bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global Industrial Efficiency Revolutionized by Automated Lathe
Authors: Rokhman Fatori , Fahruddin A'rasy 
Published: May 2024

The implementation of an automatic lathe, utilizing hydraulic drive and PLC programming, stands as a significant advancement in industrial engineering aimed at augmenting productivity while circumventing additional labor requirements. Through meticulous research, it was discerned that the predominant issue plaguing the production process was the occurrence of stoppages due to irregular or unstable dimensions in the output, particularly concerning the Item Bush K0JA Left. Employing methodologies such as SIPOC analysis and the 4M framework revealed that the root cause primarily stemmed from machine-related factors, exacerbated by fluctuations in the installation, resulting in misalignments and subsequent dimension inaccuracies. Addressing this, a targeted intervention involving the addition of locking bolts to the material fixture mitigated the stoppage occurrences significantly, with the operator rectifying positioning irregularities on average 1200 minutes per month. This intervention not only enhanced operational efficiency but also yielded tangible cost savings, amounting to Rp 578,000 monthly or Rp 6,936,000 annually, attributed to the prevention of production downtime. The implications of this study underscore the importance of proactive measures in streamlining manufacturing processes, thereby optimizing resource utilization and economic viability. Further research avenues could delve into the optimization of maintenance protocols for automated machinery and the exploration of advanced sensor technologies to preemptively detect dimensional inconsistencies, thus bolstering overall production resilience and efficacy.
Highlight:

Advanced machinery integration boosts manufacturing efficiency.
Root cause analysis reveals process optimization insights.
Cost savings demonstrate practical impact of production enhancements.

Keywoard: Automatic lathe, Industrial engineering, Productivity enhancement, Dimensional accuracy, Cost savings
A step by step protocol for stabilizing specific DNA G-quadruplexes by CRISPR guided G-quadruplex binding proteins and ligands
Authors: qin Geng , Liu Zhenqi , Yang Jie , Liao Xiaofeng , Zhao Chuanqi , Ren Jinsong , Qu Xiaogang 
Published: May 2024

Abstract

Despite the importance of DNA G-quadruplexes (G4s) in health and disease being demonstrated, technologies to readily manipulate specific G4 folding for functional analysis and therapeutic purposes are lacking. Here, we employ the G4-stabilizing protein/ligand in conjunction with CRISPR system to selectively facilitate single or multiple targeted G4 folding within specific genomic loci. We demonstrate that fusion of nucleolin with a catalytically inactive Cas9 (dCas9) can specifically stabilize G4s in the promoter of oncogene MYC and muscle associated gene Itga7 as well as telomere G4s, leading to cell proliferation arrest, inhibition of myoblast differentiation and cell senescence, respectively. Furthermore, CRISPR system can confer intra-G4 selectivity to G4-binding compounds PDC and PDS. Compared with traditional G4 ligands, CRISPR-guided Bio-PDC enables a more precise investigation into the biological functionality of de novo G4s. Our study provides insights that will enhance understanding of G4 functions and therapeutic interventions.
Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs
Authors: Angelucci Alessandra , Hassanpour Mahlega , Merlin Sam , Federer Frederick , Zaidi Qasim 
Published: May 2024

Abstract

In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically- identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs.
Carotid Body Tumor (CBT): Incidental Discovery and Individualized Management
Authors: Senapati Sidhartha Gautam , Kattamuri Lakshmi Prasanna Vaishnavi , Deoker Abhizith 
Published: May 2024

Abstract

Carotid Body Tumors (CBT) are uncommon neuroendocrine neoplasms that develop near the carotid bifurcation. Most cases are asymptomatic and discovered incidentally. Diagnosis is based on imaging, with computed tomography being the preferred initial investigation. Surgical excision is the preferred treatment, with preoperative embolization and angiography aiding in the planning process. In cases where surgery is not feasible, radiotherapy may be considered. Here we present a 75-year-old female with recurrent vomiting, severe headache, and newly diagnosed atrial fibrillation. Workup incidentally detected carotid body paraganglioma. The patient's treatment included metoprolol for heart rate and blood pressure control and radiotherapy for CBT due to age and comorbidities. The discussion delves into the characteristics of paragangliomas, their chemoreceptor role, prevalence, and genetic factors. The case report emphasizes the importance of early detection and individualized approaches in managing CBTs, considering patient profiles and medical conditions.
A gene cluster of mitochondrial complexes contributes to the cognitive decline of COVID-19 infection
Authors: xu Wen-tao , An Xiao-bin , Ma Jing , Wang Xu-qiao , Yang Ji-nan , Wang Qin , Wang Dong-yang , Wu Yan , Zeng Lu , Qu Yang , Zhao Bo-wen , Ai Jing 
Published: May 2024

Abstract

"Brain fog," a persistent cognitive impairment syndrome, stands out as a significant neurological aftermath of coronavirus disease 2019 (COVID-19). Yet, the underlying mechanisms by which COVID-19 induces cognitive deficits remain elusive. In our study, we observed an upregulation in the expression of genes linked to the inflammatory response and oxidative stress, whereas genes associated with cognitive function were downregulated in the brains of patients infected with COVID-19. Through single-nucleus RNA sequencing (snRNA-seq) analysis, we found that COVID-19 infection triggers the immune responses in microglia and astrocytes. This activation, in turn, exacerbates oxidative stress in oligodendrocytes, oligodendrocyte progenitors (OPCs), and neurons. Further investigations revealed that COVID-19 infection elevates LUC7L2 expression, which inhibits mitochondrial oxidative phosphorylation (OXPHOS), and suppresses the expression of mitochondrial complex genes such as MT-ND1, MT-ND2, MT-ND3, MT-ND4L, MT-CYB, MT-CO3, and MT-ATP6. A holistic approach to protect mitochondrial complex function, rather than targeting a single molecular should be an effective therapeutic strategy to prevent and treat the long-term consequences of "long COVID."
Joint multi-omics discriminant analysis with consistent representation learning using PANDA
Authors: Wu Jia , Aminu Muhammad , Hong Lingzhi , Vokes Natalie , Schmidt Stephanie , Saad Maliazurina B. , Zhu Bo , Li Xiuning , Cascone Tina , Sheshadri Ajay , Jaffray David , Futreal Andrew , Lee Jack , Byers Lauren , Gibbons Don , Heymach John , Chen Ken , Cheng Chao , Zhang Jianjun , Wang Bo 
Published: May 2024

Abstract

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis–based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.
Effect of antenatal care on birth outcomes in The Gambia: a propensity score matching analysis
Authors: Kumar Santosh , Suso Alasana , Wood Elizabeth 
Published: May 2024

Abstract

Background
The prevalence of low birth weight (LBW) is disproportionately high in developing countries, particularly in African countries. LBW infants have a greater risk of mortality and suffer from physical and cognitive impairment. Adequate antenatal care (ANC) can improve birth outcomes and mitigate the risk of LBW; however, there is limited evidence on the effectiveness of ANC on birth outcomes in resource-poor settings. The objective of this study was to estimate the causal association between ANC and birth outcomes in the Gambia.
Methods
The 2019-20 Gambian Demographic and Health Survey (GHDS) was used for the analysis. The GHDS collected birth weight information for 8,362 children born in the five years preceding the survey. After excluding observations with missing birth weight information, the final analytical sample included 4,443 children. A multivariate regression model and propensity score matching method were used to assess the associations between ANC and birth outcomes. The regression models were adjusted for sex and birth order of the child, maternal age and education, wealth quintiles, marital status, rural area, number of children under five years of age, and region fixed effects.
Results
Multivariate regression analysis revealed that one additional ANC visit increased birthweight by 22 grams and the probability of LBW by 1.2 percentage points. Mothers who had four or more ANC visits (ANC 4+) had a 3.9 percentage point lower likelihood of giving birth to LBW infants than mothers with fewer than four ANC visits. The matching analysis revealed that the average treatment effect of ANC 4 + was a 71-gram increase in birth weight and a 4.7 percentage point reduction in LBW.
Conclusions
Having more ANC visits is associated with improved birth outcomes in Gambia after controlling for the socioeconomic status of the households. Thus, it is important to design health policies that can ensure that women have adequate ANC visits during pregnancy to reduce the disproportionate burden of LBW in countries with limited resources.
Assessment of oral changes in 2692 intensive care unit patients: a cross-sectional study
Authors: Silva Weslay Rodrigues da , Sobral Ana Paula Veras , de Oliveira Hugo Angelo Gomes , Briano Aylanne Xavier de Lacerda Cavalcante Timoteo , Albuquerque Antonio Carlos Moura Melo , Gominho Luciana Ferraz , Cavalcanti Lúcia de Fátima Santos , Cavalcanti Ully Dias Nascimento Távora , Romeiro Kaline 
Published: May 2024

Abstract

Objectives
To identify oral changes in patients admitted to an intensive care unit (ICU).
Materials and Methods
Retrospective, cross-sectional, descriptive study. The following data were collected at a high-complexity referral center in northeastern Brazil between 2021 and 2024: sex, age, cause of hospitalization, ventilatory support, dental condition, and type of oral changes.
Results
A total of 2,692 patients admitted to the ICU were evaluated; 52.1% of the patients were male and the mean age was 69.98 years (± 19.53). Oral changes were detected in 56.98% of the patients and odontogenic infections were the most prevalent (41.49%). The presence of oral changes was significantly associated with male sex (p < 0.001), age between the 6th and 7th decade of life (p < 0.001), ventilatory support (p = 0.037), being partially dentate (p < 0.001), and the number of teeth (p < 0.001).
Conclusion
The prevalence of oral changes was high in the sample and male patients had a higher risk of developing these changes.
Clinical Relevance:
The results permit to characterize the incidence of oral changes in this patient profile, reinforcing the importance of the dentist as a member of multidisciplinary care teams.
Navigating the Maze of Attention: Exploring Interactions Among Stimulus, Individual Characteristics, and Context in Threat Perception
Authors: Moscona Gal , Gozansky Einav , Abado Elinor , Okon-Singer Hadas 
Published: May 2024

Abstract

An ongoing debate exists in the literature regarding the reliability of attention bias to threats. The current study directly examined how the interaction between available attention resources, personality traits, and stimuli visual characteristics moderates attention bias to task-irrelevant threatening stimuli. To this end, the current study conducted a comprehensive series of four experiments in which an emotional modification of the perceptual load task was employed. Participants with high and low fear of spiders, as well as participants diagnosed with arachnophobia, performed the task under high and low perceptual loads while ignoring task-irrelevant distracting spiders. As expected, all participants, regardless of fear level, were affected to some extent by the threatening distracting spider pictures, known to evoke threat due to their evolutionary value. However, the results show that high fear and phobia groups exhibit consistent attention bias to threats, depending on the threat’s ecological value. The low fear groups, on the other hand, showed a similar but weaker attention bias to threat, only when attentional resources were available.These results deomstratethe variance in individuals’ capacity to inhibit distracting threats and focus on current goals.
Adsorptive Desulfurization of Liquid Fuels Over cost effective Cu-Ni Bimetallic Nanoparticles-Modified Alum Sludge
Authors: Musa Aminu , Dara Ibrahim Ahmad , Salisu Ahmed , Muhammad Abubakar 
Published: May 2024

Abstract

The elimination of sulfur from liquid fuels in the refining process to a particular range to adhere to the Standard Operation of Nigeria (SON) maximum level of 50 ppm is an important process. To address the limitation of hydro-desulfurization, several techniques have been used including adsorptive desulfurization. Adsorptive desulfurization has been used because of its efficiency and cost-effectiveness of the materials. In this study, Alum Sludge was modified to prepare alum sludge coupled with copper AS/Cu, alum sludge coupled with nickel AS/Ni, and alum sludge coupled with copper-nickel nanoparticles AS/Cu-Ni NPs. A comparative study was performed for the sulfur removal from liquid fuels diesel and kerosene by the Alum sludge coupled with metallic nanoparticles. The produced AS, AS/Cu, AS/Ni, and AS/Cu-Ni NPs were evaluated by Brunauer-Emmett-Teller (BET) analysis, Fourier Transform Infrared spectroscopy (FTIR), Scanning Electron Microscope (SEM), X-ray Diffraction (XRD), and X-ray Fluorescence (XRF). According to the BET results, AS/Cu had a surface area that was relatively higher at 557.9 (m2/g), while AS/Ni had a surface area that was lower at 127 (m2/g). The percentage rise of Cu metallic NPs from (0.00725% - 1.69%) and Ni metallic NPs from 0.0085% Ni to 0.0595% Ni was shown by the XRF data. Researchers looked into the effects of adsorbent dose, loaded metal nanoparticles, and adsorption process isotherms. The percentage removal of sulfur increases reaching ≈100%. In comparison to the alum sludge combined with copper/nickel (AS/Cu-Ni) and the alum sludge combined with nickel (AS/Ni) NPs from both liquid fuels, the AS/Cu-Ni yields a higher percentage of sulfur removal. The experimental adsorption results for kerosene and diesel, which had higher correlation values, were better adapted to the Langmuir model than to the Freundlich and Temkin model, according to the data. It was discovered that the adsorption processes were exothermic, advantageous, and spontaneous.
Living donor liver transplantation for the treatment of recurrent pediatric acute liver failure with neuroblastoma amplified sequence gene mutation: a literature review
Authors: Zheng Zebing , Liu Yuanmei , Xia Xinrong , Tang Chengyan , Gong Yuan , Huang Lu , Du Qing , Zhu Daiwei , Zhou Wangkan , Jin Zhu 
Published: May 2024

Abstract

Background: Biallelic mutations in the neuroblastoma amplified sequence (NBAS) gene can cause recurrent acute liver failure (RALF) and multi-systemic disease.
Case presentation: Herein, we report a 3-year-old Chinese boy with RALF due to a novel heterozygote mutation c.3596G> A(p.C1199Y)/c.1028G> A(p.S343N) in the NBAS gene, which was identified by whole-exome sequencing. The missense mutation c.3596G> A(p.C1199Y) was inherited from his father, and c.1028G> A(p.S343N) was inherited from his mother. He had suffered six acute liver crises triggered by fever. He eventually underwent living donor liver transplantation (LDLT) at 44 months, with his father donating the left lateral lobe liver, and is now healthy with no recurrence of ALF.
Conclusion: We describe a novel pathogenic mutation in the NBAS gene of a patient with RALF and report that LDLT is a safe and efficient treatment for RALF caused by the NBAS gene mutation.
Deep Symbolic Regression for Numerical Formulation of Fundamental Period in Concentrically Steel-Braced RC Frames
Authors: Rahman Taimur , Sultana Shamima , Ahmed Tanjir , Momin Md. Farhad , Provasha Afra Anam 
Published: May 2024

Abstract

This research explores the use of Deep Symbolic Regression (DSR) to develop a sophisticated predictive model for the fundamental period of vibration in concentrically steel-braced reinforced concrete (RC) frames. Traditional empirical models often overlook complex interactions within structural dynamics during seismic events, a gap this study addresses by deriving tailored equations for various bracing configurations such as Cross bracing, Diagonal bracing, and Chevron bracing. The model development incorporates an iterative refinement process utilizing DSR techniques to enhance accuracy and applicability in predicting seismic responses. Further refinement and optimization are achieved using the L-BFGS-B algorithm, ensuring robustness and adherence to safety standards. Validation against actual structural data reveals that our proposed equations achieve high predictive accuracy, with R-squared values up to 0.8247 and RMSE values as low as 0.2119, consistently presenting lower error metrics across various configurations compared to those found in established seismic design standards, such as ASCE, Eurocode, and Japan’s Building Standards. Comparative analyses and Bland-Altman plots confirm that the models not only match but often surpass the accuracy of traditional formulas, validating their potential as reliable tools in structural engineering for earthquake resilience planning. The findings demonstrate DSR’s potential to revolutionize traditional practices in formulating empirical equations, offering a scientifically rigorous, data-driven methodology for more accurately predicting the dynamic responses of structures under seismic loads.
Text2Price: Adeep Learning Model for Predicting Electronic Product Prices from Descriptive Text Sequences
Authors: Al-Majmar Nashwan , Alsubari Akram , Marhabi waheeb Al 
Published: May 2024

Abstract

This study investigates deep learning models for predicting electronic product prices through text sequence (Text2Price). The study examined the performance of these models in terms of price prediction, the factors influencing predictions, the model's comprehension of numerical and expressive text, and the efficacy of the developed price prediction model. The primary aim is to create a model skilled at forecasting product prices using textual sequences containing product names, brands, and features. Methodologically, the research employs the T5-BESD model, a transformer-based architecture trained on a dataset of 22,000 electronic products from the Amazon. Data preprocessing involves cleaning and integrating features to create text sequence. In the model definition phase, a custom neural network architecture, T5Regressor, predicts product prices from textual descriptions. This architecture comprises a transformer-based language model (T5) and a linear regression layer. The T5 model comprehends and encodes the input text, while the linear regression layer predicts the numerical output (price). The linear regression layer involves a transformation with a weight matrix W and bias vector b. Additionally, the L1 loss, used for training, measures the absolute difference between the predicted and true values. In the initialization and setup phase, critical components, including the optimizer (Adam_W), learning rate scheduler, and loss function, are initialized. The learning rate scheduler dynamically adjusts the learning rate during training, incorporating a warm-up phase. The results demonstrate a consistent improvement in accuracy from 38.48–54.86% over five epochs, with the test accuracy reaching 52.38%. The mean squared error decreases from 45057.29 to 19783.88, indicating enhanced prediction accuracy, and the mean absolute error drops from 66.87 to 47.34, reflecting reduced disparities between the predicted and actual values. The research concludes by providing insights into the effectiveness of the T5-BESD model's effectiveness, emphasizing the importance of comprehensive data and suggesting avenues for improvement.