This study spatially estimates degraded lands in Indonesia that have limited functions for food production, carbon storage, and conservation of biodiversity and native vegetation, and examines their suitability to grow biodiesel species (Calophyllum inophyllum, Pongamia pinnata and Reutealis trisperma) and biomass species (Calliandra calothyrsus and Gliricidia sepium). Results showed that Indonesia has ∼3.5 million ha of degraded lands potentially suitable for these species. With the all-five-species scenario, these lands had the potential to produce 1105 PJ yr⁻¹ of biomass and 3 PJ yr⁻¹ of biodiesel. With the biodiesel-only-species scenario, these lands showed the potential to produce 10 PJ yr⁻¹ of biodiesel. Despite this energy potential, however, the land sizes were too small to support economies of scale for biofuel production. The study findings contribute to identifying lands with limited functions, modeling biofuel-species growth on regional lands and estimating carbon stocks of restored degraded lands in Indonesia.

Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of optical character recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degree of document image, researchers have proposed a variety of solutions. This paper reviews the main binarization techniques, including both traditional algorithms and deep learning-based algorithms. We also summarize some difficulties and challenges in the field of document image binarization. Here, we evaluate various image binarization techniques to identify shortcomings in current methods and provide some help for future research.

Metagenomics have opened our eyes to the otherwise enigmatic microbial consortia that relies on different human body niches that impact on disease pathogenesis. This work originally aims to re-analyse a public shotgun metagenomics dataset to glean insights on the microbial species that may partake in stomach cancer pathogenesis. However, a random selection of sequenced reads reveals poor percent identities (79 to 94%) of the microbial identification obtained in a BLAST search. This surprising result suggests possible stomach acid induced degradation or modification of the sequenced read that reduced the effectiveness of the metagenomics approach in microbial identification. Further analysis of the dataset highlights that poor percent identities did not arise due to highly fragmented nature of the genetic material where there is a good abundance of sequenced reads in the 100 to 1300 base pair category. More intriguingly, attempts to obtain a correlation between percent identity and read length did not reveal a correlation which meant that stomach acid did not modify all types of nucleotides. More likely, stomach acid only modified low abundance nucleotide in this case, which point to the probability of identifying a microbe in the modified gene fragment in totally unpredictable ways. Overall, data reported in this work suggests caution in the interpretation of results of shotgun metagenomics studies of stomach cancer microbiota where stomach acid likely degraded the genetic material of microbes that may result in mis-identification.

Ca-bentonite (CB) alone and in a mixture with limestone (L), tobacco biochar (TB) and zeolite (Z) on the fixation, geochemical fractions and absorption of Cd and Zn by Chinese cabbage in smelter heavily polluted (S-HP) and smelter low polluted (S-LP) soils were investigated. The results showed that the CB+TB and CB+L+TB treatments significantly immobilized Cd up to 22.03% and 29.68%, respectively, and reduced uptake by Chinese cabbage shoot to 35.98% with CB+Z+L and 61.35% with CB+L in S-HP and S-LP soils compared with the control. The CB+ Z+ L+TB treatment mobilized Cd up to 4.45% and increased absorption in the shoot by 9.85% in S-HP soil. The greatest immobilization of Zn was 53.18% and 58.20% with the CB+Z+L+TB treatment, which reduced Zn uptake in the plant shoot by 9.94% with CB + L and 58.04 with CB+Z+L+TB in S-HP and S-LP soils. The CB+Z+TB and CB+TB treatments mobilized Zn up to 35.40% and 4.80%, respectively, in both soils. Furthermore, the uptake of Zn in plant shoot was observed by 58.96% and 7.82% with application of CB+Z and CB+TB treatments, respectively, in S-HP and S-LP soils. Overall, our results suggest that Ca-bentonite alone and in mixtures with different amendments can be used to reduce the phyto-extraction of Cd and Zn in Zn-smelter polluted soils.

Appropriate vaccine selection is crucial in the control of foot-and-mouth disease (FMD). Vaccination can prevent clinical disease and reduces viral shedding, but there is a lack of cross-protection between the seven serotypes and their sublineages, making the selection of an adequately protective vaccine difficult. Since the exact composition of their vaccines is not consistently disclosed by all manufacturers, incompatibility of the strains used for vaccination with regionally circulating strains can cause vaccination campaigns to fail. Here, we present a deep sequencing approach for polyvalent inactivated FMD vaccines that can identify all component strains by their genome sequences. The genomes of all strains of a commercial pentavalent FMD vaccine were de-novo assembled and the vaccine composition determined semi-quantitatively. The genome assembly required high stringency parameters to prevent misassemblies caused by conserved regions of the genome shared by related strains. In contrast, reference-guided assembly is only recommended in cases where the number of strains is previously known and appropriate reference sequences are available. The presented approach can be applied not only to any inactivated whole-virus FMD vaccine, but also to vaccine quality testing in general and allows for better decision-making for vaccines with unknown composition.