC-2SOA was 156.79% more than that of GNRFET-IFFC-2SOA. To confirm the workability of the proposed circuits, an integrator was designed and simulated as its application. The CNTFET-based IFFC-2SOA integrators showed better integration action than the traditional CMOS-IFFC-2SOA.
High-Performance MOS-C Realization of Mixed-Mode Third-Order Sinusoidal Oscillator
Authors: Kumar Atul , Chaturvedi Bhartendu , Jagga Shafali
Published: November 2023

A newly designed third-order mixed-mode orthogonally tuned quadrature oscillator, generating sinusoidal voltage and current waveforms, is introduced in this paper. The reported circuit is realized using two dual-X current conveyor transconductance amplifiers (DXCCTAs), two active MOS-based grounded resistors and three grounded capacitors. MOS-C realization of the proposed oscillator with all grounded capacitors is the best choice for modern integration. In this study, the ideal and non-ideal analyses and frequency stability aspect of the circuit are elucidated. The proposed third-order oscillator circuit has the advantages of orthogonally controllable frequency of oscillation & condition of oscillation, very good operating frequency (34.25[Formula: see text]MHz) and low total harmonic distortion (within 1.8%). The theoretical analyses have been validated by both pre-layout and post-layout simulations using general process design kit 0.18[Formula: see text][Formula: see text]m technology and the results have been compared with relevant state-of-the-art works. Layout of the used active block, DXCCTA, is developed with Cadence Virtuoso that measures an area of 38[Formula: see text][Formula: see text]m.
Krill Herd and Feed Forward Optimization System-Based Routing Protocol for IoT-MANET Environment
Authors: Sugumaran S. , Sivasankaran V. , Chitra M. G.
Published: November 2023

The Internet of Things (IoT) is a developing technology in the world of communication and embedded systems. The IoT consists of a wireless sensor network with Internet service. The data size of the sensor node is small, but the routing of the data and energy consumption are important issues that need to be advocated. The Mobile Adhoc Network (MANET) plays a very important role in IoT services. In MANET, nodes are moving within the network. So, routes are created dynamically on demand and do not have any centralized units. The route optimization method addresses issues like selecting the best routes in terms of overhead, loop free, traffic control, balancing, throughput, route maintenance, and so on. In this paper, IoT routes are created between sensors to sink through MANET nodes with WSN routing ideology. The Krill Herd and Feed Forward Optimization (KH-FFO)-based method discovers the routes. The Krill herd algorithm clusters the network. This method increases network speed and reduces energy waste. Feed-forward optimization involves learning all the nodes in the network and identifying the shortest and most energy-efficient route from source to sink. The overall performance of the KH-FFO protocol has improved the network’s capacity, reduced packet loss, and increased the energy utilization of the nodes in the network. The ns-3 simulation for KH-FFO is tested in different node densities and observed energy utilization is increased by 28%, network life is increased by 7%, Packet delivery ratio improved by 7.5%, the End-to-End delay improved by 31% and the Throughput is 3%. These metrices are better than the existing works in the network.
Few-shot segmentation using distribution adaption networks
Authors: Niu Xiaoming , He Guanghui
Published: November 2023

Few-shot segmentation aims to achieve object-specific semantic segmentation in query images by conditioning on specific objects identified within support images. Previous methodologies predominantly relied on evaluating similarities between semantic-level prototypes of support features or comprehensive pixelwise support information and query images to inform predictions for queries. However, a significant domain discrepancy has been identified between the extracted query features and their support counterparts, resulting in a significant misalignment in the distribution of support and query features. As a result, direct analogies between support and query information cannot ensure the precision of query predictions. In this investigation, a distribution adaptation network is introduced, meticulously designed to learn discriminative representations and alleviate the distributional disparities between support features and query features. The adaptation network embeds both support and query features into Reproducing Kernel Hilbert Spaces (RKHS) and optimally aligns their distributions. The distributional gap is effectively bridged through optimal kernel embedding alignment, facilitated by a multiple kernel learning strategy. Extensive experimentation on datasets PASCAL-5i, COCO-20i and FSS-1000 unequivocally demonstrates that the proposed Kernel-Aligned Distribution Network (KADN) achieves state-of-the-art performance.
Electromagnetic duality and discrete symmetries for dyon in macroscopic media
Authors: Joshi Ila
Published: November 2023

This paper investigates discrete symmetries for dyon and the invariance of Maxwell’s field equations in the macroscopic media (material medium). Using advanced mathematical approaches, the paper derived electromagnetic duality and a unique form of Poynting theorem for dyon in macroscopic media. Here, we demonstrate that the combination of parity [Formula: see text], charge conjugation [Formula: see text], and time reversal [Formula: see text] symmetry: [Formula: see text], is an exact symmetry for dyon in macroscopic media. Furthermore, a comprehensive analysis of the electromagnetic wave equation for dyons in conducting media is also derived. These findings contribute significantly to the understanding of dyon behavior in electromagnetic fields.
Testing the effect of anisotropy on the parametrization of deceleration parameter from recent observational data
Authors: Zarei Z. , Aghamohammadi A. , Fayaz V. , Terohid S. A. A.
Published: November 2023

In this work, we investigate Bianchi type I universe in the presence of dark energy and dark matter by three parameterizations of deceleration parameter models, which are considered to find solutions of the models. In the framework of an anisotropic cosmology, we constrain the parameters of these models and compared the results with the [Formula: see text]CDM model, by using the datasets from different observational, namely, the Hubble data and type Ia Supernovae Pantheon data. Additionally, the evolution of kinematic cosmographic parameters is investigated. In particular, we analyze the effects of anisotropy on the physical behavior of our mentioned models and compare them with standard [Formula: see text]CDM model.
Numerical Investigation on Sloshing Phenomena in U-Shaped Containers Subjected to Near-Fault Strong Ground Motions
Authors: Das Anupam , Konar Tanmoy , Maity Damodar
Published: November 2023

In a laterally excited, partially liquid-filled U-shaped container, oscillation is the predominant motion of the liquid. However, there would be some sloshing in the liquid free surface at the vertical limbs of the container. Due to the presence of high-energy velocity pulses, near-fault ground motions can cause significant sloshing in the vertical limbs of the container. Thus, it is necessary to examine the effect of near-fault earthquake excitations on the sloshing liquid height, sloshing-induced hydrodynamic pressure, and shear stress at the walls of the U-shaped containers. The same is attempted in this paper. The standard “k-epsilon” turbulence model with enhanced wall function is considered in this study. The outcome of this study reveals that the time-wise frequency variation of the near-fault ground motions significantly affects the sloshing response of the liquid stored in the U-shaped containers. It is observed that the sloshing-induced hydrodynamic pressure is severely influenced by the frequency content of the ground motion records rather than their peak ground acceleration.
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning
Authors: Liu Haoyu , Diallo Alec F. , Patras Paul
Published: November 2023

Embedding covert streams into a cover channel is a common approach to circumventing Internet censorship, due to censors' inability to examine encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.). However, recent advances in machine learning (ML) enable detecting a range of anti-censorship systems by learning distinct statistical patterns hidden in traffic flows. Therefore, designing obfuscation solutions able to generate traffic that is statistically similar to innocuous network activity, in order to deceive ML-based classifiers at line speed, is difficult.

In this paper, we formulate a practical adversarial attack strategy against flow classifiers as a method for circumventing censorship. Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design. Amoeba works by interacting with censoring classifiers without any knowledge of their model structure, but by crafting packets and observing the classifiers' decisions, in order to guide the sequence generation process. Our experiments using data collected from two popular anti-censorship systems demonstrate that Amoeba can effectively shape adversarial flows that have on average 94% attack success rate against a range of ML algorithms. In addition, we show that these adversarial flows are robust in different network environments and possess transferability across various ML models, meaning that once trained against one, our agent can subvert other censoring classifiers without retraining.
Dances with Blues: Harnessing Multi-Frequency Carriers for Commodity Bluetooth Backscatter
Authors: Jiang Maoran , Gong Wei
Published: November 2023

We present DanBlue, a commodity Bluetooth backscatter system that can take multi-frequency signals as excitations. Unlike all prior systems, DanBlue leverages ambient Bluetooth signals of various frequencies to backscatter in the standard Bluetooth-hopping way. To do so, we first introduce an edge proxy to identify uncontrolled ambient Bluetooth signals. Then, we design a wideband channel hopping to enable fast frequency shifts for low-power tags, empowering backscatter hopping much like active Bluetooth hopping. We prototype the DanBlue tag using off-the-shelf FPGAs and the DanBlue edge with commercial off-the-shelf (COTS) chips. Through comprehensive field studies, we show that DanBlue supports hopping from any-frequency Bluetooth excitations to any-frequency Bluetooth channels. Furthermore, the accuracy of frequency identification is as high as 99% with less than 7.1 ms latency. For the first time, we demonstrate DanBlue can emulate the Bluetooth protocol stack and seamlessly build connectivity with multiple active Bluetooth radios. We believe DanBlue is taking a crucial step forward on fully functioning battery-free Bluetooth.
Dissecting the Performance of Satellite Network Operators
Authors: Raman Aravindh , Varvello Matteo , Chang Hyunseok , Sastry Nishanth , Zaki Yasir
Published: November 2023

The rapid growth of satellite network operators (SNOs) has revolutionized broadband communications, enabling global connectivity and bridging the digital divide. As these networks expand, it is important to evaluate their performance and efficiency. This paper presents the first comprehensive study of SNOs. We take an opportunistic approach and devise a methodology which allows to identify public network measurements performed via SNOs. We apply this methodology to both M-Lab and RIPE public datasets which allowed us to characterize low level performance and footprint of up to 18 SNOs operating in different orbits. Finally, we identify and recruit paid testers on three popular SNOs (Starlink, HughesNet, and ViaSat) to evaluate the performance of popular applications like web browsing and video streaming.
Divided at the Edge - Measuring Performance and the Digital Divide of Cloud Edge Data Centers
Authors: Martin Noah , Dogar Fahad
Published: November 2023

Cloud providers are highly incentivized to reduce latency. One way they do this is by locating data centers as close to users as possible. These “cloud edge” data centers are placed in metropolitan areas and enable edge computing for residents of these cities. Therefore, which cities are selected to host edge data centers determines who has the fastest access to applications requiring edge compute — creating a digital divide between those closest and furthest from the edge. In this study we measure latency to the current and predicted cloud edge of three major cloud providers around the world. Our measurements use the RIPE Atlas platform targeting cloud regions, AWS Local Zones, and network optimization services that minimize the path to the cloud edge. An analysis of the digital divide shows rising inequality as the relative difference between users closest and farthest from cloud compute increases. We also find this inequality unfairly affects lower income census tracts in the US. This result is extended globally using remotely sensed night time lights as a proxy for wealth. Finally, we demonstrate that low earth orbit satellite internet can help to close this digital divide and provide more fair access to the cloud edge.
EXPLORA: AI/ML EXPLainability for the Open RAN
Authors: Fiandrino Claudio , Bonati Leonardo , D'Oro Salvatore , Polese Michele , Melodia Tommaso , Widmer Joerg
Published: November 2023

The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a system of disaggregated, virtualized, and software-based components. These self-optimize the network through programmable, closed-loop control, leveraging Artificial Intelligence (AI) and Machine Learning (ML) routines. In this context, Deep Reinforcement Learning (DRL) has shown great potential in addressing complex resource allocation problems. However, DRL-based solutions are inherently hard to explain, which hinders their deployment and use in practice. In this paper, we propose EXPLORA, a framework that provides explainability of DRL-based control solutions for the Open RAN ecosystem. EXPLORA synthesizes network-oriented explanations based on an attributed graph that produces a link between the actions taken by a DRL agent (i.e., the nodes of the graph) and the input state space (i.e., the attributes of each node). This novel approach allows EXPLORA to explain models by providing information on the wireless context in which the DRL agent operates. EXPLORA is also designed to be lightweight for real-time operation. We prototype EXPLORA and test it experimentally on an O-RAN-compliant near-real-time RIC deployed on the Colosseum wireless network emulator. We evaluate EXPLORA for agents trained for different purposes and showcase how it generates clear network-oriented explanations. We also show how explanations can be used to perform informative and targeted intent-based action steering and achieve median transmission bitrate improvements of 4% and tail improvements of 10%.
Millions of Low-latency State Insertions on ASIC Switches
Authors: Caiazzi Tommaso , Scazzariello Mariano , Chiesa Marco
Published: November 2023

Key-value data structures are an essential component of today's stateful packet processors such as load balancers, packet schedulers, and more. Realizing key-value data structures entirely in the data-plane of an ASIC switch would bring enormous energy savings. Yet, today's implementations are ill-suited for stateful packet processing as they support only a limited amount of flow-state insertions per second into these data structures. In this paper, we present SWITCHAROO, a mechanism for realizing key-value data structures on programmable ASIC switches that supports both high-frequency insertions and fast lookups entirely in the data plane. We show that SWITCHAROO can be realized on ASIC, supports millions of flow-state insertions per second with only limited amount of packet recirculation.
Revocation Speedrun: How the WebPKI Copes with Fraudulent Certificates
Authors: Friess Jens , Schulmann Haya , Waidner Michael
Published: November 2023

The TLS ecosystem depends on certificates to bootstrap secure connections. Certificate Authorities (CAs) are trusted to issue these correctly. However, as a result of security breaches or attacks, certificates may be issued fraudulently and need to be revoked prematurely.

Revocation, as a reactive measure, is fundamentally damage control and, as such, time is critical. Therefore, measuring reaction delay is the first step to identifying how well the revocation system functions.

In this paper we attempt to characterize the current performance of the WebPKI in dealing with fraudulent certificates. We present measurements of each step in the revocation process: the detection of certificate issuance through Certificate Transparency (CT) monitoring, the administrative revocation process at popular CAs, and the revocation checking behavior of end-user clients, both in a controlled virtualized environment and in the wild. We perform two live measurements, in 2022 and 2023, respectively, to provide a longitudinal comparison.

We find that detection and revocation of fraudulent certificates is quick and efficient when leveraging CT and can be completed within 6.5 hours on average. Furthermore, CT is being increasingly enforced by some browsers. However, ∼83% of the clients we observed, across popular browsers, brands and OSes, completely disregard a certificate's status, whileall of the studied browsers still display soft-fail behavior, making them vulnerable to attackers capable of interfering with the network. Of the clients that do check revocation, we find that 35% can be made to accept a revoked certificate through the use of OCSP Stapling. We expect this number to grow with client-side adoption of OCSP Stapling [RFC6961]. Current OCSP expiration times allow a revoked certificate to remain fully valid for up to 7 days for the majority of CAs, exposing clients to attacks.
Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting
Authors: Wang Tianyi , Chen Shu-Ching
Published: November 2023

Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic spatio-temporal relations in the traffic data, which are crucial in capturing the traffic dynamic. In this work, we propose AJSTGL, a novel adaptive joint spatio-temporal graph learning network for traffic data forecasting. The proposed model utilizes static and adaptive graph learning modules to capture the static and dynamic spatial traffic patterns and optimize the graph learning process. A sequence-to-sequence fusion model is proposed to learn the temporal correlation and combine the output of multiple parallelized encoders. We also develop a spatio-temporal graph transformer module to complement the sequence-to-sequence fusion module by dynamically capturing the time-evolving node relations in long-term intervals. Experiments on three large-scale traffic flow datasets demonstrate that our model could outperform other state-of-the-art baseline methods.
Biased Polar Questions
Authors: Romero Maribel
Published: November 2023

Although the following polar question forms raise the same issue, the positive question Is Jane coming?, the low negation question Is Jane not coming?, and the high negation question Isn't Jane coming? cannot be used interchangeably because they are sensitive to the expectations that the speaker may originally have (original speaker bias) and to contextual evidence that becomes available during the conversational exchange (contextual evidence bias). This article summarizes the aspects of these constructions on which agreement has been reached and identifies central points of empirical and theoretical divergence in the literature; further, it critically reviews current attempts to derive original speaker bias in high negation questions as well as the asymmetry between positive questions and low negation questions with respect to contextual evidence bias.
Mitochondrial Dysfunction in Kidney Tubulopathies
Authors: Hoogstraten Charlotte A. , Hoenderop Joost G. , de Baaij Jeroen H.F.
Published: November 2023

Mitochondria play a key role in kidney physiology and pathology. They produce ATP to fuel energy-demanding water and solute reabsorption processes along the nephron. Moreover, mitochondria contribute to cellular health by the regulation of autophagy, (oxidative) stress responses, and apoptosis. Mitochondrial abundance is particularly high in cortical segments, including proximal and distal convoluted tubules. Dysfunction of the mitochondria has been described for tubulopathies such as Fanconi, Gitelman, and Bartter-like syndromes and renal tubular acidosis. In addition, mitochondrial cytopathies often affect renal (tubular) tissues, such as in Kearns-Sayre and Leigh syndromes. Nevertheless, the mechanisms by which mitochondrial dysfunction results in renal tubular diseases are only scarcely being explored. This review provides an overview of mitochondrial dysfunction in the development and progression of kidney tubulopathies. Furthermore, it emphasizes the need for further mechanistic investigations to identify links between mitochondrial function and renal electrolyte reabsorption.