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Insects often overcome unfavorable seasons in a hormonally regulated state of diapause during which their activity ceases, development is arrested, metabolic rate is suppressed, and tolerance of environmental stress is bolstered. Diapausing insects pass through a stereotypic succession of eco-physiological phases termed “diapause development.” The phasing is varied in the literature, and the whole concept is sometimes criticized as being too artificial. Here we present the results of transcriptional profiling using custom microarrays representing 1,042 genes in the drosophilid fly, Chymomyza costata. Fully grown, third-instar larvae programmed for diapause by a photoperiodic (short-day) signal were assayed as they traversed the diapause developmental program. When analyzing the gradual dynamics in the transcriptomic profile, we could readily distinguish distinct diapause developmental phases associated with induction/initiation, maintenance, cold acclimation, and termination by cold or by photoperiodic signal. Accordingly, each phase is characterized by a specific pattern of gene expression, supporting the physiological relevance of the concept of diapause phasing. Further, we have dissected in greater detail the changes in transcript levels of elements of several signaling pathways considered critical for diapause regulation. The phase of diapause termination is associated with enhanced transcript levels in several positive elements stimulating direct development (the 20-hydroxyecdysone pathway: Ecr, Shd, Broad; the Wnt pathway: basket, c-jun) that are countered by up-regulation in some negative elements (the insulin-signaling pathway: Ilp8, PI3k, Akt; the target of rapamycin pathway: Tsc2 and 4EBP; the Wnt pathway: shaggy). We speculate such up-regulations may represent the early steps linked to termination of diapause programming.

Type 4 pili (T4P) are important virulence factors, which belong to a superfamily of nanomachines ubiquitous in prokaryotes, called type 4 filaments (T4F). T4F are defined as helical polymers of type 4 pilins. Recent advances in cryo-electron microscopy (cryo-EM) led to structures of several T4F, revealing that the long N-terminal α-helix (α1) – the trademark of pilins – packs in the centre of the filaments to form a hydrophobic core. In diderm bacteria – all available bacterial T4F structures are from diderm species – a portion of α1 is melted (unfolded). Here we report that this architecture is conserved in phylogenetically distant monoderm species by determining the structure of Streptococcus sanguinis T4P. Our 3.7 Å resolution cryo-EM structure of S. sanguinis heteropolymeric T4P and the resulting full atomic model including all minor pilins highlight universal features of bacterial T4F and have widespread implications in understanding T4F biology. Here, Anger et al. report the structure of the type 4 pilus (T4P) from Streptococcus sanguinis . They show that the T4P architecture seen in diderm bacteria – where the N-terminal α-helices of pilin subunits are partially unfolded upon polymerisation – is conserved in distant monoderm species.

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype–environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha−1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha−1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.

People differ in their general tendency to endorse conspiracy theories (that is, conspiracy mentality). Previous research yielded inconsistent findings on the relationship between conspiracy mentality and political orientation, showing a greater conspiracy mentality either among the political right (a linear relation) or amongst both the left and right extremes (a curvilinear relation). We revisited this relationship across two studies spanning 26 countries (combined N = 104,253) and found overall evidence for both linear and quadratic relations, albeit small and heterogeneous across countries. We also observed stronger support for conspiracy mentality among voters of opposition parties (that is, those deprived of political control). Nonetheless, the quadratic effect of political orientation remained significant when adjusting for political control deprivation. We conclude that conspiracy mentality is associated with extreme left- and especially extreme right-wing beliefs, and that this non-linear relation may be strengthened by, but is not reducible to, deprivation of political control.Across 26 countries, Imhoff et al. find that conspiracy mentality is more prevalent at both ends of the political spectrum than the centre. This U-shaped pattern is accentuated for supporters of political parties not in government, particularly on the political right.

Throughout the history of technological progress, attempts have been made to build a machine that looks and behaves like humans. This paper presents a semi-anthropomimetic robot. The robot structure consists of a human-like upper body mounted on a mobile platform (mobile base, cart). The robot uses the three-wheeled mobile platform with two driving wheels and one passive (caster) wheel. The configuration and model of the upper body are represented as an anthropomimetic, compliant robot with antagonistically coupled drives. Robust control is evaluated in order to ensure stability of the robot position. The aim of this work is not the synthesis of control, but rather the examination of the limits of the adopted robot control strategy and the robot behaviour under disturbances (analysis of tip-over stability). The paper analyses both disturbances from the cart motion and external disturbances due to interaction with the environment (external impulse and long term external force). In order to analyse the balance of the robot and to avoid tipping over, different situations are tested and the appropriate dimensions of the cart are estimated (relying on the ZMP calculation).

COPD is a complex, heterogeneous condition. Even in the early clinical stages, COPD carries a significant burden, with breathlessness frequently leading to a reduction in exercise capacity and changes that correlate with long-term patient outcomes and mortality. Implementation of an effective management strategy is required to reduce symptoms, preserve lung function, quality of life, and exercise capacity, and prevent exacerbations. However, current clinical practice frequently differs from published guidelines on the management of COPD. This review focuses on the current scientific evidence and expert opinion on the management of moderate COPD: the symptoms arising from moderate airflow obstruction and the burden these symptoms impose, how physical activity can improve disease outcomes, the benefits of dual bronchodilation in COPD, and the limited evidence for the benefits of inhaled corticosteroids in this disease. We emphasize the importance of maximizing bronchodilation in COPD with inhaled dual-bronchodilator treatment, enhancing patient-related outcomes, and enabling the withdrawal of inhaled corticosteroids in COPD in well-defined patient groups.

Type 4 pili (T4P) are important virulence factors, which belong to a superfamily of nanomachines ubiquitous in prokaryotes, called type 4 filaments (T4F). T4F are defined as helical polymers of type 4 pilins. Recent advances in cryo-electron microscopy (cryo-EM) led to structures of several T4F. This revealed that the long N-terminal α-helix, the trademark of pilins, packs in the centre of the filaments to form a hydrophobic core, which in bacteria is accompanied by the melting (unfolding) of a portion of α1. Since all available bacterial T4F structures are from diderm species, we tested whether this architecture is conserved in phylogenetically distant species by determining the structure of the T4P of the monoderm Streptococcus sanguinis. Our 3.7 A resolution cryo-EM structure of this heteropolymeric T4P, and the resulting full atomic model including all minor pilins, highlight universal features of bacterial T4F and have widespread implications in understanding their biology.