Systemic lupus erythematosus with initial presentation of empyematous pleural effusion in an elderly male patient: A diagnostic challenge

Systemic lupus erythematosus (SLE) poses great difficulty in making an early diagnosis in elderly males, often presenting with atypical manifestations. Acute onset of empyematous pleural effusion has rarely been seen. Herein, we report a 66-year-old man with SLE presenting with rapid progression of bilateral pleural effusion. Diagnostic thoracocentesis disclosed neutrophil-predominant exudates and chest computed tomography revealed multiple loculated pleural effusions. Nevertheless, optimal antibiotic therapy plus surgical decortication of the pleura did not improve his condition. The diagnosis of SLE was readily established after LE cells were accidentally found in the pleural effusion. Large amounts of pleural effusion subsided soon after high dose corticosteroid therapy

Accelerating and Improving AlphaZero Using Population Based Training

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.Comment: accepted by AAAI2020 as oral presentation. In this version, supplementary materials are adde

Partial wave effects in the heavy quarkonium radiative electromagnetic decays

In a previous paper \cite{Bc}, it was pointed out that the wave functions of all particles are not pure waves, besides the main partial waves, they all contain {other partial waves}. It is very interesting to know what role these different partial waves play in particle transitions. Therefore, by using the Bethe-Salpeter equation method, we study the radiative electromagnetic decays $\psi\rightarrow\gamma\chi_{_{cJ}}$ and $\Upsilon\rightarrow\gamma\chi_{_{bJ}}$ ($J=0,1,2$). We find that for the $S$ and $P$ wave dominated states, like the $\psi(2S)$, $\Upsilon(2S)$, $\chi_{_{cJ}}(1P)$, and $\chi_{_{bJ}}(1P)$ etc., the dominant $S$ and $P$ waves provide main and nonrelativistic contrition to the decays; other partial waves mainly contribute to the relativistic correction. For the states like the $\psi(1D)$, $\Upsilon(2D)$, $\chi_{c2}(1F)$, and $\chi_{b2}(1F)$ etc., they are the $S-P-D$ mixing state dominated by $D$ wave or the $P-D-F$ mixing state dominated by $F$ wave. Large decay widths are found in the transitions $\psi(2D)\to \chi_{c2}(1F)$, $\Upsilon(1D)\to \chi_{bJ}(1P)$, and $\Upsilon(2D)\to \chi_{bJ}(2P)$ etc., which may be helpful to study the missing states $\chi_{c2}(1F)$, $\Upsilon(1D)$, and $\Upsilon(2D)$.Comment: 31 pages, 19 table

Evaluation of Biological Toxicity of CdTe Quantum Dots with Different Coating Reagents according to Protein Expression of Engineering Escherichia coli

The results obtained from toxicity assessment of quantum dots (QDs) can be used to establish guidelines for the application of QDs in bioimaging. This paper focused on the design of a novel method to evaluate the toxicity of CdTe QDs using engineering Escherichia coli as a model. The toxicity of mercaptoacetic acid (MPA), glutathione (GSH), and L-cysteine (Cys) capped CdTe QDs was analyzed according to the heterologous protein expression in BL21/DE3, engineering Escherichia coli extensively used for protein expression. The results showed that the MPA-CdTe QDs had more serious toxicity than the other two kinds of CdTe QDs. The microscopic images and SEM micrographs further proved that both the proliferation and the protein expression of engineering Escherichia coli were inhibited after treatment with MPA-CdTe QDs. The proposed method is important to evaluate biological toxicity of both QDs and other nanoparticles

A Local-Pattern Related Look-Up Table

This paper describes a Relevance-Zone pattern table (RZT) that can be used to replace a traditional transposition table. An RZT stores exact game values for patterns that are discovered during a Relevance-Zone-Based Search (RZS), which is the current state-of-the-art in solving L&D problems in Go. Positions that share the same pattern can reuse the same exact game value in the RZT. The pattern matching scheme for RZTs is implemented using a radix tree, taking into consideration patterns with different shapes. To improve the efficiency of table lookups, we designed a heuristic that prevents redundant lookups. The heuristic can safely skip previously queried patterns for a given position, reducing the overhead to 10% of the original cost. We also analyze the time complexity of the RZT both theoretically and empirically. Experiments show the overhead of traversing the radix tree in practice during lookup remain flat logarithmically in relation to the number of entries stored in the table. Experiments also show that the use of an RZT instead of a traditional transposition table significantly reduces the number of searched nodes on two data sets of 7x7 and 19x19 L&D Go problems.Comment: Submitted to IEEE Transactions on Games (under review