Reactivity of Acyclic (Pentadienyl) iron (1+) Cations with Phosphorous-stabilized Carbon Nucleophiles and with Nitrogen Nucleophiles

A lot of studies regard organoiron compounds have been made in recent years.Nucleophilic addition with soft nucleophiles such as malonate anions or phosphonatestabilized carbon nucleophiles, or harder nucleophiles such as organolithium or Grignardreagents to an acyclic (pentadienyl)iron cation results in the (pentenediyl)iron complexes. Reaction of (pentadienyl)iron complexes with paraformaldehyde via Horner-Emmonsolefination will give enolate complex. Oxidation of the enolate leads to the formation ofcyclopropane carboxylates. Further oxidation of divinylcyclopropane carboxylates canform cycloheptadiene. The reacivity of acyclic (pentadienyl)iron cations with nitrogen nucleophiles such aspotassium phthalimide were examined, which could be used as potential routes tosynthesis natural product. Click chemistry was also introduced to see the reactivity oforganoiron azide with terminal alkynes. We have also proposed to synthesize a model of bicycle [4,1,0] heptanes via the ringclosing metathesis of vinylcyclopropanes

Reactivity of Acyclic (pentadienyl)iron(1+) Cations with Phosphonate Stabilized Nucleophiles: Application to the Synthesis of Oxygenated Metabolites of Carvone

The addition of phosphonate stabilized carbon nucleophiles to acyclic (pentadienyl)iron(1+) cations proceeds predominantly at an internal carbon to afford (pentenediyl)iron complexes. Those complexes bearing an electron withdrawing group at the σ-bound carbon (i.e., 13/14) are stable and isolable, while complexes which do not contain an electron withdrawing group at the σ-bound carbon undergo CO insertion, reductive elimination and conjugation of the double bond to afford cyclohexenone products (21/22). Deprotonation of the phosphonate 13/14 or 21 and reaction with paraformaldehyde affords the olefinated products. This methodology was utilized to prepare oxygenated carvone metabolites (±)-25 and (±)-26

Fractional strong matching preclusion for two variants of hypercubes

Let F be a subset of edges and vertices of a graph G. If G-F has no fractional perfect matching, then F is a fractional strong matching preclusion set of G. The fractional strong matching preclusion number is the cardinality of a minimum fractional strong matching preclusion set. In this paper, we mainly study the fractional strong matching preclusion problem for two variants of hypercubes, the multiply twisted cube and the locally twisted cube, which are two of the most popular interconnection networks. In addition, we classify all the optimal fractional strong matching preclusion set of each

Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation

In the rapidly evolving field of AI research, foundational models like BERT and GPT have significantly advanced language and vision tasks. The advent of pretrain-prompting models such as ChatGPT and Segmentation Anything Model (SAM) has further revolutionized image segmentation. However, their applications in specialized areas, particularly in nuclei segmentation within medical imaging, reveal a key challenge: the generation of high-quality, informative prompts is as crucial as applying state-of-the-art (SOTA) fine-tuning techniques on foundation models. To address this, we introduce Segment Any Cell (SAC), an innovative framework that enhances SAM specifically for nuclei segmentation. SAC integrates a Low-Rank Adaptation (LoRA) within the attention layer of the Transformer to improve the fine-tuning process, outperforming existing SOTA methods. It also introduces an innovative auto-prompt generator that produces effective prompts to guide segmentation, a critical factor in handling the complexities of nuclei segmentation in biomedical imaging. Our extensive experiments demonstrate the superiority of SAC in nuclei segmentation tasks, proving its effectiveness as a tool for pathologists and researchers. Our contributions include a novel prompt generation strategy, automated adaptability for diverse segmentation tasks, the innovative application of Low-Rank Attention Adaptation in SAM, and a versatile framework for semantic segmentation challenges

Fractional matching preclusion for butterfly derived networks

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu [18] recently introduced the concept of fractional matching preclusion number. The fractional matching preclusion number (FMP number) of G, denoted by fmp(G), is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The fractional strong matching preclusion number (FSMP number) of G, denoted by fsmp(G), is the minimum number of vertices and edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for butterfly network, augmented butterfly network and enhanced butterfly network

Large Distance Modification of Newtonian Potential and Structure Formation in Universe

In this paper, we study the effects of super-light brane world perturbative modes on structure formation in our universe. As these modes modify the large distance behavior of Newtonian potential, they effect the clustering of a system of galaxies. So, we explicitly calculate the clustering of galaxies interacting through such a modified Newtonian potential. We use a suitable approximation for analyzing this system of galaxies, and discuss the validity of such approximations. We observe that such corrections also modify the virial theorem for such a system of galaxies.Comment: 13 pages, 3 captioned figure

Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network

Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content. However, the conversion of SDRTV to HDRTV often amplifies the existing coding artifacts in SDRTV which deteriorate the visual quality of the output. In this study, we propose a dual inverse degradation SDRTV-to-HDRTV network DIDNet to address the issue of coding artifact restoration in converted HDRTV, which has not been previously studied. Specifically, we propose a temporal-spatial feature alignment module and dual modulation convolution to remove coding artifacts and enhance color restoration ability. Furthermore, a wavelet attention module is proposed to improve SDRTV features in the frequency domain. An auxiliary loss is introduced to decouple the learning process for effectively restoring from dual degradation. The proposed method outperforms the current state-of-the-art method in terms of quantitative results, visual quality, and inference times, thus enhancing the performance of the SDRTV-to-HDRTV method in real-world scenarios.Comment: 10 page

Robust Multimodal Failure Detection for Microservice Systems

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches