SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a large number of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To address the aforementioned issues, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. The proposed SCCAM method is tested on a continuous stirred tank heater and the Tennessee Eastman industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis

Macrocycle encapsulation triggered supramolecular pKa shift: A fluorescence indicator for detecting octreotide in aqueous solution

Supramolecular pKa shifts, have attracted much attention in catalytic and biomimetic studies because of their excellent property to modify the acidity or basicity of the substrate in aqueous media by host-guest inclusion. Here, Fluorescence indicator displacement based on cucurbit[8]uril encapsulation of the dye acridine leads to the recognition of the peptide drug octreotide in aqueous solution via distinctive pH signals was expolited. This is thought to be a result of competitive host-guest interactions involving a supramolecular pKa shift

Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models

Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have attracted significant attention due to their exceptional performance in zero-shot learning scenarios. Similarly, in the field of visual learning, models like Grounding DINO and the Segment Anything Model (SAM) have exhibited remarkable progress in open-set detection and instance segmentation tasks. It is undeniable that these FMs will profoundly impact a wide range of real-world visual learning tasks, ushering in a new paradigm shift for developing such models. In this study, we concentrate on the remote sensing domain, where the images are notably dissimilar from those in conventional scenarios. We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg. The pipeline is benchmarked on several widely-used remote sensing datasets, and we present preliminary results to demonstrate its effectiveness. Through this work, we aim to provide insights into maximizing the applicability of visual FMs in specific contexts with minimal model tuning. The code is available at https://github.com/Douglas2Code/Text2Seg.Comment: 10 pages, 6 figure

The role of macrophages in gastric cancer

As one of the deadliest cancers of the gastrointestinal tract, there has been limited improvement in long-term survival rates for gastric cancer (GC) in recent decades. The poor prognosis is attributed to difficulties in early detection, minimal opportunity for radical resection and resistance to chemotherapy and radiation. Macrophages are among the most abundant infiltrating immune cells in the GC stroma. These cells engage in crosstalk with cancer cells, adipocytes and other stromal cells to regulate metabolic, inflammatory and immune status, generating an immunosuppressive tumour microenvironment (TME) and ultimately promoting tumour initiation and progression. In this review, we summarise recent advances in our understanding of the origin of macrophages and their types and polarisation in cancer and provide an overview of the role of macrophages in GC carcinogenesis and development and their interaction with the GC immune microenvironment and flora. In addition, we explore the role of macrophages in preclinical and clinical trials on drug resistance and in treatment of GC to assess their potential therapeutic value in this disease

Atomically dispersed Cu-N3 on hollow spherical carbon nitride for acetaminophen degradation: Generation of 1O2 from H2O2

Discharge of recalcitrant pharmaceuticals into aquatic systems has caused severe impacts on public health and ecosystem. Advanced oxidation processes (AOPs) are effective for eliminating these refractory pollutants, for which single-atom catalysts (SACs) become the state-of-the-art materials owing to the maximized exposure of active metal sites. In this work, hollow spherical graphitic carbon nitride (hsCN) was fabricated to incorporate copper species to develop Fenton-like catalysts for acetaminophen (ACT) removal. Through pyrolysis of supramolecular assemblies derived from melamine-Cu complex and cyanuric acid, single atom Cu-N3 sites were anchored on hsCN by N-coordination to obtain SACu-hsCN. In virtue of the atomically dispersed Cu-N3 sites as well as the hollow structure of hsCN providing smooth channels for the interactions between single Cu atoms and reactants, the optimal 5.5SACu-hsCN removed 94.8% of ACT after 180 min of Fenton-like reactions, which was superior to that of 5.5AGCu-hsCN with aggregated Cu particles on hsCN (56.7% in 180 min). Moreover, 5.5SACu-hsCN was still active after four cycles of regeneration. The mechanism investigation demonstrated that both hydroxyl radicals (OH) and singlet oxygen (1O2) contributed to ACT degradation in 5.5SACu-hsCN/H202 system, in which non-radical 1O2 played the dominant role

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care

PharmacyGPT: The AI Pharmacist

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable insights into the potential applications and limitations of LLMs in the field of clinical pharmacy, with implications for both patient care and the development of future AI-driven healthcare solutions. By evaluating the performance of PharmacyGPT, we aim to contribute to the ongoing discourse surrounding the integration of artificial intelligence in healthcare settings, ultimately promoting the responsible and efficacious use of such technologies

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change