Common and distinct equity preferences in children and adults

Fairness plays a crucial role in children’s social life and has garnered considerable attention. However, previous research and theories primarily examined the development of children’s fairness behaviors in the conflict between self-interest motivation and fairness-complying motivation, neglecting the influence of advantage-seeking motivation. Moreover, despite the well-established role of gain/loss frame in human decision-making, it remains largely unclear whether the framing effect modulates fairness behaviors in children. It was hypothesized that children would exhibit advantage-seeking motivation resulting in more selfish behaviors in the loss context. To examine the hypothesis, we combined an adapted dictator game and computational modeling to investigate various motivations underlying fairness behaviors of children in both loss and gain contexts and to explore the developmental directions by contrasting children and adults. In addition, the current design enabled the dissociation between fairness knowledge and behaviors by asking participants to decide for themselves (the first-party role) or for others (the third-party role). This study recruited a total of 34 children (9–10 years, Mage = 9.82, SDage = 0.38, 16 females) and 31 college students (Mage = 19.81, SDage = 1.40, 17 females). The behavioral results indicated that children behaved more selfishly in first-party and more fairly in third-party than adults, without any significant framing effects. The computational results revealed that both children and adults exhibited aversion to advantageous and disadvantageous inequity in third-party. However, they showed distinct preferences for advantageous inequity in first-party, with advantage-seeking preferences among children and aversion to advantageous inequity among adults. These findings contribute to a deeper understanding of children’s social preferences and their developmental directions

Expression of an extremely acidic β-1,4-glucanase from thermoacidophilic Alicyclobacillus sp. A4 in Pichia pastoris is improved by truncating the gene sequence

<p>Abstract</p> <p>Background</p> <p><it>Alicyclobacillus </it>sp. A4 is thermoacidophilic and produces many glycoside hydrolases. An extremely acidic β-1,4-glucanase (CelA4) has been isolated from <it>Alicyclobacillus </it>sp. A4 and purified. This glucanase with a molecular mass of 48.6 kDa decreases the viscosity of barley-soybean feed under simulated gastric conditions. Therefore, it has the potential to improve the nutrient bioavailability of pig feed. For the study reported herein, the full-length gene, <it>CelA4</it>, of this glucanase (CelA4) was identified using the sequences of six peptides and cloned from strain A4. The gene fragment (<it>CelA4</it>_<it>F</it>) encoding the mature protein was expressed in <it>Pichia pastoris</it>. Sequence truncation and glycosylation were found for recombinant CelA4_F, both of which affected the expression efficiency. The physical properties of various forms of CelA4 as they affected enzymatic activity were characterized.</p> <p>Results</p> <p>We located the full-length 2,148-bp gene for CelA4 (<it>CelA4</it>) in the genome of <it>Alicyclobacillus </it>sp. A4. <it>CelA4 </it>encodes a 715-residue polypeptide with a calculated molecular mass of 71.64 kDa, including an N-terminal signal peptide (residues 1-39), a catalytic domain (residues 39-497), and a C-terminal threonine-rich region (residues 498-715). Its deduced amino acid sequence and that of an <it>Alicyclobacillus acidocaldarius </it>endo-β-1,4-glucanase were identical at 44% of the residue positions. When the experimental molecular mass of CelA4_F--a recombinant protein designed to mimic the CelA4 sequence lacking the N-terminal signal peptide that had been expressed in <it>Pichia pastoris</it>--was compared with its hypothetical molecular mass, it was apparent that CelA4_Fwas truncated, possibly at residue 497. An artificially truncated gene fragment (<it>CelA4</it>_<it>T</it>) without C-terminal threonine-rich region was expressed in <it>P. pastoris</it>, and the expression efficiency of CelA4_Twas substantially greater than that of CelA4_F. Purified CelA4_Fand CelA4_Thad similar molecular masses (~60 kDa) and enzymatic properties (optimum pH, 3.4; optimum temperature, 60°C); they were relatively stable between pH 1.2 and 8.2 at 70°C and resistant to acidic and neutral proteases. However, their molecular masses and thermostabilities differed from those of CelA4 isolated from <it>Alicyclobacillus </it>sp. A4. A deglycosylated form of CelA4 (CelA4_D) had properties similar to that of CelA4 except that it was thermoliable at 60°C.</p> <p>Conclusions</p> <p>Truncation during expression of CelA4_For artificial truncation of its gene--both of which produced a form of CelA4 lacking a threonine-rich region that includes a putative linker--increased the level of enzyme produced in comparison with that produced by cultivation of <it>Alicyclobacillus </it>sp. A4. Glycosylation increased the thermostability of CelA4. Of the four forms of CelA4 studied, CelA4_Twas produced in highest yield and had the most favorable physical properties; therefore, it has potential for use in the feed industry.</p

Augmenting Large Language Model Translators via Translation Memories

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to ``understand'' prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.Comment: Accepted to Findings of ACL 202

Large Language Models are Parallel Multilingual Learners

In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities. To test this capability, we design extensive experiments encompassing 8 typical datasets, 7 languages and 8 state-of-the-art multilingual LLMs. Experimental results show that (1) incorporating more languages help PiM surpass the conventional ICL further; (2) even combining with the translations that are inferior to baseline performance can also help. Moreover, by examining the activated neurons in LLMs, we discover a counterintuitive but interesting phenomenon. Contrary to the common thought that PiM would activate more neurons than monolingual input to leverage knowledge learned from diverse languages, PiM actually inhibits neurons and promotes more precise neuron activation especially when more languages are added. This phenomenon aligns with the neuroscience insight about synaptic pruning, which removes less used neural connections, strengthens remainders, and then enhances brain intelligence.Comment: Working in proces

Interrogating Bromodomain Inhibitor Resistance in KMT2A-Rearranged Leukemia Through Combinatorial CRISPR Screens

Bromo- and extra-terminal domain inhibitors (BETi) have exhibited therapeutic activities in many cancers. However, the mechanisms controlling BETi response and resistance are not well understood. We conducted genome-wide loss-of-function CRISPR screens using BETi-treated KMT2A-rearranged (KMT2A-r) cell lines. We revealed that Speckle-type POZ protein (SPOP) gene (Speckle Type BTB/POZ Protein) deficiency caused significant BETi resistance, which was further validated in cell lines and xenograft models. Proteomics analysis and a kinase-vulnerability CRISPR screen indicated that cells treated with BETi are sensitive to GSK3 perturbation. Pharmaceutical inhibition of GSK3 reversed the BETi-resistance phenotype. Based on this observation, a combination therapy regimen inhibiting both BET and GSK3 was developed to impede KMT2A-r leukemia progression in patient-derived xenografts in vivo. Our results revealed molecular mechanisms underlying BETi resistance and a promising combination treatment regimen of ABBV-744 and CHIR-98014 by utilizing unique ex vivo and in vivo KMT2A-r PDX models

Pollution status and distribution characteristics of indoor air bacteria in subway stations and compartments in a city of Central South China

BackgroundBacteria are the most diverse and widely sourced microorganisms in the indoor air of subway stations, where pathogenic bacteria can spread through the air, leading to increased health risks. ObjectiveTo understand the status and distribution characteristics of indoor air bacterial pollution in subway stations and compartments in a city of Central South China, and to provide a scientific basis for formulating intervention measures to address indoor air bacteria pollution in subways. MethodsThree subway stations and the compartments of trains parking there in a city in Central South China were selected according to passenger flow for synchronous air sampling and monitoring. Temperature, humidity, wind speed, carbon dioxide (CO2), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) were measured by direct reading method. In accordance with the requirements of Examination methods for public places-Part 3: Airborne microorganisms (GB/T 18204.3-2013), air samples were collected at a flow rate of 28.3 L·min−1, and total bacterial count was estimated. Bacterial microbial species were identified with a mass spectrometer and pathogenic bacteria were distinguished from non-pathogenic bacteria according to the Catalogue of pathogenic microorganisms transmitted to human beings issued by National Health Commission. Kruskal-Wallis H test was used to compare the subway hygiene indicators in different regions and time periods, and Bonferroni test was used for pairwise comparison. Spearman correlation test was used to evaluate the correlation between CO2 concentration and total bacterial count. ResultsThe pass rates were 100.0% for airborne total bacteria count, PM2.5, and PM10 in the subway stations and train compartments, 94.4% for temperature and wind speed, 98.6% for CO2, but 0% for humidity. The overall median (P25, P75) total bacteria count was 177 (138,262) CFU·m−3. Specifically, the total bacteria count was higher in station halls than in platforms, and higher during morning peak hours than during evening peak hours (P<0.05). A total of 874 strains and 82 species were identified by automatic microbial mass spectrometry. The results of identification were all over 9 points, and the predominant bacteria in the air were Micrococcus luteus (52.2%) and Staphylococcus hominis (9.8%). Three pathogens, Acinetobacter baumannii (0.3%), Corynebacterium striatum (0.1%), and Staphylococcus epidermidis bacilli (2.2%) were detected in 23 samples (2.6%), and the associated locations were mainly distributed in train compartments during evening rush hours. ConclusionThe total bacteria count in indoor air varies by monitoring sites of subway stations and time periods, and there is a risk of opportunistic bacterial infection. Attention should be paid to cleaning and disinfection during peak passenger flow hours in all areas

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc