STaSy: Score-based Tabular data Synthesis

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity. Code is available at https://github.com/JayoungKim408/STaSy.Comment: 27 pages, Accepted by ICLR 2023 for spotlight presentation, Official code: https://github.com/JayoungKim408/STaS

Menthol, a unique urinary volatile compound, is associated with chronic inflammation in interstitial cystitis.

Chronic inflammation is a potential systemic risk factor for many bladder dysfunctions, including interstitial cystitis (IC). However, the underlying mechanism through which a healthy bladder protects itself from inflammatory triggers remains unknown. In this study, we identified odor compounds in urine obtained from IC patients and healthy controls. Using comprehensive solid-phase microextraction-gas chromatography-time-of-flight-mass spectrometry (SPME-GC-TOF-MS) profiling and bioinformatics, we found that levels of urinary volatile metabolites, such as menthol, were significantly reduced in IC patients, compared to healthy controls. In an attempt to understand the mechanistic meaning of our volatile metabolites data and the role of menthol in the immune system, we performed two independent experiments: (a) cytokine profiling, and (b) DNA microarray. Our findings suggest that lipopolysaccharide (LPS)-stimulated inflammatory events, such as the production and secretion of inflammatory cytokines (e.g., TNF-α, IL-6, and IL-1β) and the activation of NF-κB and associated proteins within a large signaling network (e.g., Akt, TLR1, TNFAIP3, and NF-κB), are suppressed by the presence of menthol. These findings broaden our knowledge on the role of urinary menthol in suppressing inflammatory events and provide potential new strategies for alleviating both the odor and inflammation associated with IC

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page

A New and Versatile Synthesis of 1,3-Dioxan-5-yl-pyrimidine and Purine Nucleoside Analogues

1,3-Dioxan-5-yl pyrimidine and purine nucleoside analogues were prepared following a new and versatile synthetic strategy. These analogues were synthesized via nucleophilic addition of the selected nucleobase to a 1,3-dioxane scaffold that presents an appropriate leaving group in position 5. In particular cis and trans isomers of purine/pyrimidine nucleosides and their halogenated homologues were obtained. NMR experiments, carried out on the cis isomers, led to assignment of an equatorial orientation to the 2-hydroxymethyl group and axial orientation to the nucleobase in position 5 of the 1,3-dioxane. The trans isomers showed a diequatorial orientation of these groups. These assignments were confirmed by X-ray crystallographic studie

Reliability and Validity of Modified Algometer in Abdominal Examination

Objective. Abdominal examination (AE) is one of the essential diagnostic methods in traditional Korean medicine that has been widely used for deciding treatment, cause, and prognosis of the disease. AE majorly depends on the experience of practitioners; therefore, standardization and quantification of AE are desperately needed. However, few studies have tried to objectify AE and established its standard. We assessed the reliability and validity of newly developed diagnostic device for AE called modified algometer (MA). Methods. Thirty-six subjects with functional dyspepsia were allocated into one of 2 groups according to gold standard of AE: epigastric discomfort without tenderness (n=23) group or epigastric discomfort with tenderness (n=13) group. Pressure pain threshold was evaluated at participants’ epigastric region with algometer and MA. We assessed reliability and validity (sensitivity and specificity) and calculated optimal cutoff value. Results. MA showed high intertrial reliability (ICC 0.849; 0.703–0.923; P<0.000) and validity (sensitivity: 76.92%; specificity: 60.87%), and cutoff value was 330.0 mmHg. Algometer and MA showed moderate correlation (r=0.583, P≤0.000). Conclusion. MA can be reliable and valid diagnostic device for AE and has the possibility of practical use for quantification and standardization of AE

Decreased DBC1 Expression Is Associated With Poor Prognosis in Patients With Non-Muscle-Invasive Bladder Cancer

Purpose The deleted in bladder cancer 1 (DBC1) gene is located within chromosome 9 (9q32-33), a chromosomal region that frequently shows loss of heterozygosity in bladder cancer (BC). It is suspected that it acts as a tumor suppressor gene, but its prognostic value remains unclear. The aim of the present study was to investigate the value of DBC1 as a prognostic marker in BC. Materials and Methods The expression of DBC1 was determined by real-time polymerase chain reaction analysis in 344 patients with BC (220 non-muscle-invasive BC [NMIBC] and 124 muscle-invasive BC [MIBC]) and in 34 patients with normal bladder mucosa. The results were compared with clinicopathologic parameters, and the prognostic value of DBC1 was evaluated by Kaplan-Meier analysis and a multivariate Cox regression model. Results: DBC1 expression was significantly decreased in patients with MIBC compared with those diagnosed with NMIBC (p=0.010). Patients with aggressive tumor characteristics had lower DBC1 expression levels in NMIBC (each, p<0.05). By multivariate Cox regression analysis, low DBC1 expression was a predictor of progression to MIBC (hazard ratio, 7.104; p=0.013). Kaplan-Meier estimates revealed a significant difference in tumor recurrence, progression to MIBC, and cancer-specific survival depending on the level of DBC1 expression in NMIBC (log-rank test, each, p<0.05). Conclusions: The expression of DBC1 was associated with tumor aggressiveness, progression to MIBC, and survival in NMIBC. Our results suggest that DBC1 expression can be a useful prognostic marker for patients with NMIBC

Comparative absorption, distribution, and excretion of titanium dioxide and zinc oxide nanoparticles after repeated oral administration

Background The in vivo kinetics of nanoparticles is an essential to understand the hazard of nanoparticles. Here, the absorption, distribution, and excretion patterns of titanium dioxide (TiO2) and zinc oxide (ZnO) nanoparticles following oral administration were evaluated. Methods Nanoparticles were orally administered to rats for 13 weeks (7 days/week). Samples of blood, tissues (liver, kidneys, spleen, and brain), urine, and feces were obtained at necropsy. The level of Ti or Zn in each sample was measured using inductively coupled plasma-mass spectrometry. Results TiO2 nanoparticles had extremely low absorption, while ZnO nanoparticles had higher absorption and a clear dose-response curve. Tissue distribution data showed that TiO2 nanoparticles were not significantly increased in sampled organs, even in the group receiving the highest dose (1041.5 mg/kg body weight). In contrast, Zn concentrations in the liver and kidney were significantly increased compared with the vehicle control. ZnO nanoparticles in the spleen and brain were minimally increased. Ti concentrations were not significantly increased in the urine, while Zn levels were significantly increased in the urine, again with a clear dose-response curve. Very high concentrations of Ti were detected in the feces, while much less Zn was detected in the feces. Conclusions Compared with TiO2 nanoparticles, ZnO nanoparticles demonstrated higher absorption and more extensive organ distribution when administered orally. The higher absorption of ZnO than TiO2 nanoparticles might be due to the higher dissolution rate in acidic gastric fluid, although more thorough studies are needed