Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Untangling Microbiota Diversity and Assembly Patterns in the World's Largest Water Diversion Canal.

Large water diversion projects are important constructions for reallocation of human-essential water resources. Deciphering microbiota dynamics and assembly mechanisms underlying canal water ecosystem services especially during long-distance diversion is a prerequisite for water quality monitoring, biohazard warning and sustainable management. Using a 1432-km canal of the South-to-North Water Diversion Projects as a model system, we answer three central questions: how bacterial and micro-eukaryotic communities spatio-temporally develop, how much ecological stochasticity contributes to microbiota assembly, and which immigrating populations better survive and navigate across the canal. We applied quantitative ribosomal RNA gene sequence analyses to investigate canal water microbial communities sampled over a year, as well as null model- and neutral model-based approaches to disentangle the microbiota assembly processes. Our results showed clear microbiota dynamics in community composition driven by seasonality more than geographic location, and seasonally dependent influence of environmental parameters. Overall, bacterial community was largely shaped by deterministic processes, whereas stochasticity dominated micro-eukaryotic community assembly. We defined a local growth factor (LGF) and demonstrated its innovative use to quantitatively infer microbial proliferation, unraveling taxonomically dependent population response to local environmental selection across canal sections. Using LGF as a quantitative indicator of immigrating capacities, we also found that most micro-eukaryotic populations (82%) from the source water sustained growth in the canal and better acclimated to the hydrodynamical water environment than bacteria (67%). Taxa inferred to largely propagate include Limnohabitans sp. and Cryptophyceae, potentially contributing to water auto-purification. Combined, our work poses first and unique insights into the microbiota assembly patterns and dynamics in the world's largest water diversion canal, providing important ecological knowledge for long-term sustainable water quality maintenance in such a giant engineered system.

Engineering Alternate Ligand Recognition in the PurR Topology: A System of Novel Caffeine Biosensing Transcriptional Antirepressors.

Recent advances in synthetic biology and protein engineering have increased the number of allosteric transcription factors used to regulate independent promoters. These developments represent an important increase in our biological computing capacity, which will enable us to construct more sophisticated genetic programs for a broad range of biological technologies. However, the majority of these transcription factors are represented by the repressor phenotype (BUFFER), and require layered inversion to confer the antithetical logical function (NOT), requiring additional biological resources. Moreover, these engineered transcription factors typically utilize native ligand binding functions paired with alternate DNA binding functions. In this study, we have advanced the state-of-the-art by engineering and redesigning the PurR topology (a native antirepressor) to be responsive to caffeine, while mitigating responsiveness to the native ligand hypoxanthine-i.e., a deamination product of the input molecule adenine. Importantly, the resulting caffeine responsive transcription factors are not antagonized by the native ligand hypoxanthine. In addition, we conferred alternate DNA binding to the caffeine antirepressors, and to the PurR scaffold, creating 38 new transcription factors that are congruent with our current transcriptional programming structure. Finally, we leveraged this system of transcription factors to create integrated NOR logic and related feedback operations. This study represents the first example of a system of transcription factors (antirepressors) in which both the ligand binding site and the DNA binding functions were successfully engineered in tandem.

Effect of the Adaptive Response on the Wear Behavior of PVD and CVD Coated Cutting Tools during Machining with Built Up Edge Formation.

The relationship between the wear process and the adaptive response of the coated cutting tool to external stimuli is demonstrated in this review paper. The goal of the featured case studies is to achieve control over the behavior of the tool/workpiece tribo-system, using an example of severe tribological conditions present under machining with intensive built-up edge (BUE) formation. The built-ups developed during the machining process are dynamic structures with a dual role. On one hand they exhibit protective functions but, on the other hand, the process of built-up edge formation is similar to an avalanche. Periodical growth and breakage of BUE eventually leads to tooltip failure and catastrophe of the entire tribo-system. The process of BUE formation is governed by the stick-slip phenomenon occurring at the chip/tool interface which is associated with the self-organized critical process (SOC). This process could be potentially brought under control through the engineered adaptive response of the tribo-system, with the goal of reducing the scale and frequency of the occurring avalanches (built-ups). A number of multiscale frictional processes could be used to achieve this task. Such processes are associated with the strongly non-equilibrium process of self-organization during friction (nano-scale tribo-films formation) as well as physical-chemical and mechanical processes that develop on a microscopic scale inside the coating layer and the carbide substrate. Various strategies for achieving control over wear behavior are presented in this paper using specific machining case studies of several hard-to-cut materials such as stainless steels, titanium alloy (TiAl6V4), compacted graphitic iron (CGI), each of which typically undergoes strong built-up edge formation. Various categories of hard coatings deposited by different physical vapor deposition (PVD) and chemical vapor deposition (CVD) methods are applied on cutting tools and the results of their tribological and wear performance studies are presented. Future research trends are outlined as well.