RESUMEN La contaminación por neblina, caracterizada principalmente por la baja visibilidad, es uno de los principales problemas ambientales a los que se enfrenta China en la actualidad. El pronóstico exacto de la neblina es útil para la aplicación de medidas preventivas orientadas a controlar emisiones de contaminantes atmosféricos y aliviar la contaminación por neblina. Sin embargo, no es fácil predecir con precisión los acontecimientos de baja visibilidad causados por neblina, lo cual requiere no sólo un pronóstico riguroso de los factores meteorológicos, sino también una actualización detallada y en tiempo real de los inventarios de emisiones de las fuentes. Con el fin de obtener una herramienta de predicción fiable, se estudian varios métodos populares de aprendizaje automático, como la máquina vectorial de apoyo (SVM, por su sigla en inglés), el k-vecino más próximo, el bosque aleatorio y varios métodos de aprendizaje profundo. A partir de los principales factores que influyen en la visibilidad, se analizan las relaciones entre velocidad y dirección del viento, temperatura, humedad y visibilidad. El método de aprendizaje automático se utiliza para entrenamiento y pronóstico. La precisión de estos métodos para pronosticar la visibilidad se verifica mediante varios parámetros (error de raíz cuadrada media, error absoluto medio y error porcentual absoluto medio). Los resultados muestran que: 1) entre todos los parámetros meteorológicos, la velocidad del viento puede reflejar mejor la variación de la visibilidad; 2) el rendimiento de las redes neuronales recurrentes de memoria a largo-corto plazo (LSTM RNN) y el método de unidades recurrentes cerradas (GRU) en la predicción de visibilidad a corto plazo (es decir, 1, 3 y 6 h) es casi igual; 3) el método clásico de aprendizaje automático (SVM) funciona bien para el pronóstico de visibilidad a medio y largo plazo, y 4) el método de aprendizaje automático también tiene cierto grado de precisión en el pronóstico, incluso en un periodo de tiempo más largo (p. ej., 72 h).

ABSTRACT Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and thereby mitigate haze pollution. However, it is not easy to accurately predict low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine (SVM), k-nearest neighbor, and random forest, as well as several deep learning methods, on visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) long short-term memory recurrent neural networks (LSTM RNN), and gated recurrent unit (GRU) methods perform almost equally well on short-term visibility forecasts (i.e., 1, 3, and 6 h); (3) a classical machine learning method (i.e., the SVM) performs well in mid- and long-term visibility forecasts; (4) machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g., 7 2h).

RESUMO OBJETIVO: O objetivo deste estudo é investigar a expressão de Id-1 em tecidos de adenocarcinoma colorretal em humanos e investigar sua correlação com os parâmetros patológicos clínicos de câncer colorretal. MÉTODOS: Os níveis de expressão de proteína e mRNA Id-1 em 50 amostras de tecido colorretal normal e 50 amostras de tecido de adenocarcinoma colorretal foram detectados através de reação em cadeia de polimerase precedida de transcrição reversa e western blot. Além disso, a proteína Id-1 foi detectada através de imuno-histoquímica. A correlação entre a expressão de Id-1 e características clínico-patológicas foi analisada. RESULTADOS: O nível de expressão de mRNA Id-1 em tecidos de adenocarcinoma colorretal e tecidos colorretais normais foi de 0,96 ± 0,03 versus 0,20 ± 0,04, respectivamente; a diferença foi estatisticamente significativa (P= 0,011). Além disso, a expressão da proteína Id-1 foi maior em tecidos de adenocarcinoma colorretal do que em tecidos colorretais normais (0,82 ± 0,04 versus 0,31 ± 0,02, P= 0,020). Além disso, a taxa de expressão positiva de proteínas Id-1 foi maior em tecidos de adenocarcinoma colorretal do que em tecidos colorretais normais (72,00% vs. 24,00%, X2=23,431, p=0,000). A expressão de Id-1 foi correlacionada com a profundidade da invasão tumoral, estágio TNM, metástases linfonodais, invasão vascular e metástase hepática (P<0,01). Todavia, essa expressão não se correlacionou com o tamanho do tumor e graus de diferenciação (P>0,05). CONCLUSÃO: A alta expressão de Id-1 em tecidos de adenocarcinoma colorretal desempenham um importante papel no processo do câncer, e é esperado que se torne um novo indicador de monitoramento de tumores para o diagnóstico clínico, tratamento e estimativa de prognóstico.

SUMMARY BACKGROUND: This study aims to investigate the expression of Id-1 in human colorectal adenocarcinoma tissues and explore its correlation with the clinical pathological parameters of colorectal cancer. METHODS: The Id-1 mRNA and protein expression levels of 50 specimens of normal colorectal tissues and 50 specimens of colorectal adenocarcinoma tissues were detected using reverse-transcription polymerase chain reaction and western blot. Furthermore, Id-1 protein was detected using immunohistochemistry. The correlation between the expression of Id-1 and clinicopathologic features was analyzed. RESULTS: The mRNA expression level of Id-1 in colorectal adenocarcinoma tissues and normal colorectal tissues was 0.96 ± 0.03 vs. 0.20 ± 0.04, respectively; and the difference was statistically significant (P=0.011). Furthermore, Id-1 protein expression was higher in colorectal adenocarcinoma tissues than in normal colorectal tissues (0.82 ± 0.04 vs. 0.31 ± 0.02, P=0.020). In addition, the positive protein expression rate of Id-1 was higher in colorectal adenocarcinoma tissues than in normal colorectal tissues (72.00% vs. 24.00%, X2=23.431, P=0.000). The expression of Id-1 was correlated with the depth of tumor invasion, TNM stage, lymph node metastasis, vessel invasion, and liver metastasis (P<0.01). However, this expression was not correlated with tumor size and differentiation degrees (P>0.05). CONCLUSIONS: The high Id-1 expression in colorectal adenocarcinoma tissues play an important role in the process of cancer, and is expected to become a new tumor monitoring indicator for clinical diagnosis, treatment, and prognosis judgment.

Heliconema hainanensis sp. nov. collected from Uroconger lepturus (Richardson) (Anguilliformes: Congridae), Muraenesox cinereus (Forsskål) and Congresox talabonoides (Bleeker) (Anguilliformes: Muraenesocidae) in the South China Sea was described using light and scanning electron microscopy. The new species differs from its congeners by the following morphology: pseudolabia, the number and arrangement of caudal papillae (4 pairs of pedunculate precloacal papillae arranged in 2 groups of 2 and 2 pairs and 6 pairs of pedunculate postcloacal papillae arranged in 4 groups of 1, 2, 1 and 2 pairs), the length of spicules [left spicule 0.51-0.69 mm, right spicule 0.20-0.27 mm, spicule (right:left) ratio 1:2.20-2.69] and the morphology of the female tail tip. In addition, specimens of the new species collected from the three different hosts and specimens of an unidentified species of Heliconema collected from U. lepturus were characterised using molecular methods by sequencing the internal transcribed spacer (ITS) of ribosomal DNA. Analyses and comparison of the ITS sequence of H. hainanensis sp. nov. with Heliconema sp. support the validity of the new species based on morphological observations. An identification key to the species of Heliconema is also provided.

The changes of cell ultra structure as well as Ca2+ homeostasis involved in the drought-induced maize leaf senescence was investigated. Meanwhile, many indicatives of leaf senescence including thiobarbituric acid reactive substance (MDA), electrolyte leakage (EL), and chlorophyll along with soluble proteins were also detected during the process. The Polyethylene glycol6000(PEG6000)-incubated detached leaves showed a slight increase in the MDA content and electrolyte leakage during the first 30 min of our detection, which was corresponded to an unobvious alteration of the cell ultrastructure. Other typical senescence parameters measured in whole leaf exhibited a moderate elevation as well. Thereafter, however, the EL and MDA rose to a large extent, which was correlated with a dramatic damage to the cell ultrastructure with concomitant sharp decrease in the chlorophyll and soluble proteins content. The deposits of calcium antimonite, being an indicator for Ca2+ localization, were observed in the vacuoles as well as intercellular spaces in the leaves grown under normal condition. Nevertheless, after PEG treatment, it was revealed a distinct increment of Ca2+ in the cytoplasm as well as chloroplasts and nuclei. Moreover, with long-lasting treatment of PEG to the detached leaves, the concentration of Ca2+ as described above showed a continuous increment which was consist with the remarked alteration of physiological parameters and severe damage to the ultrastructure of cells, all of which indicated the leaf senescence. Such drought-induced leaf senescence might result from a loss of the cell's capability to extrude Ca2+. All above findings give us a good insight into the important role of Ca2+ homeostasis in the process of leaf senescence accelerated by the drought stress.