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Background Long non-protein-coding RNAs (ncRNAs) are emerging as important regulators of cellular differentiation and are widely expressed in the brain. Results Here we show that many long ncRNAs exhibit dynamic expression patterns during neuronal and oligodendrocyte (OL) lineage specification, neuronal-glial fate transitions, and progressive stages of OL lineage elaboration including myelination. Consideration of the genomic context of these dynamically regulated ncRNAs showed they were part of complex transcriptional loci that encompass key neural developmental protein-coding genes, with which they exhibit concordant expression profiles as indicated by both microarray and in situ hybridization analyses. These included ncRNAs associated with differentiation-specific nuclear subdomains such as Gomafu and Neat1 , and ncRNAs associated with developmental enhancers and genes encoding important transcription factors and homeotic proteins. We also observed changes in ncRNA expression profiles in response to treatment with trichostatin A, a histone deacetylase inhibitor that prevents the progression of OL progenitors into post-mitotic OLs by altering lineage-specific gene expression programs. Conclusion This is the first report of long ncRNA expression in neuronal and glial cell differentiation and of the modulation of ncRNA expression by modification of chromatin architecture. These observations explicitly link ncRNA dynamics to neural stem cell fate decisions, specification and epigenetic reprogramming and may have important implications for understanding and treating neuropsychiatric diseases.

The presence of microplastics and nanoplastics (MnPs) in the atmosphere and their transport on a global scale has previously been demonstrated. However, little is known about their environmental impacts in the atmosphere. MnPs could act as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs), affecting cloud formation processes. In sufficient quantities, they could change the cloud albedo, precipitation and lifetime, collectively impacting the Earth’s radiation balance and climate. In this Perspective, we evaluate the potential impact of MnPs on cloud formation by assessing their ability to act as CCN or INPs. Based on an analysis of their physicochemical properties, we propose that MnPs can act as INPs and potentially as CCN after environmental aging processes such as photochemical weathering and the sorption of macromolecules or trace soluble species onto the particle surface. The actual climate impact(s) of MnPs depend on their abundance relative to other aerosols. The concentration of MnPs in the atmosphere is currently low, so they are unlikely to make a substantial contribution to radiative forcing in regions exposed to other aerosols, either from natural sources or anthropogenic pollution. Nevertheless, MnPs will potentially cause non-negligible perturbations in unpolluted remote or marine clouds and generate local climate impacts, particularly in view of an increase in the release of MnPs to the environment in the future. Further measurements, coupled with better characterization of the physiochemical properties of MnPs, will enable a more accurate assessment of the climate impacts of MnPs acting as INPs and CCN. Microplastics and nanoplastics may affect cloud formation processes by acting as ice-nucleating particles and cloud condensation nuclei.

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

Premature ovarian insufficiency (POI) is a major cause of female infertility due to early loss of ovarian function. POI is a heterogeneous condition, and its molecular etiology is unclear. To identify genetic variants associated with POI, here we performed whole-exome sequencing in a cohort of 1,030 patients with POI. We detected 195 pathogenic/likely pathogenic variants in 59 known POI-causative genes, accounting for 193 (18.7%) cases. Association analyses comparing the POI cohort with a control cohort of 5,000 individuals without POI identified 20 further POI-associated genes with a significantly higher burden of loss-of-function variants. Functional annotations of these novel 20 genes indicated their involvement in ovarian development and function, including gonadogenesis ( LGR4 and PRDM1 ), meiosis ( CPEB1 , KASH5 , MCMDC2 , MEIOSIN , NUP43 , RFWD3 , SHOC1 , SLX4 and STRA8 ) and folliculogenesis and ovulation ( ALOX12 , BMP6 , H1-8 , HMMR , HSD17B1 , MST1R , PPM1B , ZAR1 and ZP3 ). Cumulatively, pathogenic and likely pathogenic variants in known POI-causative and novel POI-associated genes contributed to 242 (23.5%) cases. Further genotype–phenotype correlation analyses indicated that genetic contribution was higher in cases with primary amenorrhea compared to that in cases with secondary amenorrhea. This study expands understanding of the genetic landscape underlying POI and presents insights that have the potential to improve the utility of diagnostic genetic screenings. Whole-exome sequencing analyses in a cohort of 1,030 patients with premature ovarian insufficiency identify new likely pathogenic variants and reveal distinct genetic architectures between primary and secondary amenorrhea.

In this paper, a new fractional-order (FO) PIλDµ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the controller, and the two additional adjustable factors make the FO PIλDµ controller have better accuracy and robustness. Simulations are carried out for comparison with a ship’s digital PID autopilot. The results show that the FO PIλDµ controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO PIλDµ controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PIλDµ controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO PIλDµ controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.

Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal circuits to facilitate lifelong learning. Inspired by this, we develop a corticohippocampal circuits-based hybrid neural network (CH-HNN) that emulates these dual representations, significantly mitigating catastrophic forgetting in both task-incremental and class-incremental learning scenarios. Our CH-HNNs incorporate artificial neural networks and spiking neural networks, leveraging prior knowledge to facilitate new concept learning through episode inference, and offering insights into the neural functions of both feedforward and feedback loops within corticohippocampal circuits. Crucially, CH-HNN operates as a task-agnostic system without increasing memory demands, demonstrating adaptability and robustness in real-world applications. Coupled with the low power consumption inherent to SNNs, our model represents the potential for energy-efficient, continual learning in dynamic environments. Energy-efficient, task-agnostic continual learning is a key challenge in Artificial Intelligence frameworks. Here, authors propose a hybrid neural network that emulates dual representations in corticohippocampal circuits, reducing the effect of catastrophic forgetting.

Background Millions of nucleotide variants are identified through cancer genome sequencing and it is clinically important to identify the pathogenic variants among them. By introducing base substitutions at guide RNA target regions in the genome, CRISPR-Cas9-based base editors provide the possibility for evaluating a large number of variants in their genomic context. However, the variability in editing efficiency and the complexity of outcome mapping are two existing problems for assigning guide RNA effects to variants in base editing screens. Results To improve the identification of pathogenic variants, we develop a framework to combine base editing screens with sgRNA efficiency and outcome mapping. We apply the method to evaluate more than 9000 variants across all the exons of BRCA1 and BRCA2 genes. Our efficiency-corrected scoring model identifies 910 loss-of-function variants for BRCA1/2 , including 151 variants in the noncoding part of the genes such as the 5′ untranslated regions. Many of them are identified in cancer patients and are reported as “benign/likely benign” or “variants of uncertain significance” by clinicians. Our data suggest a need to re-evaluate their clinical significance, which may be helpful for risk assessment and treatment of breast and ovarian cancer. Conclusions Our results suggest that base editing screens with efficiency correction is a powerful strategy to identify pathogenic variants in a high-throughput manner. Applying this strategy to assess variants in both coding and noncoding regions of the genome could have a direct impact on the interpretation of cancer variants.

The analysis of super-dyadic relations through hypergraphs is gradually gaining attention, with its community structure analysis playing a crucial role in computational social science. However, few scholars have paid attention to the impact of hyperedge diversity on the community structure of hypergraphs, especially the impact generated by heterogeneous hyperedges. This paper expands hypergraphs into signed hypergraphs and proposes a framework for community structure in signed hypergraphs along with a variant of modularity. Simultaneously, an optimization algorithm is introduced in this paper to detect potential communities by maximizing modularity. Experimental results reveal that the proposed method can effectively optimize the objective function and detect community structures.

Combining biomass, a clean and renewable energy source, with waste plastic, which serves as a good auxiliary fuel, can produce high-quality clean fuel. The performance of biomass-derived fuel can be improved by torrefaction. This study optimized the co-torrefaction of fungus bran and polypropylene (PP) waste plastic to obtain clean solid biofuel with high calorific value and low ash content (AC) using response surface methodology. Two sets of mixed biochars were investigated using a multiobjective optimization method: mass yield–higher heating value–ash content (MY-HHV-AC) and energy yield–ash content (EY-AC). PP increased the heat value, decreased AC, and acted as a binder. The optimal operating conditions regarding reaction temperature, reaction time, and PP blending ratio were 230.68 °C, 30 min, and 20%, respectively, for the MY-HHV-AC set and 220 °C, 30 min, 20%, respectively, for the EY-AC set. The MY-HHV-AC set had properties close to those of peat and lignite. Furthermore, compared with that of the pure biochar, the AC of the two sets decreased by 15.71% and 14.88%, respectively, indicating that the prepared mixed biochars served as ideal biofuels. Finally, a circular economy framework for biobriquette fuel was proposed and prospects for preparing pellets provided.

Habitat quality (HQ) is of great significance to regional sustainable development, which is a key link for regional ecological security and human welfare. Assessing the temporal and spatial evolution of HQ caused by land-use change could provide a scientific basis for regional ecological protection and land management. Here, based on the ArcGIS platform and the InVEST model, this study quantified the spatial and temporal evolutions of land use, landscape patterns, and HQ from 1985 to 2015, in Taihu Lake Basin (TLB). Hotspot analysis tools were used to identify the spatial agglomeration and evolution characteristics of HQ in TLB. The results showed that, (1) the land use and landscape pattern in TLB experienced dramatic change process during 1985–2015, with the dominating conversion being from farmland to construction land, which led to an increase in landscape heterogeneity and fragmentation. (2) The HQ was generally reduced in the past 30 years. In particular, the decline of HQ was extremely severe in the peripheral area of cities and roads, due to urban sprawl. (3) Regarding the spatial distribution of HQ, the northern and eastern areas were generally higher, while the western and southern areas were generally lower. The hotspots areas were mainly located in the southwestern mountain area and west-central lake area, while the cold spots areas were mainly located in urban areas in the north and the east. (4) The area and degree of habitat degradation were both increased significantly due to land-use change, and the degradation of the Taihu Lake was highlighted. Strengthening the ecological environment management and reducing the threat of urban expansion to the HQ is urgently required. This study could help understand HQ of the TLB and provide a scientific basis for decision-makers.