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Purpose A daily quality assurance (QA) check in proton therapy is ensuring that the range of each proton beam energy in water is accurate to 1 mm. This is important for ensuring that the tumor is adequately irradiated while minimizing damage to surrounding healthy tissue. It is also important to verify the total charge collected against the beam model. This work proposes a time-efficient method for verifying the range and total charge of proton beams at different energies using a multilayer Faraday collector (MLFC). Methods We used an MLFC-128-250 MeV comprising 128 layers of thin copper foils separated by thin insulating Kapton TM layers. Protons passing through the collector induce a charge on the metallic foils, which is integrated and measured by a multichannel electrometer. The charge deposition on the foils provides information about the beam range. Results Our results show that the proton beam range obtained using MLFC correlates closely with the range obtained from commissioning water tank measurements for all proton energies. Upon applying a range calibration factor, the maximum deviation is 0.4 g/cm 2 . The MLFC range showed no dependence on the number of monitor units and the source-to-surface distance. Range measurements collected over multiple weeks exhibited stability. The total charge collected agrees closely with the theoretical charge from the treatment planning system beam model for low- and mid-range energies. Conclusions We have calibrated and commissioned the use of the MLFC to easily verify range and total charge of proton beams. This tool will improve the workflow efficiency of the proton QA.

Lysosomal enzymes are synthesized in the endoplasmic reticulum (ER) and transferred to the Golgi complex by interaction with the Batten disease protein CLN8 (ceroid lipofuscinosis, neuronal, 8). Here we investigated the relationship of this pathway with CLN6, an ER-associated protein of unknown function that is defective in a different Batten disease subtype. Experiments focused on protein interaction and trafficking identified CLN6 as an obligate component of a CLN6-CLN8 complex (herein referred to as EGRESS: ER-to-Golgi relaying of enzymes of the lysosomal system), which recruits lysosomal enzymes at the ER to promote their Golgi transfer. Mutagenesis experiments showed that the second luminal loop of CLN6 is required for the interaction of CLN6 with the enzymes but dispensable for interaction with CLN8. In vitro and in vivo studies showed that CLN6 deficiency results in inefficient ER export of lysosomal enzymes and diminished levels of the enzymes at the lysosome. Mice lacking both CLN6 and CLN8 did not display aggravated pathology compared with the single deficiencies, indicating that the EGRESS complex works as a functional unit. These results identify CLN6 and the EGRESS complex as key players in lysosome biogenesis and shed light on the molecular etiology of Batten disease caused by defects in CLN6.

In this paper, the outage behavior and diversity order of the mixture transceiver architecture for multiple-input single-output broadcast channels are analyzed. The mixture scheme groups users with closely-aligned channels and applies superposition coding and successive interference cancellation decoding to each group composed of users with closely-aligned channels, while applying zero-forcing beamforming across semi-orthogonal user groups. In order to enable such analysis, closed-form lower bounds on the achievable rates of a general multiple-input single-output broadcast channel with superposition coding and successive interference cancellation are newly derived. By employing channel-adaptive user grouping and proper power allocation, which ensures that the channel subspaces of user groups have an angle larger than a certain threshold, it is shown that the mixture transceiver architecture achieves full diversity order in multiple-input single-output broadcast channels and opportunistically increases the multiplexing gain while achieving full diversity order. Furthermore, the achieved full diversity order is the same as that of the single-user maximal ratio transmit beamforming. Hence, the mixture scheme can provide reliable communication under channel fading for ultra-reliable low latency communication. The numerical results validate our analysis and show the outage superiority of the mixture scheme over conventional transceiver designs for multiple-input single-output broadcast channels.

In this paper, we use quarterly stock price index data to test the weak form of the efficient market hypothesis for 13 art market indices. The fact that hypotheses can be efficiently tested by performing unit root tests is well known. The conventional unit root test results show that most art indices have unit roots, which means that these art markets are efficient. However, the results of the non-linear quantile unit root tests we conduct over a range of quantiles lead us to reject the null of the unit root test for several art markets. To acquire more information, we perform the test over sub-quantile intervals, finding asymmetric market efficiency between the low and high quantile intervals: we cannot reject the unit root null in upper quantile intervals for most art price indices. [ABSTRACT FROM AUTHOR]

Objective: A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation. Method: Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments. Results: Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%. Conclusions: This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors. Clinical Impact: Accurately predicting a patient’s functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

Abstract Background Brain metastases frequently occur in patients with non‐small cell lung cancer (NSCLC) resulting in a poor prognosis. Here, we investigated the association between PD‐L1 expression and brain metastasis in patients with NSCLC and its clinical significance. Methods A total of 270 patients diagnosed with metastatic NSCLC who underwent PD‐L1 testing on their tumor tissue between January 2017 and March 2019 were retrospectively reviewed. The VENTANA PD‐L1 (SP263) assay was used, and positive PD‐L1 expression was defined as staining in ≥1% of tumor cells. Results Positive PD‐L1 expression was observed in 181 (67.0%) patients, and 74 (27.4%) patients had brain metastasis at diagnosis. Synchronous brain metastases were more frequently observed in PD‐L1‐positive compared with PD‐L1‐negative patients (31.5% vs. 19.1%, p = 0.045). Multiple logistic regression analysis identified positive PD‐L1 expression (odds ratio [OR]: 2.24, p = 0.012) as an independent factor associated with synchronous brain metastasis, along with the histological subtype of nonsquamous cell carcinoma (OR: 2.84, p = 0.003). However, the incidence of central nervous system (CNS) progression was not associated with PD‐L1 positivity, with a two‐year cumulative CNS progression rate of 26.3% and 28.4% in PD‐L1‐positive and PD‐L1‐negative patients, respectively (log rank p = 0.944). Furthermore, positive PD‐L1 expression did not affect CNS progression or overall survival in patients with synchronous brain metastasis (long rank p = 0.513 and 0.592, respectively). Conclusions Initial brain metastases are common in NSCLC patients with positive PD‐L1 expression. Further studies are necessary to understand the relationship between early brain metastasis and cancer immunity.

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter λ, which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of λ increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification.

Purpose: A daily quality assurance (QA) check in proton therapy is ensuring that the range of each proton beam energy in water is accurate to 1 mm. This is important for ensuring that the tumor is adequately irradiated while minimizing damage to surrounding healthy tissue. It is also important to verify the total charge collected against the beam model. This work proposes a time-efficient method for verifying the range and total charge of proton beams at different energies using a multilayer Faraday collector (MLFC). Methods: We used an MLFC-128-250 MeV comprising 128 layers of thin copper foils separated by thin insulating KaptonTM layers. Protons passing through the collector induce a charge on the metallic foils, which is integrated and measured by a multichannel electrometer. The charge deposition on the foils provides information about the beam range. Results: Our results show that the proton beam range obtained using MLFC correlates closely with the range obtained from commissioning water tank measurements for all proton energies. Upon applying a range calibration factor, the maximum deviation is 0.4 g/cm2. The MLFC range showed no dependence on the number of monitor units and the source-to-surface distance. Range measurements collected over multiple weeks exhibited stability. The total charge collected agrees closely with the theoretical charge from the treatment planning system beam model for low- and mid-range energies. Conclusions: We have calibrated and commissioned the use of the MLFC to easily verify range and total charge of proton beams. This tool will improve the workflow efficiency of the proton QA. [ABSTRACT FROM AUTHOR]