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Plasmodium vivax is the most widespread cause of malaria, especially in subtropical and temperate regions such as Asia-Pacific and America. P. vivax lactate dehydrogenase (PvLDH), an essential enzyme in the glycolytic pathway, is required for the development and reproduction of the parasite. Thus, LDH from these parasites has garnered attention as a diagnostic biomarker for malaria and as a potential molecular target for developing antimalarial drugs. In this study, we prepared a transformed Escherichia coli strain for the overexpression of PvLDH without codon optimization. We introduced this recombinant plasmid DNA prepared by insertion of the PvLDH gene in the pET-21a(+) expression vector, into the Rosetta(DE3), an E. coli strain suitable for eukaryotic protein expression. The time, temperature, and inducer concentration for PvLDH expression from this E. coli Rosetta(DE3), containing the original PvLDH gene, were optimized. We obtained PvLDH with a 31.0 mg/L yield and high purity (>95%) from this Rosetta(DE3) strain. The purified protein was characterized structurally and functionally. The PvLDH expressed and purified from transformed bacteria without codon optimization was successfully demonstrated to exhibit its potential tetramer structure and enzyme activity. These findings are expected to provide valuable insights for research on infectious diseases, metabolism, diagnostics, and therapeutics for malaria caused by P. vivax.

One of the determinants of the neuronal subtype produced from retinal progenitor cells is their proliferative potential. Here the authors show that mTORC1 promotes progenitor cell cycle progression and hence accelerated development in mouse retina through induction of the immunoproteasome which enhances the degradation of cyclins.

The current study aims to investigate a prediction model from reflection values and the spectral angle mapper (SAM) obtained from hyperspectral imaging (HSI) technology for the detection of three types of foreign materials – a branch, a knife, and rubber – that are associated with the various fruit-processed products. The study found that the maximum and minimum reflection values in the 900 to 1700 nm range for juices (apple, grape, tomato) and jams (strawberry, peach, tomato) differed depending on the presence or absence of physical hazards. The presence of physical hazards was also confirmed by the color difference in the SAM image. The partial least squares discriminant analysis model (PLS-DA) and logistic regression implemented on the Jupyter Notebook platform through the Anaconda prompt provided accuracy, F1 score, specificity, and sensitivity based on the confusion matrix. The maximum value was 100.0 %, while the minimum value was 97.6 % of the result of the PLS-DA modeling in the testing set. Logistic regression modeling also had a similar result: the maximum value was 100.0 %, while the minimum value was 97.8 % in the testing set. The sensitivity value in the testing set, which is a meaningful result for detecting physical hazards, was a maximum of 100.0 % for both PLS-DA and logistic regression. Results from the current study suggest that the reflection value and SAM data obtained through hyperspectral imaging could build a big data platform for early determination of physical hazards during agricultural product processing.

Recognizing traffic accident events in driving videos is a challenging task and has emerged as a crucial area of interest in autonomous driving applications in recent years. To ensure safe driving alongside human drivers and anticipation of their behaviors, methods to efficiently and accurately detect traffic accidents from a first-person viewpoint must be developed. This paper proposes a novel model, named the TempoLearn network, which leverages spatio-temporal learning to detect traffic accidents. The proposed approach incorporates temporal convolutions, given their effectiveness in identifying abnormalities, and a dilation factor for achieving large receptive fields. The TempoLearn network has two key components: accident localization, for predicting when the accident occurs in a video, and accident classification based on the localization results. To evaluate the performance of the proposed network, we conduct experiments using a traffic accident dashcam video benchmark dataset, i.e., the detection of traffic anomaly (DoTA) dataset, which is currently the largest and most complex traffic accident dataset. The proposed network achieves excellent performance on the DoTA dataset, and the accident localization score, measured in terms of AUC, is 16.5% higher than that of the existing state-of-the-art model. Moreover, we demonstrate the effectiveness of the TempoLearn network through experiments conducted on another benchmark dataset, i.e., the car crash dataset (CCD).

Abstract Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.

Purpose: The adductor pollicis muscle is frequently targeted for botulinum neurotoxin injective treatment for spasticity. However, there are no injective guidelines for delivering injection to the muscle. Materials and Methods: A method known as the modified Sihler’s method was used to stain the adductor pollicis muscle in 16 specimens to reveal intramuscular neural distribution of the muscle. Results: The most intramuscular neural distribution was located on 1/5 to 3/5 of the muscle regarding midline of 3rd metacarpal bone (0) to the base of the 1st proximal phalanx (5/5). The nerve entry point was mostly located on 0 to 1/5 of the muscle. Conclusion: The result suggests that botulinum neurotoxin should be delivered at the middle of second metacarpal bone via deep injection. [ABSTRACT FROM AUTHOR]

Rotenone, an inhibitor of mitochondrial complex I of the mitochondrial respiratory chain, is known to elevate mitochondrial reactive oxygen species and induce apoptosis via activation of the caspase-3 pathway. Bee venom (BV) extracted from honey bees has been widely used in oriental medicine and contains melittin, apamin, adolapin, mast cell-degranulating peptide, and phospholipase A2. In this study, we tested the effects of BV on neuronal cell death by examining rotenone-induced mitochondrial dysfunction. NSC34 motor neuron cells were pretreated with 2.5 μg/mL BV and stimulated with 10 μM rotenone to induce cell toxicity. We assessed cell death by Western blotting using specific antibodies, such as phospho-ERK1/2, phospho-JNK, and cleaved capase-3 and performed an MTT assay for evaluation of cell death and mitochondria staining. Pretreatment with 2.5 μg/mL BV had a neuroprotective effect against 10 μM rotenone-induced cell death in NSC34 motor neuron cells. Pre-treatment with BV significantly enhanced cell viability and ameliorated mitochondrial impairment in rotenone-treated cellular model. Moreover, BV treatment inhibited the activation of JNK signaling and cleaved caspase-3 related to cell death and increased ERK phosphorylation involved in cell survival in rotenone-treated NSC34 motor neuron cells. Taken together, we suggest that BV treatment can be useful for protection of neurons against oxidative stress or neurotoxin-induced cell death.