Structure of mushy layers grown from perfectly and imperfectly conducting boundaries. Part 1. Diffusive solidification

We model transient mushy-layer growth for a binary alloy solidifying from a cooled boundary, characterising the impact of liquid composition and thermal growth conditions on the mush porosity and growth rate. We consider cooling from a perfectly conducting isothermal boundary, and from an imperfectly conducting boundary governed by a linearised thermal boundary condition. For an isothermal boundary we characterise different growth regimes depending on a concentration ratio, which can also be viewed as characterising the ratio of composition-dependent freezing point depression versus the temperature difference across the mushy layer. Large concentration ratio leads to high porosity throughout the mushy layer and an asymptotically simplified model for growth with an effective thermal diffusivity accounting for latent heat release from internal solidification. Low concentration ratio leads to low porosity throughout most of the mushy layer, except for a high-porosity boundary layer localised near the mush–liquid interface. We identify scalings for the boundary-layer thickness and mush growth rate. An imperfectly conducting boundary leads to an initial lag in the onset of solidification, followed by an adjustment period, before asymptoting to the perfectly conducting state at large time. We develop asymptotic solutions for large concentration ratio and large effective heat capacity, and characterise the mush structure, growth rate and transition times between the regimes. For low concentration ratio the high porosity zone spans the full mush depth at early times, before localising near the mush–liquid interface at later times. Such variation of porosity has important implications for the properties and biological habitability of mushy sea ice

Prediabetes and risk of all-cause and cause-specific mortality: a prospective study of 114,062 adults in Mexico City

Background Prediabetes has been associated with increased all-cause and cardiovascular mortality. However, no large-scale studies have been conducted in Mexico or Latin America examining these associations. Methods We analyzed data from 114,062 adults without diabetes (diagnosed or undiagnosed) from the Mexico City Prospective Study. Participants were followed until January 1st, 2021, for cause-specific mortality. We defined prediabetes according to the American Diabetes Association (ADA, HbA1c ≥5.7% to <6.5%) and the International Expert Committee (IEC, HbA1c ≥6.0 to <6.5%) definitions. Cox regression adjusted for confounders was used to estimate all-cause and cause-specific mortality rate ratios (RR) for deaths occurring at ages 35-74 years associated with prediabetes. Results After 18.4 (IQR 17.6-19.7) median years’ follow-up, individuals with prediabetes had higher risk of all-cause mortality at ages 35-74 compared to those without prediabetes (RR 1.13 [1.07-1.20] for ADA-defined and 1.27 [1.17-1.38] for IEC-defined prediabetes), as well as higher risk of cardiovascular (RR 1.23 [1.11-1.37] and 1.44 [1.24-1.67], respectively), renal (RR 1.33 [1.06-1.66] and 1.62 [1.18-2.23], respectively), and acute diabetic deaths (RR 2.62 [1.75-3.93] and 3.42 [2.09-5.61], respectively). The absolute excess risk associated with ADA-defined prediabetes at ages 35-74 accounted for 7% of cardiovascular, 9% of renal, and 31% of acute diabetic deaths. IEC-defined prediabetes accounted for 4%, 5% and 14% of cardiovascular, renal, and acute diabetic deaths. Prediabetes-associated excess mortality risks were, at least in part, explained by adiposity. Conclusion Prediabetes is a significant risk factor for all-cause, cardiovascular, renal, and acute diabetic deaths in Mexican adults. Early identification and timely management of prediabetes among individuals at risk of this condition could reduce premature mortality in this population

Immersion patterns alone can predict vessel following by albatrosses

Many pelagic seabird species are threatened by bycatch in fisheries. Bycatch risk assessments benefit from quantifying the frequency, duration, and location of individual seabird interactions with fishing vessels. However, proximity-based interaction analyses are limited by the availability and spatiotemporal resolution of bird and vessel tracking data. Here, we examined whether patterns in seabird landing and take-off behaviour (immersion) derived from GLS-immersion loggers (0.167 Hz) can detect vessel interactions when tracking data are lacking or incomplete. We identified closeproximity seabird-vessel interactions by spatiotemporally matching high-resolution GPS data (0.02-1 Hz) from 45 black-browed albatrosses (Thalassarche melanophris) to Automatic Identification System (AIS) data from trawler vessels. We used random forest models to investigate whether immersion patterns alone could distinguish these vessel interactions from natural foraging behaviours. We observed multiple seabird-vessel interaction types, with active vessel “following” (with multiple landings) comprising only 59% of discrete interaction events. Other interaction types included “stopping” (with 1 landing) and “passing” (with 0 landings). Using immersion patterns alone, we could distinguish vessel following in >80% of both foraging timesteps and discrete foraging bouts, with false positive vessel following detections totalling <10% of true positives. We found that GLS-immersion loggers sometimes remain wet following take-off, leading to inflated durations of on-water periods. However, leaving this error uncorrected only slightly reduced the performance of our random forest models. Policy implications. We demonstrate that seabird immersion patterns alone can detect high-risk seabird-vessel interactions, even in the absence of locational data for both seabirds and vessels. Our approach could allow for more comprehensive seabird bycatch risk assessments that quantify previously hidden seabird-vessel interactions, such as those involving migratory life history stages and illegal, unreported, or unregulated (IUU) fishing vessels

Local mapping of the nanoscale viscoelastic properties of fluid membranes by AFM nanorheology

Biological membranes are intrinsically dynamic entities that continually adapt their biophysical properties and molecular organisation to support cellular function. Current microscopy techniques can derive high-resolution structural information of labelled molecules but quantifying the associated viscoelastic behaviour with nanometre precision remains challenging. Here, we develop an approach based on atomic force microscopy in conjunction with fast nano-actuators to map the viscoelastic response of unlabelled supported membranes with nanometre spatial resolution. On fluid membranes, we show that the method can quantify local variations in the molecular mobility of the lipids and derive a diffusion coefficient. We confirm our experimental approach with molecular dynamics simulations, also highlighting the role played by the water at the interface with the membrane on the measurement. Probing ternary model bilayers reveals spatial correlations in the local diffusion over distances of ≈20 nm within liquid disordered domains. This lateral correlation is enhanced in native bovine lens membranes, where the inclusion of protein-rich domains induces four-fold variations in the diffusion coefficient across < 100 nm of the fluid regions, consistent with biological function. Our findings suggest that diffusion is highly localised in fluid biomembranes

Interactive Panel Summaries of the 2024 Voice AI Symposium

The 2024 Voice AI Symposium presented by the Bridge2AI-Voice Consortium, was a 2-day event which took place May 1st-May 2nd in Tampa, FL. The event included four interactive panel sessions, which are summarized here. All four interactive panels featured an innovative format, designed to maximize engagement and facilitate deep discussions. Each panel began with a 45 min segment where moderators posed targeted questions to expert panelists, delving into complex topics within the field of voice AI. This was followed by a 45 min “stakeholder forum,” during which audience members asked questions and engaged in live interactive polls. Interactive polls stimulated meaningful conversation between panelists and attendees, and brought to light diverse viewpoints. Workshops were audio recorded and transcripts were assembled with assistance from generative A.I tools including Whisper Version 7.13.1 for audio transcription and ChatGPT version 4.0 for content summation. Content was then reviewed and edited by authors

The multi-dimensional environmental impact of global crop commodities

Agriculture is one of the leading causes of detrimental environmental impacts, including greenhouse gas emissions, biodiversity loss and depletion of freshwater resources. Such impacts can be assessed by environmental sustainability indices; however, limitations in current indicators necessitate the development of more robust and standardized crop-specific environmental sustainability indices. Here we developed the crop environmental sustainability index (PLANTdex), a spatially explicit index (5 arcmin resolution) quantifying crop production’s environmental impacts. PLANTdex includes globally standardized indicators of environmental impacts assessing water stress and biodiversity loss via five emissions pathways: greenhouse gas emissions, water consumption, land occupation, and nitrogen and phosphorus fertilizer application. We applied PLANTdex to 16 crops, revealing high variability across production system efficiencies, crop types and local context environmental sensitivities. Globally and nationally, no clear correlation emerged between PLANTdex scores and crop production, but stronger correlations were evident at finer spatial scales and for individual crops. Sugar cane showed the strongest negative correlation (low impacts in high-production areas), while oil palm had the strongest positive correlation (high impacts in high-production areas), highlighting the importance of sub-national, crop-specific assessments. PLANTdex’s spatial resolution and crop specificity make it valuable for initiatives such as the Taskforce on Nature-related Financial Disclosures and corporate sustainability strategies

A graph-theoretic framework for free-parafermion solvability

We present a graph-theoretic characterization of when a quantum spin model admits an exact solution via a mapping to free parafermions. Our characterization is based on the concept of a frustration graph, which represents the commutation relations between Weyl operators of a Hamiltonian. We show that a quantum spin system has an exact free-parafermion solution if its frustration graph is an oriented indifference graph. Furthermore, we show that if the frustration graph of a model can be dipath oriented via switching operations, then the model is integrable in the sense that there is a family of commuting independent set charges. Additionally, we establish an efficient algorithm for deciding whether this is possible. Our characterization extends that given for free-fermion solvability. Finally, we apply our results to solve three qudit spin models

Loss of the NF-κB negative regulator Pirk in Drosophila links brain and gut immunity to neurodegeneration

A gut–brain axis influenced by host innate immunity and resident microbiota has been implicated in neurological conditions including Alzheimer’s disease. However, the precise connection of innate immunity to Alzheimer’s disease remains unclear. Using Pirk, a negative regulator of the IMD/NF-κB pathway in Drosophila, we studied the neurological phenotypes induced when genetically predisposing flies to chronically over-active immunity. Pirk mutants exhibited age-dependent neurological phenotypes such as reduced locomotion and altered sleep patterns coupled to an increased number of brain lesions. Gut-specific pirk-RNA interference led to earlier onset of the neurological phenotypes which, alongside changes in intestinal bacteria in pirk mutants, highlighted a potential early role for the intestinal ecosystem in the onset of neurodegeneration. In contrast, glia-specific RNA interference of pirk resulted in late onset of the relevant phenotypes suggesting a later contribution of the nervous system to the underlying neuropathology. Knockout of the antimicrobial peptide (AMP) gene AttacinD or rearing flies in axenic conditions recovered some of the neurological phenotypes, suggesting both chronic AMP gene expression as well as gut bacteria changes as mediators. Our results indicate an evolutionarily conserved path to neurodegeneration linked to dysregulated immunity. They also reveal that in this context, age-dependent neurodegeneration can happen in less complex non-vertebrate brains in the absence of beta-amyloid or tau aggregation

Integrating TSPO-PET imaging with metabolomics for enhanced prognostic accuracy in multiple sclerosis

Background: Predicting disease progression in multiple sclerosis (MS) remains challenging. PET imaging with 18 kDa translocator protein (TSPO) radioligands can detect microglial and astrocyte activation beyond MRI-visible lesions, which has been shown to be highly predictive of disease progression. We previously demonstrated that nuclear magnetic resonance (NMR)-based metabolomics could accurately distinguish between relapsing-remitting (RRMS) and secondary progressive MS (SPMS). This study investigates whether combining TSPO imaging with metabolomics enhances predictive accuracy in a similar setting. Methods: Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand 11C-PK11195 in Finland. Patient disability was assessed using the expanded disability status scale (EDSS) at baseline and 1 year later. Serum metabolomics was performed to identify biomarkers associated with TSPO binding and disease progression. Results: Greater TSPO availability in the normal-appearing white matter and perilesional regions correlated with higher EDSS. Serum metabolites glutamate (p=0.02), glutamine (p=0.006), and glucose (p=0.008), detected by NMR, effectively distinguished future progressors. These three metabolites alone predicted progression with the same accuracy as TSPO-PET imaging (AUC 0.78; p=0.0001), validated in an independent cohort. Combining serum metabolite data with PET imaging significantly improved predictive power, achieving an AUC of 0.98 (p<0.0001). Conclusion: Measuring three specific serum metabolites is as effective as TSPO imaging in predicting MS progression. However, integrating TSPO imaging with serum metabolite analysis substantially enhances predictive accuracy. Given the simplicity and affordability of NMR analysis, this approach could lead to more personalised, accessible treatment strategies and serve as a valuable tool for clinical trial stratification