IBP reduction coefficients made simple

We present an efficient method to shorten the analytic integration-by-parts (IBP) reduction coefficients of multi-loop Feynman integrals. For our approach, we develop an improved version of Leinartas' multivariate partial fraction algorithm, and provide a modern implementation based on the computer algebra system Singular. Furthermore, We observe that for an integral basis with uniform transcendental (UT) weights, the denominators of IBP reduction coefficients with respect to the UT basis are either symbol letters or polynomials purely in the spacetime dimension $D$. With a UT basis, the partial fraction algorithm is more efficient both with respect to its performance and the size reduction. We show that in complicated examples with existence of a UT basis, the IBP reduction coefficients size can be reduced by a factor of as large as $\sim 100$. We observe that our algorithm also works well for settings without a UT basis.Comment: minor changes, typos correcte

A computation of two-loop six-point Feynman integrals in dimensional regularization

We compute three families of two-loop six-point massless Feynman integrals in dimensional regularization, namely the double-box, the pentagon-triangle, and the hegaxon-bubble family. This constitutes the first analytic computation of two-loop master integrals with eight scales. We use the method of canonical differential equations. We describe the corresponding integral basis with uniform transcendentality, the relevant function alphabet, and analytic boundary values at a particular point in the Euclidean region up to the fourth order in the regularization parameter $\epsilon$. The results are expressed as one-fold integrals over classical polylogarithms suitable for fast and high-precision evaluation.Comment: 35 pages, 5 figure

Pulmonary Cryptococcosis Diagnosed by Metagenomic Next-Generation Sequencing in a Young Patient With Normal Immune Function: A Case Report

BackgroundPulmonary cryptococcosis (PC) is a serious opportunistic fungal infection that usually occurs in immunocompromised patients. This disease is often difficult to diagnose in time due to its clinical manifestations and radiological feature similar to other pulmonary infections, as well as the low sensitivity of conventional diagnostic methods. Cryptococcosis in immune-competent patients is rare.Case PresentationHere we report a case of PC in an immune-competent patient. Tuberculosis was suspected according to radiological features due to the positive T-lymphocyte spot test and pure protein derivative skin test. To further detect the pathogen, bronchoalveolar lavage fluid (BALF) was collected for metagenomic next-generation sequencing (mNGS). Cryptococcus neoformans (one specific read) was identified by mNGS, indicating the PC of this patient. The following BALF culture and cryptococcal antigen lateral flow assay (CrAg-LFA) test also showed Cryptococcus infection, confirming the mNGS detection. Voriconazole (0.4 g daily) was given orally according to the subsequent susceptibility results. After seven months of treatment, the patient's condition improved.ConclusionMetagenomic next-generation sequencing (mNGS) is a better diagnostic tool to help clinicians distinguish pulmonary cryptococcosis from other atypical pulmonary infections

Predicting cervical intraepithelial neoplasia and determining the follow-up period in high-risk human papillomavirus patients

PurposeDespite strong efforts to promote human papillomavirus (HPV) vaccine and cervical cancer screening, cervical cancer remains a threat to women’s reproductive health. Some high-risk HPV types play a crucial role in the progression of cervical cancer and precancerous lesions. Therefore, HPV screening has become an important means to prevent, diagnose, and triage cervical cancer. This study aims to leverage artificial intelligence to predict individual risks of cervical intraepithelial neoplasia (CIN) in women with high-risk HPV infection and to recommend the appropriate triage strategy and follow-up period according to the risk level.Materials and methodsA total of 475 cases were collected in this study. The sources were from the Department of Gynecology and Obstetrics in a tertiary hospital, a case report on HPV from the PubMed website, and clinical data of cervical cancer patients from The Cancer Genome Atlas (TCGA) database. Through in-depth study of the interaction between high-risk HPV and its risk factors, the risk factor relationship diagram structure was constructed. A Classification of Lesion Stages (CLS) algorithm was designed to predict cervical lesion stages. The risk levels of patients were analyzed based on all risk factors, and follow-up periods were formulated for each risk level.ResultsOur proposed CLS algorithm predicted the probability of occurrence of CIN3—the precancerous lesion stage of cervical cancer. This prediction was based on patients’ HPV-16 and -18 infection status, age, presence of persistent infection, and HPV type. Follow-up periods of 3–6 months, 6–12 months, and 3- to 5-year intervals were suggested for high-risk, medium-risk, and low-risk patients, respectively.ConclusionA lesion prediction model was constructed to determine the probabilities of occurrence of CIN by analyzing individual data, such as patient lifestyle, physical assessments, and patient complaints, in order to identify high-risk patients. Furthermore, the potential implications of the calculated features were mined to devise prevention strategies

TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision

Numerous large language model (LLM) agents have been built for different tasks like web navigation and online shopping due to LLM's wide knowledge and text-understanding ability. Among these works, many of them utilize in-context examples to achieve generalization without the need for fine-tuning, while few of them have considered the problem of how to select and effectively utilize these examples. Recently, methods based on trajectory-level retrieval with task meta-data and using trajectories as in-context examples have been proposed to improve the agent's overall performance in some sequential decision making tasks. However, these methods can be problematic due to plausible examples retrieved without task-specific state transition dynamics and long input with plenty of irrelevant context. In this paper, we propose a novel framework (TRAD) to address these issues. TRAD first conducts Thought Retrieval, achieving step-level demonstration selection via thought matching, leading to more helpful demonstrations and less irrelevant input noise. Then, TRAD introduces Aligned Decision, complementing retrieved demonstration steps with their previous or subsequent steps, which enables tolerance for imperfect thought and provides a choice for balance between more context and less noise. Extensive experiments on ALFWorld and Mind2Web benchmarks show that TRAD not only outperforms state-of-the-art models but also effectively helps in reducing noise and promoting generalization. Furthermore, TRAD has been deployed in real-world scenarios of a global business insurance company and improves the success rate of robotic process automation.Comment: Codes available at: https://github.com/skyriver-2000/TRAD-Officia

Novel frameless LINAC radiosurgery solution for uveal melanoma

IntroductionRadiation treatment has replaced enucleation as an organ-preservation treatment for patients with uveal melanoma (UM). We developed a novel non-invasive, frameless LINAC based solution for fractionated stereotactic radiosurgery (fSRS) treatment.MethodsWe designed and constructed the a stereotactic ocular localization box that can be attached and indexed to a stereotactic LINAC tabletop. It contains adjustable LED lights as a gaze focus point and CCD camera for monitoring of the patient’s eye position. The device also has 6 infrared spheres compatible with the ExacTRAC IGRT system. Treatment plans were developed using iPLAN Dose version 4.5, with conformal dynamic arcs and 6MV photon beam in flattening filter free mode, dosed to 50Gy in 5 fractions. During treatment, patients were instructed to stare at the light when a radiation beam is prepared and ready for delivery. Eye movement was tracked throughout treatment. Residual setup errors were recorded for evaluation.ResultsThe stereotactic ocular localization box was 3D-printed with polylactic acid material and attached to the stereotactic LINAC tabletop. 10 patients were treated to evaluate the feasibility, tolerability and setup accuracy. Median treatment time for each arc is 17.3 ± 2.4 seconds (range: 13.8-23.4). After ExacTRAC setup, the residual setup errors are -0.1 ± 0.3 mm laterally, -0.1 ± 0.3 mm longitudinally, and 0 ± 0.2 mm vertically. The residue rotational errors are -0.1 ± 0.3 degree pitch, 0.1 ± 0.2 degree roll, and 0 ± 0.2 degree couch rotation. All patients received treatment successfully.ConclusionWe successfully developed a novel non-invasive frameless mask-based LINAC solution for SRS for uveal melanoma, or other ocular tumors. It is well tolerated with high set up accuracy. Future directions for this localization box would include a multi-center trial to assess the efficacy and reproducibility in the fabrication and execution of such a solution for UM therapy

Novel Frameless LINAC Radiosurgery Solution for Uveal Melanoma

INTRODUCTION: Radiation treatment has replaced enucleation as an organ-preservation treatment for patients with uveal melanoma (UM). We developed a novel non-invasive, frameless LINAC based solution for fractionated stereotactic radiosurgery (fSRS) treatment. METHODS: We designed and constructed the a stereotactic ocular localization box that can be attached and indexed to a stereotactic LINAC tabletop. It contains adjustable LED lights as a gaze focus point and CCD camera for monitoring of the patient\u27s eye position. The device also has 6 infrared spheres compatible with the ExacTRAC IGRT system. Treatment plans were developed using iPLAN Dose version 4.5, with conformal dynamic arcs and 6MV photon beam in flattening filter free mode, dosed to 50Gy in 5 fractions. During treatment, patients were instructed to stare at the light when a radiation beam is prepared and ready for delivery. Eye movement was tracked throughout treatment. Residual setup errors were recorded for evaluation. RESULTS: The stereotactic ocular localization box was 3D-printed with polylactic acid material and attached to the stereotactic LINAC tabletop. 10 patients were treated to evaluate the feasibility, tolerability and setup accuracy. Median treatment time for each arc is 17.3 ± 2.4 seconds (range: 13.8-23.4). After ExacTRAC setup, the residual setup errors are -0.1 ± 0.3 mm laterally, -0.1 ± 0.3 mm longitudinally, and 0 ± 0.2 mm vertically. The residue rotational errors are -0.1 ± 0.3 degree pitch, 0.1 ± 0.2 degree roll, and 0 ± 0.2 degree couch rotation. All patients received treatment successfully. CONCLUSION: We successfully developed a novel non-invasive frameless mask-based LINAC solution for SRS for uveal melanoma, or other ocular tumors. It is well tolerated with high set up accuracy. Future directions for this localization box would include a multi-center trial to assess the efficacy and reproducibility in the fabrication and execution of such a solution for UM therapy

Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study

ObjectiveThe purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.MethodsThirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.ResultCompared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = −2.175, p = 0.034), gamma (t = −2.332, p = 0.023), and local efficiency (t = −2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = −3.581, p = 0.0007), and the network efficiency values (t = −2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.ConclusionOur study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP

Collaborative environmental management in the Pearl River Delta : an urban operation research approach for electricity consumption

Electricity generation is the major emission source of air pollutants in the highly industrialized Pearl River Delta Region. In a compact region like the Pearl River Delta, pollutants can easily transfer from one city to another. The research question of this study is to construct an optimal and mutual agreeable scheme to reduce electricity consumption in the Pearl River Delta Region, which involves the collaboration of all cities in the Region. The main objective of the study is to conduct a cooperative scheme that internalizes the external social cost of electricity consumption through optimal electricity consumption reduction. This research first surveys papers on urban environmental problems, especially environmental problems caused in Pearl River Delta Region. Literature review indicates that public electricity generation is the major emission source of air pollutants in this region. Secondly, this research reviews literatures on the social costs of electricity consumption. Reviews show that external costs of electricity consumption in different countries differ widely, ranging from 13% to 700% of electricity price. This study adopts the lower quartile of this range, which is 13% of electricity price. Thirdly, urban operations research is reviewed, and a major policy instrument for environmental improvement, environmental tax, is investigated. This study develops a hierarchical structure of urban operations research to study the collaborative management of electricity consumption reduction in the Pearl River Delta Region. This urban operations research model includes seven essential steps: problem definition; objectives identification; performance measures; data analysis; analytical framework construction; model solution and courses of actions; and policy implementation. Moreover, this novel urban operations research model is applied in collaborative management of electricity consumption reduction in the Pearl River Delta. This research uses statistical and mathematical methods to estimate the parameters relevant to GDP, electricity consumption, external costs of electricity consumption, and environmental tax, and then formulates the operational model. Then, this model is employed to evaluate non-cooperative equilibrium condition among the eleven Pearl River Delta cities under a non-cooperative market outcome; to derive individual city’s external cost of electricity; to derive environmental levy and optimal electricity consumption reduction; and to design a compensation plan. In the compensation plan, under cooperation, in both 2013 and 2014, four cities (Guangzhou, Shenzhen, Foshan, and Dongguan) have to pay for their net spillover external cost of electricity consumption. The other seven cities (Zhuhai, Jiangmen, Huizhou, Zhaoqing, Hong Kong, Macao, and Zhongshan) would receive compensation. The urban operations research model for regional cooperation in electricity consumption reduction developed in this study provides an instrument to deal with the pollution problem in the Pearl River Delta Region. It facilitates the exploration of hitherto intractable problems in regional environmental cooperation and established solution plans. The urban operations model is expected to provide practical policy choices for a Pearl River Delta environmental collaboration scheme. This research represents the first attempt on an application of urban operations research model of collaborative management scheme of electricity consumption reduction in Pearl River Delta Region.published_or_final_versionUrban Planning and DesignDoctoralDoctor of Philosoph