Effects Of Different Pathways Research Articles

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance. The latest China National Medical Licensing Examination (CNMLE-2022) served as the benchmark. We collected 53 medical books and 381149 medical questions to construct the medical knowledge base and question bank. The proposed Knowledge and Few-shot Enhancement In-context Learning (KFE) framework leverages the in-context learning ability of LLMs to integrate diverse external clinical knowledge sources. We evaluated KFE with ChatGPT (GPT-3.5), GPT-4, Baichuan2-7B, Baichuan2-13B, and QWEN-72B in CNMLE-2022 and further investigated the effectiveness of different pathways for incorporating LLMs with medical knowledge from 7 distinct perspectives. Directly applying ChatGPT failed to qualify for the CNMLE-2022 at a score of 51. Cooperated with the KFE framework, the LLMs with varying sizes yielded consistent and significant improvements. The ChatGPT's performance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This surpasses the qualification threshold (60) and exceeds the average human score of 68.70, affirming the effectiveness and robustness of the framework. It also enabled a smaller Baichuan2-13B to pass the examination, showcasing the great potential in low-resource settings. This study shed light on the optimal practices to enhance the capabilities of LLMs in non-English medical scenarios. By synergizing medical knowledge through in-context learning, LLMs can extend clinical insight beyond language barriers in healthcare, significantly reducing language-related disparities of LLM applications and ensuring global benefit in this field.

(Invited) Green Hydrogen Technologies: Status and Trends

There is growing societal consensus that hydrogen is an absolutely necessary part of energy portfolio to reach the COP26 goal to secure global net-zero by mid-century. Hydrogen will play a major role in hard to decarbonize sectors such as industrial (production of steel, cement, and chemicals including ammonia) and heavy-duty, long-haul transportation. By different estimations, hydrogen production volume will be anywhere from 240 to 800 million metric tons per year (MMTY). More realistic predictions are in the range 500 – 600 MMTY that is 7 – 8.5 times more than current global hydrogen production, which predominantly uses fossil fuels and emits around 830 MMTY of carbon dioxide. It is assumed that hydrogen produced by water splitting became predominant by 2050.With less than 0.1% of current global hydrogen production delivered from water electrolysis, electrolytic hydrogen has tremendous potential for growth. Two commercial (alkaline and PEM) and two emerging (SOEC and AEM) will be compared based on current status and trends of technology (catalysts, membranes, system manufacturability, and capital cost). These technologies could benefit from the integration with energy sources (e.g., nuclear power) or downstream utilization (e.g., ammonia production). Suitability of these technologies for exemplary environments with different energy inputs, electricity prices, and capacity factors will be analyzed. In addition, the effect of different pathways for hydrogen delivery (pure and in the form of a hydrogen carrier) on the levelized cost of hydrogen will be considered.Development of advanced green hydrogen technologies including early stage research will be illustrated with projects funded by DOE Advanced Research Projects Agency (ARPA-E) and Hydrogen and Fuel Cell technologies Office (HFTO), and their role in DOE Hydrogen Program and Hydrogen Earthshot will be discussed.

Incorporating environmental forcing in developing ecosystem-based fisheries management strategies

AbstractThis study incorporated two pathways of environmental forcing (i.e. “larval mortality forcing” and “somatic growth forcing”) into an end-to-end ecosystem model (Object-oriented Simulator of Marine ecOSystEms, OSMOSE) developed for the Pacific North Coast Integrated Management Area (PNCIMA) off western Canada, in order to evaluate alternative fisheries management strategies under environmental changes. With a suite of ecosystem-level indicators, the present study first compared the ecosystem effects of different pathways of environmental forcing scenarios; and then evaluated the alternative fisheries management strategies which encompassed a series of fishing mortality rates relative to FMSY (the fishing mortality rate that produces maximum sustainable yield) and a set of precautionary harvest control rules (HCRs). The main objectives of this study were to (i) explore the ecosystem effects of different environmental forcing scenarios; (ii) identify the impacts of different fishing mortality rates on marine ecosystem structure and function; and (iii) evaluate the ecosystem-level performance of various levels of precautionary HCRs. Results indicated that different pathways of environmental forcing had different ecosystem effects and incorporating appropriate HCRs in the fisheries management process could help maintain ecosystem health and sustainable fisheries. This study provides important information on future fisheries management options within similar marine ecosystems that are facing global changes.

Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling

More than one billion people will face water scarcity within the next ten years due to climate change and unsustainable water usage, and this number is only expected to grow exponentially in the future. At current water use rates, supply-side demand management is no longer an effective way to combat water scarcity. Instead, many municipalities and water agencies are looking to demand-side solutions to prevent major water loss. While changing conservation behavior is one demand-based strategy, there is a growing movement toward the adoption of water conservation technology as a way to solve water resource depletion. Installing technology into one’s household requires additional costs and motivation, creating a gap between the overall potential households that could adopt this technology, and how many actually do. This study identified and modeled a variety of demographic and household characteristics, social network influence, and external factors such as water price and rebate policy to see their effect on residential water conservation technology adoption. Using Agent-based Modeling and data obtained from the City of Miami Beach, the coupled effects of these factors were evaluated to examine the effectiveness of different pathways towards the adoption of more water conservation technologies. The results showed that income growth and water pricing structure, more so than any of the demographic or building characteristics, impacted household adoption of water conservation technologies. The results also revealed that the effectiveness of rebate programs depends on conservation technology cost and the affluence of the community. Rebate allocation did influence expensive technology adoption, with the potential to increase the adoption rate by 50%. Additionally, social network connections were shown to have an impact on the rate of adoption independent of price strategy or rebate status. These findings will lead the way for municipalities and other water agencies to more strategically implement interventions to encourage household technology adoption based on the characteristics of their communities.

Raman Deuterium Isotope Probing Reveals Microbial Metabolism at the Single-Cell Level.

We illustrate that single-cell Raman microspectroscopy, coupled with deuterium isotope probing (Raman-DIP), provides a culture-independent and nondestructive approach to probe metabolic pathways of carbon substrates at the single-cell level. We found a distinguishable C-D vibration band at 2070-2300 cm-1 in single-cell Raman spectra (SCRS) when Escherichia coli used deuterated glucose and Pseudomonas sp. used deuterated naphthalene as sole carbon sources. The intensity of the C-D band is proportional to the extent of deuteration in the carbon source, and as little as 5% deuteration can be distinguished by analysis of SCRS. It suggests that Raman-DIP could be used to semiquantitatively and sensitively indicate the metabolism of deuterated carbon source in microbes. A lower lipid conversion rate of deuterated naphthalene compared to that of deuterated glucose was observed, presumably owing to different anabolic pathways and membrane alteration. Apart from the C-D band shift from C-H, SCRS also reveal several isotopic shifts of the phenylalanine band, of which the positions correlate well with a computational model. A reduction in phenylalanine deuteration in Pseudomonas sp. compared to that in E. coli is due to the dilution effect of different pathways of phenylalanine biosynthesis in Pseudomonas sp. Collectively, we demonstrate that Raman-DIP can not only indicate metabolic activity using deuterated carbon sources but also reveal different metabolic pathways by analyzing SCRS. By harnessing such low-cost and versatile deuterated substrates, Raman-DIP has the potential to probe a wide range of metabolic pathways and functions at the single-cell level.

A computational study of ethylene–air sooting flames: Effects of large polycyclic aromatic hydrocarbons

An updated reduced gas-phase kinetic mechanism was developed and integrated with aerosol models to predict soot formation characteristics in ethylene nonpremixed and premixed flames. A primary objective is to investigate the sensitivity of the soot formation to various chemical pathways for large polycyclic aromatic hydrocarbons (PAH). The gas-phase chemical mechanism adopted the KAUST-Aramco PAH Mech 1.0, which utilized the AramcoMech 1.3 for gas-phase reactions validated for up to C2 fuels. In addition, PAH species up to coronene (C24H12 or A7) were included to describe the detailed formation pathways of soot precursors. In this study, the detailed chemical mechanism was reduced from 397 to 99 species using directed relation graph with expert knowledge (DRG-X) and sensitivity analysis. The method of moments with interpolative closure (MOMIC) was employed for the soot aerosol model. Counterflow nonpremixed flames at low strain rate sooting conditions were considered, for which the sensitivity of soot formation characteristics to different nucleation pathways were investigated. Premixed flame experiment data at different equivalence ratios were also used for validation. The findings show that higher PAH concentrations result in a higher soot nucleation rate, and that the total soot volume and average size of the particles are predicted in good agreement with experimental results. Subsequently, the effects of different pathways, with respect to pyrene- or coronene-based nucleation models, on the net soot formation rate were analyzed. It was found that the nucleation processes (i.e., soot inception) are sensitive to the choice of PAH precursors, and consideration of higher PAH species beyond pyrene is critical for accurate prediction of the overall soot formation.

Life review and life story books for people with mild to moderate dementia: a randomised controlled trial

Objectives: To evaluate the effect of different pathways for developing a life story book (LSB) for people with dementia.Method: Preliminary randomised control trial; 23 people with dementia in care homes (mean age 86) randomly assigned to receive either 12 individual life review sessions and co-creating a LSB or a personal LSB created by their relatives as a ‘gift’Results: No difference in quality of life (quality of life–Alzheimer's disease (QOL–AD)) was observed between the two groups, six weeks after having received the LSB (F(1,20) = 0.08, p = 0.77). At this point, QOL–AD had improved for both groups, but there was a significant between-group difference at an intermediate assessment immediately after the life review sessions had been completed, before the LSBs were received (F(1, 20) = 5.11, p = 0.035), in favour of life review. A similar pattern was observed on autobiographical memory (extended autobiographical memory interview), with the life review group improving significantly more than the gift group during the life review sessions, but no difference was observed once all participants had had their LSB for six weeks. After the LSBs were produced – by either pathway – quality of relationship as rated by relatives improved significantly (F(2, 39) = 19.37, p < 0.001) and staff knowledge regarding the resident and attitudes to dementia improved.Conclusion: The creation of LSBs – either through a life review process or by relatives without involving the person with dementia – has benefits for people with dementia, relatives and staff in care homes. However, undertaking a life review requires training and supervision.

Multiple imputation for left‐censored biomarker data based on Gibbs sampling method

Biomarkers, increasingly used in biomedical studies for the diagnosis and prognosis of acute and chronic diseases, provide insight into the effectiveness of treatments and potential pathways that can be used to guide future treatment targets. The measurement of these markers is often limited by the sensitivity of the given assay, resulting in data that are censored either at the lower or at the upper limit of detection. For the Genetic and Inflammatory Markers of Sepsis (GenIMS) study, many different biomarkers were measured to examine the effect of different pathways on the development of sepsis. In this study, the left-censoring of several important inflammatory markers has led to the need for statistical methods that can incorporate this censoring into any analysis of the biomarker data. This paper focuses on the development of multiple imputation methods for the inclusion of multiple left-censored biomarkers in a logistic regression analysis. We assume a multivariate normal distribution to account for the correlations between biomarkers and use the Gibbs sampler for the estimation of the distributional parameters and the imputation of the censored markers. We evaluate and compare the proposed methods with some simple imputation methods through simulation. We use a data set of inflammatory and coagulation markers from the GenIMS study for illustration.