Comparative expression of recombinant phospholipase A2 in Komagataella phaffii depending on the modification of the alpha-factor signaling peptide

Currently, the Russian market of phospholipase A2 enzyme preparations is represented by commercial preparations of foreign manufacturers: Nagase (Japan) and Maxapal (the Netherlands). However, the growing demand and the need to reduce the cost of production of phospholipase A2 require the development of new super-producers of phospholipase A2. In this connection, the aim of the work is to compare the expression of recombinant phospholipase A2 in Komagataella phaffii depending on the modification of the alpha-factor signaling peptide. The object of the study is the recipient yeast strain Komagataella phaffii X-33. The studies were conducted in accordance with generally accepted norms and approaches. Phospholipase A2 genes from Streptomyces violaceoruber were used for this worK. The target sequences were synthesized in the company "Eurogen" (Russia) and cloned as part of the TE vector pUC57. In the course of the work, the genetic constructs pPICZaA-Pla2 and PPICZmf4iA-Pla2 containing the Streptomyces violaceoruber phospholipase A2 gene were assembled under the native signal a-MF and its modification mf4i. The transformation of the yeast Komagataella phaffii X-33 with the obtained genetic constructs was also carried out. As a result of the conducted studies, it was shown that on average, there were no significant differences in the level of expression and specific activity of recombinant phospholipase A2 in methylotrophic yeast K. Phaffii X-33 when using the native a-MF secretion signal and its modified version mf4i. However, the use of the secretion factor mf4i allows for higher production of phospholipase A2 in individual clones. The obtained data indicate the prospects of using the secretion factor mf4i to create super-producers of enzymes based on yeast K. Phaffii X-33.

Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statements

The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to this paper have been open-sourced on Github1.

A NOVEL ENHANCED MPPT METHOD COMBINING FRACTIONAL-ORDER AND FUZZY LOGIC CONTROL

A fractional-order fuzzy logic control (FOFLC) method for maximum power point tracking (MPPT) in a photovoltaic (PV) system is presented. By combining the robustness of fuzzy logic with the accuracy of fractional order, the proposed method can improve the tracking accuracy in weather variations compared with the conventional fuzzy MPPT. First, the fractionalorder factor is carefully selected according to the dynamic range of the fuzzy controller. It takes a bigger alpha factor in the first place to expand the fuzzy domain and shortens the time of searching for the MPP. When the maximum power point is approached, it uses a smaller the alpha factor to contract the fuzzy domain and eliminates the oscillations at the MPP. Therefore, the FOFLC in a PV system has rapid dynamic responses under environment variations and high tracking accuracy of the maximum power point. Second, MATLAB/Simulink software is employed to simulate a PV power system and verify the proposed algorithm by various simulations. The enhanced MPPT algorithm has been implemented on a field programmable gate array (FPGA) board. Finally, a boost dc–dc converter experiment has been carried out to evaluate the system performance. The simulation and experiment results show that this method can improve the transient and steady-state performance simultaneously.

Reformulating the bromine alpha factor and equivalent effective stratospheric chlorine (EESC): evolution of ozone destruction rates of bromine and chlorine in future climate scenarios

Future trajectories of the stratospheric trace gas background will alter the rates of bromine- and chlorine-mediated catalytic ozone destruction via changes in the partitioning of inorganic halogen reservoirs and the underlying temperature structure of the stratosphere. The current formulation of the bromine alpha factor, the ozone-destroying power of stratospheric bromine atoms relative to stratospheric chlorine atoms, is invariant with the climate state. Here, we refactor the bromine alpha factor, introducing normalization to a benchmark chemistry–climate state, and formulate Equivalent Effective Stratospheric Benchmark-normalized Chlorine (EESBnC) to reflect changes in the rates of both bromine- and chlorine-mediated ozone loss catalysis with time. We show that the ozone-processing power of the extrapolar stratosphere is significantly perturbed by future climate assumptions. Furthermore, we show that our EESBnC-based estimate of the extrapolar ozone recovery date is in closer agreement with extrapolar ozone recovery dates predicted using more sophisticated 3-D chemistry–climate models than predictions made using equivalent effective stratospheric chlorine (EESC).

Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America (Preprint)

BACKGROUND The present work is a statistical analysis of the COVID-19 pandemic. As the number of cases worldwide overtakes one million, data reveals closed outbreaks in Hubei and South Korea, with a new slight increase in the number of infected people in the latter. Both of these countries have reached a plateau in the number of Total Confirmed Cases per Million (TCCpM) residents, suggesting a trend to be followed by other affected regions. OBJECTIVE Using Hubei's data as a basis of analysis, we have studied the spreading rate of COVID-19 and modelled the epidemic center for 10 European countries. We have also given the final TCCpM curves for Italy and Lombardia. The introduction of the $\boldsymbol{\alpha}$-factor allows us to analyse the different stages of the outbreak, compare the European countries amongst each other, and, finally, to confront the initial phase of the disease between Europe and South America. METHODS By dividing the TCCpM curves in multiple sections spanning short time frames we were able to fit each section to a linear model. By pairing then the angular coefficient ( factor) of each section to the total number of confirmed infections at the center of the corresponding time interval, we have analysed how the spreading rate of Covid-19 changes as more people are infected. Also, by modelling the TCCpM curves with an asymmetrical time integral of a Normal Distribution, we were able to study, by fitting progressively larger data ensembles, how the fitting parameters change as more data becomes available. RESULTS The data analysis shows that the spreading rate of COVID-19 increases similarly for all countries in its early stage, but changes as the number of TCCpM in each country grows. Regarding the modelling of the TCCpM curves, we have found that the fitting parameters oscillate with time before reaching constant values. The estimation of such values allows the determination of better parameters for the model, which in turn leads to more trustworthy forecasts on the pandemic development. CONCLUSIONS The analysis of the oscillating fitting parameters allows an early prediction of the TCC, epidemic center and standard deviation of the outbreak. The alpha factor and the recovered over confirmed cases ratio can be used to understand the pandemic development in each country and to compare the protective measures taken by local authorities and their impact on the spreading of the disease. INTERNATIONAL REGISTERED REPORT RR2-doi.org/10.1101/2020.04.06.20055327

Temporal Evolution of the Bromine Alpha Factor and Equivalent Effective Stratospheric Chlorine in Future Climate Scenarios

Future trajectories of the stratospheric trace gas background will alter the rates of bromine- and chlorine-mediated catalytic ozone destruction via changes in the partitioning of inorganic halogen reservoirs and the underlying temperature structure of the stratosphere. The current formulation of the bromine alpha factor, the ozone-destroying power of stratospheric bromine atoms relative to stratospheric chlorine atoms, is invariant with climate state. Here, we refactor the bromine alpha factor, introducing climate normalization to a benchmark climate state, and reformulate Equivalent Effective Stratospheric Chlorine (EESC) to reflect changes in the rates of both chlorine- and bromine-mediated ozone loss catalysis with time. We show that the ozone-processing power of the extrapolar stratosphere is significantly perturbed by future climate assumptions. Furthermore, we show that our EESC-based estimate of the extrapolar ozone-recovery date is in closer agreement with extrapolar ozone recovery dates predicted using more sophisticated 3-D chemistry-climate models than prior formulations of EESC that employ climate-invariant values of the bromine alpha factor.