bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developmental exposure to domoic acid targets reticulospinal neurons and leads to aberrant myelination in the spinal cord
Authors: Panlilio Jennifer M. , Hammar Katherine M. , Aluru Neelakanteswar , Hahn Mark E. 
Published: February 2023

AbstractHarmful algal blooms (HABs) produce neurotoxins that affect human health. Developmental exposure of zebrafish embryos to the HAB toxin domoic acid (DomA) causes myelin defects, loss of reticulospinal neurons, and behavioral deficits. However, it is unclear whether DomA primarily targets myelin sheaths, leading to the loss of reticulospinal neurons, or reticulospinal neurons, causing myelin defects. Here, we show that while exposure to DomA at 2 dpf did not reduce the number of oligodendrocyte precursors prior to myelination, it led to fewer myelinating oligodendrocytes that produced shorter myelin sheaths and aberrantly wrapped neuron cell bodies. DomA-exposed larvae lacked Mauthner neurons prior to the onset of myelination, suggesting that axonal loss is not secondary to myelin defects. The loss of the axonal targets may have led oligodendrocytes to inappropriately myelinate neuronal cell bodies. Consistent with this, GANT61, a GLI1/2 inhibitor that reduces oligodendrocyte number, caused a reduction in aberrantly myelinated neuron cell bodies in DomA-exposed fish. Together, these results suggest that DomA initially alters reticulospinal neurons and the loss of axons causes aberrant myelination of nearby cell bodies. The identification of initial targets and perturbed cellular processes provides a mechanistic understanding of how DomA alters neurodevelopment, leading to structural and behavioral phenotypes.
Interpretable patent recommendation with knowledge graph and deep learning
Authors: Chen Han , Deng Weiwei 
Published: February 2023

AbstractPatent transfer is a common practice for companies to obtain competitive advantages. However, they encounter the difficulty of selecting suitable patents because the number of patents is increasingly large. Many patent recommendation methods have been proposed to ease the difficulty, but they ignore patent quality and cannot explain why certain patents are recommended. Patent quality and recommendation explanations affect companies’ decision-making in the patent transfer context. Failing to consider them in the recommendation process leads to less effective recommendation results. To fill these gaps, this paper proposes an interpretable patent recommendation method based on knowledge graph and deep learning. The proposed method organizes heterogeneous patent information as a knowledge graph. Then it extracts connectivity and quality features from the knowledge graph for pairs of patents and companies. The former features indicate the relevance of the pairs while the latter features reflect the quality of the patents. Based on the features, we design an interpretable recommendation model by combining a deep neural network with a relevance propagation technique. We conduct experiments with real-world data to evaluate the proposed method. Recommendation lists with varying lengths show that the average precision, recall, and mean average precision of the proposed method are 0.596, 0.636, and 0.584, which improve corresponding performance of best baselines by 7.28%, 18.35%, and 8.60%, respectively. Besides, our method interprets recommendation results by identifying important features leading to the results.
Changes in physio-biochemical parameters and expression of metallothioneins in Avena sativa L. in response to drought
Authors: Konieczna Wiktoria , Warchoł Marzena , Mierek-Adamska Agnieszka , Skrzypek Edyta , Waligórski Piotr , Piernik Agnieszka , Dąbrowska Grażyna B. 
Published: February 2023

AbstractDrought is one of the major threats to food security. Among several mechanisms involved in plant stress tolerance, one protein family—the plant metallothioneins (MTs)—shows great promise for enhancing drought resistance. Plant metallothioneins in oat (Avena sativaL.) have not yet been deeply analysed, and the literature lacks a comprehensive study of the whole family of plant MTs in response to drought. In this study, we showed that the number and nature ofcis-elements linked with stress response in promoters ofAsMTs1–3differed depending on the MT type. Drought stress in oat plants caused an increase in the expression ofAsMT2andAsMT3and a decrease in the expression ofAsMT1compared to well-watered plants. Moreover, the low values of relative water content, water use efficiency, net photosynthesis (PN), transpiration (E), stomatal conductance (gs), chlorophylla, and carotenoid were accompanied by high levels of electrolyte leakage, internal CO2concentration (Ci) and abscisic acid content, and high activity of antioxidants enzymes in plants under drought stress. The present study puts forward the idea thatAsMTsare crucial for oat response to drought stress not only by regulating antioxidant activity but also by changing the plant water regime and photosynthesis. Our results support the hypothesis that structural differences among types of plant MTs reflect their diversified physiological roles.
A 10-year microbiological study of Pseudomonas aeruginosa strains revealed the circulation of populations resistant to both carbapenems and quaternary ammonium compounds
Authors: Pottier Marine , Gravey François , Castagnet Sophie , Auzou Michel , Langlois Bénédicte , Guérin François , Giard Jean-Christophe , Léon Albertine , Le Hello Simon 
Published: February 2023

AbstractPseudomonas aeruginosa is one of the leading causes of healthcare-associated infections. For this study, the susceptibility profiles to antipseudomonal antibiotics and a quaternary ammonium compound, didecyldimethylammonium chloride (DDAC), widely used as a disinfectant, were established for 180 selected human and environmental hospital strains isolated between 2011 and 2020. Furthermore, a genomic study determined resistome and clonal putative relatedness for 77 of them. During the ten-year study period, it was estimated that 9.5% of patients’ strains were resistant to carbapenems, 11.9% were multidrug-resistant (MDR), and 0.7% were extensively drug-resistant (XDR). Decreased susceptibility (DS) to DDAC was observed for 28.0% of strains, a phenotype significantly associated with MDR/XDR profiles and from hospital environmental samples (p < 0.0001). According to genomic analyses, the P. aeruginosa population unsusceptible to carbapenems and/or to DDAC was diverse but mainly belonged to top ten high-risk clones described worldwide by del Barrio-Tofiño et al. The carbapenem resistance appeared mainly due to the production of the VIM-2 carbapenemase (39.3%) and DS to DDAC mediated by MexAB-OprM pump efflux overexpression. This study highlights the diversity of MDR/XDR populations of P. aeruginosa which are unsusceptible to compounds that are widely used in medicine and hospital disinfection and are probably distributed in hospitals worldwide.
Magnetization dynamics at finite temperature in CoFeB–MgO based MTJs
Authors: Sampan-A-Pai Sutee , Phoomatna Rattaphon , Boonruesi Worawut , Meo Andrea , Chureemart Jessada , Evans Richard F. L. , Chantrell Roy W. , Chureemart Phanwadee 
Published: February 2023

AbstractThe discovery of magnetization switching via spin transfer torque (STT) in PMA-based MTJs has led to the development of next-generation magnetic memory technology with high operating speed, low power consumption and high scalability. In this work, we theoretically investigate the influence of finite size and temperature on the mechanism of magnetization switching in CoFeB–MgO based MTJ to get better understanding of STT-MRAM fundamentals and design. An atomistic model coupled with simultaneous solution of the spin accumulation is employed. The results reveal that the incoherent switching process in MTJ strongly depends on the system size and temperature. At 0 K, the coherent switching mode can only be observed in MTJs with the diameter less than 20 nm. However, at any finite temperature, incoherent magnetization switching is thermally excited. Furthermore, increasing temperature results in decreasing switching time of the magnetization. We conclude that temperature dependent properties and thermally driven reversal are important considerations for the design and development of advanced MRAM systems.
Caldendrin represses neurite regeneration and growth in dorsal root ganglion neurons
Authors: Lopez Josue A. , Yamamoto Annamarie , Vecchi Joseph T. , Hagen Jussara , Lee Kyungmoo , Sonka Milan , Hansen Marlan R. , Lee Amy 
Published: February 2023

AbstractCaldendrin is a Ca2+ binding protein that interacts with multiple effectors, such as the Cav1 L-type Ca2+ channel, which play a prominent role in regulating the outgrowth of dendrites and axons (i.e., neurites) during development and in response to injury. Here, we investigated the role of caldendrin in Cav1-dependent pathways that impinge upon neurite growth in dorsal root ganglion neurons (DRGNs). By immunofluorescence, caldendrin was localized in medium- and large- diameter DRGNs. Compared to DRGNs cultured from WT mice, DRGNs of caldendrin knockout (KO) mice exhibited enhanced neurite regeneration and outgrowth. Strong depolarization, which normally represses neurite growth through activation of Cav1 channels, had no effect on neurite growth in DRGN cultures from female caldendrin KO mice. Remarkably, DRGNs from caldendrin KO males were no different from those of WT males in terms of depolarization-dependent neurite growth repression. We conclude that caldendrin opposes neurite regeneration and growth, and this involves coupling of Cav1 channels to growth-inhibitory pathways in DRGNs of females but not males.
Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea
Authors: Maglietta Rosalia , Saccotelli Leonardo , Fanizza Carmelo , Telesca Vito , Dimauro Giovanni , Causio Salvatore , Lecci Rita , Federico Ivan , Coppini Giovanni , Cipriano Giulia , Carlucci Roberto 
Published: February 2023

AbstractAlthough the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso’s dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.
Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
Authors: Ferreira E. K. G. D. , Lara D. S. D. , Silveira G. F. 
Published: February 2023

AbstractHigh Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the different models. In lineage A549, the best model showed a positive correlation with R = 0.953. In Huh7, the best correlation of the model was R = 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R = 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.
Engineered kirigami design of PVDF-Pt core–shell nanofiber network for flexible transparent electrode
Authors: Park Heesung , Si Hyeokjun , Gu Junseo , Lee Donghyun , Park Donghyuck , Lee Young-In , Kim Kwanlae 
Published: February 2023

AbstractNanofiber networks comprising polymer-metal core–shell structures exhibit several advantages, such as high uniformities and considerable flexibilities. Additionally, the flexibility of the nanofiber network may be further enhanced by engineering the network topology. Therefore, in this study, the topologies of polyvinylidene fluoride (PVDF)-Pt core–shell nanofiber (CS NF) networks were engineered, and their performances as flexible transparent electrodes were comprehensively evaluated. Three distinct topologies of nanofiber networks were induced using circular, square, and rectangular electrode collectors. A highly uniform nanofiber network was obtained using the square electrode collector, which generated a high density of nanofiber junctions (nodes). Consequently, this nanofiber network exhibited the smallest sheet resistance $$\left({R}_{\mathrm{s}}\right)$$

R
s

and lowest optical transmittance $$\left(T\right)$$

T

among the three CS NF networks. In contrast, nanofiber bundles were frequently formed in the randomly aligned CS NF network prepared using the circular electrode collector, reducing the node density. As a result, it simultaneously exhibited a very small $${R}_{\mathrm{s}}$$

R
s

and high $$T$$
T
, generating the largest percolation figure of merit $$\left(\Pi =330.5\right)$$

Π
=
330.5

. Under certain strain directions, the CS NF network with the engineered topology exhibited a significantly enhanced mechanical durability. Finally, a flexible piezoelectric pressure sensor with CS NF network electrodes was fabricated and its sensing performance was excellent.
Sequence dependent UV damage of complete pools of oligonucleotides
Authors: Kufner Corinna L. , Krebs Stefan , Fischaleck Marlis , Philippou-Massier Julia , Blum Helmut , Bucher Dominik B. , Braun Dieter , Zinth Wolfgang , Mast Christof B. 
Published: February 2023

AbstractUnderstanding the sequence-dependent DNA damage formation requires probing a complete pool of sequences over a wide dose range of the damage-causing exposure. We used high throughput sequencing to simultaneously obtain the dose dependence and quantum yields for oligonucleotide damages for all possible 4096 DNA sequences with hexamer length. We exposed the DNA to ultraviolet radiation at 266 nm and doses of up to 500 absorbed photons per base. At the dimer level, our results confirm existing literature values of photodamage, whereas we now quantified the susceptibility of sequence motifs to UV irradiation up to previously inaccessible polymer lengths. This revealed the protective effect of the sequence context in preventing the formation of UV-lesions. For example, the rate to form dipyrimidine lesions is strongly reduced by nearby guanine bases. Our results provide a complete picture of the sensitivity of oligonucleotides to UV irradiation and allow us to predict their abundance in high-UV environments.
Spatio-temporal dynamics enhance cellular diversity, neuronal function and further maturation of human cerebral organoids
Authors: Saglam-Metiner Pelin , Devamoglu Utku , Filiz Yagmur , Akbari Soheil , Beceren Goze , Goker Bakiye , Yaldiz Burcu , Yanasik Sena , Biray Avci Cigir , Erdal Esra , Yesil-Celiktas Ozlem 
Published: February 2023

AbstractThe bioengineerined and whole matured human brain organoids stand as highly valuable three-dimensional in vitro brain-mimetic models to recapitulate in vivo brain development, neurodevelopmental and neurodegenerative diseases. Various instructive signals affecting multiple biological processes including morphogenesis, developmental stages, cell fate transitions, cell migration, stem cell function and immune responses have been employed for generation of physiologically functional cerebral organoids. However, the current approaches for maturation require improvement for highly harvestable and functional cerebral organoids with reduced batch-to-batch variabilities. Here, we demonstrate two different engineering approaches, the rotating cell culture system (RCCS) microgravity bioreactor and a newly designed microfluidic platform (µ-platform) to improve harvestability, reproducibility and the survival of high-quality cerebral organoids and compare with those of traditional spinner and shaker systems. RCCS and µ-platform organoids have reached ideal sizes, approximately 95% harvestability, prolonged culture time with Ki-67 + /CD31 + /β-catenin+ proliferative, adhesive and endothelial-like cells and exhibited enriched cellular diversity (abundant neural/glial/ endothelial cell population), structural brain morphogenesis, further functional neuronal identities (glutamate secreting glutamatergic, GABAergic and hippocampal neurons) and synaptogenesis (presynaptic-postsynaptic interaction) during whole human brain development. Both organoids expressed CD11b + /IBA1 + microglia and MBP + /OLIG2 + oligodendrocytes at high levels as of day 60. RCCS and µ-platform organoids showing high levels of physiological fidelity a high level of physiological fidelity can serve as functional preclinical models to test new therapeutic regimens for neurological diseases and benefit from multiplexing.
Early Identification of Children at Risk of Communication Disorders: Introducing a Novel Battery of Dynamic Assessments for Infants
Authors: Spicer-Cain Helen , Camilleri Bernard , Hasson Natalie , Botting Nicola 
Published: February 2023

Purpose:
Many children with communication disorders (CDs) experience lengthy gaps between parental reporting of concerns and formal identification by professionals. This means that children with CDs are denied access to early interventions that may help support the development of communication skills and prevent possible negative sequelae associated with long-term outcomes. This may be due, in part, to the lack of assessment instruments available for children younger than 3 years of age. This study therefore reports on promising preliminary data from a novel set of valid dynamic assessment (DA) measures designed for infants.

Method:

We recruited 53 low-risk children and two groups of children considered to be at high risk for CDs (
n
= 17, social high risk, and
n
= 22, language high risk) due to family members with language and social communication difficulties. The children were between 1 and 2 years of age and were assessed using a battery of five DA tasks related to receptive vocabulary, motor imitation, response to joint attention, turn taking, and social requesting. A set of standardized measures were also used.

Results:
The DA tasks showed high levels of interrater reliability and relationships with age across a cross-sectional sample of children from the low-risk group. Three tasks showed moderate to strong correlations with standardized measures taken at the same age, with particularly strong correlations between the DA of receptive vocabulary and other receptive language measures. The DA of receptive vocabulary was also the only task to discriminate between the three risk groups, with the social high risk group scoring lower.

Conclusions:
These results provide preliminary information about early DA tasks, forming the basis for further research into their utility. DA tasks might eventually facilitate the development of new methods for detecting CDs in very young children, allowing earlier intervention and support.
Privacy‐preserving remote sensing images recognition based on limited visual cryptography
Authors: Zhang Denghui , Shafiq Muhammad , Wang Liguo , Srivastava Gautam , Yin Shoulin 
Published: February 2023

AbstractWith the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
Relaxation‐based bi‐lever reformulation and decomposition algorithm‐based collaborative optimization of multi‐microgrid for cloud energy storage
Authors: Feng Fei , Du Xin , Si Qiang , Cai Hao 
Published: February 2023

AbstractEnergy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi‐lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation‐based bi‐lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi‐lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG.