EFFECTS OF LATE PLANTING DATES, MATURITY GROUPS AND MANAGEMENT SYSTEMS ON GROWTH, DEVELOPMENT AND YIELD OF SOYBEAN IN SOUTH CAROLINA

Planting date plays a significant role in determining soybean growth, development and seed yield. The objectives of this experiment were to evaluate the effects of late planting date, management system, and maturity group on the growth, development and seed yield of maturity group VII and VIII soybean under dry land conditions in the Southeastern coastal plain of the United States. Plant growth and development, seed yield, yield components, and seed oil and protein concentrations were evaluated throughout the season. These experiments were conducted in South Carolina at the Edisto Research and Education Center near Blackville and the Pee Dee Research and Education Center near Florence. Soybean was planted at four weekly intervals starting on 15-June in both 2011 and 2012. Pioneer 97M50 (a MG VII determinate variety) and Prichard Roundup Ready (a MG VIII determinate variety) were selected based on their adaptation to the Southeast. The two management systems were: a strip-till (ST) system using a John Deere MaxEmerge Vaccum planter + Unverferth 300 strip till with 96-cm row spacing and a drilled no-till (NT) planting system with 19-cm row spacing. Plant growth was evaluated based on leaf area index (LAI), Normalized Difference Vegetation Index (NDVI), and plant height (HT). Plant development was calculated based on the duration (days) of growth stages. Growth stages were recorded weekly from 10 randomly selected plants in each plot. The beginning of each stage was determined when at least 50% of plants were at that stage. Overall, planting after 22 June appeared to reduce seed yield. The ST system increased the seed yield compared to the drilled NT system. Yields were greater for the MG VIII variety than the MG VII variety. LAI, NDVI, and HT at R2 and R4 were generally reduced with delayed planting dates. Later planting shortened the duration of both vegetative and reproductive growth stages for both MG VII and VIII soybeans. Shortened duration of vegetative growth and seed filling period might have contributed most to the lower yields observed in delayed planting dates. Planting date did not affect either protein or oil concentration. Protein concentration in the seed was found to be significantly higher and oil concentration lower in soybean grown in the ST system than in the drilled NT system. Positive correlations were found between: seed yield and LAI, NDVI, and HT at R2 and R4; seed yield and duration of vegetative and seed filling growth period; and seed yield and dry weight of each plant part (branches, stems, petioles, leaves, and pods)

Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models

Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have attracted significant attention due to their exceptional performance in zero-shot learning scenarios. Similarly, in the field of visual learning, models like Grounding DINO and the Segment Anything Model (SAM) have exhibited remarkable progress in open-set detection and instance segmentation tasks. It is undeniable that these FMs will profoundly impact a wide range of real-world visual learning tasks, ushering in a new paradigm shift for developing such models. In this study, we concentrate on the remote sensing domain, where the images are notably dissimilar from those in conventional scenarios. We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg. The pipeline is benchmarked on several widely-used remote sensing datasets, and we present preliminary results to demonstrate its effectiveness. Through this work, we aim to provide insights into maximizing the applicability of visual FMs in specific contexts with minimal model tuning. The code is available at https://github.com/Douglas2Code/Text2Seg.Comment: 10 pages, 6 figure

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

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care

The role of macrophages in gastric cancer

As one of the deadliest cancers of the gastrointestinal tract, there has been limited improvement in long-term survival rates for gastric cancer (GC) in recent decades. The poor prognosis is attributed to difficulties in early detection, minimal opportunity for radical resection and resistance to chemotherapy and radiation. Macrophages are among the most abundant infiltrating immune cells in the GC stroma. These cells engage in crosstalk with cancer cells, adipocytes and other stromal cells to regulate metabolic, inflammatory and immune status, generating an immunosuppressive tumour microenvironment (TME) and ultimately promoting tumour initiation and progression. In this review, we summarise recent advances in our understanding of the origin of macrophages and their types and polarisation in cancer and provide an overview of the role of macrophages in GC carcinogenesis and development and their interaction with the GC immune microenvironment and flora. In addition, we explore the role of macrophages in preclinical and clinical trials on drug resistance and in treatment of GC to assess their potential therapeutic value in this disease

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Multi-Target Tracking Using Windowed Fourier Single-Pixel Imaging

The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing 6K measurements and calculating 2K windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI

Efficient Beampattern Synthesis for Sparse Frequency Diverse Array via Matrix Pencil Method

Due to the introduction of frequency offsets, the pattern synthesis problem of sparse Frequency diverse array (FDA) becomes more complicated than that of the phased array. A typical way to solve this problem is to use a global optimization algorithm, but this is usually time-consuming. In this paper, we propose an efficient non-iterative beampattern synthesis approach for sparse FDA. For a given reference pattern, which can be generated by other synthesis methods, we first sample it uniformly and construct the Hankel matrix with the sampled data. By low-rank processing, a low-rank approximation version of the Hankel matrix can then be obtained. Finally, the matrix enhancement and matrix pencil (MEMP) and matrix pencil (MP) methods are applied to estimate the antenna positions, frequency offsets, and excitations of the obtained array from the approximated matrix. Besides this, two typical FDA frameworks including multi-carrier FDA (MCFDA) and standard FDA (SFDA) are considered. Numerical simulation results prove that the proposed method outperforms the existing methods in terms of synthesis error, average runtime, and percentage of saving elements

BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks

Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks. However, it has been found that SAM does not always perform satisfactorily when faced with challenging downstream tasks. This has led downstream users to demand a customized SAM model that can be adapted to these downstream tasks. In this paper, we present BadSAM, the first backdoor attack on the image segmentation foundation model. Our preliminary experiments on the CAMO dataset demonstrate the effectiveness of BadSAM.Comment: 2 pages, 3 figure

The association between erector spinae muscle content and chronic heart failure and its severity

Abstract Aims Previous studies have shown a significant reduction in skeletal muscle content in patients with chronic heart failure (CHF). The present study focused on the erector spinae muscle (ESM) to determine whether ESM content is associated with the development and severity of CHF. Methods and results A total of 652 patients were included in this trial for the study. According to the diagnostic criteria of CHF, 652 patients were divided into two groups, namely, the control group (268 patients) and the CHF group (384 patients). Meanwhile, to assess whether the ESM is associated with the severity of CHF, patients in the CHF group were divided into two groups according to left ventricular ejection fraction (LVEF) values: heart failure with preserved ejection fraction (HFpEF, LVEF ≥50%, 256 patients) and heart failure with reduced ejection fraction (HFrEF, LVEF ≤40%, 68 patients). Receiver operating curve analysis was performed to assess whether ESM content could predict CHF and determine its severity. Compared with the control group, the patients in the CHF group were older, the prevalence of coronary heart disease (CHD) and atrial fibrillation was higher, the colour ultrasound results showed that LVEF decreased significantly, and the left ventricular end‐diastolic internal diameter and left ventricular end‐systolic internal diameter increased significantly. Besides, patients in the CHF group had significantly lower ESM content, and ESM is an independent predictor of heart failure, with an odds ratio of 0.713 (CHF group vs. control group, 95% confidence interval 0.626–0.811, P < 0.001). Compared with the HFpEF group, the HFrEF group has a lower prevalence of CHD, LVEF decreased significantly, the left ventricular end‐diastolic internal diameter and left ventricular end‐systolic internal diameter increased significantly, also patients in the HFrEF group had significantly lower ESM content compared with patients in the HFpEF group, and ESM is an independent predictor of the severity of heart failure, with an odds ratio of 0.514 (HFrEF group vs. HFpEF group, 95% confidence interval (0.418–0.633, P < 0.05). The results of receiver operating curve analysis showed that the sensitivity and specificity of ESM content for the diagnosis of CHF were 65.6% and 71.6%, respectively, while the sensitivity and specificity of ESM content for predicting the severity of CHF were 47.1% and 89.1%, respectively. Conclusions The ESM is of great value in predicting the onset and severity of CHF