Gut-Brain Axis: Probiotic, <em>Bacillus subtilis</em>, Prevents Aggression via the Modification of the Central Serotonergic System

Intestinal bacteria release various neuroactive compounds directly or indirectly regulating brain function to modulate host health and behavior through the gut-brain axis. Probiotics have been used as dietary supplements to target gut microbiota (microbiome) for prevention or therapeutic treatment of various diseases including mental disorders. In our study, chickens were used as an animal model to assess, if dietary supplementation of probiotic, Bacillus subtilis, reduces aggressive behaviors following social challenge. Chickens of an aggressive line were housed in single-hen cages. At 24 weeks of age, the hens were paired with similar body weight to identify the dominance rank (day 0). The subordinate and dominant of each pair were fed a regular layer diet or the diet mixed with 250 ppm probiotics for 2 weeks, then the second behavior test was performed between the same pair (day 14). The display of aggressive behaviors in the regular diet-fed chickens was not affected between the levels at day 0 and day 14, while the frequency of threat and aggressive pecking were reduced in the probiotic-fed chickens compared to the levels at day 0. These results suggest dietary probiotic, Bacillus subtilis, could be a suitable strategy for increasing hosts’ mental health

Carbohydrates generated via hot water as catalyst for CO2 reduction reaction

Combining terrestrial biomass with submarine-type hydrothermal environments for CO2 reduction is a possible approach for realizing new energies while achieving sustainable circulation of carbon. Herein, carbohydrateenabled CO2 reduction based on NaHCO3 conversion to formate revealed that hydrothermal environments facilitated direct hydrogen transfer from carbohydrates (glucose, cellulose) to CO2/NaHCO3 with hot water (250–300 °C, 5–20 MPa) acting as homogeneous catalyst in absence of any conventional catalysts giving CO2/ NaHCO3 reduction efficiencies as high as 76% for cellulose. Time-resolved operando hydrothermal DRIFTS spectra of glycolaldehyde in hot water (250 °C, autogenous pressure) verified that water catalyzed NaHCO3 reduction by converting the -CHO group in the carbohydrate to its hydrated state as -CH(OH)2, which enabled NaHCO3 reduction by direct hydrogen transfer and that the ratio of hydrogen transfer from water:- glycolaldehyde for NaHCO3 reduction was about 13:87 on an atom basis. For cellulose exploited as energy input, a greater than 3.4% solar-to-formate efficiency can be theoretically attained, which is unprecedented compared with present literature values. These findings provide basic data for future studies on biomass-enabled CO2 reduction and broaden the scope of hydrothermal chemistry for developing net-zero emission processes

Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?

Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through few-shot prompt tuning. We find that such a prompt tuning process is highly robust to label noises. This intrigues us to study the key reasons contributing to the robustness of the prompt tuning paradigm. We conducted extensive experiments to explore this property and find the key factors are: 1) the fixed classname tokens provide a strong regularization to the optimization of the model, reducing gradients induced by the noisy samples; 2) the powerful pre-trained image-text embedding that is learned from diverse and generic web data provides strong prior knowledge for image classification. Further, we demonstrate that noisy zero-shot predictions from CLIP can be used to tune its own prompt, significantly enhancing prediction accuracy in the unsupervised setting. The code is available at https://github.com/CEWu/PTNL.Comment: Accepted by ICCV202

The Online Data Quality Monitoring System at BESIII

The online Data Quality Monitoring (DQM) plays an important role in the data taking process of HEP experiments. BESIII DQM samples data from online data flow, reconstructs them with offline reconstruction software, and automatically analyzes the reconstructed data with user-defined algorithms. The DQM software is a scalable distributed system. The monitored results are gathered and displayed in various formats, which provides the shifter with current run information that can be used to find problems early. This paper gives an overview of DQM system at BESIII.Comment: Already submit to Chinese Physics