bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the prevalence of Non-Verbal Learning Disability (NVLD) from the ABCD sample
Authors: Coccaro Ambra , Banich Marie , Mammarella Irene C. , Liotti Mario
Published: April 2024

Non-Verbal Learning Disability (NVLD) is a neurodevelopmental disorder characterized by deficits in processing visuospatial information but with age-appropriate verbal skills. This cognitive profile has been hypothesized to be associated with atypical white matter, but at the present there is a lack of evidence for this hypothesis. Currently, the condition is not characterized within the main diagnostic systems, in part because no clear set of criteria for characterizing the disorder exists. This report is the first attempt to estimate NVLD prevalence, using two sets of diagnostic criteria, in a large sample of over 11,000 children who were selected without regards to problems of specific nature, either psychological, neurological, physical and/or social. Furthermore, it examined the association between the profile of cognitive abilities and aspects of whole-brain white matter measures in children with and without symptoms associated with NVLD. Participants were drawn from the Adolescent Brain Cognitive Development (ABCD) study, a 10-year longitudinal study of 11,876 children in the U.S. The data used in the present study were drawn from the initial testing point at which the children were 9–10 years old. Prevalence of NVLD based on two distinct sets of criteria, correlations between the measures used to create the criteria, correlations between criteria measures and measures of white matter integrity. The cognitive criteria included measures of visuospatial processing, reading, intelligence and social skills. By varying the cut-offs applied to social skills in conjunction with visuo-spatial difficulties, spared reading skills and intelligence scores, we calculated prevalence for two NVLD groups. White matter characteristics were measures of volume, fractional anisotropy and mean diffusivity. Based on the criteria used, the estimated prevalence of NVLD varied from 1 to 8%. Furthermore, children with NVLD showed a dissociation between measures of visuo-spatial processing not observed in non-NVLD children. At the neurological level, findings provide preliminary evidence of associations between the cognitive profile of NVLD and abnormalities in white matters tracts. The present study documents that exists, within this large non-selected sample, a proportion of youth who show evidence of NVLD. Given those results, it appears essential to establish the best diagnostic criteria, to improve the treatment options and quality of life for children with this disorder.
Blockchain integration for in-vehicle CAN bus intrusion detection systems with ISO/SAE 21434 compliant reporting
Authors: Andreica Tudor , Musuroi Adrian , Anistoroaei Alfred , Jichici Camil , Groza Bogdan
Published: April 2024

The development of Intrusion Detection Systems (IDS) for in-vehicle buses has gained a lot of momentum in recent years as the number of reported vulnerabilities and the degree of interconnectivity for modern vehicles are on the rise. Since intrusion detection is resource consuming, it can be performed on computationally capable Android head units that are now present inside vehicles. Moreover, these units are connected to the internet, which enables the use of more complex algorithms that run in cloud environments. In this work we develop one such approach: an IDS that consists of a locally installed copy, running on head units, and a centralized instance of it that runs in the cloud and monitors traffic for groups of similar vehicles. Additionally, the centralized instance is part of a cloud service for intrusion detection which is continuously updated with the most recent types of attacks. The classification results of the cloud-based service are further analyzed by an incident response team which confirms the presence of known attacks, analyzes new types of attacks and assesses their impact. The output of this activity is stored on the Blockchain as ISO/SAE 21434 compliant reports, ensuring the transparency and traceability of the reported incidents.
Assessing the impact of climate change on water requirement and yield of sugarcane over different agro-climatic zones of Tamil Nadu
Authors: Guhan V. , Annadurai K. , Easwaran S. , Marimuthu M. , Balu D. , Vigneswaran S. , Navinkumar C.
Published: April 2024

The DSSAT CANEGRO model was calibrated and verified using field experimental data from five Tamil Nadu Agroclimatic Zones (1981–2022). The genetic coefficients of the sugarcane cultivar (CO-86032) were calculated. R2 obtained between measured and simulated stalk fresh mass was 0.9 with the nRMSE (0.01) and RMSE (1.6) and R2 between measured and simulated sucrose mass was 0.9 with the nRMSE (0.16) and RMSE (1.2). For yield R2 obtained between measured and simulated was 0.9 with the nRMSE (0.01) and RMSE (1.6). As a result, the CANEGRO model may be used to mimic the phenology and yield features of the sugarcane cultivar in Tamil Nadu's Agro Climatic Zones. Temperature increases in Agro Climatic Zones resulted in varying yield reductions, with 2 °C increases causing a 3% loss, 3 °C increases 5%, and 4 °C increases 9%. The Water Requirement rose throughout all of the ACZ due to the high temperature, but to differing degrees. A 2 °C increase often results in an average 4% increase in the WR. 3 °C rise in temperature increased WR to 9% and WR rose by 13% when the temperature was raised by 4 °C.
A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions
Authors: Barbieri Marco , Hooijmans Melissa T. , Moulin Kevin , Cork Tyler E. , Ennis Daniel B. , Gold Garry E. , Kogan Feliks , Mazzoli Valentina
Published: April 2024

This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.
Simple and rapid determination of tartrazine in fake saffron using the metal organic framework (Fe SA MOF@CNF) by HPLC/PDA
Authors: salehi Ali , Shariatifar Nabi , Jahed-Khaniki Gholamreza , Sadighara Parisa , Hozoori Mohammad
Published: April 2024

The present study of a novel metal–organic framework containing Fe single atoms doped on electrospun carbon nanofibers (Fe SA-MOF@CNF) based on dispersive micro solid phase extraction (D-μ-SPE) using HPLC–PDA for detection tartrazine in fake saffron samples was designed. The Fe SA-MOF@CNF sorbent was extensively characterized through various techniques including N2 adsorption–desorption isotherms, X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The specific area of surface of the sorbent was 577.384 m2/g. The study variables were optimized via the central composite design (CCD), which included a sorbent mass of 15 mg, a contact time of 6 min, a pH of 7.56, and a tartrazine concentration of 300 ng/ml. Under the optimum condition, the calibration curve of this method was linear in the range of 5–1000 ng/mL, with a correlation coefficient of 0.992. The LOD and LOQ values were ranged 0.38–0.74 and 1.34–2.42 ng/ml, respectively. This approach revealed significant improvements, including high extraction recovery (98.64), recovery rates (98.43–102.72%), and accuracy (RSDs < 0.75 to 3.6%). the enrichment factors were obtained in the range of 80.6–86.4 with preconcentration factor of 22.3. Consequently, the D-μ-SPE method based on synthesized Fe SA-MOF@CNF could be recommended as a sustainable sorbent for detecting tartrazine in saffron samples.
Microbial induced wettability alteration with implications for Underground Hydrogen Storage
Authors: Boon Maartje , Buntic Ivan , Ahmed Kadir , Dopffel Nicole , Peters Catherine , Hajibeygi Hadi
Published: April 2024

Characterization of the microbial activity impacts on transport and storage of hydrogen is a crucial aspect of successful Underground Hydrogen Storage (UHS). Microbes can use hydrogen for their metabolism, which can then lead to formation of biofilms. Biofilms can potentially alter the wettability of the system and, consequently, impact the flow dynamics and trapping mechanisms in the reservoir. In this study, we investigate the impact of microbial activity on wettability of the hydrogen/brine/rock system, using the captive-bubble cell experimental approach. Apparent contact angles are measured for bubbles of pure hydrogen in contact with a solid surface inside a cell filled with living brine which contains sulphate reducing microbes. To investigate the impact of surface roughness, two different solid samples are used: a “rough” Bentheimer Sandstone sample and a “smooth” pure Quartz sample. It is found that, in systems where buoyancy and interfacial forces are the main acting forces, the impact of biofilm formation on the apparent contact angle highly depends on the surface roughness. For the “rough” Bentheimer sandstone, the apparent contact angle was unchanged by biofilm formation, while for the smooth pure Quartz sample the apparent contact angle decreased significantly, making the system more water-wet. This decrease in apparent contact angle is in contrast with an earlier study present in the literature where a significant increase in contact angle due to microbial activity was reported. The wettability of the biofilm is mainly determined by the consistency of the Extracellular Polymeric Substances (EPS) which depends on the growth conditions in the system. Therefore, to determine the impact of microbial activity on the wettability during UHS will require accurate replication of the reservoir conditions including surface roughness, chemical composition of the brine, the microbial community, as well as temperature, pressure and pH-value conditions.
Response accuracy of ChatGPT 3.5 Copilot and Gemini in interpreting biochemical laboratory data a pilot study
Authors: Kaftan Ahmed Naseer , Hussain Majid Kadhum , Naser Farah Hasson
Published: April 2024

With the release of ChatGPT at the end of 2022, a new era of thinking and technology use has begun. Artificial intelligence models (AIs) like Gemini (Bard), Copilot (Bing), and ChatGPT-3.5 have the potential to impact every aspect of our lives, including laboratory data interpretation. To assess the accuracy of ChatGPT-3.5, Copilot, and Gemini responses in evaluating biochemical data. Ten simulated patients' biochemical laboratory data, including serum urea, creatinine, glucose, cholesterol, triglycerides, low-density lipoprotein (LDL-c), and high-density lipoprotein (HDL-c), in addition to HbA1c, were interpreted by three AIs: Copilot, Gemini, and ChatGPT-3.5, followed by evaluation with three raters. The study was carried out using two approaches. The first encompassed all biochemical data. The second contained only kidney function data. The first approach indicated Copilot to have the highest level of accuracy, followed by Gemini and ChatGPT-3.5. Friedman and Dunn's post-hoc test revealed that Copilot had the highest mean rank; the pairwise comparisons revealed significant differences for Copilot vs. ChatGPT-3.5 (P = 0.002) and Gemini (P = 0.008). The second approach exhibited Copilot to have the highest accuracy of performance. The Friedman test with Dunn's post-hoc analysis showed Copilot to have the highest mean rank. The Wilcoxon Signed-Rank Test demonstrated an indistinguishable response (P = 0.5) of Copilot when all laboratory data were applied vs. the application of only kidney function data. Copilot is more accurate in interpreting biochemical data than Gemini and ChatGPT-3.5. Its consistent responses across different data subsets highlight its reliability in this context.
Toward the eco-friendly cosmetic cleansing assisted by the micro-bubbly jet
Authors: Kang Yeeun , Park Jooyeon , Park Hyungmin
Published: April 2024

While numerous types of chemical cosmetic cleansers have been presented, those with sensitive skin may still experience some irritation while using them. Moreover, the environmental issue of chemical agents has been documented repeatedly. To address these, we suggest the potential application of a micro-sized bubble-laden water jet to cleanse the cosmetics without (or less) using chemical detergents. We devised a venturi-type nozzle with a mesh and air holes capable of generating massive fine bubbles. By testing with the foundation and lip tint (known to be highly adhesive) coated on the synthetic leather and artificial skin surfaces, we measured that the cleansing performance of the bubbly jet is much better (even without the chemical agent) than the single-phase liquid jet. As a mechanism for enhanced removal, it is understood that the greater kinetic energy of the jet due to the acceleration of the effective liquid–air mixture flow and the direct bubble-cosmetic collisions play essential roles. We believe that the present results will spur the development of environment-friendly cleaning methods.
Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions
Authors: Zaghba Layachi , Borni Abdelhalim , Benbitour Messaouda Khennane , Fezzani Amor , Alwabli Abdullah , Bajaj Mohit , Dost Mohammadi Shir Ahmad , Ghoneim Sherif S. M.
Published: April 2024

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.
Numerical simulation study on the evolution law of mechanical properties of different metamorphic coals after heat treatment
Authors: Zhang Xun , Lai Hongyu , Huang Ge , Lu Bing , Liang Huimin
Published: April 2024

In order to study the effect of temperature on the structure and mechanical properties of coal with different metamorphic degree. Three coal samples with varying degrees of metamorphism were chosen for analysis. The discrete element software PFC2D is used to simulate the heat treatment and compression of coal. The findings indicate that during the heating process, low-order coal exhibits noticeable thermal cracks at an early stage, while thermal crack development in middle-order coal is concentrated in the later stages. In contrast, high-order coal demonstrates a more stable macroscopic structure. The strength and stiffness of low rank coal show the lowest value and decrease significantly within 135 °C. However, the strength and stiffness of medium rank coal decrease significantly after 135 °C. The changes of mechanical properties and damage modes of coal caused by thermal damage are often ignored, which may lead to the deviation of design and research results from the actual situation. Therefore, this study is of great significance to the prevention and control of coal mine disasters.
Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting
Authors: Prosz Aurel , Sahgal Pranshu , Huffman Brandon M. , Sztupinszki Zsofia , Morris Clare X. , Chen David , Börcsök Judit , Diossy Miklos , Tisza Viktoria , Spisak Sandor , Likasitwatanakul Pornlada , Rusz Orsolya , Csabai Istvan , Cecchini Michael , Baca Yasmine , Elliott Andrew , Enzinger Peter , Singh Harshabad , Ubellaker Jessalyn , Lazaro Jean-Bernard , Cleary James M. , Szallasi Zoltan , Sethi Nilay S.
Published: April 2024

Homologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in cancer. HR-deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of HR deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Using whole exome and genome sequencing data, we found that a significant subset of GEA, but very few colorectal adenocarcinomas, show evidence of HR deficiency by mutational signature analysis (HRD score). High HRD gastric cancer cell lines demonstrated functional HR deficiency by RAD51 foci assay and increased sensitivity to platinum chemotherapy and PARP inhibitors. Of clinical relevance, analysis of three different GEA patient cohorts demonstrated that platinum treated HR deficient cancers had better outcomes. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by two photoproduct repair assays. Single-cell RNA-sequencing revealed that, in addition to inducing apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, validated by intracellular GSH assay. Overall, our study provides preclinical evidence that a subset of GEAs harbor genomic features of HR and NER deficiency and may therefore benefit from platinum chemotherapy and PARP inhibitors.
Klebsiella pneumoniae peptide hijacks a Streptococcus pneumoniae permease to subvert pneumococcal growth and colonization
Authors: Lux Janine , Portmann Hannah , Sánchez García Lucía , Erhardt Maria , Holivololona Lalaina , Laloli Laura , Licheri Manon F. , Gallay Clement , Hoepner Robert , Croucher Nicholas J. , Straume Daniel , Veening Jan-Willem , Dijkman Ronald , Heller Manfred , Grandgirard Denis , Leib Stephen L. , Hathaway Lucy J.
Published: April 2024

Treatment of pneumococcal infections is limited by antibiotic resistance and exacerbation of disease by bacterial lysis releasing pneumolysin toxin and other inflammatory factors. We identified a previously uncharacterized peptide in the Klebsiella pneumoniae secretome, which enters Streptococcus pneumoniae via its AmiA-AliA/AliB permease. Subsequent downregulation of genes for amino acid biosynthesis and peptide uptake was associated with reduction of pneumococcal growth in defined medium and human cerebrospinal fluid, irregular cell shape, decreased chain length and decreased genetic transformation. The bacteriostatic effect was specific to S. pneumoniae and Streptococcus pseudopneumoniae with no effect on Streptococcus mitis, Haemophilus influenzae, Staphylococcus aureus or K. pneumoniae. Peptide sequence and length were crucial to growth suppression. The peptide reduced pneumococcal adherence to primary human airway epithelial cell cultures and colonization of rat nasopharynx, without toxicity. We identified a peptide with potential as a therapeutic for pneumococcal diseases suppressing growth of multiple clinical isolates, including antibiotic resistant strains, while avoiding bacterial lysis and dysbiosis.
A neural speech decoding framework leveraging deep learning and speech synthesis
Authors: Chen Xupeng , Wang Ran , Khalilian-Gourtani Amirhossein , Yu Leyao , Dugan Patricia , Friedman Daniel , Doyle Werner , Devinsky Orrin , Wang Yao , Flinker Adeen
Published: April 2024

Decoding human speech from neural signals is essential for brain–computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.
Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
Authors: McDonald Robert I. , Biswas Tanushree , Chakraborty T. C. , Kroeger Timm , Cook-Patton Susan C. , Fargione Joseph E.
Published: April 2024

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO2e yr−1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO2e yr−1. The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.
Machine learning for sustainable organic waste treatment: a critical review
Authors: Gupta Rohit , Ouderji Zahra Hajabdollahi , Uzma  , Yu Zhibin , Sloan William T. , You Siming
Published: April 2024

Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven modeling techniques for organic treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, andk-nearest neighbors. The application of these techniques is explored in terms of their capacity for optimizing complex processes. Additionally, the study delves into physics-informed neural networks, highlighting the significance of integrating domain knowledge for improved model consistency. Comparative analyses are carried out to provide insights into the strengths and weaknesses of each technique, aiding practitioners in selecting appropriate models for diverse applications. Transfer learning and specialized neural network variants are also discussed, offering avenues for enhancing predictive capabilities. This work contributes valuable insights to the field of data-driven modeling, emphasizing the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios.
Quantitative phase imaging with a compact meta-microscope
Authors: Wang Junyi , Yu Rongtao , Ye Xin , Sun Jiacheng , Li Jian , Huang Chunyu , Xiao Xingjian , Ji Jitao , Shen Wenjing , Tie Zuoxiu , Chen Chen , Zhu Shining , Li Tao
Published: April 2024

Quantitative phase imaging (QPI) based on the transport-of-intensity equation (TIE) is a powerful technique in label-free microscopy. The image stack required for a successful TIE-QPI is traditionally obtained by translating the object or image plane, and the optical elements used in the conventional TIE-QPI systems are usually bulky and cumbersome. Stable and compact TIE-QPI methods capable of non-motion optical zooming can significantly facilitate applications that demand portability. Here, we propose a non-motion TIE-QPI method based on a dispersive metalens. The dispersive nature of the metalens is utilized to provide a spectral focal tuning. With fixed object and image planes, seven through-focus intensity images are captured by changing the illumination wavelength. The QPI performance is validated by retrieving the surface phase profiles of a microlens array and a phase resolution target, showing a high phase detection accuracy (deviation less than 0.03 wavelength). Subsequently, we established a compact meta-microscope by integrating the metalens with a commercially available CMOS image sensor, which shows good performance in microscopic imaging of unstained bio-samples. Our approach, based on the large-dispersive metalens, facilitates a compact and robust QPI system for optical metrology and label-free microscopy.
A missense mutation in human INSC causes peripheral neuropathy
Authors: Yeh Jui-Yu , Chao Hua-Chuan , Hong Cheng-Li , Hung Yu-Chien , Tzou Fei-Yang , Hsiao Cheng-Tsung , Li Jeng-Lin , Chen Wen-Jie , Chou Cheng-Ta , Tsai Yu-Shuen , Liao Yi-Chu , Lin Yu-Chun , Lin Suewei , Huang Shu-Yi , Kennerson Marina , Lee Yi-Chung , Chan Chih-Chiang
Published: April 2024

PAR3/INSC/LGN form an evolutionarily conserved complex required for asymmetric cell division in the developing brain, but its post-developmental function and disease relevance in the peripheral nervous system (PNS) remains unknown. We mapped a new locus for axonal Charcot–Marie-Tooth disease (CMT2) and identified a missense mutation c.209 T > G (p.Met70Arg) in the INSC gene. Modeling the INSCM70R variant in Drosophila, we showed that it caused proprioceptive defects in adult flies, leading to gait defects resembling those in CMT2 patients. Cellularly, PAR3/INSC/LGN dysfunction caused tubulin aggregation and necrotic neurodegeneration, with microtubule-stabilizing agents rescuing both morphological and functional defects of the INSCM70R mutation in the PNS. Our findings underscore the critical role of the PAR3/INSC/LGN machinery in the adult PNS and highlight a potential therapeutic target for INSC-associated CMT2.
Comparison of mechanochemical methods in the synthesis of binaphthol–benzoquinone based cocrystals
Authors: Nagapudi Siddarth , Nagapudi Karthik
Published: April 2024

Mechanochemical methods either under neat or liquid assisted conditions have proven to be successful in making the cocrystals with different stoichiometries.
Infrared bands of neutral gas-phase carbon clusters in a broad spectral range
Authors: Ferrari Piero , Lemmens Alexander K. , Redlich Britta
Published: April 2024

The gas-phase infrared spectra of neutral carbon clusters are measured in a wide spectral range.
1H and 13C chemical shift–structure effects in anhydrous β-caffeine and four caffeine–diacid cocrystals probed by solid-state NMR experiments and DFT calculations
Authors: Majhi Debashis , Stevensson Baltzar , Nguyen Tra Mi , Edén Mattias
Published: April 2024

Solid-state NMR experiments and DFT calculations were utilized for structure refinements and resonance assignments of caffeine-based cocrystals and their precursors.
Bridging the gap: viable reaction pathways from tetrahedrane to benzyne
Authors: Cole Taylor A. , Davis Steven R. , Flint Athena R. , Fortenberry Ryan C.
Published: April 2024

The addition of sp-carbon-containing molecules to polycyclic sp3 tetrahedrane (c-C4H4) results in the formation of both o-benzyne (c-C6H4) and benzene (c-C6H6). Horsehead Nebula by Travis Rector (NOAO), with thanks to NASA and the Space Telescope Science Institute (STScI).
Prolonging the n-type conduction of thermoelectric carbon nanotubes exposed to warm air by mixing hydrated water into the adsorbed dopant layers composed of Li+-receptor molecules
Authors: Hata Shinichi , Nakagawa Chika , Taketoshi Ayako , Murayama Toru , Ishida Tamao , Du Yukou , Shiraishi Yukihide , Toshima Naoki
Published: April 2024

The n-type properties of carbon nanotubes are stabilized in the ambient atmosphere by hydrated water on a cationic molecular adsorption layer supplemented with Li+, which slows the diffusion of thermally active oxygen molecules.
Shining light on tryptamine-derived isocyanides: access to constrained spirocylic scaffolds
Authors: Wu Minghui , Saya Jordy M. , Han Peiliang , Walia Rajat , Pradhan Bapi , Honing Maarten , Ranjan Prabhat , Orru Romano V. A.
Published: April 2024

Tryptamine-derived isocyanides form aggregates at higher concentration that can form a charge transfer complex under visible light irradiation thus enabling a crucial single electron transfer step to generate carbon-based-radical intermediates.
Covalent-assisted seeding of Si nanoparticles into a dual-matrix design toward advanced Si-based Li-ion batteries
Authors: Do Kwanghyun , Park Changyong , Hwang Jeonguk , Kim Sucheol , Jung Yeju , Lee Se Hun , Lim Hee-Dae , Ahn Heejoon
Published: April 2024

This study presents an innovative method for covalently embedding Si nanoparticles within a Sn/SiOC dual-matrix, demonstrating robust adhesion and low electrode resistance. This approach provides valuable insights for design of Si-based composites.
Enhanced biosensing of tumor necrosis factor-alpha based on aptamer-functionalized surface plasmon resonance substrate and Goos–Hänchen shift
Authors: Borg Kathrine Nygaard , Jaffiol Rodolphe , Ho Yi-Ping , Zeng Shuwen
Published: April 2024

This study introduces an aptamer-based surface plasmon resonance biosensor for biomolecule detection, showcasing heightened sensitivity with the Goos–Hänchen shift occurring upon analyte-aptamer binding.
Novel advanced materials and magnetic solid phase extraction as approaches in sample preparation to enhance the analysis of ochratoxin A in peanuts
Authors: Wang Bingchen , Wang Yifan , Zhang Xiuyuan , He Kuo
Published: April 2024

In order to improve the detection efficiency of ochratoxin, a superparamagnetic nano-material was synthesized for solid phase extraction of ochratoxin.
Analysis of O-acetylated sialic acids by 3-nitrophenylhydrazine derivatization combined with LC-MS/MS
Authors: Zhao Chenhao , Wang Xingdan , Wu Jing , Hu Yeli , Zhang Qiwei , Zheng Qi
Published: April 2024

The 3-NPH-labeled sialic acids produce a high abundance of reporter ions, which can improve the accuracy of quantitative analysis of O-acetylated species.
Exploring sulfur donor atom coordination chemistry with La(ii), Nd(ii), and Tm(ii) using a terphenylthiolate ligand
Authors: Gilbert-Bass Kito , Stennett Cary R. , Grotjahn Robin , Ziller Joseph W. , Furche Filipp , Evans William J.
Published: April 2024

To expand the range of donor atoms known to stabilize 4fn5d1 Ln(ii) ions beyond C, N, and O first row main group donor atoms, the Ln(iii) terphenylthiolate iodides, LnIII(SAriPr6)2I (AriPr6 = C6H3-2,6-(C6H2-2,4,6-iPr3)2, Ln = La, Nd) were reduced to LnII(SAriPr6)2 complexes.
Red-emitting Cs2NaScCl6:Sm flexible films for high-resolution X-ray imaging
Authors: Li Yuxia , Luo Qi , Huang Xin , Lu Hao , Yuan Yazhou , Xu Xieming , Wang Shuaihua , Wu Shaofan
Published: April 2024

A study of the radiation detection properties of Cs2NaScCl6:Sm and imaging of flexible scintillation films.
Cage doping of Ti, Zr, and Hf-based 13-atom nanoclusters: two sides of the same coin
Authors: Piotrowski Maurício J. , Palheta João Marcos T. , Fournier René
Published: April 2024

Our DFT study unveils new bimetallic icosahedral nanoclusters with tunable magnetic properties, elucidating their stability through metal cluster electronic and atomic shell structures, paving the way for designing superatoms and new materials.
Vibrational synchronization and its reaction pathway influence from an entropic intermediate in a dirhodium catalyzed allylic C–H activation/Cope rearrangement reaction
Authors: Schaefer Anthony J. , Ess Daniel H.
Published: April 2024

In the dynamically stepwise reaction pathway C–H insertion versus Cope selectivity is highly influenced by whether or not vibrational synchronization occurs in the nonstatistical entropic intermediate.
Metallic Re3O2 with mixed-valence states
Authors: Li Wenjing , Li Fei , Zhang Xiaohua , Wu Jinhui , Yang Guochun
Published: April 2024

A novel P63/mmc Re3O2 with mixed-valence states is discovered through first-principles structural search calculations.
TMAO perturbs intermolecular vibrational motions of water revealed by low-frequency modes
Authors: Liu Tsung-Han , Okuno Masanari
Published: April 2024

TMAO effectively immobilizes surrounding water molecules through forming complexes. Hyper-Raman spectroscopy enables us to observe that collective motions of water are much more restricted, revealed by the librational modes of water.
Plasma-engraved Co3O4 nanorods with enriched oxygen vacancies for efficient electrocatalytic ammonia synthesis
Authors: Lan Bingru , Wan Jieying , Guan Chuang , Yang Ji , Yang Na , Zheng Jiageng , Li Xiaodong , Zhang Hao
Published: April 2024

Plasma-engraved Co3O4 with enriched oxygen vacancies exhibits an FE of 98.9% and a noteworthy ammonia yield of 27.5 mg cm−2 h−1.
Dispersions of magnetic nanoparticles in water/ionic liquid mixtures
Authors: Fiuza T. , Sarkar M. , Riedl J. , Cousin F. , Demouchy G. , Depeyrot J. , Dubois E. , Perzynski R. , Peyre V.
Published: April 2024

Nanoparticles of iron oxide are dispersed in mixtures of water and ionic liquid, here ethylammonium nitrate, and the NP/NP and NP/solvent interactions are studied.
Microfluidic particle counter visualizing mucosal antibodies against SARS-CoV-2 in the upper respiratory tract for rapid evaluation of immune protection
Authors: Li Jiaheng , Chu Lok Ting , Hartanto Hogi , Guo Guihuan , Liu Lu , Wu Jianpeng , Wu Minghui , Cui Chenyu , Wang Gaobo , Liu Wengang , Kwong Hoi Kwan , Wu Siying , Chen Ting-Hsuan
Published: April 2024

A microfluidic particle counter for visualizing mucosal antibody levels against SARS-CoV-2 in the upper respiratory tract for rapid evaluation of immune protection.
Dielectrophoretic characterization and selection of non-spherical flagellate algae in parallel channels with right-angle bipolar electrodes
Authors: Chen Xiaoming , Liu Shun , Shen Mo , Shi Jishun , Wu Chungang , Song Zhipeng , Zhao Yong
Published: April 2024

We develop a microfluidic device with closed right-angle bipolar electrodes to realize dielectrophoretic characterization and selection of non-spherical flagellate algae.
Side-view optical microscopy-assisted atomic force microscopy for thickness-dependent nanobiomechanics
Authors: Yang Yanqi , Li Mi
Published: April 2024

This work combines atomic force microscopy (AFM) with a side-view optical microscopy module to measure the nanomechanical properties of biomaterials.
Long-term in vivo dissolution of thermo- and pH-responsive, 19F magnetic resonance-traceable and injectable polymer implants
Authors: Jirát-Ziółkowska Natalia , Vít Martin , Groborz Ondřej , Kolouchová Kristýna , Červený David , Sedláček Ondřej , Jirák Daniel
Published: April 2024

Thermo- and pH-responsive polymers' dissolution in vivo can be broadly fine-tuned. Polymeric implant can be detected in vivo using “hot-spot” 19F MR measurement. Long-term in vivo dissolution of polymeric implant shows no side effects.
Selenol-ene chemistry-based one pot reaction for unsymmetric selenides synthesis and rate regulation of selenide oxidative elimination
Authors: Xiang Yu , Zhang Jiandong , Xu Yiming , Wang Dianliang , Zhang Zhengbiao , Zhu Jian , Lu Weihong , He Hanliang , Pan Xiangqiang
Published: April 2024

The electronic and steric effects exerted by the substituents on selenide significantly influence the selenide oxidative elimination progress.
Graphene and metal–organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation
Authors: Tran Anh Tuan Trong , Hassan Kamrul , Tung Tran Thanh , Tripathy Ashis , Mondal Ashok , Losic Dusan
Published: April 2024

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost.
Lysosome passivation triggered by silver nanoparticles enhances subcellular-targeted drug therapy
Authors: Wang Xueqian , Zhao Shulin , Fang Guiqian , Wang Ran , Lyu Xinxing , Shao Xintian , Ling Peixue , Meng CaiCai , Chen Jia , Mu Yanling
Published: April 2024

Lysosome passivation strategy for enhancing subcellular-targeted drug therapy.
NHC and photoredox catalysis dual-catalyzed 1,4-mono-/di-fluoromethylative acylation of 1,3-enynes
Authors: Xia Jiuli , Ma Ruiyang , Wang Lihong , Sun Jiaqiong , Zheng Guangfan , Zhang Qian
Published: April 2024

NHC and photocatalysis dual-catalyzed mono/difluoromethylative acylation of 1,3-enynes was realized, providing fluormethyl-substituted allenyl ketones. SO2 might play a critical role in achieving high reactivity and selectivity.
Exploring the potential of MB2 MBene family as promising anodes for Li-ion batteries
Authors: Han Ying , Wang Lianli , Zheng Bin , Wang Jinlei , Zhang Li , Xiao Beibei
Published: April 2024

A series of 2D transition metal borides (MBenes) are reported and their properties as anode materials for LIBs are investigated.
Application of a distinctly bent, trinuclear, end-to-end azide bridged, mixed valence cobalt(iii/ii/iii) complex in the fabrication of photosensitive Schottky barrier diodes
Authors: Bhunia Sudip , Das Mainak , Banerjee Snehasis , Drew Michael. G. B. , Ray Partha Pratim , Chattopadhyay Shouvik
Published: April 2024

A trinuclear cobalt(iii)-cobalt(ii)-cobalt(iii) complex has been synthesized and used to fabricate a Schottky barrier diode. The conductivity of the synthesized complex-based device improves considerably in illumination conditions from the non-illuminated conditions.
Deconvoluting nitric oxide–protein interactions with spatially resolved multiplex imaging
Authors: Li Yi , Pan Kaijun , Gao Yanan , Li Jia , Zang Yi , Li Xin
Published: April 2024

Simultaneous imaging of nitric oxide (NO) and its proximal proteins should facilitate the deconvolution of NO–protein interactions.
Softness matters: effects of compression on the behavior of adsorbed microgels at interfaces
Authors: Gerelli Yuri , Camerin Fabrizio , Bochenek Steffen , Schmidt Maximilian M. , Maestro Armando , Richtering Walter , Zaccarelli Emanuela , Scotti Andrea
Published: April 2024

As lateral compression is applied, microgel particles with different softness exhibit distinct behaviours. Hard microgels (green particles) are pushed away from the air–water interface, whereas soft ones (orange particles) are less deformed.
A bioinspired sulfur-surrounded iron catalyst for ammonia synthesis
Authors: Zhang Na , Si Yubing , Fu Qiang , Chen Xing
Published: April 2024

A nitrogenase-inspired inorganic compound consisting of iron (Fe) and molybdenum disulfide (MoS2) is designed with a promising capability of N2 fixation using first principles.
Zn alloying strategy to improve the photoluminescence of CuGaS2/ZnS core/shell quantum dots
Authors: Haque Mohammed Abdul , Lohar Amruta , Jadhav Yogesh , Kumar Ravi , Jha S. N. , Bhattacharyya D. , Jadkar Sandesh , Sartale Shrikrishna , Mahamuni Shailaja
Published: April 2024

Research efforts are being devoted to realize colloidal, heavy metal ion free, and luminescent quantum dots.
OSFS‐Vague: Online streaming feature selection algorithm based on vague set
Authors: Yang Jie , Wang Zhijun , Wang Guoyin , Liu Yanmin , He Yi , Wu Di
Published: April 2024

Online streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high‐dimensional data. In real big data‐related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. However, existing OSFS methods rely on presented and fixed hyperparameters, which undoubtedly lead to poor selection performance when encountering dynamic features. To make up for the existing shortcomings, the authors propose a novel OSFS algorithm based on vague set, named OSFS‐Vague. Its main idea is to combine uncertainty and three‐way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous method. OSFS‐Vague also improves the calculation method of correlation between features and labels. Moreover, OSFS‐Vague uses the distance correlation coefficient to classify streaming features into relevant features, weakly redundant features, and redundant features. Finally, the relevant features and weakly redundant features are filtered for an optimal feature set. To evaluate the proposed OSFS‐Vague, extensive empirical experiments have been conducted on 11 datasets. The results demonstrate that OSFS‐Vague outperforms six state‐of‐the‐art OSFS algorithms in terms of selection accuracy and computational efficiency.
Distributed optimal Volt/Var control in power electronics dominated AC/DC hybrid distribution network
Authors: Zhang Rufeng , Song Yiting , Qu Rui
Published: April 2024

The integration of large‐scale distributed power sources increases the voltage fluctuation in AC/DC hybrid distribution network (AD‐HDN). Power electronics devices such as photovoltaic (PV) inverters, soft open point (SOP), and voltage source converters (VSCs) can be utilized for voltage/var control (VVC) to alleviate the risk of voltage fluctuation and violation. This paper proposes a distributed optimal VVC method in power electronics dominated AD‐HDN. Firstly, the reactive power and voltage characteristics of PV inverters, SOP, and VSCs are analysed, and an optimal VVC optimization model for AD‐HDN to minimize node voltage deviation, PV curtailment, and network loss is proposed. Then, the second‐order cone (SOC) relaxation technique is used to re‐formulate the model into a convex optimization model. A distributed optimal VVC framework based on the alternating direction method of multipliers (ADMM) is constructed. Based on the residual balance principle and relaxation technique, an accelerated ADMM method is further proposed to solve the proposed model. Finally, case studies are conducted on the IEEE 33‐node and 85‐node systems to verify the superiority and effectiveness of the proposed method.
A game theoretic approach to wireless body area networks interference control
Authors: Alabdel Abass Ahmed A. , Alshaheen Hisham , Takruri Haifa
Published: April 2024

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work.