bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermotactic behaviour in lacustrine and riverine forms of Salmo trutta and its relevance to an emerging parasitic disease (PKD) in the wake of climate change
Authors: Ros Albert , Brinker Alexander 
Published: June 2024

AbstractThe thermotactic response of brown trout (Salmo trutta) was examined with the goal to investigate potential effects of the emerging temperature-dependent fatal trout disease PKD (proliferative kidney disease). First the differences in cold-water preferences of two forms of brown trout, lacustrine (migratory) and riverine, were determined. Second, it was studied whether this preference was changed in fish infected with PKD. The experiment involved a one-week habituation period at 14 °C in a two-chamber runway followed by a week of 3 °C temperature difference between the two runways. The fish could freely move between lanes via an opening at the end where food was provided. The temperature manipulation was repeated twice, and there were 3 trials per experimental group. All fish developed a clear spatial preference in the test. Lacustrine trout demonstrated a preference for warmer water, while riverine trout preferred cooler water. This may increase the risk to PKD in the lacustrine form. Most strikingly, riverine trout experimentally exposed to Tetracapsuloides bryosalmonae, the parasite that causes PKD, demonstrated stronger cold-seeking behaviour than control fish. Cold seeking behaviour suggests the occurrence of a disease-induced behavioural chill response, which may play an important role in disease recovery. This demonstrates the significance of protecting river connectivity and cold-water sanctuaries as management strategies for preserving salmonid populations in a warming climate.
Protein embeddings predict binding residues in disordered regions
Authors: Jahn Laura R. , Marquet Céline , Heinzinger Michael , Rost Burkhard 
Published: June 2024

AbstractThe identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding residues often err for intrinsically disordered proteins or regions (IDPs/IDPRs), often also referred to as molecular recognition features (MoRFs). Here, we presented a novel machine learning (ML) model trained to specifically predict binding regions in IDPRs. The proposed model, IDBindT5, leveraged embeddings from the protein language model (pLM) ProtT5 to reach a balanced accuracy of 57.2 ± 3.6% (95% confidence interval). Assessed on the same data set, this did not differ at the 95% CI from the state-of-the-art (SOTA) methods ANCHOR2 and DeepDISOBind that rely on expert-crafted features and evolutionary information from multiple sequence alignments (MSAs). Assessed on other data, methods such as SPOT-MoRF reached higher MCCs. IDBindT5’s SOTA predictions are much faster than other methods, easily enabling full-proteome analyses. Our findings emphasize the potential of pLMs as a promising approach for exploring and predicting features of disordered proteins. The model and a comprehensive manual are publicly available at https://github.com/jahnl/binding_in_disorder.
DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering
Authors: Gao Qingyun , Ai Qing 
Published: June 2024

AbstractSingle-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data, but existing clustering algorithms struggle with the inherent high dimensionality, noise, and sparsity of scRNA-seq data. To overcome these limitations, we propose a clustering algorithm: the Dual Correlation Reduction network-based Extreme Learning Machine (DCRELM). First, DCRELM obtains the low-dimensional and dense result features of scRNA-seq data in an extreme learning machine (ELM) random mapping space. Second, the ELM graph distortion module is employed to obtain a dual view of the resulting features, effectively enhancing their robustness. Third, the autoencoder fusion module is employed to learn the attributes and structural information of the resulting features, and merge these two types of information to generate consistent latent representations of these features. Fourth, the dual information reduction network is used to filter the redundant information and noise in the dual consistent latent representations. Last, a triplet self-supervised learning mechanism is utilized to further improve the clustering performance. Extensive experiments show that the DCRELM performs well in terms of clustering performance and robustness. The code is available at https://github.com/gaoqingyun-lucky/awesome-DCRELM.
Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation
Authors: Kudo Genki , Hirao Takumi , Harada Ryuhei , Hirokawa Takatsugu , Shigeta Yasuteru , Yoshino Ryunosuke 
Published: June 2024

AbstractDNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor–protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.
Investigation of bio-based rigid polyurethane foams synthesized with lignin and castor oil
Authors: Kim Hyeon Jeong , Jin Xuanjun , Choi Joon Weon 
Published: June 2024

AbstractIn this study, polyurethane (PU) foams were manufactured using kraft lignin and castor oil as bio-based polyols by replacing 5–20 wt% and 10–100 wt% of conventional polyol, respectively. To investigate the effects of unmodified bio-based polyols on PU foam production, reactivity and morphology within PU composites was analyzed as well as mechanical and thermal properties of the resulting foams. Bio-based PU foam production was carried out after characterizing the reagents used in the foaming process (including hydroxyl group content, molecular weight distribution, and viscosity). To compare the resulting bio-based PU foams, control foam were produced without any bio-based polyol under the same experimental conditions. For lignin-incorporated PU foams, two types, LPU and lpu, were manufactured with index ratio of 1.01 and 1.3, respectively. The compressive strength of LPU foams increased with lignin content from 5 wt% (LPU5: 147 kPa) to 20 wt% (LPU20: 207 kPa), although it remained lower than that of the control foam (PU0: 326 kPa). Similarly, the compressive strength of lpu foams was lower than that of the control foam (pu0: 441 kPa), with values of 164 kPa (lpu5), 163 kPa (lpu10), 167 kPa (lpu15), and 147 kPa (lpu20). At 10 wt% lignin content, both foams (LPU10 and lpu10) exhibited the smallest and most homogenous pore sizes and structures. For castor oil-incorporated PU foams with an index of 1.01, denoted as CPU, increasing castor oil content resulted in larger cell sizes and void fractions, transitioning to an open-cell structure and decreasing the compressive strength of the foams from 284 kPa (CPU10) to 23 kPa (CPU100). Fourier transform infrared (FT-IR) results indicated the formation of characteristic urethane linkages in PU foams and confirmed that bio-based polyols were less reactive with isocyanate compared to traditional polyol. Thermogravimetric analysis (TGA) showed that incorporating lignin and castor oil affected the thermal decomposition behavior. The thermal stability of lignin-incorporated PU foams improved as the lignin content increased with char yields increasing from 11.5 wt% (LPU5) to 15.8 wt% (LPU20) and from 12.4 wt% (lpu5) to 17.5 wt% (lpu20). Conversely, the addition of castor oil resulted in decreased thermal stability, with char yields decreasing from 10.6 wt% (CPU10) to 4.2 wt% (CPU100). This research provides a comprehensive understanding of PU foams incorporating unmodified biomass-derived polyols (lignin and castor oil), suggesting their potential for value-added utilization as bio-based products.
A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon’s grid-connected systems
Authors: Molu Reagan Jean Jacques , Naoussi Serge Raoul Dzonde , Bajaj Mohit , Wira Patrice , Mbasso Wulfran Fendzi , Das Barun K. , Tuka Milkias Berhanu , Singh Arvind R. 
Published: June 2024

Abstract
Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.
A randomized controlled trial to evaluate the progressive muscle relaxation technique in hip fracture patients
Authors: Mashhadi-Naser Sahar , Shirvani Saeid , Vasli Parvaneh 
Published: June 2024

AbstractThe purpose of this research was to ascertain how progressive muscle relaxation (PMR) technique affected hip fracture patients' anxiety, sleep quality, and post-operative pain. This parallel randomized controlled trial was conducted on 100 patients with hip fracture hospitalized in one of the reference orthopedic hospitals in Tehran, Iran who were selected using convenience sampling and randomly were placed in two PMR group (n = 50) and control group (n = 50). Data were collected by Demographic information questionnaire, Visual analogue scale for pain rating, Pittsburgh Sleep Quality Index and State-Trait Anxiety Inventory. The PMR technique was the progressive muscle relaxation technique, which was started the night after the surgery for three nights before going to bed. Data were collected on two occasions, including before the PMR technique and the day after the last stage of the PMR technique. The data were analyzed by SPSS software using descriptive and inferential statistics. The results revealed significant within-group changes in both groups' post-operative pain, sleep quality, and anxiety scores (P < 0.001). The progressive muscle relaxation group experienced decreased post-operative pain and anxiety scores and increased sleep quality scores (P < 0.001). The linear mixed model showed that the absolute changes in the follow-up post-operative pain, sleep quality, and anxiety scores were 1.19 and 7.94 units, significantly lower than the baseline, respectively. The results revealed significant within-group changes in both groups' post-operative pain, sleep quality, and anxiety scores (P < 0.001). The progressive muscle relaxation group experienced decreased post-operative pain and anxiety scores and increased sleep quality scores (P < 0.001). The study's findings demonstrated the beneficial effects of progressive muscle relaxation on hip fracture patients' outcomes, such as their level of anxiety, sleep quality, and post-operative pain. The study's findings can be applied by medical professionals to improve patient satisfaction and care quality.This clinical trial has been registered with the Iranian Registry of Clinical Trials under the code IRCT20231120060119N1, which was approved on 7/12/2023.
Quantitative study of the ossification centers of the body of sphenoid bone in the human fetus
Authors: Grzonkowska Magdalena , Baumgart Mariusz , Szpinda Michał 
Published: June 2024

AbstractThe aim of the present study was to examine the growth dynamics of the two ossification centers of the body of sphenoid bone in the human fetus, based on their linear, planar and volumetric parameters. The examinations were carried out on 37 human fetuses of both sexes aged 18–30 weeks of gestation, which had been preserved in 10% neutral formalin solution. Using CT, digital image analysis software, 3D reconstruction and statistical methods, we evaluated the size of the presphenoid and postsphenoid ossification centers. The presphenoid ossification center grew proportionately in sagittal diameter, projection surface area and volume, and logarithmically in transverse diameter. The postsphenoid ossification center increased logarithmically in sagittal diameter, transverse diameter and projection surface area, while its volumetric growth followed proportionately. The numerical findings of the presphenoid and postsphenoid ossification centers may be considered age-specific reference values of potential relevance in monitoring the normal fetal growth and screening for congenital disorders in the fetus. The obtained results may contribute to a better understanding of the growing fetal skeleton, bringing new numerical information regarding its diagnosis and development.
Malate dehydrogenase (MDH) in cancer: a promiscuous enzyme, a redox regulator, and a metabolic co-conspirator
Authors: Leverett Betsy , Austin Shane , Tan-Arroyo Jason 
Published: June 2024

Abstract
Malate dehydrogenase (MDH) is an essential enzyme in the tricarboxylic acid cycle that functions in cellular respiration and redox homeostasis. Recent studies indicate that MDH facilitates metabolic plasticity in tumor cells, catalyzing the formation of an oncometabolite, contributing to altered epigenetics, and maintaining redox capacity to support the rewired energy metabolism and biosynthesis that enables cancer progression. This minireview summarizes current findings on the unique supporting roles played by MDH in human cancers and provides an update on targeting MDH in cancer chemotherapy.
A mm‐wave LNA employing current re‐use and non‐linearity cancellation in 28 nm CMOS for automotive RADAR and 6G receivers
Authors: Kalita Ellora , Khan Barha , Rahman Mustafijur 
Published: June 2024

AbstractThis letter reports an 85 GHz low noise amplifier (LNA) employing derivative superposition based non‐linearity cancellation and a current re‐use topology. The LNA employs a two‐stage stacked architecture, each featuring neutralized differential pairs utilizing the same DC current. In derivative superposition, an auxiliary branch consisting of neutralized differential pairs cells is added in the LNA in parallel to stage 1 to ensure non‐linearity cancellation. Layout‐based capacitive neutralization is implemented to improve GMAX, resulting in simplified routing, reduced parasitics, and a more compact layout. The proposed LNA is fabricated in TSMC 28 nm CMOS process and achieves a peak gain of 8.4 dB at 84.2 GHz with a measured 3 dB bandwidth () from 79.4 to 92 GHz. The minimum measured noise figure is 12.8 dB. The LNA draws 34 mA of DC current from a 1.2 V supply. The highly linear LNA with IIP3 +6.5 dBm is tailored for automotive RADAR and 6G receivers.
SAGP: A spectral attention‐based global pooling
Authors: Yu Xiang , Xu Longzheng , Han Yi , Geng Zhe , Zhu Daiyin 
Published: June 2024

AbstractPooling operations, essential for neural networks, reduce feature map dimensions while preserving key features and enhancing spatial invariance. Traditional pooling methods often miss the feature maps' alternating currentcomponents. This study introduces a novel global pooling technique utilizing spectral self‐attention, leveraging the discrete cosine transform for spectral analysis and a self‐attention mechanism for assessing frequency component significance. This approach allows for efficient feature synthesis through weighted averaging, significantly boosting TOP‐1 accuracy with minimal parameter increase, outperforming existing models.
Regulation of mixed convective flow in a horizontal channel with multiple slots using P, PI, and PID controllers
Authors: Debnath Sonjoy Chandra , Chowdhury Shuvo , Asaduzzaman Md , Nahar Most. Naznin , Sattar Ankita Binte , Saha Sumon 
Published: June 2024

AbstractThis study numerically investigates mixed convective cooling in a two‐dimensional horizontal channel containing periodically heated blocks by applying proportional (P), proportional‐integral (PI), and proportional‐integral‐derivative (PID) controllers. Three different controller configurations regulate the amount of cold air entering the chamber. The air's non‐dimensional temperature is continuously monitored at the set point to compare the controllers’ performance, and the percentage of overshoot and the steady‐state error are analysed. The investigated chamber comprises one inlet and two exit ports, a temperature sensor, and two heated blocks that are isotherm heat sources. The Galerkin finite element approach computationally solves the equations of continuity, momentum, and energy to analyse the thermo‐fluid phenomena occurring within the chamber. Parametric simulation is carried for different values of the proportional gain (Kp = 0.005, 0.010, 0.050 m s−1 K−1), the integral gain (Ki = 0.05, 0.10, 0.15 m s−2 K−1), the derivative gain (Kd = 10−5, 10−4, 10−3 m K−1) to achieve a consistent and expeditious response. Variations of Reynolds, Richardson, and mean Nusselt numbers with time are plotted to compare the system's performance. The investigation indicates that the PI controller produces a comparable level of performance with the PID controller, reducing the necessity to add a derivative controller.
The SRG/eROSITA All-Sky Survey
Authors: Seppi R. , Comparat J. , Ghirardini V. , Garrel C. , Artis E. , Sánchez A. G. , Liu A. , Clerc N. , Bulbul E. , Grandis S. , Kluge M. , Reiprich T. H. , Merloni A. , Zhang X. , Bahar Y. E. , Shreeram S. , Sanders J. , Ramos-Ceja M. , Krumpe M. 
Published: June 2024

Context. The spatial distribution of galaxy clusters provides a reliable tracer of the large-scale distribution of matter in the Universe. The clustering signal depends on intrinsic cluster properties and cosmological parameters.
Aims. The ability of eROSITA on board Spectrum-Roentgen-Gamma (SRG) to discover galaxy clusters allows the association of extended X-ray emission with dark matter haloes to be probed. We measured the projected two-point correlation function to study the occupation of dark matter haloes by clusters and groups detected by the first eROSITA all-sky survey (eRASS1).
Methods. We created five volume-limited samples probing clusters with different redshifts and X-ray luminosity values. We interpreted the correlation function with halo occupation distribution (HOD) and halo abundance matching (HAM) models. We simultaneously fit the cosmological parameters and halo bias of a flux-limited sample of 6493 clusters with purity > 96%.
Results. We obtained a detailed view of the halo occupation for eRASS1 clusters. The fainter population at low redshift (S0: L̄X = 4.63 × 1043 erg s−1, 0.1 < z < 0.2) is the least biased compared to dark matter, with b = 2.95 ± 0.21. The brightest clusters up to higher redshift (S4: L̄X = 1.77 × 1044 erg s−1, 0.1 < z < 0.6) exhibit a higher bias b = 4.34 ± 0.62. Satellite groups are rare, with a satellite fraction < 14.9% (8.1) for the S0 (S4) sample. We combined the HOD prediction with a HAM procedure to constrain the scaling relation between LX and mass in a new way, and find a scatter of ⟨σLx⟩ = 0.36. We obtain cosmological constraints for the physical cold dark matter density ωc = 0.12−0.02+0.03 and an average halo bias b = 3.63−0.85+1.02.
Conclusions. We modelled the clustering of galaxy clusters with a HOD approach for the first time, paving the way for future studies combining eROSITA with 4MOST, SDSS, Euclid, Rubin, and DESI to unravel the cluster distribution in the Universe.