ELECTRONICALLY RECORDED SELF-WEIGHING IN BEHAVIORAL TREATMENT FOR WEIGHT LOSS

Background: Self-weighing is a recommended but understudied weight loss strategy. Objectives: 1) Examine the mediating effects of adherence to energy intake (EI) and energy expenditure (EE) goals on the association between self-weighing and weight changes; 2) Identify self-weighing patterns and examine differences in adherence to EI/EE goals and weight changes across self-weighing patterns; 3) Explore participants’ experience of daily self-weighing. Methods: The study included two methodological approaches. In the quantitative component, we conducted a secondary analysis of self-weighing data from a clinical trial (SELF) and a longitudinal, descriptive study of behavioral treatment for weight loss (EMPOWER). Outcome weight was measured every 6 months in the project office. Adherence to self-weighing protocols was calculated using data from electronic scales in the participants’ homes. Adherence to EI/EE goals was obtained from the self-monitoring data. Linear mixed modeling, mediation analysis and group-based trajectory modeling were used for analysis. In the qualitative component, we conducted three focus groups to explore participants’ experience of daily weighing. Content analysis was used to identify themes. Results: During the first six months of the SELF study, there was a significant mediation effect of adherence to EI and EE goals on the association between adherence to self-weighing and percent weight change (indirect effect: b=-0.26, p=0.02; b=-0.23, p=0.02). Using EMPOWER study data, three patterns of self-weighing were identified: high/consistent (75.0% self-weighed ≥6 days/week regularly); moderate/declined (16.2% declined from 4-5 to 2 days/week); minimal/declined (8.8% declined from 5-6 to 0 days/week). The high/consistent group achieved greater weight loss than the other two groups at 6 months (10.19%, 5.45%, and 2.00%) and 12 months (9.90%, 5.62%, and 0.65%). Focus group data revealed reasons for daily self-weighing included feeling motivated, providing feedback for eating and exercise behaviors, and feeling in control. Reasons for not weighing daily included interruption of routine and weight gain. The main suggestion for future users of this strategy was learning to accept a normal range of weight fluctuation. Conclusions: Findings suggest that the majority of participants were able to sustain a habit of daily self-weighing, which impacts weight changes directly and indirectly through changes in EI and EE

Monetary Theory from a Chinese Historical Perspective

We discuss monetary thought in ancient China from the perspective of Western monetary theory. It sets out the structure of economic activity in the various dynasties of ancient China and emphasizes the differences in monetary structure from Europe (and later North America). Imperial China was a politically integrated structure with regional segmentation of economic activities and hence with regional money. Monetary policy was one body conducted at regional level, but overseen naturally politically before national integration under the Ming dynasty (14th century). In various regions different forms of money circulated, with gold, silver, copper, and paper all present at various times. Monetary policy was guided by monetary thought, such as later in Europe. Basic concepts such as monetary function, the velocity of circulation, inflation, interest rate parity and the quantity theory were all present. The economics of Imperial China witnessed boom and bust, inflation and deflation and monetary control much like Europe to follow. Monetary thought thus seemingly preceded Western thought, and had remarkable similarities. Whether much of this thought travelled down the silk road remains unknown, but the possibility is intriguing.

The utility of traditional Chinese medicine (Shenmai) in the cardiac rehabilitation after coronary artery bypass grafting: A single-center randomized clinical trial

Objective examine the efficacy and safety of Shenmai to the cardiac rehabilitation in patients received coronary artery bypass grafting. Design a single-center randomized, single blind clinical trial. Setting Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Subjects Patients with coronary artery disease who received coronary artery bypass grafting in our center were studied. They must be competent to complete the 6-minute walking test without any assistance and without any severe comorbidity. Interventions in Shemmai group, the participants were treated with Shenmai injection (100 ml/day) right after the surgery to discharge for 9.28 ± 3.75 days and then capsule (3.6 g/day) sequentially for 30 days in addition to the cardiac rehabilitation. In control group, only cardiac rehabilitation was conducted. Main measures the 6-Minute Walking Test was measured at three time points: one day before operation, on the day of discharge and 30 days follow up. Results The sample (n = 166) was predominately male (84%), with mean age was 61.12 ± 9.13 years. There was no significant difference between groups in baseline characteristics and the procedural characteristics. There was one death in control group and one stroke in Shenmai group right after the surgery. Overall, there was group (p = .005) and time effect (p < .001) on the 6-minute walking distance. Participants in the Shenmai group walked longer distance in meters compared with control group on the day of discharge (314.54 ± 64.14 vs. 271.29 ± 76.82, P < .001), while no significant differences before operation (399.72 ± 93.19 vs. 403.67 ± 91.99, p = .78) and on 30-day follow up (436.54 ± 67.64 vs. 421.64 ± 83.53, p = .21). Conclusion Shenmai improves the exercise tolerance in the early stage of the cardiac rehabilitation for patients received coronary artery bypass grafting

Degradable mesoporous semimetal antimony nanospheres for near-infrared II multimodal theranostics.

Metallic and semimetallic mesoporous frameworks are of great importance owing to their unique properties and broad applications. However, semimetallic mesoporous structures cannot be obtained by the traditional template-mediated strategies due to the inevitable hydrolytic reaction of semimetal compounds. Therefore, it is yet challenging to fabricate mesoporous semimetal nanostructures, not even mention controlling their pore sizes. Here we develop a facile and robust selective etching route to synthesize monodispersed mesoporous antimony nanospheres (MSbNSs). The pore sizes of MSbNSs are tunable by carefully controlling the partial oxidation of Sb nuclei and the selective etching of the as-formed Sb2O3. MSbNSs show a wide absorption from visible to second near-infrared (NIR-II) region. Moreover, PEGylated MSbNSs are degradable and the degradation mechanism is further explained. The NIR-II photothermal performance of MSbNSs is promising with a high photothermal conversion efficiency of ~44% and intensive NIR-II photoacoustic signal. MSbNSs show potential as multifunctional nanomedicines for NIR-II photoacoustic imaging guided synergistic photothermal/chemo therapy in vivo. Our selective etching process would contribute to the development of various semimetallic mesoporous structures and efficient multimodal nanoplatforms for theranostics

Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BACKGROUND: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing

The Correlation Between SPP1 and Immune Escape of EGFR Mutant Lung Adenocarcinoma Was Explored by Bioinformatics Analysis

BackgroundImmune checkpoint inhibitors have achieved breakthrough efficacy in treating lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFR), leading to the revision of the treatment guidelines. However, most patients with EGFR mutation are resistant to immunotherapy. It is particularly important to study the differences in tumor microenvironment (TME) between patients with and without EGFR mutation. However, relevant research has not been reported. Our previous study showed that secreted phosphoprotein 1 (SPP1) promotes macrophage M2 polarization and PD-L1 expression in LUAD, which may influence response to immunotherapy. Here, we assessed the role of SPP1 in different populations and its effects on the TME.MethodsWe compared the expression of SPP1 in LUAD tumor and normal tissues, and in samples with wild-type and mutant EGFR. We also evaluated the influence of SPP1 on survival. The LUAD data sets were downloaded from TCGA and CPTAC databases. Clinicopathologic characteristics associated with overall survival in TCGA were assessed using Cox regression analysis. GSEA revealed that several fundamental signaling pathways were enriched in the high SPP1 expression group. We applied CIBERSORT and xCell to calculate the proportion and abundance of tumor-infiltrating immune cells (TICs) in LUAD, and compared the differences in patients with high or low SPP1 expression and wild-type or mutant EGFR. In addition, we explored the correlation between SPP1 and CD276 for different groups.ResultsSPP1 expression was higher in LUAD tumor tissues and in people with EGFR mutation. High SPP1 expression was associated with poor prognosis. Univariate and multivariate cox analysis revealed that up-regulated SPP1 expression was independent indicator of poor prognosis. GSEA showed that the SPP1 high expression group was mainly enriched in immunosuppressed pathways. In the SPP1 high expression group, the infiltration of CD8+ T cells was lower and M2-type macrophages was higher. These results were also observed in patients with EGFR mutation. Furthermore, we found that the SPP1 expression was positively correlated with CD276, especially in patients with EGFR mutation.ConclusionSPP1 levels might be a useful marker of immunosuppression in patients with EGFR mutation, and could offer insight for therapeutics

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Compressing large scale urban trajectory data

With the increasing size of trajectory data generated by location-based services and applications which are built from inexpensive GPS-enabled devices in urban environments, the need for com- pressing large scale trajectories becomes obvious. This paper pro- poses a scalable urban trajectory compression scheme (SUTC) that can compress a set of trajectories collectively by exploiting com- mon movement behaviors among the urban moving objects such as vehicles and smartphone users. SUTC exploits that urban objects moving in similar behaviors naturally, especially large-scale of hu- man and vehicle which are moving constrained by some geograph- ic context (e.g., road networks or routes). To exploit redundancy across a large set of trajectories, SUTC first transforms trajectory sequences from Euclidean space to network-constrained space and represents each trajectory with a sequence of symbolic positions in textual domain. Then, SUTC performs compression by encoding the symbolic sequences with general-purpose compression meth-ods. The key challenge in this process is how to transform the tra-jectory data from spatio-temporal domain to textual domain with-out introducing unbounded error. We develop two strategies (i.e.,velocity-based symbolization, and beacon-based symbolization) to enrich the symbol sequences and achieves high compression ratios by sacrificing a little bit the decoding accuracy. Besides, we al-so optimize the organization of trajectory data in order to adapt it to practical compression algorithms, and increase the efficiency of compressing processes. Our experiments on real large-scale trajec-tory datasets demonstrate the superiority and feasibility of the our proposed algorithms. Copyrigh

COSBench: Cloud Object Storage Benchmark

ABSTRACT With object storage systems being increasingly recognized as a preferred way to expose one&apos;s storage infrastructure to the web, the past few years have witnessed an explosion in the acceptance of these systems. Unfortunately, the proliferation of available solutions and the complexity of each individual one, coupled with a lack of dedicated workload, makes it very challenging for one to evaluate and tune the performance of different systems. To help address this problem, we present the Cloud Object Storage Benchmark (COSBench). It is a benchmark tool that we have developed at Intel with the goal of facilitating both performance comparison and system optimization of these systems. In this paper, we describe the design and implementation of this tool, focusing on its extensibility and scalability. In addition, we discuss how people can use this tool to perform system characterization and how the latter can facilitate system comparison and optimization. To demonstrate the value of our tool, we report the results of our experiments conducted on two Swift setups we built in our lab. We also share some of our experiences in turning our setups to achieve higher performance