Just five U.S. universities have trained 1-in-8 tenure-track faculty members serving at the nation’s institutions of higher learning, according to new CU �鶹ӰԺ research.

The study, , takes the most exhaustive look yet at the structure of the American professoriate—capturing data on nearly 300,000 tenure-track faculty (including where they received their own graduate degrees) at more than 10,000 university departments at 368 PhD-granting institutions from 2011 to 2020.

The study reveals that in all fields of academia, most professors come from a small number of institutions.

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Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.

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We have known for decades that long noncoding RNAs (lncRNAs) can play essential functions across most forms of life. The maintenance of chromosome length requires an lncRNA (e.g., hTERC) and two lncRNAs in the ribosome that are required for protein synthesis. Thus, lncRNAs can represent powerful RNA machines. More recently, it has become clear that mammalian genomes encode thousands more lncRNAs. Thus, we raise the question: Which, if any, of these lncRNAs could also represent RNA-based machines? Here we synthesize studies that are beginning to address this question by investigating fundamental properties of lncRNA genes, revealing new insights into the RNA structure–function relationship, determining cis- and trans-acting lncRNAs in vivo, and generating new developments in high-throughput screening used to identify functional lncRNAs. Overall, these findings provide a context toward understanding the molecular grammar underlying lncRNA biology.

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Genome-wide sequencing has led to the discovery of thousands of long non-coding RNA (lncRNA) loci in the human genome, but evidence of functional significance has remained controversial for many lncRNAs. Genetically engineered model organisms are considered the gold standard for linking genotype to phenotype. Recent advances in CRISPR–Cas genome editing have led to a rapid increase in the use of mouse models to more readily survey lncRNAs for functional significance. Here, we review strategies to investigate the physiological relevance of lncRNA loci by highlighting studies that have used genetic mouse models to reveal key in vivo roles for lncRNAs, from fertility to brain development. We illustrate how an investigative approach, starting with whole-gene deletion followed by transcription termination and/or transgene rescue strategies, can provide definitive evidence for the in vivo function of mammalian lncRNAs.

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Many of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to biology. Here, we present massively parallel RNA assay combined with immunoprecipitation (MPRNA-IP) for in vivo high-throughput dissection of RNA–protein interactions and describe statistical models for identifying RNA domains and parsing the structural contributions of RNA. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, MPRNA-IP is able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest, including the long noncoding RNA NORAD, bacteriophage MS2 RNA, and human telomerase RNA, and we use it to interrogate the hitherto unknown sequence or structural RNA-binding preferences of the DNA-looping factor CTCF. By integrating systematic mutation analysis with crosslinking immunoprecipitation, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA–protein interactions in vivo.

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Purpose of Review

Bone mineral density and systemic factors are used to assess skeletal health in astronauts. Yet, even in a general population, these measures fail to accurately predict when any individual will fracture. This review considers how long-duration human spaceflight requires evaluation of additional bone structural and material quality measures that contribute to microgravity-induced skeletal fragility.

Recent Findings

In both humans and small animal models following spaceflight, bone mass is compromised via reduced bone formation and elevated resorption levels. Concurrently, bone structural quality (e.g., trabecular microarchitecture) is diminished and the quality of bone material is reduced via impaired tissue mineralization, maturation, and maintenance (e.g., mediated by osteocytes).

Summary

Bone structural and material quality are both affected by microgravity and may, together, jeopardize astronaut operational readiness and lead to increased fracture risk upon return to gravitational loading. Future studies need to directly evaluate how bone quality combines with diminished bone mass to influence bone strength and toughness (e.g., resistance to fracture). Bone quality assessment promises to identify novel biomarkers and therapeutic targets.

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Photopolymerizable semicrystalline thermoplastics resulting from thiol-ene polymerizations were formed via fast polymerizations and achieved excellent mechanical properties. These materials have been shown to produce materials desirable for additive manufacturing (3D printing), especially for recyclable printing and investment casting. However, while well-resolved prints were previously achieved with the thiol-ene thermoplastics, the remarkable elongation at break (ϵ_max) and toughness (T) attained in bulk were not realized for 3D printed components (ϵ_max,bulk ~ 790%, T_bulk ~ 102 MJ m^-3 vs. ϵ_max,print < 5%, T_print < 0.5 MJ m^-3). In this work, small concentrations (5-10 mol%) of a crosslinker were added to the original thiol-ene resin composition without sacrificing crystallization potential to achieve semicrystalline, covalently crosslinked networks with enhanced mechanical properties. Improvements in ductility and overall toughness were observed for printed crosslinked structures, and substantial mechanical augmentation was further demonstrated with post-manufacture thermal conditioning of printed materials above the melting temperature (T_m). In some instances, this thermal conditioning to reset the crystalline component of the crosslinked prints yielded mechanical properties that were comparable or superior to its bulk counterpart (ϵ_max ~ 790%, T ~ 95 MJ m^-3). These unique photopolymerizations and their corresponding monomer compositions exhibited concurrent polymerization and crystallization along with mechanical properties that were tunable by changes to the monomer composition, photopolymerization conditions, and post-polymerization conditioning. This is the first example of a 3D printed semicrystalline, crosslinked material with thermally tunable mechanical properties that are superior to many commercially-available resins.

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Hypothesis

Flocculation performance using polyelectrolytes is influenced by critical design parameters including molecular weight, amount and sign of the ionic charge, and polymer architecture. It is expected that systematic variation of these characteristics will impact not only flocculation efficiency (FE) achieved but that charge density and architecture, specifically, can alter the flocculation mechanism. Therefore, it should be possible to tune these design parameters for a desired flocculation application.

Experiments

Cationic-neutral and polyampholytic copolymers, exhibiting a range of molecular weights (10³–10⁶ g/mol), varying charge levels (0–100% cationic, neutral and anionic), and random or block copolymer architecture, were applied to dilute suspensions of silica microparticles (control) and Chlorella vulgaris. FE and zeta potential values were determined over a range of flocculant doses to evaluate effectiveness and mechanism achieved.

Findings

These different classes of copolymers provide specific benefits for flocculation, with many achieving >95% flocculation. Block copolymer flocculants exhibit a proposed, dominant bridging mechanism, therefore reducing flocculant dosage required for effective flocculation when compared to analogous random copolymer flocculants. Polyampholytic copolymers applied to C. vulgaris generally exhibited a bridging mechanism and increased FE compared to equivalent cationic-neutral copolymers, indicating a benefit of the anionic component on a more, complex, diversely charged suspension.

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Mechanical cues are delivered to resident cells by the extracellular matrix and play an important role in directing cell processes, ranging from embryonic development and cancer metastasis to stem cell differentiation. Recently, cellular responses to viscoelastic and elastic mechanical cues have been studied; however, questions remain as to how cells identify and transduce these cues differently. We present a synthetic cell culture substrate with viscoelastic properties based on thioester exchange chemistry that can be modulated in situ with the photoinitiated thiol-ene ‘click’ reaction. With this method, stress relaxation in thioester hydrogels with an average relaxation time of 740,000 s can be switched off in the presence of cells without change to the elastic modulus. NIH 3T3 fibroblasts, cultured for 48 h on viscoelastic compared to elastic thioester substrates, displayed increased cell area (660–560 μm²) and increased nuclear to cytoplasmic YAP/TAZ ratios (2.4 to 2.2) when cultured on elastic compared to viscoelastic hydrogels, respectively. Next, when the viscoelasticity was switched off after 24 h, the fibroblasts responded to this change and exhibited an average cell area of 540 μm², and nuclear to cytoplasmic YAP/TAZ ratio of 2.1, approaching that of the control elastic gels. Phototunable viscoelastic thioester hydrogels provide a tunable materials system to investigate time-dependent cellular responses to viscoelasticity and should prove useful for understanding the dynamics of mechanoresponsive cellular pathways.

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Ionic Zn²⁺ has increasingly been recognized as an important neurotransmitter and signaling ion in glutamatergic neuron pathways. Intracellular Zn²⁺ transiently increases as a result of neuronal excitation, and this Zn²⁺ signal is essential for neuron plasticity, but the source and regulation of the signal is still unclear. In this study, we rigorously quantified Zn²⁺, Ca²⁺, and pH dynamics in dissociated mouse hippocampal neurons stimulated with bath application of high KCl or glutamate. While both stimulation methods yielded Zn²⁺ signals, Ca²⁺ influx, and acidification, glutamate stimulation induced more sustained high intracellular Ca²⁺ and a larger increase in intracellular Zn²⁺. However, the stimulation-induced pH change was similar between conditions, indicating that a different cellular change is responsible for the stimulation-dependent difference in Zn²⁺ signal. This work provides the first robust quantification of Zn²⁺ dynamics in neurons using different methods of stimulation.

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