Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.

Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.

An astonishing regularity in student learning rate.

Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or, do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial prepractice performance, at about 65% accuracy. Despite being in the same course, students' initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.

Another look at rational torsion of modular Jacobians.

We study the rational torsion subgroup of the modular Jacobian [Formula: see text] for N a square-free integer. We give a proof of a result of Ohta on a generalization of Ogg's conjecture: For a prime number [Formula: see text], the p-primary part of the rational torsion subgroup equals that of the cuspidal subgroup. Whereas previous proofs of this result used explicit computations of the cardinalities of these groups, we instead use their structure as modules for the Hecke algebra.

Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient.

Local adaptation is commonly cited to explain species distribution, but how fitness varies along continuous geographical gradients is not well understood. Here, we combine thermal biology and life-history theory to demonstrate that Drosophila populations along a 2500 km latitudinal cline are adapted to local conditions. We measured how heat tolerance and viability rate across eight populations varied with temperature in the laboratory and then simulated their expected cumulative Darwinian fitness employing high-resolution temperature data from their eight collection sites. Simulations indicate a trade-off between annual survival and cumulative viability, as both mortality and the recruitment of new flies are predicted to increase in warmer regions. Importantly, populations are locally adapted and exhibit the optimal combination of both traits to maximize fitness where they live. In conclusion, our method is able to reconstruct fitness surfaces employing empirical life-history estimates and reconstructs peaks representing locally adapted populations, allowing us to study geographic adaptation in silico.

Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature.

Rate-temperature scaling relationships have fascinated biologists for nearly two centuries and are increasingly important in our era of global climate change. These relationships are hypothesized to originate from the temperature-dependent kinetics of rate-limiting biochemical reactions of metabolism. Several prominent theories have formalized this hypothesis using the Arrhenius model, which characterizes a monotonic temperature dependence using an activation energy E. However, the ubiquitous unimodal nature of biological temperature responses presents important theoretical, methodological, and conceptual challenges that restrict the promise for insight, prediction, and progress. Here we review the development of key hypotheses and methods for the temperature-scaling of biological rates. Using simulations, we examine the constraints of monotonic models, illustrating their sensitivity to data nuances such as temperature range and noise, and their tendency to yield variable and underestimated E, with critical consequences for climate change predictions. We also evaluate the behaviour of two prominent unimodal models when applied to incomplete and noisy datasets. We conclude with recommendations for resolving these challenges in future research, and advocate for a shift to unimodal models that better characterize the full range of biological temperature responses.

Is the voltage-gated sodium channel ß3 subunit (SCN3B) a biomarker for glioma?

Recent studies suggest a need for reliable biomarkers enhancing prognosis prediction and treatment strategies in cancer. Here, we performed a data analysis bearing on the expression of SCN3B, voltage-gated sodium channel (VGSC) ß3 subunit, as a possible candidate for the development of a glioma biomarker for the first time. This extends our previous review article that mentioned the potential of SCN3B as a prognostic biomarker for glioma survival, further examining its association with existing indicators and immune responses. We utilized clinical and genomic data from multiple glioma cohorts. These include the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We employed analytical techniques including time-dependent receiver operating characteristic (ROC) analysis, decision curves analysis (DCA), and correlation studies with immune checkpoint markers. Our findings indicate a differential SCN3B expression between glioma grades, and that this significantly correlates with patient survival, particularly in oligodendroglioma subtypes. The DCA curves suggested that the inclusion of SCN3B in the prognostic model would improve decision-making in these subtypes. Moreover, SCN3B expression positively correlated with the presence of key immune cells and negatively correlated with several immune checkpoint inhibitors. This suggests potential roles in modulating immune responses in glioma. Thus, SCN3B emerges as a promising potential prognostic biomarker for glioma, especially for oligodendroglioma. Its dual correlations with prognosis and immune regulation present a compelling case for further experimental and clinical investigations to establish its utility in enhancing glioma management strategies. These findings underscore the importance of integrating novel biomarkers with traditional prognostic models to refine treatment paradigms and improve patient outcomes.

Uncovering Broad Macroecological Patterns by Comparing the Shape of Species' Distributions along Environmental Gradients.

AbstractSpecies' distributions can take many different forms. For example, fat-tailed or skewed distributions are very common in nature, as these can naturally emerge as a result of individual variability and asymmetric environmental tolerances, respectively. Studying the basic shape of distributions can teach us a lot about the ways climatic processes and historical contingencies shape ecological communities. Yet we still lack a general understanding of how their shapes and properties compare to each other along gradients. Here, we use Bayesian nonlinear models to quantify range shape properties in empirical plant distributions. With this approach, we are able to distil the shape of plant distributions and compare them along gradients and across species. Studying the relationship between distribution properties, we revealed the existence of broad macroecological patterns along environmental gradients-such as those expected from Rapoport's rule and the abiotic stress limitation hypothesis. We also find that some aspects of the shape of observed ranges-such as kurtosis and skewness of the distributions-could be intrinsic properties of species or the result of their historical contexts. Overall, our modeling approach and results untangle the general shape of plant distributions and provide a mapping of how this changes along environmental gradients.

Clinical effectiveness and safety of olaparib in BRCA-mutated, HER2-negative metastatic breast cancer in a real-world setting: final analysis of LUCY.

PURPOSE: The interim analysis of the phase IIIb LUCY trial demonstrated the clinical effectiveness of olaparib in patients with germline BRCA-mutated (gBRCAm), human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (mBC), with median progression-free survival (PFS) of 8.11 months, which was similar to that in the olaparib arm of the phase III OlympiAD trial (7.03 months). This prespecified analysis provides final overall survival (OS) and safety data. METHODS: The open-label, single-arm LUCY trial of olaparib (300 mg, twice daily) enrolled adults with gBRCAm or somatic BRCA-mutated (sBRCAm), HER2-negative mBC. Patients had previously received a taxane or anthracycline for neoadjuvant/adjuvant or metastatic disease and up to two lines of chemotherapy for mBC. RESULTS: Of 563 patients screened, 256 (gBRCAm, n = 253; sBRCAm, n = 3) were enrolled. In the gBRCAm cohort, median investigator-assessed PFS (primary endpoint) was 8.18 months and median OS was 24.94 months. Olaparib was clinically effective in all prespecified subgroups: hormone receptor status, previous chemotherapy for mBC, previous platinum-based chemotherapy (including by line of therapy), and previous cyclin-dependent kinase 4/6 inhibitor use. The most frequent treatment-emergent adverse events (TEAEs) were nausea (55.3%) and anemia (39.2%). Few patients (6.3%) discontinued olaparib owing to a TEAE. No deaths associated with AEs occurred during the study treatment or 30-day follow-up. CONCLUSION: The LUCY patient population reflects a real-world population in line with the licensed indication of olaparib in mBC. These findings support the clinical effectiveness and safety of olaparib in patients with gBRCAm, HER2-negative mBC. CLINICAL TRIAL REGISTRATION: Clinical trials registration number: NCT03286842.

Cool-edge populations of the kelp Ecklonia radiata under global ocean change scenarios: strong sensitivity to ocean warming but little effect of ocean acidification.

Kelp forests are threatened by ocean warming, yet effects of co-occurring drivers such as CO2 are rarely considered when predicting their performance in the future. In Australia, the kelp Ecklonia radiata forms extensive forests across seawater temperatures of approximately 7-26°C. Cool-edge populations are typically considered more thermally tolerant than their warm-edge counterparts but this ignores the possibility of local adaptation. Moreover, it is unknown whether elevated CO2 can mitigate negative effects of warming. To identify whether elevated CO2 could improve thermal performance of a cool-edge population of E. radiata, we constructed thermal performance curves for growth and photosynthesis, under both current and elevated CO2 (approx. 400 and 1000 µatm). We then modelled annual performance under warming scenarios to highlight thermal susceptibility. Elevated CO2 had minimal effect on growth but increased photosynthesis around the thermal optimum. Thermal optima were approximately 16°C for growth and approximately 18°C for photosynthesis, and modelled performance indicated cool-edge populations may be vulnerable in the future. Our findings demonstrate that elevated CO2 is unlikely to offset negative effects of ocean warming on the kelp E. radiata and highlight the potential susceptibility of cool-edge populations to ocean warming.

Hemoglobin Threshold for Blood Transfusion in Young Children Hospitalized with Iron Deficiency Anemia.

Current recommendations advise against blood transfusion in hemodynamically stable children with iron deficiency anemia. In an observational study of 125 children aged 6 through 36 months, hospitalized with iron deficiency anemia, we found that hemoglobin level predicted red blood cell transfusion (area under the curve 0.8862). A hemoglobin of 39 g/L had sensitivity 92% and specificity 72% for transfusion.