Immune Evasion Strategies of Pre-Erythrocytic Malaria Parasites

Malaria is a mosquito-borne infectious disease of humans. It begins with a bite from an infected female Anopheles mosquito and leads to the development of the pre-erythrocytic and blood stages. Blood-stage infection is the exclusive cause of clinical symptoms of malaria. In contrast, the pre-erythrocytic stage is clinically asymptomatic and could be an excellent target for preventive therapies. Although the robust host immune responses limit the development of the liver stage, malaria parasites have also evolved strategies to suppress host defenses at the pre-erythrocytic stage. This paper reviews the immune evasion strategies of malaria parasites at the pre-erythrocytic stage, which could provide us with potential targets to design prophylactic strategies against malaria

OpenAGI: When LLM Meets Domain Experts

Human intelligence has the remarkable ability to assemble basic skills into complex ones so as to solve complex tasks. This ability is equally important for Artificial Intelligence (AI), and thus, we assert that in addition to the development of large, comprehensive intelligent models, it is equally crucial to equip such models with the capability to harness various domain-specific expert models for complex task-solving in the pursuit of Artificial General Intelligence (AGI). Recent developments in Large Language Models (LLMs) have demonstrated remarkable learning and reasoning abilities, making them promising as a controller to select, synthesize, and execute external models to solve complex tasks. In this project, we develop OpenAGI, an open-source AGI research platform, specifically designed to offer complex, multi-step tasks and accompanied by task-specific datasets, evaluation metrics, and a diverse range of extensible models. OpenAGI formulates complex tasks as natural language queries, serving as input to the LLM. The LLM subsequently selects, synthesizes, and executes models provided by OpenAGI to address the task. Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF) mechanism, which uses the task-solving result as feedback to improve the LLM's task-solving ability. Thus, the LLM is responsible for synthesizing various external models for solving complex tasks, while RLTF provides feedback to improve its task-solving ability, enabling a feedback loop for self-improving AI. We believe that the paradigm of LLMs operating various expert models for complex task-solving is a promising approach towards AGI. To facilitate the community's long-term improvement and evaluation of AGI's ability, we open-source the code, benchmark, and evaluation methods of the OpenAGI project at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table

GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset

Enzymolytic soybean meal—impact on growth performance, nutrient digestibility, antioxidative capacity, and intestinal health of weaned piglets

Enzymolytic soybean meal (ESBM) enriches free amino acids and small peptides, while mitigating anti-nutritional factors. Substituting soybean meal with ESBM enhances animal performance, though optimal piglet dietary supplementation levels vary. The present study aimed to assess the impact of ESBM on the growth performance, nutrient digestibility, antioxidative capacity and intestinal health of weaned piglets. A total of 120 piglets (initial body weight, 7.0 ± 0.4 kg) were randomly allocated into 4 dietary groups, each comprising 5 replicates with 6 piglets per replicate. The control group received the basal diet, while the experimental groups were fed diets containing 2, 4% or 8% ESBM as a replacement for soybean meal over 28 days. Compared with the control group, piglets supplemented with 4% ESBM exhibited a significant increase (p < 0.05) in average daily gain and the apparent total tract digestibility of dry matter, ether extract and gross energy (p < 0.05), alongside a notable decrease (p < 0.05) in diarrhea incidence. Fed ESBM linearly increased (p < 0.05) the villus height in the ileum of piglets. The levels of superoxide dismutase and total antioxidant capacity in serum of piglets increased (p < 0.05) in the 2 and 4% ESBM groups, while diamine oxidase content decreased (p < 0.05) in the 4 and 8% ESBM group. ESBM inclusion also upregulated (p < 0.05) the expression of superoxide dismutase 1 (SOD-1), Catalase (CAT) and claudin-1 mRNA. In terms of cecal fermentation characteristics, ESBM supplementation resulted in a increase (p < 0.05) in valerate content and a linear rise (p < 0.05) in propionate, butyrate, and total short-chain fatty acids levels, accompanied by a decrease (p < 0.05) in the concentrations of tryptamine and NH3 in cecal digesta. ESBM had no discernible effect on cecal microbial composition. In summary, substitution of soybean meal with ESBM effectively improved the growth performance of piglets by enhancing nutrient digestibility, antioxidant capacity, intestinal barrier and cecal microbial fermentation characteristics, with the optimal replacement level identified at 4%

Improving Long-Term Adherence to Monitoring/Treatment in Underserved Asian Americans with Chronic Hepatitis B (CHB) through a Multicomponent Culturally Tailored Intervention: A Randomized Controlled Trial

Background: Although Asian Americans make up 6% of the U.S. population, they account for 58% of Americans with chronic hepatitis B (CHB). Yet, adherence to monitoring and antiviral treatment guidelines among Asian American CHB patients remains suboptimal. Methods: The purpose of this study was to evaluate the efficacy of a multicomponent intervention on adherence to CHB monitoring among Asian Americans with CHB. The intervention components included virtual patient education, patient navigation, and mobile health reminders delivered by bilingual community health educators. Chi-square test and t -test were used to compare demographic characteristics and two CHB measures: CHB clinical follow-up and CHB laboratory monitoring by the time of the 12-month follow-up assessment. A generalized linear mixed-effects model (GLMM) was fitted to assess the effectiveness of the intervention. Results: The study sample consisted of 358 Chinese and Vietnamese Americans living with CHB, including 181 in the intervention group and 177 in the control group. The intervention group had a significantly higher rate of CHB clinical follow- up (86.2%) and CHB laboratory monitoring (79.0%) than did the control group (54.2% and 45.2%, respectively). Results of the GLMM showed significant intervention effects on CHB clinical follow-up (odds ratio = 7.35, 95% confidence interval = 4.06–13.33) and CHB laboratory monitoring (odds ratio = 6.60, 95% confidence interval = 3.77–11.56) at the 12-month follow-up assessment. Conclusion: The multicomponent intervention was effective in improving adherence to CHB monitoring among Asian Americans. Additional implementation research is needed to better understand and apply effective interventions to other underserved populations

Factors Associated with Hepatitis B Medication Adherence and Persistence among Underserved Chinese and Vietnamese Americans

Background: Hepatitis B virus (HBV) infection disproportionately affects Asian Amer- icans in the United States, while this population faces low adherence to HBV treatment. Using the information–motivation–behavioral skills model (IMB), the study aims to examine medication adherence and persistence among Chinese and Vietnamese people with HBV. Methodology: Study participants were recruited between March 2019 and March 2020 and were enrolled through multiple recruitment approaches in the Greater Philadelphia Area and New York City. The study is an assess- ment of the baseline data on medication adherence, HBV-related knowledge, motivation of HBV med- ication treatment, self-efficacy about HBV medication treatment, and socioeconomic status. Results: Among 165 participants, 77.6% were Chinese and 22.4% were Vietnamese Americans. HBV-related knowledge/information, motivation, and self-efficacy were all positively associated with having medium/high medication adherence. Multilevel mixed-effects generalized linear regression revealed that living more than 10 years in the U.S. (OR = 4.24; p = 0.028) and greater information–knowledge about HBV (OR = 1.46; p = 0.004) were statistically associated with higher odds of medium/high medication adherence. Moreover, greater HBV-related knowledge/information ( OR = 1.49; p = 0.023) and greater motivation towards HBV treatment adherence (OR = 1.10; p = 0.036) were both associated with a higher likelihood of medication persistence. Conclusion: Our findings provided significant im- plications in designing behavioral interventions focused on self-efficacy, information, and motivation to promote better medication adherence among Asian Americans living with HBV

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Immune Evasion Strategies of Pre-Erythrocytic Malaria Parasites

Malaria is a mosquito-borne infectious disease of humans. It begins with a bite from an infected female Anopheles mosquito and leads to the development of the pre-erythrocytic and blood stages. Blood-stage infection is the exclusive cause of clinical symptoms of malaria. In contrast, the pre-erythrocytic stage is clinically asymptomatic and could be an excellent target for preventive therapies. Although the robust host immune responses limit the development of the liver stage, malaria parasites have also evolved strategies to suppress host defenses at the pre-erythrocytic stage. This paper reviews the immune evasion strategies of malaria parasites at the pre-erythrocytic stage, which could provide us with potential targets to design prophylactic strategies against malaria

A Deep few-shot learning algorithm for hyperspectral image classification

For hyperspectral image classification problem of small sample, this paper proposes a depth of less sample learning algorithm, this algorithm through the simulation of the small sample classification in the process of training is to train the depth 3D convolution neural network feature extraction, the extraction of characteristic with smaller class span and large spacing between classes, more suitable for small sample classification problem, and can be used for different hyperspectral data, has better generalization ability. The trained model is used to extract the features of the target data set, and then the nearest neighbor classifier and support vector machine classifier are combined for supervised classification. Three groups of hyperspectral image data of Pavia university, Indian Pines and Salinas were used in the classification experiment. The experimental results showed that the algorithm could achieve a better classification accuracy than the traditional semi-supervised classification method under the condition of fewer training samples (only 5 marked samples were selected for each type of feature as training samples)

Interaction between drinking and dietary inflammatory index affects prostate specific antigen: a cross-sectional study

Abstract Background Numerous studies have shown that the dietary inflammatory index (DII) is associated with adverse health effects. However, the relationship between DII and prostate cancer (PCa) remains controversial. Although alcohol is included in DII as a dietary factor, the various adverse health effects of alcohol consumption are not only related to inflammation. On the other hand, it has been a long-standing debate whether alcohol consumption is linked to the risk of PCa. Therefore, to clarify whether drinking affects the relationship between DII and PCa, we evaluated the correlation between DII and prostate-specific antigen (PSA) based on the National Health and Nutrition Examination Survey (NHANES) database. Methods We used data from the NHANES spanning from 2005 to 2010 to analyze the relationship between PCa and DII. Out of the 31,034 NHANES participants, we enrolled 4,120 individuals in our study, utilizing dietary intake data from a twenty-four-hour period to determine DII scores. Demographic data, physical and laboratory test results were collected to compare between low PSA and high PSA groups, and to calculate the odds ratio between both groups, we employed a logistic regression analysis. Results In this cross-sectional investigation of PCa, drinkers and non-drinkers had different relationships between DII and PSA levels (OR: 1.2, 95% Cl: 1-1.44 vs. OR: 0.98, 95% Cl: 0.9–1.07), and DII and abstaining from alcohol were effective in reducing the incidence of PSA (p-value for significant interaction = 0.037). Conclusion The results of our study suggest that drinking may influence the relationship between DII and PSA levels. DII is likely to be a reliable indicator for estimating PSA levels among non-drinkers, who may limit their intake of pro-inflammatory ingredients to lower the incidence and death of PCa