RRescue: Ranking LLM Responses to Enhance Reasoning Over Context

Effectively using a given context is paramount for large language models. A context window can include task specifications, retrieved documents, previous conversations, and even model self-reflections, functioning similarly to episodic memory. While efforts are being made to expand the context window, studies indicate that LLMs do not use their context optimally for response generation. In this paper, we present a novel approach to optimize LLMs using ranking metrics, which teaches LLMs to rank a collection of contextually-grounded candidate responses. Rather than a traditional full ordering, we advocate for a partial ordering. This is because achieving consensus on the perfect order for system responses can be challenging. Our partial ordering is more robust, less sensitive to noise, and can be acquired through human labelers, heuristic functions, or model distillation. We test our system's improved contextual understanding using the latest benchmarks, including a new multi-document question answering dataset. We conduct ablation studies to understand crucial factors, such as how to gather candidate responses, determine their most suitable order, and balance supervised fine-tuning with ranking metrics. Our approach, named RRescue, suggests a promising avenue for enhancing LLMs' contextual understanding via response ranking

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development

MicroRNA-24-3p Attenuates Myocardial Ischemia/Reperfusion Injury by Suppressing RIPK1 Expression in Mice

Background/Aims: This study was developed to investigate a potential therapeutic method for myocardial ischemia/reperfusion injury involving the promotion of miR-24-3p expression. Methods: Microarray analysis was used to screen differentially expressed genes in a myocardial ischemia/reperfusion (I/R) injury mouse model. Gene set enrichment analysis was utilized to determine vital signaling pathways. Targeting verification was conducted with a luciferase reporter assay. Myocardial I/R injury was developed in mice, and the expression levels of RIPK1 and miR-24-3p were investigated by qRT-PCR and Western blot. Hemodynamic parameters and the activity of serum myocardial enzymes were measured to evaluate cardiac function. Infarct area was observed through HE and TTC staining. Myocardial cell apoptosis was examined by TUNEL staining and caspase-3 activity analysis. Results: RIPK1 was an upregulated mRNA found by microarray analysis and a verified target of the downregulated miRNA miR-24-3p. The upregulation of RIPK1 (1.8-fold) and the downregulation of miR-24-3p (0.3-fold) were confirmed in I/R mice. RIPK1 led to impaired cardiac function indexes, increased infarct area and cell apoptosis, while miR-24-3p could reverse the injury by regulating RIPK1. The TNF signaling pathway was proven to be involved in myocardial I/R injury through the detection of the dysregulation of related proteins. Conclusion: In conclusion, RIPK1 was upregulated and miR-24-3p was downregulated in a myocardial I/R injury mouse model. RIPK1 could aggravate myocardial I/R injury via the TNF signaling pathway, while miR-24-3p could suppress RIPK1 and therefore exert cardioprotective effects in myocardial I/R injury

Some Rare Earth Elements Analysis by Microwave Plasma Torch Coupled with the Linear Ion Trap Mass Spectrometry

A sensitive mass spectrometric analysis method based on the microwave plasma technique is developed for the fast detection of trace rare earth elements (REEs) in aqueous solution. The plasma was produced from a microwave plasma torch (MPT) under atmospheric pressure and was used as ambient ion source of a linear ion trap mass spectrometer (LTQ). Water samples were directly pneumatically nebulized to flow into the plasma through the central tube of MPT. For some REEs, the generated composite ions were detected in both positive and negative ion modes and further characterized in tandem mass spectrometry. Under the optimized conditions, the limit of detection (LOD) was at the level 0.1 ng/mL using MS2 procedure in negative mode. A single REE analysis can be completed within 2~3 minutes with the relative standard deviation ranging between 2.4% and 21.2% (six repeated measurements) for the 5 experimental runs. Moreover, the recovery rates of these REEs are between the range of 97.6%–122.1%. Two real samples have also been analyzed, including well and orange juice. These experimental data demonstrated that this method is a useful tool for the field analysis of REEs in water and can be used as an alternative supplement of ICP-MS

Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

Multi-task learning (MTL) has received considerable attention, and numerous deep learning applications benefit from MTL with multiple objectives. However, constructing multiple related tasks is difficult, and sometimes only a single task is available for training in a dataset. To tackle this problem, we explored the idea of using unsupervised clustering to construct a variety of auxiliary tasks from unlabeled data or existing labeled data. We found that some of these newly constructed tasks could exhibit semantic meanings corresponding to certain human-specific attributes, but some were non-ideal. In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task. Experimental results across five image datasets demonstrated that the proposed method significantly outperformed existing single task learning, semi-supervised learning, and some data augmentation methods, including an improvement of more than 9% on the Omniglot dataset

Recent progress and controversies in the treatment of metastatic hormone-sensitive prostate cancer

Androgen deprivation therapy (ADT) has long been the only treatment for metastatic hormone-sensitive prostate cancer (mHSPC). In recent years, with the use of docetaxel chemotherapy and the emergence of various novel hormone therapy drugs, such as Abiraterone, Enzalutamide, Apalutamide, Darolutamide, and Rezvilutamide, the treatment strategies for mHSPC have been greatly changed. Furthermore, local treatment has been added to the treatment of low tumor burden mHSPC and triple therapy has been regarded as an important treatment choice for high tumor burden mHSPC. The survival rate of mHSPC patients has increased significantly and the quality of life also improved with these new treatment strategies. This article reviews the latest advances and controversies in the current treatment of mHSPC. Ongoing clinical trials are introduced and further directions are also discussed in this mini-review

The impact of renal function on the prognostic value of N-terminal pro–B-type natriuretic peptide in patients with coronary artery disease

Background: The impact of renal function on the prognostic value of N-terminal pro–B-type natriureticpeptide (NT-proBNP) remains unclear in coronary artery disease (CAD). This study sought toinvestigate the value of using NT-proBNP level to predict prognoses of CAD patients with differentestimated glomerular filtration rates (eGFRs).Methods: A retrospective analysis was conducted from a single registered database. 2087 consecutivepatients with CAD confirmed by coronary angiography were enrolled. The primary endpoint was allcausemortality.Results: The mean follow-up time was 26.4 ± 11.9 months and death events occurred in 197 cases.The NT-proBNP levels increased with the deterioration of renal function, as well as the optimal cutoffvalues based on eGFR stratification to predict endpoint outcome (179.4 pg/mL, 1443.0 pg/mL,3478.0 pg/mL, for eGFR ≥ 90, 60–90 and < 60 mL/min/1.73 m2, respectively). Compared with theroutine cut-off value or overall optimal one, stratified optimal ones had superior predictive ability forendpoint in each eGFR group (all with the highest Youden’s J statistics). And the prognostic value becameweaker as eGFR level decreased (eGFR ≥ 90 vs. 60–90 vs. < 60 mL/min/1.73 m2, odds ratio [OR]7.7; 95% confidence interval [CI] 1.7–33.9 vs. OR 4.8; 95% CI 2.7–8.5 vs. OR 3.0; 95% CI 1.5–6.2).Conclusions: This study demonstrated that NT-proBNP exhibits different predictive values for prognosisfor CAD patients with different levels of renal function. Among the assessed values, the NT-proBNPcut-off value determined using renal function improve the accuracy of the prognosis prediction of CAD.Moreover, lower eGFR is associated with a higher NT-proBNP cut-off value for prognostic prediction

A role for the cerebellum in motor-triggered alleviation of anxiety

Physical exercise is known to reduce anxiety, but the underlying brain mechanisms remain unclear. Here, we explore a hypothalamo-cerebello-amygdalar circuit that may mediate motor-dependent alleviation of anxiety. This three-neuron loop, in which the cerebellar dentate nucleus takes center stage, bridges the motor system with the emotional system. Subjecting animals to a constant rotarod engages glutamatergic cerebellar dentate neurons that drive PKCδ+ amygdalar neurons to elicit an anxiolytic effect. Moreover, challenging animals on an accelerated rather than a constant rotarod engages hypothalamic neurons that provide a superimposed anxiolytic effect via an orexinergic projection to the dentate neurons that activate the amygdala. Our findings reveal a cerebello-limbic pathway that may contribute to motor-triggered alleviation of anxiety and that may be optimally exploited during challenging physical exercise.</p