Discovering Genes Involved in the Synthesis of Secondary Metabolites From the Seeds of Moringa Oleifera Through Transcriptome Analysis

Moringa oleifera is a widely used crop that produces seeds with a plethora of benefits encompassing health and nutrition. Secondary metabolite compounds were determined in the seeds of Moringa oleifera that possess nutritional and pharmacological benefits. Although various phytochemical researchers reported the presence of secondary metabolites in M. oleifera seeds, there is a lack of research on the genes encoding for enzymes that catalyze the synthesis of secondary metabolites in the seeds of M. oleifera. In the present study, RNA sequencing was used to analyze the transcriptome of the mature seed embryos of M. oleifera. Biological pathway analysis revealed 416 upregulated genes encoding for 11 enzymes involved in the catalytic steps of the phenylpropanoid and flavonoid pathways, and 63 unigenes encoding for 8 enzymes involved in the catalytic steps of the alkaloid pathway. These findings however need further validation using qRT-PCR which is a reliable and robust technique in order to validate the presence and expression of genes encoding for enzymes leading to the synthesis of secondary metabolites in the mature seed embryos of M. oleifera

Photovoltaic Performance of Ultrasmall PbSe Quantum Dots

We investigated the effect of PbSe quantum dot size on the performance of Schottky solar cells made in an ITO/PEDOT/PbSe/aluminum structure, varying the PbSe nanoparticle diameter from 1 to 3 nm. In this highly confined regime, we find that the larger particle bandgap can lead to higher open-circuit voltages (~0.6 V), and thus an increase in overall efficiency compared to previously reported devices of this structure. To carry out this study, we modified existing synthesis methods to obtain ultrasmall PbSe nanocrystals with diameters as small as 1 nm, where the nanocrystal size is controlled by adjusting the growth temperature. As expected, we find that photocurrent decreases with size due to reduced absorption and increased recombination, but we also find that the open-circuit voltage begins to decrease for particles with diameters smaller than 2 nm, most likely due to reduced collection efficiency. Owing to this effect, we find peak performance for devices made with PbSe dots with a first exciton energy of ~1.6 eV (2.3 nm diameter), with a typical efficiency of 3.5%, and a champion device efficiency of 4.57%. Comparing the external quantum efficiency of our devices to an optical model reveals that the photocurrent is also strongly affected by the coherent interference in the thin film due to Fabry-Pérot cavity modes within the PbSe layer. Our results demonstrate that even in this simple device architecture, fine-tuning of the nanoparticle size can lead to substantial improvements in efficiency

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Use of probabilistic phrases in a coordination game: human versus GPT-4

English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that the median human participant and GPT4 assigned probability estimates that were in good agreement (proportions of variance accounted for close to .90). GPT4's estimates of probability both in the investment and Medical contexts were as close or closer to that of the human participants as the human participants' estimates were to one another. Estimates of probability for both the human participants and GPT4 were little affected by context. In contrast, human and GPT4 estimates of ambiguity were not in such good agreement.Comment: Corrected typos, extended discussion, added reference

Evaluation of workplace safety performance in the Chinese petroleum industry

Reform of the Chinese petroleum industry has entered its second phase since early 1999. The productivity of the petroleum industry has been greatly improved, while the safety performance and records are not satisfactory. This paper investigates the critical factors for improving safety performance in the Chinese petroleum industry. The data used for the analysis are from a questionnaire survey administered to 480 professionals in the petroleum industry in which 143 valid responses were received. Statistical analysis techniques are used to analyze the data collected. The findings revealed that the most significant source of the safety problem is due to the combination of several reasons, including (a) violation on operating procedures, (b) obsolete facilities and equipment failures, (c) insufficient safety management system, (d) improper commands, number of casualties, and (e) production performances and operating skills. The three most essential protective methods include safety training and increasing staff's safety consciousness, cultivating safety culture, and enhancing equipment management and detecting hazards in time

Contribution of infrastructure to the township's sustainable development in Southwest China

Townships in Southwest China are usually located in mountainous regions, which are abundant in natural and cultural landscape resources. There are additional requirements for the township’s sustainable development in these areas. However, insufficient infrastructures, due to limited resources, constrain the sustainable development of these townships. Sustainable contribution of infrastructure (SCOI) in this study is defined as the performance of infrastructure as a contribution to the coordinated development among economic, social, and environmental dimensions of township’s sustainable development. It is necessary to assess these infrastructures according to SCOI and provide choices for investment to maximize resource utilization. Therefore, an assessing model of SCOI with 26 general indicators was developed, which covers five most urgently needed infrastructures of these townships in Southwest China, including road transport, sewage treatment, waste disposal, water supply, and gas. In this model, quantitative and qualitative methods are combined to acquire different SCOI of each infrastructure. The result of the SCOI would be an important reference for infrastructure investment. A case study of Jiansheng Town, that is located in the Dadukou district of Chongqing, demonstrates the applicability of the model. It shows the assessing model of SCOI is efficient to identify the most valuable infrastructure that is appropriate for investment with the goal of township’s sustainable development. This study can provide insights for infrastructure investment and management in townships or areas

OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning

Recently, there has been a surge of explainable AI (XAI) methods driven by the need for understanding machine learning model behaviors in high-stakes scenarios. However, properly evaluating the effectiveness of the XAI methods inevitably requires the involvement of human subjects, and conducting human-centered benchmarks is challenging in a number of ways: designing and implementing user studies is complex; numerous design choices in the design space of user study lead to problems of reproducibility; and running user studies can be challenging and even daunting for machine learning researchers. To address these challenges, this paper presents OpenHEXAI, an open-source framework for human-centered evaluation of XAI methods. OpenHEXAI features (1) a collection of diverse benchmark datasets, pre-trained models, and post hoc explanation methods; (2) an easy-to-use web application for user study; (3) comprehensive evaluation metrics for the effectiveness of post hoc explanation methods in the context of human-AI decision making tasks; (4) best practice recommendations of experiment documentation; and (5) convenient tools for power analysis and cost estimation. OpenHEAXI is the first large-scale infrastructural effort to facilitate human-centered benchmarks of XAI methods. It simplifies the design and implementation of user studies for XAI methods, thus allowing researchers and practitioners to focus on the scientific questions. Additionally, it enhances reproducibility through standardized designs. Based on OpenHEXAI, we further conduct a systematic benchmark of four state-of-the-art post hoc explanation methods and compare their impacts on human-AI decision making tasks in terms of accuracy, fairness, as well as users' trust and understanding of the machine learning model

Rapid detection of BRCA1/2 recurrent mutations in Chinese breast and ovarian cancer patients with multiplex SNaPshot genotyping panels.

BRCA1/2 mutations are significant risk factors for hereditary breast and ovarian cancer (HBOC), its mutation frequency in HBOC of Chinese ethnicity is around 9%, in which nearly half are recurrent mutations. In Hong Kong and China, genetic testing and counseling are not as common as in the West. To reduce the barrier of testing, a multiplex SNaPshot genotyping panel that targeted 25 Chinese BRCA1/2 mutation hotspots was developed, and its feasibility was evaluated in a local cohort of 441 breast and 155 ovarian cancer patients. For those who tested negative, they were then subjected to full-gene testing with next-generation sequencing (NGS). BRCA mutation prevalence in this cohort was 8.05% and the yield of the recurrent panel was 3.52%, identifying over 40% of the mutation carriers. Moreover, from 79 Chinese breast cancer cases recruited overseas, 2 recurrent mutations and one novel BRCA2 mutation were detected by the panel and NGS respectively. The developed genotyping panel showed to be an easy-to-perform and more affordable testing tool that can provide important contributions to improve the healthcare of Chinese women with cancer as well as family members that harbor high risk mutations for HBOC

Review on green building rating tools worldwide : recommendations for Australia

Buildings could be led to adverse impacts on environment, such as generation of construction and demolition waste, and emission of greenhouse gases (GHG). Therefore, promotion on development of green buildings is in need. With the increasing awareness in sustainable development, various rating tools are promoted to evaluate the performance of green buildings. Nowadays, these tools function as a guideline for green building development. There are various green building rating tools developed worldwide, and various countries follow different rules, incentives and regulations. However, despite of promotion of green building rating tools, environmental issues from buildings are still significant in Australia. This research compared green building rating tools in Australia and other countries or regions around the world. This research found that rating tools in Australia lack of (1) mandatory criteria and (2) regulations and incentives. This paper recommended that governmental incentives should be promoted