High accurate and explainable multi-pill detection framework with graph neural network-assisted multimodal data fusion.

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern that needs to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identification problem. However, most published works consider only single-pill identification and fail to distinguish hard samples with identical appearances. Also, most existing pill image datasets only feature single pill images captured in carefully controlled environments under ideal lighting conditions and clean backgrounds. In this work, we are the first to tackle the multi-pill detection problem in real-world settings, aiming at localizing and identifying pills captured by users during pill intake. Moreover, we also introduce a multi-pill image dataset taken in unconstrained conditions. To handle hard samples, we propose a novel method for constructing heterogeneous a priori graphs incorporating three forms of inter-pill relationships, including co-occurrence likelihood, relative size, and visual semantic correlation. We then offer a framework for integrating a priori with pills' visual features to enhance detection accuracy. Our experimental results have proved the robustness, reliability, and explainability of the proposed framework. Experimentally, it outperforms all detection benchmarks in terms of all evaluation metrics. Specifically, our proposed framework improves COCO mAP metrics by 9.4% over Faster R-CNN and 12.0% compared to vanilla YOLOv5. Our study opens up new opportunities for protecting patients from medication errors using an AI-based pill identification solution.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea.

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

Evaluation of Carbon Dioxide Laser-Assisted Treatment for Gingival Melanin Hyperpigmentation.

BACKGROUND: Smile aesthetics has a vital role to play in an individual's life and one of the factors affecting the beauty of the smile is gingival color. A gingival color change or gingival hyperpigmentation causes an unesthetic smile line, especially in patients with a gummy smile, which is also known as a black gummy smile. Numerous gingival depigmentation methods have been performed successfully for ablating gingival melanin pigmented epithelium. Thus, the aim of this study is to evaluate the treatment efficacy of gingival hyperpigmentation by using a carbon dioxide (CO2) laser. METHODS: A cross-sectional descriptive study was carried out with 38 patients at a hospital in Vietnam. Ponnaiyan classification and the Hedin melanin index were used to assess the distribution and extent of gingival pigmentation in the study. Pain assessment was performed using the Visual Analog Scale (VAS) to evaluate the intensity of pain during the laser treatment. In addition, clinical evaluation (i.e., wound healing) of each treatment procedure was conducted using the three level Dummett-Gupta Oral Pigmentation Index (DOPI) assessment. RESULTS: This study showed that less pain was experienced by patients treated by CO2 laser; the rates of no pain, mild pain and moderate pain after treatment were, respectively, 21%, 76% and 2.6%; there was 100% complete epithelization after 1 week. The DOPI rates for turning from a DOPI score of 1, 2 or 3 to a DOPI score of 0 after a 12-week treatment were 87.5%, 76.9% and 24%, respectively. CONCLUSIONS: Using a CO2 laser for gingival melanin pigmentation treatment is a safe and effective procedure.

Immunologically distinct responses occur in the CNS of COVID-19 patients

One third of COVID-19 patients develop significant neurological symptoms, yet SARS-CoV-2 is rarely detected in central nervous system (CNS) tissue, suggesting a potential role for parainfectious processes, including neuroimmune responses. We therefore examined immune parameters in cerebrospinal fluid (CSF) and blood samples from a cohort of patients with COVID-19 and significant neurological complications. We found divergent immunological responses in the CNS compartment, including increased levels of IL-12 and IL-12-associated innate and adaptive immune cell activation. Moreover, we found increased proportions of B cells in the CSF relative to the periphery and evidence of clonal expansion of CSF B cells, suggesting a divergent intrathecal humoral response to SARS-CoV-2. Indeed, all COVID-19 cases examined had anti-SARS-CoV-2 IgG antibodies in the CSF whose target epitopes diverged from serum antibodies. We directly examined whether CSF resident antibodies target self-antigens and found a significant burden of CNS autoimmunity, with the CSF from most patients recognizing neural self-antigens. Finally, we produced a panel of monoclonal antibodies from patients CSF and show that these target both anti-viral and anti-neural antigens--including one mAb specific for the spike protein that also recognizes neural tissue. This exploratory immune survey reveals evidence of a compartmentalized and self-reactive immune response in the CNS meriting a more systematic evaluation of neurologically impaired COVID-19 patients. One Sentence SummaryA subset of COVID-19 patients with neurologic impairment show cerebrospinal fluid-specific immune alterations that point to both neuroinvasion and anti-neural autoimmunity as potential causes of impairment.

Graphite and Graphene Fairy Circles: A Bottom-Up Approach for the Formation of Nanocorrals.

A convenient covalent functionalization approach and nanopatterning method of graphite and graphene is developed. In contrast to expectations, electrochemically activated dediazotization of a mixture of two aryl diazonium compounds in aqueous media leads to a spatially inhomogeneous functionalization of graphitic surfaces, creating covalently modified surfaces with quasi-uniform spaced islands of pristine graphite or graphene, coined nanocorrals. Cyclic voltammetry and chronoamperometry approaches are compared. The average diameter (45-130 nm) and surface density (20-125 corrals/µm2) of these nanocorrals are tunable. These chemically modified nanostructured graphitic (CMNG) surfaces are characterized by atomic force microscopy, scanning tunneling microscopy, Raman spectroscopy and microscopy, and X-ray photoelectron spectroscopy. Mechanisms leading to the formation of these CMNG surfaces are discussed. The potential of these surfaces to investigate supramolecular self-assembly and on-surface reactions under nanoconfinement conditions is demonstrated.

Liquid Nickel Salts: Synthesis, Crystal Structure Determination, and Electrochemical Synthesis of Nickel Nanoparticles.

New nickel-containing ionic liquids were synthesized, characterized and their electrochemistry was investigated. In addition, a mechanism for the electrochemical synthesis of nanoparticles from these compounds is proposed. In these so-called liquid metal salts, the nickel(II) cation is octahedrally coordinated by six N-alkylimidazole ligands. The different counter anions that were used are bis(trifluoromethanesulfonyl)imide (Tf2 N(-) ), trifluoromethanesulfonate (OTf(-) ) and methanesulfonate (OMs(-) ). Several different N-alkylimidazoles were considered, with the alkyl sidechain ranging in length from methyl to dodecyl. The newly synthesized liquid metal salts were characterized by CHN analysis, FTIR, DSC, TGA and viscosity measurements. An odd-even effect was observed for the melting temperatures and viscosities of the ionic liquids, with the complexes with an even number of carbon atoms in the alkyl chain of the imidazole having a higher melting temperature and a lower viscosity than the complexes with an odd number of carbons. The crystal structures of several of the nickel(II) complexes that are not liquid at room temperature were determined. The electrochemistry of the compounds with the lowest viscosities was investigated. The nickel(II) cation could be reduced but surprisingly no nickel deposits were obtained on the electrode. Instead, nickel nanoparticles were formed at 100 % selectivity, as confirmed by TEM. The magnetic properties of these nanoparticles were investigated by SQUID measurements.