A novel method, GeneGPT, is presented in this paper to teach LLMs how to leverage NCBI's Web APIs for answering questions pertaining to genomics. Using in-context learning and an augmented decoding algorithm that recognizes and executes API calls, we prompt Codex to resolve the GeneTuring tests employing NCBI Web APIs. GeneGPT, evaluated on the GeneTuring benchmark, exhibited state-of-the-art performance across eight tasks, averaging 0.83. This decisively surpasses the performance of retrieval-augmented LLMs like Bing (0.44), biomedical LLMs like BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Further study indicates that (1) API demonstrations show significant cross-task generalizability, exceeding the usefulness of documentations for in-context learning; (2) GeneGPT demonstrates generalization to longer API call sequences and accurately answers multi-hop queries in the GeneHop dataset; (3) Varying types of errors are apparent in different tasks, providing valuable insight for future refinements.

Competition acts as a pivotal force that structures biodiversity and dictates the conditions for species coexistence. Historically, a prominent approach to this question has been the geometrical examination of Consumer Resource Models, or CRMs. From this, we derive broadly applicable principles, for example, Tilman's $R^*$ and species coexistence cones. To expand upon these arguments, we develop a novel geometric approach to understanding species coexistence, using convex polytopes within the consumer preference space. Using the geometric structure of consumer preferences, we illustrate the prediction of species coexistence, the identification of stable ecological steady states, and the description of transitions between these states. The implications of these results are profound, marking a qualitatively distinct understanding of how species traits contribute to ecosystem structure, particularly within the context of niche theory.

The process of transcription frequently involves cyclical bursts, transitioning between active (ON) and inactive (OFF) states. Despite the unknown mechanisms governing transcriptional bursts, the spatiotemporal regulation of transcriptional activity remains elusive. Utilizing live transcription imaging with single polymerase sensitivity, we examine key developmental genes in the fly embryo. Genomics Tools Quantifying single-allele transcription rates and multi-polymerase bursts demonstrates consistent bursting patterns throughout all genes, both temporally and spatially, while considering cis and trans perturbations. The allele's ON-probability constitutes the primary factor impacting the transcription rate, with variations in the transcription initiation rate possessing a less significant influence. A predefined ON probability uniquely defines the average ON and OFF periods, upholding a consistent bursting duration. Our study demonstrates that the convergence of diverse regulatory processes chiefly affects the probability of the ON-state, consequently influencing mRNA synthesis rather than modifying the ON and OFF duration of any particular mechanism. Software for Bioimaging Subsequently, our results encourage and direct future studies into the mechanisms behind these bursting rules and their influence on transcriptional regulation.

Two 2D, orthogonal kV X-ray images are utilized for patient alignment in certain proton therapy facilities, captured at fixed, oblique angles, as 3D imaging directly on the treatment bed isn't provided. The tumor's depiction in kV images is restricted because the three-dimensional structure of the patient is rendered onto a two-dimensional plane, significantly when the tumor is situated behind high-density regions, for example, bone. This factor can contribute to considerable mistakes in the patient's setup procedure. A reconstruction of the 3D CT image from kV images acquired at the isocenter, while in the treatment position, constitutes a solution.
An asymmetric autoencoder network architecture, composed of vision transformer blocks, was implemented. The dataset was compiled from one patient with head and neck pathology, including two orthogonal kV images (1024×1024 voxels), a single 3D CT scan with padding (512x512x512 voxels) acquired from the in-room CT-on-rails prior to kV imaging, and two digitally reconstructed radiographs (DRRs) (512×512 pixels) derived from the CT. Resampling kV images at 8-voxel intervals and DRR/CT images at 4-voxel intervals produced a dataset of 262,144 samples, each with a 128-voxel dimension along each spatial axis. The encoder's training involved the utilization of both kV and DRR images, and was further tasked with generating a consistent feature map from both input sources. In the course of testing, solely kV images that were independent in nature were used. The model's output of sCTs was arranged according to their spatial data, allowing for their concatenation to create the full-size synthetic CT (sCT). A determination of synthetic CT (sCT) image quality was made through the application of mean absolute error (MAE) and the per-voxel absolute CT number difference volume histogram (CDVH).
The model exhibited a speed of 21 seconds and a mean absolute error (MAE) that remained 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.
A novel vision transformer network, designed specifically for each patient, was developed and validated as accurate and efficient for the reconstruction of 3D CT images from kV images.
A network architecture based on vision transformers, designed for individual patient data, demonstrated accuracy and efficiency in reconstructing 3D CT images from kV radiographic inputs.

The manner in which the human brain interprets and processes information deserves meticulous consideration. The present study used functional magnetic resonance imaging to evaluate the selectivity and inter-individual differences in how the human brain reacts to presented images. In our inaugural experiment, images projected to achieve maximum activation levels based on a group-level encoding model generated more substantial responses compared to images predicted for average activation levels, the gain in activation directly correlating with the accuracy of the encoding model. Beyond this, aTLfaces and FBA1 showed elevated activation levels when presented with optimal synthetic images, differing from their response to optimal natural images. Our second experiment demonstrated that synthetic images generated by a personalized encoding model yielded a stronger response than those produced by group-level or other subject encoding models. The observed preference of aTLfaces for synthetic images over natural images was validated in a subsequent replication. Our research highlights the potential use of data-driven and generative approaches to adjust responses of macro-scale brain regions, enabling investigation of inter-individual variations and functional specialization within the human visual system.

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. To overcome the challenges posed by individual differences in cognitive and computational modeling, an ideal neural conversion tool is expected to produce authentic neural signals from one subject, replicating them from those of another subject. This research presents a groundbreaking individual-to-individual EEG converter, designated as EEG2EEG, drawing on the principles of generative models within computer vision. For 9 subjects, the THINGS EEG2 data was used to build and assess 72 distinct EEG2EEG models, each connected to a unique pair of subjects. click here EEG2EEG's performance in learning the correspondence of neural representations from one individual's EEG signals to another's is highlighted by our results, indicating a high degree of conversion accuracy. The EEG signals, moreover, present more distinct representations of visual information in comparison with what's obtainable from real-world datasets. The methodology detailed here establishes a novel and advanced framework for converting EEG signals into neural representations. This framework provides a flexible and high-performance mapping from one individual's brain to another, offering insights for both neural engineering and cognitive neuroscience.

Every encounter between a living entity and its environment embodies a gamble. Bearing only partial understanding of a probabilistic environment, the living entity needs to determine its subsequent action or short-term approach, an action that inherently or overtly entails adopting a model of this surrounding world. Better environmental statistics can refine betting strategies, but real-world constraints on gathering this information frequently restrict progress. We posit that optimal inference dictates difficulty in inferring 'complex' models due to bounded information, ultimately causing larger prediction errors. We propose a 'playing it safe' principle; under conditions of restricted information-gathering capacity, biological systems ought to favor simpler representations of reality, leading to less risky betting strategies. An optimally safe adaptation strategy, determined by the Bayesian prior, emerges from Bayesian inference. Our “playing it safe” approach, when incorporated into the study of stochastic phenotypic switching in bacteria, results in an increased fitness (population growth rate) of the bacterial community. The principle, we contend, applies extensively to the intricacies of adaptation, learning, and evolution, thereby elucidating the environments that sustain thriving organisms.

Neocortical neuron spiking activity exhibits an impressive range of variability, even when driven by identical stimuli. The approximate Poissonian discharge of neurons suggests a hypothesis concerning the asynchronous operation of these neural networks. Neurons, when operating asynchronously, fire independently, significantly decreasing the chance of a neuron experiencing simultaneous synaptic inputs.