Biosynthesis of cannabinoid precursor olivetolic acid by overcoming rate-limiting steps in genetically engineered Yarrowia lipolytica

Natural products acting on our central nervous systems are in utmost demand to fight against pain and mental disorders. Cannabinoids (CBDs) are proven neuroactive agents to treat anxiety, depression, chronic pain diseases, seizure, strokes and neurological disorders. The scarcity of the hemp-sourced CBD products and the prohibitive manufacturing cost limit the wide application of CBDs. Yeast metabolic engineering offers the flexibility to meet the ever-increasing market demand. In this work, we took a retrosynthetic approach and sequentially identified the rate-limiting steps to improve the biosynthesis of the CBD precursor olivetolic acid (OLA) in Yarrowia lipolytica. We debottlenecked the critical enzymatic steps to overcome the supply of hexanoyl-CoA, malonyl-CoA, acetyl-CoA, NADPH and ATPs to redirect carbon flux toward OLA. Implementation of these strategies led to an 83-fold increase in OLA titer in shaking flask experiment. This work may serve as a baseline for engineering CBD biosynthesis in oleaginous yeast species.

Building a training dataset for classification under a cost limitation

Purpose A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results. Design/methodology/approach In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances. Findings Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy. Originality/value No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.

Technical inspection as an inventory item of fixed assets in the accounting of a healthcare organization

The article considers the features of forming a new inventory object for Russian accounting — technical inspection of this object of fixed assets as part of a complex object of fixed assets of a medical organization. Such an inventory item as a technical inspection is formed in accordance with FSB 6/2020 “Fixed assets” if it meets such important criteria as the cost exceeding the cost limit set by each health organization in its accounting policy, as well as the useful life exceeding 12 months. The article presents an example of accounting for technical inspection on the example of such an object of fixed assets as a pipeline system of compressed medical gases, gases for driving surgical instruments and vacuum.

Using a reactive emulsifier to construct simple and convenient nanocapsules loaded with lambda-cyhalothrin to achieve efficient foliar delivery and insecticidal synergies

Nanocapsules are a promising controlled release formulation for foliar pest control. However, the complicated process and high cost limit widespread use in agriculture, so a simpler and more convenient preparation...

Dual Performing Copper-Platinum Core-Shell Nanozyme for Environmental Electrochemistry- Electrocatalytic Oxidation and Electroanalysis of Ammonia

Platinum (Pt) is one of the most celebrated catalytic/electrocatalytic material for many of the important (electro)chemical conversion reactions. Low availability and high cost limit their commercial applications. In the quest...

Binary-Addition Tree Algorithm-Based Resilience Assessment for Binary-State Network Problems

Applications in real life are composed of different kinds of network systems; these networks may be interfered by uncontrollable or unpredictable disruptive events involving natural disasters, human errors, evil-intentioned attacks, or other disturbances. Any of these disruptive events will cause networks to malfunction and possibly result in large economic losses. As a result, it is important to assess network resilience which is a measure to describe how a network system recovers its performance and functionality to a satisfactory level from a disruptive event. Inspired by the measures of reliability evaluation used in binary-state networks, this paper proposes a binary-addition tree algorithm-based resilience assessment for binary-state networks and applies it on a wildfire network with wireless sensors. Considering the stochastic nature of disruptive events, the proposed binary-addition tree algorithm-based resilience assessment comprehensively enumerates all the possible disruptive events and all the corresponding recovery strategies, and then calculate the network resilience. Furthermore, recovery cost limit is concerned in this paper for decision makers who choose the recovery strategies with their recovery cost limit and resilience requirement.

Iterative Budgeted Exponential Search

We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require Ω(2ⁿ) expansions, where n is the number of states within the final f bound. Existing algorithms that address this problem like B and B’ improve this bound to Ω(n²). For tree search, IDA* can also require Ω(n²) expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is O(n log C*), where C* is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*.