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Abstract Economic growth has brought about escalating environmental challenges. The purist of socioeconomic and environmental coordination necessitates a deep understanding of their interplay. Therefore, a game theory stepwise-cluster factorial coupling coordination model (GSFCM) has been developed to investigate the spatial and temporal patterns and driving forces of socioeconomic and environmental coordination in the Yangtze River Delta Region. Results reveal an upward in socioeconomic and environmental status from 2015 to 2020. All 41 cities in the region show progress towards coordination, coupled with spatial inequalities. Hangzhou, Shanghai, and Zhoushan demonstrate the highest coupling coordination degree (CCD), while Bozhou, Suzhou, and Fuyang located in Anhui exhibit the lowest levels. Socioeconomic factors contribute 82.98% to the enhancement of CCD, environmental factors contribute 11.05%, and their interaction accounts for 5.92%. Main drivers influencing CCD include Gross Domestic Product (GDP) per capita, the proportion of the added value of tertiary industry, PM2.5 concentration, and their interactions. Policy related to regional collaboration, industrial upgrades, and investment in environmental infrastructure are recomended to achieve a harmonious balance between economic growth and environmental protection. These insights are expected to support policy decisions in sustainable development of the region.

Abstract Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

Abstract The traditional decomposed ensemble prediction model decomposes the entire rainfall sequence into several sub-sequences, dividing them into training and testing periods for modeling. During sample construction, future information is erroneously mixed into the training data, making it challenging to apply in practical rainfall forecasting. This paper proposes a novel stepwise decomposed ensemble coupling model, realized through variational mode decomposition (VMD) and bidirectional long short-term memory neural network (BiLSTM) models. Model parameters are optimized using an improved particle swarm optimization (IPSO). The performance of the model was evaluated using rainfall data from the Southern Four Lakes basin. The results indicate that: (1) Compared to the PSO algorithm, the IPSO algorithm-coupled model shows a minimum decrease of 2.70% in MAE and at least 2.62% in RMSE across the four cities in the Southern Four Lakes basin; the IPSO algorithm results in a minimum decrease of 25.58% in MAE and at least 28.19% in RMSE for the VMD-BiLSTM model. (2) When compared to IPSO-BiLSTM, the VMD-IPSO-BiLSTM based on the stepwise decomposition technique exhibits a minimum decrease of 26.54% in MAE and at least 34.16% in RMSE. (3) The NSE for the testing period of the VMD-IPSO-BiLSTM model in each city surpasses 0.88, indicating higher prediction accuracy and providing new insights for optimizing rainfall forecasting.

Abstract Ornamental foliage plants that have a dense appearance are highly valued. One way to achieve this is by using plant growth regulators as a tool for plant growth management. In a greenhouse with a mist irrigation system, a study was conducted on dwarf schefflera, an ornamental foliage plant, which was exposed to foliar application of gibberellic acid and benzyladenine hormones. The hormones were sprayed on dwarf schefflera leaves at 0, 100, and 200 mg/l concentrations, at 15-day intervals in three stages. The experiment was conducted as a factorial based on a completely randomized design, with four replicates. The combination of gibberellic acid and benzyladenine at 200 mg/l concentration had a significant effect on leaf number, leaf area, and plant height. The treatment also resulted in the highest content of photosynthetic pigments. Furthermore, the highest soluble carbohydrate to reducing sugars ratio was observed in treatments of 100 and 200 mg/l benzyladenine, and 200 mg/l gibberellic acid + benzyladenine. Stepwise regression analysis showed that root volume was the first variable to enter the model, explaining 44% of variations. The next variable was root fresh weight, and the two-variable model explained 63% of variations in leaf number. The greatest positive effect on leaf number was related to root fresh weight (0.43), which had a positive correlation with leaf number (0.47). The results showed that 200 mg/l concentration of gibberellic acid and benzyladenine significantly improved morphological growth, chlorophyll and carotenoid synthesis, and reducing sugar and soluble carbohydrate contents in dwarf schefflera.

Abstract Vascularized composite allografts (VCAs) present unique challenges in transplant medicine, owing to their complex structure and vulnerability to ischemic injury. Innovative preservation techniques are crucial for extending the viability of these grafts, from procurement to transplantation. This study addresses these challenges by integrating cryoprotectant agent (CPA) optimization, advanced thermal tracking, and stepwise CPA loading strategies within an ex vivo rodent model. CPA optimization focused on various combinations, identifying those that effectively suppress ice nucleation while mitigating cytotoxicity. Thermal dynamics were monitored using invasive thermocouples and non-invasive FLIR imaging, yielding detailed temperature profiles crucial for managing warm ischemia time and optimizing cooling rates. The efficacy of stepwise CPA loading versus conventional flush protocols demonstrated that stepwise (un)loading significantly improved arterial resistance and weight change outcomes. In summary, this study presents comprehensive advancements in VCA preservation strategies, combining CPA optimization, precise thermal monitoring, and stepwise loading techniques. These findings hold potential implications for refining transplantation protocols and improving graft viability in VCA transplantation.

Abstract Recent research has revealed that hemodynamic changes caused by lung recruitment maneuvers (LRM) with continuous positive airway pressure can be used to identify fluid responders. We investigated the usefulness of stepwise LRM with increasing positive end-expiratory pressure and constant driving pressure for predicting fluid responsiveness in patients under lung protective ventilation (LPV). Forty-one patients under LPV were enrolled when PPV values were in a priori considered gray zone (4% to 17%). The FloTrac-Vigileo device measured stroke volume variation (SVV) and stroke volume (SV), while the patient monitor measured pulse pressure variation (PPV) before and at the end of stepwise LRM and before and 5 min after fluid challenge (6 ml/kg). Fluid responsiveness was defined as a ≥ 15% increase in the SV or SV index. Seventeen were fluid responders. The areas under the curve for the augmented values of PPV and SVV, as well as the decrease in SV by stepwise LRM to identify fluid responders, were 0.76 (95% confidence interval, 0.61–0.88), 0.78 (0.62–0.89), and 0.69 (0.53–0.82), respectively. The optimal cut-offs for the augmented values of PPV and SVV were > 18% and > 13%, respectively. Stepwise LRM -generated augmented PPV and SVV predicted fluid responsiveness under LPV.

Abstract Osteoporosis is a metabolic bone disease that impairs bone mineral density, microarchitecture, and strength. It requires continuous management, and further research into new treatment options is necessary. Osteoprotegerin (OPG) inhibits bone resorption and osteoclast activity. The objective of this study was to investigate the effects of stepwise administration of OPG-encoded minicircles (mcOPG) and a bone formation regulator, parathyroid hormone-related peptide (PTHrP)-encoded minicircles (mcPTHrP) in osteoporosis. The combined treatment with mcOPG and mcPTHrP significantly increased osteogenic marker expression in osteoblast differentiation compared with the single treatment groups. A model of postmenopausal osteoporosis was established in 12-week-old female rats through ovariectomy (OVX). After 8 weeks of OVX, mcOPG (80 µg/kg) was administered via intravenous injection. After 16 weeks of OVX, mcPTHrP (80 µg/kg) was injected once a week for 3 weeks. The bone microstructure in the femur was evaluated 24 weeks after OVX using micro-CT. In a proof-of-concept study, stepwise treatment with mcOPG and mcPTHrP on an OVX rat model significantly improved bone microstructure compared to treatment with mcOPG or mcPTHrP alone. These results suggest that stepwise treatment with mcOPG and mcPTHrP may be a potential treatment for osteoporosis.

Abstract Graphene oxide (GO) is a monolayer of oxidized graphene which is a convenient and potential candidate in a wide range of fields of applications like electronics, photonics, optoelectronics, energy storage, catalysis, chemical sensors, and many others. GO is often composed of various oxygen-containing groups such as hydroxyl, carboxyl, and epoxy. One appealing method for achieving graphene-like behavior with sp2 hybridized carbon is the reduction of GO i.e. formation of reduced graphene oxide (RGO). A stepwise reduction GO to form a family of RGO, containing various quantities of oxygen-related defects was carried out. Herein, the defects related chemical and physical properties of GO and the RGO family were studied and reported in an effort to understand how the properties of RGO vary with the reduction rate. Although there are several reports on various features and applications of GO and RGO but a systematic investigation of the variation of the physical and chemical properties in RGO with the varying quantities of oxygeneous defects is imperative for the engineered physical properties in achieving the desired field of applications. We have attempted to look at the role of sp2 and sp3 carbon fractions, which are present in RGO-based systems, and how they affect the electrical, optoelectronic, and adsorption characteristics.

Abstract One of the well-known medicinal plants in the Falcaria genus is Sickleweed. Falcaria species exhibit a high degree of genetic variability, posing challenges in the examination of genetic diversity due to the significant potential for hybridization and introgression among them. Utilizing morphological traits and molecular markers may prove to be a valuable approach in evaluating and harnessing germplasm, considering the current obstacles faced in breeding this medicinal herb. In 2021, fifteen Sickleweed populations were cultivated in pots under field conditions, employing a randomized complete block design with three replications. This aimed to assess genetic diversity and conduct marker-trait association analyses utilizing morpho-physiological characteristics and SSR markers. The Sickleweed populations displayed considerable genetic diversity across all traits. Through cluster analysis of traits and the utilization of the UPGMA method based on the Gower distance matrix, the population was classified into three distinct clusters. Upon examining all genotypes, 52 polymorphic bands were detected, with an average of 8.68 bands per primer. The average expected heterozygosity across all loci was 0.864, while the average PIC was 0.855. Molecular data analysis employing the Jaccard similarity index and UPGMA method revealed the division of Sickleweed populations into two major groups. Furthermore, the results of molecular variance analysis indicated that variation within the population exceeded that between populations. Thirty-two SSR fragments were found to be significantly associated with genomic regions controlling the studied traits, determined through the application of stepwise regression. Selection based on molecular markers offers a rapid method for breeding programs, with the genetic information obtained from these markers playing a crucial role. Therefore, alongside traits, selecting superior genotypes and populations of high value in breeding programs becomes feasible. The findings highlight that certain markers are linked to multiple traits, emphasizing the critical importance of this characteristic in plant breeding for the simultaneous improvement of numerous traits. The study’s insights regarding markers hold potential for application in Sickleweed breeding programs.

Abstract Although infection with the methicillin-resistant Staphylococcus aureus (MRSA) clone USA300 is extremely rare in Japan, the uniquely evolved clone ΨUSA300 has been reported in Japan. An outbreak of a distinct USA300 clone was recently reported in an HIV/AIDS referral hospital in Tokyo. The present study investigated the evolutionary origin and genetic diversity of USA300-related clones causing regional outbreaks among people living with HIV (PLWHIV) in Tokyo. MRSA isolates collected from PLWHIV in an HIV/AIDS referral center in Tokyo were subjected to whole-genome sequencing and their genetic features were compared with those of previously described USA300 MRSA genomes. Of the 28 MRSAs isolated in 2016–2019, 23 (82.1%) were identified as USA300, with 22 (95.6%) of the latter identified as ΨUSA300. Although the genomic structure of ΨUSA300 was identical to the structures of reference USA300 strains, one clade (cluster A) was found to have acquired 29 previously identified lineage-specific mutations in a stepwise manner. The estimated divergence dates of ΨUSA300 and Cluster A were 2009 and 2012, respectively. These findings suggested that the ΨUSA300 clone had spread among PLWHIVs in Tokyo in the early 2010s, with stepwise acquisition of lineage-specific nonsynonymous mutations.

Abstract Utilizing the red mud sustainably is now a challenging issue. Red mud due to its wide production, presence of some radioactive elements, high alkalinity, and salinity, has a dramatic potential to contaminate soil and groundwater. Notwithstanding its drawbacks, Red mud consists several elements, including Ca, Al, Ti, Si, and Fe, in various mineral forms. In this study, stepwise leaching was applied as a proper method to separate and purify the main valuable elements using available and affordable HCl. The pre-leaching step under optimized conditions using HCl (0.2 M) at room temperature for 2 h removed 89% of the calcium content from red mud. To selectively remove the solid silica, the residue was treated with concentrated HCl (3.0 M, L/S of 20 mL/g) at 95 °C, resulting in the dissolution of iron and aluminum content with up to 90% efficiency. After precipitation of the Fe3+ and Al3+, they were characterized using FT-IR, BET, EDS, XRD, SEM and TEM monographs, confirming the formation of nanosized hematite (α-Fe2O3) and mesoporous gamma alumina (γ-Al2O3). Consequently, inexpensive red mud was converted into highly valuable nano-sized metal oxides using simple, sustainable techniques and cheap reagents. Moreover, this technique generates the lowest amounts of waste during the leaching process and all reagents can be recycled for further uses, making this method a sustainable utilization.

Abstract To investigate the outcomes of the modified radial tongue-shaped flap following stepwise surgery release for treating Benson type I camptodactyly of the 5th digit. A retrospective analysis involving patients with Benson type I camptodactyly of the 5th digit was performed. A total of 8 patients with 12 affected digits were included. Extent of surgical release depended on the degree of soft tissue contracture. Skin release, subcutaneous fascial release, and flexor digitorum superficialis tenotomy were performed in all 12 digits, sliding volar plate release in 2 digits, and intrinsic tendon transfer in 1 digit. The mean total passive motion of proximal interphalangeal joint significantly increased from 32.5° ± 16° to 86.3° ± 20.4°, while mean total active motion significantly increased from 22° ± 10.5° to 73.8° ± 27.5° (P

Abstract Neural regeneration is extremely difficult to achieve. In traumatic brain injuries, the loss of brain parenchyma volume hinders neural regeneration. In this study, neuronal tissue engineering was performed by using electrically charged hydrogels composed of cationic and anionic monomers in a 1:1 ratio (C1A1 hydrogel), which served as an effective scaffold for the attachment of neural stem cells (NSCs). In the 3D environment of porous C1A1 hydrogels engineered by the cryogelation technique, NSCs differentiated into neuroglial cells. The C1A1 porous hydrogel was implanted into brain defects in a mouse traumatic damage model. The VEGF-immersed C1A1 porous hydrogel promoted host-derived vascular network formation together with the infiltration of macrophages/microglia and astrocytes into the gel. Furthermore, the stepwise transplantation of GFP-labeled NSCs supported differentiation towards glial and neuronal cells. Therefore, this two-step method for neural regeneration may become a new approach for therapeutic brain tissue reconstruction after brain damage in the future.

Abstract We present a theoretical model describing photoelectron transport dynamics in stepwise-doped GaAs photocathodes. Built-in electric field caused by the doping structure is analyzed, and the time-evolution of electron concentration in the active layer induced by a femtosecond laser pulse is solved. The predictions of the model show excellent agreement with the experimental data measured with pump-probe transient reflectometry, demonstrating the capability of the theoretical model in predicting photoelectron behaviors in real devices. Comparisons are also made between this stepwise doping model and the conventional gradient doping model with a continuous doping profile, thereby providing the first quantitative evaluation of the effectiveness and the limitation of the gradient doping model in describing actual stepwise-doped devices.

Abstract Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance.

Abstract The true slime mould, Physarum polycephalum, develops as a vascular network of protoplasm, connecting node-like sources of food in an effort to solve multi-objective transport problems. The organism first establishes a dense and continuous mesh, reinforcing optimal pathways over time through constructive feedbacks of protoplasmic streaming. Resolved vascular morphologies are the result of an evolutionarily-refined mechanism of computation, which can serve as a versatile biological model for network design at the urban scale. Existing digital Physarum models typically use positive reinforcement mechanisms to capture meshing and refinement behaviours simultaneously. While these automations generate accurate descriptions of sensory and constructive feedback, they limit stepwise design control, reducing flexibility and applicability. A model that decouples the two “phases” of Physarum behaviour would enable multistage control over network growth. Here we introduce such a system, first by producing a site-responsive mesh from a population of nutrient-attracted agents, and then by independently calculating from it a flexible, proximity-defined shortest-walk to produce a final network. We develop and map networks within existing urban environments that perform similarly to those biologically grown, establishing a versatile tool for bio-inspired urban network design.

Abstract The aim of this study was to analyze changes in refraction and evaluate the variables in school children who received atropine as myopic control for 10 years. Low-concentration atropine (0.05%) was prescribed initially, and the dose was increased in a stepwise manner if rapid myopic progression (≥ 0.5D per half year) was noted during the regular follow-up visit. 23 children with a mean age of 6.96 ± 1.07 years were included. The initial spherical equivalent was − 1.25 ± 0.84 D. The overall mean myopic progression was − 0.30 ± 0.27 D/year. Younger initial age, female, higher initial spherical equivalent and the need of higher concentration of atropine were found to be risk factors for myopic progression in multivariate mixed-effect analysis (p = 0.013, 0.017, 0.024 and 0.014). Children who kept using a lower concentration of atropine (≤ 0.1%) tended to have slower myopic progression throughout the 10-year course than those who shifted to higher concentrations (> 0.1%) (p ≤ 0.001). Stepwise low concentration of atropine might be effective for long-term myopic control in school students. Those who had poor response to lower concentration of atropine may have the risk of faster progression, even with high concentration of atropine. Additional or alternative treatment might be considered.

Abstract Conventional neuronal network pattern formation techniques cannot control the arrangement of axons and dendrites because network structures must be fixed before neurite differentiation. To overcome this limitation, we developed a non-destructive stepwise microfabrication technique that can be used to alter microchannels within agarose to guide neurites during elongation. Micropatterns were formed in thin agarose layer coating of a cultivation dish using the tip of a 0.7 $$\upmu \mathrm{m}$$ μ m -diameter platinum-coated glass microneedle heated by a focused 1064-nm wavelength infrared laser, which has no absorbance of water. As the size of the heat source was 0.7 $$\upmu \mathrm{m}$$ μ m , which is smaller than the laser wavelength, the temperature fell to 45 $$^\circ \hbox {C}$$ ∘ C within a distance of 7.0 $$\upmu \mathrm{m}$$ μ m from the edge of the etched agarose microchannel. We exploited the fast temperature decay property to guide cell-to-cell connection during neuronal network cultivation. The first neurite of a hippocampal cell from a microchamber was guided to a microchannel leading to the target neuron with stepwise etching of the micrometer resolution microchannel in the agarose layer, and the elongated neurites were not damaged by the heat of etching. The results indicate the potential of this new technique for fully direction-controlled on-chip neuronal network studies.

Abstract Conventionally, random forests are built from “greedy” decision trees which each consider only one split at a time during their construction. The sub-optimality of greedy implementation has been well-known, yet mainstream adoption of more sophisticated tree building algorithms has been lacking. We examine under what circumstances an implementation of less greedy decision trees actually yields outperformance. To this end, a “stepwise lookahead” variation of the random forest algorithm is presented for its ability to better uncover binary feature interdependencies. In contrast to the greedy approach, the decision trees included in this random forest algorithm, each simultaneously consider three split nodes in tiers of depth two. It is demonstrated on synthetic data and financial price time series that the lookahead version significantly outperforms the greedy one when (a) certain non-linear relationships between feature-pairs are present and (b) if the signal-to-noise ratio is particularly low. A long-short trading strategy for copper futures is then backtested by training both greedy and stepwise lookahead random forests to predict the signs of daily price returns. The resulting superior performance of the lookahead algorithm is at least partially explained by the presence of “XOR-like” relationships between long-term and short-term technical indicators. More generally, across all examined datasets, when no such relationships between features are present, performance across random forests is similar. Given its enhanced ability to understand the feature-interdependencies present in complex systems, this lookahead variation is a useful extension to the toolkit of data scientists, in particular for financial machine learning, where conditions (a) and (b) are typically met.

Abstract Although incompletely understood, microbiota-host interactions are assumed to be altered in irritable bowel syndrome (IBS). We, therefore, aimed to develop a novel analysis pipeline tailored for the integrative analysis of microbiota-host interactions and association to symptoms and prove its utility in a pilot cohort. A multilayer stepwise integrative analysis pipeline was developed to visualize complex variable associations. Application of the pipeline was demonstrated on a dataset of IBS patients and healthy controls (HC), using the R software package to analyze colonic host mRNA and mucosal microbiota (16S rRNA gene sequencing), as well as gastrointestinal (GI) and psychological symptoms. In total, 42 IBS patients (57% female, mean age 33.6 (range 18–58)) and 20 HC (60% female, mean age 26.8 (range 23–41)) were included. Only in IBS patients, mRNA expression of Toll-like receptor 4 and genes associated with barrier function (PAR2, OCLN, TJP1) intercorrelated closely, suggesting potential functional relationships. This host genes-based “permeability cluster” was associated to mucosa-adjacent Chlamydiae and Lentisphaerae, and furthermore associated to satiety as well as to anxiety, depression and fatigue. In both IBS patients and HC, chromogranins, secretogranins and TLRs clustered together. In IBS patients, this host genes-based “immune-enteroendocrine cluster” was associated to specific members of Firmicutes, and to depression and fatigue, whereas in HC no significant association to microbiota was identified. We have developed a stepwise integrative analysis pipeline that allowed identification of unique host-microbiota intercorrelation patterns and association to symptoms in IBS patients. This analysis pipeline may aid in advancing the understanding of complex variable associations in health and disease.

Abstract Telomerases elongate the ends of chromosomes required for cell immortality through their reverse transcriptase activity. By using the model organism Saccharomyces cerevisiae we defined the order in which the holoenzyme matures. First, a longer precursor of the telomerase RNA, TLC1 is transcribed and exported into the cytoplasm, where it associates with the protecting Sm-ring, the Est and the Pop proteins. This partly matured telomerase is re-imported into the nucleus via Mtr10 and a novel TLC1-import factor, the karyopherin Cse1. Remarkably, while mutations in all known transport factors result in short telomere ends, mutation in CSE1 leads to the amplification of Y′ elements in the terminal chromosome regions and thus elongated telomere ends. Cse1 does not only support TLC1 import, but also the Sm-ring stabilization on the RNA enableling Mtr10 contact and nuclear import. Thus, Sm-ring formation and import factor contact resembles a quality control step in the maturation process of the telomerase. The re-imported immature TLC1 is finally trimmed into the 1158 nucleotides long mature form via the nuclear exosome. TMG-capping of TLC1 finalizes maturation, leading to mature telomerase.

In Escherichia coli DNA replication yields interlinked chromosomes. Controlling topological changes associated with replication and returning the newly replicated chromosomes to an unlinked monomeric state is essential to cell survival. In the absence of the topoisomerase topoIV, the site-specific recombination complex XerCD- dif-FtsK can remove replication links by local reconnection. We previously showed mathematically that there is a unique minimal pathway of unlinking replication links by reconnection while stepwise reducing the topological complexity. However, the possibility that reconnection preserves or increases topological complexity is biologically plausible. In this case, are there other unlinking pathways? Which is the most probable? We consider these questions in an analytical and numerical study of minimal unlinking pathways. We use a Markov Chain Monte Carlo algorithm with Multiple Markov Chain sampling to model local reconnection on 491 different substrate topologies, 166 knots and 325 links, and distinguish between pathways connecting a total of 881 different topologies. We conclude that the minimal pathway of unlinking replication links that was found under more stringent assumptions is the most probable. We also present exact results on unlinking a 6-crossing replication link. These results point to a general process of topology simplification by local reconnection, with applications going beyond DNA.

Abstract In this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

Abstract Hearing loss is one of the most common birth disorders in humans, with an estimated prevalence of 1–3 in every 1000 newborns. This study investigates the molecular etiology of a hearing loss cohort using a stepwise strategy to effectively diagnose patients and address the challenges posed by the genetic heterogeneity and variable mutation spectrum of hearing loss. In order to target known pathogenic variants, multiplex PCR plus next-generation sequencing was applied in the first step; patients which did not receive a diagnosis from this were further referred for exome sequencing. A total of 92 unrelated patients with nonsyndromic hearing loss were enrolled in the study. In total, 64% (59/92) of the patients were molecularly diagnosed, 44 of them in the first step by multiplex PCR plus sequencing. Exome sequencing resulted in eleven diagnoses (23%, 11/48) and four probable diagnoses (8%, 4/48) among the 48 patients who were not diagnosed in the first step. The rate of secondary findings from exome sequencing in our cohort was 3% (2/58). This research presents a molecular diagnosis spectrum of 92 non-syndromic hearing loss patients and demonstrates the benefits of using a stepwise diagnostic approach in the genetic testing of nonsyndromic hearing loss.

Abstract Trypanosoma brucei is a protozoan parasite that causes important human and livestock diseases in sub-Saharan Africa. By overexpressing a single RNA-binding protein, RBP6, in non-infectious procyclics trypanosomes, we previously recapitulated in vitro the events occurring in the tsetse fly vector, namely the development of epimastigotes and infectious, quiescent metacyclic parasites. To identify genes involved in this developmental progression, we individually targeted 86 transcripts by RNAi in the RBP6 overexpression cell line and assessed the loss-of-function phenotypes on repositioning the kinetoplast, an organelle that contains the mitochondrial genome, the expression of BARP or brucei alanine rich protein, a marker for epimastigotes, and metacyclic variant surface glycoprotein. This screen identified 22 genes that positively or negatively regulate the stepwise progression towards infectivity at different stages. Two previously uncharacterized putative nucleic acid binding proteins emerged as potent regulators, namely the cold shock domain-containing proteins CSD1 and CSD2. RNA-Seq data from a selected group of cell lines further revealed that the components of gene expression regulatory networks identified in this study affected the abundance of a subset of transcripts in very similar fashion. Finally, our data suggest a considerable overlap between the genes that regulate the formation of stumpy bloodstream form trypanosomes and the genes that govern the development of metacyclic form parasites.