Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The ...YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network's learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.
Remarkable improvement relative to traditional approaches in the treatment of hematological malignancies by chimeric antigen receptor (CAR) T-cell therapy has promoted sequential approvals of eight ...commercial CAR T products within last 5 years. Although CAR T cells' productization is now rapidly boosting their extensive clinical application in real-world patients, the limitation of their clinical efficacy and related toxicities inspire further optimization of CAR structure and substantial development of innovative trials in various scenarios. Herein, we first summarized the current status and major progress in CAR T therapy for hematological malignancies, then described crucial factors which possibly compromise the clinical efficacies of CAR T cells, such as CAR T cell exhaustion and loss of antigen, and finally, we discussed the potential optimization strategies to tackle the challenges in the field of CAR T therapy.
A method for intelligent data analysis was designed by combining electrochemical sensing with machine learning (ML). Specifically, a voltammetric sensor is described for determination of the ...phytoinhibitor maleic hydrazide in crop samples. Carboxyl-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT-C4-COOH) was electro-synthesized in aqueous micellar solution by direct anodic oxidation of its monomer. A nanosensor was then prepared by placing copper nanoparticles (CuNPs) on the PEDOT-C4-COOH film via electro-deposition of Cu (II) from aqueous micellar solutions. An artificial neural network (ANN) served as a powerful ML model to realize intelligent data analysis and smart transformation for digital output. Different established regression methods were selected for evaluating the ANN-based method that was found to be superior to known methods. The sensor has a wide working range (from 0.06–1000 μM), a low limit of detection (10 nM), good stability, selectivity and practicality. The method was applied to the determination of maleic hydrazide in (spiked) samples of onion, rice, potato and cotton leaf. Satisfactory results demonstrate that the feature of simultaneous data acquisition and analysis is highly attractive.
Graphical abstract
Schematic representation of an electrochemical sensor based on carboxyl-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT-C4-COOH) and copper nanoparticles (CuNPs) by differential pulse voltammetry (DPV) to detect maleic hydrazide (MH). PEDOT-C4-COOH was electro-synthesized in 0.1 M LiClO
4
aqueous micellar solution with 0.1 M sodium dodecyl benzene sulfonate (SDBS) by amperometry (CA). CuNPs was prepared by cyclic voltammetry (CV).
Multi-label feature selection, which is an efficient and effective pre-processing step in machine learning and data mining, can select a feature subset that contains more contributions for ...multi-label classification while improving the performance of the classifiers. In real-world applications, an instance may be associated with multiple related labels with different relative importances, and the process of obtaining different features usually requires different costs, containing money, and time, etc. However, most existing works with regard to multi-label feature selection do not take into consideration the above two critical issues simultaneously. Therefore, in this paper, we exploit the idea of neighborhood granularity to enhance the traditional logical labels into label distribution forms to excavate the deeper supervised information hidden in multi-label data, and further consider the effect of the test cost under three different distributions, simultaneously. Motivated by these issues, a novel test cost multi-label feature selection algorithm with label enhancement and neighborhood granularity is designed. Moreover, the proposed algorithm is tested upon ten publicly available benchmark multi-label datasets with six widely-used metrics from two different aspects. Finally, two groups of experimental results demonstrate that the proposed algorithm achieves the satisfactory and superior performance over other four state-of-the-art comparing algorithms, and it is effective for improving the learning performance and decreasing the total test costs of the selected feature subset.
Chimeric antigen receptor (CAR) T cells represent a potentially curative therapy for patients with advanced hematological cancers; however, uncertainties surround the cell-intrinsic fitness as well ...as the exhaustion that restrict the capacity of CAR-T. Decitabine (DAC), a DNA demethylating agent, has been demonstrated to reverse exhaustion-associated DNA-methylation programs and to improve T cell responses against tumors. Here we show that DAC significantly enhances antileukemia functions of CD123 CAR-T cells
and
. Additionally, it inhibits the expression of DMNT3a and DNMT1. Using the Illumina Methylation EPIC BeadChip (850 K), we identified differentially methylated regions, most of which undergo hypomethylated changes. Transcriptomic profiling revealed that CD123 CAR-T cells treated with DAC were enriched in genes associated with naive, early memory T cells, as well as non-exhausted T cells. DAC treatment also results in upregulation of immune synapse-related genes. Finally, our data further suggest that DAC works through the regulation of cellular differentiation characterized by naive and memory phenotypes. Taken together, these findings demonstrate that DAC improves the anti-leukemia properties of CD123-directed CAR-T cells, and provides a basis for rational combinatorial CAR-T-based immunotherapy for patients with acute myeloid leukemia (AML).
Heavy flavour physics provides excellent opportunities to indirectly search for new physics at very high energy scales and to study hadron properties for deep understanding of the strong interaction. ...The LHCb experiment has been playing a leading role in the study of heavy flavour physics since the start of the LHC operations about ten years ago, and made a range of high-precision measurements and unexpected discoveries, which may have far-reaching implications on the field of particle physics. This review highlights a selection of the most influential physics results on CP violation, rare decays, and heavy flavour production and spectroscopy obtained by LHCb using the data collected during the first two operation periods of the LHC. The upgrade plan of LHCb and the physics prospects are also briefly discussed.
Feature selection is an important strategy for knowledge reduction in rough set. Interval-valued data, as an extension of single values, can better express uncertain information from the perspective ...of uncertainty measure. However, for applications in the real world, feature values in interval-valued data vary with time evolving. For dynamic interval-valued data, it is time-consuming to employ existing approaches to choose the feature subset because they need to recalculate the interval-valued data from scratch when feature values vary. Motived by this, we research the incremental methods of feature selection in dynamic interval-valued data environment, which can select new feature subset according to previous results. At first, the incremental updatings of
θ
-conditional entropy are proposed, which measures the significance of candidate features along with the change of feature values of a single object and multiple objects, respetively. On this basis, aiming at dynamic interval-valued data, the homologous incremental feature selection algorithms are put forward. Finally, by comparing the results of feature subset selection between incremental algorithm and non-incremental algorithm on public data sets, it can be concluded that the proposed two incremental algorithms are more efficient and effective, especially, as multiple objects change feature values, two incremental algorithms proposed in this paper have achieved satisfactory results in computing time.
Oncolytic virotherapy has emerged as a promising treatment for human cancers owing to an ability to elicit curative effects via systemic administration. Tumor cells often create an unfavorable ...immunosuppressive microenvironment that degrade viral structures and impede viral replication; however, recent studies have established that viruses altered via genetic modifications can serve as effective oncolytic agents to combat hostile tumor environments. Specifically, oncolytic vaccinia virus (OVV) has gained popularity owing to its safety, potential for systemic delivery, and large gene insertion capacity. This review highlights current research on the use of engineered mutated viruses and gene-armed OVVs to reverse the tumor microenvironment and enhance antitumor activity in vitro and in vivo, and provides an overview of ongoing clinical trials and combination therapies. In addition, we discuss the potential benefits and drawbacks of OVV as a cancer therapy, and explore different perspectives in this field.
Induction chemotherapy based on high-dose methotrexate is considered as the standard approach for newly diagnosed primary central nervous system lymphomas (PCNSLs). However, the best combination ...chemotherapeutic regimen remains unclear. This study aimed to determine the efficacy and toxicities of rituximab with methotrexate (R-M regimen). Consecutive 37 Chinese patients receiving R-M regimen as induction chemotherapy were retrospectively identified from January 2015 to June 2020 from our center in eastern China. Fourteen patients receiving rituximab plus methotrexate with cytarabine (R-MA regimen) at the same period were identified as the positive control group. The response rates, survival, toxicities, length of hospital stay (LOS), and cost were compared. Compared with the R-MA regimen, the R-M regimen showed comparable response rate and survival outcomes, but had fewer grade 3-4 hematological toxicities, shorter LOS, lower mean total hospitalization cost and lower mean total antibiotic cost. Complete remission at the end of induction chemotherapy and ECOG > 3 were independent prognostic factors for overall survival. In conclusion, R-M regimen is an effective and cost-effective combination treatment for PCNSLs, which warrants further evaluation in randomized trials.
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