Original Target Research Articles

A novel approach to the assessment of higher-order rule learning in male mice.

Historically, the development of valid and reliable methods for assessing higher-order cognitive abilities (e.g., rule learning and transfer) has been difficult in rodent models. To date, limited evidence supports the existence of higher cognitive abilities such as rule generation and complex decision-making in mice, rats, and rabbits. To this end, we sought to develop a task that would require mice to learn and transfer a rule. We trained mice to visually discriminate a series of images (image set, six total) of increasing complexity following three stages: (1) learn a visual target, (2) learn a rule (ignore any new images around the target), and finally (3) apply this rule in abstract form to a comparable but new image set. To evaluate learning for each stage, we measured (1) days (and performance by day) to discriminate the original target at criterion, (2) days (and performance by day) to get back to criterion when images in the set were altered by the introduction of distractors (rule learning), and (3) overall days (and performance by day) to criterion when experienced versus naïve cohorts of mice were tested on the same image set (rule transfer). Twenty-seven wild-type male C57 mice were tested using Bussey-Saksida touchscreen operant conditioning boxes (Lafayette Instruments). Two comparable black-white image sets were delivered sequentially (counterbalanced for order) to two identical cohorts of mice. Results showed that all mice were able to effectively learn their initial target image and could recall it >80 d later. We also found that mice were able to quickly learn and apply a "rule" : Ignore new distractors and continue to identify their visual target embedded in more complex images. The presence of rule learning was supported because performance criterion thresholds were regained much faster than initial learning when distractors were introduced. On the other hand, mice appeared unable to transfer this rule to a new set of stimuli. This is supported because visual discrimination curves for a new image set were no better than an initial (naïve) learning by a matched cohort of mice. Overall results have important implications for phenotyping research and particularly for the modeling of complex disorders in mice.

Hippocampal-Sparing Radiotherapy in Primary Sinonasal and Cutaneous Head and Neck Malignancies: A Feasibility Study

Patients with primary sinonasal and cutaneous head and neck (H&N) malignancies often receive meaningful hippocampal doses, but the hippocampus is not a classic avoidance structure in radiation planning of these primary sites. This series characterizes the feasibility and tradeoffs of hippocampal-sparing radiotherapy (HSRT) for patients with primary sinonasal and cutaneous H&N malignancies. We retrospectively identified patients at a single institution treated definitively for primary sinonasal or cutaneous malignancies of the H&N. Each patient received (chemo)radiation and all received clinically-significant radiation dose to one or both hippocampi. We created new HSRT plans for each patient with intensity-modulated radiotherapy using original target and organ-at-risk (OAR) volumes. Hippocampi were contoured based on Radiation Therapy Oncology Group guidelines. Absolute and relative differences in radiation dose to the hippocampi, planning target volumes (PTV), and OARs were recorded. We used paired-samples t-tests to compare hippocampal and PTV dosimetric measures with and without HSRT. Thirty-seven patients were included (22 sinonasal, 11 cutaneous H&N, and 4 parotid primary tumors). Median prescription dose was 6600cGy (range: 5000-7440cGy). The most common fractionation regimens were 200cGy/fraction daily (51%, 19/37 patients) and 120cGy/fraction twice daily (41%, 15/37 patients). There were significant decreases in hippocampal Dmax and D100% using HSRT without compromising PTV coverage (Table 1). HSRT resulted in a relative increase of mean lacrimal gland dose by an average of 3.8%, optic chiasm Dmax by 1.3%, and whole brain Dmax of 1.2%. However, other OAR doses were lower with HSRT, including parotid gland mean dose, lens Dmax, optic nerve Dmax, cochlea mean dose, brainstem Dmax, and whole brain mean dose. HSRT is feasible and results in meaningful radiation dose reduction to the hippocampi without reducing PTV coverage or increasing dose to other OARs. The hippocampi should be regularly included as avoidance structures when treating primary sinonasal and cutaneous H&N tumors with radiation. We suggest target hippocampal constraints of Dmax < 1600cGy and D100% < 500cGy when feasible (without compromising PTV coverage). The clinical significance of HSRT in patients with primary H&N tumors should be investigated prospectively.

Quantifying Cardiac Chamber Motion for Noninvasive Stereotactic Cardiac Radiosurgery Using Deformable Image Registration

Noninvasive Stereotactic Cardiac Radiosurgery (NSCR) is a novel treatment for drug resistant ventricular tachycardia. One of the major issues with this new treatment style is uncertainty in the target location due to cardiac and respiratory motion. This motion uncertainty results in planning target volumes that are 3 to 4 times the original target volume. Quantification of this motion requires accurate segmentation of the heart chambers throughout cardiac and respiratory phases. Manual segmentation of the heart is incredibly time consuming and not feasible to be done for each patient. This work aimed to develop a semi-automated workflow that takes manual contours of the heart chambers in one phase of a breath-hold ECG cardiac 4DCT and warp these contours onto all other phases. Four chambers of the heart and the aorta were manually contoured on one phase of a patient's breath-hold ECG cardiac 4DCT. A single shot deep learning deformable image registration code called GroupRegNet was used to propagate the contours onto the additional phases. This code uses a global smoothness parameter that does not work well in cardiac cases. To account for the type of motion that is expected in the cardiac 4DCTs, a piecewise smoothing parameter that only enforces smoothness within each of the original contours was added to the code. To determine the accuracy of the registration, a second phase with significant motion from the contoured phase was also manually contoured. The Dice coefficient and surface-to-surface distance between the manual and propagated contours were calculated. The tables below show the improvement of the contour propagation accuracy, measured as the Dice coefficients and the mean surface-to-surface distance using the original global smoothing and piecewise-smoothing only within the original contours. These results show an improvement in registration accuracy due to using a piecewise smoothing factor instead of global smoothness. Along with improvements in registration accuracy, this work shows promise for this semi-automated methodology to be further improved with the hope of fully automating the process and eventually implemented into the workflow for NSCR treatment to quantify cardiac chamber motion more accurately and eventually reduce the margins during radiation treatment.

The effectiveness and cost-effectiveness of a group domestic abuse perpetrator programme: protocol for a randomised controlled trial

BackgroundIn contrast to evidence for interventions supporting victim/survivors of domestic violence and abuse (DVA), the effectiveness of perpetrator programmes for reduction of abuse is uncertain. This study aims to estimate the effectiveness and cost-effectiveness of a perpetrator programme for men.MethodsPragmatic two-group individually randomised controlled trial (RCT) with embedded process and economic evaluation. Five centres in southwest England and South Wales aim to recruit 316 (reduced from original target of 366) male domestic abuse perpetrators. These will be randomised 2:1 to a community-based domestic abuse perpetrator programme (DAPP) or usual care comparator with 12-month follow-up. Female partners/ex-partners will be invited to join the study.The intervention for men comprises 23 weekly sessions of a group programme delivered in voluntary sector domestic abuse services. The intervention for female partners/ex-partners is one-to-one support from a safety worker. Men allocated to usual care receive no intervention; however, they are free to access other services. Their partners/ex-partners will be signposted to support services.Data is collected at baseline, and 4, 8 and 12 months’ follow-up. The primary outcome is men’s self-reported abusive behaviour measured by the Abusive Behaviour Inventory (ABI-29) at 12 months. Secondary measures include physical and mental health status and resource use alongside the abuse measure ABI (ABI-R) for partners/ex-partners and criminal justice contact for men.A mixed methods process evaluation and qualitative study will explore mechanisms of effectiveness, judge fidelity to the intervention model using interviews and group observations.The economic evaluation, over a 1-year time horizon from three perspectives (health and social care, public sector and society), will employ a cost-consequences framework reporting costs alongside economic outcomes (Quality-Adjusted Life Years derived from EQ-5D-5L, SF-12 and CHU-9D, and ICECAP-A) as well as the primary and other secondary outcomes.DiscussionThis trial will provide evidence of the (cost)effectiveness of a DAPP. The embedded process evaluation will further insights in the experiences and contexts of participants and their journey through a perpetrator programme, and the study will seek to address the omission in other studies of economic evaluations.Trial registrationISRCTN15804282, April 1, 2019

Residual Attention Mechanism for Remote Sensing Target Hiding

In this paper, we investigate deep-learning-based image inpainting techniques for emergency remote sensing mapping. Image inpainting can generate fabricated targets to conceal real-world private structures and ensure informational privacy. However, casual inpainting outputs may seem incongruous within original contexts. In addition, the residuals of original targets may persist in the hiding results. A Residual Attention Target-Hiding (RATH) model has been proposed to address these limitations for remote sensing target hiding. The RATH model introduces the residual attention mechanism to replace gated convolutions, thereby reducing parameters, mitigating gradient issues, and learning the distribution of targets present in the original images. Furthermore, this paper modifies the fusion module in the contextual attention layer to enlarge the fusion patch size. We extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset proved the efficiency of RATH for image inpainting and target hiding. RATH had the highest similarity, with a 90.44% structural similarity index metric (SSIM), for edge-guided target hiding. The training parameters had 1M fewer values than gated convolution (Gated Conv). Finally, we present two automated target-hiding techniques that integrate semantic segmentation with direct target hiding or edge-guided synthesis for remote sensing mapping applications.

Small molecule activators of TAK1 promotes its activity-dependent ubiquitination and TRAIL-mediated tumor cell death

TAK1 is a key modulator of both NF-κB signaling and RIPK1. In TNF signaling pathway, activation of TAK1 directly mediates the phosphorylation of IKK complex and RIPK1. In a search for small molecule activators of RIPK1-mediated necroptosis, we found R406/R788, two small molecule analogs that could promote sustained activation of TAK1. Treatment with R406 sensitized cells to TNF-mediated necroptosis and RIPK1-dependent apoptosis by promoting sustained RIPK1 activation. Using click chemistry and multiple biochemical binding assays, we showed that treatment with R406 promotes the activation of TAK1 by directly binding to TAK1, independent of its original target Syk kinase. Treatment with R406 promoted the ubiquitination of TAK1 and the interaction of activated TAK1 with ubiquitinated RIPK1. Finally, we showed that R406/R788 could promote the cancer-killing activities of TRAIL in vitro and in mouse models. Our studies demonstrate the possibility of developing small molecule TAK1 activators to potentiate the effect of TRAIL as anticancer therapies.

Canagliflozin reduces chemoresistance in hepatocellular carcinoma through PKM2-c-Myc complex-mediated glutamine starvation

Cisplatin (CPT) is one of the standard treatments for hepatocellular carcinoma (HCC). However, its use is limits as a monotherapy due to drug resistance, and the underlying mechanism remains unclear. To solve this problem, we tried using canagliflozin (CANA), a clinical drug for diabetes, to reduce chemoresistance to CPT, and the result showed that CANA could vigorously inhibit cell proliferation and migration independent of the original target SGLT2. Mechanistically, CANA reduced aerobic glycolysis in HCC by targeting PKM2. The downregulated PKM2 directly bound to the transcription factor c-Myc in the cytoplasm to form a complex, which upregulated the level of phosphorylated c-Myc Thr58 and promoted the ubiquitination and degradation of c-Myc. Decreased c-Myc reduced the expression of GLS1, a key enzyme in glutamine metabolism, leading to impaired glutamine utilization. Finally, intracellular glutamine starvation induced ferroptosis and sensitized HCC to CPT. In conclusion, our study showed that CANA re-sensitized HCC to CPT by inducing ferroptosis through dual effects on glycolysis and glutamine metabolism. This is a novel mechanism to increase chemosensitivity, which may provide compatible chemotherapy drugs for HCC.

Strong duality of a conic optimization problem with a single hyperplane and two cone constraints

Strong (Lagrangian) duality of general conic optimization problems (COPs) has long been studied and its profound and complicated results appear in different forms in a wide range of literatures. As a result, characterizing the known and unknown results can sometimes be difficult. The aim of this article is to provide a unified and geometric overview of strong duality of COPs for the known results, and to explain the essential ideas of the duality results with the simplest geometry. For our framework, we employ a COP minimizing a linear function in a vector variable subject to a single hyperplane constraint and two cone constraints , . It can be identically reformulated as a simpler COP with the single hyperplane constraint and the single cone constraint . This simple COP and its dual as well as their duality relation can be represented geometrically, and they have no duality gap without any constraint qualification. The dual of the original target COP is equivalent to the dual of the reformulated COP if the Minkowski sum of the duals of the two cones and is closed or if the dual of the reformulated COP satisfies a certain Slater condition. Thus, these two conditions make it possible to transfer all duality results, including the existence and/or boundedness of optimal solutions, on the reformulated COP to the ones on the original target COP, and further to the ones on a standard primal-dual pair of COPs with symmetry.

Truncated quadratic norm minimization for bilinear factorization based matrix completion

Low-rank matrix completion is an important research topic with a wide range of applications. One prevailing way for matrix recovery is based on rank minimization. Directly solving this problem is NP hard. Therefore, various rank surrogates are developed, like nuclear norm. However, nuclear norm regularization minimizes the sum of all the singular values, and hence the rank is not well approximated. We propose a new rank substitution named truncated quadratic norm that performs the corresponding truncated quadratic operation on the singular values. This function takes the square of the minor singular values and maps large singular values to one. In order to reduce computational complexity, the original target matrix is factorized into two small matrices on which the truncated quadratic norm is imposed. The resultant problem is then solved by alternating minimization. We also prove that the solution sequence is able to converge to a critical point. Experimental results on synthetic data and real-world images demonstrate the excellent performance of our method in terms of recovery accuracy.

How E-, L-, and P-Selectins Bind to sLex and PSGL-1: A Quantification of Critical Residue Interactions.

Selectins and their ability to interact with specific ligands are a cornerstone in cell communication. Over the last three decades, a considerable wealth of experimental and molecular modeling insights into their structure and modus operandi were gathered. Nonetheless, explaining the role of individual selectin residues on a quantitative level remained elusive, despite its importance in understanding the structure-function relationship in these molecules and designing their inhibitors. This work explores essential interactions of selectin-ligand binding, employing a multiscale approach that combines molecular dynamics, quantum-chemical calculations, and residue interaction network models. Such an approach successfully reproduces most of the experimental findings. It proves to be helpful, with the potential for becoming an established tool for quantitative predictions of residue contribution to the binding of biomolecular complexes. The results empower us to quantify the importance of particular residues and functional groups in the protein-ligand interface and to pinpoint differences in molecular recognition by the three selectins. We show that mutations in the E-, L-, and P-selectins, e.g., different residues in positions 46, 85, 97, and 107, present a crucial difference in how the ligand is engaged. We assess the role of sulfation of tyrosine residues in PSGL-1 and suggest that TyrSO3- in position 51 interacting with Arg85 in P-selectin is a significant factor in the increased affinity of P-selectin to PSGL-1 compared to E- and L-selectins. We propose an original pharmacophore targeting five essential PSGL-binding sites based on the analysis of the selectin···PSGL-1 interactions.

Pengaruh Penerimaan Pajak Hotel, Pajak Restoran, Pajak Hiburan, Pajak Penerangan Jalan dan Pajak Reklame Terhadap Pendapatan Asli Daerah Kota Batu

Indonesia as a government that adheres to the principle of regional autonomy, each region is given the authority to regulate its own government. Where to organize their own local government requires a source of income. One source of local revenue is local taxes and regional levies. The purpose of this study was to determine the effect of hotel taxes, restaurant taxes, street lighting taxes and advertisement taxes on local revenue This type of research is quantitative with a descriptive approach. The data used in the secondary data research contained in the Batu City regional financial institutions are in the form of regional tax target and realization reports and 2012-2018 regional original revenue target and realization reports. The research method used is multiple linear regression analysis to see the effect of hotel taxes, restaurant taxes, entertainment taxes, street lighting taxes and advertisement taxes on local revenue. The results of testing the hypothesis using the t test succeeded in proving that the hotel tax did not have a significant effect on local revenue, while the restaurant tax, entertainment tax, street lighting tax and advertisement tax had a significant effect on regional original income. The test results using the f test prove that the hotel tax, restaurant tax, entertainment tax, street lighting tax, and advertisement tax simultaneously affect local revenue. Keyword: Regional Autonomy, Revenue, Regression Analysis, Taxes, Realization Report.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes. To overcome this issue, we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm. This enhances the algorithm loading capability of the fuze. In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms, we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square (SAF-LMS) algorithm. We also designed an algorithm pause module to retain the original trend of the target echo peak, improving the time discrimination accuracy and anti-interference capability of the fuze. Finally, experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals. The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than 31 dB, with an increase of up to 76% in extreme detection distance.

Research on improved level set image segmentation method.

Aiming at the shortcomings of the traditional level set model which only has good robustness to the weak boundary and strong noise of the original target image, this paper proposes an improved algorithm based on the no-weight initialization level set model, introducing bilateral filters and using implicit surface level sets to extract and segment the original target image object more accurately, clearly and intuitively in the evolution process. The experimental simulation results show that, compared with the traditional non-reinitialized level set model segmentation method, the improved method can more accurately extract the edge contours of the target image object, and has better edge contour extraction effect, and the original target noise reduction effect of the improved model is better than that of the model before the improvement. The original target image object edge contour takes less time to extract than the conventional non-reinitialized level set model before the improvement.

Preliminary design of the Hayabusa2 extended mission to the fast-rotating asteroid 1998 KY26

The near-Earth asteroid 1998 KY26 is a rendezvous target of the Hayabusa2 extended mission, with a diameter as small as 20–40m and a spin period as short as 10.7min. This research aims to develop a plan for Hayabusa2 operations around this particular asteroid constrained by the remaining spacecraft resources. The main part of this paper begins with the characterization of the orbital and surface dynamics about 1998 KY26. The analysis demonstrates that the orbital motion of the spacecraft in the microgravity environment is dominated by solar radiation pressure, while the centrifugal force due to the high spin rate plays a prominent role at the surface. The dynamical environment significantly differs from that of the original target Ryugu, for which Hayabusa2 was specifically designed, posing technical challenges in close-proximity operations. In particular, this article highlights the feasibility of two primary operations: hovering for station-keeping and controlled descent for close-up observations. The discussion is further extended to more advanced operations, such as those using the projectile and target marker aboard the spacecraft. Our research pushes the envelope to explore one of the smallest members of the Solar System.

The CLARIN infrastructure as an interoperable language technology platform for SSH and beyond

AbstractCLARIN is a European Research Infrastructure Consortium developing and providing a federated and interoperable platform to support scientists in the field of the Social Sciences and Humanities in carrying-out language-related research. This contribution provides an overview of the entire infrastructure with a particular focus on tool interoperability, ease of access to research data, tools and services, the importance of sharing knowledge within and across (national) communities, and community building. By taking into account FAIR principles from the very beginning, CLARIN succeeded in becoming a successful example of a research infrastructure that is actively used by its members. The benefits CLARIN members reap from their infrastructure secure a future for their common good that is both sustainable and attractive to partners beyond the original target groups.

A study of antibody conjugates for the treatment of inflammatory breast cancer.

e15016 Background: Inflammatory breast cancer (inflammatory carcinoma) is a rare metastatic malignant tumor, it accounts for about 2% of all breast cancer cases. Despite its low incidence, inflammatory breast cancer is highly invasive, its five-year survival rate is less than 41%, making its prognosis the worst of all breast cancer subtypes, and effective drugs are urgently needed in clinical practice. Therefore, our study develops novel antibody conjugate drugs that can target overexpressed-ICAM-1 in inflammatory breast cancer cells as an original molecular target precisely. Methods: In this study, flow cytometry was used to perform precise and quantitative target screening of inflammatory breast cancer cell lines (SUM-149, KPL4), the endocytosis efficiency of ADC drugs was verified by flow cytometry efficiency analysis and single-photon confocal analysis, and the cytotoxicity of different drugs and cell lines was explored by CCK8 experiment. A nude mouse model of orthotopic xenograft inflammatory breast cancer was constructed, and the in vivo therapeutic effect of inflammatory breast cancer was observed by intravenous injection of two ICAM-1 antibody conjugates (ICAM-1-MMAE and ICAM-1-DXD), also the ADC drug structure with the best therapeutic effect for inflammatory breast cancer was screened. Results: ICAM-1 molecule in a variety of inflammatory breast cancer cell lines were highly over-expressed, and the antibody internalization rate was 51% at 4h, and after the peak the endocytosis efficiency slowed down into the plateau stage; ICAM1-MMAE and ICAM1-DXD are safer than first-line chemotherapy agents for breast cancer; The tumor volume was significantly reduced after administration into the orthotopic breast cancer xenograft model, which further verified the obvious effect of ADC drugs in vivo. Conclusions: Both ICAM-1-MMAE and ICAM-1-DXD have shown significant anti-inflammatory breast cancer effects in vitro and in vivo, and this study provides potential antibody conjugate drugs for targeted therapy of inflammatory breast cancer, which is expected to help improve the poor prognosis of inflammatory breast cancer.

Using transfer-learning-based algorithms as data reduction strategies for volatile organic compounds classification using plasma spectroscopy

This work presents the use of transfer-learning-based algorithms as data reduction strategies for the classification of volatile organic compounds (VOCs) using the optical emission spectroscopy of plasmas. The plasma used is generated with a home-made microplasma generation device (MGD) ignited in the mixtures of Ar and VOCs. The spectra are acquired from ten MGDs. The VOCs tested are methanol, ethanol, and isopropanol. VOCs are classified using a convolutional neural network. In addition, gradient-weighted class activation mapping is used as the explainable artificial intelligent technique. It ensures the model classification is based upon rational plasma physics by considering appropriate wavelengths. The VOC concentrations are then quantified using linear regression and an artificial neural network (ANN). The transfer learning-based algorithms tested are parameter transfer, REPTILE, and self-training. Spectral data from ten MGDs are grouped into source and target datasets. Ten MGDs are tested individually using a model that was trained on the other nine MGDs. The three MGDs with the lowest accuracy are chosen as the target dataset, while the other seven MGDs make up the source dataset. The original target dataset has 22 500 spectra and is further reduced to 12 600, 9000, 1800, 225, and 22 spectra to test the behavior of each algorithm. With 225 spectra used for training, the model trained with the random initial model shows an accuracy of 0.82. The models trained with parameter transfer and REPTILE have accuracies of 0.98 and 0.95, respectively. Finally, an ANN model is used to quantify the VOC concentration with an R 2 value of 0.9996. The results demonstrate the potential using transfer-learning-based algorithms as the data reduction strategies for classification of spectroscopic data.

Re-Synthesis of CIGS Nanocrystallites Using Oxidation Roasting Pretreatment from Spent CIGS Targets

The CIGS (Cu(In, Ga)Se2) thin film solar cell sputtering process utilizes only 30% of the original target. The remaining 70% of the target must be recycled to achieve In, Ga, and Se rare metal sustainable use. It is, therefore, very important to establish spent CIGS target recycling technology to reduce environmental damage. CIGS is a tetrahedrally bonded semiconductor with a chalcopyrite crystal structure. Chalcopyrite is resistant to attack by the oxidants used in dissolution due to forming a passivation surface layer. Therefore, increasing the reaction temperature, lixiviating agent, and oxidant concentrations is necessary to enhance CIGS dissolution. The oxidation roasting pretreatment effects on the recovery and leaching of spent CIGS targets are investigated in this study. The results indicated that the proper oxidation roasting pretreatment process could significantly enhance CIGS leaching, reducing costs and increasing the reaction rate. This can be explained by the fact that the chalcopyrite structure was decomposed and transformed into easier dissolvable Cu2SeO4, In2O3, and amorphous Ga2O3 after roasting in the air. Cu, In, and Ga recoveries can reach above 99.9% by leaching CIGS roasted at 500 °C in 1 M H2SO4 at 60 °C for 1 h. As the roasting temperature was increased to 600 °C, the Ga recovery rate decreased due to the formation of difficult dissolvable β-Ga2O3. Mono-dispersed, near-stoichiometric CIGS nanoparticles with a mean crystallite size of 9 nm can be obtained using a direct recycling process combining oxidation, leaching, and re-synthesis processes.

Research on Detection Algorithm of Safety helmet based on improved YOLOX

In the industrial production, construction and other fields, safety helmet as an important safety protective equipment, the wearing situation of workers personal safety and property safety is of great significance. Therefore, this paper proposes a safety helmet detection method based on the improved YOLOX algorithm. First, the 5000 pictures of safety helmet wearing at construction sites are labelled. The Squeeze and-Excitation module is introduced in the YOLOX network structure. The original Loss function is replaced with varifocal Loss. After experimental verification, compared with the original YOLOX target detection algorithm, our algorithm improves by 2.13 percentage points, enhances the model's focus on key areas and optimizes the model training effect, while the number of model parameters does not increase significantly. In conclusion, our algorithm has a wide range of application prospects and research value.

Abstract 6563: Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients

Abstract Adult diffuse gliomas are heterogeneous and the most common primary brain tumors. Gliomas have high tissue invasion, proliferation and therapeutic resistance potential. Although much knowledge has been recently gained regarding glioma biology and evolution, many questions are still open, such as which is the cell of origin, what are its characteristics and how the tumor propagation occurs. The cancer stem cell (CSC) model proposes a population of cells with high self-renew capacity and capable of propagating the tumor with more differentiated cells, generating intratumoral heterogeneity. Based on the CSC model, our work aims to integrate the most advanced techniques available such as scRNAseq and machine learning models to estimate the enrichment of glioma stem cells (GSCs) in tumors, by defining a GSC-Stemness Index (GSCsi). To build the prediction model, we used public scRNA-seq data from glioblastoma (GBM)-enriched GSCs. We used the standard Seurat pipeline for quality control, normalization and downstream analysis. The GSC single cell data was used to train a prediction model using the One Class Logistic Regression (OCLR) algorithm. Several models were tested, including overdispersed genes, differentially expressed genes between GSCs and the whole tumor, GSCs from each patient individually and a model with all GSCs. The prediction models were applied to gene expression data from TCGA, GLASS, and publicly available scRNA-seq data from different glioma subtypes. The model built with all the GSCs and all genes showed the best performance, being able to identify with higher indices grade 4 gliomas and IDHwt in the TCGA and GLASS data. Survival analysis resulted in a hazard ratio greater than 20, indicating a high correlation between increased GSCsi and poor prognosis. By applying the model to scRNA-seq data from gliomas, clusters of high GSCsi were identified. We can partially conclude that the GSCsi obtained with the model is capable of identifying grade 4 gliomas and IDHwt and we are performing analyzes with the genes with the highest correlation (positive and negative) with the GSCsi in the TCGA and GLASS data. The analysis of these genes together with genes differentially expressed in the high GSCsi clusters in scRNA-seq data can elucidate pathways responsible for the therapeutic resistance and propagation of gliomas, in addition to proposing theories about the cell of origin and potential therapeutic targets to improve the diagnosis and treatment of these patients. Citation Format: Renan L. Simões, Maycon Marção, Tathiane M. Malta. Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6563.