Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystal Structures of Human Choline Kinase Isoforms in Complex with Hemicholinium-3

Human choline kinase (ChoK) catalyzes the first reaction in phosphatidylcholine biosynthesis and exists as ChoKα (α1 and α2) and ChoKβ isoforms. Recent studies suggest that ChoK is implicated in tumorigenesis and emerging as an attractive target for anticancer chemotherapy. To extend our understanding of the molecular mechanism of ChoK inhibition, we have determined the high resolution x-ray structures of the ChoKα1 and ChoKβ isoforms in complex with hemicholinium-3 (HC-3), a known inhibitor of ChoK. In both structures, HC-3 bound at the conserved hydrophobic groove on the C-terminal lobe. One of the HC-3 oxazinium rings complexed with ChoKα1 occupied the choline-binding pocket, providing a structural explanation for its inhibitory action. Interestingly, the HC-3 molecule co-crystallized with ChoKβ was phosphorylated in the choline binding site. This phosphorylation, albeit occurring at a very slow rate, was confirmed experimentally by mass spectroscopy and radioactive assays. Detailed kinetic studies revealed that HC-3 is a much more potent inhibitor for ChoKα isoforms (α1 and α2) compared with ChoKβ. Mutational studies based on the structures of both inhibitor-bound ChoK complexes demonstrated that Leu-401 of ChoKα2 (equivalent to Leu-419 of ChoKα1), or the corresponding residue Phe-352 of ChoKβ, which is one of the hydrophobic residues neighboring the active site, influences the plasticity of the HC-3-binding groove, thereby playing a key role in HC-3 sensitivity and phosphorylation.
Theoretical thermal efficiency of conventional steam reforming of groundnut shell

The aim of the study was to determine the theoretical thermal efficiency of conventional steam reforming of groundnut shell (CSR-GS) using the ‘DH ratio’ factor. It was found that the energy cost of CSR-GS is dominated by the enthalpy of heating liquid water from 298 K (25 ℃ ) to reforming temperature. Thus, energy cost increases with the increase in steam-to-carbon ratio . It was concluded that the conventional steam reforming of groundnut shell has poor thermal efficiency at all the investigated steam-to-carbon ratios and temperatures (700 to 1200 K). However, at a steam-to-carbon ratio of 1 combined with a temperature of 700 K, the DH ratio was 1.0, which is almost more thermodynamically advantageous than water splitting.
Potential targets for the treatment of MI: GRP75-mediated Ca2+ transfer in MAM

Background: After myocardial infarction (MI), there is a notable disruption in cellular calcium ion homeostasis and mitochondrial function. These alterations are believed to be linked to endoplasmic reticulum (ER) stress, though the specific mechanisms are not fully understood. This research endeavors to elucidate the involvement of glucose regulated protein 75 (GRP75) in post-MI calcium ion homeostasis and mitochondrial function. Results: Excessive oxidative stress was activated in humans' post-myocardial infarction, with most differentially expressed genes being enriched in metabolic pathways, especially the calcium signaling pathway. In MI rats, symptoms of myocardial injury were accompanied by an increase in the activation of PERK, ATF6, and IRE1, as well as elevated Binding immunoglobulin protein (Bip) expression. Moreover, in oxygen-glucose deprivation (OGD)-induced cardiomyocytes, it was confirmed that inhibiting PERK exacerbated intracellular Ca2+ disruption and cell apoptosis. More importantly, in cardiomyocytes undergoing Tunicamycin-induced ER stress, Ca2+ accumulated in both the ER and mitochondria. Concurrently, the co-localization of GRP75 with IP3R and VDAC1 increased under ER stress in cardiomyocytes. In OGD-induced cardiomyocytes, knockdown of GRP75 not only reduced the Ca2+ levels in both the ER and mitochondria and improved the ultrastructure of cardiomyocytes, but it also increased the number of contact points between ER and mitochondria, reducing MAM formation, and decreased cell apoptosis. Significantly, knockdown of GRP75 did not affect the protein expression of PERK and hypoxia-inducible factor 1 (HIF-1). Transcriptome analysis of cardiomyocytes revealed that knockdown of GRP75 mainly influenced the molecular functions of sialyltransferase and IP3R, as well as the biosynthesis of glycosphingolipids and lactate metabolism. In OGD-induced cardiomyocytes, the knockdown of GRP75 lowered the protein expression levels of glucose transporter-1 (Glut1), pyruvate kinase M2 (PKM2), and lactate dehydrogenase A (LDHA), and decreased the metabolic products of glycolysis. Conclusion: The complex interaction between the ER and mitochondria, driven by the GRP75 and its associated IP3R1-GRP75-VDAC1 complex, is crucial for calcium homeostasis and cardiomyocyte's adaptive response to ER stress. Modulating GRP75 could offer a strategy to regulate calcium dynamics, diminish glycolysis, and thereby mitigate cardiomyocyte apoptosis.
Predominance of Trichophyton tonsurans causing tinea capitis: A 12-year retrospective study in the north of Iran

Background and Purpose: Among different clinical entities of dermatophytosis, tinea capitis (TC) is considered a major public health challenge in the world, especially in regions with poor health and low income. Therefore, this study aimed to provide a retrospective analysis of the patients suspected of TC who were referred to the medical mycology laboratory of Mazandaran, a northern province of Iran. Materials and Methods: A retrospective analysis was performed on the patients suspected of TC who were referred to the medical mycology laboratory from July 2009 to April 2022. Hair roots and skin scrapings were collected from the participants. The laboratory diagnosis was confirmed by direct microscopic examination and culture. Finally, 921 out of 11095 (8.3%) patients were suspected of TC. Results: Based on the findings, TC was confirmed in 209 out of 921 patients (22.7%). In terms of gender, 209 TC patients (75.1%) were male. Moreover, the male to female ratio of TC patients was 1:3.0. Trichophyton tonsurans (146/174, 83.91%) was the most etiological agent, followed by T. mentagrophytes (13/174, 7.47%), T. violaceum (9/174, 5.17%), Microsporum canis (3/174, 1.71%), T. verrucosum (2/174, 1.15%) and T. rubrum (1/174, 0.57%). Besides, endothrix (77.0%) was the most prevalent type of hair invasion. Conclusion: The results revealed the predominance of T. tonsurans, as a causative agent of TC. Despite the prevalence of TC, the absence of appropriate consideration highlights that it is a neglected complication among children.
Explainable Depression Symptom Detection in Social Media

Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals' health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are centred on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. We present two approaches: i) train a model to classify, and another one to explain the classifier's decision separately and ii) unify the two tasks simultaneously using a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational LLMs when using in-context learning. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms, enhancing trust in the automated process. We evaluate our approach using recent symptom-based datasets, employing both offline and expert-in-the-loop metrics to assess the quality of the explanations generated by our models. The experimental results show that it is possible to achieve good classification results while generating interpretable symptom-based explanations.
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language

With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR). However, despite the commonalities of input format and task goal, there are huge gaps between the behavioral and textual information, which obstruct thoroughly modeling SR as language modeling via PLM. To bridge the gap, we propose a novel Unified pre-trained language model enhanced sequential recommendation (UPSR), aiming to build a unified pre-trained recommendation model for multi-domain recommendation tasks. We formally design five key indicators, namely naturalness, domain consistency, informativeness, noise&ambiguity, and text length, to guide the text->item adaptation and behavior sequence->text sequence adaptation differently for pre-training and fine-tuning stages, which are essential but under-explored by previous works. In experiments, we conduct extensive evaluations on seven datasets with both tuning and zero-shot settings and achieve the overall best performance. Comprehensive model analyses also provide valuable insights for behavior modeling via PLM, shedding light on large pre-trained recommendation models. The source codes will be released in the future.
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings

This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation. The potential advantage of DistillCSE is its self-enhancing feature: using a base model to provide additional supervision signals, a stronger model may be learned through knowledge distillation. However, the vanilla DistillCSE through the standard implementation of knowledge distillation only achieves marginal improvements due to severe overfitting. The further quantitative analyses demonstrate the reason that the standard knowledge distillation exhibits a relatively large variance of the teacher model's logits due to the essence of contrastive learning. To mitigate the issue induced by high variance, this paper accordingly proposed two simple yet effective solutions for knowledge distillation: a Group-P shuffling strategy as an implicit regularization and the averaging logits from multiple teacher components. Experiments on standard benchmarks demonstrate that the proposed DistillCSE outperforms many strong baseline methods and yields a new state-of-the-art performance.
Understanding Generative AI in Art: An Interview Study with Artists on G-AI from an HCI Perspective

The emergence of Generative Artificial Intelligence (G-AI) has changed the landscape of creative arts with its power to compose novel artwork and thus brought ethical concerns. Despite the efforts by prior works to address these concerns from technical and societal perspectives, there exists little discussion on this topic from an HCI point of view, considering the artists as human factors. We sought to investigate the impact of G-AI on artists, understanding the relationship between artists and G-AI, in order to motivate the underlying HCI research. We conducted semi-structured interviews with artists ($N=25$) from diverse artistic disciplines involved with G-AI in their artistic creation. We found (1) a dilemma among the artists, (2) a disparity in the understanding of G-AI between the artists and the AI developers(3) a tendency to oppose G-AI among the artists. We discuss the future opportunities of HCI research to tackle the problems identified from the interviews.
Higher or Lower: Challenges in Object based SLAM

Simultaneous localization and mapping, as a fundamental task in computer vision, has gained higher demands for performance in recent years due to the rapid development of autonomous driving and unmanned aerial vehicles. Traditional SLAM algorithms highly rely on basic geometry features such as points and lines, which are susceptible to environment. Conversely, higher-level object features offer richer information that is crucial for enhancing the overall performance of the framework. However, the effective utilization of object features necessitates careful consideration of various challenges, including complexity and process velocity. Given the advantages and disadvantages of both high-level object feature and low-level geometry features, it becomes essential to make informed choices within the SLAM framework. Taking these factors into account, this paper provides a thorough comparison between geometry features and object features, analyzes the current mainstream application methods of object features in SLAM frameworks, and presents a comprehensive overview of the main challenges involved in object-based SLAM.
Towards Multi-Subsession Conversational Recommendation

Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS mainly focuses on the single conversation (subsession) that user quits after a successful recommendation, neglecting the common scenario where user has multiple conversations (multi-subsession) over a short period. Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession. To fill the gap in this new CRS scenario, we devise a novel framework called Multi-Subsession Conversational Recommender with Activation Attributes (MSCAA). Specifically, we first develop a context-aware recommendation module, comprehensively modeling user interests from historical interactions, previous subsessions, and feedback in the current subsession. Furthermore, an attribute selection policy module is proposed to learn a flexible strategy for asking appropriate attributes to elicit user interests. Finally, we design a conversation policy module to manage the above two modules to decide actions between asking and recommending. Extensive experiments on four datasets verify the effectiveness of our MSCAA framework for the MSMCR setting.
The $d_\gamma/2$-variation of distance profiles in $\gamma$-Liouville quantum gravity

For Brownian surfaces with boundary and an interior marked point, a natural observable to consider is the distance profile, defined as the process of distances from the marked point to a variable point $x$ lying on the boundary. When the boundary is parametrized by the natural length measure on it, this distance profile turns out to be locally absolutely continuous to Brownian motion, and as a result, the boundary length measure itself has a natural interpretation as the quadratic variation process of the distance profile. In this paper, we extend this interpretation to $\gamma$-Liouville quantum gravity ($\gamma$-LQG), a one-parameter family of models of random geometry which is known to specialize to the case of Brownian geometry for the case $\gamma=\sqrt{8/3}$. With $d_\gamma$ denoting the Hausdorff dimension of $\gamma$-LQG, we show that for a $\gamma$-LQG surface with boundary, the natural boundary length measure can be interpreted (up to a constant factor) as the $d_\gamma/2$-variation process of the distance profile from an interior point.
Energy-variational solutions for viscoelastic fluid models

In this article, we introduce the concept of energy-variational solutions for a large class of systems of nonlinear evolutionary partial differential equations. Under certain convexity assumptions, the existence of such solutions can be shown constructively by an adapted minimizing movement scheme. Weak-strong uniqueness follows by a suitable relative energy inequality. Our main motivation is to apply the general framework to viscoelastic fluid models. Therefore, we give a short overview on different versions of such models and their derivation. The abstract result is applied to two of these viscoelastic fluid models in full detail. In the conclusion, we comment on further applications of the general theory and its possible impact.
Stable Nonconvex-Nonconcave Training via Linear Interpolation

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss landscape and show how linear interpolation can help by leveraging the theory of nonexpansive operators. We construct a new optimization scheme called relaxed approximate proximal point (RAPP), which is the first explicit method without anchoring to achieve last iterate convergence rates for $\rho$-comonotone problems while only requiring $\rho>-\tfrac{1}{2L}$. The construction extends to constrained and regularized settings. By replacing the inner optimizer in RAPP we rediscover the family of Lookahead algorithms for which we establish convergence in cohypomonotone problems even when the base optimizer is taken to be gradient descent ascent. The range of cohypomonotone problems in which Lookahead converges is further expanded by exploiting that Lookahead inherits the properties of the base optimizer. We corroborate the results with experiments on generative adversarial networks which demonstrates the benefits of the linear interpolation present in both RAPP and Lookahead.
InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution

Over recent decades, significant advancements in cross-modal retrieval are mainly driven by breakthroughs in visual and linguistic modeling. However, a recent study shows that multi-modal data representations tend to cluster within a limited convex cone (as representation degeneration problem), which hinders retrieval performance due to the inseparability of these representations. In our study, we first empirically validate the presence of the representation degeneration problem across multiple cross-modal benchmarks and methods. Next, to address it, we introduce a novel method, called InvGC, a post-processing technique inspired by graph convolution and average pooling. Specifically, InvGC defines the graph topology within the datasets and then applies graph convolution in a subtractive manner. This method effectively separates representations by increasing the distances between data points. To improve the efficiency and effectiveness of InvGC, we propose an advanced graph topology, LocalAdj, which only aims to increase the distances between each data point and its nearest neighbors. To understand why InvGC works, we present a detailed theoretical analysis, proving that the lower bound of recall will be improved after deploying InvGC. Extensive empirical results show that InvGC and InvGC w/LocalAdj significantly mitigate the representation degeneration problem, thereby enhancing retrieval performance. Our code is available at https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval
Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets

Opinion summarization is expected to digest larger review sets and provide summaries from different perspectives. However, most existing solutions are deficient in epitomizing extensive reviews and offering opinion summaries from various angles due to the lack of designs for information selection. To this end, we propose SUBSUMM, a supervised summarization framework for large-scale multi-perspective opinion summarization. SUBSUMM consists of a review sampling strategy set and a two-stage training scheme. The sampling strategies take sentiment orientation and contrastive information value into consideration, with which the review subsets from different perspectives and quality levels can be selected. Subsequently, the summarizer is encouraged to learn from the sub-optimal and optimal subsets successively in order to capitalize on the massive input. Experimental results on AmaSum and Rotten Tomatoes datasets demonstrate that SUBSUMM is adept at generating pros, cons, and verdict summaries from hundreds of input reviews. Furthermore, our in-depth analysis verifies that the advanced selection of review subsets and the two-stage training scheme are vital to boosting the summarization performance.
NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring. However, the development of automatic recognition systems in this field is currently hindered by the scarcity of appropriately labeled datasets. The existing video datasets pose several limitations: 1) these datasets are small-scale in size to support comprehensive investigations of nursing activity; 2) they primarily focus on single procedures, lacking expert-level annotations for various nursing procedures and action steps; and 3) they lack temporally localized annotations, which prevents the effective localization of targeted actions within longer video sequences. To mitigate these limitations, we propose NurViD, a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours, making it approximately four times longer than the existing largest nursing activity datasets. Notably, it encompasses 51 distinct nursing procedures and 177 action steps, providing a much more comprehensive coverage compared to existing datasets that primarily focus on limited procedures. To evaluate the efficacy of current deep learning methods on nursing activity understanding, we establish three benchmarks on NurViD: procedure recognition on untrimmed videos, procedure and action recognition on trimmed videos, and action detection. Our benchmark and code will be available at \url{https://github.com/minghu0830/NurViD-benchmark}.
Tuna: Instruction Tuning using Feedback from Large Language Models

Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effective way to align model behaviors with human preferences. However, the instruction-tuned model has only seen one response per instruction, lacking the knowledge of potentially better responses. In this paper, we propose finetuning an instruction-tuned LLM using our novel \textit{probabilistic ranking} and \textit{contextual ranking} approaches to increase the likelihood of generating better responses. Probabilistic ranking enables the instruction-tuned model to inherit the relative rankings of high-quality and low-quality responses from the teacher LLM. On the other hand, learning with contextual ranking allows the model to refine its own response distribution using the contextual understanding ability of stronger LLMs. Furthermore, we apply probabilistic ranking and contextual ranking sequentially to the instruction-tuned LLM. The resulting model, which we call \textbf{Tuna}, consistently improves the performance on Super Natural Instructions (119 test tasks), LMentry (25 test tasks), Vicuna QA, and can even obtain better results than several strong reinforcement learning baselines. Our code and data are available at \url{ https://github.com/microsoft/LMOps}.