This paper showcases GeneGPT, a novel method for enabling LLMs to utilize the Web APIs of the NCBI to effectively address queries on genomics. The GeneTuring tests are resolved by Codex utilizing NCBI Web APIs, this resolution is achieved through in-context learning, and an enhanced decoding algorithm, capable of detecting and executing API calls. GeneGPT's experimental data on the GeneTuring benchmark highlights remarkable performance across eight tasks, achieving a strong average score of 0.83, substantially surpassing the performance of comparable models such as retrieval-augmented LLMs (e.g., the new Bing with 0.44), biomedical LLMs (e.g., BioMedLM with 0.08 and BioGPT with 0.04), GPT-3 (0.16) and ChatGPT (0.12). Our subsequent investigation suggests that (1) API demonstrations show strong generalizability across tasks, proving more helpful than documentation for in-context learning; (2) GeneGPT demonstrates the capacity to generalize to extended sequences of API calls and respond to complex multi-hop queries in GeneHop, a novel dataset introduced; (3) Various types of errors are prevalent in different tasks, offering valuable insights for future improvements.
The complex interactions and effects of competition are central to understanding species coexistence and biodiversity in ecological systems. Geometric analysis of Consumer Resource Models (CRMs) has, historically, been a crucial approach to this inquiry. This has resulted in generally applicable concepts, including Tilman's $R^*$ and species coexistence cones. This work extends the previous arguments by presenting a unique geometrical perspective on species coexistence, specifically using convex polytopes to describe the consumer preference space. We expose the capacity of consumer preference geometry to foresee species coexistence, to list stable ecological equilibrium points, and to delineate transitions among them. These findings collectively present a novel qualitative perspective on the relationship between species characteristics and ecosystem development, underpinned by niche theory.
Transcriptional processes frequently exhibit a pattern of on-and-off bursts, with periods of intense activity (ON) followed by periods of dormancy (OFF). The mystery of how transcriptional bursts are regulated to determine the precise spatial and temporal activity patterns still needs to be deciphered. Live transcription imaging, with single polymerase precision, is applied to study key developmental genes within the fly embryo. Avapritinib datasheet Quantifiable single-allele transcription rates and multi-polymerase bursts exhibit shared bursting phenomena among all genes, encompassing both temporal and spatial aspects, and considering cis- and trans-perturbations. The allele's ON-probability serves as the crucial determinant for the transcription rate, and the changes in the transcription initiation rate are relatively constrained. The likelihood of an ON state dictates a particular average ON and OFF duration, while maintaining a consistent characteristic burst duration. From our study, a convergence of regulatory processes is found to primarily affect the ON-state's likelihood, thereby controlling mRNA production, avoiding any mechanism-specific adjustment of the ON and OFF durations. Avapritinib datasheet The results we obtained thus motivate and facilitate new research into the mechanisms operating behind these bursting rules and managing transcriptional control.
Two orthogonal 2D kV images, captured at predefined oblique angles, are instrumental for patient alignment in some proton therapy facilities, given the absence of 3D imaging capabilities on the treatment table. kV images face a limitation in revealing tumors, given the reduction of the patient's three-dimensional body to a two-dimensional form; this effect is particularly pronounced when the tumor is positioned behind dense structures, like bone. This often leads to a significant margin of error in patient positioning. To resolve this, one can reconstruct the 3D CT image from the kV images taken at the treatment isocenter's position during the treatment procedure.
A vision-transformer-based, asymmetric autoencoder network was constructed. Data collection involved a single head and neck patient, utilizing 2 orthogonal kV images (resolution: 1024×1024 voxels), 1 3D CT scan with padding (512x512x512 voxels) acquired from the in-room CT-on-rails system pre-kV exposure, and 2 digitally-reconstructed radiographs (DRR) (512×512 voxels) created from the 3D CT. Our dataset, composed of 262,144 samples, was constructed by resampling kV images every 8 voxels and DRR/CT images every 4 voxels. Each image in the dataset had a dimension of 128 voxels in each direction. kV and DRR image data were both used in training, consequently stimulating the encoder's learning of a combined feature map from both types. Independent kV images were the sole images used during the testing procedures. Consecutive sCTs, derived from the model and possessing spatial context, were linked together to construct the full-size synthetic CT (sCT). Mean absolute error (MAE) and the per-voxel-absolute-CT-number-difference volume histogram (CDVH) were used to assess the image quality of the synthetic CT (sCT).
The model's speed clocked in at 21 seconds, while its mean absolute error (MAE) was below 40HU. The CDVH report concluded that a fraction of voxels, specifically less than 5%, experienced a per-voxel absolute CT number difference exceeding 185 Hounsfield Units.
3D CT images were effectively reconstructed from kV images using a patient-specific vision transformer network, exhibiting accuracy and efficiency in the process.
A network, specifically designed for each patient's anatomy using vision transformers, was developed and validated as accurate and efficient for reconstructing 3D CT images from lower-energy kV images.
Insight into the human brain's procedures for interpreting and processing information is significant. Using functional MRI, we examined the selectivity and individual variations in human brain responses to visual stimuli. Utilizing a group-level encoding model, our initial experiment uncovered that images predicted to reach maximal activation evoked stronger responses than images anticipated to achieve average activation, and this increase in activation was positively correlated with the accuracy of the encoding model. Consequently, aTLfaces and FBA1 experienced enhanced activation in response to maximal synthetic images, as opposed to maximal natural images. Our second experimental phase demonstrated that synthetic images produced by a personalized encoding model provoked a more substantial response compared to those created by group-level or other subjects' models. A further replication of the finding demonstrated aTLfaces' bias towards synthetic images as opposed to natural images. Our results demonstrate the prospect of employing data-driven and generative methods to control large-scale brain region activity, facilitating examination of inter-individual variations in the human visual system's functional specializations.
Models in cognitive and computational neuroscience trained on only one subject's data often fail to translate their findings to other individuals, which can be attributed to individual disparities. A neural converter, ideally designed for individual-to-individual transfer, is predicted to produce genuine neural signals of one person from another's signals, thereby resolving the issue of individual variations for both cognitive and computational models. We posit, in this study, a novel individual EEG converter, designated EEG2EEG, inspired by the analogous generative models that dominate the computer vision landscape. Employing the THINGS EEG2 dataset, we constructed and assessed 72 independent EEG2EEG models, each representing a unique pair from 9 subjects. Avapritinib datasheet The results unequivocally show that EEG2EEG adeptly learns the correspondence of neural representations in EEG signals between different subjects, achieving superior conversion outcomes. Beyond that, the EEG signals created reveal a more apparent and detailed portrayal of visual information in contrast to the data extracted from real-world sources. A new and advanced framework for neural conversion of EEG signals is presented in this method, enabling flexible and high-performance mapping between individual brains, thereby illuminating insights pertinent to both neural engineering and cognitive neuroscience.
A living being's relationship with its environment is fundamentally a matter of placing bets. Armed with a fragmented understanding of a probabilistic world, the entity must determine its next step or immediate tactic, an action that inevitably incorporates a model of the world, either explicitly or implicitly. By providing more robust environmental statistics, the accuracy of betting can be improved; nevertheless, practical limitations on information acquisition resources often persist. We contend that optimal inference theories suggest that models of 'complexity' are more challenging to infer with limited information, resulting in elevated prediction inaccuracies. Therefore, we advocate a principle of 'playing it safe,' wherein, considering limited capacity for information acquisition, biological systems ought to favor simpler models of reality, and consequently, less hazardous wagering approaches. The Bayesian prior dictates the optimal, safe adaptation strategy within the realm of Bayesian inference. Our research demonstrates that, in bacterial populations undergoing stochastic phenotypic switching, the utilization of our “playing it safe” principle results in an enhanced fitness (population growth rate) for the collective. We contend that the principle generally applies across problems of adaptation, learning, and evolution, illuminating the environments in which organisms can achieve their maximum potential.
Neocortical neuron spiking activity displays a remarkable degree of fluctuation, regardless of whether the networks are stimulated by identical inputs. The approximate Poissonian discharge of neurons suggests a hypothesis concerning the asynchronous operation of these neural networks. Independent firing of neurons characterizes the asynchronous state, making the likelihood of synchronous synaptic input to a single neuron exceptionally low.