Computer vision-based wood identification has been successfully applied to recognize tree species using digital images of wood sections or surfaces. However, this image-to- species approach can... Show moreComputer vision-based wood identification has been successfully applied to recognize tree species using digital images of wood sections or surfaces. However, this image-to- species approach can only recognize a limited number of species due to two main reasons: 1) the lack of a good reference database requiring high-quality standardized images from multiple individuals of hundreds or even thousands of traded timber species, and 2) species not included in the reference database cannot be identified without expert knowledge. Another bottleneck is that the feature extraction process used by these species recognition approaches is a black box, thereby creating a discrepancy between machine learning features and wood anatomical features. This discrepancy prevents wood anatomists from understanding how these machine learning algorithms work. Here, we survey currently existing methods used in feature extraction, classification, and deep learning methods applied in wood identification along with their pitfalls and opportunities. As an example of how the field could move forward, we launch the idea of building an image-to-features-to-species identification approach based on microscopic wood images as well as text files comprising wood anatomical descriptions. This would boost wood identification in two ways: (1) extensive reference databases for each species would become less crucial as the databases are ordered at the trait level, (2) timber identification would become more feasible for species that have not yet been included in the reference database as long as wood anatomical descriptions are available. Show less
The development of novel anti-infectives requires unprecedented strategies targeting pathways which are solely present in pathogens but absent in humans. Following this principle, we developed... Show moreThe development of novel anti-infectives requires unprecedented strategies targeting pathways which are solely present in pathogens but absent in humans. Following this principle, we developed inhibitors of lipoic acid (LA) salvage, a crucial pathway for the survival of LA auxotrophic bacteria and parasites but non-essential in human cells. An LA-based probe was selectively transferred onto substrate proteins via lipoate protein ligase (LPL) in intact cells, and their binding sites were determined by mass spectrometry. Probe labeling served as a proxy of LPL activity, enabling in situ screenings for cell-permeable LPL inhibitors. Profiling a focused compound library revealed two substrate analogs (LAMe and C3) as inhibitors, which were further validated by binding studies and co-crystallography. Importantly, LAMe exhibited low toxicity in human cells and achieved killing of Plasmodium falciparum in erythrocytes with an EC50 value of 15 μM, making it the most effective LPL inhibitor reported to date. Show less
Objective. The development of effective cancer treatments depends on the availability of cell lines that faithfully recapitulate the cancer in question. This study definitively re-assigns the... Show moreObjective. The development of effective cancer treatments depends on the availability of cell lines that faithfully recapitulate the cancer in question. This study definitively re-assigns the histologic identities of two ovarian cancer cell lines, COV434 (originally described as a granulosa cell tumour) and TOV-112D (originally described as grade 3 endometrioid carcinoma), both of which were recently suggested to represent small cell carcinoma of the ovary, hypercalcemic type (SCCOHT), based on their shared gene expression profiles and sensitivity to EZH2 inhibitors. Methods. For COV434 and TOV-112D, we re-reviewed the original pathology slides and obtained clinical followup on the patients, when available, and performed immunohistochemistry for SMARCA4, SMARCA2 and additional diagnostic markers on the original formalin-fixed, paraffin-embedded (FFPE) clinical material, when available. For COV434, we further performed whole exome sequencing and validated SMARCA4 mutations by Sanger sequencing. We studied the growth of the cell lines at baseline and upon re-expression of SMARCA4 in vitro for both cell lines and evaluated the serum calcium levels in vivo upon injection into immunodeficient mice for COV434 cells.Results. The available morphological, immunohistochemical, genetic, and clinical features indicate COV434 is derived from SCCOHT, and TOV-112D is a dedifferentiated carcinoma. Transplantation of COV434 into mice leads to increased serum calcium level. Re-expression of SMARCA4 in either COV434 and TOV-112D cells suppressed their growth dramatically. Conclusions. COV434 represents a bona fide SCCOHT cell line. TOV-112D is a dedifferentiated ovarian carcinoma cell line.(c) 2020 Elsevier Inc. All rights reserved. Show less
