Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models

The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005). Thus, the same situation can often result in different emotional experiences. While the detection of emotion is a well-established task, there is very limited work so far on the automatic prediction of cognitive appraisals. This work fills the gap by presenting CovidET-Appraisals, the most comprehensive dataset to-date that assesses 24 appraisal dimensions, each with a natural language rationale, across 241 Reddit posts. CovidET-Appraisals presents an ideal testbed to evaluate the ability of large language models -- excelling at a wide range of NLP tasks -- to automatically assess and explain cognitive appraisals. We found that while the best models are performant, open-sourced LLMs fall short at this task, presenting a new challenge in the future development of emotionally intelligent models. We release our dataset at https://github.com/honglizhan/CovidET-Appraisals-Public.Comment: EMNLP 2023 (Findings) Camera-Ready Versio

Unsupervised Extractive Summarization of Emotion Triggers

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained supervised models to both detect emotions and explain emotion triggers (events and appraisals) via abstractive summarization. However, obtaining timely and qualitative abstractive summaries is expensive and extremely time-consuming, requiring highly-trained expert annotators. In time-sensitive, high-stake contexts, this can block necessary responses. We instead pursue unsupervised systems that extract triggers from text. First, we introduce CovidET-EXT, augmenting (Zhan et al. 2022)'s abstractive dataset (in the context of the COVID-19 crisis) with extractive triggers. Second, we develop new unsupervised learning models that can jointly detect emotions and summarize their triggers. Our best approach, entitled Emotion-Aware Pagerank, incorporates emotion information from external sources combined with a language understanding module, and outperforms strong baselines. We release our data and code at https://github.com/tsosea2/CovidET-EXT.Comment: ACL 2023 Camera-Read

Multiwindow SRS imaging using a rapid widely tunable fiber laser

Spectroscopic stimulated Raman scattering (SRS) imaging has become a useful tool finding a broad range of applications. Yet, wider adoption is hindered by the bulky and environmentally sensitive solid-state optical parametric oscillator (OPO) in a current SRS microscope. Moreover, chemically informative multiwindow SRS imaging across C-H, C-D, and fingerprint Raman regions is challenging due to the slow wavelength tuning speed of the solid-state OPO. In this work, we present a multiwindow SRS imaging system based on a compact and robust fiber laser with rapid and wide tuning capability. To address the relative intensity noise intrinsic to a fiber laser, we implemented autobalanced detection, which enhances the signal-to-noise ratio of stimulated Raman loss imaging by 23 times. We demonstrate high-quality SRS metabolic imaging of fungi, cancer cells, and Caenorhabditis elegans across the C-H, C-D, and fingerprint Raman windows. Our results showcase the potential of the compact multiwindow SRS system for a broad range of applications.R35 GM136223 - NIGMS NIH HHS; R01 AI141439 - NIAID NIH HHS; R01 CA224275 - NCI NIH HHSAccepted manuscrip

Down-Regulation of MicroRNA-214 Contributed to the Enhanced Mitochondrial Transcription Factor A and Inhibited Proliferation of Colorectal Cancer Cells

Background/Aims: Colon cancer, also known as colorectal cancer (CRC), is one of the most common malignant tumors globally. Although significant advances have been made for developing novel therapeutics, the mechanisms of progression of colorectal cancer are still poorly understood. Methods: In this study, we identified down-regulation of microRNA-214 (miR-214) as the contributing factor for CRC. Mitochondrial transcription factor A (TFAM) and miR-214 expression in tumor samples from colorectal cancer patients and cancer cell lines were examined by reverse transcription and real-Time PCR (qPCR) or Western Blotting. Results: Our data demonstrated that miR-214 was significantly down-regulated in the tissue samples from CRC patients as well as CRC derived cell lines. TFAM overexpression was also observed in CRC patients and identified as a target for miR-214. Knockdown of TFAM by miR-214 mimics significantly inhibited the proliferation of CRC cell lines. Also, down-regulation of TFAM inhibited nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) nuclear translocation and the expression of NF-κB depended genes. Conclusion: In conclusion, our data suggested that down-regulation of MiR-214 contributed to the enhanced TFAM expression and decreased proliferation of CRC cells

Discovery of Pod Shatter-Resistant Associated SNPs by Deep Sequencing of a Representative Library Followed by Bulk Segregant Analysis in Rapeseed

Background: Single nucleotide polymorphisms (SNPs) are an important class of genetic marker for target gene mapping. As of yet, there is no rapid and effective method to identify SNPs linked with agronomic traits in rapeseed and other crop species. Methodology/Principal Findings: We demonstrate a novel method for identifying SNP markers in rapeseed by deep sequencing a representative library and performing bulk segregant analysis. With this method, SNPs associated with rapeseed pod shatter-resistance were discovered. Firstly, a reduced representation of the rapeseed genome was used. Genomic fragments ranging from 450–550 bp were prepared from the susceptible bulk (ten F2 plants with the silique shattering resistance index, SSRI,0.10) and the resistance bulk (ten F2 plants with SSRI.0.90), and also Solexa sequencingproduced 90 bp reads. Approximately 50 million of these sequence reads were assembled into contigs to a depth of 20-fold coverage. Secondly, 60,396 ‘simple SNPs ’ were identified, and the statistical significance was evaluated using Fisher’s exact test. There were 70 associated SNPs whose –log10p value over 16 were selected to be further analyzed. The distribution of these SNPs appeared a tight cluster, which consisted of 14 associated SNPs within a 396 kb region on chromosome A09. Our evidence indicates that this region contains a major quantitative trait locus (QTL). Finally, two associated SNPs from this region were mapped on a major QTL region

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Biophysical characterization of the allosteric transition in lactose repressor protein (LacI)

Allosteric transition, the basis of signal transduction and central to the function of regulatory proteins (e.g., transcriptional factors), is widely involved in biological systems with conformational change as a key characteristic. Although end-state structures are known for many proteins, less is known about the underlying detailed mechanism of the allosteric transition. We have used LacI as a model system to investigate this process at the atomic level. The work in this thesis focuses on three regions of LacI: the core pivot region, the N-subdomain monomer-monomer interface, and the hinge region. Characterization of representative mutants (L148F, S151P, P320A, and Q60G/L148F) demonstrated that the core pivot region exerts long-range effects on LacI function. For L148F and S151P, operator and inducer binding are altered in an inverse fashion with binding for one ligand strengthened, and binding for the other ligand weakened. Further characterization of L148F and S 151P has indicated that the conformational equilibrium is shifted towards the induced state in L148F and towards the repressed end in S151P. This conclusion is supported by detailed thermodynamic ligand binding assays and UV difference spectra. Detailed unfolding/refolding studies further suggest that the intrinsic ligand-binding properties of L148F and S151P are altered. Global fitting of all ligand-binding data is underway to further characterize these shifts. Our data for K84 hydrophobic variants (K84A/L) disclose impeded allosteric response to inducer, a state that is supported by a unique pattern in UV difference spectra. Operator release kinetics for K84A/L in response to IPTG suggest that two inducer molecules are required to release operator DNA. Characterization of 13 substitutions at V52, including binding to operator sequence variants, indicates a dominant role of the protein-operator interaction in LacI allostery and high affinity operator binding. Moreover, subsets of mutants that decouple inducer binding and conformational change were identified. In summary, this thesis work emphasizes the key role of several regions in LacI allostery, identifies several LacI allosteric intermediates, and discloses intermediates trapped along the allosteric pathway by mutation that correlate with points along the TMD simulation