Bioavailability of soil organic carbon and Fe as influenced by forestry practices in a subtropical coastal catchment

Potential impacts of plantation forestry practices on soil organic carbon and Fe available to microorganisms were investigated in a subtropical coastal catchment. The impacts of harvesting or replanting were largely limited to the soil top layer (0–10 cm depth). The thirty-year-old Pinus plantation showed low soil moisture content (Wc) and relatively high levels of soil total organic carbon (TOC). Harvesting and replanting increased soil Wc but reduced TOC levels. Mean dissolved organic carbon (DOC) and microbial biomass carbon (MBC) increased in harvested or replanted soils, but such changes were not statistically significant (P > 0.05). Total dithionite-citrate and aqua regia-extractable Fe did not respond to forestry practices, but acid ammonium oxalate and pyrophosphate-extractable, bioavailable Fe decreased markedly after harvesting or replanting. Numbers of heterotrophic bacteria were significantly correlated with DOC levels (P < 0.05), whereas Fe-reducing bacteria and S-bacteria detected using laboratory cultivation techniques did not show strong correlation with either soil DOC or Fe content

Enhancing Diffusion Models with Text-Encoder Reinforcement Learning

Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research addresses this issue by refining the diffusion U-Net using human rewards through reinforcement learning or direct backpropagation. However, many of them overlook the importance of the text encoder, which is typically pretrained and fixed during training. In this paper, we demonstrate that by finetuning the text encoder through reinforcement learning, we can enhance the text-image alignment of the results, thereby improving the visual quality. Our primary motivation comes from the observation that the current text encoder is suboptimal, often requiring careful prompt adjustment. While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder. Therefore, we propose to use reinforcement learning with low-rank adaptation to finetune the text encoder based on task-specific rewards, referred as \textbf{TexForce}. We first show that finetuning the text encoder can improve the performance of diffusion models. Then, we illustrate that TexForce can be simply combined with existing U-Net finetuned models to get much better results without additional training. Finally, we showcase the adaptability of our method in diverse applications, including the generation of high-quality face and hand images

S100A8 is a prognostic signature and associated with immune response in diffuse large B-cell lymphoma

BackgroundS100A8, a calcium-binding protein belonging to the S100 family, is involved in immune responses and multiple tumor pathogens. Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of B-cell lymphoma and remains incurable in 40% of patients. However, the role of S100A8 and its regulation of the immune response in DLBCL remain unclear.MethodsThe differential expression of S100A8 was identified via the GEO and TCGA databases. The prognostic role of S100A8 in DLBCL was calculated using the Kaplan-Meier curve. The function enrichment of differentially expressed genes (DEGs) was explored through GO, KEGG, GSEA, and PPI analysis. In our cohort, the expression of S100A8 was verified. Meanwhile, the biological function of S100A8 was applied after the inhibition of S100A8 in an in vitro experiment. The association between S100A8 and immune cell infiltration and treatment response in DLBCL was analyzed.ResultsS100A8 was significantly overexpressed and related to a poor prognosis in DLBCL patients. Function enrichment analysis revealed that DEGs were mainly enriched in the IL-17 signaling pathway. Our cohort also verified this point. In vitro experiments suggested that inhibition of S100A8 should promote cell apoptosis and suppress tumor growth. Single-cell RNA sequence analysis indicated that S100A8 might be associated with features of the tumor microenvironment (TME), and immune infiltration analyses discovered that S100A8 expression was involved in TME. In terms of drug screening, we predicted that many drugs were associated with preferable sensitivity.ConclusionElevated S100A8 expression is associated with a poor prognosis and immune infiltration in DLBCL. Inhibition of S100A8 could promote cell apoptosis and suppress tumor growth. Meanwhile, S100A8 has the potential to be a promising immunotherapeutic target for patients with DLBCL

Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs on low-level visual perception and understanding. To address this gap, we present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment. a) To evaluate the low-level perception ability, we construct the LLVisionQA dataset, consisting of 2,990 diverse-sourced images, each equipped with a human-asked question focusing on its low-level attributes. We then measure the correctness of MLLMs on answering these questions. b) To examine the description ability of MLLMs on low-level information, we propose the LLDescribe dataset consisting of long expert-labelled golden low-level text descriptions on 499 images, and a GPT-involved comparison pipeline between outputs of MLLMs and the golden descriptions. c) Besides these two tasks, we further measure their visual quality assessment ability to align with human opinion scores. Specifically, we design a softmax-based strategy that enables MLLMs to predict quantifiable quality scores, and evaluate them on various existing image quality assessment (IQA) datasets. Our evaluation across the three abilities confirms that MLLMs possess preliminary low-level visual skills. However, these skills are still unstable and relatively imprecise, indicating the need for specific enhancements on MLLMs towards these abilities. We hope that our benchmark can encourage the research community to delve deeper to discover and enhance these untapped potentials of MLLMs. Project Page: https://vqassessment.github.io/Q-Bench.Comment: 25 pages, 14 figures, 9 tables, preprint versio

Glomerular Organization in the Antennal Lobe of the Oriental Fruit Fly Bactrocera dorsalis

The oriental fruit fly, Bactrocera dorsalis is one of the most destructive pests of horticultural crops in tropical and subtropical Asia. The insect relies heavily on its olfactory system to select suitable hosts for development and reproduction. To understand the neural basis of its odor-driven behaviors, it is fundamental to characterize the anatomy of its olfactory system. In this study, we investigated the anatomical organization of the antennal lobe (AL), the primary olfactory center, in B. dorsalis, and constructed a 3D glomerular atlas of the AL based on synaptic antibody staining combined with computerized 3D reconstruction. To facilitate identification of individual glomeruli, we also applied mass staining of olfactory sensory neurons (OSNs) and projection neurons (PNs). In total, 64 or 65 glomeruli are identifiable in both sexes based on their shape, size, and relative spatial relationship. The overall glomerular volume of two sexes is not statistically different. However, eight glomeruli are sexually dimorphic: four (named AM2, C1, L2, and L3) are larger in males, and four are larger in females (A3, AD1, DM3, and M1). The results from anterograde staining, obtained by applying dye in the antennal lobe, show that three typical medial, media lateral, and lateral antennal-lobe tracts form parallel connections between the antennal lobe and protocerebrum. In addition to these three tracts, we also found a transverse antennal-lobe tract. Based on the retrograde staining of the calyx in the mushroom body, we also characterize the arrangement of roots and cell body clusters linked to the medial antennal-lobe tracts. These data provide a foundation for future studies on the olfactory processing of host odors in B. dorsalis

Tungsten Nanoparticles Accelerate Polysulfides Conversion: A Viable Route toward Stable Room-Temperature Sodium–Sulfur Batteries

Room-temperature sodium–sulfur (RT Na–S) batteries are arousing great interest in recent years. Their practical applications, however, are hindered by several intrinsic problems, such as the sluggish kinetic, shuttle effect, and the incomplete conversion of sodium polysulfides (NaPSs). Here a sulfur host material that is based on tungsten nanoparticles embedded in nitrogen-doped graphene is reported. The incorporation of tungsten nanoparticles significantly accelerates the polysulfides conversion (especially the reduction of Na2S4 to Na2S, which contributes to 75% of the full capacity) and completely suppresses the shuttle effect, en route to a fully reversible reaction of NaPSs. With a host weight ratio of only 9.1% (about 3–6 times lower than that in recent reports), the cathode shows unprecedented electrochemical performances even at high sulfur mass loadings. The experimental findings, which are corroborated by the first-principles calculations, highlight the so far unexplored role of tungsten nanoparticles in sulfur hosts, thus pointing to a viable route toward stable Na–S batteries at room temperatures

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation models have shown exciting potentials on low-level visual tasks, their related abilities are still preliminary and need to be improved. In order to enhance these models, we conduct a large-scale subjective experiment collecting a vast number of real human feedbacks on low-level vision. Each feedback follows a pathway that starts with a detailed description on the low-level visual appearance (*e.g. clarity, color, brightness* of an image, and ends with an overall conclusion, with an average length of 45 words. The constructed **Q-Pathway** dataset includes 58K detailed human feedbacks on 18,973 images with diverse low-level appearance. Moreover, to enable foundation models to robustly respond to diverse types of questions, we design a GPT-participated conversion to process these feedbacks into diverse-format 200K instruction-response pairs. Experimental results indicate that the **Q-Instruct** consistently elevates low-level perception and understanding abilities across several foundational models. We anticipate that our datasets can pave the way for a future that general intelligence can perceive, understand low-level visual appearance and evaluate visual quality like a human. Our dataset, model zoo, and demo is published at: https://q-future.github.io/Q-Instruct.Comment: 16 pages, 11 figures, page 12-16 as appendi

A nomogram to predict the treatment benefit of perampanel in drug-resistant epilepsy patients

ObjectiveThe objective of this study was to identify the factors that affect the efficacy of added perampanel for the treatment of drug-resistant epilepsy (DRE), and to develop a reliable nomogram to predict the benefit of this addition.MethodsA retrospective clinical analysis was conducted on DRE patients who received perampanel treatment and who were followed up for at least 6 months from January 2020 and September 2023 at the Epilepsy Center of Fujian Medical University Union Hospital. Data from January 2020 to December 2021 were used as development dataset to build model, while the data from January 2022 to September 2023 were used as validation dataset for internal validation. The predictive factors that affected the efficacy of perampanel as DRE treatment were included in the final multivariate logistic regression model, and a derived nomogram was established.ResultsA total of 119 DRE patients who received perampanel treatment were included in this study (development datasets: n = 76; validation data: n = 43). Among them, 72.3% (n = 86) showed a 50% or greater reduction in seizure frequency after perampanel treatment. Of all the parameters of interest, sex, age, history of generalized tonic-clonic seizures, and the number of antiseizure medications were identified as significant predictors for estimating the benefit of adding perampanel for the treatment of DRE. A model incorporating these four variables was developed, and a nomogram was constructed to calculate the probability of benefit of adding perampanel using the model coefficients. The C-index of the predictive model was 0.838, and the validation C-index was 0.756. The goodness-of-fit test showed good calibration of the model (p = 0.920, 0.752 respectively).ConclusionThe proposed nomogram has significant clinical potential for predicting the probability of benefit of perampanel as DRE treatment. This nomogram can be used to identify DRE patients who could benefit from the early addition of perampanel to their treatment regimen