bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Time-History Terms on Reservoir Dynamics and Prediction Accuracy in Echo State Networks
Authors: Ebato Yudai , Nobukawa Sou , Sakemi Yusuke , Nishimura Haruhiko , Kanamaru Takashi , Sviridova Nina , Aihara Kazuyuki 
Published: January 2024

Abstract
The echo state network (ESN) is an excellent machine learning model for processing time series data. This model, utilizing the response of a recurrent neural network called a reservoir to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesized that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with time-history terms, namely Leaky-Integrator ESNs (LI-ESN) and chaotic echo state networks (ChESN). The results suggest that, compared to ESNs without time-history terms, the reservoir dynamics of LI-ESN and ChESN can maintain diversity and stability while possessing higher delay capacity, leading to their superior performance. Explaining ESN performance through dynamical metrics are crucial for evaluating the numerous ESN architectures recently proposed from a general perspective and for the development of more sophisticated architectures, and this study contributes to such efforts.
Galectin-3 Impairs Calcium Transients and β-Cell Function
Authors: LI PINGPING , Jiang Qian , Zhao Qijin , Chen Yibing , Ma Chunxiao , Peng Xiaohong , Wu Xi , Liu Xingfeng , Wang Ruoran , Hou Shaocong , Kong Lijuan , Wan Yanjun , Wang Shusen , Meng Zhuo-Xian , Cui Bing , Chen Liangyi 
Published: January 2024

Abstract

In diabetes, macrophages and inflammation are increased in the islets, along with β-cell dysfunction. Here, we demonstrate that galectin-3 (Gal3), mainly produced and secreted by macrophages, is elevated in islets from both high-fat diet (HFD)-fed and diabetic db/db mice. Gal3 acutely reduces glucose-stimulated insulin secretion (GSIS) inβ-cell lines and primary islets in mice and humans. Importantly, Gal3 binds to calcium voltage-gated channel auxiliary subunit gamma 1 (CACNG1) and inhibits calcium influx via the cytomembrane and subsequent GSIS.β-Cell CACNG1 deficiency phenocopies Gal3 treatment. Inhibition of Gal3 through either genetic or pharmacologic loss of function improves GSIS and glucose homeostasis in both HFD- fed and db/db mice. Our results highlight a novel role of Gal3 in pancreatic β-cell dysfunction, and Gal3 could be a new therapeutic target for the treatment of type 2 diabetes.
Impulsivity mediates the association between narcissism and substance-related problems beyond the degree of substance use: a longitudinal observational study
Authors: Hildebrandt Malin K. , Noack Josepha , Wuellhorst Raoul , Endrass Tanja , Jauk Emanuel 
Published: January 2024

Abstract
Background
Narcissism has been implied as a putative risk factor for substance use disorders (SUDs). However, previous research did not disentangle the degree of substance use from substance-related problems, the symptoms of SUDs. This preregistered study addressed the open question whether grandiose and vulnerable narcissism and their constituent traits convey specific SUD risk, that is, explain substance-related problems beyond the degree of use. Furthermore, we tested whether impulsivity or substance use motives linked to narcissistic self-regulation mediate this association.
Methods
Narcissism, impulsivity, substance use motives, past-year substance use, and substance-related problems were assessed in 139 (poly-)substance users, 121 of whom completed a one-year follow-up. For significant longitudinal associations between narcissism factors and substance-related problems controlled for the degree of use, we tested impulsivity and substance use motives as mediators.
Results
Grandiose narcissism (r = .24, p = .007) and its constituent factors antagonistic (r = .27, p = .003) and agentic narcissism (r = .18, p = .050), but not vulnerable narcissism, prospectively predicted substance-related problems beyond the degree of substance use. Associations of grandiose narcissism and antagonistic narcissism with substance-related problems were fully mediated by impulsivity, but not substance use motives.
Discussion
We demonstrate that grandiose narcissism – particularly antagonistic but also agentic narcissism – is specifically linked to substance-related problems beyond the degree of substance use. The mediating effect of impulsivity but not substance use motives suggests that impulsivity may be a more important mechanism than narcissistic self-regulation in promoting SUD in narcissism. However, future studies may use more targeted measures than substance use motives to further probe the role of self-regulation. Similar result patterns for alcohol compared to all substances together indicate that mechanisms may be alike across substances. In conclusion, narcissistic individuals may not use substances more but have a higher SUD risk, informing prevention and treatment.
Deep learning for schizophrenia classification based on natural language processing—A pilot study
Authors: Lin Pei-Yun , Chen Ying-Hsuan , Chang Yuh-Jer , Ho Tsung-Tse , Shih Tai-Chuan , Ko Chih-Hung , Lai Ying-Hui 
Published: January 2024

Abstract

Background：The correct diagnosis of schizophrenia is essential to reduce the economic burden and avoid worsening patients’ comorbidities. However, current clinical diagnosis is subjective and time consuming. We propose a deep learning method using the bidirectional encoder representations from transformers (BERT) to identify lexical incoherence related to schizophrenia.
Methods：We use a fine-tuned BERT model to extract schizophrenia-related text features and detect possible schizophrenia. Our study involves the enrollment of 13 participants diagnosed with schizophrenia and 13 participants without schizophrenia. Following the collection of speech data, we create a training set by sampling from 10 speakers in each group. Subsequently, the remaining speakers' data is reserved for external testing to assess the model's performance.
Results：After adjusting the parameters of the BERT model, we achieve excellent detection results, with an average accuracy of 84%, 95% of true positives, and an F1 score of 0.806. These results underscore the efficacy of our proposed system in identifying lexical incoherence related to schizophrenia.
Conclusions：Our proposed method, leveraging the deep learning BERT model, shows promise in contributing to schizophrenia diagnosis. The model's self-attention mechanism successfully extracts representative schizophrenia-related text features, providing an objective indicator for psychiatrists. With ongoing refinement, the BERT model serves as a valuable auxiliary tool for expedited and objective schizophrenia diagnosis, ultimately alleviating societal economic burdens and preventing major complications in patients.
Rapid Market Screening to Assess Lead Concentrations in Consumer Products across 25 Low- and Middle-Income Countries
Authors: Sargsyan Aelita , Nash Emily , Binkhorst Gordon , Forsyth Jenna E. , Jones Barbara , Ibarra Gabriel Sanchez , Berg Sarah , McCartor Andrew , Fuller Richard , Bose-O’Reilly Stephan 
Published: January 2024

Abstract

Background: Lead exposure can have serious consequences for health and development. The neurological and behavioral effects of lead are considered irreversible. Young children are particularly vulnerable to lead poisoning. In 2020, Pure Earth and UNICEF estimated that one in three children had elevated blood lead levels above 5 µg/dL. The sources of lead exposure vary around the world and can range from household products, such as spices or cookware, to environmental pollution from nearby industries. The aim of this study was to analyze common products from markets in low- and middle-income countries (LMICs) for their lead content to determine whether they are plausible sources of exposure.
Methods: In 25 LMICs, the research teams systematically collected consumer products (metal cookware, ceramics, cosmetics, paints, toys, spices and other foods). The items were analyzed on site for detectable lead above 2 ppm using an X-ray fluorescence analyzer. For quality control purposes, a subset of the samples was analyzed in the USA using inductively coupled plasma mass spectrometry. The lead concentrations of the individual product types were compared with established regulatory thresholds.
Results: Out of 5007 analyzed products, threshold values (TV) were surpassed in 51% for metal cookware (TV 100 ppm), 45% for ceramics (TV 100 ppm), and 41% for paints (TV 90 ppm).
Conclusions: Sources of exposure in LMICs can be diverse, and consumers in LMICs lack adequate protection from preventable sources of lead exposure. Rapid Market Screening is an innovative, simple, and useful tool to identify risky products that could be sources of lead exposure.
Selective enhancement of fear extinction by inhibiting neuronal adenylyl cyclase 1 (AC1) in aged mice
Authors: Shi Wantong , Chen Qi-Yu , Ma Yujie , Wan Jinjin , Li Xu-Hui , Zhuo Min 
Published: January 2024

Abstract
Adenylyl cyclase 1 (AC1) is a selective subtype of ACs, which is selectively expressed in neurons. The activation of AC1 is activity-dependent, and AC1 plays an important role in cortical excitation that contributes to chronic pain and related emotional disorders. Previous studies have reported that human-used NB001 (hNB001, a selective AC1 inhibitor) produced analgesic effects in different animal models of chronic pain. However, the potential effects of hNB001 on learning and memory have been less investigated. In the present study, we found that hNB001 affected neither the induction nor the expression of trace fear, but selectively enhanced the relearning ability during the extinction in aged mice. By contrast, the same application of hNB001 did not affect recent, remote auditory fear memory, or remote fear extinction in either adult or aged mice. Furthermore, a single or consecutive 30-day oral administration of hNB001 did not affect acute nociceptive response, motor function, or anxiety-like behavior in either adult or aged mice. Our results are consistent with previous findings that inhibition of AC1 did not affect general sensory, emotional, and motor functions in adult mice, and provide strong evidence that inhibiting the activity of AC1 may be beneficial for certain forms of learning and memory in aged mice.
Rab8a serves as a valuable biomarker of esophageal squamous cell carcinoma
Authors: Liu Rong , Kang Zhanting , Gan Ting , Wang Yangruonan , Huangfu Zhiyuan , Cui Jing , Wang Yongxia 
Published: January 2024

Abstract
Background
Esophageal carcinoma (ESCA) is a digestive tract malignancy with high morbidity in China, among which esophageal squamous cell carcinoma (ESCC) accounts for 90% of the confirmed cases. Rab8a is a member of the Ras small GTPase superfamily, and it has been shown to play an important role in endometrial cancer (EC) and hepatocellular carcinoma (HCC). However, the function of Rab8a in ESCC are currently unclear. This study aimed to investigate Rab8a as a biomarker for the diagnosis of ESCC.
Methods
The study first used TIMER2.0, GEPIA and UALCAN to analyze the expression of Rab8a in a variety of clinically common malignancies including ESCC, followed by real-time PCR (quantitative real-time PCR, qPCR), Western blot, immunohistochemical (IHC) ，and a series of in vitro biological experiments
Results
Rab8a is highly expressed in the esophageal cancer cells and tissues.overexpression of Rab8a can promote the proliferation and migration of ESCC ,while knockdown its expression can inhibit the proliferation and migration of ESCC. Correlation analysis revealed the positive correlation between the expression of Rab8a and NDUFA1, CYC1 in public GEO databases. Therefore, Rab8a may promote ESCC progression by activating mitochondrial respiration.
Conclusions
This study demonstrated that Rab8a is upregulated in ESCC and may promote ESCC cell proliferation and migration by activating mitochondrial respiration. This study provides a rationale for clinical diagnosis and screening of new therapeutic targets for ESCC.
Postpartum Anemia and Associated Factors among Lactating Women Visiting Health Centers in OdoShakisso District, Oromia Region, South west Ethiopia: a cross sectional study.
Authors: Shebiti Tadesse , Dessie Yadeta , Gena Hussein Mohammed , Egata Gudina 
Published: January 2024

Abstract
Background
Anemia is one of the most serious public health problems worldwide. Although anemia during pregnancy has been adequately investigated, very little is known about postpartum anemia in low income countries like Ethiopia.
Objective
to assess the magnitude of anemia and associated factors among lactating women visiting health centers in the study area.
Methods
Facility -based cross-sectional study design was used among randomly selected 509 lactating women who have given birth in the last 6 months prior to the study period. Data were collected using a pretested structured questionnaire. Blood samples were collected to determine hemoglobin concentration using a battery-operated portable Hemo Cue analyzer. Hemoglobin (Hgb) level was measured in gram per deciliter (g/dl). The stool examination was performed for ova and parasite using direct wet mount technique. Descriptive statistics was used to describe the data using numerical summary measures, tables, and percentages. Bivariable binary logistic regression analyses were used to see the association between each independent variable and the outcome variable. Multivariable binary logistic regression model was fitted to control for all possible confounders and to identify predictors of anemia. Odds ratio alongside 95% confidence intervals (CIs) were estimated to measure the strength of the association between study variables. Level of statistical significance was declared at p value < 0.05.
Results
The overall magnitude of postpartum anemia was 30.2%; 95%CI :(26.3%, 34.2%)], mild anemia being [22.5% ; 95%CI:(18.6% ,26.3%)],moderate anemia [5.7%; (95%Cl:(3.8%, 7.7%)] and severe anemia [2%; 95%Cl:( 0.8%,3.2%)] respectively. Maternal primary level of education [adjusted odds ratio [(AOR) = 2.6, 95% CI:(1.6.0 ,6.34)], paternal illiteracy [(AOR = 2.7; (95%CI:1.18,6.19)], being infected with ascaris lumbricoids [AOR = 5.28, 95% CI: (1.81,15.43)] and Hook worm [(AOR = 5.38, 95% CI:(1.05,27.57)], multiparity [(AOR = 2.5, 95% CI:(1.45,4.31)], and lack of iron supplementation during pregnancy [(AOR = 2.89; 95%CI:(1.65,4.65)] were significantly associated with anemia.
Conclusions
The magnitude of postpartum anemia was high. Maternal and paternal education, intestinal parasitic infections, multiparty and lack of iron supplementation during pregnancy were predictors of postpartum anemia. Hence, all concerned bodies should strengthen strategies to contain parasitic infections, improve iron supplementation, birth spacing, and postnatal deworming to prevent postpartum anemia.
Arrangement and feedback effects of droplet swarms in a parallel microchannel device
Authors: Li Shouchuan , He Chengxiang , Wang Zhongdong , Zhu Chunying , Ma Youguang , Fu Taotao 
Published: January 2024

Abstract
Droplet swarms are the dominant form of highly dispersed microdroplets in cavities. In the self-assembly of droplet swarms, the continuous phase tends to flow in the path of the minimum resistance, the arrangement of the droplet swarms tends to minimize the potential energy, and the two mechanisms compromise in competition. In this paper, whether droplet swarms can timely adjust the morphology is measured by the ratio of the average flow rate of the two-phase flow to that of the droplet swarms, so as to clarify the dominant mechanism of the arrangement of droplet swarms. The arrangement of droplet swarms dominated by different mechanisms and the distribution of that are introduced, and the prediction method for the arrangement of droplet swarms is proposed. The mechanism of the breakup of the microdroplet in the cavity is elucidated, and three modes of the breakup in the cavity are introduced. Furthermore, a resistance model for the microdevice is established to quantify the fluctuations of the pressure difference and the flow rate caused by the formation of droplet swarms. The influence of the feedback effects of droplet swarms on the uniformity of droplet formation and flow patterns is analyzed, indicating the ideal flow range for the formation of highly dispersed droplets in microchannels. This paper clarifies the arrangement and feedback effects of droplet swarms, which will guide the application of microdevices in reaction and mass transfer processes.
The Efficacy of The Stephens-Sarlos Development Program on Sensorimotor Development, Auditory and Visual Skills of Schoolchildren
Authors: Stephens-Sarlós Erzsébet , Stephens Patrick 
Published: January 2024

Abstract
Background
Around 800 million young children worldwide have cognitive-developmental limitations due to biological, environmental, and psychosocial reasons, while in Europe, an estimated 15 million children have special educational needs. This leads to educational challenges, limited skill levels, and higher unemployment rates. Therefore, timely intervention within institutional settings to address the reasons underlying these problems is of critical importance.
Objective
The “S. Sensorimotor Development Program” (SSDP) was developed by the authors as a key intervention to develop the skills essential for starting school. This longitudinal field study investigated the impact of the SSDP in an institutional setting focusing on the specific auditory and visual skills required for learning in kindergarten and primary school children.
Methods
The SSDP consists of 120 training sessions focused on sensorimotor maturation. 772 children aged 5–8 took part in the study, with 704 in the experimental and 68 in the control group, each containing an equal ratio of boys and girls. The study lasted six to eight months and consisted of three to five weekly training sessions.
Results
Results obtained following the intervention showed significant improvements in sensorimotor maturity in the experimental compared to the no-intervention control group (p<.001, effect size (d)=.483; auditory skillsr=.605 p <.001, d=.366; visual skills r= .542, p< .001, d =.294). The intervention group also improved compared to its baseline measurements.
Conclusion
These results show that implementing the SSDP in school settings can improve sensorimotor development, which has a strong effect on auditory and visual abilities in children aged 5–8.
Sustainability Challenges and Opportunities: A Qualitative Inquiry into the Metal Industry's Response to Renewable Energy Price Volatility
Authors: Reynolds Samantha , Bennett Noah 
Published: January 2024

Abstract
This research explores the intricate relationship between the metal industry and renewable energy price volatility, aiming to uncover the challenges and opportunities that shape the industry's journey towards sustainability. In a world increasingly focused on mitigating climate change, understanding how energy-intensive sectors such as the metal industry respond to the dynamics of renewable energy prices is crucial for fostering environmentally responsible practices. Through a qualitative inquiry involving semi-structured interviews with key industry stakeholders—executives, policymakers, and environmental experts—the study provides a comprehensive examination of the metal industry's perspectives, decision-making processes, and strategies. The findings reveal a shared awareness among participants of the inherent volatility in renewable energy prices, attributed to factors such as market forces, government policies, and technological advancements. The impact of renewable energy price volatility on decision-making processes within the metal industry is a central theme. Participants articulated the delicate balance between short-term economic considerations and long-term sustainability goals, emphasizing the challenges in making confident, large-scale investments in renewable energy infrastructure. Regulatory uncertainties and the perceived lack of a level playing field for renewables compared to traditional energy sources emerged as significant impediments. Despite these challenges, the study identifies several strategies employed by the metal industry to navigate renewable energy price volatility. Diversification of energy sources, adoption of energy-efficient technologies, and collaborative initiatives with government bodies were highlighted as key approaches. These strategies showcase a proactive stance by the industry in mitigating risks and integrating sustainability into operational practices. The research not only outlines the challenges faced by the metal industry but also identifies opportunities for sustainability. Integration of advanced technologies, such as artificial intelligence and data analytics, and collaborative efforts with renewable energy providers and policymakers emerged as pathways to enhance operational efficiency and create a more stable environment for sustainable practices. The implications of this research extend to industry stakeholders, policymakers, and researchers. Recommendations emphasize the importance of stable regulatory frameworks, financial incentives, and collaborative partnerships to empower the metal industry in making strategic and sustainable choices. The findings contribute to the ongoing discourse on sustainable practices, informing future policies, business strategies, and research directions. Ultimately, this study seeks to guide the metal industry towards a more sustainable and environmentally responsible future in the face of renewable energy price volatility.
A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT
Authors: Wang Shukai , Xu Bingyan 
Published: January 2024

Abstract
This study addressed the growing challenge of evasive malware detection by introducing an innovative approach that integrates binary opcode analysis with the advanced machine learning capabilities of BERT (Bidirectional Encoder Representations from Transformers). Focusing on the rapidly evolving landscape of cybersecurity, this study explored the intricacies of malware behavior, particularly in the context of opcode obfuscation and direct OS lower-level API access. Through a comprehensive methodology, the study demonstrated the effectiveness of combining traditional disassembly techniques with state-of-the-art deep learning algorithms. The findings revealed a significant distinction between malware and benign software in terms of their opcode structures and system interactions. This study not only offered a novel perspective in malware analysis, but also set the stage for future advancements in cybersecurity tools and techniques. The integration of binary opcode analysis and BERT represented a vital development in cybersecurity, providing enhanced detection capabilities and a deeper understanding of sophisticated malware threats.