Advancing Open Science

Background: Dulaglutide, a GLP-1-IgG4 Fc fusion, is a long-acting GLP-1 receptor agonist used for type 2 diabetes therapy and other emerging indications. It is produced commercially in Chinese hamster ovary (CHO) cells. The supply of the original drug is now limited in some regions, so creation of highly productive biosimilar manufacturing platforms is important. Methods: Two expression plasmids (p1.1-Tr2-Dul, p1.2-GS-Dul) encoding dulaglutide were sequentially transfected into apoptosis-resistant CHO 4BGD cells. Two-step transgene amplifications with methotrexate (MTX), followed by methionine sulfoximine (MSX) selection and subsequent cell cloning pipeline, were employed. Candidate clonal cell lines were selected using fed-batch culturing and long-term productivity testing. Results: Transfection with a second plasmid encoding glutamine synthetase (p1.2-GS-Dul) and selection with MSX resulted in a further ~30% increase titer in polyclonal population even after MTX-driven amplification. Top clone 4BGD/Dul #73 reached 1.05 g/L product titer in fed-batch culture (qP up to 22 pg·cell⁻¹·day⁻¹) and remained stable for 69 days in medium without MTX/MSX. Size exclusion-high-performance liquid chromatography showed ≥95% monomer; EC₅₀ of the purified GLP-1-Fc in a GLP-1R/CRE-Luc assay was 52 pM for the obtained product versus 76 pM for the original reference drug. Conclusions: The sequential transfection and dual-marker selection approach enables the efficient generation of a robust, high-yield, and glutamine-independent CHO producer, representing a productive strategy suitable for industrial biosimilar development.

Recently, the installed generation capacity of wind energy has expanded significantly, and the doubly fed induction generator (DFIG) has gained a prominent position amongst wind generators owing to its superior performance. It is extremely vital to enhance the low-voltage ride-through (LVRT) capability for the wind turbine DFIG system because the DFIG is very sensitive to faults in the electrical grid. The major concept of LVRT is to keep the DFIG connected to the electrical grid in the case of an occurrence of grid voltage sags. The currents of rotor and DC-bus voltage rise during voltage dips, resulting in damage to the power electronic converters and the windings of the rotor. There are many protection approaches that deal with LVRT capability for the wind turbine DFIG system. A popular approach for DFIG protection is the crowbar technique. The resistance of the crowbar must be precisely chosen owing to its impact on both the currents of the rotor and DC-bus voltage, while also ensuring that the rotor speed does not exceed its maximum limit. Therefore, this paper aims to obtain the optimal values of crowbar resistance to minimize the crowbar energy losses and ensure stable DFIG operation during grid voltage dips. A recent optimization technique, the Starfish Optimization (SFO) algorithm, was used for cropping the optimal crowbar resistance for improving LVRT capability. To validate the accuracy of the results, the SFO results were compared to the well-known optimization algorithm, particle swarm optimizer (PSO). The performance of the wind turbine DFIG system was investigated by using Matlab/Simulink at a rated wind speed of 13 m/s. The results demonstrated that the increases in DC-link voltage and rotor speed were reduced by 42.5% and 45.8%, respectively.

This research focuses on the additional valorization of olive leaves, a by-product of regular olive pruning, by increasing their secondary metabolite content through the combined application of biochar and a phenolic extract from olive leaves. A suspension of biochar, obtained by the pyrolysis of grapevine pruning residues, was prepared by mixing it in demineralized water (1.5 g; 5 L; 24 h). The phenolic extract was obtained by extracting lyophilized and ground olive leaves in demineralized water (50 g; 5 L; 24 h), while the combined preparation was obtained in an analogous manner (1.5 g biochar; 50 g olive leaf powder; 5 L water; 24 h). Treatments were applied at the beginning of July, 50 days after anthesis (May 16th) and included the following: (i) control treatment (demineralized water), (ii) biochar solution, (iii) phenolic extract solution, and (iv) a combined aqueous preparation of biochar and phenolic extract, all with the addition of a wetting agent. Trees of the olive cultivars Leccino and Istarska bjelica were sprayed with the corresponding preparation until runoff. Olive leaves were sampled three weeks after treatment (July 26th) and, after washing and drying, and were prepared for LC-MSMS analysis. Both biochar-based treatments induced the most potent effects, although responses differed between cultivars. In particular, apigenin derivatives, hydroxytyrosol, luteolin-7-rutinoside, and the secoiridoid oleacein showed apparent differences between biochar treatments and the control. Overall, higher concentrations of the sum of detected secoiridoids were observed in the leaf samples of ‘Istarska bjelica’ under BCH and BCH+PH treatments, whereas no such differences were found for ‘Leccino’ cultivar. Further research is needed to clarify the cultivar-dependent response of secondary metabolism in these olive cultivars and the mechanisms by which biochar foliar application modulates metabolite profiles.

This study presents a decision tree model-based approach to classify rural net migration across Colombian departments using sociodemographic and economic variables. In the model formulation, immigration is considered the movement of people to a destination area to settle there, while emigration is the movement of people from that specific area to other places. The target variable was defined as a binary category representing positive (when the immigration is greater than emigration) or negative net migration. Four classification models were trained and evaluated: Decision Tree, Random Forest, AdaBoost, and XGBoost. Data were preprocessed using cleaning techniques, categorical variable encoding, and class balance assessment. Model performance was evaluated using various metrics, including accuracy, precision, sensitivity, $F_1$ score, and the area under the ROC curve. The results show that Random Forest achieves the highest accuracy, precision, sensitivity, and F1 score in the 10-variable and 15-variable settings, while XGBoost is competitive but not dominant. Furthermore, the importance of the model was analyzed to identify key factors influencing migration patterns. This approach allows for a more precise understanding of regional migration dynamics in Colombia and can serve as a basis for designing informed public policies.

Public attitudes toward renewable energy sources (RES) have been widely studied at the household level. However, less is known about how citizens differ in their broader perceptions, knowledge, and behavioral orientations toward renewable energy. This study explores such heterogeneity within Polish society using survey data from a representative sample of 974 respondents. An exploratory factor analysis identified six dimensions of renewable energy attitudes: environmental concern, knowledge and awareness, social and economic support, perceived ease of use, perceived benefits, and behavioral intentions. Using these attitudinal dimensions, cluster analysis revealed two distinct consumer segments that differ in their overall level of engagement with renewable energy. The first cluster comprises pro-green and engaged individuals who express strong concern for environmental issues, have a greater awareness of the benefits of renewable energy, and are more ready to adopt such technologies. The second cluster represents respondents who are less engaged or skeptical, with weaker environmental and behavioral commitment. The comparison of sociodemographic characteristics across clusters showed no statistically significant differences in gender, age, education, or place of residence, and only a marginal effect for income. The findings suggest that support for renewable energy in Poland is not driven by demographics but somewhat shaped by cognitive and value-based factors, offering valuable insights for policymakers and communication strategists promoting the energy transition.

Microtopography regulates soil erosion by shaping surface heterogeneity, but the mechanism of loess slope soil loss remains insufficiently quantified. This study combined laboratory rainfall simulations and machine learning to investigate how tillage-induced microtopography modulates soil loss through surface heterogeneity and hydrodynamic processes. Simulations used loess soil (silty loam) with a 5° slope, 60 mm/h rainfall intensity, and 5–30 min rainfall durations (RD). Results indicated that the mean weight diameter (MWD) and aggregate stability index (ASI) of structural, transition, and depositional crusts under micro-terrain decreased by 36~65% and 41~60%, respectively, while the fractal dimension (D) increased by 10~19%. Negative relationships were observed between ASI/MWD and D (R² = 0.83~0.98). Horizontal cultivation (T_HC, surface roughness [SR] = 1.76, average depression storage [ADS] = 2.34 × 10⁻² m³) delayed runoff connectivity and reduced cumulative soil loss (LS) by 42–58% compared to hoeing cultivation (T_HE, SR = 1.47, ADS = 3.23 × 10⁻⁴ m³). Abrupt hydrodynamic transitions occurred at 10 min RD (T_HE) and 15 min RD (artificial digging [T_AD]), driven by trench connectivity and depression overflow. LS exhibited a significant positive correlation with D and RD and was inversely correlated with ASI, MWD, and SR. A three-hidden-layer BPNN exhibited high predictive accuracy for LS (mean square error = 0.07), verifying applicability in complex scenarios with significant microtopographic heterogeneity and multi-factor coupling. This study demonstrated that surface roughness and depression storage were the dominant microtopographic controls on loess slope soil loss. BPNN provided a reliable tool for soil loss prediction in heterogeneous microtopographic systems. The findings provide critical insights into optimizing tillage-based soil conservation strategies for sloping loess farmlands.

Human Papillomavirus (HPV)-related cancers constitute a major global health problem, accounting for 4.5% of all human cancers. Studying the composition of the tumor microenvironment (TME) of HPV-related cancers may help develop therapeutic strategies or identify prognostic biomarkers with potential clinical significance. Among all the components of TME, mast cells (MCs) appear to be particularly relevant in HPV-related tumors. MCs are myeloid-derived immune cells that release a wide range of inflammatory mediators. It is now recognized that these immune cells are important players within the TME, where they can exert both anti- and pro-tumor activities depending on the type of MC-derived inflammatory mediators released. MCs may play an important role in the processes associated with cell transformation, development, and the progression of HPV-associated tumors; however, their specific functions in these neoplasms are not yet fully understood. This review addresses the current state of knowledge on MCs and their contribution to the molecular biology of HPV-related cancers. In addition, it highlights MCs’ roles in the pro- or anti-tumor paradigm and discusses their emerging potential as therapeutic targets or prognostic biomarkers.

Sedimentary dynamics in the Palamós Canyon are influenced by river inputs and storm resuspension, as well as by bottom trawling on the canyon flanks. In this study, we estimate recent sediment deposition patterns along the canyon axis using the excess activity concentration of the short-lived radiotracer ²³⁴Th (half-life of 24.1 days). Sediment cores were obtained at various locations along the canyon axis from a depth of approximately 800 m to 2100 m in June 2023 and August 2024. Excess ²³⁴Th (²³⁴Th_xs) was detected in all sampled sites with variable penetration depths (0.5–3.5 cm). ²³⁴Th_xs-derived estimations of mixing rates decreased downcanyon from up to 15.6 cm² y⁻¹ at the canyon head (~800 m) to negligible mixing at the canyon mouth (~2100 m). ²³⁴Th_xs inventories, a proxy of recent sediment deposition, were high (1800–3490 Bq m⁻²) at the canyon head and at the upper canyon (~1400 m) close to fishing grounds and decreased downcanyon (82–694 Bq m⁻²) at the lower canyon (~1800 m) and canyon mouth. Inventories varied 2-fold across years presumably attributed to enhanced riverine and bottom trawling sediment fluxes. Similar ²³⁴Th-derived sediment deposition patterns can be found in submarine canyons worldwide, highlighting the value of this radiotracer for sedimentary dynamics studies in such complex environments.