bluuebunny/crossref_metadata_2025 huggingface dataset:
Training for Elite Team-Pursuit Track Cyclists—Part I: A Profile of General Training Characteristics
Authors: Stadnyk Antony M.J. , Stanley Jamie , Decker Tim , Slattery Katie M.
Published: August 2024

Purpose: To profile the training characteristics of an elite team pursuit cycling squad and assess variations in training intensity and load accumulation across the 36-week period prior to a world-record performance at the 2018 Commonwealth Games. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 21.9 [3.52] y; 4.4 [0.16] W·kg−1 at anaerobic threshold; 6.2 [0.28] W·kg−1 maximal oxygen uptake 68.7 [2.99] mL kg·min−1) were analyzed with weekly total training volume and heart rate, power output, and torque intensity distributions calculated with reference to their 3:49.804 min:s.ms performance requirements for a 4-km team pursuit. Results: Athletes completed 543 (37) h−1 of training across 436 (16) sessions. On-bike activities accounted for 69.9% of all training sessions, with participants cycling 11,246 (1139) km−1 in the training period of interest, whereas 12.7% of sessions involved gym/strength training. A pyramidal intensity distribution was evident with over 65% and 70% of training, respectively, performed at low-intensity zone heart rate and power output, whereas 5.3% and 7.7% of training was performed above anaerobic threshold. The athletes accumulated 4.4% of total training volume at, or above, their world-record team pursuit lead position torque (55 N·m). Conclusions: These data provide updated and novel insight to the power and torque demands and load accumulation contributing to world-record team pursuit performance. Although the observed pyramidal intensity distribution is common in endurance sports, the lack of shift toward a polarized intensity distribution during taper and competition peaking differs from previous research.
External Training Loads and Soft-Tissue Injury Occurrence During Congested Versus Noncongested Periods in Football
Authors: Barreira Paulo , Vaz João R. , Ferreira Ruben , Araújo João Pedro , Tavares Francisco
Published: August 2024

Objective: To analyze the influence of congested and noncongested fixture periods during 2 seasons in a professional male football team on soft-tissue injury incidence and external load. Methods: Thirty-three professional football players from a Portuguese Liga I team participated in this study. Weekly external load and soft-tissue injury rate and burden of 2 consecutive seasons (2021–22 and 2022–23) were analyzed. Results: Total soft-tissue injury rate and burden for the 2 seasons were 3.9 and 3.2 injuries per 1000 hours and 71.8 and 60.5 days per 1000 hours for congested and noncongested periods, respectively. No significant differences were observed between congested and noncongested periods. Total high-speed running, sprint distance, distance above 80% and 90% of maximal velocity, and meters accelerating and decelerating above 2 m/s2 were significantly higher for noncongested weeks. Match accelerations and decelerations above 3 m/s2 were higher during congested periods and training during noncongested periods. No differences between the 2 periods were observed for the total number of accelerations and decelerations above 3 m/s2. Overall, physical outputs per week were higher for training during noncongested weeks, whereas matches during congested periods registered higher external load. Conclusions: No effect of a congested schedule was observed on soft-tissue injury rates and injury burden. Higher match exposure during congested periods increased external load performed per week, and during noncongested periods, training load was superior to congested weeks.
Uncontrolled deforestation and population growth threaten a tropical island’s water and land resources in only 10 years
Authors: Foucher Anthony , Evrard Olivier , Rabiet Laura , Cerdan Olivier , Landemaine Valentin , Bizeul Rémi , Chalaux-Clergue Thomas , Marescaux Jonathan , Debortoli Nicolas , Ambroise Valentin , Desprats Jean-François
Published: August 2024

Rapid demographic growth in tropical islands can exacerbate conflicts and pressures on natural resources, as illustrated by the French island of Mayotte where resources are limited. In only 10 years, uncontrolled migration and population growth (+80% of population between 2002 and 2021) have led to a pronounced 3600% increase in deforestation rates (2010–2014) and an intensification of agricultural practices, escalating conflicts over limited land, water, and biodiversity resources. Implementing an original multi-proxy approach to sediment cores, our study reveals a staggering 300% acceleration in erosion during the first wave of migration (2011–2015), followed by a further 190% increase (2019–2021) under sustained migratory and demographic pressures. Sedimentary DNA analysis provided insights into increased connectivity and community changes. By 2050, the population of this region will increase by 74 and 103%, in Comoros and Madagascar islands, respectively. Urgent conservation measures are needed to avoid major socio-environmental crises and to protect resources for future generations.
Semantic memory–based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision
Authors: Zhang Yue , Zhang Woyu , Wang Shaocong , Lin Ning , Yu Yifei , He Yangu , Wang Bo , Jiang Hao , Lin Peng , Xu Xiaoxin , Qi Xiaojuan , Wang Zhongrui , Zhang Xumeng , Shang Dashan , Liu Qi , Cheng Kwang-Ting , Liu Ming
Published: August 2024

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory–based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.
In situ implantable DNA hydrogel for diagnosis and therapy of postoperative rehemorrhage following intracerebral hemorrhage surgery
Authors: Yu Wenyan , Gong Enpeng , Wang Changlin , Che Chengyuan , Zhao Yuzhen , Wu Xinyun , Yang Yi , Shi Haiyu , Chen Mengjuan , Li Mingge , Xie Li , Guo Yue , Guo Mingming , Mu Liya , Wang Zhenya , Zhang Zhenzhong , Zhang Kaixiang , Liu Junjie , Shi Jinjin
Published: August 2024

Postoperative rehemorrhage following intracerebral hemorrhage surgery is intricately associated with a high mortality rate, yet there is now no effective clinical treatment. In this study, we developed a hemoglobin (Hb)–responsive in situ implantable DNA hydrogel comprising Hb aptamers cross-linked with two complementary chains and encapsulating deferoxamine mesylate (DFO). Functionally, the hydrogel generates signals upon postoperative rehemorrhage by capturing Hb, demonstrating a distinctive “self-diagnosis” capability. In addition, the ongoing capture of Hb mediates the gradual disintegration of the hydrogel, enabling the on-demand release of DFO without compromising physiological iron-dependent functions. This process achieves self-treatment by inhibiting the ferroptosis of neurocytes. In a collagenase and autologous blood injection model–induced mimic postoperative rehemorrhage model, the hydrogel exhibited a 5.58-fold increase in iron absorption efficiency, reducing hematoma size significantly (from 8.674 to 4.768 cubic millimeters). This innovative Hb-responsive DNA hydrogel not only offers a therapeutic intervention for postoperative rehemorrhage but also provides self-diagnosis feedback, holding notable promise for enhancing clinical outcomes.
Origin of reconnecting current sheets in shocked turbulent plasma
Authors: Wang Shimou , Lu San , Lu Quanming , Wang Rongsheng , Ren Junyi , Gao Xinliang , Guo Jin
Published: August 2024

Magnetic reconnection, the rearrangement of magnetic field topologies, is a fundamental plasma process throughout the universe, which converts magnetic energy to plasma kinetic energy and results in particle energization. A current sheet is a prerequisite for the occurrence of magnetic reconnection. It has been well documented that reconnecting current sheets are prevalent in turbulent plasmas. However, how these current sheets are formed remains unclear. Among natural plasmas, the region downstream of the Earth’s bow shock, the magnetosheath, is one of the most turbulent. Here, we show that the reconnecting current sheets in the turbulent magnetosheath originate from the waves in the region upstream of the shock. Once excited, the upstream waves are transmitted across the shock, compressed, and then transformed into current sheets in the downstream region. Magnetic reconnection subsequently occurs in these downstream current sheets. This process can be generalized to various shocked plasmas in astrophysical and laboratorial environments where turbulent magnetic reconnection should be common.
High energy density picoliter-scale zinc-air microbatteries for colloidal robotics
Authors: Zhang Ge , Yang Sungyun , Yang Jing Fan , Gonzalez-Medrano David , Miskin Marc Z. , Koman Volodymyr B. , Zeng Yuwen , Li Sylvia Xin , Kuehne Matthias , Liu Albert Tianxiang , Brooks Allan M. , Kumar Mahesh , Strano Michael S.
Published: August 2024

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10−12liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.
Acyl-CoA binding protein for the experimental treatment of anorexia
Authors: Chen Hui , Moriceau Stéphanie , Joseph Adrien , Mailliet Francois , Li Sijing , Tolle Virginie , Duriez Philibert , Dardennes Roland , Durand Sylvère , Carbonnier Vincent , Stoll Gautier , Sauvat Allan , Lachkar Sylvie , Aprahamian Fanny , Alves Costa Silva Carolina , Pan Hui , Montégut Léa , Anagnostopoulos Gerasimos , Lambertucci Flavia , Motiño Omar , Nogueira-Recalde Uxía , Bourgin Mélanie , Mao Misha , Pan Yuhong , Cerone Alexandra , Boedec Erwan , Gouveia Zelia L. , Marmorino Federica , Cremolini Chiara , Derosa Lisa , Zitvogel Laurence , Kepp Oliver , López-Otín Carlos , Maiuri Maria Chiara , Perez Franck , Gorwood Philip , Ramoz Nicolas , Oury Franck , Martins Isabelle , Kroemer Guido
Published: August 2024

Extracellular acyl-coenzyme A binding protein [ACBP encoded by diazepam binding inhibitor (DBI)] is a phylogenetically ancient appetite stimulator that is secreted in a nonconventional, autophagy-dependent fashion. Here, we show that low ACBP/DBI plasma concentrations are associated with poor prognosis in patients with anorexia nervosa, a frequent and often intractable eating disorder. In mice, anorexia induced by chronic restraint stress (CRS) is accompanied by a reduction in circulating ACBP/DBI concentrations. We engineered a chemical-genetic system for the secretion of ACBP/DBI through a biotin-activatable, autophagy-independent pathway. In transgenic mice expressing this system in hepatocytes, biotin-induced elevations in plasma ACBP/DBI concentrations prevented anorexia induced by CRS or chemotherapeutic agents including cisplatin, doxorubicin, and paclitaxel. ACBP/DBI reversed the CRS or cisplatin-induced increase in plasma lipocalin-2 concentrations and the hypothalamic activation of anorexigenic melanocortin 4 receptors, for which lipocalin-2 is an agonist. Daily intravenous injections of recombinant ACBP/DBI protein or subcutaneous implantation of osmotic pumps releasing recombinant ACBP/DBI mimicked the orexigenic effects of the chemical-genetic system. In conclusion, the supplementation of extracellular and peripheral ACBP/DBI might constitute a viable strategy for treating anorexia.
Concurrent
Ascaris
infection modulates host immunity resulting in impaired control of
Salmonella
infection in pigs
Authors: Midha Ankur , Oser Larissa , Schlosser-Brandenburg Josephine , Laubschat Alexandra , Mugo Robert M. , Musimbi Zaneta D. , Höfler Philipp , Kundik Arkadi , Hayani Rima , Adjah Joshua , Groenhagen Saskia , Tieke Malte , Elizalde-Velázquez Luis E. , Kühl Anja A. , Klopfleisch Robert , Tedin Karsten , Rausch Sebastian , Hartmann Susanne
Published: August 2024

Ascaris
is one of the most widespread helminth infections, leading to chronic morbidity in humans and considerable economic losses in pig farming. In addition, pigs are an important reservoir for the zoonotic salmonellosis
,
where pigs can serve as asymptomatic carriers. Here, we investigated the impact of an ongoing
Ascaris
infection on the immune response to
Salmonella
in pigs. We observed higher bacterial burdens in experimentally coinfected pigs compared to pigs infected with
Salmonella
alone. The impaired control of
Salmonella
in the coinfected pigs was associated with repressed interferon gamma responses in the small intestine and with the alternative activation of gut macrophages evident in elevated CD206 expression.
Ascaris
single and coinfection were associated with a rise of CD4
-
CD8α
+
FoxP3
+
Treg in the lymph nodes draining the small intestine and liver. In addition, macrophages from coinfected pigs showed enhanced susceptibility to
Salmonella
infection
in vitro
and the
Salmonella-
induced monocytosis and tumor necrosis factor alpha production by myeloid cells was repressed in pigs coinfected with
Ascaris
. Hence, our data indicate that acute
Ascaris
infection modulates different immune effector functions with important consequences for the control of tissue-invasive coinfecting pathogens.

IMPORTANCE

In experimentally infected pigs, we show that an ongoing infection with the parasitic worm
Ascaris suum
modulates host immunity, and coinfected pigs have higher
Salmonella
burdens compared to pigs infected with
Salmonella
alone. Both infections are widespread in pig production and the prevalence of
Salmonella
is high in endemic regions of human Ascariasis, indicating that this is a clinically meaningful coinfection. We observed the type 2/regulatory immune response to be induced during an
Ascaris
infection correlates with increased susceptibility of pigs to the concurrent bacterial infection.
Combinatorial control of
Pseudomonas aeruginosa
biofilm development by quorum-sensing and nutrient-sensing regulators
Authors: Chen Gong , Fanouraki Georgia , Anandhi Rangarajan Aathmaja , Winkelman Bradford T. , Winkelman Jared T. , Waters Christopher M. , Mukherjee Sampriti
Published: August 2024

The human pathogen
Pseudomonas aeruginosa
, a leading cause of hospital-acquired infections, inhabits and forms sessile antibiotic-resistant communities called biofilms in a wide range of biotic and abiotic environments. In this study, we examined how two global sensory signaling pathways—the RhlR quorum-sensing system and the CbrA/CbrB nutritional adaptation system—intersect to control biofilm development. Previous work has shown that individually these two systems repress biofilm formation. Here, we used biofilm analyses, RNA-seq, and reporter assays to explore the combined effect of information flow through RhlR and CbrA on biofilm development. We find that the Δ
rhlR
Δ
cbrA
double mutant exhibits a biofilm morphology and an associated transcriptional response distinct from wildtype and the parent Δ
rhlR
and Δ
cbrA
mutants indicating codominance of each signaling pathway. The Δ
rhlR
Δ
cbrA
mutant gains suppressor mutations that allow biofilm expansion; these mutations map to the
crc
gene resulting in loss of function of the carbon catabolite repression protein Crc. Furthermore, the combined absence of RhlR and CbrA leads to a drastic reduction in the abundance of the Crc antagonist small RNA CrcZ. Thus, CrcZ acts as the molecular convergence point for quorum- and nutrient-sensing cues. We find that in the absence of antagonism by CrcZ, Crc promotes the expression of biofilm matrix components—Pel exopolysaccharide, and CupB and CupC fimbriae. Therefore, this study uncovers a regulatory link between nutritional adaption and quorum sensing with potential implications for anti-biofilm targeting strategies.

IMPORTANCE

Bacteria often form multicellular communities encased in an extracytoplasmic matrix called biofilms. Biofilm development is controlled by various environmental stimuli that are decoded and converted into appropriate cellular responses. To understand how information from two distinct stimuli is integrated, we used biofilm formation in the human pathogen
Pseudomonas aeruginosa
as a model and studied the intersection of two global sensory signaling pathways—quorum sensing and nutritional adaptation. Global transcriptomics on biofilm cells and reporter assays suggest parallel regulation of biofilms by each pathway that converges on the abundance of a small RNA antagonist of the carbon catabolite repression protein, Crc. We find a new role of Crc as it modulates the expression of biofilm matrix components in response to the environment. These results expand our understanding of the genetic regulatory strategies that allow
P. aeruginosa
to successfully develop biofilm communities.
Development and validation of a novel questionnaire to describe and assess sensations and triggers associated with refractory and unexplained chronic cough
Authors: Galgani Shannon , Sawyer Chelsea , King Jenny , Dockry Rachel , Wingfield-Digby James , Holt Kimberly , Mitchell Joanne , Sen Shilpi , Birchall Danielle , Solari Francesca , Smith Jacky , Yorke Janelle
Published: August 2024

Introduction

Refractory or unexplained chronic cough (RUCC) is a common clinical problem with no effective diagnostic tools. The Sensations and Triggers Provoking Cough questionnaire (TOPIC) was developed to characterise cough in RUCC versus cough in other conditions.

Methods

Content analysis of participant interviews discussing the sensations and triggers of chronic cough informed TOPIC development. Participants with chronic cough completed the draft-TOPIC (a subset repeating 5–7 days later), St George’s Respiratory Questionnaire (SGRQ), Cough Severity Diary (CSD) and Global Rating of Change Scale. The draft-TOPIC item list was reduced in hierarchical and Rasch analysis to refine the questionnaire to the TOPIC.

Results

49 items describing the triggers and sensations of cough were generated from participant interviews (RUCC n=14, chronic obstructive pulmonary disease (COPD) n=11, interstitial lung disease (ILD) n=10, asthma n=11, bronchiectasis n=3, cystic fibrosis n=7). 140 participants (median age 60.0 (19.0–88.0), female 56.4%; RUCC n=39, ILD n=38, asthma n=45, COPD n=6, bronchiectasis n=12) completed draft-TOPIC, where items with poor ‘fit’ for RUCC were removed to create TOPIC (8 trigger items, 7 sensation items). Median TOPIC score was significantly higher in RUCC (37.0) vs ILD (24.5, p=0.009) and asthma (7.0, p<0.001), but not bronchiectasis (20.0, p=0.318) or COPD (18.5, p=0.238), likely due to small sample sizes. The Rasch model demonstrated excellent fit in RUCC (χ2=22.04, p=0.85; PSI=0.88); as expected. When all participant groups were included, fit was no longer demonstrated (χ2=66.43, p=0.0001, PSI=0.89) due to the increased heterogeneity (CI=0.077). TOPIC correlated positively with SGRQ (r=0.47, p<0.001) and CSD (r=0.63, p<0.001). The test–retest reliability of TOPIC (intraclass correlation coefficient) was excellent (r=0.90, p<0.001).

Conclusions

High TOPIC scores in the RUCC patients suggest their cough is characterised by specific sensations and triggers. Validation of TOPIC in cough clinics may demonstrate value as an aid to identify features of RUCC versus cough in other conditions.
Psychological well-being trajectories preceding incident mild cognitive impairment and dementia
Authors: Guo Jie , Wang Jiao , Dove Abigail , Bennett David A , Xu Weili
Published: August 2024

Background

Poorer psychological well-being has been related to an increased dementia risk, but changes in psychological well-being along the dementia course are unclear. We explored psychological well-being trajectories before and after the diagnosis of mild cognitive impairment (MCI) and dementia.

Methods

Within the Rush Memory and Aging Project, 910 cognitively intact older adults were followed annually for up to 14 years to detect incident MCI and dementia. Psychological well-being and its six components (self-acceptance, autonomy, environmental mastery, purpose in life, positive relation with others, and personal growth) were annually measured based on Ryff’s Scales of Psychological Well-Being. Data were analysed using mixed-effect models with a backward timescale.

Results

Compared with participants who remained cognitively intact, those who developed incident MCI had a faster decline in psychological well-being (β −0.015, 95% CI −0.027 to –0.003), leading to lower well-being 2 years before MCI diagnosis (mean difference at year −2, −0.099, 95% CI −0.187 to –0.012). Considering different well-being components, those who developed MCI had lower levels of purpose in life and personal growth beginning 3 years (−0.126, 95% CI −0.251 to –0.001) and 6 years (−0.139, 95% CI −0.268 to –0.009) before MCI, respectively. The slope of psychological well-being decline was similar before and after MCI diagnosis for each component except for positive relation with others, which had an accelerated decline after MCI (β −0.042, 95% CI−0.075 to –0.009). Well-being trajectories remained similar for individuals with MCI regardless of whether they later developed dementia.

Conclusions

Psychological well-being (specifically purpose in life and personal growth) became significantly lower before MCI diagnosis.
Un/Making Data Imaginaries: The Data Epics
Authors: Desjardins Audrey , Benabdallah Gabrielle , Kaneko Maya A.
Published: August 2024

With the increase of Internet of Things devices in home environments, data will become an even more dominant part of people’s everyday lives. The invisibility of data leads us to rely on our imagination to make sense of them, yet this imagination is heavily shaped by a technocentric lens that views data as neutral and transparent. In response, in this article, we present the Data Epics project, where we commissioned seven fiction writers to write short stories based on smart home device data provided by seven households. We offer an analysis of the writers and households’ experiences with the project, presenting seven ways in which data imaginaries are made and unmade. We contribute a reflection around how making new data imaginaries unmakes common ones, the friction in unmaking certain imaginaries, and how we might further disseminate alternative data imaginaries.
Estimating Exposure to Information on Social Networks
Authors: Nettasinghe Buddhika , Kadoma Kowe , Naaman Mor , Krishnamurthy Vikram
Published: August 2024

Estimating exposure to information on a social network is a problem with important consequences for our society. The exposure estimation problem involves finding the fraction of people on the network who have been exposed to a piece of information (e.g., a URL of a news article on Facebook, a hashtag on Twitter). The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information. Often, both features are not publicly available (i.e., access to the two features is limited only to the internal administrators of the platform) and are difficult to estimate from data. As a solution, we propose two methods to estimate the exposure to a piece of information in an unbiased manner: a vanilla method that is based on sampling the network uniformly and a method that non-uniformly samples the network motivated by the Friendship Paradox. We provide theoretical results that characterize the conditions (in terms of properties of the network and the piece of information) under which one method outperforms the other. Further, we outline extensions of the proposed methods to dynamic information cascades (where the exposure needs to be tracked in real time). We demonstrate the practical feasibility of the proposed methods via experiments on multiple synthetic and real-world datasets.
Multi-Scale and Multi-Layer Lattice Transformer for Underwater Image Enhancement
Authors: Hsu Wei-Yen , Hsu Yu-Yu
Published: August 2024

Underwater images are often subject to color deviation and a loss of detail due to the absorption and scattering of light. The challenge of enhancing underwater images is compounded by variations in wavelength and distance attenuation, as well as color deviation that exist across different scales and layers, resulting in different degrees of color deviation, attenuation, and blurring. To address these issues, we propose a novel multi-scale and multi-layer lattice transformer (MMLattFormer) to effectively eliminate artifacts and color deviation, prevent over-enhancement, and preserve details across various scales and layers, thereby achieving more accurate and natural results in underwater image enhancement. The proposed MMLattFormer model integrates the advantage of LattFormer to enhance global perception with the advantage of “multi-scale and multi-layer” configuration to leverages the differences and complementarities between features of various scales and layers to boost local perception. The proposed MMLattFormer model is comprised of multi-scale and multi-layer LattFormers. Each LattFormer primarily encompasses two modules: Multi-head Transposed-attention Residual Network (MTRN) and Gated-attention Residual Network (GRN). The MTRN module enables cross-pixel interaction and pixel-level aggregation in an efficient manner to extract more significant and distinguishable features, whereas the GRN module can effectively suppress under-informed or redundant features and retain only useful information, enabling excellent image restoration exploiting the local and global structures of the images. Moreover, to better capture local details, we introduce depthwise convolution in these two modules before generating global attention maps and decomposing images into different features to better capture the local context in image features. The qualitative and quantitative results indicate that the proposed method outperforms state-of-the-art approaches in delivering more natural results. This is evident in its superior detail preservation, effective prevention of over-enhancement, and successful removal of artifacts and color deviation on several public datasets.
Enhancing Visual Homing in Robotics: A Study on Blockchain Integration and Consensus Algorithms
Authors: Paykari Nasim , Lyons Damian M. , Rahouti Mohamed
Published: August 2024

Creating an immutable repository for vital robot and environmental data, ensuring long-term accessibility, and functioning in the absence of GPS or mapping are crucial for visual homing navigation systems. We focus on the intersection of blockchain and robotics, particularly in visual homing. Our research involves an in-depth analysis of various blockchain consensus mechanisms, highlighting their suitability for visual homing applications. The heart of blockchain functionality lies in its consensus mechanism, which facilitates agreement among network nodes. In our first study part, we conduct a comprehensive comparative analysis of key consensus algorithms, emphasizing visual homing’s decentralization, fault tolerance, latency, and throughput requirements. This analysis serves as a valuable reference for researchers and developers, emphasizing the importance of aligning the chosen consensus mechanism with specific blockchain application needs. The second part of our work involves extensive experiments exploring the connection between blockchain and visual homing. We assess prominent consensus mechanisms like Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Proof of Authority (PoA) within a virtual environment in Gazebo, leveraging wide area visual navigation (WAVN). Our research implementation is grounded in the ROS framework and the Gazebo simulation environment.
Neuron Semantic-Guided Test Generation for Deep Neural Networks Fuzzing
Authors: Huang Li , Sun Weifeng , Yan Meng , Liu Zhongxin , Lei Yan , Lo David
Published: August 2024

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: ❶
Testing Adequacy
: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. ❷
Interpretability
: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which incorrect attributes or patterns of the model are triggered by the test inputs. This lack of interpretability hampers the subsequent debugging and fixing process. Therefore, there is an urgent need for a novel fuzzing criterion that offers improved testing adequacy, better interpretability, and more effective failure detection capabilities for DNNs.

To alleviate these limitations, we propose NSGen, an approach for DNN fuzzing that utilizes neuron semantics as guidance during test generation. NSGen identifies critical neurons, translates their high-level semantic features into natural language descriptions, and then assembles them into human-readable DNN decision paths (representing the internal decision of the DNN). With these decision paths, we can generate more fault-revealing test inputs by quantifying the similarity between original test inputs and mutated test inputs for fuzzing. We evaluate NSGen on popular DNN models (VGG16_BN, ResNet50, and MobileNet_v2) using CIFAR10, CIFAR100, Oxford 102 Flower, and ImageNet datasets. Compared to 12 existing coverage-guided fuzzing criteria, NSGen outperforms all baselines, increasing the number of triggered faults by 21.4% to 61.2% compared to the state-of-the-art coverage-guided fuzzing criterion. This demonstrates NSGen's effectiveness in generating fault-revealing test inputs through guided input mutation, highlighting its potential to enhance DNN testing and interpretability.
CARL: Unsupervised Code-Based Adversarial Attacks for Programming Language Models via Reinforcement Learning
Authors: Yao Kaichun , Wang Hao , Qin Chuan , Zhu Hengshu , Wu Yanjun , Zhang Libo
Published: August 2024

Code based adversarial attacks play a crucial role in revealing vulnerabilities of software system. Recently, pre-trained programming language models (PLMs) have demonstrated remarkable success in various significant software engineering tasks, progressively transforming the paradigm of software development. Despite their impressive capabilities, these powerful models are vulnerable to adversarial attacks. Therefore, it is necessary to carefully investigate the robustness and vulnerabilities of the PLMs by means of adversarial attacks. Adversarial attacks entail imperceptible input modifications that cause target models to make incorrect predictions. Existing approaches for attacking PLMs often employ either identifier renaming or the greedy algorithm, which may yield sub-optimal performance or lead to high inference times. In response to these limitations, we propose CARL, an unsupervised black-box attack model that leverages reinforcement learning to generate imperceptible adversarial examples. Specifically, CARL comprises a programming language encoder and a perturbation prediction layer. In order to achieve more effective and efficient attack, we cast the task as a sequence decision-making process, optimizing through policy gradient with a suite of reward functions. We conduct extensive experiments to validate the effectiveness of CARL on code summarization, code translation, and code refinement tasks, covering various programming languages and PLMs. The experimental results demonstrate that CARL surpasses state-of-the-art code attack models, achieving the highest attack success rate across multiple tasks and PLMs while maintaining high attack efficiency, imperceptibility, consistency, and fluency.
Is Bare-metal I/O Performance with User-defined Storage Drives Inside VMs Possible? Benchmarking libvfio-user vs. Common Storage Virtualization Configurations
Authors: Rolón Sebastián , Balmau Oana
Published: August 2024

It is a critical concern for cloud computing providers to identify and adopt optimal virtual machine (VM) I/O storage paths. In Linux, these involve NVMe hardware, PCIe passthrough with vfio, and userspace drivers (e.g. SPDK). One project tying together these host-side improvements with the guests is vfio-user, a library and protocol for inter-process communication of I/O operations over sockets. QEMU/KVM VMs using vfio-user and SPDK are theoretically expected to attain low latencies due to mostly userspace processing, but this VM datapath configuration is still experimental and performance data is lacking in the literature.
Synthesis, Studies of Shocked Pulse of 50 Scale on 100 Gy Irradiated Zinc Acetate Crystals for Photonic, NLO, Phase-Matching and Electronic Utilities
Authors: N Balamurugapandian , K Suganya , R Hariharasuthan , T Jayanalina , V Swarnalatha , K SenthilKannan , R Manikandan
Published: August 2024

The Zinc Acetate - ZA is grown in 16–17 days as colorless crystal by slow evaporation manner; in the present work, the shocked pulses of range of 50 scaling of ZA-100 Gy irradiated sample is represented after the pulsed reference of shock waves as ZA-100Gy-50S in physical reference, the single crystalline XRD data of ZA-100Gy-50S is having 15.108, 9.254, 4.7999 of a, b, c values in Å and beta as 99.12° with P21/c as space group and monoclinic as the system; the UV-visible spectral cut-off wavelength for ZA-100Gy-50S is 224 nm; the band gap in eV as 5.53 from Tauc’s plot and is 5.5357 eV from UV-visible data. The Fluorescence wavelength gives the energy impact of 2.4047 eV. The NLO-SHG as 1.34 times than the standard KDP and the phase matching proviso of ZA-100Gy-50S is 93.8 mV The surface morphological impact of ZA-100Gy-50S crystal is with small cracks, no major flaws; the third-order NLO of ZA-100Gy-50S is of 13.92 × 10–6 esu by Z-Scan; the frequency doubling circuit and frequency tripling circuit are analyzed for the ZA-100Gy-50S and are 2.28 times, 3.23 times the input. The (111) based displaying of the display action with Red-Green-Blue filled polygonal effect.
A ketogenic diet, regardless of fish oil content, does not affect glucose homeostasis or muscle insulin response in male rats
Authors: Budd Joshua M. , Notaro Nicole M. , MacLeod Blair , Mutch David M. , Dyck David J.
Published: August 2024

Ketogenic diets (KDs) improve glucose metabolism in humans and rodents that are insulin resistant, but their impact is unclear in a healthy context. Furthermore, standard KDs typically lack beneficial omega-3 long-chain polyunsaturated fatty acids (n3-LCPUFA). This study assessed whether supplementing a KD with n3-LCPUFA could alter glucose homeostasis or skeletal muscle insulin response. No differences were observed between a standard KD and a KD with n3-LCPUFA when energy intake was controlled.
Alterations of urine microRNA-7977/G6PD level in patients with diabetic kidney disease and its association with dysfunction of albumin-induced autophagy in proximal epithelial tubular cells
Authors: Shi Zhenzhen , Li Xinran , Zhang Liyi , Xie Jinlan , Zhong Feifei , Guo Zhenhong , Gao Zhongai , Wang Jingyu , Mahto Roshan Kumar , Li Yuan , Wang Shenglan , Chang Baocheng , Stanton Robert C. , Yang Juhong
Published: August 2024

This study provides new evidence that reduced glucose-6-phosphate dehydrogenase (G6PD) may damage the endocytosis of renal tubular epithelial cells by reducing albumin-induced autophagy. More importantly, for the first time, our study has provided evidence from humans that the decrease in urinary G6PD activity is positively associated with renal injury, and abnormal glucose and lipid metabolism may be important reasons for reduced G6PD levels. Increased miR-7977 may at least in part explain the downregulation of G6PD.
Efficient Removing of Pharmaceutical Contaminants of Commercial Carbamazepine by Natural Pyrite‐Induced Fenton Reaction in a Wide pH Range
Authors: Shi Tingting , Xi Jianjun , Shao Yidan , Tong Qiao , Zou Xi , Zhou Hongping , Liu Shourong , Gao Xiaoya
Published: August 2024

Pharmaceutical contaminants are causing great attention for the public people, urgently requiring to be eliminated from the environment. Herein, commercial pharmaceutical carbamazepine was chosen as a model to be degraded by a heterogeneous Fenton system using natural pyrite as catalyst. The catalyst was characterized by X‐ray diffraction apparatus, high‐resolution transmission electron microscopy, and X‐ray photoelectron spectroscopy. The elements composition and chemical states of pyrite changed significantly after the Fenton reaction, which promoted the production of active species in the electron transfer process. The degradation efficiency of carbamazepine (2.5 mg/L) can reach 99.71% after 30 min with 0.3 g/L of pyrite and 5 mM of H2O2, respectively. The reaction system was suitable for a wide range of pH (3–9). The environmental adaptability examination of pyrite showed that humic acid (HA) had inhibitory effects on the degradation of carbamazepine. While the changes of degradation efficiency of carbamazepine were inconspicuous in the presence of and , indicating strong anti‐interference to environment common anions. Free‐radical capturers suggested that hydroxyl radical (∙OH), superoxide radical (), and singlet oxygen (1O2) participated in the degradation of carbamazepine. This research has offered a new strategy of “treating wastes with wastes” to use pyrite to remove emerging pollutants.
Insights Into the Chromatin Structure of Thermoplasma volcanium: Archaeal HU Regulates Alba‐Mediated DNA Compaction
Authors: Aycardo Kirk Amerigo B. , Prieto Eloise I.
Published: August 2024

Archaeal species encode a diversity of chromatin proteins that play distinct roles in genome compaction. Although our understanding of the individual proteins has been growing, their contributions to higher‐order folding in the archaeal chromosome remain to be explored. In this study, we investigated the chromatin structure of Thermoplasma volcanium, a euryarchaeon that encodes a heat‐unstable (HU) family protein (HUTvo) instead of histones. Atomic force microscopy revealed that the T. volcanium chromatin contains nucleoprotein structures of progressively increasing sizes, from 20 nm filaments to ~50 nm globules, indicative of higher‐order DNA folding. In vitro reconstitution showed that HUTvo wraps DNA similar to histones and forms highly compact yet disorganized nucleoprotein clusters on its own. Remarkably, HUTvo only dampens the formation of higher‐order structures by Alba. Our results provide an insight into how the interplay of chromatin proteins regulates genome compaction in histone‐free archaea.
Technology in Suicide Prevention: Fears and Functionality for Crisis Supporters
Authors: Hopkins Danielle , Mazzer Kelly , Rickwood Debra
Published: August 2024

Background: Crisis supporters at Lifeline Australia consistently engage with distressed and often suicidal help‐seekers. The development of technological methods, such as machine learning (ML), in suicide prevention may complement their support work. Investigating attitudes towards the use of ML in crisis support is an important first step.

Aims: The current study is aimed at investigating crisis supporters’ attitudes towards ML in crisis support/suicide prevention, beliefs about the effect of technology on the service and help‐seeking, and concerns/opinions about any future technology implementation.

Methods: Two hundred fifty‐five crisis supporters aged 20–84 years were recruited through Lifeline Australia. Participants voluntarily completed an anonymous questionnaire, including measures of attitudes towards technology as well as open‐text options, which provided the data for a thematic analysis.

Results: Crisis supporters were neutral to negative on an adapted measure of ML use in crisis support. Less than one‐third held the belief that technology would enhance Lifeline services, and over half of the participants felt help‐seekers would be less likely to contact Lifeline if technology was implemented. Thematic analysis of the open‐text questions revealed loss of human connection and mistrust of algorithms to be the most prominent barriers to future technological adoption by Lifeline crisis supporters.

Limitations: Clearly defining terms of ML and technology was difficult to do in this hypothetical context, potentially impacting the attitudes expressed.

Conclusions: Any new technology to support crisis supporters needs to be carefully codesigned with the workforce to ensure effective implementation and avoid any potential or perceived negative impacts on help‐seekers.
Breast Cancer Prediction: A Fusion of Genetic Algorithm, Chemical Reaction Optimization, and Machine Learning Techniques
Authors: Islam Md. Rafiqul , Islam Md. Shahidul , Majumder Saikat
Published: August 2024

Breast cancer is currently one of the most prevalent cancers affecting women globally. Uncontrolled growth and division of breast cells lead to the formation of tumors, marking the onset of breast cancer. Predicting breast cancer is essential for early detection, making treatment plans, and implementing preventive measures, ultimately improving patient outcomes and reducing mortality rates. In recent years, numerous studies have been published to predict breast cancer where researchers use a variety of methods. Most investigations have been conducted using narrow and specific datasets, often resulting in a lack of accuracy. Such methods may not be suitable for clinical use. The study aims to address the limitations of existing models in terms of robustness and generalization across diverse datasets. In our study, we employed two metaheuristic algorithms, namely, genetic algorithm (GA) and chemical reaction optimization (CRO) with machine learning techniques, including support vector machine (SVM), decision tree, random forest, and XGBoost. GA and CRO are used to optimize the feature selection process. It enables machine learning algorithms to predict more accurately. Experiments were conducted on three datasets, namely, Wisconsin Breast Cancer (WBC), Breast Cancer‐the University of California, Irvine (BC‐UCI), and Breast Cancer Coimbra (BCC) datasets. The datasets contain 569, 286, and 116 instances, respectively. The classifiers with optimized features consistently outperformed the classifiers without feature optimization in terms of accuracy, precision, recall, specificity, and F1score. Among the compared methods published recently, our method attained the highest accuracies of 99.64% in the WBC dataset and 98% in the BCC dataset, as well as the second highest accuracy of 99.12% in the BC‐UCI dataset. Comparative analysis demonstrated the superiority of our approach over existing methods.
Ferroptosis‐Related Genes in IgA Nephropathy: Screening for Potential Targets of the Mechanism
Authors: Zhu Wenhui , Chen Yao , Xiao Jing , Cheng Chuchu , Ma Guijie , Wang Yang , Zhang Yonggang , Chen Ming
Published: August 2024

Aims: Exploring key genes and potential molecular pathways of ferroptosis in immunoglobulin A nephropathy (IgAN).

Methods: The IgAN datasets and ferroptosis‐related genes (FRGs) were obtained in the Gene Expression Omnibus (GEO) and FerrDb database. Differentially expressed genes (DEGs) were identified using R software and intersected with FRGs to obtain differentially expressed FRGs (DE‐FRGs). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (PEA) and Gene Ontology (GO) functional annotation were performed on DE‐FRGs. In the Search Tool for the Retrieval of Interacting Genes (STRING) website, we construct a protein–protein interaction (PPI) network. The PPI network was further investigated with screening hub genes with Cytoscape software. The core genes were then subjected to gene set enrichment analysis (GSEA). Finally, the samples were analyzed for immune infiltration in R, and the correlation between hub genes and immune cells was analyzed.

Results: A total of 347 DEGs were identified. CD44, CDO1, CYBB, IL1B, RRM2, AKR1C1, activated transcription factor‐3 (ATF3), CDKN1A, GDF15, JUN, MGST1, MIOX, MT1G, NR4A1, PDK4, TNFAIP3, and ZFP36 were determined as DE‐FRGs. JUN, IL1B, and ATF3 were then screened as hub genes. GSEA and immune infiltration analysis revealed that the hub genes were closely associated with immune inflammatory responses such as NOD‐like receptor signaling, IL‐17 signaling, and TNF signaling.

Conclusions: Our results show that JUN and ATF3 are possibly critical genes in the process of IgAN ferroptosis and may be related with immune cell infiltration.