A cost-effective and universal strategy for complete prokaryotic genomic sequencing proposed by computer simulation

Background: Pyrosequencing techniques allow scientists to perform prokaryotic genome sequencing to achieve the draft genomic sequences within a few days. However, the assemblies with shotgun sequencing are usually composed of hundreds of contigs. A further multiplex PCR procedure is needed to fill all the gaps and link contigs into complete chromosomal sequence, which is the basis for prokaryotic comparative genomic studies. In this article, we study various pyrosequencing strategies by simulated assembling from 100 prokaryotic genomes. Findings. Simulation study shows that a single end 454 Jr. run combined with a paired end 454 Jr. run (8 kb library) can produce: 1) ∼90% of 100 assemblies with 99.99%; 4) average false gene duplication rate is < 0.7%; 5) average false gene loss rate is < 0.4%. Conclusions: A single end 454 Jr. run combined with a paired end 454 Jr. run (8 kb library) is a cost-effective way for prokaryotic whole genome sequencing. This strategy provides solution to produce high quality draft assemblies for most of prokaryotic organisms within days. Due to the small number of assembled scaffolds, the following multiplex PCR procedure (for gap filling) would be easy. As a result, large scale prokaryotic whole genome sequencing projects may be finished within weeks. © 2012 Jiang et al; BioMed Central Ltd.published_or_final_versio

Photoinduced Segregation Behavior in 2D Mixed Halide Perovskite: Effects of Light and Heat

Photoinduced halide segregation (PHS) is a process of critical importance for the performance of perovskite solar cells with mixed halide absorber layers. However, PHS is still not well understood, especially in the case of layered mixed halide perovskites (MHPs), which are less commonly studied compared to their 3D counterparts. Here, we investigated temperature- and light-induced PHS in 2D MHPs with a phenylpropylammonium (PPA) spacer. We found that 2D PPA-based MHPs exhibited complex segregation behavior dependence on temperature and illumination intensity with the suppression of segregation observed at high temperature (attributed to the highly exothermic nature of the process) as well as moderate illumination intensities, illustrating the importance of additional processes present in this particular material, which exhibits distinctly different behavior compared to 2D MHPs with other aromatic cations

Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models

Column statistics of normalized polyamine contents (μmol/g of creatinine) in different subsets.

<p>Column statistics of normalized polyamine contents (μmol/g of creatinine) in different subsets.</p

Distribution of (A) normalized Put, (B) normalized Spd, (C) normalized Spm values in PCa, BPH and HC.

<p>The black bar in the figures indicates the mean value of each subset while the error bar indicates the corresponding SEM.</p