Analysis of the Psychological Factors Faced by the Final Year College Students of China During Job Interviews and While Choosing Careers.

Career choice research has attracted the attention of recruiters and young graduates. The study aims to investigate the psychological factors that influence college students' employment choices. As a result, data for the study were gathered from 250 final-year college students in China via an online questionnaire survey. The study identified the psychological barriers faced by college students during job interviews through interviews with 120 h recruiters. The SPPS tool is used for data analysis. The study identified personal interest, self-efficacy, and self- esteem; social responsibilities; confidence; professional development opportunities; and future orientation as the important psychological factors that influence the career choice of college students. The study also found that the barriers faced by the college students during the interview were anxiety, inferiority complex, cowardice, and pride. Therefore, the study suggests that the college provides job-oriented training for college students' employment choices. The college should take the initiative to provide students with career opportunities and proper training to avoid psychological barriers during interviews.

A new approach to optimizing aeration using XGB-Bi-LSTM via the online monitoring of oxygen transfer efficiency and oxygen uptake rate.

In wastewater treatment plants (WWTPs), aeration is vital for microbial oxygen needs. To achieve carbon neutrality, optimizing aeration for energy and emissions reduction is imperative. Machine learning (ML) is used in wastewater treatment to reveal complex rules in large data sets has become a trend. In this vein, the present paper proposes an aeration optimization approach based on the extreme gradient boosting-bidirectional long short-term memory (XGB-Bi-LSTM) model via the online monitoring of oxygen transfer efficiency (OTE) and oxygen uptake rate (OUR), thus allowing WWTPs to conserve energy and reduce indirect carbon emissions. The approach uses gain algorithm of XGB to calculate the importance of features and identify important parameters, and then uses Bi-LSTM to predict the target with important parameters as features. Operational data from a WWTP in Suzhou, China, is employed to train and test the approach, the performance of which is compared with ML models suitable for regression prediction tasks (XGB, random forest, light gradient boosting machine, gradient boosting and LSTM). Experimental results show the approach requires only a small number of input parameters to achieve good performance and outperforms other machine-learning models. When OTE and dissolved oxygen (DO) are used as features to predict the alpha factor (αF; since diffusers were used, multiply by the pollution factor F), the R-squared (R2) is 0.9977, the root mean square error (RMSE) is 0.0043, the mean absolute percentage error (MAPE) is 0.0069 and the median absolute error (MedAE) is 0.0032. When the predicted αF and the OUR are used as features to predict the air flow rate of an aeration unit, the R2 is 0.9901, the RMSE is 3.6150, the MAPE is 0.0209 and the MedAE is 1.5472. Using our optimized aeration approach, the energy consumption can be reduced by 23%.

Oxygen transfer parameters and oxygen uptake rates revisited.

This paper provides a review of the essential equations and parameters that are used to design bioreactor aeration systems. Major objectives were to determine if the log-deficit method (LDM) and non-linear regression method (NLRM) yielded different results for the volumetric oxygen mass transfer coefficient (KL a)T and dissolved oxygen (DO) saturation concentration (Cs)T . Another objective was to compare and evaluate oxygen uptake rates (OURs) from the traditional BOD bottle technique with OURs determined from mass balances around completely-mixed activated sludge (CMAS) reactors. Full- and bench-scale, non-steady state reaeration tests were performed using sodium sulfite and cobalt chloride in addition to operating bench-scale CMAS reactors. Full-and bench-scale reaeration testing indicated there was no significant difference in the estimate of (KL a)T in tap water using the LDM or NLRM. Similarly, (Cs)T values were the same for the LDM and NLRM for 70 out of 119 reaeration tests. Actual oxygen uptake rates (AOURs) measured using the BOD bottle technique versus calculated oxygen uptake rates (COURS) were the same for 26 out of 51 data sets evaluated. The alpha-factor (α) increased as the solids retention time (SRT) was increased.

An improved secretion signal enhances the secretion of model proteins from Pichia pastoris.

BACKGROUND: Proteins can be secreted from a host organism with the aid of N-terminal secretion signals. The budding yeast Pichia pastoris (Komagataella sp.) is widely employed to secrete proteins of academic and industrial interest. For this yeast, the most commonly used secretion signal is the N-terminal portion of pre-pro-α-factor from Saccharomyces cerevisiae. However, this secretion signal promotes posttranslational translocation into the endoplasmic reticulum (ER), so proteins that can fold in the cytosol may be inefficiently translocated and thus poorly secreted. In addition, if a protein self-associates, the α-factor pro region can potentially cause aggregation, thereby hampering export from the ER. This study addresses both limitations of the pre-pro-α-factor secretion signal. RESULTS: We engineered a hybrid secretion signal consisting of the S. cerevisiae Ost1 signal sequence, which promotes cotranslational translocation into the ER, followed by the α-factor pro region. Secretion and intracellular localization were assessed using as a model protein the tetrameric red fluorescent protein E2-Crimson. When paired with the α-factor pro region, the Ost1 signal sequence yielded much more efficient secretion than the α-factor signal sequence. Moreover, an allelic variant of the α-factor pro region reduced aggregation of the E2-Crimson construct in the ER. The resulting improved secretion signal enhanced secretion of E2-Crimson up to 20-fold compared to the levels obtained with the original α-factor secretion signal. Similar findings were obtained with the lipase BTL2, which exhibited 10-fold enhanced secretion with the improved secretion signal. CONCLUSIONS: The improved secretion signal confers dramatic benefits for the secretion of certain proteins from P. pastoris. These benefits are likely to be most evident for proteins that can fold in the cytosol and for oligomeric proteins.

Identification and expression analysis of lipopolysaccharide-induced TNF-alpha factor gene in Chinese mitten crab Eriocheir sinensis.

Lipopolysaccharide-induced TNF-alpha factor (LITAF) is an important transcription factor in transcriptional regulation of TNF-alpha and other cytokines. Here we identified a full-length LITAF homolog cDNA, termed EsLITAF, that contains a 71 bp 5'-untranslated sequence, an open reading frame consisting of 381 bp, and a 208 bp 3'-untranslated sequence in Chinese mitten crab (Eriocheir sinensis), an economically important freshwater crustacean species in China. EsLITAF protein possesses a conserved C-terminal LITAF-like domain with two CXXC motifs and is comprised of 126 amino acids with a theoretical molecular mass of 13.1 kDa and an isoelectric point of 6.36. Blast search against GenBank database revealed that EsLITAF protein shared the highest sequence identity (84%) with the counterpart of Pacific white shrimp (Litopenaeus vannamei). EsLITAF mRNA transcript expresses ubiquitously in all examined tissues with the highest expression in hepatopancreas and lowest expression in haemocytes. Furthermore, EsLITAF mRNA expression could be significantly and rapidly induced in haemocytes by LPS and Poly(I:C) stimulations in vitro. Moreover, EsLITAF gene expression was up-regulated in haemocytes, gill and hepatopancreas after Edwardsiella tarda and Vibrio anguillarum challenges. Taken together, we have identified and characterized a new crustacean LITAF homolog from the Chinese mitten crab.

Characterization of the interaction between the Sec61 translocon complex and ppαF using optical tweezers.

The Sec61 translocon allows the translocation of secretory preproteins from the cytosol to the endoplasmic reticulum lumen during polypeptide biosynthesis. These proteins possess an N-terminal signal peptide (SP) which docks at the translocon. SP mutations can abolish translocation and cause diseases, suggesting an essential role for this SP/Sec61 interaction. However, a detailed biophysical characterization of this binding is still missing. Here, optical tweezers force spectroscopy was used to characterize the kinetic parameters of the dissociation process between Sec61 and the SP of prepro-alpha-factor. The unbinding parameters including off-rate constant and distance to the transition state were obtained by fitting rupture force data to Dudko-Hummer-Szabo models. Interestingly, the translocation inhibitor mycolactone increases the off-rate and accelerates the SP/Sec61 dissociation, while also weakening the interaction. Whereas the translocation deficient mutant containing a single point mutation in the SP abolished the specificity of the SP/Sec61 binding, resulting in an unstable interaction. In conclusion, we characterize quantitatively the dissociation process between the signal peptide and the translocon, and how the unbinding parameters are modified by a translocation inhibitor.

Dynamic alpha factors: Prediction in time and evolution along reactors.

The performance of aeration - one of the most costly processes at water resource recovery facilities - is heavily impacted by actual wastewater characteristics which are commonly taken into account using the alpha factor (α). This factor varies depending on hydraulic and organic loading; such variance includes both time and spatial fluctuations. In standard design practice, it is often considered as a fixed number, or at best, a predefined time series. The objective of this paper is to propose a new method of predicting plantwide trends in the α factor through the use of process modelling which can accommodate diurnal and seasonal variations. The authors' concept takes into account the dependence of α on sludge retention time in the form of degradation kinetics, the effects of organic loading (influent filtered COD), the presence or absence of anoxic zones, diffuser depth, and the impact of high MLSS found in certain, e.g., MBR, technologies. The developed model was calibrated using data from numerous facilities, relying on off-gas measurements and tests in clean and process water. Model validation was carried out against averaged α factor gradient data from one plant, and against diurnal air flow measurements from another. The Benchmark Simulation Model 1 configuration was used to demonstrate the applicability of the proposed model - in estimation of blower energy consumption and peak air flow requirements - comparing it with constant and scheduled α factor-based approaches.

Bioreactor-scale cell performance and protein production can be substantially increased by using a secretion signal that drives co-translational translocation in Pichia pastoris.

Pichia pastoris (Komagataella spp.) has become one of the most important host organisms for production of heterologous proteins of biotechnological interest, many of them extracellular. The protein secretion pathway has been recognized as a limiting process in which many roadblocks have been pinpointed. Recently, we have identified a bottleneck at the ER translocation level. In earlier exploratory studies, this limitation could be largely overcome by using an improved chimeric secretion signal to drive proteins through the co-translational translocation pathway. Here, we have further tested at bioreactor scale the improved secretion signal consisting of the pre-Ost1 signal sequence, which drives proteins through co-translational translocation, followed by the pro region from the secretion signal of the Saccharomyces cerevisiae α-factor mating pheromone. For comparison, the commonly used full-length α-factor secretion signal, which drives proteins through post-translational translocation, was tested. These two secretion signals were fused to three different model proteins: the tetrameric red fluorescent protein E2-Crimson, which can be used to visualize roadblocks in the secretory pathway; the lipase 2 from Bacillus thermocatenulatus (BTL2); and the Rhizopus oryzae lipase (ROL). All strains were tested in batch cultivation to study the different growth parameters obtained. The strains carrying the improved secretion signal showed increased final production of the proteins of interest. Interestingly, they were able to grow at significantly higher maximum specific growth rates than their counterparts carrying the conventional secretion signal. These results were corroborated in a 5 L fed-batch cultivation, where the final product concentration and volumetric productivity were also shown to be improved.

Model-based monitoring of diffuser fouling using standard sensors.

Fouling of fine-pore diffusers can cause substantial aeration energy wastage. It remains challenging to monitor their condition and decide the optimal time for labour-intensive and costly cleaning actions. In this study, we show that data from standard sensors (airflow rate, dissolved oxygen concentration, pressure and airflow valve position), which are fed to simple models, can track the diffuser's condition. Additionally, the parameter estimation of diffuser dynamic wet pressure, oxygen transfer rate, respiration rate and the joint alpha fouling factor ( α F ) was facilitated by an active fault detection inspired method. The method executes a sequence with piecewise constant valve positions via the control system. As a result, airflow rates in a sequence similar to a staircase are obtained, which simplifies the estimation of dissolved oxygen dynamics and airflow rate dynamics. The proposed method was evaluated on a full scale over 18 months and successfully detected a reduced cleaning in the diffusers and several sensor-related disturbances. Ultimately, the findings motivate further research on how modelling combined with repetitive process disturbances can leverage data-driven insights from standard instrumentation.

Analyses of rRNA gene chromatin in cell cycle arrested Saccharomyces cerevisiae cells.

The existence of two chromatin structures in the rDNA locus was previously demonstrated for a large variety of organisms, ranging from yeast to human. In yeast there are about 150-200 rRNA genes organized in tandem repeats. Almost half of them are transcribed and largely depleted of nucleosomes (active/open), the other half is not transcribed and is assembled in regular arrays of nucleosomes (inactive/closed). It is proposed that RNA polymerase-I (RNAPI) transcription-elongation removes nucleosomes from closed rRNA genes (opening), and that soon after DNA replication there is deposition of nucleosomes on the open rRNA genes (closing). In G1 arrested cells, nearly all rRNA genes are depleted of nucleosomes, but most of them are not transcribed (inactive/open). In relation to the research article by Charton et al. (Mutat. Res.), the data presented here are on the hydroxyurea concentration-dependent inhibition of yeast culture growth, on cell cycle arrest before completion of genome replication, and on the opening of rRNA gene chromatin. As comparison, data are presented for yeast arrested in the G1-phase of the cell cycle by the pheromone α-factor.

Limitations imposed by conventional fine bubble diffusers on the design of a high-loaded membrane bioreactor (HL-MBR).

The operation of membrane bioreactors (MBRs) at higher than usual mixed liquor suspended solids (MLSS) concentrations may enhance the loading rate treatment capacity while minimizing even further the system's footprint. This requires operating the MBR at the highest possible MLSS concentration and biomass activity (e.g., at high loading rates and low solid retention times (SRTs)). Both a negative effect of the MLSS concentrations and a positive effect of the SRT on the oxygen transfer have been reported when using conventional fine bubble diffusers. However, most of the evaluations have been carried out either at extremely high SRTs or at low MLSS concentrations eventually underestimating the effects of the MLSS concentration on the oxygen transfer. This research evaluated the current limitations imposed by fine bubble diffusers in the context of the high-loaded MBR (HL-MBR) (i.e., high MLSS and short SRT-the latter emulated by concentrating municipal sludge from a wastewater treatment plant (WWTP) operated at a short SRT of approximately 5 days). The high MLSS concentrations and the short SRT of the original municipal sludge induced a large fraction of mixed liquor volatile suspended solids (MLVSS) in the sludge, promoting a large amount of sludge flocs that eventually accumulated on the surface of the bubbles and reduced the free water content of the suspension. Moreover, the short SRTs at which the original municipal sludge was obtained eventually appear to have promoted the accumulation of surfactants in the sludge mixture. This combination exhibited a detrimental effect on the oxygen transfer. Fine bubble diffusers limit the maximum MLSS concentration for a HL-MBR at 30 g L-1; beyond that point is either not technically or not economically feasible to operate; an optimum MLSS concentration of 20 g L-1 is suggested to maximize the treatment capacity while minimizing the system's footprint.

Modelling oxygen transfer using dynamic alpha factors.

Due to the importance of wastewater aeration in meeting treatment requirements and due to its elevated energy intensity, it is important to describe the real nature of an aeration system to improve design and specification, performance prediction, energy consumption, and process sustainability. Because organic loadings drive aeration efficiency to its lowest value when the oxygen demand (energy) is the highest, the implications of considering their dynamic nature on energy costs are of utmost importance. A dynamic model aimed at identifying conservation opportunities is presented. The model developed describes the correlation between the COD concentration and the α factor in activated sludge. Using the proposed model, the aeration efficiency is calculated as a function of the organic loading (i.e. COD). This results in predictions of oxygen transfer values that are more realistic than the traditional method of assuming constant α values. The model was applied to two water resource recovery facilities, and was calibrated and validated with time-sensitive databases. Our improved aeration model structure increases the quality of prediction of field data through the recognition of the dynamic nature of the alpha factor (α) as a function of the applied oxygen demand. For the cases presented herein, the model prediction of airflow improved by 20-35% when dynamic α is used. The proposed model offers a quantitative tool for the prediction of energy demand and for minimizing aeration design uncertainty.

Methods of Synchronization of Yeast Cells for the Analysis of Cell Cycle Progression.

Cell division is a fascinating and fundamental process that sustains life. By this process, unicellular organisms reproduce and multicellular organisms sustain development, growth, and tissue repair. Division of a mother cell gives rise to two daughter cells according to an ordered set of events within four successive phases called G1 (gap1), S (DNA Synthesis), G2 (gap2), and M (Mitosis) phase. How these different phases are orchestrated to ensure the physical separation of the two daughter cells is a tightly regulated process. Indeed, inappropriate cell division could lead to uncontrolled cell proliferation and ultimately to cancer. Saccharomyces cerevisiae is an excellent model system for unraveling the secrets of cell division. A large community of researchers has chosen budding yeast as a model because of its advantages: rapid growth in simple and economical media, tractable genetics, powerful biochemistry, cell biology, and proteomics approaches. Furthermore, the cell cycle mechanisms, as elucidated in yeast, are conserved in higher eukaryotes. The ability to synchronize and get large numbers of cells in a particular stage of the cell cycle is crucial to properly explore the mechanisms of the cell cycle. An overview of the most common yeast synchronization techniques has been compiled in this chapter.

Synchronization of the Budding Yeast Saccharomyces cerevisiae.

A number of model organisms have provided the basis for our understanding of the eukaryotic cell cycle. These model organisms are generally much easier to manipulate than mammalian cells and as such provide amenable tools for extensive genetic and biochemical analysis. One of the most common model organisms used to study the cell cycle is the budding yeast Saccharomyces cerevisiae. This model provides the ability to synchronise cells efficiently at different stages of the cell cycle, which in turn opens up the possibility for extensive and detailed study of mechanisms regulating the eukaryotic cell cycle. Here, we describe methods in which budding yeast cells are arrested at a particular phase of the cell cycle and then released from the block, permitting the study of molecular mechanisms that drive the progression through the cell cycle.

Simulation of Factor Structures (SIMFAK).

A general method for the simulation of factor structures is developed, which contains different possibilities for simulation. The generalization of the method SIMFAK shows, that it is possible to simulate factor structures of maximum likelihood method, of minres method and of alpha factor analysis. Computation results demonstrate the practicability of SIMFAK.

Identification and characterization of lipopolysaccharide-induced TNF-alpha factor gene from Japanese flounder Paralichthys olivaceus.

Lipopolysaccharide-induced TNF-α factor (LITAF) is an important transcription factor participating in innate immunity through regulating TNF-α and other inflammatory cytokines expression. However, the expression and biological relevance of LITAF in fish is still very limited. In this study, a full-length LITAF cDNA, termed PoLITAF, was identified from Japanese flounder Paralichthys olivaceus. PoLITAF contains a 67 bp 5'-untranslated sequence, a 435 bp open reading frame, and a 647 bp 3'-untranslated sequence. PoLITAF protein is comprised of 144 amino acids with a conserved C-terminal LITAF-like domain and shows 51-76% sequence similarity and 40-65% sequence identity with other LITAF homologues. Characterization of this new gene revealed that PoLITAF mRNA was detected in all examined tissues with the highest expression in gill. In head kidney primary culture, the expression of Japanese flounder PoLITAF and TNF-α was significantly up-regulated in response to Poly(I:C) and bacterial endotoxin LPS stimulation. Further in vivo experiments demonstrated that PoLITAF expression was up-regulated in head kidney, gill and spleen post bacterial challenge with Edwardsiella tarda. Moreover, the up-regulated expression of Japanese flounder TNF-α following the enhanced expression of PoLITAF was detected as early as 4h in both gill and head kidney tissues and 12h in spleen after the bacterial infection in vivo. Our findings suggest that PoLITAF is a novel inducible gene possibly involved in Japanese flounder innate immunity.

Recovery from stress - a cell cycle perspective.

We develop a Boolean model to explore the dynamical behaviour of budding yeast in response to osmotic and pheromone stress. Our model predicts that osmotic stress halts the cell cycle progression in either of four possible arrest points. The state of the cell at the onset of the stress dictates which arrest point is finally reached. According to our study and consistent with biological data, these cells can return to the cell cycle after removal of the stress. Moreover, the Boolean model illustrates how osmotic stress alters the state transitions of the cell. Furthermore, we investigate the influence of a particular pheromone based method for the synchronisation of the cell cycles in a population of cells. We show this technique is not a suitable method to study one of the arrest points under osmotic stress. Finally, we discuss how an osmotic stress can cause some of the so called frozen cells to divide. In this case the stress can move these cells to the cell cycle trajectory, such that they will replicate again.