Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and ...experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.
With the aim of providing student affairs practitioners and faculty with the tools they need to increase their comfort level and enable their ability to engage in discussions about belief both in and ...out of the classroom, the contributors provide foundational knowledge, concrete teaching ideas, sample activities, and case studies that can be used in a variety of settings.
IMPORTANCE: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) ...assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. OBJECTIVE: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit’s r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose “which response was better” and judged both “the quality of information provided” (very poor, poor, acceptable, good, or very good) and “the empathy or bedside manner provided” (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. RESULTS: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 17-62 words vs 211 168-245 words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. CONCLUSIONS: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.
Neural heterogeneity promotes robust learning Perez-Nieves, Nicolas; Leung, Vincent C H; Dragotti, Pier Luigi ...
Nature communications,
10/2021, Letnik:
12, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models ...which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.
Immunotherapy induces durable responses in a subset of patients with cancer. High tumor mutational burden (TMB) may be a response biomarker for PD-1/PD-L1 blockade in tumors such as melanoma and ...non-small cell lung cancer (NSCLC). Our aim was to examine the relationship between TMB and outcome in diverse cancers treated with various immunotherapies. We reviewed data on 1,638 patients who had undergone comprehensive genomic profiling and had TMB assessment. Immunotherapy-treated patients (
= 151) were analyzed for response rate (RR), progression-free survival (PFS), and overall survival (OS). Higher TMB was independently associated with better outcome parameters (multivariable analysis). The RR for patients with high (≥20 mutations/mb) versus low to intermediate TMB was 22/38 (58%) versus 23/113 (20%;
= 0.0001); median PFS, 12.8 months vs. 3.3 months (
≤ 0.0001); median OS, not reached versus 16.3 months (
= 0.0036). Results were similar when anti-PD-1/PD-L1 monotherapy was analyzed (
= 102 patients), with a linear correlation between higher TMB and favorable outcome parameters; the median TMB for responders versus nonresponders treated with anti-PD-1/PD-L1 monotherapy was 18.0 versus 5.0 mutations/mb (
< 0.0001). Interestingly, anti-CTLA4/anti-PD-1/PD-L1 combinations versus anti-PD-1/PD-L1 monotherapy was selected as a factor independent of TMB for predicting better RR (77% vs. 21%;
= 0.004) and PFS (
= 0.024). Higher TMB predicts favorable outcome to PD-1/PD-L1 blockade across diverse tumors. Benefit from dual checkpoint blockade did not show a similarly strong dependence on TMB.
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A robust system for automatic processing and assignment of raw
13
C and
1
H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. ...Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated
13
C and
1
H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at
https://github.com/KristapsE/DP4-AI
.
A robust system for automatic processing and assignment of raw
13
C and
1
H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow.
GIAO NMR shift calculation has been applied to the challenging task of reliably assigning stereochemistry with quantifiable confidence when only one set of experimental data are available. We have ...compared several approaches for assigning a probability to each candidate structure and have tested the ability of these methods to distinguish up to 64 possible diastereoisomers of 117 different molecules, using NMR shifts obtained in rapid and computationally inexpensive single-point calculations on molecular mechanics geometries without time-consuming ab initio geometry optimization. We show that a probability analysis based on the errors in each 13C or 1H shift is significantly more successful at making correct assignments with high confidence than are probabilities based on the correlation coefficient and mean absolute error parameters. Our new probability measure, which we have termed DP4, complements the probabilities obtained from our previously developed CP3 parameter, which applies to the case of assigning a pair of diastereoisomers when one has both experimental data sets. We illustrate the application of DP4 to assigning the stereochemistry or structure of 21 natural products that were originally misassigned in the literature or that required extensive synthesis of diastereoisomers to establish their stereochemistry.
The order Carnivora, which currently includes 296 species classified into 16 families, is distributed across all continents. The phylogeny and the timing of diversification of members of the order ...are still a matter of debate. Here, complete mitochondrial genomes were analysed to reconstruct the phylogenetic relationships and to estimate divergence times among species of Carnivora. We assembled 51 new mitogenomes from 13 families, and aligned them with available mitogenomes by selecting only those showing more than 1% of nucleotide divergence and excluding those suspected to be of low-quality or from misidentified taxa. Our final alignment included 220 taxa representing 2,442 mitogenomes. Our analyses led to a robust resolution of suprafamilial and intrafamilial relationships. We identified 21 fossil calibration points to estimate a molecular timescale for carnivorans. According to our divergence time estimates, crown carnivorans appeared during or just after the Early Eocene Climatic Optimum; all major groups of Caniformia (Cynoidea/Arctoidea; Ursidae; Musteloidea/Pinnipedia) diverged from each other during the Eocene, while all major groups of Feliformia (Nandiniidae; Feloidea; Viverroidea) diversified more recently during the Oligocene, with a basal divergence of Nandinia at the Eocene/Oligocene transition; intrafamilial divergences occurred during the Miocene, except for the Procyonidae, as Potos separated from other genera during the Oligocene.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Highlights • Vasopressin 1a receptor binding is diffuse and widespread in the titi monkey brain. • Oxytocin receptor binding is much more limited, with the densest binding in the hippocampus. • There ...is considerable interspecies variation in neuropeptide receptor expression in primates. • ALS-II-69 is a selective antagonist for the titi monkey oxytocin receptor. • SR49059 is a selective antagonist for the titi monkey vasopressin 1a receptor.
•This study employs spatial filtering of occurrence data.•The aim was to reduce overfitting to sampling bias in ecological niche models.•We quantified overfitting and model performance.•Spatially ...filtered models showed lower overfitting and better performance.
This study employs spatial filtering of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this issue, we used Maxent, bioclimatic variables, and occurrence localities of a broadly distributed Malagasy tenrec, Microgale cowani (Tenrecidae: Oryzorictinae). We modeled the abiotically suitable area of this species using three distinct datasets: unfiltered, spatially filtered, and rarefied unfiltered localities. To quantify overfitting and model performance, we calculated evaluation AUC, the difference between calibration and evaluation AUC (=AUCdiff), and omission rates. Models made with the filtered dataset showed lower overfitting and better performance than the other two suites of models, having lower omission rates and AUCdiff, and a higher AUCevaluation. Additionally, the rarefied unfiltered dataset performed better than the unfiltered one for three evaluation metrics, likely because the larger one reinforced the biases. These results indicate that spatial filtering of occurrence localities may allow biogeographers to produce better models.
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