Background Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM. Material and Methods For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machine-learning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machine-learning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Scene text detection is the task of detecting word boxes in given images. The accuracy of text detection has been greatly elevated using deep learning models, especially convolutional neural networks. Previous studies commonly aimed at developing more accurate models, but their models became computationally heavy and worse in efficiency. In this paper, we propose a new efficient model for text detection. The proposed model, namely Compact and Accurate Scene Text detector (CAST), consists of MobileNetV2 as a backbone and balanced decoder. Unlike previous studies that used standard convolutional layers as a decoder, we carefully design a balanced decoder. Through experiments with three well-known datasets, we then demonstrated that the balanced decoder and the proposed CAST are efficient and effective. The CAST was about 1.1x worse in terms of the F1 score, but 30∼115x better in terms of floating-point operations per second (FLOPS).

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted de novo design approach to identify a novel inhibitor of FLT-3 (D835Y). A recurrent neural network containing long short-term memory cells (LSTM) was implemented to generate potential candidates related to our in-house hit compound (PCW-1001). Approximately 10,416 hits were generated from 20 epochs, and the generated hits were further filtered using various toxicity and synthetic feasibility filters. Based on the docking and free energy ranking, the top compound was selected for synthesis and screening. Of these three compounds, PCW-A1001 proved to be highly selective for the FLT-3 (D835Y) mutant, with an IC50 of 764 nM, whereas the IC50 of FLT-3 WT was 2.54 μM.

Understanding how individuals recognize an emerging technology can have a profound impact on how successfully the technology is adopted. The authors look into how people with different cognitive styles interpret a new technology and arrive at different beliefs of the same technology, which influences the recognition and acceptance of the technology. An experiment was conducted to test whether an individual's cognitive style impacts their beliefs and intention to use RFID, an emerging technology. The study suggests that a person's cognitive style does influence how he/she perceives the usefulness and ease of the technology, as well as his/her attitude and intention to use it. People with introversion, thinking and judging cognitive styles tend to perceive higher ease of use of RFID than those with extroversion, feeling and perceiving cognitive styles. Also, judging types are more likely to better perceive higher usefulness of RFID. The authors also provide discussion on the managerial and theoretical implications of our findings.

In France, the regional airport’s demand for services is facing challenges due to the continuous expansion of the high-speed train, high-speed line, and highway networks. This study focuses on the viability of regional airports in France through technical efficiency using data envelopment, principle component analysis, Malmquist productivity index, and regression analysis using bootstrapping. To face the current competitive environment, the regional airports in France adopted strategies, such as the construction of low-cost carrier (LCC)-dedicated terminals (LCCTs) with lower expenses to attract more LCCs, increasing non-aeronautical revenue, and hosting regional hubs of LCCs. This is the first study that analyzes all of the French regional airports. The findings indicate that the existence of LCCTs positively affects technical efficiency on the airport’s performance, and share of LCCs at a regional airport leads to neither the efficiency nor the profit level.