Modified solid carbon sources with nitrate adsorption capability combined with nZVI improve the denitrification performance of constructed wetlands.

In this study, various modified agricultural wastes (modified canna leaves (MCL), modified rice straw (MRS) and modified peanut shells (MPS)) as solid carbon sources (SCSs) were used to remove nitrate in constructed wetlands (CWs). Then, modified SCSs combined with nZVI (SCSN) as co-electrons further enhanced both heterotrophic denitrification (HD) and autotrophic denitrification (AD) performance of CWs. The results showed that NO3--N removal efficiencies in CWs with SCSNs (75.3-91.1%) and in CWs with SCSs (63.3-65.5%) were significantly higher than that in CK-CW (47.0%). The presence of SCSs reduced the accumulation of NO2--N in CWs. Compared to the addition of SCSs, the addition of SCSNs decreased the effluent COD concentration in CWs, avoiding secondary pollution. In addition, the solid-phase denitrifiers Silanimonas and Thauera were enriched in MPS-CW. Thermomonas, an autotrophic denitrifying bacteria (ADB), and Azospira, a nitrate-reducing Fe (II) oxidation bacteria (NRFOB), exhibited high relative abundance in MPN-CW.

Statistical validation of traditional chinese medicine theories.

The theories of Traditional Chinese Medicine (TCM) originated from experiences doctors had with patients in ancient times. We ask the question whether aspects of TCM theories can be reconstructed through data analysis. To answer the question, we have developed a data analysis method called latent tree models and have used it to analyze several TCM data sets. This paper reports the results we obtained on one of the data sets and explains how they provide statistical validation to the relevant TCM theories.

Latent tree models and diagnosis in traditional Chinese medicine.

OBJECTIVE: TCM (traditional Chinese medicine) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain skeptical and even critical of TCM because of a number of its shortcomings. One key shortcoming is the lack of objective diagnosis standards. We endeavor to alleviate this shortcoming using machine learning techniques. METHOD: TCM diagnosis consists of two steps, patient information gathering and syndrome differentiation. We focus on the latter. When viewed as a black box, syndrome differentiation is simply a classifier that classifies patients into different classes based on their symptoms. A fundamental question is: do those classes exist in reality? To seek an answer to the question from the machine learning perspective, one would naturally use cluster analysis. Previous clustering methods are unable to cope with the complexity of TCM. We have therefore developed a new clustering method in the form of latent tree models. We have conducted a case study where we first collected a data set about a TCM domain called kidney deficiency and then used latent tree models to analyze the data set. RESULTS: Our analysis has found natural clusters in the data set that correspond well to TCM syndrome types. This is an important discovery because (1) it provides statistical validation to TCM syndrome types and (2) it suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation. In this paper, we provide a summary of research work on latent tree models and report the aforementioned case study.