Addressing bias in artificial intelligence for public health surveillance.

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

Examination of Firefighting as an Occupational Exposure Criteria for Lung Cancer Screening.

PURPOSE: Firefighting is known to be carcinogenic to humans. However, current lung cancer screening guidelines do not account for occupational exposure. We hypothesize that firefighting is an independent risk factor associated with the development of high-risk lung nodules on low-dose CT (LDCT). METHODS: Members of a firefighter's union underwent LDCT at a single institution between April 2022 and June 2023 within a lung cancer screening program. Results were interpreted by designated chest radiologists and reported using the Lung-RADS scoring system. Demographic and radiographic data were recorded, and summary statistics are reported. RESULTS: 1347 individuals underwent lung cancer screening, with a median age of 51 years (IQR 42-58), including 56 (4.2%) females. Overall, 899 (66.7%) were never smokers, 345 (25.6%) were former smokers, and 103 (7.7%) were current smokers. There were 41 firefighters (3.0%) who had high-risk (Lung-RADS 3 or 4) nodules requiring intervention or surveillance, of which 21 (1.5%) were Lung-RADS 3 and 20 (1.5%) that were Lung-RADS 4. Of the firefighters with high-risk nodules, only 6 (14.6%) were eligible for LDCT based on current screening guidelines. There were 7 high-risk nodules (0.5%) that required procedural intervention, 6 (85.7%) of which were from the non-screening eligible cohort. There were also 20 never-smoking firefighters (57.1%) with high-risk nodules that were non-screening eligible. CONCLUSION: Firefighting, even in the absence of smoking history, may be associated with the development of high-risk lung nodules on LDCT. Carefully selected occupational exposures should be considered in the development of future lung cancer screening guidelines.

Long-term clinical outcomes of gastric mucosa-associated lymphoid tissue lymphoma in real-world experience.

This long-term, retrospective, single-center study evaluated real-world clinical outcomes of gastric mucosa-associated lymphoid tissue (MALT) lymphoma using different therapeutic modalities and analyzed factors affecting survival outcomes and long-term prognosis. We enrolled 203 patients with pathologically confirmed low-grade gastric MALT lymphoma and examined their treatment responses. Helicobacter pylori eradication was performed in all patients with H. pylori infection (HPI) and localized stage gastric MALT lymphoma. All patients underwent pre-treatment and physical evaluations, with complete blood count, biochemistry panel, and staging workup. Among 144 HPI-positive patients with stage I or II1-2 disease who underwent H. pylori eradication, 112 (77.8%) achieved complete remission (CR). All HPI-negative patients who received first-line radiotherapy achieved CR (100%), but only 22 of 27 first-line chemotherapy-treated patients achieved CR (81.5%). Lesions in the proximal upper-third or in multiple locations and an invasion depth to the submucosa or deeper were associated with poor response to eradication, and HPI negativity was significantly correlated with poor progression-free survival. HPI eradication treatment should be the first-line treatment for patients with localized stage HPI-positive gastric MALT lymphoma. The "watch-and-wait" strategy should be adopted for delayed responders. We suggest radiotherapy for patients with a localized HPI-negative status or when eradication has failed.

Post-intensive care syndrome and health-related quality of life in long-term survivors of intensive care unit.

OBJECTIVE: The objective of this study was to provide preliminary data for improving the health-related quality of life of long-term intensive care unit survivors by identifying the relationship between health-related quality of life and post-intensive care syndrome. METHODS: Using a descriptive correlation research design, data from patients who visited the outpatient department for continuous treatment after discharge from the intensive care unit were analysed. Post-intensive care syndrome was measured by physical, cognitive, and mental problems. Data were collected from 1st August to 31st December, 2019, and 121 intensive care unit survivors participated in the study. RESULTS: Health-related quality of life showed a negative correlation with physical, mental, and cognitive problems. The factors associated with health-related quality of life were physical and mental problems, education level, sedatives and neuromuscular relaxants, and marital status. CONCLUSIONS: To improve the health-related quality of life of intensive care unit survivors, post-intensive care syndrome prevention is important, and a systematic strategy is required through a long-term longitudinal trace study. In addition, intensive care unit nurses and other healthcare professionals need to provide early interventions to reduce post-intensive care syndrome.

Circumferential submucosal incision prior to endoscopic mucosal resection versus conventional endoscopic mucosal resection for colorectal lesions with endoscopic features of sessile serrated lesions.

BACKGROUND AND AIMS: Sessile serrated lesions (SSLs) are more prone to incomplete resection than conventional adenomas. This study evaluated whether circumferential submucosal incision prior to endoscopic mucosal resection (CSI-EMR) can increase the rate of complete and en bloc resections of colorectal lesions with endoscopic features of SSL. METHODS: Retrospective analyses and propensity score matching were performed for the resection of colorectal lesions ≥ 10 mm with endoscopic features of SSL. RESULTS: After 1:1 ratio matching, 127 lesions in the CSI-EMR group and 127 in the EMR group were selected for analysis. The median size of the lesions was 15 mm (IQR 12-16) in both groups. There was no significant difference in either the complete resection rate or en bloc resection rate between CSI-EMR and EMR groups (96.9% vs. 92.9%, P = 0.155; 92.1% vs. 89.0%, P = 0.391). By contrast, the R0 resection rate was significantly higher in the CSI-EMR group than in the EMR group (89.8% vs. 59.8%, P < 0.001). The median procedure time was significantly longer in the CSI-EMR group than in the EMR group (6.28 min vs. 2.55 min, P < 0.001), whereas there was no significant difference between the two groups in the incidence of adverse events or recurrence rate. Multivariate analysis showed that CSI-EMR was the only factor significantly associated with R0 resection (P < 0.001). CONCLUSIONS: For colorectal lesions with endoscopic features of SSL, CSI-EMR does not increase the complete or en bloc resection rate, but does increase the R0 resection rate. The procedure time is longer for CSI-EMR than EMR. The association of CSI-EMR with R0 resection and non-recurrence should be further evaluated.

Hot snare polypectomy versus endoscopic mucosal resection for small colorectal polyps: a randomized controlled trial.

BACKGROUND AND AIMS: This study was performed to compare endoscopic mucosal resection (EMR) with hot snare polypectomy (HSP) in terms of the complete resection rate and the incidence of adverse events for resecting small (5-10 mm) colorectal polyps. METHODS: Small colorectal polyps (5-10 mm) with neoplastic features were randomly allocated to either the HSP or EMR group. A submucosal injection was performed prior to hot snaring in the EMR group only. Complete resection was defined as the absence of neoplastic tissue from two additional biopsies of the polypectomy site. R0 resection was defined as the absence of neoplastic tissue at the margin of the resected specimen. RESULTS: A total of 362 colon polyps from 272 patients were included, and 167 polyps in the HSP group and 155 polyps in the EMR group were analyzed. Between the polypectomy techniques, there was no significant difference in the complete resection rates, which were 96.4% (161/167) in the HSP group and 95.5% (148/155) in the EMR group (P = 0.67). The R0 resection rate in the HSP and EMR groups was significantly different, with 49.7% (83/167) and 74.8% (116/155), respectively (P < 0.001). There was no significant difference in the incidence of adverse events between the two groups. CONCLUSIONS: The complete resection rates for small (5-10 mm) polyps were not different between HSP and EMR. TRIAL REGISTRY: ClincialTrials.gov number NCT02239536.

How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks.

Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver-vehicle interaction, which must match the driver's in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver's input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers' needs.

Quality of randomized controlled trials published in the International Urogynecology Journal 2007-2016.

INTRODUCTION AND HYPOTHESIS: Randomized controlled trials (RCTs) provide the best quality clinical evidence. The aim of this study was to assess the quality of RCTs published by the International Urogynecology Journal (IUJ) in 2007-2016. METHODS: RCTs in original articles were extracted from PubMed and IUJ homepage. Change in RCT quality over time was assessed with Jadad and van Tulder scales and Cochrane Collaboration's risk of bias tool (CCRBT). Jadad scores of 3-5 or van Tulder scores of >5 indicated high-quality RCTs. The effect on RCT quality of including funding source and institutional review board (IRB) approval statements and describing the intervention was assessed. In addition, changes in RCT topics over time were assessed. RESULTS: Annual RCT frequencies did not change significantly (6.7-15.7%): 36.1% and 25.7% described blinding and allocation concealment, respectively. Both tended to increase between 2013 and 2016, particularly 2013 and 2014. Funding statement inclusion (39.1% overall) and intervention description (78.2% overall) tended to increase steadily. IRB statement inclusion (60.4% overall) increased significantly (p < 0.01). Jadad scores and van Tulder rose significantly until 2014 (p < 0.01). Frequencies of high-quality RCTs tended to rise. CCRBT indicated that RCTs with a low risk of bias tended to increase until 2014. However, from 2015, Jadad scores, van Tulder, and CCRBT the low risk tended to decreased. RCTs with funding and IRB approval statements had higher Jadad and van Tulder scores than unfunded RCTs (p < 0.01 and p < 0.01, respectively). Intervention description did not associate with better quality. CONCLUSIONS: RCT quality improved over time, but a dip in quality was observed in 2015-2016 because of decreased blinding and allocation concealment.

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.

MOTIVATION: Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. RESULTS: We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal. AVAILABILITY AND IMPLEMENTATION: The code is available at: http://www.taehyunlab.org CONTACT: jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability.

Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder.

Engagement plays a key role in improving the cognitive and motor development of children with autism spectrum disorder (ASD). Sensing and recognizing their engagement is crucial before sustaining and improving the engagement. Engaging technologies involving interactive and multi-sensory stimuli have improved engagement and alleviated hyperactive and stereotyped behaviors. However, due to the scarcity of data on engagement recognition for children with ASD, limited access to and small pools of participants, and the prohibitive application requirements such as robots, high cost, and expertise, implementation in real world is challenging. However, serious games have the potential to overcome those drawbacks and are suitable for practical use in the field. This study proposes Engagnition, a dataset for engagement recognition of children with ASD (N = 57) using a serious game, "Defeat the Monster," based on enhancing recognition and classification skills. The dataset consists of physiological and behavioral responses, annotated by experts. For technical validation, we report the distributions of engagement and intervention, and the signal-to-noise ratio of physiological signals.

Supramolecular Association of a Block Copolymer via Strong Hydrogen Bonding to Form Self-Healable Ionogels.

The drive to enhance the operational durability and reliability of stretchable and wearable electronic and electrochemical devices has led to the exploration of self-healing materials that can recover from both physical and functional failures. In the present study, we fabricated a self-healable solid polymer electrolyte, referred to as an ionogel, using reversible hydrogen bonding between the ureidopyrimidone units of a block copolymer (BCP) network swollen in an ionic liquid (IL). The BCP consisted of poly(styrene-b-(methyl acrylate-r-ureidopyrimidone methacrylate)) [poly(S-b-(MA-r-UPyMA)], with the IL-phobic polystyrene forming micellar cores that were interconnected via intercorona hydrogen bonding between the ureidopyrimidone units. By precisely regulating the molecular weight and the composition of the hydrogen-bondable motifs, the mechanical, electrical, and self-healing characteristics of the ionogel were systematically evaluated. The resulting ionogel samples exhibited suitable stretchability, ionic conductivity, and room-temperature self-healability due to reversible hydrogen bonding. To highlight the applicability of the self-healing ionogel as a high-capacitance gate insulator, an electrolyte-gated transistor (EGT) was fabricated using a poly(3-hexylthiophene-2,5-diyl) semiconductor, and the performance of the EGT was fully recovered from a complete cut without any external stimuli.

Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review.

Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.

Chemical Structure-Physicochemical Property Relationships of Copolymers Utilizable for Negative-Tone Photoimaging via Chemical Amplification.

We demonstrate an understanding of different physicochemical properties of copolymers induced by systematic changes in their structural parameters, i.e., the chemical structure of the comonomer unit, composition, molecular weight, and dispersity. The terpolymers were designed to be implemented in a chemically amplified resist (CAR) to form negative-tone patterns. With two basic repeating units of 4-hydroxystyrene and 2-ethyl-2-methacryloxyadamantane as monomers for conventional CARs, the pendant group of the third methacrylate comonomer was varied from aromatic, aliphatic lactone to lactone rings to modulate the interaction capability of the copolymer chains with n-butyl acetate, which is a negative-tone developer. Along with these structures, the monomer composition, molecular weight, and dispersity were also controlled. Physicochemical properties of the synthesized copolymers having controlled structures, i.e., dissolution behaviors and quantified Hansen solubility parameters, surface wetting characteristics, and surface roughness, which can be important properties affecting patterning capability in high-resolution lithography, were explored. Furthermore, the feasibility to use experimentally determined Hansen solubility parameters of the copolymers for the prediction of pattern formation using a coarse-grained model was assessed. Our comprehensive studies on the correlation of the structural parameters of the copolymers with final properties offer fundamental avenues to attain effective designs of the complex CAR system toward the lithographic process to achieve a sub-10 nm dimension, which is close to a single-chain dimension.

MultiSenseBadminton: Wearable Sensor-Based Biomechanical Dataset for Evaluation of Badminton Performance.

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

Flexible multilevel resistive memory with controlled charge trap B- and N-doped carbon nanotubes.

B- and N-doped carbon nanotubes (CNTs) with controlled workfunctions were successfully employed as charge trap materials for solution processable, mechanically flexible, multilevel switching resistive memory. B- and N-doping systematically controlled the charge trap level and dispersibility of CNTs in polystyrene matrix. Consequently, doped CNT device demonstrated greatly enhanced nonvolatile memory performance (ON-OFF ratio >10(2), endurance cycle >10(2), retention time >10(5)) compared to undoped CNT device. More significantly, the device employing both B- and N-doped CNTs with different charge trap levels exhibited multilevel resistive switching with a discrete and stable intermediate state. Charge trapping materials with different energy levels offer a novel design scheme for solution processable multilevel memory.

Measuring Impact of Simulation-Based Informed Consent Training on Surgical Intern's Long-Term Confidence.

OBJECTIVE: Our objective was to evaluate the outcome of a training program on long-term confidence of interns and attending physicians. DESIGN: In this prospective cohort study, general surgery interns underwent a training program on informed consent that involved didactics, standardized patient encounters, and supplemental procedure specific guides at the start of the academic year. At the end of the academic year, we surveyed interns from the classes of 2020 (trained) and 2019 (untrained) about their experience and confidence in obtaining an informed consent. Further, we queried attending physicians on their experience and confidence in the interns at the end of each academic year. SETTING: Single academic general surgery residency program based at 2 urban tertiary hospitals. PARTICIPANTS: General surgery interns including unmatched preliminary residents and categorical interns from general surgery, interventional radiology, and urology. RESULTS: Twenty-four incoming interns participated in the training program. Intern confidence discussing operation benefits improved from a median score of 4 to 5 (p = 0.03), and total confidence improved from a median score of 15 to 17.5 (p = 0.08). There was no difference in median total confidence scores (15 vs. 17.5; p = 0.21) between classes. Attending physicians had similar median total confidence scores following intervention (10 vs. 11; p = 0.87). Intern satisfaction was 80% with the didactic session, and 90% with standardized patient encounters. Twenty percent of learners used the supplemental procedure specific guides. CONCLUSIONS: Implementation of an intern targeted program on informed consent that incorporated didactics and standardized patient encounters was viewed as useful and may contribute to long-term improvements in confidence.

Exploring mobility data for enhancing HIV care engagement in Black/African American and Hispanic/Latinx individuals: a longitudinal observational study protocol.

INTRODUCTION: Increasing engagement in HIV care among people living with HIV, especially those from Black/African American and Hispanic/Latinx communities, is an urgent need. Mobility data that measure individuals' movements over time in combination with sociostructural data (eg, crime, census) can potentially identify barriers and facilitators to HIV care engagement and can enhance public health surveillance and inform interventions. METHODS AND ANALYSIS: The proposed work is a longitudinal observational cohort study aiming to enrol 400 Black/African American and Hispanic/Latinx individuals living with HIV in areas of the USA with high prevalence rates of HIV. Each participant will be asked to share at least 14 consecutive days of mobility data per month through the study app for 1 year and complete surveys at five time points (baseline, 3, 6, 9 and 12 months). The study app will collect Global Positioning System (GPS) data. These GPS data will be merged with other data sets containing information related to HIV care facilities, other healthcare, business and service locations, and sociostructural data. Machine learning and deep learning models will be used for data analysis to identify contextual predictors of HIV care engagement. The study includes interviews with stakeholders to evaluate the implementation and ethical concerns of using mobility data to increase engagement in HIV care. We seek to study the relationship between mobility patterns and HIV care engagement. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Institutional Review Board of the University of California, Irvine (#20205923). Collected data will be deidentified and securely stored. Dissemination of findings will be done through presentations, posters and research papers while collaborating with other research teams.

Flexible memristive memory array on plastic substrates.

The demand for flexible electronic systems such as wearable computers, E-paper, and flexible displays has recently increased due to their advantages over present rigid electronic systems. Flexible memory is an essential part of electronic systems for data processing, storage, and communication and thus a key element to realize such flexible electronic systems. Although several emerging memory technologies, including resistive switching memory, have been proposed, the cell-to-cell interference issue has to be overcome for flexible and high performance nonvolatile memory applications. This paper describes the development of NOR type flexible resistive random access memory (RRAM) with a one transistor-one memristor structure (1T-1M). By integration of a high-performance single crystal silicon transistor with a titanium oxide based memristor, random access to memory cells on flexible substrates was achieved without any electrical interference from adjacent cells. The work presented here can provide a new approach to high-performance nonvolatile memory for flexible electronic applications.