The synthesis and spectroscopic characterisation of hydrotalcite formed from aluminate solutions

Raman spectroscopy has been used to characterise nine hydrotalcites prepared from aluminate and magnesium solutions (magnesium chloride and seawater). The aluminate hydrotalcites are proposed to have the following formula Mg6Al2(OH)16(CO32-).xH2O, Mg6Al2(OH)16(CO32-,SO42-).xH2O, and Mg6Al2(OH)16(SO42-).xH2O. The synthesis of these hydrotalcites using seawater results in the intercalation of sulfate anions into the hydrotalcite interlayer. The spectra have been used to assess the molecular assembly of the cations and anions in the hydrotalcite structures. The spectra have been conveniently subdivided into spectral features based upon the carbonate anion, the hydroxyl units and water units. This investigation has shown the ideal conditions to form hydrotalcite from aluminate solutions is at pH 14 using magnesium chloride. Changes in synthesis conditions resulted in the formation of impurity products aragonite, thenardite, and gypsum.

Expression pattern of Oct-4, Sox2, and c-Myc in the primary culture of human dental pulp derived cells

Dental pulp cells (DPCs) have shown promising potential in dental tissue repair and regeneration. However, during in vitro culture, these cells undergo replicative senescence and result in significant alteration in cell proliferation and differentiation. Recently, the transcription factors of Oct-4, Sox2, c-Myc, and Klf4 have been reported to play a regulatory role in the stem cell self-renewal process, namely cell reprogramming. Therefore, it is interesting to know whether the replicative senescence during the culture of dental pulp cells is related to the diminishing of the expression of these transcription factors. In this study, we investigated the expression of the reprogramming markers Oct-4, Sox2, and c-Myc in the in vitro explant cultured dental pulp tissues and explant cultured dental pulp cells (DPCs) at various passages by immunofluorescence staining and real-time polymerase chain reaction analysis. Our results demonstrated that Oct-4, Sox2, and c-Myc translocated from nucleus in the first 2 passages to cytoplasm after the third passage in explant cultured DPCs. The mRNA expression of Oct-4, Sox2, and c-Myc elevated significantly over the first 2 passages, peaked at second passage (P < .05), and then decreased along the number of passages afterwards (P < .05). For the first time we demonstrated that the expression of reprogramming markers Oct-4, Sox2, and c-Myc was detectable in the early passaged DPCs, and the sequential loss of these markers in the nucleus during DPC cultures might be related to the cell fate of dental pulp derived cells during the long-term in vitro cultivation under current culture conditions.

Synthesis and Raman spectroscopic characterisation of hydrotalcites based on the formula Ca6Al2(CO3)(OH)16· 4H2O

We have successfully synthesized hydrotalcites (HTs) contg. calcium, which are naturally occurring minerals. Insight into the unique structure of HTs has been obtained using a combination of X-ray diffraction (XRD) as well as IR and Raman spectroscopies. Calcium-contg. hydrotalcites (Ca-HTs) of the formula Ca4Al2(CO3)(OH)12·4H2O (2:1 Ca-HT) to Ca8Al2(CO3)(OH)20· 4H2O (4:1 Ca-HT) have been successfully synthesized and characterised by XRD and Raman spectroscopy. XRD has shown that 3:1 calcium HTs have the largest interlayer distance. Raman spectroscopy complemented with selected IR data has been used to characterize the synthesized Ca-HTs. The Raman bands obsd. at around 1086 and 1077 cm-1 were attributed to the ν1 sym. stretching modes of the (CO32-) units of calcite and carbonate intercalated into the HT interlayer. The corresponding ν3 CO32- antisym. stretching modes are found at around 1410 and 1475 cm-1.

The effect of synthesis temperature on the formation of hydrotalcites in Bayer liquor : a vibrational spectroscopic analysis

The seawater neutralisation process is currently used in the Alumina industry to reduce the pH and dissolved metal concentrations in bauxite refinery residues, through the precipitation of Mg, Al, and Ca hydroxide and carbonate minerals. This neutralisation method is very similar to the co-precipitation method used to synthesise hydrotalcite (Mg6Al2(OH)16CO3•4H2O). This study looks at the effect of temperature on the type of precipitates that form from the seawater neutralisation process of Bayer liquor. The Bayer precipitates have been characterised by a variety of techniques, including X-ray diffraction, Raman spectroscopy and infrared spectroscopy. The mineralogical composition of Bayer precipitates largely includes hydrotalcite, hydromagnesite, and calcium carbonate species. XRD determined that Bayer hydrotalcites that are synthesised at 55 °C have a larger interlayer distance, indicating more anions are removed from Bayer liquor. Vibrational spectroscopic techniques have identified an increase in hydrogen bond strength for precipitates formed at 55 °C, suggesting the formation of a more stable Bayer hydrotalcite. Raman spectroscopy identified the intercalation of sulfate and carbonate anions into Bayer hydrotalcites using these synthesis conditions.

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Rough sets based reasoning and pattern mining for a two-stage information filtering system

This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern- based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experiments have been conducted to compare the proposed two-stage filtering (T-SM) model with other possible "term-based + pattern-based" or "term-based + term-based" IF models. The results based on the RCV1 corpus show that the T-SM model significantly outperforms other types of "two-stage" IF models.