bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining surface drifters and high resolution global simulations enables the mapping of internal tide surface energy
Authors: Caspar-Cohen Zoé , Ponte Aurélien , Lahaye Noé , Zaron Edward D. , Arbic Brian K. , Yu Xiaolong , LeGentil Sylvie , Menemenlis Dimitris 
Published: July 2024

Abstract

By dissipating energy and generating mixing, internal tides (ITs) play a major role in the climatological evolution of the ocean. Our understanding of this class of ocean variability is however hindered by the rarity of observations capable of capturing ITs with global coverage. The data provided by the Global Drifter Program (GDP) offer high temporal resolution and quasi-global coverage, thus bringing promising perspectives. However, due to their inherent drifting nature, these instruments provide a distorted view of the IT signal. By theoretically rationalizing this distortion and leveraging a massive synthetic drifter numerical simulation, we are able to map semi-diurnal IT energy levels from GDP data and compare it to three datasets (two numerical simulations, and a satellite altimetry IT atlas). We find that all numerical simulations exhibit biases. Nonetheless, the simulation that benefited from dedicated attention towards ITs representation performs best. This supports renewed efforts in the concurrent numerical modeling of ITs / ocean circulation. The substantial deficit of energy in the IT atlas highlights the inability for altimetric estimates to measure incoherent and finer scale ITs and strongly supports the need to isolate ITs signature in the data collected by the new wide-swath altimetry mission SWOT.
Multivalent LecA/LecB Inhibitors based on the Co-assemblies of Perylene Monoimide-carbohydrate Conjugates for Antibiotic-free Antibacterial and Wound Healing
Authors: Wang Ke-Rang , Yang Jian-Xing , Li Hai-Qing , Yin Fangqian , Yin Wen-Juan 
Published: July 2024

Abstract

Pathogenic infection is becoming a global health threat to human health. Especially for the treatment of P. aeruginosa remains particularly challenging. Fortunately, it is interestingly found that the LecA and LecB lectins of P. aeruginosa played crucial roles in bacterial adhesion, biofilm formation, virulence, and host cell invasion. Herein, a co-assemble strategy to prepare antibiotic-free antibacterial and antibiofilm agents by using two kinds of perylene-carbohydrate conjugates (PMI-3Gal and PMI-3Fuc) with synergistic targeting for two lectins of P. aeruginosa LecA and LecB was developed. Due to the strong multivalent carbohydrate-lectin interactions both for LecA and LecB lectins, the co-assembly PMI-3Gal@PMI-3Fuc showed selective adhesion effects, inhibition activity of biofilm formation and potent photothermal antibacterial activities for P. aeruginosa and a clinical-isolated P. aeruginosa strain, and showed the acceleration effect for the wound healing in mice. This result opens a supramolecular principle for antibiotic-free antibacterial and antibiofilm effects based on multivalent glycoconjugates.
Imaging semiconductor-to-metal transition and topological flat bands of twisted bilayer MoTe2
Authors: Wang Shiyong , Liu Yufeng , Gu Yu , Bao Ting , Mao Ning , Li Can , Jiang Shudan , Liu Liang , Guan Dandan , Li Yaoyi , Zheng Hao , Liu Canhua , Watanabe Kenji , Taniguchi Takashi , Duan Wenhui , Jia Jinfeng , Liu Xiaoxue , Zhang Yang , Li Tingxin 
Published: July 2024

Abstract

Two-dimensional (2D) moiré materials have emerged as a highly tunable platform for investigating novel quantum states of matter arising from strong electronic correlations and nontrivial band topology1-4. Recently, topological flat bands formed in 2D semiconducting moiré superlattices have attracted great interests5-32. In particular, a series of topological quantum phases, including the long-sought fractional quantum anomalous Hall (FQAH) effect, have recently been experimentally observed14-22 in twisted bilayer MoTe2 (tMoTe2). However, the microscopic information of tMoTe2 moiré superlattice and its electronic structure is still lacking. Here, we present scanning tunneling microscopy and spectroscopy (STM/STS) studies of the tMoTe2 moiré superlattice, with twist angles ranging from about 2.3° to 2.8°. We developed a contact-STM mode to apply pressure on tMoTe2 and observed a phase transition from band insulator to metal of tMoTe2 under pressure at the charge neutrality point. STM imaging reveals a pronounced in-plane lattice reconstruction with periodic strain redistribution in the tMoTe2, which serves as gauge fields for generating topological moiré bands. Importantly, the electronic states of the low-energy moiré flat bands primarily concentrate at the XM and MX regions as revealed by STS imaging. Such spatial distributions are nicely reproduced by our first principal calculations with a large-scale basis, suggesting the low-energy moiré flat bands are formed through the hybridization of K valley bands of the top layer and K’ valley bands of the bottom layer. Overall, our findings provide compelling real-space evidence of electronic structure under pressure and topological flat bands of tMoTe2, paving the way for further STM/STS investigations of correlated topological states within the topological flat band in gate-tunable tMoTe2 devices.
Retrieval-Augmented Generation in Large Language Models through Selective Augmentation
Authors: Quintela Joao , Sapateiro Marquinhos 
Published: July 2024

Abstract

The increasing complexity and demands of natural language processing tasks have driven the need for more advanced and contextually aware language models. The integration of selective augmentation within Retrieval-Augmented Generation (RAG) frameworks represents a significant advancement, enhancing the relevance and accuracy of generated responses by dynamically incorporating pertinent information during inference. This research carefully developed and implemented a selective augmentation algorithm tailored to GPT-Neo, demonstrating substantial improvements in performance metrics such as BLEU, ROUGE, and F1 scores. Data preprocessing and model fine-tuning were conducted rigorously, ensuring a robust foundation for the selective augmentation mechanism. Experimental results confirmed that the enhanced RAG model not only provided more accurate and contextually relevant responses but also exhibited superior coherence compared to the baseline model. The implications of these findings are profound, suggesting that selective augmentation can significantly elevate the capabilities of language models, making them more reliable and effective for a wide range of applications. This study contributes valuable insights into the optimization of augmentation processes, paving the way for future advancements in natural language processing technologies.
Well-being of Older Adults in Continuing Education: Age and Gender Exploration
Authors: Rosser Pablo , Soler  Seila 
Published: July 2024

Abstract

This pilot study, a precursor to more in-depth research, examines the relationship between age, gender, and well-being in older adults participating in educational programs. Employing a quantitative design, descriptive analyses, T-Tests, ANOVA, and correlations were used to assess participants' responses. The results highlight an imbalanced age and gender distribution, with a predominance of individuals aged 66–75 and a higher proportion of women. The analyses reveal significant differences in well-being based on gender and age. Correlations indicate that as age increases, so does satisfaction with past and future life, although interest in new challenging experiences decreases. This study underscores the importance of considering demographic differences when designing and implementing educational programs for older adults, emphasizing the need for specific adaptations to maximize well-being and participation.
Estimating extreme values drought risk through classical and Bayesian paradigm
Authors: Ahmad Touqeer , Sabir Safoorah , Arshad Irshad Ahmad , Albalawi Olayan 
Published: July 2024

Abstract

Drought poses significant challenges to both the environment and the economy, necessitating proactive mitigation strategies. This study introduces both classical and Bayesian Markov Chain Monte Carlo (MCMC) extreme value probabilistic models for quantifying drought risk. The models utilize the generalized extreme value distribution to characterize the distribution of standardized precipitation index and non-stationary standardized precipitation index variables. Drought risk is probabilistically assessed across five regions in Baluchistan (a drought-prone area of Pakistan) over two 20-year periods per region. The study represents an innovative approach in probabilistic quantification models, demonstrating slight performance improvement with the Bayesian MCMC paradigm, as evaluated by the continuously ranked probability scoring. Moreover, the application of the presented methodology can be extended to other climatic zones using Bayesian MCMC with informative priors constructed from historical records of the neighboring regions.
Unexpectedly Disabling Headache Revealing Moyamoya Disease in a Child in Sub-Saharan Africa: A Case Report and Review of the Literature.
Authors: Kouassi Kouamé Léonard , Broh N’Guessan Yves Constant , Yeo Nawa Samuel , Dihissou Monsouratou Ayan Niyi 
Published: July 2024

Abstract

Introduction
Moyamoya disease is a chronic cerebral vasculopathy of unknown origin. It is rarely described in Africa, especially in sub-Saharan Africa. Stroke is the major clinical manifestation. Headache, on the other hand, is a rare presenting symptom, especially when it is recent and disabling. Here, we report a case of moyamoya disease in a child in sub-Saharan Africa that was unexpectedly diagnosed by recent disabling headaches.
Case presentation
A 9-year-old male child with a two-week history of disabling headaches was admitted to the neurology department. These headaches were associated with vomiting, without fever, seizures or loss of consciousness, and without signs of localization. The cranioencephalic CT scan performed on the day of admission was normal. Twenty-four hours later, a left hemicorporeal motor deficit and visual disturbances appeared. Cerebral MRI and TOF angio-MRI revealed bilateral infarcts in the territories of the right middle cerebral artery and posterior cerebral arteries, with stenosis of both ends of the distal internal carotid arteries, giving a "smoke cloud" appearance to the cerebral vessels, suggestive of moya moya disease. The results of secondary etiology studies were unremarkable.
Conclusions
This case report alerts practitioners to an unusual initial clinical presentation of moyamoya disease in children and prompts them to prescribe cerebral MRI coupled with cerebral angio-MRI in the presence of an inaugural, disabling headache. This case also adds to the sub-Saharan literature on this disease.
Psychometric properties of the Arabic Anti-Mattering Scale in a non-clinical sample of Arabic-speaking adolescents.
Authors: Cherri Sarah , Malaeb Diana , Sakr Fouad , Dabbous Mariam , Hallit Souheil , Fekih-Romdhane Feten , Obeid Sahar 
Published: July 2024

Abstract

Background: The urge to be valued and to value others is known as mattering, and it is essential to children, adolescents, and college students' development of psychological health and wellbeing. Even with its significance, a lot of teenagers believe they are insignificant to their community. This study's objective was to examine, among a sample of adolescents from Lebanon, the psychometric properties of the Anti-Mattering Scale translated into Arabic.
Methods: Data for this cross-sectional study was gathered using a link in a Google Form. during November 2023. Using a snowball sampling technique, participants ranged in age from 13 to 18 and were Lebanese citizens.
Results: Seven hundred ninety-three adolescents took part in this study; the mean age was 16.08 ± 1.74 years. and 62.4% females. According to CFA, the AMS scale's one-factor model fit was satisfactory: RMSEA = .097 (90% CI .072, .125), SRMR = .027, CFI = .979, TLI = .957. For the entire sample, the composite reliability of scores was deemed sufficient (ω =.87 / α =.87). This model had a borderline convergent validity, as AVE = .57. In terms of anti-mattering scores, there was no discernible difference between the genders (10.73 ± 3.52 vs 11.14 ± 3.64; t(791) = -1.53; p = .126). Furthermore, there was a notable correlation between higher psychological distress and higher anti-mattering scores (r = .53; p < .001) and lower wellbeing (r = -.37; p < .001).
Conclusion: The Anti-Mattering Scale translated into Arabic appears to be a valid self-assessment measure to capture the presence of anti-mattering feeling among adolescents. It is anticipated that the Arabic Anti-Mattering Scale will be advantageous for healthcare professionals and researchers working with Arabic-speaking people around the world.
Over 20% external quantum efficiency in near-infrared quantum dot light-emitting diodes by using photo-crosslinked hole-transport layers
Authors: Li Binghan , Wang Yu , Zhang Jiancheng , Lin Qingli , Sun Ruijia , Shen Huaibin , Ji Botao 
Published: July 2024

Abstract

Visible quantum dot light-emitting diodes (QD-LEDs) have satisfied the requirements for commercial displays. However, the performance of near-infrared (NIR) QD-LEDs lags considerably behind, due to the comparatively inferior quality of NIR QDs and the current limitations on device architecture suitable for NIR electroluminescence. Here, we present an efficient and reproducable strategy using inorganic fluoride as a key additive to kinetically homogenize shell growth of ZnSe on various core QD facets, producing heavy-metal-free, highly regular InAs/InP/ZnSe/ZnS QDs with near 100% quantum yields. Moreover, we developed an approach of in-situ photo-crosslinking blended hole-transport materials for accurate modulation of energy levels of hole-transport layers (HTLs). The crosslinked HTLs substantially enhance hole transfer to the emitting layer for balanced carrier dynamics in QD-LEDs. The resulting NIR QD-LEDs exhibit a peak external quantum efficiency of 20.5%, a high radiance of 581.4 W sr−1 m−2 and an ultralong half-lifetime of 550 h at 50 W sr−1 m−2, making the device the best-performing solution-processed NIR QD-LEDs to date. This study represents a significant step towards practical application of NIR QD-LEDs.
Результаты терапии первичной медиастинальной В-крупноклеточной лимфомы. Опыт МРНЦ им. А.Ф. Цыба (Обнинск)
Authors: Терехова Алёна Юрьевна , Фалалеева Н. А. , Галицына А. Б. , Челмаков А. М. , Вовченко А. А. , Рябикина Н. А. , Даниленко А. А. , Богатырева Т. И. 
Published: July 2024

AIM. To assess chemotherapy outcomes in patients with primary mediastinal large B-cell lymphoma (PMBCL) treated at the AF Tsyb Medical Radiological Research Center from 2016 to 2023.
MATERIALS & METHODS. The analysis focused on the data from 58 patients with the morphologically verified diagnosis of PMBCL. The patients were aged 17–62 years (median 34 years), there were 39 women and 19 men. The median follow-up was 51.2 months (range 0.4–200.9 months). Depending on drug chemotherapy regimens, patients were divided into 3 groups: R-NHL-BFM-90 (n = 9), R-CHOP (n = 17), and R-MACOP-B (n = 32). Most patients (90 %) received mediastinal radiotherapy of total 30–46 Gy.
RESULTS. The 5-year overall survival in the therapy groups was 66.7 %, 88.2 %, and 100 %, respectively (p = 0.007), progression-free survival was 66.7 %, 70.6 %, and 96.9 % (p = 0.006), and event-free survival was 66.7 %, 54.7 %, and 90.6 % (p = 0.038). On the whole, the toxicity profile of chemotherapy was quite acceptable. Neither low blood values nor other adverse events essentially affected a complete implementation of chemoradiotherapy program. The intermediate outcomes were based on PET-CT data after 2–4 therapy cycles in 37 (64 %) patients. PET-CT showed that at the stage of drug chemotherapy, complete response (CR) was achieved in 27 (73 %) patients, and partial response (PR) was achieved in 4 (11 %) patients. With respect to the CR and PR criteria, there were 6 (16 %) non-responders.
CONCLUSION. PMBCL is one of extranodal lymphomas with thymic B-cells being primary source of tumor growth. PMBCL is characterized by aggressive course and extreme heterogeneity of clinical manifestations. Up to now, the first-line chemotherapy decision making in PMBCL has remained an issue with practical importance. This paper reports immediate and long-term outcomes of the program chemoradiotherapy regimen R-MACOP-B with subsequent consolidation radiotherapy. The results obtained can be termed quite satisfactory and noninferior to the data from national and international sources. Nevertheless, more effective chemoradiotherapy programs for PMBCL continue to be elaborated. In this context, immune checkpoint inhibitors as part of therapy programs seem to hold promise for the treatment of newly diagnosed PMBCL.
Investigating the Association between LncRNA NR2F2-AS1, miR-320b, and BMI1 in Gastric Cancer: Insights into Expression Profiles as Potential Biomarkers for Disease Management
Authors: Ghorbanzadeh Shadi , Pourghasem Navid , Amiz Roghayeh , Afsa Masoomeh , Malekzadeh Kianoosh 
Published: July 2024

Aim:
This study aims to investigate the potential role of lncRNA NR2F2-AS1 in the
development of gastric cancer by affecting the levels of miR-320b and BMI1.

Background:
Gastric cancer is a high-mortality malignancy, and understanding the underlying
molecular mechanisms is crucial. Non-coding RNAs play an important role in gene expression,
and their dysregulation can lead to tumor initiation and progression

Objective:
This study aims to determine the pathological role of LncRNA NR2F2-AS1 in gastric cancer progression and its association with the clinicopathological characteristics of patients.

Methods:
Bioinformatics databases were used to predict the expression levels and interactions between the studied factors to achieve this objective. The expression pattern of NR2F2-AS1/miR-320b/BMI1 in 40 pairs of tumor and adjacent normal tissues was examined using RT-PCR, IHC, and western blot. The correlation, ROC curve, and survival analyses were also conducted for the aforementioned factors.

Results:
The results showed an increase of more than 2-fold for BMI-1 and lncRNA NR2F2-AS1 in lower stages, and the elevation continued with the increasing stage of the disease. This correlated with significant downregulation of miR-320b and PTEN, indicating their association with gastric cancer progression and decreased patient survival. LncRNA NR2F2-AS1 acts as an oncogene by influencing the level of miR-320b, altering the amount of BMI1. A reduction in the amount of miR-320b against lncRNA NR2F2-AS1 and BMI1 directly correlates with a reduced overall survival rate of patients, especially if this disproportion is more than 3.0. ROC curve analysis indicated that alteration in the lncRNA NR2F2-AS1 level showed more than 98.0% sensitivity and specificity to differentiate the lower from higher stages of GC and predict the early onset of metastasis.

Conclusion:
In conclusion, these results suggest that NR2F2-AS1/miR-320b/BMI1 has the potential to be a prognostic as well as diagnostic biomarker for gastric cancer.
Comparison of Changes in the Neutrophil-lymphocyte Ratio (NLR) and Post-surgery Outcomes in Child Patients with COVID-19 and Non-COVID-19
Authors: Syukur Rusmin B. , Utariani Arie , Achmad Muh R. , Andriyanto Lucky , Octaliansah Ezra , Purnomo Windhu , Adhi Mahendratama P. 
Published: July 2024

Background
Pediatric patients with COVID-19 who undergo surgery have two actual problems, both the presence of a viral inflammatory response and an inflammatory response to surgery, which can affect the outcome of surgery and therapy. NLR is considered a fairly accurate predictor of surgery and COVID-19 infection.

Objective
This study aims to investigate the effect of changes in neutrophil-lymphocyte (NLR) on post-surgery outcomes in pediatric patients with COVID-19 infection.

Methods
It is a retrospective cohort study with an observational analysis. This research is a multi-center study on COVID-19 patients using a data bank of pediatric patients with COVID-19 and control patients non COVID-19 children undergoing surgery in three hospitals. Data recorded were age, gender, nutritional status, anesthetic status before surgery, length of stay, repeat surgery, sepsis, septic shock, and mortality. Statistical analysis was performed by the U-mann Whitney test.

Results
The study sample consisted of 87 total samples consisting of 2 groups, namely the group of pediatric patients with 29 samples with COVID-19 and 58 non-COVID-19 samples. Gender, nutritional status, length of stay, the incidence of reoperation, sepsis, septic shock, and mortality were not significantly different between the two groups with a p<0.05. Risk analysis by looking at the incidence of sepsis and septic shock obtained an intersection point of 2.175 and 2.225, respectively. Analysis of changes in the NLR (delta-NLR) in the mortality events of the COVID-19 group and non-COVID-19 found that the NLR of the COVID-19 group was two times greater than the cut-off NLR of 2,175.

Conclusion
There is no difference in the outcome of surgery for children with COVID-19 and non-COVID-19 based on the NLR change analysis.
A Novel Hybrid Feature Extraction Methods for EEG Based Emotions Classification Using Ensemble Approaches in HIED Dataset
Authors: A Stephen Dass , J Prabhu 
Published: July 2024

Introduction:
Emotions play a significant role in human relationships in everyday
challenges. Human-machine interactions and the complexities of its research include humanoid
robots which rely significantly on emotional queries. The present study recommends a novel
methodology for human emotion identification using electroencephalogram (EEG) signals.
Machine learning (ML) techniques based on electroencephalogram have demonstrated impressive
results in distinguishing emotions.

Method:
The present study investigates five ensemble learning-based machine learning
(EML) and five conventional machine learning (CML) algorithms for achieving the recognition
of human emotions from EEG signals. The main objective of the current research study is to
implement the optimal approach for classifying emotions and subsequently will achieve it by
using physiological characteristics to categorise the seven main emotional states (stress, happy,
sadness, anger, fear, disgust, and surprise). This research follows the study investigation of
ensemble learning-based machine learning (EML) and conventional machine learning (CML)
algorithms for achieving the recognition of human emotions from EEG signals. Initially, the
EEG data in this study are categorized into theta, alpha, beta, and gamma bands using a DWT.

Result:
With this idea, the research elaborates on further decomposition based on bandseparated
EEG signals known as intrinsic mode functions (IMFs) along with empirical mode
decomposition is subsequently implemented. Then, 13 statistical characteristics are extracted
from the IMFs using five multiclass EML algorithms: adaptive boosting, rotation random forest,
bagging, random forest, and extreme gradient boost. The following procedure is carried out
to construct an ML-based system. In the final phase, 10-fold cross-validation is used to assess
the efficacy of these five EML algorithms. Accuracy, F1-score, and area-under-the-curve
(AUC) are performance assessment metrics that are employed to compare the final results of
these algorithms with the five CML techniques.

Conclusion:
The proposed method for EEG-based emotion analysis is significantly more effective
and achieves greater outcomes than all of the techniques that were compared, based on
the data from the experiments.