Bounded-Distortion Metric Learning

Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical inaccuracy. This paper presents {\it bounded-distortion metric learning} (BDML), a new metric learning framework which amounts to finding an optimal Mahalanobis metric space with a bounded-distortion constraint. An efficient solver based on the multiplicative weights update method is proposed. Moreover, we generalize BDML to pseudo-metric learning and devise the semidefinite relaxation and a randomized algorithm to approximately solve it. We further provide theoretical analysis to show that distortion is a key ingredient for stability and generalization ability of our BDML algorithm. Extensive experiments on several benchmark datasets yield promising results

Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.

BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database. METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram. RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001). CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis

Application of diaminium iodides in binary ionic liquid electrolytes for dye-sensitized solar cells

N-(2-hydroxyethyl)ethylenediaminium iodides (HEEDAIs) and N-(2-hydroxyethyl)piperazinium iodides (HEPIs) were synthesized, and their thermal properties were analysed. The influence of HEEDAI and HEPI on I 3 − /I − redox behavior in binary ionic liquid was investigated. The result revealed that HEEDAI can suppress the recombination between I 3 − and the injected electrons in TiO 2 conduction band and be used as the alternative of 4-tert-butylpyridine in the electrolyte of dye-sensitized solar cells. The electrolyte C, 0.15 mol·L −1 I 2 , HEEDAI and MPII with mass ratio of 1 : 4, gave the short-circuit photocurrent density of 9.36 mA·cm −2 , open-circuit photovoltage of 0.67 V, fill factor of 0.52, and the corresponding photoelectric conversion efficiency of 3.24% at the illumination (air mass 1.5, 100 mW·cm −2 , active area 0.25 cm 2 )

Current insights in the preclinical study of palatal wound healing and oronasal fistula after cleft palate repair

Poor palatal wound healing after cleft palate repair could lead to unfavorable prognosis such as oronasal fistula (ONF), which might affect the patient’s velopharyngeal function as well as their quality of life. Thus, restoring poor palatal wound healing for avoiding the occurrence of ONF should be considered the key to postoperative care after cleft palate repair. This review provided current insights in the preclinical study of poor palatal wound healing after cleft palate repair. This review comprehensively introduced the animal model establishment for palatal wound healing and related ONF, including the models by mice, rats, piglets, and dogs, and then demonstrated the aspects for investigating poor palatal wound healing and related treatments, including possible signaling pathways that could be involved in the formation of poor palatal wound healing, the related microbiota changes because of the deformity of palatal structure, and the studies for potential therapeutic strategies for palatal wound healing and ONF. The purpose of this review was to show the state of the art in preclinical studies about palatal wound healing after cleft palate repair and to show the promising aspects for better management of palatal wound healing

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

Mutations in TUBB8 and Human Oocyte Meiotic Arrest

BACKGROUND Human reproduction depends on the fusion of a mature oocyte with a sperm cell to form a fertilized egg. The genetic events that lead to the arrest of human oocyte maturation are unknown. METHODS We sequenced the exomes of five members of a four-generation family, three of whom had infertility due to oocyte meiosis I arrest. We performed Sanger sequencing of a candidate gene, TUBB8, in DNA samples from these members, additional family members, and members of 23 other affected families. The expression of TUBB8 and all other β-tubulin isotypes was assessed in human oocytes, early embryos, sperm cells, and several somatic tissues by means of a quantitative reverse- transcriptase–polymerase-chain-reaction assay. We evaluated the effect of the TUBB8 mutations on the assembly of the heterodimer consisting of one α-tubulin polypeptide and one β-tubulin polypeptide (α/β-tubulin heterodimer) in vitro, on microtubule architecture in HeLa cells, on microtubule dynamics in yeast cells, and on spindle assembly in mouse and human oocytes. RESULTS We identified seven mutations in the primate-specific gene TUBB8 that were responsible for oocyte meiosis I arrest in 7 of the 24 families. TUBB8 expression is unique to oocytes and the early embryo, in which this gene accounts for almost all the expressed β-tubulin. The mutations affect chaperone-dependent folding and assembly of the α/β-tubulin heterodimer, disrupt microtubule behavior on expression in cultured cells, alter microtubule dynamics in vivo, and cause catastrophic spindle-assembly defects and maturation arrest on expression in mouse and human oocytes. CONCLUSIONS TUBB8 mutations have dominant-negative effects that disrupt microtubule behavior and oocyte meiotic spindle assembly and maturation, causing female infertility. (Funded by the National Basic Research Program of China and others.