High-Throughput Screening of Acyl-CoA Thioesterase I Mutants Using a Fluid Array Platform

Screening target microorganisms from a mutated recombinant library plays a crucial role in advancing synthetic biology and metabolic engineering. However, conventional screening tools have several limitations regarding throughput, cost, and labor. Here, we used the fluid array platform to conduct high-throughput screening (HTS) that identified Escherichia coli ???TesA thioesterase mutants producing elevated yields of free fatty acids (FFAs) from a large (106) mutant library. A growth-based screening method using a TetA-RFP fusion sensing mechanism and a reporter-based screening method using high-level FFA producing mutants were employed to identify these mutants via HTS. The platform was able to cover >95% of the mutation library, and it screened target cells from many arrays of the fluid array platform so that a post-analysis could be conducted by gas chromatography. The ???TesA mutation of each isolated mutant showing improved FFA production in E. coli was characterized, and its enhanced FFA production capability was confirmed

Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.Comment: 15 pages, 7 figure

Unified Hierarchical Relationship Between Thermodynamic Tradeoff Relations

Recent years have witnessed a surge of discoveries in the studies of thermodynamic inequalities: the thermodynamic uncertainty relation (TUR) and the entropic bound (EB) provide a lower bound on the entropy production (EP) in terms of nonequilibrium currents; the classical speed limit (CSL) expresses the lower bound on the EP using the geometry of probability distributions; the power-efficiency (PE) tradeoff dictates the maximum power achievable for a heat engine given the level of its thermal efficiency. In this study, we show that there exists a unified hierarchical structure encompassing all of these bounds, with the fundamental inequality given by a novel extension of the TUR (XTUR) that incorporates the most general range of current-like and state-dependent observables. By selecting more specific observables, the TUR and the EB follow from the XTUR, and the CSL and the PE tradeoff follow from the EB. Our derivations cover both Langevin and Markov jump systems, with the first proof of the EB for the Markov jump systems and a more generalized form of the CSL. We also present concrete examples of the EB for the Markov jump systems and the generalized CSL.Comment: 19 pages, 4 figure

Trends of improved water and sanitation coverage around the globe between 1990 and 2010: inequality among countries and performance of official development assistance.

BACKGROUND: As the Millennium Development Goals ended, and were replaced by the Sustainable Development Goals, efforts have been made to evaluate the achievements and performance of official development assistance (ODA) in the health sector. In this study, we explore trends in the expansion of water and sanitation coverage in developing countries and the performance of ODA. DESIGN: We explored inequality across developing countries by income level, and investigated how ODA for water and sanitation was committed by country, region, and income level. Changes in inequality were tested via slope changes by investigating the interaction of year and income level with a likelihood ratio test. A random effects model was applied according to the results of the Hausman test. RESULTS: The slope of the linear trend between economic level and sanitation coverage has declined over time. However, a random effects model suggested that the change in slope across years was not significant (e.g. for the slope change between 2000 and 2010: likelihood ratio χ2 = 2.49, probability > χ2 = 0.1146). A similar pro-rich pattern across developing countries and a non-significant change in the slope associated with different economic levels were demonstrated for water coverage. Our analysis shows that the inequality of water and sanitation coverage among countries across the world has not been addressed effectively during the past decade. Our findings demonstrate that the countries with the least coverage persistently received far less ODA per capita than did countries with much more extensive water and sanitation coverage, suggesting that ODA for water and sanitation is poorly targeted. CONCLUSION: The most deprived countries should receive more attention for water and sanitation improvements from the world health community. A strong political commitment to ODA targeting the countries with the least coverage is needed at the global level

All-rounder: A flexible DNN accelerator with diverse data format support

Recognizing the explosive increase in the use of DNN-based applications, several industrial companies developed a custom ASIC (e.g., Google TPU, IBM RaPiD, Intel NNP-I/NNP-T) and constructed a hyperscale cloud infrastructure with it. The ASIC performs operations of the inference or training process of DNN models which are requested by users. Since the DNN models have different data formats and types of operations, the ASIC needs to support diverse data formats and generality for the operations. However, the conventional ASICs do not fulfill these requirements. To overcome the limitations of it, we propose a flexible DNN accelerator called All-rounder. The accelerator is designed with an area-efficient multiplier supporting multiple precisions of integer and floating point datatypes. In addition, it constitutes a flexibly fusible and fissionable MAC array to support various types of DNN operations efficiently. We implemented the register transfer level (RTL) design using Verilog and synthesized it in 28nm CMOS technology. To examine practical effectiveness of our proposed designs, we designed two multiply units and three state-of-the-art DNN accelerators. We compare our multiplier with the multiply units and perform architectural evaluation on performance and energy efficiency with eight real-world DNN models. Furthermore, we compare benefits of the All-rounder accelerator to a high-end GPU card, i.e., NVIDIA GeForce RTX30390. The proposed All-rounder accelerator universally has speedup and high energy efficiency in various DNN benchmarks than the baselines

Cost-benefit analysis of water source improvements through borehole drilling or rehabilitation: an empirical study based on a cluster randomized controlled trial in the Volta Region, Ghana.

BACKGROUND: Despite remarkable progress in water coverage improvements, diseases associated with poor water remain a considerable public health problem in many developing countries. OBJECTIVE: We aimed to estimate the costs and benefits of drilling or rehabilitating boreholes with handpumps in resource-poor settings and hard-to-reach areas. METHODS: Diarrheal reduction in the population was predicted on the basis of the empirical findings from a cluster randomized controlled trial. The full investment and estimated annual running costs were used to calculate the intervention costs. Direct economic benefits of avoiding child diarrheal disease, indirect economic benefits related to health improvements, and non-health benefits related to water improvement were estimated. One-way and multi-way sensitivity analyses were performed to determine the robustness of the findings. RESULTS: Our analysis found that the return on a US$1 investment was US$ 9.4 for borehole drilling and US$ 14.1 for borehole rehabilitation. Time savings were the main contributor, accounting for 68% of the benefits, followed by the economic benefits of averted child deaths, which contributed to 15% of the benefits. The sensitivity analyses suggested that improving water sources yields high returns under all circumstances, and that borehole rehabilitation is more efficient than borehole drilling. CONCLUSION: This study explicitly justifies increased investment in water improvement in rural areas and demonstrates the high returns of rehabilitating boreholes. We hope that this study will be used as evidence for informing the policy decisions of governments or international agencies regarding further investments in improved water coverage in rural areas and the selection of appropriately designed interventions