Constraints on Scalar-Field Dark Energy from the Cosmic Lens All-Sky Survey Gravitational Lens Statistics

We use the statistics of strong gravitational lensing based on the Cosmic Lens All-Sky Survey (CLASS) data to constrain cosmological parameters in a spatially-flat, inverse power-law potential energy density, scalar-field dark energy cosmological model. The lensing-based constraints are consistent with, but weaker than, those derived from Type Ia supernova redshift-magnitude data, and mildly favor the Einstein cosmological constant limit of this dark energy model.Comment: 10 pages, 1 figure; ApJL, in press; minor additions, new referenc

SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity

Recently, sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks. However, they neglect to evaluate the quality of their constructed sentiment representations; they just focus on improving the fine-tuning performance, which overshadows the representation quality. We argue that without guaranteeing the representation quality, their downstream performance can be highly dependent on the supervision of the fine-tuning data rather than representation quality. This problem would make them difficult to foray into other sentiment-related domains, especially where labeled data is scarce. We first propose Sentiment-guided Textual Similarity (SgTS), a novel metric for evaluating the quality of sentiment representations, which is designed based on the degree of equivalence in sentiment polarity between two sentences. We then propose SentiCSE, a novel Sentiment-aware Contrastive Sentence Embedding framework for constructing sentiment representations via combined word-level and sentence-level objectives, whose quality is guaranteed by SgTS. Qualitative and quantitative comparison with the previous sentiment-aware PLMs shows the superiority of our work. Our code is available at: https://github.com/nayohan/SentiCSEComment: 14 pages, 8 figure

When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings

Complex chemical structures, like drugs, are usually defined by SMILES strings as a sequence of molecules and bonds. These SMILES strings are used in different complex machine learning-based drug-related research and representation works. Escaping from complex representation, in this work, we pose a single question: What if we treat drug SMILES as conventional sentences and engage in text classification for drug classification? Our experiments affirm the possibility with very competitive scores. The study explores the notion of viewing each atom and bond as sentence components, employing basic NLP methods to categorize drug types, proving that complex problems can also be solved with simpler perspectives. The data and code are available here: https://github.com/azminewasi/Drug-Classification-NLP.Comment: 7 pages, 2 figures, 5 tables, Accepted (invited to present) to the The Second Tiny Papers Track at ICLR 2024 (https://openreview.net/forum?id=VUYCyH8fCw

CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions

Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potential to pave the way for creating and advancing innovative medications applicable in practical settings. However, existing DDI prediction models continue to face challenges related to generalization in extreme cases, robust feature extraction, and real-life application possibilities. We aim to address these challenges by leveraging the effectiveness of context-aware deep graph learning by introducing a novel framework named CADGL. Based on a customized variational graph autoencoder (VGAE), we capture critical structural and physio-chemical information using two context preprocessors for feature extraction from two different perspectives: local neighborhood and molecular context, in a heterogeneous graphical structure. Our customized VGAE consists of a graph encoder, a latent information encoder, and an MLP decoder. CADGL surpasses other state-of-the-art DDI prediction models, excelling in predicting clinically valuable novel DDIs, supported by rigorous case studies.Comment: 8 Pages, 4 Figures; In revie

ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning

In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-attention ViT to squeeze the best of the data. We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions. To ensure correct label classification and improve the models' discriminative power, we introduce anchor loss, which encourages large margins between anchor points. Additionally, the multi-head self-attention mechanism, which is also trainable, plays an integral role in identifying accurate labels. This approach provides critical elements for improving the reliability of predictions and has a substantial positive effect on final prediction capabilities. Our adaptive model can be integrated with any deep neural network to forestall challenges in various recognition tasks. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.Comment: 10 pages, 7 figures. Code: https://github.com/takihasan/ARBE

A Generalized Look at Federated Learning: Survey and Perspectives

Federated learning (FL) refers to a distributed machine learning framework involving learning from several decentralized edge clients without sharing local dataset. This distributed strategy prevents data leakage and enables on-device training as it updates the global model based on the local model updates. Despite offering several advantages, including data privacy and scalability, FL poses challenges such as statistical and system heterogeneity of data in federated networks, communication bottlenecks, privacy and security issues. This survey contains a systematic summarization of previous work, studies, and experiments on FL and presents a list of possibilities for FL across a range of applications and use cases. Other than that, various challenges of implementing FL and promising directions revolving around the corresponding challenges are provided.Comment: 9 pages, 2 figure

Efficient mutation screening for cervical cancers from circulating tumor DNA in blood

Background Early diagnosis and continuous monitoring are necessary for an efficient management of cervical cancers (CC). Liquid biopsy, such as detecting circulating tumor DNA (ctDNA) from blood, is a simple, non-invasive method for testing and monitoring cancer markers. However, tumor-specific alterations in ctDNA have not been extensively investigated or compared to other circulating biomarkers in the diagnosis and monitoring of the CC. Therfore, Next-generation sequencing (NGS) analysis with blood samples can be a new approach for highly accurate diagnosis and monitoring of the CC. Method Using a bioinformatics approach, we designed a panel of 24 genes associated with CC to detect and characterize patterns of somatic single-nucleotide variations, indels, and copy number variations. Our NGS CC panel covers most of the genes in The Cancer Genome Atlas (TCGA) as well as additional cancer driver and tumor suppressor genes. We profiled the variants in ctDNA from 24 CC patients who were being treated with systemic chemotherapy and local radiotherapy at the Jeonbuk National University Hospital, Korea. Result Eighteen out of 24 genes in our NGS CC panel had mutations across the 24 CC patients, including somatic alterations of mutated genes (ZFHX3-83%,KMT2C-79%, KMT2D-79%, NSD1-67%,ATM-38% andRNF213-27%). We demonstrated that theRNF213mutation could be used potentially used as a monitoring marker for response to chemo- and radiotherapy. Conclusion We developed our NGS CC panel and demostrated that our NGS panel can be useful for the diagnosis and monitoring of the CC, since the panel detected the common somatic variations in CC patients and we observed how these genetic variations change according to the treatment pattern of the patient

Ki67 Antigen as a Predictive Factor for Prognosis of Sinonasal Mucosal Melanoma

ObjectivesSinonasal mucosal melanoma is a rare and aggressive disease. The aim of this study was to analyze the clinical features of patients with sinonasal mucosal melanoma and to determine the role of Ki67 antigen as a predictor of prognosis in sinonasal mucosal melanoma.MethodsThis was a retrospective case-series study at a single institution, an academic tertiary referral center. From 1995 to 2007, 27 patients with sinonasal mucosal melanoma were reviewed retrospectively, and the expression of Ki67 antigen was assessed by immunohistochemistry.ResultsThe overall 5-yr survival rate was 33.9%. No significant differences were observed in 5-yr survival according to age, sex, stage, or the presence of melanin. The rates of local failure, regional failure, and distant failure were 37.0%, 14.8%, and 11.1%, respectively. Patients with spindle or mixed cell types had better prognoses than those with other cell types. At a cut-off value of 35%, patients with lower Ki67 scores showed better survival than those with higher Ki67 scores.ConclusionThe presence of spindle or mixed cell types may indicate a better prognosis than other cell types. Ki67 immunostaining may be a useful predictor of prognosis in patients with mucosal malignant melanoma of the sinonasal tract

Preparation and In Vitro

Magnesium ion substituted biphasic calcium phosphate (Mg-BCP) bioceramic microscaffolds with spherical and porous morphology were successfully prepared using in situ coprecipitation and rotary spray drying atomization process for application of tissue engineering combined with human adipose tissue-derived mesenchymal stem cells (hAT-MSCs). After 4 weeks of immersion in Hanks’ balanced salt solution (HBSS), Mg-BCP micro-scaffolds showed the enhanced biodegradation and bioactivity due to the substituted Mg2+ ion present in the BCP structure. In this study, it was observed that hAT-MSCs have clearly attached on the surface of Mg-BCP micro-scaffolds. In addition, Mg-BCP micro-scaffolds exhibited the improved biocompatibility and osteoconductivity via in vitro and in vivo biological tests with hAT-MSCs. Therefore, these bioceramic micro-scaffolds had potential to be used as hAT-MSCs microcarriers for biomedical applications