Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic Sensitization of High-Grade Serous Ovarian Cancer Cells Lacking Caspase-8 Expression to Chemotherapeutics Using Combinations of Small-Molecule BRD4 and CDK9 Inhibitors

Simple Summary Despite the recent advancement in the treatment of ovarian cancer, it continues to be a largely incurable disease, plagued by de novo and acquired resistance to standard chemotherapeutics and novel second-line therapeutics. Therefore, identifying novel biomarkers and resistance mechanisms is critical to overcoming resistance, developing newer treatment strategies, and improving patient survival. In this study, we have demonstrated that low Caspase-8 expression correlates with poor prognosis in ovarian cancer patients. Moreover, the lack of Caspase-8 alters transcriptional regulation in ovarian cancer cells. In Caspase-8 knockout cells, increased BRD4 expression, increased CDK9 activity, and resistance to Carboplatin and Paclitaxel were observed. We have also shown that combining BRD4 inhibitors with Carboplatin, Paclitaxel, and CDK9 inhibitors synergistically sensitized these cells to undergo cell death. Abstract Ovarian cancer is one of the most lethal gynecological cancers worldwide, with approximately 70% of cases diagnosed in advanced stages. This late diagnosis results from the absence of early warning symptoms and is associated with an unfavorable prognosis. A standard treatment entails a combination of primary chemotherapy with platinum and taxane agents. Tumor recurrence following first-line chemotherapy with Carboplatin and Paclitaxel is detected in 80% of advanced ovarian cancer patients, with disease relapse occurring within 2 years of initial treatment. Platinum-resistant ovarian cancer is one of the biggest challenges in treating patients. Second-line treatments involve PARP or VEGF inhibitors. Identifying novel biomarkers and resistance mechanisms is critical to overcoming resistance, developing newer treatment strategies, and improving patient survival. In this study, we have determined that low Caspase-8 expression in ovarian cancer patients leads to poor prognosis. High-Grade Serous Ovarian Cancer (HGSOC) cells lacking Caspase-8 expression showed an altered composition of the RNA Polymerase II-containing transcriptional elongation complex leading to increased transcriptional activity. Caspase-8 knockout cells display increased BRD4 expression and CDK9 activity and reduced sensitivities to Carboplatin and Paclitaxel. Based on our work, we are proposing three potential therapeutic approaches to treat advanced ovarian cancer patients who exhibit low Caspase-8 expression and resistance to Carboplatin and/or Paclitaxel—combinations of (1) Carboplatin with small-molecule BRD4 inhibitors; (2) Paclitaxel with small-molecule BRD4 inhibitors, and (3) small-molecule BRD4 and CDK9 inhibitors. In addition, we are also proposing two predictive markers of chemoresistance—BRD4 and pCDK9.
Water Quality Index Assessment of Bada Talab, Chhatarpur, (M.P.) India

This study focuses on assessing the water quality of “Bada Talab” at five different sampling sites over the course of one year, from January 2022 to December 2022.In this study, various physico-chemical parameters were used to assess the water quality. The parameters tested are Nitrate, DO,Temperature, pH, EC, Turbidity, TS,TDS, TSS, Fluoride, Total Hardness, BOD and Iron on Pre-monsoon, Monsoon and post-Monsoon basis. The Water Quality Index (WQI) is a composite index that takes into account several water quality parameters to provide an overall assessment of water quality. In this study, the calculated WQI values indicate that the lake is moderately and slightly polluted. As a result, the water may not be suitable for direct domestic use without treatment. However, it is considered acceptable for irrigation and other non-potable purposes.
Antibody Banding Patterns on the Enzyme-Linked Immunoelectrotransfer Blot (EITB) Assay Clearly Discriminate Viable Cysticercosis in Naturally Infected Pigs

Enzyme-linked immunoelectrotransfer blot (EITB) detects antibodies against seven Taenia solium larvae antigens in three protein families (GP50, T24/42, and 8-kDa) with different structures and functions. EITB banding patterns against these antigens in pigs provide information about the course of infection and may discriminate viable cysticercosis. We analyzed the banding patterns and infection outcomes (presence of viable cysts, degenerated cysts, and any cysts) of 512 rural pigs. Banding patterns were grouped into homogenous classes using latent class analysis, and relationships with infection outcomes were assessed. Four classes were identified: 1 (n = 308, EITB-negative or positive for the GP50 family), 2 (n = 127, positive for GP50 (GP50 family), GP42-39 and GP24 (T24/42 family), but negative for 8-kDa antigens), 3 (n = 22, positive for GP50 and T24/42 antigens (GP42-39 and GP24), as well as to 8-kDa bands GP13, GP14, and GP18, but negative for GP21), and 4 (n = 55, positive for GP50 and T24/42 antigens, as well as to 8-kDa antigens GP21 and GP18 in combination). Pigs in classes 3 and 4 were more likely to have viable cysts (72.6% and 96.4%, respectively) than pigs in classes 1 and 2 (0.7% and 27.6%, respectively; p < 0.001). The number of infections with any cysts was higher in classes 3 and 4 (77.3% and 98.2%, respectively) and lower in classes 2 and 1 (34.7% and 4.9%, respectively; p < 0.001). Pigs with viable cysts represented >90% of pigs with any cysts in classes 3 and 4 (94.1% and 98.2%, respectively), while degenerated cysts were frequent among pigs with any cysts in classes 1, 3, and 2 (86.7%, 47.1%, and 43.2%, respectively; p < 0.001). EITB banding patterns strongly correlate with cysticercosis infection status in rural pigs, with classes 3 and 4 being more predictive of viable infections.
Fast Frequency Regulation Utilizing Non-Aggregate Thermostatically Controlled Loads Based On Edge Intelligent Terminals

--Thermostatically controlled loads (TCLs) have promising regulation potential and are regarded as valuable flexible resources. Due to the relatively lower operating power, TCLs are always aggregated to participate in various regulation programs. However, the intrinsic response delays of aggregation always result in relatively slower speed and limit further implementation in the fast frequency regulation (FFR) service, which is urgently needed by renewable power systems without sufficient inertias. Therefore, a non-aggregate method based on edge intelligent terminal (EIT) is proposed to utilize TCLs to provide FFR without unacceptable rebound effects. Firstly, the FFR infrastructure, consisting of the distributed frequency response and centralized recovery optimization, is developed based on functions of the EIT, such as local measurement, edge computation, and flexible control. Then, the equivalent mirror recovery strategy is proposed to mitigate power fluctuations and reduce occupations of other regulation resources. On this basis, the optimal recovery problem of TCLs is formulated to achieve flexible power control without violations of FFR requirements. To overcome the curse of dimensionality, the dynamic clustering method is proposed to divide non-aggregate TCLs into tens of groups, thereby the optimization problem is solved by EITs separately and the solution speed is independent of system scales. Finally, the proposed FFR is validated based on several simulation cases, and results show that TCLs significantly contribute to enhancing frequency resilience for low-inertia power systems.
Heavy-traffic analysis of a multiple-phase network with discriminatory processor sharing

textabstractWe analyze a generalization of the Discriminatory Processor Sharing (DPS) queue in a heavy-traffic setting. Customers present in the system are served simultaneously at rates controlled by a vector of weights. We assume that customers have phase-type distributed service requirements and allow that customers have different weights in various phases of their service. In our main result we establish a state-space collapse for the queue length vector in heavy traffic. The result shows that in the limit, the queue length vector is the product of an exponentially distributed random variable and a deterministic vector. This generalizes a previous result by Rege and Sengupta (1996) who considered a DPS queue with exponentially distributed service requirements. Their analysis was based on obtaining all moments of the queue length distributions by solving systems of linear equations. We undertake a more direct approach by showing that the probability generating function satisfies a partial differential equation that allows a closed-form solution after passing to the heavy-traffic limit. Making use of the state-space collapse result, we derive interesting properties in heavy traffic: (i) For the DPS queue we obtain that, conditioned on the number of customers in the system, the residual service requirements are asymptotically i.i. d. according to the forward recurrence times. (ii) We then investigate how the choice for the weights influences the asymptotic performance of the system. In particular, for the DPS queue we show that the scaled holding cost reduces as classes with a higher value for d_k/E(B_k^fwd) obtain a larger share of the capacity, where d_k is the cost associated to class k, and E(B_k^fwd) is the forward recurrence time of the class-k service requirement. The applicability of this result for a moderately loaded system is investigated by numerical experiments.
CR-YOLOv8: Multiscale Object Detection in Traffic Sign Images

Due to the large-scale changes of different forms of traffic signs and the rapid speed of vehicles, the detection accuracy and real-time performance of general object detectors are greatly challenged, especially the detection accuracy of small objects. In order to solve this problem, a multi-scale traffic sign detection model CR-YOLOv8 is proposed based on the latest YOLOv8. In the feature extraction stage, the attention module is introduced to enhance the channel and spatial features, so that the network can learn the key information of the small objects more easily. The RFB module is introduced in the feature fusion stage, which improves the feature diversity with less computational overhead and improves the network’s ability to detect multi-scale objects. By improving the loss function to enable the model to effectively balance multi-scale objectives during training, the model generalization ability is improved.The experimental results on TT100k dataset show that compared with the baseline network, the average detection accuracy of the improved method is increased by 2.3 %, and the detection accuracy of small objects is increased by 1.6 %, which effectively reduces the detection accuracy gap among different scales.
Investigation on the Impact of Excitation Amplitude on AFM-TM Microcantilever Beam System’s Dynamic Characteristics and Implementation of an Equivalent Circuit

Alterations in the dynamical properties of an atomic force microscope microcantilever beam system in tapping mode can appreciably impact its measurement precision. Understanding the influence mechanism of dynamic parameter changes on the system’s motion characteristics is vital to improve the accuracy of the atomic force microscope in tapping mode (AFM-TM). In this study, we categorize the mathematical model of the AFM-TM microcantilever beam system into systems 1 and 2 based on actual working conditions. Then, we analyze the alterations in the dynamic properties of both systems due to external excitation variations using bifurcation diagrams, phase trajectories, Lyapunov indices, and attraction domains. The numerical simulation results show that when the dimensionless external excitation g < 0.183, the motion state of system 2 is period 1. When g < 0.9, the motion state of system 1 is period 1 motion. Finally, we develop the equivalent circuit model of the AFM-TM microcantilever beam and perform related software simulations, along with practical circuit experiments. Our experimental results indicate that the constructed equivalent circuit can effectively analyze the dynamic characteristics of the AFM-TM microcantilever beam system in the presence of complex external environmental factors. It is observed that the practical circuit simulation attenuates high-frequency signals, resulting in a 31.4% reduction in excitation amplitude compared to numerical simulation results. This provides an essential theoretical foundation for selecting external excitation parameters for AFM-TM cantilever beams and offers a novel method for analyzing the dynamics of micro- and nanomechanical systems, as well as other nonlinear systems.
Exploring the Three-Dimensional Frontier: Advancements in MSC Spheroids and Their Implications for Breast Cancer and Personalized Regenerative Therapies

To more accurately replicate the in vivo three-dimensional (3D) mesenchymal stem cell (MSC) niche and enhance cellular phenotypes for superior in vivo treatments, MSC functionalization through in vitro 3D culture approaches has gained attention. The organization of MSCs in 3D spheroids results in altered cell shape, cytoskeleton rearrangement, and polarization. Investigations have revealed that the survival and secretory capability of MSCs are positively impacted by moderate hypoxia within the inner zones of MSC spheroids. The spheroid hypoxic microenvironment enhances the production of angiogenic and anti-apoptotic molecules, including HGF, VEGF, and FGF-2. Furthermore, it upregulates the expression of hypoxia-adaptive molecules such as CXCL12 and HIF-1, inhibiting MSC death. The current review focuses on the latest developments in fundamental and translational research concerning three-dimensional MSC systems. This emphasis extends to the primary benefits and potential applications of MSC spheroids, particularly in the context of breast cancer and customized regenerative therapies.
When Vectorization Meets Change Detection

In long-term Earth observation, change detection (CD) is a crucial and intricate task with applications spanning diverse fields, including land resource planning and natural disaster monitoring. Most existing CD approaches typically output segmentation results in raster format. However, raster format results suffer from higher memory usage, poorer shape accuracy, magnified distortions, and challenges in topological editing. To address the issues of raster format, we propose a novel end-to-end change vectorization network (CVNet), which is the first attempt to extract changes using vector format. The CVNet directly learns the vector components of changed objects and uses them to construct vectors. Specifically, since the vectorization of CD faces the inherent imbalance between changed and unchanged samples, we first introduce the Change-Collector to collect the changed regions and combine them into more compact samples. Next, the vector components learning model (VCLM) is introduced to capture the fundamental components for constructing the vectors, including change maps, junction positions, and segmentation masks. Finally, the changed instances obtained from the masks are used to divide and connect junctions to generate the vector output. To verify the effectiveness of the proposed framework, we construct two building change vectorization datasets by modifying the WHU-CD and LEVIR-CD benchmarks. Experimental results demonstrate that the CVNet outperforms the existing postprocess vectorization methods in terms of the visual effect and all evaluation metrics. The dataset and source code will be made publicly available at https://github.com/yyyyll0ss/CVNet.
Synthesis of Highly Luminescent Silica-Coated Upconversion Nanoparticles from Lanthanide Oxides or Nitrates Using Co-Precipitation and Sol–Gel Methods

Upconversion nanoparticles (UCNPs) are under consideration for their use as bioimaging probes with enhanced optical performance for real time follow-up under non-invasive conditions. Photostable and core-shell NaYF4:Yb3+, Er3+-SiO2 UCNPs obtained by a novel and simple co-precipitation method from lanthanide nitrates or oxides were herein synthesized for the first time. The sol–gel Stöber method followed by oven or supercritical gel drying was used to confer biocompatible surface properties to UCNPs by the formation of an ultrathin silica coating. Upconversion (UC) spectra were studied to evaluate the fluorescence of UCNPs upon red/near infrared (NIR) irradiation. ζ-potential measurements, TEM analyses, XRD patterns and long-term physicochemical stability were also assessed and confirmed that the UCNPs co-precipitation synthesis is a shape- and phase-controlling approach. The bio- and hemocompatibility of the UCNPs formulation with the highest fluorescence intensity was evaluated with murine fibroblasts and human blood, respectively, and provided excellent results that endorse the efficacy of the silica gel coating. The herein synthesized UCNPs can be regarded as efficient fluorescent probes for bioimaging purposes with the high luminescence, physicochemical stability and biocompatibility required for biomedical applications.
Latest Hauterivian (Early Cretaceous) ammonites from Umbria-Marche Apennines (Central Italy)

This monograph presents the systematic description o f a rich ammonite fauna o f latest Hauterivian age, collected in the Maiolica formation o f the Umbria-Marche Apennines. In terms o f ammonite zones the fauna indicates the P. angulicostata A udi, zone, P. catulloi subzone. The whole fauna comes from a single bed, the so-called "Guide-bed" o f the Faraoni Level. Twenty-nine species have been recognized and som e new taxa are proposed: P seudovaldedorsella nov. genus, whose type-species is P. c ra ssid o rsa ta (Karakash), Phyllopachyceras laeviventris n. sp. , Barrem ites prim itivus n. sp. , Plesiospitidiscus breskovskii n. sp„ Psilotissotia (P.) apcnninica n. sp„ Discoidellia pseudobertrandi n. sp. , Discoidellia vermeuleni n. sp. The intraspecific variability o f the above mentioned species plus that o f Pseudothurmannia sarasini Sarkar and P. mortilleti (Pictet & De Loriol) has been thoroughly analysed. The absence of am m onites identifiable as Pseudothurm annia catulloi (Parona) sensu Hoedemaeker does not imply an older age than the P. catulloi subzone. In fact the whole ammonite assemblage belong to the typical fauna which defines the P. catulloi subzone itself. PAROLE CHIAVE Paleontologia, Sislem atica, Ammonoidea, Cretaceo, Hauteriviano, Maiolica, Appennino um bro-m archigiano, Italia. RIASSUNTO Uj presente monografia tratta della descrizione sistematica di una ricca fauna ad am m oniti del! 'Hauteriviano terminate raccolta nella fonnazione della Maiolica dell'Appennino umbro-marchigiano. In termini di zone ad am m oniti la fauna indica la zona a P. angulicostata Auctt. , ed in parlicolare al sottozona a P. catulloi. L'intera fauna proviene da un singolo straw, il “Guide-bed" del Livello Faraoni. Sono riconosciute ventinove specie e proposte le istituzioni dei seguenti nuovi taxa: Pseudovaldedorsella nov. genus. , la cui specie tipo I P. crassidorsata (Karakash), Phyllopachyceras laeviventris n. sp. , Barrem ites primitivus n. sp. , Plesiospitidiscus breskovskii n. sp. , Psilotissotia (P.) apenninica n. sp. , Discoidellia pseudobertrandi n. sp. and Discoidellia vermeuleni n. sp. £ ' stata dettagliatamente esaminata la variability intraspeciftca di tutte le specie succitate ed anche di Pseudothurm annia sarasini Sarkar e P. mortilleti (Pictet & De Loriol). L'assenza di Pseudothurm annia catulloi (Parona) sensu Hoedemaeker non implica un'eta piii antica della sottozona a P. catulloi. Infatii l'intera associazione ad am m oniti appartiene alia tipica fauna che caratterizza la stessa sottozona a P. catulloi. * University degli Studi di Urbino, Istituto di Geologia, Loc. Crocicchia 1-61029 Urbino Italy E-mail: cecca@ uniurb.it. ** via Sparapani 1 lc, 1-60123 Ancona Italy. *** via Venezia 42,1-61043 Cagli (PS) Italy.
Effect of Capital Structure on Shareholder Value: Evidence from Panel ARDL Approach

This study examines the effect of capital structure on the shareholders’ value in Indian pharmaceutical industry collecting data over a period from 2001 to 2020. Further, the study also investigates the long-run and short-run relationship between capital structure and shareholder value. This study employs pooled, fixed, random effect regression models and panel autoregressive distributed lag analysis. The shareholder value of Indian firms is measured by earnings per share (EPS) and market value added (MVA) while the debt-equity ratio, interest coverage ratio, and total liabilities to total assets are the proxy variables for capital structure. The study reveals that capital structure influences shareholder value significantly. The findings also explored the existence of a long-run relationship between capital structure and shareholder value.
ADMM Algorithmic Regularization Paths for Sparse and Large Scale Positive-Definite Covariance Matrix Estimation

: Estimating sparse positive-definite covariance matrices in high dimensions has received extensive attention in the past two decades. However, many existing algorithms are proposed for a single regularization parameter and little attention has been paid to estimating the covariance matrices over the full range of regularization parameters. In this paper we suggest to compute the regularization paths of estimating the positive-definite covariance matrices through a one-step approximation of the warm-starting Alternating Direction Method of Multipliers (ADMM) algorithm, which quickly outlines a sequence of sparse solutions at a fine resolution. We demonstrate the effectiveness and computational savings of our proposed algo-rithm through elaborative analysis of simulated examples.
ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios

Erasure codes are widely used in large-scale distributed storage systems due to their high efficiency and reliability, but they also face extremely high repair penalties when data corruption occurs. At present, machine learning methods can accurately predict the next failure time and type of machine nodes. Based on this, in order to solve the problem of unnecessary repair traffic caused by temporary failures, as well as the more degraded reads of high-frequency accessed data due to longer failure time of such data in existing repair methods, we propose an Adaptive Classification Predictive Repair method (ACPR) for different fault scenarios. By categorizing the failed blocks into high-risk and low-risk based on the failure type of the soon-to-fail (STF) node and the access heat of STF blocks, ACPR can perform adaptive predictive repair. By quickly repair high-risk blocks to ensure data availability while delaying the repair of low-risk blocks, a large amount of unnecessary repair traffic caused by temporary node failures in the cluster is avoided. Alibaba Cloud Elastic Compute Service (ECS) experiments results show that compared with FastPR and ECPipe, ACPR can shorten the repair time per data block by up to 15.2% and 33.5%, respectively. Moreover, ACPR can reduce repair traffic by up to 74.1% and 84.4%, respectively.