225 research outputs found
Seeding Rate and Row-Spacing Effects on Seed Yield and Yield Components of \u3cem\u3eLeymus chinensis\u3c/em\u3e (Trin.) Tzvel.
Chinese sheepgrass (Leymus chinensis (Trin.) Tzvel.) is widely distributed in the eastern portion of the Inner Mongolian Plateau and the Songnen Grassland of China. This grass is highly salt, cold and drought tolerant and has been the major source of forage for cows and other ruminants in China (Gao et al. 2012). Seed yield of this grass is very low under native conditions because of the low heading percentage and percentage of seed set (Wang et al. 2010). The Hexi Corridor, located in China’s northwestern Gansu Province, is the seed production center of China because of its dry, sunny climate and favorable irrigation conditions. Our field study was conducted to determine the optimum seeding rate and row-spacing for seed production of Chinese sheepgrass in the Hexi Corridor, where this grass has not been previously grown
A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain
Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve NLP models. Mappings of an NLP model's representations of and the
brain activities evoked by linguistic input are typically deployed to reveal
this symbiosis. However, two critical problems limit its advancement: 1) The
model's representations (artificial neurons, ANs) rely on layer-level
embeddings and thus lack fine-granularity; 2) The brain activities (biological
neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e.,
voxel/region) and thus lack integrations and interactions among brain
functions. To address those problems, in this study, we 1) define ANs with
fine-granularity in transformer-based NLP models (BERT in this study) and
measure their temporal activations to input text sequences; 2) define BNs as
functional brain networks (FBNs) extracted from functional magnetic resonance
imaging (fMRI) data to capture functional interactions in the brain; 3) couple
ANs and BNs by maximizing the synchronization of their temporal activations.
Our experimental results demonstrate 1) The activations of ANs and BNs are
significantly synchronized; 2) the ANs carry meaningful linguistic/semantic
information and anchor to their BN signatures; 3) the anchored BNs are
interpretable in a neurolinguistic context. Overall, our study introduces a
novel, general, and effective framework to link transformer-based NLP models
and neural activities in response to language and may provide novel insights
for future studies such as brain-inspired evaluation and development of NLP
models
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation
In recent years, the Segmentation Anything Model (SAM) has attracted
considerable attention as a foundational model well-known for its robust
generalization capabilities across various downstream tasks. However, SAM does
not exhibit satisfactory performance in the realm of medical image analysis. In
this study, we introduce the first study on adapting SAM on video segmentation,
called MediViSTA-SAM, a novel approach designed for medical video segmentation.
Given video data, MediViSTA, spatio-temporal adapter captures long and short
range temporal attention with cross-frame attention mechanism effectively
constraining it to consider the immediately preceding video frame as a
reference, while also considering spatial information effectively.
Additionally, it incorporates multi-scale fusion by employing a U-shaped
encoder and a modified mask decoder to handle objects of varying sizes. To
evaluate our approach, extensive experiments were conducted using
state-of-the-art (SOTA) methods, assessing its generalization abilities on
multi-vendor in-house echocardiography datasets. The results highlight the
accuracy and effectiveness of our network in medical video segmentation
PharmacyGPT: The AI Pharmacist
In this study, we introduce PharmacyGPT, a novel framework to assess the
capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in
emulating the role of clinical pharmacists. Our methodology encompasses the
utilization of LLMs to generate comprehensible patient clusters, formulate
medication plans, and forecast patient outcomes. We conduct our investigation
using real data acquired from the intensive care unit (ICU) at the University
of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable
insights into the potential applications and limitations of LLMs in the field
of clinical pharmacy, with implications for both patient care and the
development of future AI-driven healthcare solutions. By evaluating the
performance of PharmacyGPT, we aim to contribute to the ongoing discourse
surrounding the integration of artificial intelligence in healthcare settings,
ultimately promoting the responsible and efficacious use of such technologies
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