Mendeley Data Showcase

My research uncovered the resource trade-off mechanism between plant growth and immunity, which is associated with the energy sensor TOR and involves the phosphatase LOPP that plays a critical role in immunity. This dataset contains all the original protein immunoblot images from my paper. The labels on the images correspond to the data in the paper, with red boxes indicating target bands and red text denoting the antibodies used.

The widespread dissemination of religiously aggressive content on social media platforms poses significant threats to social cohesion and communal harmony. Social media has emerged as a prevalent venue for discussing diverse topics, including religion which frequently result in debates. These debates often fuel animosity, incite violence, and spread life-threatening messages that disrupt societal peace and security. To address this challenge, we developed a novel Bengali dataset, ALERT, accompanied by English translations, to identify and classify religious aggression in texts. The dataset was obtained from several online platforms, including Facebook, YouTube, blogs, online news portals, and group discussions. We executed multiple stages for data preprocessing, including the elimination of duplicates, special characters, emoji to improve the coherence of the dataset. Each instance in the dataset was annotated by two of the lists of four annotators with diverse academic, religious, and racial backgrounds, with any discrepancies resolved by expert review. The ALERT dataset is a collection of 4,003 Bangla texts categorized as 1. hate speech (1,007), 2. vandalism (998), 3. life-threatening (994), 4. no aggression (1,004). The dataset is structured with the following fields: • Annotator 1 • Annotator 2 • Final Annotation • Text • English Translation Our developed dataset contains a mix of formal and informal Bangla texts, reflecting how people communicate in real life. Instead of simply labeling content as aggressive or not, it offers more detailed categories, helping with more precise content moderation. The dataset is publicly accessible for research purposes to promote innovation and collaboration within the Bengali NLP community.

This data consists of raw immunoblotting images from the article titled "KGCLM lactylation mediated by ACAT2 promotes ferroptosis resistance in KRASG12D-mutant cancer". KRAS mutations drive tumorigenesis, but their role in ferroptosis regulation remains unclear. Here, we construct KRASWT and KRASG12D-mutant cancer cells and demonstrate that G12D-mutant cells exhibit increased viability and reduced ferroptosis upon RSL3 or erastin treatment. These cells show diminished lipid peroxidation and mitochondrial damage, indicating ferroptosis resistance. KRASG12D activates MEK-ERK signaling to phosphorylate LDHA, enhancing glycolysis and lactate production. Exogenous lactate supplementation similarly protects wild-type cells from ferroptosis. Mechanistically, G12D mutation-derived lactate induces Glutamate-cysteine ligase modifier (GCLM) lactylation, a process catalysed by Acetyl-CoA Acetyltransferase 2 (ACAT2). Inhibition of GCLM lactylation either through the mutation of the lactylation site or by knockdown of ACAT2 diminished the enzymatic activity of Glutamate-cysteine ligase (GCL) and suppressed Glutathione synthesis. Importantly, ACAT2 depletion overcomes ferroptosis resistance in KRASG12D-mutant tumors in vivo. Our findings reveal a KRASG12D-driven metabolic adaptation linking GCLM lactylation to ferroptosis resistance, proposing ACAT2 inhibition as a therapeutic strategy for KRAS-mutant cancers.

This repository contains the main input files used for the seismic hazard calculations performed with the OpenQuake engine, including the modified source model and logic tree configurations based on the 2022 Costa Rica seismic hazard model (Hidalgo-Leiva et al., 2023). It also includes main output files (e.g., hazard maps and ground motion grids, hazard curves, and disaggregation) and a filtered list of earthquakes with magnitudes Mw ≥ 7 from Arroyo-Solorzano and Linkimer (2021) used in the analysis described in the discussion for the recurrence periods in the Nicoya and Osa peninsulas.

“Data supporting this study are openly available"

Abstract: Quinazolinones have been recently recognized as preferred scaffolds for developing novel therapeutic opportunities: they indeed exhibit a wide range of biological activities, including antifungal, antitubercular, antihypertensive, anticancer, and antiviral ones. In this work, a focused library of new bioactive 4-(3-H)-quinazolinones has been synthesized, their cytotoxic action against DU-145 prostate cancer cells has been detailed and compound 4k has been revealed as the most active one. Consequently, its interactome has been characterized, using a label-free functional proteomics-based platform coupling Drug Affinity Responsive Target Stability (DARTS) and targeted Limited Proteolysis-Multiple Reaction Monitoring-Mass Spectrometry (t-LiP-MRM-MS). This multi-faced strategy has been employed to reveal few subunits of the 26S proteasome machinery as the most reliable compound 4k biological targets. This paved the way for the deepening of the protein–ligand interaction using in-vitro and in-silico bio-orthogonal techniques. Finally, the analysis of its function in living DU-145 cells prompted compound 4k as a new leading inhibitor of the chymotrypsin-like activity of the proteasomal β-5 subunit, stirring the quinazolinone framework for the development of new anticancer drugs. This data set contains the following DARTS proteomics data: exp2, part 1

Western blot data from Sulagna Mukherjee following western blots of tissue from control or Rapalink-1 treatments.

We present data on the protein release kinetics from different electrospun DegraPol® fiber meshes, where either IGF-1 was incorporated, PDGF-BB, or IGF-1 and PDGF-BB (mixed group). We present cumulative release data over a period of 30 days.

Raw data

This research used a systematic literature review approach to assess the implementation of AI agents in addressing mental health issues. The review followed the PRISMA (Preferred Reporting Items for Systematic Reviews) procedure to identify, assess, and analyze academic literature related to the use of AI agents and Large Language Models (LLM) in mental health services, specifically in emotional support and personalization based on multi-modal emotion recognition.