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As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

PurposeTo develop a patient-reported outcome measure to assess the impact of glaucoma and treatment, including minimally invasive glaucoma surgery (MIGS).DesignObservational study before and after concomitant cataract and Food and Drug Administration-approved implantable MIGS device surgery.SettingSurvey administration was on a computer, iPad, or similar device.Patient population184 adults completed the baseline survey, 124 a survey 3 months after surgery, and 106 the 1-month test-retest reliability survey. The age range was 37 to 89 (average age = 72). Most were female (57%), non-Hispanic White (81%), and had a college degree (56%).Main outcome measuresThe Glaucoma Outcomes Survey (GOS) assesses functional limitations (27 items), vision-related symptoms (7 items), psychosocial issues (7 items), and satisfaction with microinvasive glaucoma surgery (1 item). These multiple-item scales were scored on a 0 to 100 range, with a higher score indicating worse health.ResultsInternal consistency reliability estimates ranged from 0.75 to 0.93, and 1-month test-retest intraclass correlations ranged from 0.83 to 0.92 for the GOS scales. Product-moment correlations among the scales ranged from 0.56 to 0.60. Improvement in visual acuity in the study eye from baseline to the 3-month follow-up was significantly related to improvements in GOS functional limitations (r = 0.18, P = .0485), vision-related symptoms (r = 0.19, P = .0386), and psychosocial concerns (r = 0.18, P = .0503). Responders to treatment ranged from 17% for vision-related symptoms to 48% for functional limitations.ConclusionsThis study supports using the GOS for ophthalmic procedures such as MIGS. Further evaluation of the GOS in different patient subgroups and clinical settings is needed.

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little forecast skill in the troposphere: individual ensemble members are mostly uncorrelated and span a range of atmospheric evolutions that are possible for the given set of external forcings. The uncertainty of such a probabilistic forecast is then determined by this range of possible evolutions – often quantified in terms of ensemble spread. Various dynamical processes can affect the ensemble spread within a given region, including extreme events simulated in individual members. For forecasts of geopotential height at 1000 hPa (Z1000) over Europe, such extremes are mainly comprised of synoptic storms propagating along the North Atlantic storm track. We use ECMWF reforecasts from the S2S database to investigate the connection between different storm characteristics and ensemble spread in more detail. We find that the presence of storms in individual ensemble members at S2S timescales contributes about 20 % to the total Z1000 forecast uncertainty over northern Europe. Furthermore, certain atmospheric conditions associated with substantial anomalies in the North Atlantic storm track show reduced Z1000 ensemble spread over northern Europe. For example, during periods with a weak stratospheric polar vortex, the genesis frequency of Euro-Atlantic storms is reduced and their tracks are shifted equatorwards. As a result, we find weaker storm magnitudes and lower storm counts, and hence anomalously low subseasonal ensemble spread, over northern Europe.

This study explores the autism knowledge and awareness of educators in Higher Education (HE) as well as their attitudes towards accommodating autistic students within their teaching. Semi-structured interviews were used to allow for in-depth investigation of this topic. Thematic analysis uncovered a striking dissonance between educators' positive attitudes towards accommodating autistic students and their actions in the classroom. A range of additional themes and subthemes interacted with the concept of dissonance. Even though participants self-described as having expert knowledge of autism, an attitude-behaviour gap was evident. Impact on inclusivity for autistic students is discussed, alongside recommended directions for future study and practice.

Abstract Population declines of the monarch butterfly (Danaus plexippus plexippus) in North America have largely been attributed to the distribution and condition of species‐specific preferred nectar sources. In 2020, the US Fish and Wildlife Service (USFWS) listed the monarch butterfly in the US Federal Register as a candidate species under the Endangered Species Act of 1973. The USFWS ranked the availability, quality, and spatial distribution of nectar plants during autumn migration as the fourth most contributing factor to US monarch population declines. During the autumn migration through the Great Plains, monarchs seek nectar plants to accumulate lipid reserves for further migration to and overwintering in Mexico. We applied vegetation and rangeland health data from the US Department of Agriculture, Natural Resources Conservation Service, National Resources Inventory (NRI) to quantify species density and richness of monarch‐preferred nectar plants, associated rangeland conditions, and diversity of nectar sources along this autumn migration pathway. We focused specifically on longitudinal gradients W‐095‐100 and W‐100‐105 and discrete 5° latitudinal–longitudinal cells within those gradients. The respective NRI dataset spans 8211 rangeland sites sampled between 2009 and 2018. Approximately 84.4% of sites in W‐095‐100 and 72.5% of sites in W‐100‐105 contained monarch‐preferred nectar plants. Preferred nectar plants made up 7.4% of 2438 identified plant species in W‐095‐100 and 6.1% of 2371 identified plant species in W‐100‐105. For W‐095‐100, preferred nectar plant densities were highest for the 5° cell covering portions of US states Oklahoma and Kansas and lowest for the 5° cell at the US–Mexico border. In W‐100‐105, preferred nectar plant densities decreased linearly from north to south. Preferred nectar plant densities were greater for 5° cells in W‐100‐105 (50.5 billion plants) as compared with W‐095‐100 (44.4 billion plants). Consistent with trends in preferred nectar source density, rangeland conditions assessed by similarity indices and rangeland health protocols were generally lowest for 5° cells spanning the US–Mexico border. The results provide the most comprehensive assessment to date for preferred nectar sources of the monarch butterfly along the Great Plains autumn migration to Mexico and document generally decreasing nectar sources and habitat conditions at southern latitudes in this ecologically important pathway.

Ridge tillage (RT) is a conservation practice that provides several benefits such as enhanced root growth and reduced soil erosion. The objectives of this study were to develop an autosteered living mulch seeder and hoeing prototype for RT systems using RTK-GNSS (real-time kinematic global navigation satellite systems) created ridges as a guide. It was also aimed to compare weed control efficacy and crop response of ridge-hoeing compared to conventional hoeing in flat tillage (FT). It was further aimed to investigate the impact of a new RT technology (with ridge re-compaction) on maize root development, yield, soil temperature, and moisture compared to FT.Field experiments were conducted with maize in 2021 and 2022 in a two-factorial split-plot design with tillage (RT and FT) as main treatment and weed control (untreated, herbicide, twice hoeing, hoeing + living mulch) as sub-treatment factors. Weed density, coverage, biomass, crop density, weed control efficacy (WCE) and maize silage yield were assessed. Temperature loggers were installed within RT and FT to take temperature readings at 20 min. Soil moisture and root penetrability were measured every two weeks in each plot using soil samples and a penetrometer.The WCE and yield did not differ significantly between the tillage systems. Twice hoeing resulted in 71–80 % WCE, which was equal to herbicide treatment. Hoeing + living mulch achieved 70–72 % WCE. Different from previous studies with ridge tillage, temperatures in the compacted ridges did not consistently differ from the ridge valleys and flat seedbeds. Root penetration (against 1.4 MPa penetrometer cone index) was 40 % higher in RT than in FT. On average, RT maize produced more (53.6 g m−2) root biomass compared to FT. In summary, re-compacted ridges built along RTK-GNSS lines can allow post-emergent hoeing and living mulch seeding along ridges and also provide good growing conditions for maize.