Molecular insights into the dissolved organic matter of leather wastewater in leather industrial park wastewater treatment plant.

Leather wastewater (LW) effluent is characterized by complex organic matter, high salinity, and poor biodegradability. To meet the discharge standards, LW effluent is often mixed with municipal wastewater (MW) before being treated at a leather industrial park wastewater treatment plant (LIPWWTP). However, whether this method efficiently removes the dissolved organic matter (DOM) from LW effluent (LWDOM) remains debatable. In this study, the transformation of DOM during full-scale treatment was revealed using spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry. LWDOM exhibited higher aromaticity and lower molecular weight than DOM in MW (MWDOM). The DOM properties in mixed wastewater (MixW) were similar to those in LWDOM and MWDOM. The MixW was treated using a flocculation/primary sedimentation tank (FL1/PST), anoxic/oxic (A/O) process, secondary sedimentation tank (SST), flocculation/sedimentation tank, denitrification filter (FL2/ST-DNF), and an ozonation contact reactor (O3). The FL1/PST unit preferentially removed the peptide-like compounds. The A/O-SST units had the highest removal efficiencies for dissolved organic carbon (DOC) (61.34 %) and soluble chemical oxygen demand (SCOD) (52.2 %). The FL2/ST-DNF treatment removed the lignin-like compounds. The final treatment showed poor DOM mineralization efficiency. The correlation between water quality indices, spectral indices, and molecular-level parameters indicated that lignin-like compounds were strongly correlated with spectral indices and CHOS compounds considerably contributed to the SCOD and DOC. Although the effluent SCOD met the discharge standard, some refractory DOM from LW remained in the effluent. This study illustrates the composition and transformation of DOM and provides theoretical guidance for improving the current treatment processes.

Automatic regional analysis of myocardial native T1 values: left ventricle segmentation and AHA parcellations.

Native T1 value is emerging as a reliable indicator of abnormal heart conditions related to myocardial fibrosis. Investigators have extensively used the standardized myocardial segmentation of the American Heart Association (AHA) to measure regional T1 values of the left ventricular (LV) walls. In this paper, we present a fully automatic system to analyze modified Look-Locker inversion recovery images and to report regional T1 values of AHA segments. Ten healthy individuals participated in the T1 mapping study with a 3.0 T scanner after providing informed consent. First, we obtained masks of an LV blood-pool region and LV walls by using an image synthesis method and a layer-growing method. Subsequently, the LV walls were divided into AHA segments by identifying the boundaries of the septal regions and by using a radial projection method. The layer-growing method significantly enhanced the accuracy of the derived myocardium mask. We compared the T1 values that were obtained using manual region of interest selections and those obtained using the automatic system. The average T1 difference of the calculated segments was 4.6 ± 1.5%. This study demonstrated a practical and robust method of obtaining native T1 values of AHA segments in LV walls.

Effect of floral sources on the antioxidant, antimicrobial, and anti-inflammatory activities of honeys in Taiwan.

We evaluated the antioxidant, antibacterial, and anti-inflammatory activities of honey made from different floral sources, including the medicinal herb Bidens pilosa, fruit trees, Dimocarpus longan, Litchi chinensis, and Citrus maxima, the Taiwanese endemic plant Aglaia formosana, and a multifloral forest. The total phenolic and flavonoid contents of the honey made from B. pilosa were significantly higher than those of the other honeys. The honey from B. pilosa also had significantly greater scavenging activities for 1,1-diphenyl-2-picrylhydrazyl (DPPH·) and hydroxyl radical, and substantially more reducing power. In addition, the honey from B. pilosa showed greater antibacterial activity against gram-positive and gram-negative bacteria. However, B. pilosa honey showed little inhibitory activity against IL-8 secretion, whereas the other honeys did. These findings suggest that the levels of antioxidant and antibacterial activities are attributable to the total phenolic and flavonoid contents of honeys, while the IL-8 inhibition is attributable to components other than phenols.

Subject-specific and pose-oriented facial features for face recognition across poses.

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.