bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tabular Transformer Generative Adversarial Network for Heterogeneous distribution in healthcare
Authors: Kang Ha Ye Jin , Ko Minsam , Ryu Kwang Sun 
Published: March 2024

Abstract

In healthcare, the most common type of data is tabular data, which hold high significance and potential in the field of medical AI. However, privacy concerns have hindered their widespread use. Despite the emergence of synthetic data as a viable solution, the generation of healthcare tabular data (HTD) is complex owing to the extensive interdependencies between the variables within each record that incorporate diverse clinical characteristics, including sensitive information. To overcome these issues, this study proposed a tabular transformer generative adversarial network (TT-GAN) to generate synthetic data that can effectively consider the relationships between variables potentially present in the HTD dataset. Transformers can consider the relationships between the columns in each record using a multi-attention mechanism. In addition, to address the potential risk of restoring sensitive data in patient information, a Transformer was employed in a generative adversarial network (GAN) architecture, to ensure an implicit-based algorithm. To consider the heterogeneous characteristics of the continuous variables in the HTD dataset, the discretization and converter methodology were applied. The experimental results confirmed the superior performance of the TT-GAN than the Conditional Tabular GAN (CTGAN) and copula GAN. Discretization and converters were proven to be effective using our proposed Transformer algorithm. However, the application of the same methodology to Transformer-based models without discretization and converters exhibited a significantly inferior performance. The CTGAN and copula GAN indicated minimal effectiveness with discretization and converter methodologies. Thus, the TT-GAN exhibited considerable potential in healthcare, demonstrating its ability to generate artificial data that closely resembled real healthcare datasets. The ability of the algorithm to handle different types of mixed variables efficiently, including polynomial, discrete, and continuous variables, demonstrated its versatility and practicality in health care research and data synthesis.
Fusion of dual-targeting peptides with MAP30 promotes the apoptosis of MDA-MB-231 breast cancer cells
Authors: Wang Xinyi , Yang Yixuan , Chen Weiwei , Gan Li , Sun Yu , Lin Tong , Zhao Weichun , ZHU Zhenhong 
Published: March 2024

Abstract

Momordica antiviral protein 30 kDa (MAP30) is a type I ribosome-inactivating protein (RIP) with antibacterial, anti-HIV and antitumor activities but lacks the ability to target tumor cells. To increase its tumor-targeting ability, the arginine-glycine-aspartic (RGD) peptide and the epidermal growth factor receptor interference (EGFRi) peptide were fused with MAP30, which was named ELRL-MAP30. The use of targeted therapy for triple-negative breast cancer (TNBC) MDA-MB-231 cells, which lack the expression of estrogen receptor (ER), Progesterone receptor (PgR) and human epidermal growth factor receptor-2 (HER2), is limited. In this study, we focused on exploring the effect and mechanism of ELRL-MAP30 on TNBC MDA-MB-231 cells. First, we discovered that ELRL-MAP30 significantly inhibited the migration and invasion of MDA-MB-231 cells and induced MDA-MB-231 cell apoptosis. Moreover, ELRL-MAP30 treatment significantly reduced Bcl-2 protein expression and increased BAX protein expression. Furthermore, ELRL-MAP30 triggered apoptosis via the Fak/EGFR/Erk and Ilk/Akt signaling pathways. In addition, recombinant ELRL-MAP30 can also inhibit chicken embryonic angiogenesis, indicating its potential therapeutic effects on tumor angiogenesis. Collectively, these results indicate that ELRL-MAP30 has significant tumor-targeting properties in MDA-MB-231 cancer cells and reveals potential therapeutic effects on angiogenesis. These findings indicate the potential role of ELRL-MAP30 in the targeted treatment of the TNBC cell line MDA-MB-231.
Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching
Authors: xiaonan Li , Guanyu Li , Bo Ning , Xin zhou 
Published: March 2024

Abstract

In recent years, graph neural network technology has revolutionized molecular graph matching methods, offering new opportunities for drug discovery. Central to this research are two key steps: latent representation learning of molecules and molecular graph matching. However, the challenge lies in effectively capturing and expressing the intricate features of molecular graphs, given their strict chemical rules and high structural complexity. To address this, we propose a gaussian-enhanced graph matching method. This approach combines a dual-channel message-passing neural network, based in the GIN algorithm, to encode both nodes and edges of molecules, enabling precise representations of molecular graphs. Additionally, an end-to-end graph similarity calculation model is introduced, assessing similarity at both the node and global levels. By integrating these evaluations, we obtain a comprehensive similarity score for molecular graph pairs. This method enhances the accuracy and interpretability of molecular structural similarity. Experimental results demonstrate the superior performance of our model compared to state-of-the-art baseline methods, marking a significant step forward in molecular property prediction.
Oak seedling microbiome assembly under climate warming and drought
Authors: Hoefle Daniel , Sommer Milena , Wassermann Birgit , Faticov Maria , Serra Demetrio , Berg Gabriele , Tack Ayco J.M. , Abdelfattah Ahmed 
Published: March 2024

Abstract

Despite that climate change is currently one of the most pervasive challenges, its effects on the plant-associated microbiomes is still poorly studied. The aim of this study was to evaluate the impact of climate warming and drought on the microbiome assembly of oak plants from seed to seedling. In a multifactorial experimental set up, acorns were subjected to different temperature (15 °C, 20 °C, 25 °C) and soil moisture levels (drought (15 %) and control (60 %)) from germination until the seedling stage, after which the bacterial and fungal communities associated with the rhizosphere and phyllosphere of the seedlings were characterized. The interaction of temperature and drought affected both the bacterial and the fungal communities, whereas the latter was more affected. Specifically, the interaction influenced bacterial and fungal community composition in rhizosphere and fungal diversity and abundance in phyllosphere; overall, the effects were contrasting depending on soil moisture level. Further, temperature and drought separately affected the oak microbiome, though temperature had a stronger effect. Temperature affected bacterial and fungal diversity and abundance and bacterial community composition in phyllosphere and bacterial and fungal community composition and abundance in rhizosphere. In contrast, drought separately only affected fungal abundance in phyllosphere and bacterial and fungal community composition and abundance in rhizosphere. Regardless of temperature, Actinobacteriota was significantly enriched in drought conditions. This study provides new insights into climate change related impacts on the plant-associated microbiota of a major forest tree species such as oak.
A Comparative Analysis of Deep Learning Models for Multi-class Speech Emotion Detection
Authors: V Anchana. , Elango N. M. 
Published: March 2024

Abstract
In today's digital age, where communication transcends traditional boundaries, the exploration of deep learning models for Speech Emotion Recognition (SER) holds immense significance. As we increasingly interact through digital platforms, understanding and interpreting emotions becomes crucial. Deep learning models, with their ability to autonomously learn intricate patterns and representations, offer unparalleled potential in enhancing the accuracy and efficiency of SER systems. This project delves into models for multi-class speech emotion recognition on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The RAVDESS dataset contains 1440 speech audio recordings from 24 professional actors, expressing 8 different emotions: neutral, calm, happy, sad, angry, fearful, surprise, and disgust. Models including Deep Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and ensembles were developed. Additionally, data augmentation through pitch shifting, noise injection, and a combination thereof expanded the dataset. Besides spectrogram inputs, handcrafted audio features like Mel Frequency Cepstral Coefficients (MFCCs), Chroma Short-time Fourier transform, root mean square, and zero crossing rate were experimented with as inputs to further boost model performance. The best-performing models were a Temporal Convolutional Network (TCN), achieving 96.88% testing accuracy, and a Gated Recurrent Unit (GRU) achieving 97.04% testing accuracy in classifying the 8 emotions, outperforming previous benchmark results on this dataset.
Causal association between hallux valgus and osteoarthritis: a Mendelian randomization study
Authors: Ding Yuan , Xue Zichao , Xu Wenlong , Zhang Jie , Han Zhiyuan , Meng Ye 
Published: March 2024

Abstract
Background
Hallux valgus (HV) is considered to be associated with osteoarthritis (OA). However, the causal relationship between HV and OA remains confounded.
Methods
In this study, we aimed to examine the causal effect of HV and OA using two-sample Mendelian randomization (MR) analysis, which included MR Egger, weighted median, inverse variance weighting (IVW) and weighted mode method.
Results
HV was demonstrated as a causal risk factor of total OA by IVW method (OR = 1.008, 95% CI = 1.004–1.012, p = 1.758 × 10− 3). To further confirm the causal association between HV and various types of OA, we performed validation in knee OA and hip OA and showed that HV was the cause of knee OA (OR = 1.347, 95% CI = 1.100-1.649, p = 0.004), but not hip OA (OR = 1.103, 95% CI = 0.807–1.507, p = 0.539).
Conclusion
This study provided robust evidence of a causal relationship between HV and knee OA, indicating that HV should be corrected promptly to reduce the incidence of OA.
Low-dimensional intrinsic dimension reveals a phase transition in gradient-based learning of deep neural networks
Authors: Tan Chengli , Zhang Jiangshe , Liu Junmin , Zhao Zixiang 
Published: March 2024

Abstract
Deep neural networks complete a feature extraction task by propagating the inputs through multiple modules. However, how the representations evolve with the gradient-based optimization remains unknown. Here we leveraged the intrinsic dimension of the representations to study the learning dynamics and found that the training process underwent a phase transition from expansion to compression under disparate training regimes---a phenomenon that is ubiquitous across a wide variety of model architectures, optimizers, and data sets. We showed that the variation in the intrinsic dimension is consistent with the complexity of the learned hypothesis, which can be quantitatively assessed by the critical sample ratio that was rooted in adversarial robustness. Meanwhile, we mathematically demonstrated that this phenomenon can be analyzed in terms of the mutable correlation between neurons. Although the evoked activities obey a power-law decaying rule in biological circuits, we identified that the power-law exponent of the representations in deep neural networks predicted adversarial robustness well only at the end of the training but not during the training process. These results together suggest that deep neural networks are prone to producing robust representations by adaptively eliminating or retaining redundancies. The code is publicly available at \url{https://github.com/cltan023/learning2022}.
Perception of patients’ rights in clinical services among clients attending the outpatientdepartment at the Mawenzi Regional Referral Hospital, Tanzania
Authors: Ernest Bahat , Ochieng Joseph , Kwagala Betty 
Published: March 2024

Abstract
Background:Ethical principles and patients’ rights should guide all encounters with patients. Failureto observe ethical guidelines can lead to patientdissatisfaction and medical malpractice,and without anappropriate understanding of the patient’s rights, the healthcare team may at times be prone to various tendencies, such as failure to respect patientautonomy, such as acting in a paternalist manner or making clinical decisions on behalf of the patientor family. Anotherway to do socan occur, including cases in which not all options, risks or side effects may be appropriately disclosed. This study explored the perceptions of patients’ rights toclinical services among clients attending the outpatient department at the Mawenzi Regional Referral Hospital.
Methods:This was an exploratory cross-sectional study design that employed qualitative methods fordata collection. The study was carried out among 34 clients attending the outpatient department at Mawenzi Regional Referral Hospital in the Kilimanjaro region, Tanzania: the medical unit, the Care and Treatment Clinic (CTC) unit, the gynecology unit, and the TB and leprosy unit. In-depth interviews were conducted with 20 participants, while 14 participants participated in group discussions. The datawere analysed via inductive thematic analysis, which allowed the data to identifyand determine the themes in this study, which were then presented in the form of extracts or quotes. Nvivo 12 software was used to support the data analysis.
Results: The majority of the participants perceived that patients' rights were well implemented in health facilities and that they were satisfied with them. The minority stated that they are not satisfied with patients' rights because of various challenges that cause their rights to be poorly implemented. The main reasons for the poor implementation and dissatisfaction suggested were corruption in health facilities, the beating of patients, wastage of time while in health care facilities, poor communication with some health care providers, a shortage of medical equipment and healthcare providers, gaps in existing regulations, and follow-up.
Conclusion:To enhance satisfaction with patients' rights, there is a need for more sensitization of patients' rights through the client's service charter for health facilities and a relevant health policy to guide the implementation of patients' rights.
Comparison of the implementation of a flood evacuation model on two different geographical agent-based platforms: Netlogo and Gama.
Authors: Mamy RAKOTOARISOA Mahefa , Taïbi Aude Nuscia , Fleurant Cyril , Théodore Razakamanana 
Published: March 2024

Abstract
Agent-based simulation has gained interest in geography in recent years. Several platforms facilitate the development of spatially focused multi-agent models. However, each platform and language has its own specificities, and the same conceptual model can lead to different behaviors and results depending on the type of implementation used. Reproducing the same model on multiple platforms is the best way to test its reliability, while eliminating biases related to implementation. This "good practice" is still very rare, yet essential for making a more robust model. In this paper we present the implementation of the same conceptual model on two distinct platforms (Netlogo and Gama). Despite some overall similarities in the results, differences are observed in the outputs and functioning. These variabilities come from various factors, but a significant proportion results from the different ways of integrating geographical data, which differ between the two platforms. To highlight this geographical aspect, we use a conceptual model dealing with a flood evacuation.
Green synthesis of iron nanoparticles from the baru (Dipteryx alata) endocarp extract for the efficient removal of rhodamine B and caffeine from water through the heterogeneous Fenton process
Authors: Corrêa Cassiano Ricardo Reinehr , de Siqueira Adriano Buzutti , Matos Lopes Paulo Renato , Ambrosio Jéssica Aparecida Ribeiro , Simioni Andreza Ribeiro , de Vasconcelos Leonardo Gomes , de Morais Eduardo Beraldo 
Published: March 2024

Abstract

This study presents the first-time synthesis of iron nanoparticles (FeNPs) using an aqueous extract from the baru fruit endocarp (Dipteryx alata). Characterization through scanning electron microscopy and dynamic light scattering revealed spherical shapes with an average diameter of 419.2 nm. Fourier transform infrared spectroscopy identified phytochemicals from the baru fruit extract, acting as both reducing and stabilizing agents. X-ray diffraction confirmed the amorphous nature of the FeNPs. The Fenton-like catalytic efficiency of FeNPs was investigated for degrading rhodamine B (RhB) and caffeine. The impact of crucial parameters such as pH, H2O2 dosage, nanoparticles concentration, and temperature on the degradation process was assessed. At pH 3.0, with 1.0 g L−1 of FeNPs, 1% H2O2, and 45 °C, RhB and caffeine degradation reached 99.14 and 92.01%, respectively. The catalytic reaction kinetics followed a pseudo-first-order model for caffeine and a pseudo-second-order model for RhB. Phytotoxicity studies on Cucumis sativus confirmed the non-toxic nature of the degraded products of RhB and caffeine. These findings highlight the potential of FeNPs synthesized from the baru endocarp extract as a catalyst for removing organic pollutants, suggesting promising applications in environmental remediation and related fields.
Microwave Assisted Groebke-Blackburn-Bienayme Multicomponent Reaction to Synthesis of Imidazo[1,2-a]pyridine-furan Hybrids as Possible Therapeutic Option for Leukemia, Colon Cancer and Prostate Cancer
Authors: Manvar Parth , Katariya Dharmesh , Vyas Amita , Bhanderi Pooja , Khunt Ranjan 
Published: March 2024

Aims:
Microwave assisted ecofriendly catalytic protocol for the Groebke-Blackburn-Bienayme multicomponent reaction to synthesis imidazo[1,2-a]pyridine-furan hybrids as possible therapeutic option for leukemia, colon cancer and prostate cancer

Background:
Microwave synthesis has emerged as a potent tool for the more economical and environmental friendly synthesis of organic compounds, such as derivatives of imidazo[1,2-a]pyridine. Compared to traditional synthesis, microwave radiation causes molecules to be ex-cited and distributes thermal energy evenly in a shorter amount of time.

Objective:
The primary objective of the work presented in this article was to prepare imidazo[1,2-a]pyridine-furan hybrids via Groebke-Blackburn-Bienayme multicomponent reaction using PEG 400 in microwave irradiation as green approach. Characterized it and evaluated their anticancer activities.

Methods:
In a sealed microwave glass vial, 5-methylfuran-2-carbaldehyde 1, 2-aminoazines 2a-g, isocyanides 3a-c in presence of 20mol% acetic acid were dissolved in PEG 400 (polyethylene glycol 400) reaction solvent. The glass vial was sealed and irradiate in microwave with stirring at temperature of 75°C for 10 min. This method is an efficient alternative approach to synthesizing imidazo[1,2-a]pyridine-furan hybrids via Groebke-Blackburn-Bienayme multicomponent reac-tion.

Results:
We have successfully synthesised the imidazo[1,2-a]pyridine-furan hybrids via Groebke-Blackburn-Bienayme multicomponent reaction using PEG 400 in microwave irradiation as green approach. The structures of the compounds were confirmed through various spectro-scopic techniques and evaluated their anticancer activities.

Conclusion:
The reported protocol is advantageous over conventional methods of imidazo[1,2-a]pyridine derivatives. The time required for the reaction is much less as compared to the usual requirements of reflux. Compound 4e, 4f, 4n and 4o shows the most increased activity against cell line RPMI-8226, HCT-116 and PC-3 of Leukemia, Colon cancer and Prostate cancer respectively. By using the potential of imidazo[1,2-a]pyridine-furan based compounds via sustainable green approach, more effective and accurate cancer treatments can be designed in future.
Glycogen: A Novel Biopolymer Catalyst for the One-Pot Synthesis of Spirooxindoles, Spiro-Acenaphthylenes, and Spiro-2-Aminopyrans Derivatives under Mild Conditions
Authors: Ahmadi Nahid , Maghsoodlou Malek Taher , Nassiri Mahmoud , Milani Forough Jalili 
Published: March 2024

Background:
Glycogen, a naturally occurring macromolecule, in its granular form and
without any post-modification was found to be an efficient and eco-friendly bifunctional heterogeneous
organocatalyst.

Objective:
This catalyst can be useful for the domino synthesis of various spiropyren annulated derivatives
through three-component condensation of isathin, malononitrile, and diverse 1,3-dicarbonyl compounds,
activated CH-acids, through Knoevenagel-Michael-annulation sequence under mild conditions.

Methods:
Corresponding spiro derivatives were obtained in high to excellent yields after 5-15 min
stirring in 2 mL EtOH and 60°C in the presence of 0.01 g of glycogen, equimolar amounts of isatin/
acenaphthoquinone/ninhydrin, malononitrile, and 1,3-dicarbonyl compounds.

Results:
FTIR and 1H NMR spectroscopic showed there isn't any catalyst in the media and desired
products were obtained in excellent purity.

Conclusion:
Avoiding any transition metal, one-pot, and multicomponent procedure catalyzed by a
biopolymer, broad substrate scope, and operational simplicity are essential features of this methodology
for the preparation of medicinally important compounds.
Successful Autologous Blood Patch Pleurodesis: A Safe and Useful Treatment for Persistent Pneumothorax in Former Pulmonary Tuberculosis and Aspergillosis in Pregnant Women
Authors: Purnama Nori , Simatupang Elvando Tunggul Mauliate , Fauzi Zarfiardy Aksa , Indriani Sri , Yovi Indra , Hatta Hariadi , Zulmaeta  
Published: March 2024

Background: Autologous Blood Patch Pleurodesis (ABPP) is one of the treatment options for persistent pleural air leaks especially in patients who are not suitable for surgical intervention. Persistence pneumothorax is defined as pneumothorax of more than five days duration. It is associated with increased morbidity and cost of care. The most widely accepted treatment for it is pleurodesis. Several types of pleurodesis have been proposed, including surgical approaches and the instillation of different chemicals in the intrapleural space. ABPP has proven to be a simple, inexpensive, efficacious and safe method. We present the case of the first patient with Persistent Pneumothorax we have treated Successfully with ABPP. ABPP is currently rarely and uncommonly used, but it provides benefits especially in special conditions with easier, cheaper, and quite effective procedures.
Case: A female 35-year-old pregnant women patients with Former Tuberculosis (FTB) come to our hospital with complaints breathlessness and chest pain worsening since 3 days before admitted to our hospital. Chest radiography showed spontaneous pneumothorax with GeneXpert (GE) sputum Mtb Not Detected but there is Aspergillosis from Fungal Culture. Water Sealed Drainage (WSD) was inserted in right pleural for almost 1 month and this patient discharge with pneumostat. One month after that she come again with Persistence Pneumothotax, so that we do the pleurodesis with Blood Patch and get the improvement from clinical status. Antifungals are continued for an initial 4-6 weeks while an outpatient evaluation is conducted.
Conclusion: ABPP is a safe, inexpensive and efficacious treatment for persistent pleural air leak. Autologous Blood Patch administration may be considered for patients with Persistent Pneumothorax.