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A well-known algorithm for unknotting knots involves traversing a knot diagram and changing each crossing that is first encountered from below. The minimal number of crossings changed in this way across all diagrams for a knot is called the ascending number of the knot. The ascending number is bounded below by the unknotting number. We show that for knots obtained as the closure of a positive braid, the ascending number equals the unknotting number. We also present data indicating that a similar result may hold for positive knots. We use this data to examine which low-crossing knots have the property that their ascending number is realized in a minimal crossing diagram, showing that there are at most 5 hyperbolic, alternating knots with at most 12 crossings with this property. 

We developed a new Citizen Science Undergraduate Research Experience that engages two-year college students in application of the scientific method in order to increase STEM participation, interest, and skills. The research experience was designed to leverage the local, flood-prone urban setting of the two-year college with the class structure and flexibility of an 8-week course to provide students with diverse academic backgrounds and interests the opportunity to conduct an authentic research project on the consequences of flooding in central Texas. We describe our adaptable Citizen Science Research Experience course model and survey and assessment-based evaluation methods. Participation in the research experience is associated with higher rates of student success in terms of transfer and graduation rates, contributing to increased STEM retention. The research experience also increased student confidence in using the scientific method, formulating a research question, and working with numerical data, which support gains in science skills and integration into STEM culture. Close faculty mentoring contributed to the success of this course-based research experience. We recommend increasing instruction efficiency, structuring the course as a 2-term elective science credit course, and securing consistent faculty funding as ways to improve this and similar undergraduate research experiences at 2-year colleges. 

Small molecule gases such as nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H₂S) have long been recognized as endogenous signaling molecules with diverse physiological roles. Often described as “gasotransmitters”, these molecules complement other small molecule bioregulators (SMBs) that exert biological function across all kingdoms of life. One underappreciated distinction, however, is that many of these molecules – irrespective of whether or not they are gases in their native states outside of biology – exhibit similar molecular signaling potential mediated by protonation-dependent chemical speciation. In this review, we propose the new cross-cutting classification of protic small molecule bioregulators (PSMBs) to describe molecules in which biological function and reactivity are modulated by protonation state. Examples of PSMBs include the canonical gasotransmitter H2S, emerging gasotransmitters (H2Se, HCN), small molecule crosstalk species (e.g., SNO–, SSNO–, SO42–, ONOO–, NO2–, SCN–, OCl–), and other species where protonation state modulation is accessible at physiological pH. Importantly, these species exist in equilibrium between their neutral and anionic forms, with speciation governed by local pH and molecular environment, directly impacting their membrane nucleophilicity, permeability, redox activity, and interaction with metal centers. We describe the evolutionary origins, biosynthesis, and crosstalk of PSMBs, including roles in redox signaling, post-translational modification, and mitochondrial regulation. Reframing these important molecules in a class defined by their protic ability rather than gaseous state does not diminish prior gasotransmitter designations, but rather serves to recognize commonalities in chemical characteristics that drive the unique biological chemistry and regulation. 

ABSTRACT Messenger ribonucleoprotein (mRNP) complexes assemble co‐transcriptionally in the nucleus as RNA‐binding proteins (RBPs) engage nascent transcripts. Ongoing RNA processing and RBP dynamics generate a diverse set of mRNPs, often producing a mature mRNA—capped, spliced, and polyadenylated—within a compact mRNP particle poised for nuclear export. The processing, packaging, and export of nuclear mRNPs are tightly regulated to ensure the fidelity of gene expression and to reprogram cellular function under changing organismal and environmental conditions. Understanding the compositional and organizational dynamics of nuclear mRNP assembly and maturation is essential, as dysregulation is linked to viral infections and a range of human diseases, including neurological disorders and cancer. Recent structural, biochemical, and in‐cell studies have revealed key roles for the evolutionarily conserved Yra1/ALYREF proteins and the TRanscription‐EXport (TREX) complex in mRNP packaging and export, highlighting broadly conserved functions across eukaryotes. While many questions remain, these advances have deepened our understanding of nuclear mRNA metabolism and offer new opportunities to investigate how disruptions in mRNA biogenesis and export factors, and their associated processes, contribute to disease. This article is categorized under:RNA Interactions with Proteins and Other Molecules > RNA‐Protein ComplexesRNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional ImplicationsRNA Export and Localization > Nuclear Export/Import 

This study proposes an intelligent techno-economic assessment framework for wind energy end users, using a novel dual-input convolutional bidirectional long short-term memory (Dual-ConvBiLSTM) architecture to predict dynamic levelized cost of energy (LCOE). The proposed architecture separates weight matrices for wind supervisory control and data acquisition (SCADA) data and financial data. This allows the model to integrate both data streams at every time step through a custom dual-input cell. This approach is compared with five baseline architectures: Recurrent Neural Network (RNN), LSTM, BiLSTM, ConvLSTM, and ConvBiLSTM, which process data through separate parallel branches and concatenate outputs before final prediction. The Dual-ConvBiLSTM achieves an LCOE estimate of $4.0391 cents/kWh, closest to the actual value of $4.0450 cents/kWh, with a root mean squared error reduction of 51.8% compared to RNN, 47.0% to LSTM, 40.0% to BiLSTM, 36.7% to ConvLSTM, and 34.4% to ConvBiLSTM, demonstrating superior capability in capturing complex interactions between SCADA data and financial parameters. This intelligent framework potentially enhances economic assessment and enables end users to accelerate renewable energy deployment through more reliable financial prediction.