Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs.

BACKGROUND: Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace. RESULTS: This paper proposes a rapid detection method for PST in mussels using near-infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950-1700 nm for PST-contaminated and non-contaminated mussel samples were used to build the detection model. Near-Bayesian support vector machines (NBSVM) with unequal misclassification costs (u-NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non-contaminated mussels was far less than that of PST-contaminated mussels in practice. The u-NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u-NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G-mean and accuracy reached 0.9898 and 0.9944, respectively. CONCLUSION: Compared with the traditional support vector machines (SVMs) and the NBSVM, the u-NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u-NBSVM model can be used for rapid and non-destructive PST detection in mussels. © 2023 Society of Chemical Industry.

[Spatial differentiation characteristics and influencing factors of urban and rural settlements in Tibet, China]. / è¥¿è—åŸŽä¹¡å± æ°‘ç‚¹ç©ºé—´åˆ†å¼‚ç‰¹å¾ä¸Žå½±å“å› ç´ .

Located in the hinterland of the Qinghai-Tibet Plateau, Tibet has very limited space sui-table for human living. The spatial distribution of settlements in Tibet is restricted by multiple factors. To reveal the spatial distribution characteristics and explore the main influencing factors of settlements in Tibet, we extracted patch data from the high-resolution images of urban and rural settlements in Tibet based on visual interpretation. Methods such as the kernel density analysis, space hotspot detection, Geodetector and others were applied to analyze the spatial differentiation characteristics and influencing factors, with the aim to provide reference for future settlements selection and formulation of relevant policies on regional economic development in the plateau region. The results showed that urban and rural settlements in Tibet had a clustering pattern, with different overall density distribution. Lhasa was a high-density core, and Ngari Prefecture, Qamdo, and Nyingchi were the "core-edge" structures with low-density edges. The settlement density of Lhasa was as high as 220 ind·km-2, while that of Ngari Prefecture, Qamdo and Nyingchi was only 5.7 ind·km-2. The gap between the two regions was more than 43 times. The clustering of high and low value clusters in urban and rural settlements was remarkable. The number of high-value of large plaques was rare, and the number of low-values of small plaques was dominant. The area of high-value cluster accounted for only 3.7%, concentrated in Lhasa and Lhoka. The proportion of low value cluster area reached 67.2%, mainly distributed in Ngari Prefecture and Nagqu on the Qiangtang Plateau. There were six types of urban and rural settlements in Tibet, which formed two circle structures in the "One River and Two Streams" basin and the "Three Rivers" basin. From the inside to the outside, the large plaque-dominated type, medium-density and cluster-like type, high-density and point-scattered type, low-density and point-scattered type and high-altitude and uninhabited type was successively distributed. Lhasa was dominated by medium-density and cluster-like type, accounting for 31%. Lhoka was dominated by low-density and point-scattered type, accounting for 38%. Qamdo was mainly low-density and point-scattered type, accounting for 51%. The Ngari Prefecture, Nagqu and Shigatse were dominated by high-altitude and uninhabited type, and the proportion of the Ngari Prefecture was as high as 64%. The effects of different factors on the spatial distribution of urban and rural settlements in Tibet were distinctly different. The population and GDP were highly decisive for the distribution of urban and rural settlements. In addition, urban settlements showed strong road orientation, while rural settlements were more characterized by river orientation.

In-hospital Medical Costs of Infections Caused by Carbapenem-resistant Klebsiella pneumoniae.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a major health threat, but the economic impact of carbapenem resistance in K. pneumoniae infections remains largely uninvestigated. Methods: We constructed a retrospective cohort of all patients hospitalized at West China Hospital in 2017 who had CRKP- or carbapenem-susceptible K. pneumoniae (CSKP)-positive clinical samples. Propensity score matching (PSM) was used to control the impact of potential confounding variables, including demographics, comorbidities, and treatment, and to observe the impact of factors other than length of stay (LOS). Patients who survived were subjected to subgroup analyses stratified by infection type. Results: There were 267 patients with CRKP and 1328 with CSKP. Patients with CRKP had a higher crude in-hospital mortality rate (14.61% vs 5.65%, P < .05) and longer LOS (median, 31 vs 19 days; P < .05). PSM for demographics, comorbidities, and treatment generated 237 pairs. Patients with CRKP had higher medical costs than those with CSKP during the entire hospitalization (median, in US dollars, $22962 vs $11755, respectively; P < .05) and during the period after infection (median, $9215 vs $6904, respectively; P < .05). When LOS was matched, patients with CRKP still had high excess costs compared to those with CSKP (median, $22917 vs $13851, respectively, for the entire hospitalization, P < .05; $9101 vs $7001, respectively, after infection, P < .05). For infection type, the sample size generated sufficient power to compare only the patients with pneumonia. For surviving patients, high excess costs were observed in those with pneumonia caused by CRKP as compared to CSKP ($21890 vs $11698, respectively, for the entire hospitalization, P < .05; $9773 vs $5298, respectively, after infection, P < .05). Medicines other than antibacterial agents and nonmedicinal therapies contributed most (57.8%) of the excess costs associated with CRKP. Conclusions: Carbapenem resistance in K. pneumoniae was associated with increased medical costs not accounted for by the cost of antimicrobial therapy.