bluuebunny/crossref_metadata_2025 huggingface dataset:
Does recurrent catatonia manifest in a similar fashion in all the episodes of mood disorder? A case series with literature review
Authors: Nath Santanu , Bhoi Rosali , Mishra Biswaranjan , Padhy Susanta
Published: September 2021

Catatonia, originally conceptualised by Kahlbaum in 1868, is a neuropsychiatric condition that has been found to occur concomitantly with several organic and psychiatric conditions. Starting from the era of Kraepelin and Bleuler, this condition was faultily linked with schizophrenia alone; however, over time, greater associations have been found between catatonia and mood disorders. Despite the availability of several reports supporting this finding, there is a relative paucity of studies that specifically focus on catatonia to be the first symptom manifestation heralding a subsequent mood episode. In addition, there is scant literature to determine whether there are specific presentations of catatonia that show greater associations with mood disorders and whether these signs and symptoms recur in a stereotypical fashion in the subsequent mood episodes in the lifetime of an individual. We hereby report two cases with a diagnosis of mood disorders (bipolar disorder and recurrent depressive disorder) who had catatonia as the initial symptom not only at presentation but also at subsequent episodes. The report emphasises that recurrent catatonia can be the initial clinical manifestation of an underlying mood episode, which appears otherwise masked behind the catatonic presentation. These catatonic symptoms can be interestingly similar in all the subsequent episodes. A detailed clinical evaluation is thus warranted after catatonia has been duly treated to provide a holistic management.
Additive effects of variants of unknown significance in replication repair-associated DNA polymerase genes on mutational burden and prognosis across diverse cancers
Authors: Ying Jieer , Yang Lin , Yin Jiani C , Xia Guojie , Xing Minyan , Chen Xiaoxi , Pang Jiaohui , Wu Yong , Bao Hua , Wu Xue , Shao Yang , Zhu Lingjun , Cheng Xiangdong
Published: September 2021

Background

Defects in replication repair-associated DNA polymerases often manifest an ultra-high tumor mutational burden (TMB), which is associated with higher probabilities of response to immunotherapies. The functional and clinical implications of different polymerase variants remain unclear.

Methods

Targeted next-generation sequencing using a 425-cancer gene panel, which covers all exonic regions of three polymerase genes (POLE,POLD1, andPOLH), was conducted in a cohort of 12,266 patients across 16 different tumor types from January 2017 to January 2019. Prognostication ofPOLvariant-positive patients was performed using a cohort of 4679 patients from the The Cancer Genome Atlas (TCGA) datasets.

Results

The overall prevalence of somatic and germline polymerase variants was 4.2% (95% CI 3.8% to 4.5%) and 0.7% (95% CI 0.5% to 0.8%), respectively, with highest frequencies in endometrial, urinary, prostate, and colorectal cancers (CRCs). While most germline polymerase variants showed no clear functional consequences, we identified a candidate p.T466A affecting the exonuclease domain of POLE, which might be underlying the early onset in a case with childhood CRC. Low frequencies of known hot-spot somatic mutations inPOLEwere detected and were associated with younger age, the male sex, and microsatellite stability. In both the panel and TCGA cohorts,POLEdrivers exhibited high frequencies of alterations in genes in the DNA damage and repair (DDR) pathways, includingBRCA2,ATM,MSH6, andATR. Variants of unknown significance (VUS) of different polymerase domains showed variable penetrance with those in the exonuclease domain ofPOLEandPOLD1displaying high TMB. VUS inPOLgenes exhibited an additive effect as carriers of multiple VUS had exponentially increased TMB and prolonged overall survival. Similar to cases with driver mutations, the TMB-highPOLVUS samples showed DDR pathway involvement and polymerase hypermutation signatures. Combinatorial analysis ofPOLand DDR pathway status further supported the potential additive effects ofPOLVUS and DDR pathway genes and revealed distinct prognostic subclasses that were independent of cancer type and TMB.

Conclusions

Our results demonstrate the pathogenicity and additive prognostic value ofPOLVUS and DDR pathway gene alterations and suggest that genetic testing may be warranted in patients with diverse solid tumors.
Non-equilibrium phase separation in mixtures of catalytically active particles: size dispersity and screening effects
Authors: Ouazan-Reboul Vincent , Agudo-Canalejo Jaime , Golestanian Ramin
Published: September 2021

Biomolecular condensates in cells are often rich in catalytically active enzymes. This is particularly true in the case of the large enzymatic complexes known as metabolons, which contain different enzymes that participate in the same catalytic pathway. One possible explanation for this self-organization is the combination of the catalytic activity of the enzymes and a chemotactic response to gradients of their substrate, which leads to a substrate-mediated effective interaction between enzymes. These interactions constitute a purely non-equilibrium effect and show exotic features such as non-reciprocity. Here, we analytically study a model describing the phase separation of a mixture of such catalytically active particles. We show that a Michaelis–Menten-like dependence of the particles’ activities manifests itself as a screening of the interactions, and that a mixture of two differently sized active species can exhibit phase separation with transient oscillations. We also derive a rich stability phase diagram for a mixture of two species with both concentration-dependent activity and size dispersity. This work highlights the variety of possible phase separation behaviours in mixtures of chemically active particles, which provides an alternative pathway to the passive interactions more commonly associated with phase separation in cells. Our results highlight non-equilibrium organizing principles that can be important for biologically relevant liquid-liquid phase separation.

Graphic abstract
Henry Cavendish and the effect of gravity on propagation of light: a postscript
Authors: Lotze Karl-Heinz , Simionato Silvia
Published: September 2021

This paper is devoted to two hitherto unpublished original documents by Henry Cavendish (1731–1810) which provide insight into his calculations of the deflection of light by isolated celestial bodies. Together with a transcription of these documents, we comment on their contents in the present-day language of physics. Moreover, we compare them with a paper by Johann Georg von Soldner (1776–1833) on the same subject.
AOI-shapes: An Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of Interest
Authors: Li Mingzhao , Bao Zhifeng , Choudhury Farhana , Samet Hanan , Duckham Matt , Sellis Timos
Published: September 2021

Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization of user-defined urban AOIs in an interactive manner. In particular, we first define the problem of user-defined AOI visualization based on a real estate data visualization scenario, and we illustrate why a novel footprint method is needed to support the visualization. After extensively reviewing existing “footprint” methods, we propose a parameter-free footprint method, named AOI-shapes, to capture the boundary information of a user-defined urban AOI. Next, to allow interactive query refinements by the user, we propose two efficient and scalable algorithms to incrementally generate urban AOIs by reusing existing visualization results. Finally, we conduct extensive experiments with both synthetic and real-world datasets to demonstrate the quality and efficiency of the proposed methods.
A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling
Authors: Bernard Jürgen , Hutter Marco , Sedlmair Michael , Zeppelzauer Matthias , Munzner Tamara
Published: September 2021

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML)
Model Output
,
Instance Relations
,
Measure Functionality
, and
Measure Valence
. We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.
Developing Conversational Agents for Use in Criminal Investigations
Authors: Hepenstal Sam , Zhang Leishi , Kodagoda Neesha , Wong B. l. william
Published: September 2021

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed.
Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative Visualizations
Authors: Barral Oswald , Lallé Sébastien , Iranpour Alireza , Conati Cristina
Published: September 2021

We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to dynamically highlight in the visualization the datapoints that are described in the textual sentence currently being read by the user, as captured by eye-tracking. These interventions were previously evaluated in two user studies that involved 98 participants reading excerpts of real-world MSNVs during a 1-hour session. Participants’ outcomes included their subjective feedback about the guidance, and well as their reading time and score on a set of comprehension questions. Results showed that the interventions can increase comprehension of the MSNV excerpts for users with lower levels of a cognitive skill known as visualization literacy. In this article, we aim to further investigate this result by leveraging eye-tracking to analyze in depth how the participants processed the interventions depending on their levels of visualization literacy. We first analyzed summative gaze metrics that capture how users process and integrate the key components of the narrative visualizations. Second, we mined the salient patterns in the users’ scanpaths to contextualize how users sequentially process these components. Results indicate that the interventions succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels, and legend), as well as between the two modalities (text and visualization). We also show that the interventions help users with lower levels of visualization literacy to better map datapoints to the legend, which likely contributed to their improved comprehension of the documents. These findings shed light on how adaptive interventions help users with different levels of visualization literacy, informing the design of personalized narrative visualizations.
After-Action Review for AI (AAR/AI)
Authors: Dodge Jonathan , Khanna Roli , Irvine Jed , Lam Kin-ho , Mai Theresa , Lin Zhengxian , Kiddle Nicholas , Newman Evan , Anderson Andrew , Raja Sai , Matthews Caleb , Perdriau Christopher , Burnett Margaret , Fern Alan
Published: September 2021

Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to
assess
an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways—leading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways people assess reinforcement learning agents in a sequential decision-making environment. We then investigated what AAR/AI brought to human assessors in two qualitative studies. The first investigated AAR/AI to gather formative information, and the second built upon the results, and also varied the type of explanation (model-free vs. model-based) used in the AAR/AI process. Among the results were the following: (1) participants reporting that AAR/AI helped to
organize their thoughts
and
think logically
about the agent, (2) AAR/AI encouraged participants to reason about the agent from a
wide range of perspectives
, and (3) participants were able to leverage AAR/AI with the model-based explanations to
falsify
the agent’s predictions.
Byte-Select Compression
Authors: Tomei Matthew , Das Shomit , Seyedzadeh Mohammad , Bedoukian Philip , Beckmann Bradford , Kumar Rakesh , Wood David
Published: September 2021

Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.
Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning
Authors: Kim Chris , Lin Xiao , Collins Christopher , Taylor Graham W. , Amer Mohamed R.
Published: September 2021

While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.
System-level Early-stage Modeling and Evaluation of IVR-assisted Processor Power Delivery System
Authors: Zou An , Zhu Huifeng , Leng Jingwen , He Xin , Reddi Vijay Janapa , Gill Christopher D. , Zhang Xuan
Published: September 2021

Despite being employed in numerous efforts to improve power delivery efficiency, the integrated voltage regulator (IVR) approach has yet to be evaluated rigorously and quantitatively in a full power delivery system (PDS) setting. To fulfill this need, we present a system-level modeling and design space exploration framework called
Ivory
for IVR-assisted power delivery systems. Using a novel modeling methodology, it can accurately estimate power delivery efficiency, static performance characteristics, and dynamic transient responses under different load variations and external voltage/frequency scaling conditions. We validate the model over a wide range of IVR topologies with silicon measurement and SPICE simulation. Finally, we present two case studies using architecture-level performance and power simulators. The first case study focuses on optimal PDS design for multi-core systems, which achieves 8.6% power efficiency improvement over conventional off-chip voltage regulator module– (VRM) based PDS. The second case study explores the design tradeoffs for IVR-assisted PDSs in CPU and GPU systems with fast per-core dynamic voltage and frequency scaling (DVFS). We find 2
μs
to be the optimal DVFS timescale, which not only reaps energy benefits (12.5% improvement in CPU and 50.0% improvement in GPU) but also avoids costly IVR overheads.
CBIR Using Features Derived by Deep Learning
Authors: Maji Subhadip , Bose Smarajit
Published: September 2021

In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.”

In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.
Scenario-Aware Program Specialization for Timing Predictability
Authors: Benz Joscha , Bringmann Oliver
Published: September 2021

The successful application of static program analysis strongly depends on flow facts of a program such as loop bounds, control-flow constraints, and operating modes. This problem heavily affects the design of real-time systems, since static program analyses are a prerequisite to determine the timing behavior of a program. For example, this becomes obvious in worst-case execution time (WCET) analysis, which is often infeasible without user-annotated flow facts. Moreover, many timing simulation approaches use statically derived timings of partial program paths to reduce simulation overhead. Annotating flow facts on binary or source level is either error-prone and tedious, or requires specialized compilers that can transform source-level annotations along with the program during optimization. To overcome these obstacles, so-called
scenarios
can be used. Scenarios are a design-time methodology that describe a set of possible system parameters, such as image resolutions, operating modes, or application-dependent flow facts. The information described by a scenario is unknown in general but known and constant for a specific system. In this article,
1
we present a methodology for scenario-aware program specialization to improve timing predictability. Moreover, we provide an implementation of this methodology for embedded software written in C/C++. We show the effectiveness of our approach by evaluating its impact on WCET analysis using almost all of TACLeBench–achieving an average reduction of WCET of 31%. In addition, we provide a thorough qualitative and evaluation-based comparison to closely related work, as well as two case studies.
Tourism Event Analytics with Mobile Phone Data
Authors: Leng Yan , Noriega Alejandro , Pentland Alex
Published: September 2021

Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a low granularity. Anonymous call detail record (CDR) is a novel source of data with enormous potential in areas of high societal value: epidemics, poverty, and urban development. This study demonstrates the added value of CDR in event tourism, especially for the analysis and evaluation of marketing strategies, event operations, and the externalities at the local and national levels. To achieve this aim, we formalize 14 indicators in high spatial and temporal resolutions to measure both the positive and the negative impacts of the touristic events. We exemplify the use of these indicators in a tourism country, Andorra, on 22 high-impact events including sports competitions, cultural performances, and music festivals. We analyze these touristic events using the large-scale CDR data across 2 years. Our approach serves as a prescriptive and a diagnostic tool with mobile phone data and opens up future directions for tourism analytics.
Strange Attractors in Writing Competence Development from the Perspective of Complexity Theory Based on Sensor Data
Authors: Zhang Chao
Published: September 2021

Writing competence is crucial for second language learners. Studying strange attractors in the development of writing competence is essential in understanding the laws of language development of foreign students. This study is aimed at investigating the state and laws of the development of Chinese as a second language (CSL) writing competence. Mathematical modeling and phase space construction methods in sensor research were used to investigate strange attractors in high‐level Chinese learners studying in China in the development of CSL from the perspective of complexity theory based on the measurement framework of complexity, accuracy, and fluency. The results showed the following: (1) there are trends in the concentration and volatility of trigonometric function in different dimensions; (2) the group dynamic characteristics of writing development in CSL are simulated precisely by mathematical modeling; and (3) there are strange attractors with lexical density in CSL writing development. The development of CSL writing tends to maintain the state of strange attractors. The strange attractor reflects regularity in the dynamic, complex, and chaotic development of Chinese for international students, revealing the probabilistic prediction competence of different states in the development of CSL.
Fracture Characteristics and Zoning Model of Overburden during Longwall Mining
Authors: Ti Zhengyi , Li Jiazhen , Wang Meng , Li Xiaoyan , Jin Zhupeng , Tai Caiwang , Wang Kang
Published: September 2021

The fracture characteristics and zoning model of overburden during longwall mining are the basis of coal mine disaster prevention. However, the existing theoretical model is inconsistent with the field measurement. In order to further research into the strata’s fracture characteristics and optimize the overburden’s zoning model, we used the elasticity and Winkler foundation theory to establish first fracture and periodic fracture mechanics models of clamped boundary supported by an elastic foundation with a key stratum as the research object. We analyzed the stress distribution characteristics and fracture evolution pattern of the mining‐induced key stratum. We analyzed the zoning characteristics of mining‐induced overburden and established the zoning model according to different fracture mechanisms. The results show that the key stratum formed a double “O‐X” shaped interconnected fracture zone after the first fracture. The key stratum formed a double “C‐K” shaped interconnected fracture zone after the periodic fracture. We divided the mining‐induced overburden into three zones along the horizontal direction: the original rock zone, the inverted triangular compression‐shear fracture zone, and the trapezoidal tensile fracture zone. The study revealed the mechanism of inverted step fracture in the separation zone, explained the fracture mechanism of the coal pillar support zone, and has significant theoretical value for the prevention and control of water disasters, gas outbursts, and strata movement.
Quantile-Dependent Expressivity of Serum Uric Acid Concentrations
Authors: Williams Paul T.
Published: September 2021

Objective. “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., serum uric acid) is high or low relative to its distribution. Analyses were performed to test whether serum uric acid heritability is quantile-specific and whether this could explain some reported gene-environment interactions. Methods. Serum uric acid concentrations were analyzed from 2151 sibships and 12,068 offspring-parent pairs from the Framingham Heart Study. Quantile-specific heritability from offspring-parent regression slopes (

β

OP

,

h

2

=
2

β

OP

/

1
+

r

spouse

) and full-sib regression slopes (

β

FS

,

h

2

=

1
+
8

r

spouse

β

FS

0.5

−
1

/

2

r

spouse

) was robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. Results. Quantile-specific

h

2

(±SE) increased with increasing percentiles of the offspring’s sex- and age-adjusted uric acid distribution when estimated from

β

OP

 

P

trend

=
0.001

:
0.34
±
0.03

at the 10th,

0.36
±
0.03

at the 25th,

0.41
±
0.03

at the 50th,

0.46
±
0.04

at the 75th, and

0.49
±
0.05

at the 90th percentile and when estimated from

β

FS

 

P

trend

=
0.006

. This is consistent with the larger genetic effect size of (1) the SLC2A9 rs11722228 polymorphism in gout patients vs. controls, (2) the ABCG2 rs2231142 polymorphism in men vs. women, (3) the SLC2A9 rs13113918 polymorphism in obese patients prior to bariatric surgery vs. two-year postsurgery following 29 kg weight loss, (4) the ABCG2 rs6855911 polymorphism in obese vs. nonobese women, and (5) the LRP2 rs2544390 polymorphism in heavier drinkers vs. abstainers. Quantile-dependent expressivity may also explain the larger genetic effect size of an SLC2A9/PKD2/ABCG2 haplotype for high vs. low intakes of alcohol, chicken, or processed meats. Conclusions. Heritability of serum uric acid concentrations is quantile-specific.
Fabrication of Dye-Sensitized Solar Cells (DSSC) Using Mg-Doped ZnO as Photoanode and Extract of Rose Myrtle (Rhodomyrtus tomentosa) as Natural Dye
Authors: Siregar Nurdin , Motlan  , Panggabean Jonny Haratua , Sirait Makmur , Rajagukguk Juniastel , Gultom Noto Susanto , Sabir Fedlu Kedir
Published: September 2021

A dye-sensitized solar cell (DSSC) device using Mg-doped Zn thin films as photoanode and fruit extract of rose myrtle (Rhodomyrtus tomentosa) as the natural dye was investigated. The effect of annealing temperature (400-550°C) on the films of photoanode was systematically studied using an X-ray diffractometer (XRD), UV-Visible Near Infrared (UV-Vis NIR) Spectrophotometer, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). XRD confirm that all sample has the wurtzite hexagonal with crystallite size of 25 nm. The SEM images reveal particles on the surface of the Mg-doped ZnO thin film of irregular shapes. Increasing the annealing temperature leads to a larger particle size and slightly increases bandgap energy. The dye sensitizer of extracted rose myrtle (Rhodomyrtus tomentosa) has a strong absorption at the visible light region. The maximum efficiency of the DSSC device is 3.53% with Mg-ZnO photoanode annealed at 500°C.
Diagnostic Value of Color Doppler Ultrasound Combined with Superb Microvascular Imaging in the Detection of Small Renal Tumors Less than 3 cm Treated with Jinkui Shenqi Pills
Authors: Gong Yuping , Li Shuhui
Published: September 2021

The purpose of this study was to investigate the diagnostic value of color Doppler ultrasound combined with superb microvascular imaging (SMI) in the detection of small renal tumors less than 3 cm treated with Jinkui Shenqi pills. 50 cases were randomly selected from the patients with angioleiomyoma (a kind of small renal tumor) less than 3 cm confirmed by pathological examination and treated in our hospital from January 2018 to January 2020. All patients were treated with Jinkui Shenqi pills. All patients were first detected by color Doppler ultrasound and then by SMI. The results of color Doppler ultrasound were used as the control group, while those of color Doppler ultrasound combined with SMI were used as the experimental group. After that, the specificity, sensitivity, positive and negative detection results, and detection accuracy were compared between the two groups. The specificity and sensitivity in the experimental group were significantly higher than those in the control group, with statistical significance (

P
<
0.05

). The cases of positive and negative detection results in the experimental group were significantly higher than those in the control group, with statistical significance (

P
<
0.05

). The detection accuracy in the experimental group was significantly higher than that in the control group, with statistical significance (

P
<
0.05

). The specificity, sensitivity, positive and negative detection results, and detection accuracy of color Doppler ultrasound combined with SMI in the detection of small renal tumors less than 3 cm treated with Jinkui Shenqi pills were all significantly higher than those of color Doppler ultrasound; therefore, the application of color Doppler ultrasound combined with SMI for the diagnosis of small renal tumors is of high value.
Mobile Intercloud System for Edge Cloud Computing
Authors: Dou Yi , Ho Yik Him , Deng Yuxuan , Chan Henry C. B.
Published: September 2021

Recent years have seen considerable interest in mobile cloud computing and edge cloud computing. This paper presents a mobile Intercloud system for supporting mobile cloud computing in general and edge cloud computing in particular. In essence, a mobile user with a mobile terminal can set up a virtual mobile terminal with applications and data in a central/home cloud. The virtual mobile terminal can facilitate task and computation offloading and other functions. Moreover, when a mobile terminal joins an edge cloud, the virtual mobile terminal (including required applications and data) can be migrated to enhance system efficiency and the user experience (e.g., shorter access delays). An experimental prototype has been developed for evaluating certain basic object transfer functions. To support the application transfer function, we formulate both finite‐ and infinite‐horizon Markov decision models to determine decision policies (i.e., should an application be transferred to an edge cloud). The transfer decision depends on various factors, including transfer cost, duration associated with the edge cloud, usage probability, and usage cost in the central cloud and edge cloud. Based on the models, we obtain closed‐form solutions for the decision policies, which can be expressed in meaningful formulas to provide useful insights for edge cloud computing in general. To evaluate the mobile Intercloud system for edge cloud computing, we conducted extensive evaluations, including experimental evaluation for testing the basic functions and protocols, analytical evaluation for studying the analytical models, and simulation evaluation for analyzing performance in a multiuser and multicloud environment in particular. The experimental, simulation, and analytical results provide useful insights into the design and development of the mobile Intercloud system for edge cloud computing as well as decision policies for application transfer.
Athlete Rehabilitation Evaluation System Based on Internet of Health Things and Human Gait Analysis Algorithm
Authors: Lianzhen Chen , Hua Zhu
Published: September 2021

In order to improve the effect of athlete’s injury recognition and rehabilitation evaluation, this paper studies the traditional rehabilitation evaluation method and proposes a new athlete rehabilitation evaluation system combining the Internet of Health Things technology and human gait analysis algorithm. Moreover, this paper combines sports characteristics to improve the algorithm of human gait analysis. In addition, through the study of the athlete’s human body modeling and movement process, a human gait analysis algorithm that can be applied to multiple sports is proposed, and the gait parameter analysis and algorithm reliability research are carried out through simulation analysis. After confirming that the algorithm is effective, this paper combines the Internet of Health Things technology to construct a system model, obtains the system function module architecture with the support of the Internet of Health Things technology, and conducts experiments to verify the system performance. From the experimental research, it can be seen that the model constructed in this paper meets the theoretical and practical needs, and the system in this paper can be applied to practice in the future. The human gait recognition algorithm constructed in this article has a good effect and can play an important role in sports rehabilitation of athletes. At the same time, the system constructed in this article has certain advantages over traditional sports rehabilitation systems with the support of algorithms.
Armed Conflict and Child Weight in DR Congo
Authors: Le Kien
Published: September 2021

This paper investigates the extent to which armed conflict influences the weight of young children in the Democratic Republic of the Congo. Exploiting the variation across districts in exposure to armed conflict and the within-district variation in the timing of whether the child was exposed to armed conflict due to birth timing within a difference-in-differences framework, we detect adverse impacts of conflict exposure to child weight. Specifically, experiencing armed conflict makes children weigh less for their age and weigh less for their height by 0.20 and 0.24 standard deviations, respectively. Armed conflict also increases the probability of children being underweight and wasted by 4.7 and 2.7 percentage points, respectively. Our heterogeneity analyses reveal that children of disadvantaged backgrounds, i.e., those born to low-educated mothers, poor mothers, and rural mothers, tend to be disproportionately affected. Our study calls for effective measures to mitigate the detrimental repercussions of armed conflict.
Integrative Network Analysis Revealed Genetic Impact of Pyruvate Kinase L/R on Hepatocyte Proliferation and Graft Survival after Liver Transplantation
Authors: Liu Zhengtao , Zhao Junsheng , Wang Wenchao , Zhu Hai , Qian Junjie , Wang Shuai , Que Shuping , Zhang Feng , Yin Shengyong , Zhou Lin , Geng Lei , Zheng Shusen
Published: September 2021

Background. Pyruvate kinase L/R (PKLR) has been suggested to affect the proliferation of hepatocytes via regulation of the cell cycle and lipid metabolism. However, its impact on the global metabolome and its clinical implications remain unclear. Aims. We aimed to clarify the genetic impact of PKLR on the metabolomic profiles of hepatoma cells and its potential effects on grafts for liver transplantation (LT). Methods. Nontargeted and targeted metabolomic assays were performed in human hepatoma cells transfected with lentiviral vectors causing PKLR overexpression and silencing, respectively. We then constructed a molecular network based on integrative analysis of transcriptomic and metabolomic data. We also assessed the biological functions of PKLR in the global metabolome in LT grafts in patients via a weighted correlation network model. Results. Multiomic analysis revealed that PKLR perturbations significantly affected the pyruvate, citrate, and glycerophospholipid metabolism pathways, as crucial steps in de novo lipogenesis (DNL). We also confirmed the importance of phosphatidylcholines (PC) and its derivative lyso‐PC supply on improved survival of LT grafts in patients. Coexpression analysis revealed beneficial effects of PKLR overexpression on posttransplant prognosis by alleviating arachidonic acid metabolism of the grafts, independent of operational risk factors. Conclusion. This systems‐level analysis indicated that PKLR affected hepatoma cell viability via impacts on the whole process of DNL, from glycolysis to final PC synthesis. PKLR also improved prognosis after LT, possibly via its impact on the increased genesis of beneficial glycerophospholipids.
Construction Stage Seismic Vulnerability Evaluation of a Continuous Girder Bridge with the Cast‐in‐Place Cantilever Construction Method
Authors: Li Hongxu , Huang Yong , Guo Endong
Published: September 2021

To evaluate the vulnerability of bridges at various construction stages under the action of strong earthquakes, the incremental dynamic analysis (IDA) method is applied, and the vulnerabilities of a continuous girder case study bridge with the cast‐in‐place cantilever construction method, which owns five main construction stages, are evaluated and compared. The results show the following: With the increase in the peak ground acceleration (PGA), the vulnerabilities of bridges at different construction stages all increase. The fragility and vulnerability are mainly determined by the structural mechanical system condition and the mode shapes but not the modal frequency. For the working condition of seismic PGA of 0.4 g, (1) the bridge at the substructure construction stage may only experience slight or moderate damage with the exceedance probability of 8% to 5% and the mean loss ratio being only about 5%; (2) the vulnerabilities of bridges at the middle cantilever construction stage and the long cantilever construction stage are similar, the collapse damage exceedance probability is about 80%, and the mean loss ratio is about 65%; and (3) the vulnerabilities of bridges at the middle span closure construction stage and the bridge completion construction stage are nearly the same, the collapse damage exceedance probability is about 98%, and the mean loss ratio can reach 80%. The research results explore a new method for evaluating the vulnerability of bridges at different construction stages, which can provide suggestions for seismic damage defense and seismic insurance risk evaluation.
Classifying Vehicle Activity to Improve Point of Interest Extraction
Authors: Van Hinsbergh James , Griffiths Nathan , Taylor Phillip , Xu Zhou , Mouzakitis Alex
Published: September 2021

Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.
Preliminary Experiment on the Effect of 18% Substitute Salt on Home Blood Pressure Variability in Hypertensives
Authors: Li Jing , Mu Lisha , Rao Huakun , Wu Yangfeng , Wang Hao , Tao Hongbing , Mu Lihong
Published: September 2021

At present, the effect of substitute salt in reducing sodium intake and blood pressure is relatively clear. The present study is a phase I clinical trial involving 43 hypertensives in which the effect of 18% sodium substitute salt on the home blood pressure variability (BPV) was observed for 8 weeks with weekly follow-up. Finally, 4 patients were lost, and 39 patients completed the intervention and were included in the analysis. Daily home blood pressure and weekly adverse events were collected. The systolic blood pressure (SBP) in the morning (−10.0 mmHg, 95% CI: −16.5 to −3.5,

P

 = 0.003), SBP at night (−10.2 mmHg, 95% CI: −16.1 to −4.3,

P

 = 0.001), and diastolic blood pressure (DBP) at night (−4.0 mmHg, 95% CI: −7.1 to −0.8,

P

 = 0.014) decreased significantly. Also, there was no statistically significant change in morning (F = 1.137,

P

 = 0.352) and night diastolic (F = 0.344,

P

 = 0.481) BPV and morning systolic BPV (F = 0.663,

P

 = 0.930) over time during the intervention period, except for that night systolic BPV had a downward trend (F = 2.778,

P

 = 0.016) and had decreased 2.04 mmHg (95% CI: 0.84 to 3.23,

P

 = 0.001) after intervention. The use of 18% of the substitute salt did not increase BPV during the intervention and even may decrease it, which indicates its control effects on blood pressure. This study is the first one to observe the effect of 18% sodium substitute salt on the home blood pressure variability, providing a basis for further experiments.
Patterns of Cancer Care and Association with Survival among Younger Adolescents and Young Adults: A Population-Based Retrospective Cohort Study
Authors: Collins Chelsea L. , Peng Jiahao , Singh Sharn , Hamilton Ann S. , Freyer David R.
Published: September 2021

Background:

Younger adolescents and young adults (AYA) may receive care from either adult or pediatric oncologists. We explored patterns of care in this population and whether survival is associated with provider type.

Methods:

Utilizing the California Cancer Registry, we examined a cohort of 9,993 AYAs diagnosed with cancer aged 15 to 24 years from 1999 to 2008. Provider type (adult/pediatric) was determined by individual physician identifiers. For provider type, multivariable logistic regression models were adjusted for age, sex, race/ethnicity, socioeconomic status, diagnosis, and stage. For observed survival, Cox proportional hazard models were additionally adjusted for provider type. ORs and HR with 95% confidence intervals (95% CI) were determined.

Results:

Most patients saw adult providers (87.3% overall; 72.7% aged 15–19 years). Patients with acute leukemia, sarcoma, and central nervous system (CNS) malignancies more often saw pediatric providers [OR (95% CI) adult versus pediatric 0.48 (0.39–0.59), 0.74 (0.60–0.92), 0.76 (0.60–0.96), respectively]; those with germ cell tumors and other cancers, including carcinomas, more often saw adult providers [2.26 (1.72–2.98), 1.79 (1.41–2.27), respectively]. In aggregate and for most cancers individually, there was no survival difference by provider type [overall HR (95% CI) 1.00 (0.86–1.18)]. Higher survival was associated with pediatric providers for CNS malignancies [1.63 (1.12–2.37)] and rhabdomyosarcoma [2.22 (1.03–4.76)], and with adult providers for non-Hodgkin lymphoma [0.61 (0.39–0.96)].

Conclusions:

Most AYAs 15 to 24 years old are treated by medical oncologists. In general, survival was not associated with provider type.

Impact:

Current patterns of care for this population support increased collaboration between medical and pediatric oncology, including joint clinical trials.