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Centrality-dependent measurements of hadron and jet cross section attenuation in deep inelastic scattering on nuclei can shed new light on the physics of final-state interactions in nuclear matter, including the path-length dependence of the in-medium parton shower formation and evolution. Recent simulation studies have demonstrated the feasibility of experimental centrality determination in eA reactions at the electron-ion collider via neutron detection in the zero-degree calorimeter. Motivated by these results, we present the first theoretical calculation of the production rate modification for hadrons and jets in central and peripheral ePb collisions. We find that the variation in the suppression of inclusive jet cross section as a function of centrality is less than a factor of two. In more differential measurements, such as the distribution of hadrons versus the hadronization fraction zh, the difference can be enhanced up to an order of magnitude.

Abstract We derive a factorization theorem for the Higgs-boson production amplitude in gluon-gluon fusion induced by a light-quark loop, working at next-to-leading power in soft-collinear effective theory. The factorization is structurally similar to that obtained for the h → γγ decay amplitude induced by a light-quark loop, but additional complications arise because of external color charges. We show how the refactorization-based subtraction scheme developed in previous work leads to a factorization theorem free of endpoint divergences. We use renormalization-group techniques to predict the logarithmically enhanced terms in the three-loop gg → h form factor of order α s 3 ln k − M h 2 / m b 2 $$ {\alpha}_s^3{\ln}^k\left(-{M}_h^2/{m}_b^2\right) $$ with k = 6, 5, 4, 3. We also resum the first three towers of leading logarithms, α s n ln 2 n − k − M h 2 / m b 2 $$ {\alpha}_s^n{\ln}^{2n-k}\left(-{M}_h^2/{m}_b^2\right) $$ with k = 0, 1, 2, to all orders of perturbation theory.

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network–Long Short-Term Memory–Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system’s promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

Abstract Background It would be very helpful to stratify patients and direct patient selection if risk factors for quality of life were identified in a particular population. Nonetheless, it is still challenging to forecast the health-related quality of life among individuals with spinal metastases. The goal of this study was to stratify patient’s populations for whom the assessment of quality of life should be encouraged by developing and validating a nomogram to predict the quality of life among advanced cancer patients with spine metastases. Methods This study prospectively analyzed 208 advanced cancer patients with spine metastases, and collected their general characteristics, food preferences, addictions, comorbidities, therapeutic strategies, and mental health status. The functional assessment of cancer therapy-general (FACT-G) and hospital anxiety and depression scale (HADS) were used to assess quality of life and mental health, respectively. The complete cohort of patients was randomly divided into two groups: a training set and a validation set. Patients from the training set were conducted to train and develop a nomogram, while patients in the validation set were performed to internally validate the nomogram. The nomogram contained significant variables discovered using the least absolute shrinkage and selection operator (LASSO) approach in conjunction with 10-fold cross-validation. The nomogram’s predictive ability was assessed utilizing discrimination, calibration, and clinical usefulness. Internal validation was also completed using the bootstrap method after applying 500 iterations of procedures. A web calculator was also developed to promote clinical practice. Results Advance cancer patients with spinal metastases had an extremely low quality of life, as indicated by the average FACT-G score of just 60.32 ± 20.41. According to the LASSO and 10-fold cross-validation, Eastern Cooperative Oncology Group (ECOG) score, having an uncompleted life goal, preference for eating vegetables, chemotherapy, anxiety status, and depression status were selected as nomogram predictors. In the training set, the area under the receiver operating characteristic curve (AUROC) was 0.90 (95% CI: 0.84–0.96), while in the validation set, it was 0.85 (95% CI: 0.78–0.93). They were 0.50 (95% CI: 0.41–0.58) and 0.44 (95% CI: 0.33–0.56), respectively, for the discrimination slopes. The nomogram had favorable capacity to calibrate and was clinically useful, according to the calibration curve and decision curve analysis. When compared to patients in the low-risk group, patients in the high-risk group were above four times more likely to experience a poor quality of life (82.18% vs. 21.50%, P

232Th/233U fuel cycle is an alternate candidate for future nuclear energy. Neutron-induced fission cross section of 232Th is one of the important nuclear data if using thorium as the fuel in the future. 232Th(n, f) cross sections from 1 to 300 MeV were measured at China Spallation Neutron Source (CSNS) Back-streaming neutron facility (Back-n) relative to 235U(n, f) and n-p scattering. A fast ionization chamber for fission cross section measurement (FIXM) and a proton recoil telescope (PRT) were used to perform the measurement. The neutron energy in this measurement reaches up to 300 MeV which is supposed to be the highest among the current Experimental Nuclear Reaction Data (EXFOR) library. It stands as the only experimental data in the region of 200-300 MeV in EXFOR as well. The experimental and evaluated data of 232Th(n, f) cross section above 60 MeV are scarce, the measurement data of this work will be valuable references for future evaluation in high energy. For the data below 60 MeV, most of our data are more consistent with ENDF/B-VIII.0, excepting in the region of 1-2 MeV where our data are more in favor of CENDL-3.2.

Abstract Factorization theorems in soft-collinear effective theory at subleading order in power counting involve “radiative jet functions”, defined in terms of matrix elements of hard-collinear fields with a soft momentum emitted from inside the jet. Of particular importance are the radiative quark jet functions with an external photon or gluon, which arise e.g. in the factorization theorems for the Higgs-boson amplitudes h → γγ, h → gg and gg → h induced by light-quark loops. While the photon case has been studied extensively in previous work, we present here a detailed study of the radiative jet function with an external gluon. We calculate this jet function at one- and two-loop order, derive its one-loop anomalous dimension and study its renormalization-group evolution.

PurposeUsing an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.MethodsWe extracted a cohort of 124,770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of 3 months or less were considered to have had early death. To compare patients with and without early mortality, subgroup analysis was used. Patients were randomly divided into two groups: a training cohort (n = 1509, 80%) and an internal testing cohort (n = 388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models for predicting early mortality, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting, and it was able to combine the results from the multiply machine learning algorithms. The study employed both internal and external validations, and the key performance indicators included the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n = 98). Feature importance and reclassification were both operated in the study.ResultsThe early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (p = 0.019), marital status (p = 0.004), tumor stage (p = 0.025), node stage (p = 0.001), fibrosis score (p = 0.040), AFP level (p = 0.032), tumor size (p = 0.001), lung metastases (p < 0.001), cancer-directed surgery (p < 0.001), radiation (p < 0.001), and chemotherapy (p < 0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confidence interval [CI]: 0.727–0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed similar results; with an AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Feature importance demonstrated that the top three most crucial features were chemotherapy, radiation, and lung metastases based on the ensemble model. Reclassification of patients revealed a substantial difference in the two risk groups’ actual probabilities of early mortality (74.38% vs. 31.35%, p < 0.001). Patients in the high-risk group had significantly shorter survival time than patients in the low-risk group (p < 0.001), according to the Kaplan–Meier survival curve.ConclusionsThe ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases. With the aid of routinely accessible clinical characteristics, this model can be a trustworthy prognostic tool to predict the early death of those patients and facilitate clinical decision-making.

Purpose: This study aims to develop a prediction model to categorize the risk of early death among breast cancer patients with bone metastases using machine learning models.Methods: This study examined 16,189 bone metastatic breast cancer patients between 2010 and 2019 from a large oncological database in the United States. The patients were divided into two groups at random in a 90:10 ratio. The majority of patients (n = 14,582, 90%) were served as the training group to train and optimize prediction models, whereas patients in the validation group (n = 1,607, 10%) were utilized to validate the prediction models. Four models were introduced in the study: the logistic regression model, gradient boosting tree model, decision tree model, and random forest model.Results: Early death accounted for 17.4% of all included patients. Multivariate analysis demonstrated that older age; a separated, divorced, or widowed marital status; nonmetropolitan counties; brain metastasis; liver metastasis; lung metastasis; and histologic type of unspecified neoplasms were significantly associated with more early death, whereas a lower grade, a positive estrogen receptor (ER) status, cancer-directed surgery, radiation, and chemotherapy were significantly the protective factors. For the purpose of developing prediction models, the 12 variables were used. Among all the four models, the gradient boosting tree had the greatest AUC [0.829, 95% confident interval (CI): 0.802–0.856], and the random forest (0.828, 95% CI: 0.801–0.855) and logistic regression (0.819, 95% CI: 0.791–0.847) models came in second and third, respectively. The discrimination slopes for the three models were 0.258, 0.223, and 0.240, respectively, and the corresponding accuracy rates were 0.801, 0.770, and 0.762, respectively. The Brier score of gradient boosting tree was the lowest (0.109), followed by the random forest (0.111) and logistic regression (0.112) models. Risk stratification showed that patients in the high-risk group (46.31%) had a greater six-fold chance of early death than those in the low-risk group (7.50%).Conclusion: The gradient boosting tree model demonstrates promising performance with favorable discrimination and calibration in the study, and this model can stratify the risk probability of early death among bone metastatic breast cancer patients.

Fatty Acid Desaturase-1 (FADS1) or delta 5 desaturase (D5D) is a rate-limiting enzyme involved in the biosynthesis of long-chain polyunsaturated fatty acids (LC-PUFAs), i.e., arachidonic acid (ARA) and eicosapentaenoic (EPA). These LC-PUFAs and their metabolites play essential and broad roles in cancer cell proliferation, metastasis, and tumor microenvironment. However, the role of FADS1 in cancers remains incompletely understood. Utilizing The Cancer Genome Atlas (TCGA) database, we explored the role of FADS1 across different cancer types using multiple bioinformatics and statistical tools. Moreover, we studied the impact of a FADS1 inhibitor (D5D-IN-326) on proliferation of multiple cancer cell lines. We identified that FADS1 gene is a predictor for cancer survival in multiple cancer types. Compared to normal tissue, the mRNA expression of FADS1 is significantly increased in primary tumors while even higher in metastatic and recurrent tumors. Mechanistically, pathway analysis demonstrated that FADS1 is associated with cholesterol biosynthesis and cell cycle control genes. Interestingly, FADS1 expression is higher when TP53 is mutated. Tumors with increased FADS1 expression also demonstrated an increased signatures of fibroblasts and macrophages infiltration among most cancer types. Our in vitro assays showed that D5D-IN-326 significantly inhibited cell proliferation of kidney, colon, breast, and lung cancer cell lines in a dose-dependent manner. Lastly, single nucleotide polymorphisms (SNPs) which are well-established expression quantitative trait loci (eQTLs) for FADS1 in normal human tissues are also significantly correlated with FADS1 expression in tumors of multiple tissue types, potentially serving as a marker to stratify cancer patients with high/low FADS1 expression in their tumor tissue. Our study suggests that FADS1 plays multiple roles in cancer biology and is potentially a novel target for precision cancer treatment.

Deep inelastic scattering on nuclei at the Electron-Ion Collider will open new opportunities to investigate the structure of matter. Heavy flavor-tagged jets are complementary probes of the partonic composition and transport coefficients of large nuclei, but introduce a new mass scale that modifies the structure of parton showers and must be carefully accounted for in perturbative calculations. In the framework of soft-collinear effective theory with Glauber gluon interactions, we present the first calculation of inclusive charm-jet and bottom-jet cross sections in electron-nucleus collisions at next-to-leading order and compare them to the reference electron-proton case. We also show predictions for the heavy flavor-tagged jet momentum sharing distributions to further clarify the correlated in-medium modification of jet substructure.

Background: Epidemiological studies have shown Per- and polyfluoroalkyl substances (PFAS) to be associated with diseases of dysregulated lipid and sterol homeostasis such as steatosis and cardiometabolic disorders. However, the majority of mechanistic studies rely on single chemical exposures instead of identifying mechanisms related to the toxicity of PFAS mixtures. Objectives: The goal of the current study is to investigate mechanisms linking exposure to a PFAS mixture with alterations in lipid metabolism, including increased circulating cholesterol and bile acids. Methods: Male and female wild-type C57BL/6J mice were fed an atherogenic diet used in previous studies of pollutant-accelerated atherosclerosis and exposed to water containing a mixture of 5 PFAS representing legacy, replacement, and alternative subtypes (i.e., PFOA, PFOS, PFNA, PFHxS, and GenX), each at a concentration of 2 mg/L, for 12 weeks. Changes at the transcriptome and metabolome level were determined by RNA-seq and high-resolution mass spectrometry, respectively. Results: We observed increased circulating cholesterol, sterol metabolites, and bile acids due to PFAS exposure, with some sexual dimorphic effects. PFAS exposure increased hepatic injury, demonstrated by increased liver weight, hepatic inflammation, and plasma alanine aminotransferase levels. Females displayed increased lobular and portal inflammation compared to the male PFAS-exposed mice. Hepatic transcriptomics analysis revealed PFAS exposure modulated multiple metabolic pathways, including those related to sterols, bile acids, and acyl carnitines, with multiple sex-specific differences observed. Finally, we show that hepatic and circulating levels of PFOA were increased in exposed females compared to males, but this sexual dimorphism was not the same for other PFAS examined. Discussion: Exposure of mice to a mixture of PFAS results in PFAS-mediated modulation of cholesterol levels, possibly through disruption of enterohepatic circulation.

Purpose: To evaluate the safety and efficacy of continuous high-dose (6 mg) intravitreal ganciclovir injections (IVG) for cytomegalovirus (CMV) retinitis (CMVR) after haploidentical hematopoietic stem cell transplantation (Haplo-HSCT), and to explore factors that may influence the treatment procedure.Design: Prospective, randomized, single-blinded, positive-controlled, interventional, comparative study.Methods: A total of 22 patients with CMVR (32 eyes) were randomized to either high-dose group (IVG 6 mg weekly) or low-dose group (IVG 3 mg given twice weekly for 2 weeks as induction phase and weekly thereafter as maintenance phase). Patients who were recorded any positive CMV DNAemia or other active CMV diseases and needed systemic anti-CMV treatment during the study period were excluded. The vision outcome, variables of the treatment procedure, and incidence of complication and CMVR recurrence were analyzed and compared. Logistic regression was applied to determine the factors that may have an impact on the treatment process at baseline.Results: Compared to the low-dose group, the high-dose group resulted in a median of two less intravitreal injections (4 vs. 6 times, respectively, P = 0.016), while the rate of vision stability or improvement (81.2 vs. 87.5%), the incidence of complication (6.2 vs. 18.8%), and CMVR recurrence (12.5% vs. 6.2%) were similar (all P > 0.05). No drug-related toxicity was observed. Initial aqueous CMV-DNA load (OR: 6.872, 95% CI: 1.335–35.377, P = 0.021) and extension of lesion (OR: 0.942, 95% CI: 0.897 to .991, P = 0.020), but not dosing regimen (P = 0.162), were predictors of the treatment duration.Conclusions: Continuous high-dose regimen was well tolerated and resulted in less intravitreal injections, with similar vision outcomes and safety profiles. The clinical course of CMVR after Haplo-HSCT was determined by its own nature at baseline and could not be modified by treatment protocols under consistent immune background.