bluuebunny/crossref_metadata_2025 huggingface dataset:
Harmonizing Innovation: Chorus Work Creation and Innovation through Deep Learning and Generative Models
Authors: Zhang Hao
Published: May 2024

A precise characterization of the chorus's timbre is essential for accurate identification. It is imperative to provide a detailed representation of the timbre for analyzing temporal frequency domain characteristics, inverted spectrum features, sparse attributes, and ultimately, for chorus identification. Utilizing both temporal and frequency domain attributes enables effective differentiation of the chorus. This is exemplified through the examination of probability features, inverted spectrum characteristics, temporal frequency domain attributes, and inverted spectrum features. To enhance the temporal-frequency representation of the chorus's timbre and facilitate chorus identification, deep learning techniques are applied. The objective of this research is to analyze the structural elements of an intelligent music chorus detection and evaluation system employing deep learning. In response to significant classification errors, this study introduces a noise-reducing instrument identification model that mimics human hearing using a cochlear model with harmonious decomposition of music, incorporating time and frequency information in the auditory spectrum. This model utilizes probability characteristics, time-domain attributes, congestion features, and sparse attributes to evaluate the adverse impacts of instrumental interference. A deep hybrid network, integrating generative deep learning and an LSTM framework, establishes a correlation between the abstract feature extraction capability of the generative deep network and the feature expression capability of the serial-level noise cancellation encoder. The proposed method demonstrates remarkable accuracy on datasets and significantly improves the overall evaluation effectiveness.
Normative Data on Measures of Cardiovascular Autonomic Neuropathy and the Effect of Pretest Conditions in a Large Danish Non-Diabetic CVD-Free Population
Authors: Hansen Christian Stevns , Christensen Marie Mathilde Bjerg , Vistisen Dorte , Jepsen Randi , Ellervik Christina , Jørgensen Marit Eika , Fleischer Jesper
Published: May 2024

Background
Cardiovascular autonomic neuropathy (CAN) is a common diabetic complication associated with excess morbidity and mortality. Normative reference data for cardiovascular autonomic function are used to stratify individuals into those with and without CAN. However, reference thresholds for both cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV) are scarce and based on small sample sizes. The aim of the study was to establish contemporary normative reference thresholds based on a large non-diabetic population free of cardiovascular disease (CVD).
Methods
Cardiovascular autonomic function, CARTs and 5-minute HRV indices were assessed in individuals without diabetes and CVD from the Lolland-Falster Health Study (2018-2020) by applying the point-of-care device Vagus™. Age-specific normative reference thresholds were estimated by using log-transformed quantile regression models at the 5th and 10th percentile, with adjustments made for sex. Models assessing the association between age and HRV indices were further adjusted for heart rate.
Results
We present age-specific normative reference thresholds for cardiovascular autonomic function including CARTs and HRV for 875 individuals (48% females) aged 15-85 years. The reference thresholds are presented for both the 5th and 10th lower percentile. Higher age was inversely associated with all outcomes. Females tended to have a higher parasympathetic drive compared to males. Pre-test conditions did not affect CARTs significantly.
Conclusions
The presented age-related normative reference thresholds for both CARTs and HRV indices based on a large Danish cohort may facilitate improved quality of research and treatment.
Survival, Adhesion and Migration of Metastatic Breast Tumor Cells: Roles of Parvins (PARVA and PARVB)
Authors: Dylami Sadegh , Farahani Mohammad Kamalabadi , Kia Vahid , Atashi Amir
Published: May 2024

Changes in cell-ECM interaction can lead to the anchorage-independent proliferation, resistance to apoptosis, and improved motility that characterize breast metastatic cells. The parvin, alpha (Parva) and parvin, beta (Parvb) localizes to focal adhesions and play a part in cell attachment, migration, and persistence. The assumption of our work is that parvins contribute to the capacity of tumor cells to spread. Following the extraction of primary (4T1T) and metastatic (4T1B and 4T1L) breast tumor cells from mouse metastatic breast cancer model, MTT assay and scratch test were utilized to determine the vitality and motility of the cells. qPCR was performed to examine the expression of parvins. Tumor cells did not show any difference in cellular viability in 2- or 3-dimensional cell culture. Our research illustrated that metastatic tumor cells have a higher capacity of migration. Molecular analysis revealed that parvins expression was significantly increased in metastatic breast carcinoma cells. These findings revealed new information about a major change in parvins expression in metastatic breast carcinoma cells. A targeted therapeutic approach against breast cancer metastasis could be developed using examination of the genetic characteristics of metastatic tumor cells.
Model prediction of spatial non-cooperative target measurement information based on the Koopman operator
Authors: Zhang Xiaohong , Zhou Haiyin , Hou Bowen , Wang Jiongqi , Peng Jian , Zhou Xuanying
Published: May 2024

This paper proposes a prediction method for spatial non-cooperative measurement information based on the Koopman operator, aiming at the problem of incomplete or missing measurement information in optical relative navigation. Combining the global linear representation theory of the Koopman operator, the paper first projects the nonlinear measurement model into a Hilbert space using a dimensionality expansion function, and constructs a global linear optical measurement model based on the Koopman operator. Secondly, by using a linear combination of Koopman modes, eigenvalues, and feature functions, the paper achieves global linearization of the measurement model, reducing the loss of nonlinear information. Furthermore, a model algorithm based on the Dynamic Mode Decomposition (DMD) method is designed to approximate the Koopman operator, and the main modes of the operator are used to obtain the phase space topology structure of the model, realizing high-precision prediction of measurement information. Finally, Monte Carlo simulation results show that compared with existing methods, the proposed method improves the accuracy of measurement information prediction by 52.07%, providing an effective solution for high-precision prediction of measurement information for subsequent optical relative navigation targets.
A Novel and Cost-Friendly Expression Method of Taq DNA Polymerase Using Milk Powder and Effective Utilization of the Enzyme in Diagnostic Kits
Authors: Hekim Suleyman , Kaçıran Arife , Akmehmet Ayşe Nur , Şakalar Çağrı , ÇANAKÇI Sabriye , Belduz Ali Osman
Published: May 2024

Taq DNA polymerase has been used in PCR-based pathogen detection kits and the demand increased during the COVID-19 epidemic. In this study, it was aimed to produce recombinant Taq DNA polymerase in a low-cost and optimized setting and test its activity in various qPCR kits. Taq DNA polymerase gene was amplified by PCR and cloned into expression vectors. Recombinant protein expression was induced using IPTG and purification was performed using heat incubation and ammonium sulfate precipitation and dialysis. Protein expression was analyzed by SDS-polyacrylamide gel electrophoresis. Enzyme activity was tested by PCR and qPCR in different buffers. Recombinant Taq DNA polymerase was purified, and purified protein and commercial Taq DNA polymerase were compared by SDS-PAGE. The PCR activity of the recombinant enzyme was shown in different commercial and in-house buffers. Enzyme was stable for at least 12 months and kept at -20ºC. Recombinant enzyme and commercial enzyme had PCR activity at dilutions up to 1/32. Milk powder, which is an economical alternative for IPTG, was tested and, use of milk powder equivalent to 3 mM lactose was as efficient as IPTG for the induction of the recombinant protein. Recombinant Taq DNA polymerase was tested in two DNA-based qPCR kits that were developed in our laboratory and was shown to perform as well as commercial enzymes. Finally, our enzyme was tested in our commercial SARS-CoV-2 diagnosis RT-qPCR kit using in-house produced primer-probe oligonucleotides and was shown to have equivalent efficacy to the commercial enzyme.
Entropic stabilization of a structurally tolerant phase: The ionic phase of lithium alanate
Authors: Krummenacher Marco , Tayfuroglu Omer , Finkler Jonas , Huber Hannes , Goedecker Stefan
Published: May 2024

In materials simulations, formation energies can provide valuable information about the stability of different crystal polymorphs. However, to obtain a complete picture, it is often essential to go beyond simple energetics and include free energies into the analysis as many crystal phases are stabilized by entropic contributions and phase transitions at finite temperatures are common.
Traditionally, free energies of crystalline solids are computed through an analysis of the phonon band structure which is calculated within the harmonic approximation considering only the crystalline unit cell and its replication. But also methods that go beyond the harmonic approximation consider only one periodic structure to calculate free energies.
We show that such an approach can overlook substantial configurational free energy contributions and does not allow us to reach a well defined thermodynamic limit.
To overcome these problems, it is necessary to generate large periodic cells that can represent not only the perfect crystalline structure but also a very large number of defect structures.
Only with a configurational density of states that is well converged with respect to the size of the cell, it is possible to obtain reliable estimates of phase transition temperatures.
We illustrate this effect for lithium alanate where the configurational entropy contribution of the structurally tolerant ionic phase reduces the transition temperature to the polymeric phase by 150 K.
A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
Authors: Liu Hui , Yu Chenpeng , Fang Xing , Tian Shiye
Published: May 2024

The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumor types, yet the percentage of patients who benefit from them remains low. The bindings between tumor antigens and HLA-I/TCR molecules determine the antigen presentation and T-cell activation, thereby playing important role in the immunotherapy response. Some computational methods have been developed to predict peptide-HLA (pHLA) or peptide-TCR (pTCR) binding specificity, but they focus solely on one task at a time. In this paper, we propose UnifyImmun, a unified cross-attention transformer model designed to simultaneously predict the bindings of peptides to both receptors, thereby providing more comprehensive evaluation of antigen immunogenicity. We devise a two-phase progressive training strategy that enables these two tasks to mutually reinforce each other, by compelling the encoders to extract more expressive features. We also incorporate virtual adversarial training to enhance the model generalizability. Compared to over ten previously published methods for predicting pHLA and pTCR bindings, our method demonstrates better performance in both tasks on multiple independent and external test sets. Notably, on a large-scale COVID-19 pTCR binding test set, our method outperformed the current state-of-the-art methods by at least 9%. The validation experiments on three clinical cohorts confirm that our approach effectively predicts immunotherapy response and clinical outcomes. Furthermore, the cross-attention scores and integrated gradients reveal the amino-acid sites critical for peptide binding to receptors. In essence, our approach marks a significant step towards comprehensive evaluation of antigen immunogenicity.
Meta-Analysis and MaxEnt Model Prediction of the Distribution of the Phenacoccus solenopsis in China under the Background of Climate Change
Authors: liu zhiqian , Peng Yaqin , Danping Xu , Zhuo Zhihang
Published: May 2024

The Phenacoccus solenopsis poses a serious threat to global crops, and controlling climate change has become a crucial strategy. In this study,it through a comprehensive analysis of experimental data, found that within the suitable temperature range, the survival rate of the P.Tinsley increases with rising temperatures, indicating that climate plays a decisive role in its distribution. Using the MaxEnt model, in this study, it predicted that under three future climate scenarios (SSP1–2.6, SSP3–7.0, and SSP5–8.5), the distribution of the P.Tinsley will expand and move towards higher latitudes. Climate change is the primary factor influencing changes in pest distribution. In this study it was conducted a Meta-analysis of the P.Tinsley, including seven independent studies covering 221 observation results, and examined the impact of temperature ranging from 18°C to 39°C on the developmental cycle of the P.Tinsley. As the temperature rises, the development cycle of the P.Tinsley. gradually decreases.Additionally, in combination with the MaxEnt model, it predicted the current and potential future distribution range of the P.Tinsley. The results show that under future climate warming, the distribution area of P.Tinsley in China will expand.This research provides a theoretical basis for early monitoring and control of this pest's occurrence and spread. Therefore, the predictive results of this study will provide important information for managers in monitoring the P.Tinsley and help them formulate relevant control strategies.
Selective and Controllable Mechanism of the Temperature Field in Pig Skin under Radiofrequency Heating
Authors: Zhu Ping , Xie Chuanqi , Shu Fangjie , Li Liqiang , Su Zhaodi , Wang Zhichao , Mu Ziyu
Published: May 2024

Radiofrequency heating is widely used in medical aesthetics, which essentially achieves the contraction of collagen fibers in the dermis by increasing its temperature. The paper aimed to study the selective and controllable mechanism for the temperature field in 0.02 kg ex-vivo pig skin samples obtained from the 25 kg Bama pig under radiofrequency heating by the finite element method and relevant experiments. A heating model of pig skin was constructed at 4 MHz with a mixed heating mode of fixed-point and reciprocating sliding. Based on the mixed heating, the dermis was heated preferentially and uniformly due to its high electrical conductivity and thermal conductivity compared to other tissue layers. It allowed the dermis temperature 55.1 ℃ to remain at a dynamic plateau period with an error of 0.1 ℃, and the mean specific absorption rate across the entire pig was under 2 W/kg within 30 s in this period. The verification experiment on the pig skin samples under the same conditions showed consistent temperature distributions between the model and experiment, and the dermis collagen fibers structure was clear without tissue damage. The research results provide useful guidance for radiofrequency application practitioners to selectively control skin temperature distributions.
Food Insecurity and Chronic Diseases: Diabetes & Hypertension Among Older Adults in South Africa
Authors: Mgwaba Mbongeleni. Thembalihle , Masuku Mfundo Mandla
Published: May 2024

Food security is a situation where all people, at all times, have physical, social and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for a healthy and active life. In 2021, a significant number of the South African households either had food inadequacy or severe food inadequacy challenges. More than half of South African population is at risk of hunger and limited access to nutritious food.
Food insecurity is often associated with negative health outcomes, including chronic diseases such as diabetes and hypertension. A secondary data obtained from the cross sectional 2021 South African General Household Survey (GHS) was analyzed to determine the association between food insecurity (independent variable) and diabetes and hypertension (dependent variables). The data was analysed in three phases namely univariate, bivariate, and multivariate.
Stratified random sampling was employed in the primary study whereas the sample frame of this study consisted of 3,324 primary sampling units (PSUs) with a total of 33,000 dwelling units/ enumeration areas. The results of this study show that the odds of having hypertension or diabetes were considerably higher among older adults who had inadequate access to food (odds ratio (OR) 1.14, p < 0.05) compared to those who had adequate access to food. More targeted food security programs could lower the odds of diabetes and hypertension amongst older adults. The promotion and provision of free health screening services at various government health facilities could assist with the early detection of hypertension and diabetes.
Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization
Authors: wen Dusu , Li Bin , Guo Shun , Chen Biao , Chen Liaobin
Published: May 2024

Background
Frozen shoulder (FS) is characterized by the thickening and fibrosis of the joint capsule. The precise etiology responsible for these pathological changes remains elusive. Consequently, the primary aim of this study was to explore the potential involvement of pathogenic genes in FS.
Methods
Differential expression analysis and WGCNA were employed to investigate the co-expressed genes. GO and KEGG were conducted to elucidate the potential roles of these co-expressed genes. Subsequently, Mendelian randomization (MR) analysis was performed using expression quantitative trait loci datasets for these co-expressed genes, combined with the genome-wide association study of FS. The identified hub genes were further validated through reverse transcription-quantitative PCR (RT-qPCR). In addition, a nomogram model and receiver operating characteristic (ROC) curves were established to assess the diagnostic value of the hub genes. Furthermore, the infiltration of immune cells was evaluated using the CIBERSORT algorithm and the relationship between hub genes and immune infiltrating cells was subsequently analyzed.
Result
The gene co-expression network was constructed using WGCNA and the top two most correlated modules were identified. These modules were then intersected with differentially expressed genes, yielding a set of 295 overlapping co-expressed genes. Utilizing MR analysis, four hub genes, namely ADAMTS1, NR4A2, PARD6G, and SMKR1 were found to exhibit positive causal relationships with FS, which were subsequently validated through RT-qPCR analysis. Moreover, the diagnostic value of the four hub genes was demonstrated through the development of a nomogram model and the construction of ROC curves. Notably, a causal relationship between ADAMTS1 and immune cell infiltration in frozen shoulder was observed.
Conclusion
Our study suggested a potential association between the upregulated expression of ADAMTS1, NR4A2, PARD6G, and SMKR1 and an increased susceptibility to FS. Further investigations elucidating the functional roles of these genes will enhance our understanding of the pathogenesis of FS.
Astaxanthin attenuates glucose-induced liver injury in largemouth bass: role of p38MAPK and PI3K/Akt signaling pathways
Authors: Liao Zhihong , He Xuanshu , Chen Anqi , Zhong Jian , Lin Sihan , Guo Yucai , Cui Xin , Chen Baoyang , Zhao Wei , niu jin
Published: May 2024

Background
Astaxanthin (ASX) has been documented to exert beneficial influence on various processes in fish. Largemouth bass serves as a common model for studying glucose-induced liver disease, making it imperative to investigate the regulatory mechanisms underlying its liver health.
Methods
Largemouth bass were fed with a control diet (CON), a high carbohydrate diet (HC), or a HC diet supplemented astaxanthin (HCA) for 8-weeks, followed by the glucose tolerance test (GTT). Primary hepatocytes were treated with low glucose and high glucose combined with different concentrations of astaxanthin for 48 h. The histopathology, enzymology, transcriptomics, molecular biology and cell biology were combined to investigate the mechanism of liver injury.
Results
This study provides evidence for the protective effects of ASX against growth performance reduction and hepatic liver injure in largemouth bass fed HC diet. In GTT, HCA diet exhibited an improvement in glucose tolerance following glucose loading. Although HCA diet did not restore the expression of insulin resistance-related genes in livers at different time during the GTT, the addition of ASX in the long-term diet did improve the insulin resistance pathway by regulating the PTP1B/PI3K/Akt signaling pathway. Hepatic transcriptome analyses showed that ASX plays an essential role in the modulation of glucose homeostasis in response to treatment with HC diet. In in vitro study, the treatment with ASX resulted in an exaltation in cell viability and a reduction in the rate of cell apoptosis and reactive oxygen species (ROS). Additionally, astaxanthin was observed to improve apoptosis induced by high-glucose via p38MAPK/bcl-2/caspase-3 signaling pathway.
Conclusions
Astaxanthin exhibited a protective effect against apoptosis by regulating p38MAPK/bcl-2/caspase-3 pathway, and ameliorated insulin resistance by activating the PTP1B/PI3K/Akt pathway. This study elucidated the mechanism of astaxanthin in the liver injury of largemouth bass from a new perspective and provided a new target for the treatment of insulin resistance.
Space-Time Video Super-Resolution via Multi-Scale Feature Interpolation and Temporal Feature Fusion
Authors: Yang Caisong , Kong Guangqian , Duan Xun , Long Huiyun , Zhao Jian
Published: May 2024

The goal of Space-Time Video Super-Resolution (STVSR) is to simultaneously increase the spatial resolution and frame rate of low-resolution, low-frame-rate video. In response to the problem that the STVSR method does not fully consider the spatio-temporal correlation between successive video frames, which makes the video frame reconstruction results unsatisfactory, and the problem that the inference speed of large models is slow. This paper proposes a STVSR method based on Multi-Scale Feature Interpolation and Temporal Feature Fusion (MSITF). First, feature interpolation is performed in the low-resolution feature space to obtain the features corresponding to the missing frames. The feature is then enhanced using deformable convolution with the aim of obtaining a more accurate feature of the missing frames. Finally, the temporal alignment and global context learning of sequence frame features are performed by a temporal feature fusion module to fully extract and utilize the useful spatio-temporal information in adjacent frames, resulting in better quality of the reconstructed video frames. Extensive experiments on the benchmark datasets Vid4 and Vimeo-90k show that the proposed method achieves better qualitative and quantitative performance, with PSNR and SSIM on the Vid4 dataset improving by 0.8\% and 1.9\%, respectively, over the state-of-the-art two-stage method AdaCof+TTVSR, and MSITF improved by 1.2\% and 2.5\%, respectively, compared to single-stage method RSTT. The number of parameters decreased by 80.4\% and 8.2\% compared to the AdaCof+TTVSR and RSTT, respectively.
Temporal context in paced finger tapping tunes the error-correction mechanism
Authors: Silva Ariel D. , Laje Rodrigo
Published: May 2024

Sensorimotor synchronization (SMS) is the mainly specifically human ability to move in sync with a periodic external stimulus, as in keeping pace with music. The most common experimental paradigm to study its largely unknown underlying mechanism is the paced finger-tapping task, where a participant taps to a periodic sequence of brief stimuli. Contrary to reaction time, this task involves temporal prediction because the participant needs to trigger the motor action in advance for the tap and the stimulus to occur simultaneously, then an error-correction mechanism takes the past performance as input to adjust the following prediction. In a different task, it has been shown that exposure to a distribution of individual temporal intervals creates a “temporal context” that can bias the estimation/production of a single target interval. As temporal estimation and production are also involved in SMS, we asked whether a paced finger-tapping task with period perturbations would show any temporal context effect. In this work we show that temporal context can indeed be generated during paced finger tapping as in other, simpler temporal tasks, and that the shape and size of the resynchronization curve after a period perturbation depends on temporal context. Response asymmetry is also affected by temporal context, thus evidencing an interplay between context and intrinsic nonlinearities of the correction mechanism. We conclude that temporal context calibrates the underlying error-correction mechanism in SMS.
Mechanism and regulation strategy of plasma-assisted shaped tube electrochemical machining for micro-hole drilling
Authors: Zhan Shunda , Yang Xiwu , Xiao Haibing , Liu Mingjun , Jiang Kai , tang Wenming
Published: May 2024

The deep micro-hole structure has important applications in the fields of aerospace, precision mold, automotive industry and medical equipment. Plasma-assisted electrochemical machining (PA-ECM) is an important method for deep micro-hole fabrication. However, when the machining depth is >500 μm (depth to diameter ratio of about 1:1), the electrolyte is difficult to flow into the machining area (inter-electrode gap), which is easy to cause abnormal spark discharge and even short circuit, affecting the processing stability. In order to solve the problem of lack of electrolyte in the machining area, a plasma-assisted shaped tube electrochemical machining (PA-STEM) method was proposed in this paper. In PA-STEM, the current is mainly conductive through the electrolyte jet, and the electric field of the tube electrode end face and sidewall is shielded by the gas film/plasma film, so the machining accuracy would not deteriorate under high machining voltage. As a hot gas medium, plasma can create a machining environment similar to electrochemical jet machining (EJM), hence a small pit is left at the bottom of the micro-hole. The higher voltage makes the gas film and plasma film more dense, which is beneficial to improve the machining accuracy of micro-hole. The tube electrode feeding rate should not be too fast to avoid damage to the machined surface caused by high-energy spark discharge. At high pulse frequency and low electrolyte flow rate, the energy of plasma is more stable, and it is more suitable for high-quality micro-hole machining. Through the optimization of the process parameters, a deep micro-hole with inlet diameter of 980 μm, outlet diameter of 750 μm, depth of 5 mm was obtained, and the depth-to-diameter ratio of the micro-hole is 5.1:1. Moreover, there is no recast layer on the sidewall subsurface of the micro-hole.
Exploring Hidden Patterns and Interests on Twitter through Contextual Focal Structure Analysis
Authors: Alassad Mustafa , Nwana Lotenna , Agarwal Nitin
Published: May 2024

The Contextual Focal Structure Analysis (CFSA) model represents an advanced methodology designed to enhance the identification and interpretation of key influencers and interaction patterns within social networks, such as Twitter. The CFSA model stratifies data into multiple layers by employing a multiplex networks approach, enabling a detailed examination of user activities and interests across different social contexts. At its core, the CFSA model employs a user-to-user network layer to capture complex interactions and gain deeper insights into user engagement on the platform. Additionally, it incorporates a second layer consisting of hashtag co-occurrence networks. To assess the CFSA model’s efficacy, this study focused on analyzing the spread of the Cheng Ho disinformation narrative within the Indo-Pacific region. Utilizing an extensive dataset containing over 64,519 tweets and 20,000 hashtags collected from January 2019 to October 2022, the analysis provided a comprehensive view of user behavior and the contextual nuances shaped by their interactions with different hashtags. The results highlighted significant patterns and the dynamics of user communities. Notably, the study identified several impactful contextual focal structures that were pivotal in driving discussions related to disinformation narratives. Moreover, the CFSA model exposed prevalent hashtags such as "#SouthChinaSea," "#NavyPartnerships," and "#United_States," linking some of these to accounts that spread information about oil and gas exploration and drilling activities, predominantly conducted by NATO alliances and China. These findings underscore the potential of the CFSA model to uncover and interpret latent influential patterns and interests on Twitter, providing valuable insights into the mechanisms of information dissemination and public discourse in digital environments.
STFT, LASSO and EHO based Feature Extraction with Integrated Machine Learning and Metaheuristic Classification Techniques for Colon Cancer detection from Microarray Gene Expressions
Authors: Nair Ajin R , Rajaguru Harikumar , S Karthika M , C Keerthivasan
Published: May 2024

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92.
Complete mitogenome structure and phylogenetic implications of the first Indian yak breed- Arunachali (Poephagus grunniens L.)
Authors: Pukhrambam Martina , Tribhuvan Kishor U. , Das Pranab J. , Sarkar Mihir
Published: May 2024

Arunachali is the first registered breed of yak in India inhabiting Tawang and West Kameng districts of Arunachal Pradesh. Arunachali yak breed accounts for half the yak population in India, however, the number has been declining. In order to conserve and propagate this majestic and unique animal, this study reports the first ever whole mitogenome of an Indian yak breed. The mitogenome of Arunachali yak was found to be circular and double stranded with a length of 16,324 bp comprising 13 protein coding genes (PCGs), 22 tRNAs, 2 rRNAs and an 894bp non-coding control region (D-loop). Out of the 37 genes, 29 genes were encoded on H-strand and 8 on the L-strand. The overall nucleotide composition was A = 33.70%, T = 27.28%, G = 13.21% and C = 25.80% with an AT biasness. Phylogenetic analysis was done with three datasets involving 27 whole mitogenome of Chinese yak breeds and a Bos indicus outgroup sequence. The Arunachali yak shows same ancestry with the other yak breeds and more closeness to Jinchuan yak based on D-loop sequence. The findings of this study elucidate the mitochondrial genomic architecture of Arunachali yak and its evolutionary status along with providing basis for characterisation and formulation of specific breeding policies for Indian yaks.
Exaptation within structure: the challenge of external resources and risk-taking for public R&D institutions
Authors: Toscani Giulio
Published: May 2024

The Barcelona Supercomputing Center (BSC) emerges as a prominent research & development (R&D) entity in high-performance computing, facilitating interdisciplinary collaborations that integrate computational methodologies with empirical research. Through interviews with 20 BSC experts, this study elucidates the organizational dynamics of publicly funded institutions. It reveals an internal exaptation, an adaptation as pivotal mechanism, and the requisite for external exaptation to address challenges related to recruitment alignment with productivity metrics, budget constraints, and reluctance to assume leadership roles. The BSC employs exaptation tactics, facing obstacles in enlarging its workforce and bolstering support services; nonetheless, BSC's fluid environment mandates a willingness to assume risks in order to enact pioneering resolutions for enduring expansion and effective personnel administration.
Feasibility of magnetomyography with optically pumped magnetometers in a mobile magnetic shield
Authors: Nordenström Simon , Lebedev Victor , Hartwig Stefan , Kruse Marlen , Marquetand Justus , Broser Philip , Middelmann Thomas
Published: May 2024

While magnetomyography (MMG) using optically pumped magnetometers (OPMs) is a promising method for non-invasive investigation of the neuromuscular system, it has almost exclusively been performed in magnetically shielded rooms (MSRs) to date. MSRs provide extraordinary conditions for biomagnetic measurements but limit the widespread adoption of measurement methods due to high costs and extensive infrastructure. In this work, we address this issue by exploring the feasibility of mobile OPM-MMG in a setup of commercially available components. From field mapping and simulations, we find that the employed zero-field OPM can operate within a large region of the mobile shield, beyond which residual magnetic fields and perturbations become increasingly intolerable. Moreover, with digital filtering and moderate averaging a signal quality comparable to that in a heavily shielded MSR is attained. These findings facilitate practical and cost-effective implementations of OPM-MMG systems in clinical practice and research.
Inhibition of chondroitin sulphate-degrading enzyme Chondroitinase ABC by dextran sulphate
Authors: Dalal Sagar , Moh Edward S.X. , Packer Nicolle H.
Published: May 2024

Chondroitin sulphate (CS) is a sulphated glycosaminoglycan (GAG) polysaccharide found on proteoglycans (CSPGs) in extracellular and pericellular matrices. Chondroitinase ABC (CSase ABC) derived from Proteus vulgaris is an enzyme that has gained attention for the capacity to cleave chondroitin sulfate (CS) glycosaminoglycans (GAG) from various proteoglycans such as Aggrecan, Neurocan, Decorin etc. The substrate specificity of CSase ABC is well-known for targeting various structural motifs of CS chains and has gained popularity in the field of neuro-regeneration by selective degradation of CS GAG chains. Within this context, our investigation into the biochemistry of CSase ABC led us to a previously unreported inhibition of CSase ABC activity by Dextran Sulfate (DexS). To understand the inhibitory effects of DexS, we compared its inhibition of CSase ABC to that of other polysaccharides such as Heparan Sulfate, Heparin, Colominic Acid, Fucoidan, and Dextran. This analysis identified key structural factors such as monosaccharide composition and linkage, sulphation degree and overall charge as influencing CSase ABC inhibition. Remarkably, DexS emerged as a unique inhibitor of CSase ABC, with distinctive inhibitory effects that correlate with its chain length. DexS has been used to reliably induce ulcerative colitis in mice, effectively mimicking inflammatory bowel diseases in humans, and has been previously shown to inhibit both RNA polymerase and reverse transcriptase. Our investigation emphasizes the interplay between the properties of DexS and CSase ABC, providing significant insights into the utilization of polysaccharide-based inhibitors for modulating enzyme activity.
3D characterization of kinematic fields and poroelastic swellingnear the tip of a propagating crack in a hydrogel
Authors: Li Chenzhuo , Zubko Danila , Delespaul Damien , Kolinski John Martin
Published: May 2024

In fracture mechanics, polyacrylamide hydrogels have been widely used as a model material for experiments, benefited from its optical transparency, fracture brittleness, and low Rayleigh wave velocity. To describe the brittle fracture in the hydrogels, linear elastic fracture mechanics comes as the first choice. However, in soft materials such as hydrogels, the crack opening can be extremely large, leading to substantial geometric nonlinearity and material nonlinearity at the crack tip. Furthermore, poroelasticity may also modify the local mechanical state within the polymer network. Direct characterization of the kinematic fields and poroelastic effect at the crack tip is lacking.Here, based on a hybrid method of digital image correlation and particle tracking technique, we retrieved high-resolution 3D particle trajectories near the tip of a slowly propagating crack and measured the near-tip 3D kinematic fields, including the displacement fields, rotation fields, stretch fields, strain fields, and swelling fields. Results confirmed the complex multi-axial stretching near the crack tip and the substantial geometric nonlinearity, particularly on the two wakes of the crack where rotation exceeds $30^{\circ}$. Comparison between the measured and predicted displacement and strain fields, derived from linear elastic fracture mechanics, highlights a disagreement in the direct vicinity of the crack tip, particularly for displacement component $u_x$ and through-thickness strain component $\varepsilon_{zz}$. Significant swelling, due to the poroelastic solvent migration, is also observed, with a strong correlation to the local stretch. Our experimental method, without any assumption of the material properties, can be readily extended to study 3D crack tips in a huge varieties of materials, and our results can shed light on the fundamental fracture mechanics and the development of material models for soft materials undergoing large multi-axial loading and substantial swelling.
Analysis of flue gases in a solar powered napkin incinerator
Authors: R Bakiyalakshmi , Dey Rishav , Vasanthmurali Nishanth , M Smelisha Rajan
Published: May 2024

This study investigates the composition and characteristics of flue gases emitted from a solar-powered napkin incinerator, with a focus on assessing environmental impact and operational efficiency. It also involves a study and implementation of requisite filters to minimise the impact of the harmful flue gases. As the demand for sustainable waste management solutions grows, solar-powered incinerators offer a promising alternative for the disposal of sanitary napkins, addressing both hygiene concerns and environmental sustainability.Such insights are crucial for promoting the widespread adoption of sustainable waste management technologies and advancing global efforts towards a cleaner and healthier environment.
Prediction of Characteristics of Pervious Concrete by Machine Learning Technique Using Mix Parameters and Non-destructive Test Measurements
Authors: Sathiparan Navaratnarajah , Jeyananthan Pratheeba , Wijekoon Sathushka Heshan , Subramaniam Daniel Niruban
Published: May 2024

Ensuring quality in pervious concrete poses challenges, limiting its use. This work investigates the potential of machine learning to forecast its properties, offering a novel and accessible approach. Five machine learning techniques were employed on 300 experimental data points, considering mix parameters (aggregate size, ratio, compaction) and non-destructive measurement (ultrasonic velocity, resistivity). Artificial Neural Networks (ANNs) excelled, achieving high accuracy (R2 > 0.97) for prediction of porosity and compressive strength. Sensitivity analysis revealed the dominant influence of compaction energy, aggregate-to-cement ratio, and ultrasonic velocity, while aggregate size and resistivity had minimal impact. This study suggests that machine learning models, particularly ANNs, can be reliable and efficient for predicting pervious concrete properties. This has the potential to improve quality control and encourage broader adoption in the construction sector, ultimately leading to more sustainable and permeable infrastructure.
Zinc Metal Complex as NIR Emissive Probe for Real Time Dynamics and in-Vivo Embryogenic Evolution of Lysosomes using Super Resolution Microscopy
Authors: Nandi Chayan , Salam Abdul , Kaushik Kush , Mukherjee Bodhidipra , Anjum Farhan , Sapkal Goraksha , Sharma Shagun , Garg Richa
Published: May 2024

Zinc (Zn) based fluorescent metal complexes have gained increasing attention due to their non-toxicity and high brightness with marked fluorescent quantum yield (QY). However, they have scarcely been employed in super-resolution microscopy (SRM) for studying live cells and in vivo dynamics of lysosomes. Here, we present an NIR emissive highly photostable Zn-complex as multifaceted fluorescent probe for the long-term dynamical distribution of lysosomes in various cancerous and non-cancerous cells in live condition and in vivo embryogenic evolution in Caenorhabditis elegans (C. elegans). Apart from the normal fission, fusion, kiss and run, the motility and the exact location of lysosome at each point were mapped precisely. A notable difference in the lysosomal motility in the peripheral region in between cancerous and non-cancerous cells were distinctly observed which is attributed to the difference in viscosity of cytoplasmic environment. On the other hand, along with super-resolved structure of the smallest size lysosome (~ 77 nm) in live C. elegans, the complete in vivo embryogenic evolution of lysosomes and lysosome-related organelles (LROs) closely for 16 hours at different stages starting from a single cell to a fully matured C. elegans was captured.
Neutrosophic Cubic Fuzzy Bonferroni Arithematic Mean Operator and Its Application in Group Decision Making Problems
Authors: Azam Muhammad , Jadoon Atif Mehmood
Published: May 2024

Fuzzy set theory play a key role in decision making. The philosophical concepts of neutrosophic set theory were studied by many researcher around the globe. Interval valued and single valued interval valued neutrosophic set is a best way to handle fuzzy data more accurately and precisely. Beside this neutrosophic cubic Numbers are quite enough for cracking and patching of large data in fuzzy decision making environment. Under the neutrosophic cubic sets (NCFS) with Bonferroni mean (BM) the present paper developed some new novel aggregation like (NCFABM x,y) and (WNCFABM x,y) with its admirable features. Further we highlighted the remarkable grip of these operators with different parameter values towards MCDM. In last a real life example based on proposed measure under neutrosophic cubic fuzzy sets (NCFS) environment has been included in term of counter intuitive cases for showing the validity and reliability.
Genetic diversity and recurrent parent phenome recovery assessment in six distinct populations with introgression of LCVD resistant gene from hot pepper to heat tolerant bell pepper
Authors: S Tejpal Singh , Jindal Salesh Kumar , Sharma Abhishek , Chawla Neena
Published: May 2024

The development of resistant cultivars through backcross breeding is the most practical and efficient way to combat the disease. Resistance breeding for Capsicum species started in India in the late 1960s, however the majority of the screening work was done in open fields. Using marker-assisted, strict phenotypic selection and backcross breeding, we attempted, for the first time, to transfer the LCVD (Leaf Curl Virus Disease) resistant gene from the donor hot pepper parent S-343, a strong LCVD resistant line in India, into the genetic background of six heat-tolerant bell pepper inbreds, in limited generations, in order to develop new LCVD resistant lines in bell pepper. In current research it was observed that the recurring parent phenome (RPP) recovery is above 80% in all of the developed BC2F1 populations for all the studied attributes except for some characters such as fruit width (63.33%), number of fruits plant− 1 (76.32%) & 1000 seed dry weight (78.62%) in LCVRS3SM-8, and for capsaicin content (75%, in each) in LCVRS3SM-17 and LCVRS3SM-58. Likewise, paired t-test analysis also dictates that there was no significance difference among the BC2F1 populations and their respective recurrent parents for most of the attributes. The genetic diversity analysis revealed the supporting evidence for the effectiveness of stringent phenotypic selection for RPP recovery for most of the studied attributes as these traits exhibited high heritability coupled with high values of GCV and PCV along with high estimates of genetic advance and high genetic gain which suggests that these attributes were strongly influenced by additive gene action. LCVD resistant lines produced by this experiment will be used in future resistance breeding programmes for bell peppers.
Intelligent Monitoring for Anomaly Recognition using CNNand YOLOv9
Authors: PINGALE SIDDESH , HUNDALEKAR ABHISHEK , Naidu Vamshi Rajkumar , Shirsath Vishal
Published: May 2024

The prompt and precise detection of firearms is essential in today's security environments to ensure public safety. This research paper provides a novel method for real-time weapon detection using Convolutional Neural Network (CNN) techniques and YOLOv9 object recognition framework in both live and prerecorded film. By integrating YOLOv9, object detection accuracy and speed are considerably improved, facilitating the quick identification of possible threats. The presented method exhibits strong performance in various lighting settings and environments, with excellent recall rates and precision thorough testing and assessment. This approach used CNN based architecture and deep learning to effectively detect and categorize weapons in video frames which achieves 97.62 % accuracy.