Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights.

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

Fine root decomposition in forest ecosystems: an ecological perspective.

Fine root decomposition is a physio-biochemical activity that is critical to the global carbon cycle (C) in forest ecosystems. It is crucial to investigate the mechanisms and factors that control fine root decomposition in forest ecosystems to understand their system-level carbon balance. This process can be influenced by several abiotic (e.g., mean annual temperature, mean annual precipitation, site elevation, stand age, salinity, soil pH) and biotic (e.g., microorganism, substrate quality) variables. Comparing decomposition rates within sites reveals positive impacts of nitrogen and phosphorus concentrations and negative effects of lignin concentration. Nevertheless, estimating the actual fine root breakdown is difficult due to inadequate methods, anthropogenic activities, and the impact of climate change. Herein, we propose that how fine root substrate and soil physiochemical characteristics interact with soil microorganisms to influence fine root decomposition. This review summarized the elements that influence this process, as well as the research methods used to investigate it. There is also need to study the influence of annual and seasonal changes affecting fine root decomposition. This cumulative evidence will provide information on temporal and spatial dynamics of forest ecosystems, and will determine how logging and reforestation affect fine root decomposition.

Risk assessment of microplastic pollution in an industrial region of Bangladesh.

Despite the high potential for microplastics (MPs) pollution in Bangladesh, the presence of MPs in the industrial region has largely been unexplored in Bangladesh. So, this study was conducted to determine whether MP pollution is prevalent in the industrial soil of Bangladesh and the extent of its toxicity. To examine MPs, a total of 12 soil samples were collected from the industrial region of Narayanganj, and a stereoscopic microscope was used to visually identify the MPs. Prior to that the technique of density separation and sieving was applied to extract MPs from those 12 soil samples. Among the twelve investigated samples, a total of 151 MPs (Mean: 12.6 ± 7.9 particles kg-1) were identified, which were mostly white and ranged in size from 0.5 to 1 mm. Different types of MPs according to their shapes such as fibers (60.3%), fragments (19.2%), films (10.6%), and foam (9.9%) have been detected. 7 MPs (Mean: 0.58 ± 0.79) have been found in 3 urban farmland sites, 15 MPs (Mean: 1.87 ± 1.81) in two near metropolitan areas, and 129 MPs (Mean: 4.6 ± 4.39) in 7 industrial locations. Five polymers were identified by µ-FTIR, among which Polyamide predominated, followed by Polypropylene. According to risk assessments, the region falls under hazard categories II and III, suggesting a moderate to high risk. This paper gives thorough information on the toxicity of MP in an industrial location; therefore, it may be useful in the development of effective methods to address environmental issues.

Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8-30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies.

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).

Assessment of microplastics as contaminants in a coal mining region.

Pollution generated by microplastics (MPs) has become an issue of global concern because to its severe effects on the general health of the ecosystem, especially the health of the terrestrial environment. There is a scarcity of data based on MP contamination research in Bangladesh that is currently available, and no work on MP contamination has previously been done in an industrial region of Bangladesh. As a result, this research was undertaken with the aim of determining whether or not MP contamination is present in the industrial area of the Barapukuria region in Bangladesh. The method of sieving and density separation was used in the process of extracting MPs from a total of 12 soil samples that were collected from the industrial area of Barapukuria. A stereomicroscope was utilized to accomplish the visual identification of the MPs. The method of sieving and density separation was used in the process of extracting MPs from 12 soil samples that were gathered from the industrial area of Barapukuria. A stereomicroscope was utilized in order to accomplish the visual identification of the MPs. The concentration of MPs accounted for 1-15 items/100g (Mean: 6.75 ± 5.3) in the 12 sampled regions, mostly white in color and ranging in size from 0.5-1 mm. Fibers have been found to be the most prevalent among the detected MPs (films, fiber, foam, and fragments). 8-types of MPs (Mean: 0.32 ± 0.69) were detected in 5 rural farmland locations, 11 MPs (Mean: 1.1 ± 1.73) in 2 sub-urban farmland sites, 11 MPs (Mean: 2.2 ± 3.19) in 1 urban farmland site, 24 MPs (Mean: 2.4 ± 1.89) in 2 industrial locations, and 27 MPs (Mean: 2.7 ± 3.05) in 2 near metropolitan areas. Based on the land use land cover analysis, higher contamination of MPs have been detected in the industrial and coal mine region of Barapukuria whereas relatively lower amount of MPs have been found in the rural and urban regions.

A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response.

Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents' health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.

Economic resilience in times of public health shock: The case of the US states.

Does adopting social distancing policies amid a health crisis, e.g., COVID-19, hurt economies? Using a machine learning approach at the intermediate stage, we applied a generalized synthetic control method to answer this question. We utilize state policy response differences. Cross-validation, a machine learning approach, is used to produce the "counterfactual" for adopting states-how they "would have behaved" without lockdown orders. We categorize states with social distancing as the treatment group and those without as the control. We employ the state time-period for fixed effects, adjusting for selection bias and endogeneity. We find significant and intuitively explicable impacts on some states, such as West Virginia, but none at the aggregate level, suggesting that social distancing may not affect the entire economy. Our work implies a resilience index utilizing the magnitude and significance of the social distancing measures to rank the states' resilience. These findings help governments and businesses better prepare for shocks.

Association of Power Outage With Mortality and Hospitalizations Among Florida Nursing Home Residents After Hurricane Irma.

Importance: Exposure to hurricanes is associated with increased mortality and morbidity in nursing home (NH) residents, but the factors contributing to these outcomes are less understood. One hypothesized pathway could be power outages from hurricanes that expose NH residents to excess ambient heat. Objective: To determine the association of power loss from Hurricane Irma with hospitalization and mortality in NH residents in Florida. Design Setting and Participants: This retrospective cohort study of NH residents residing in Florida when Hurricane Irma landed on September 10, 2017, assessed mortality at 7 and 30 days after the storm and hospitalization at 30 days after the storm. The analysis was conducted from May 2, 2021, to June 28, 2021. All NH residents residing in Florida at landfall were eligible (N = 67 273). We excluded those younger than 65 years, missing power status information, or who were evacuated (13 178 [19.6%]). Exposure: We used state-administered surveys to determine NH power outage status. Exposed residents experienced a power outage poststorm, whereas unexposed residents did not experience a power outage poststorm. Main Outcomes and Measures: We used Medicare claims to assess mortality and hospitalization after Hurricane Irma landfall using generalized linear models with robust standard errors. Results: In the aftermath of Hurricane Irma, 27 892 residents (18 510 women [66.4%]; 3906 [14.0%] Black, 1651 [5.9%] Hispanic, and 21 756 [78.0%] White individuals) in 299 NHs were exposed to power loss and 26 203 residents (17 620 women [67.2%]; 4175 [15.9%] Black, 1030 [3.9%] Hispanic, and 20 477 [78.1%] White individuals) in 292 NHs were unexposed. Nursing homes that lost power were similar in size, quality star rating, and type of ownership compared with NHs that did not lose power. Power loss was associated with an increased adjusted odds of mortality among all residents within 7 days (odds ratio [OR],1.25; 95% CI,1.05-1.48) and 30 days (OR, 1.12; 95% CI,1.02-1.23) poststorm and hospitalization within 30 days, although only among residents aged 65 to 74 years (OR, 1.16; 95% CI, 1.03-1.33). Conclusions and Relevance: In this cohort study, power loss was associated with higher odds of mortality in all affected NH residents and hospitalization in some residents. The benefits and costs of policies that require NHs to have emergency alternate power sources should be assessed.

Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data-Driven Approach Backed by Sepsis Pathophysiology.

BACKGROUND: Considering morbidity, mortality, and annual treatment costs, the dramatic rise in the incidence of sepsis and septic shock among intensive care unit (ICU) admissions in US hospitals is an increasing concern. Recent changes in the sepsis definition (sepsis-3), based on the quick Sequential Organ Failure Assessment (qSOFA), have motivated the international medical informatics research community to investigate score recalculation and information retrieval, and to study the intersection between sepsis-3 and the previous definition (sepsis-2) based on systemic inflammatory response syndrome (SIRS) parameters. OBJECTIVE: The objective of this study was three-fold. First, we aimed to unpack the most prevalent criterion for sepsis (for both sepsis-3 and sepsis-2 predictors). Second, we intended to determine the most prevalent sepsis scenario in the ICU among 4 possible scenarios for qSOFA and 11 possible scenarios for SIRS. Third, we investigated the multicollinearity or dichotomy among qSOFA and SIRS predictors. METHODS: This observational study was conducted according to the most recent update of Medical Information Mart for Intensive Care (MIMIC-III, Version 1.4), the critical care database developed by MIT. The qSOFA (sepsis-3) and SIRS (sepsis-2) parameters were analyzed for patients admitted to critical care units from 2001 to 2012 in Beth Israel Deaconess Medical Center (Boston, MA, USA) to determine the prevalence and underlying relation between these parameters among patients undergoing sepsis screening. We adopted a multiblind Delphi method to seek a rationale for decisions in several stages of the research design regarding handling missing data and outlier values, statistical imputations and biases, and generalizability of the study. RESULTS: Altered mental status in the Glasgow Coma Scale (59.28%, 38,854/65,545 observations) was the most prevalent sepsis-3 (qSOFA) criterion and the white blood cell count (53.12%, 17,163/32,311 observations) was the most prevalent sepsis-2 (SIRS) criterion confronted in the ICU. In addition, the two-factored sepsis criterion of high respiratory rate (≥22 breaths/minute) and altered mental status (28.19%, among four possible qSOFA scenarios besides no sepsis) was the most prevalent sepsis-3 (qSOFA) scenario, and the three-factored sepsis criterion of tachypnea, high heart rate, and high white blood cell count (12.32%, among 11 possible scenarios besides no sepsis) was the most prevalent sepsis-2 (SIRS) scenario in the ICU. Moreover, the absolute Pearson correlation coefficients were not significant, thereby nullifying the likelihood of any linear correlation among the critical parameters and assuring the lack of multicollinearity between the parameters. Although this further bolsters evidence for their dichotomy, the absence of multicollinearity cannot guarantee that two random variables are statistically independent. CONCLUSIONS: Quantifying the prevalence of the qSOFA criteria of sepsis-3 in comparison with the SIRS criteria of sepsis-2, and understanding the underlying dichotomy among these parameters provides significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU.

Association Between Exposure to Hurricane Irma and Mortality and Hospitalization in Florida Nursing Home Residents.

Importance: Nursing home residents are at heightened risk for morbidity and mortality following an exposure to a disaster such as a hurricane or the COVID19 pandemic. Previous research has shown that nursing home resident mortality related to disasters is frequently underreported. There is a need to better understand the consequences of disasters on nursing home residents and to differentiate vulnerability based on patient characteristics. Objective: To evaluate mortality and morbidity associated with exposure to Hurricane Irma, a Category 4 storm that made landfall on September 10, 2017, in Cudjoe Key, Florida, among short-stay (<90-day residence) and long-stay (≥90-day residence) residents of nursing homes. Design, Setting, and Participants: Cohort study of Florida nursing home residents comparing residents exposed to Hurricane Irma in September 2017 to a control group of residents residing at the same nursing homes over the same time period in calendar year 2015. Data were analyzed from August 28, 2019, to July 22, 2020. Exposure: Residents who experienced Hurricane Irma were considered exposed; those who did not were considered unexposed. Main Outcome and Measures: Outcome variables included 30-day and 90-day mortality and first hospitalizations after the storm in both the short term and the long term. Results: A total of 61 564 residents who were present in 640 Florida nursing home facilities on September 7, 2017, were identified. A comparison cohort of 61 813 residents was evaluated in 2015. Both cohorts were mostly female (2015, 68%; 2017, 67%), mostly White (2015, 79%; 2017, 78%), and approximately 40% of the residents in each group were over the age of 85 years. Compared with the control group in 2015, an additional 262 more nursing home deaths were identified at 30 days and 433 more deaths at 90 days. The odds of a first hospitalization for those exposed (vs nonexposed) were 1.09 (95% CI, 1.05-1.13) within the first 30 days after the storm and 1.05 (95% CI, 1.02-1.08) at 90 days; the odds of mortality were 1.12 (95% CI, 1.05-1.18) at 30 days and 1.07 (95% CI, 1.03-1.11) at 90 days. Among long-stay residents, the odds of mortality for those exposed to Hurricane Irma were 1.18 (95% CI, 1.08-1.29) times those unexposed and the odds of hospitalization were 1.11 (95% CI, 1.04-1.18) times those unexposed in the post 30-day period. Conclusions and Relevance: The findings of this study suggest that nursing home residents are at considerable risk to the consequences of disasters. These risks may be underreported by state and federal agencies. Long-stay residents, those who have resided in a nursing home for 90 days or more, may be most vulnerable to the consequences of hurricane disasters.

Design of ultra-small mode area all-dielectric waveguides exploiting the vectorial nature of light.

The wave nature and diffraction of light pose a significant bottleneck to the continued performance and efficiency scaling of a wide variety of integrated photonic devices, often necessitating solutions based on resonance, slow-light, or plasmonics to derive enhanced light-matter interaction. Here, we introduce all-dielectric waveguides that exploit the vectorial nature of light to achieve strong subdiffraction confinement in high index dielectrics, enabling characteristic mode dimensions below λ02/1000 without metals or plasmonics. We further show how these ultra-small mode areas may coincide or diverge from the nonlinear effective mode area. The work opens the door to new types of waveguide-based devices featuring strong near-field confinement, Purcell factors, and nonlinear effects, with broad applications spanning classical and quantum optics.

Assisted Living Communities During Hurricane Irma: The Decision to Evacuate or Shelter in Place and Resident Acuity.

OBJECTIVES: To investigate the factors associated with whether assisted living communities (ALCs) in Florida evacuated or sheltered in place for Hurricane Irma in 2017, focusing on license type as a proxy for acuity of care. DESIGN: Cross-sectional study using data collected by the state through its emergency reporting system and a post-hurricane survey. SETTING AND PARTICIPANTS: Analyses included all 3112 ALCs in the emergency reporting system. A subset of 1880 that completed the survey provided supplementary data. METHODS: χ2 tests were used to examine differences between ALC characteristics (license type, size, payment, profit status, rural location, geographical region, and being under an evacuation order) and whether they evacuated. Logistic regression was used to test associations between characteristics and evacuation status. RESULTS: Of 3112 ALCs, 560 evacuated and 2552 sheltered in place. Bivariate analysis found significant associations between evacuation status and evacuation order, license type (mental health care), payment, and region. In the adjusted analysis, medium and larger ALCs were 43% (P < .001) and 53% (P < .001) less likely to evacuate than ALCs with fewer than 25 beds. Compared with ALCs in the Southeast, nearly every region was more likely to evacuate, with the highest likelihood in the Central West (odds ratio 1.76, 95% confidence interval 1.35‒2.30). ALCs under an evacuation order were 8 times more likely to evacuate (P < .001). We found no relationship between evacuation status and having a license to provide higher care. CONCLUSIONS AND IMPLICATIONS: Prior research highlighting harm associated with evacuation has led to recommendations that long-term care facilities carefully consider resident impairment in evacuation decision-making. Evidence that small ALCs are more likely to evacuate and that having a higher-care license is not associated with evacuation likelihood shows research is needed to understand how ALCs weigh resident risks in decisions to evacuate or shelter in place.

Prevalence, Antimicrobial Resistance, and Pathogenic Potential of Enterotoxigenic and Enteropathogenic Escherichia coli Associated with Acute Diarrheal Patients in Tangail, Bangladesh.

In this study, the prevalence and antimicrobial resistance of enterotoxigenic Escherichia coli (ETEC) and enteropathogenic Escherichia coli (EPEC) were investigated. Altogether 100 stool samples were collected from diarrheal patients attending the Sheikh Hasina Medical College and Hospital, Tangail, Bangladesh, during the period from March 1 to May 30, 2018. In vivo pathogenic potential of ETEC and EPEC using a Caenorhabditis elegans infection model was investigated. Among 100 diarrheal patients, 31% were positive for both ETEC and EPEC strains, 23% were lt positive for ETEC strains, and 8% were bfpA positive for EPEC strains. It was detected that 82.60%, 65.21%, 73.91%, 78.26%, 47.82%, 60.86%, and 47.82% of ETEC strains were resistant to amoxicillin-clavulanic acid (AMC), tetracycline (TE), nalidixic acid (NA), azithromycin, ciprofloxacin, ampicillin (AMP), and erythromycin (E), respectively. Whereas it was detected that 87.5% strains were resistant to AMC, AMP, and E, 75% were resistant to TE and NA, respectively. Both strains developed multidrug resistance to commonly prescribed antibiotics. EPEC showed higher pathogenicity than ETEC as 67.75% and 60% of C. elegans died after 18 h postinfection with EPEC and ETEC, respectively. The high rate of antimicrobial resistance of EPEC and ETEC highlights the necessity for the prudent use of antimicrobials in Bangladesh.

Effect of postoperative radiotherapy on outcome in resectable stage IIIA-N2 non-small-cell lung cancer: an updated meta-analysis.

BACKGROUND: In a previous meta-analysis-based modeling study, it was hypothesized that modern postoperative radiotherapy (PORT) may improve both local recurrence and overall survival (OS) in stage IIIA-N2 non-small-cell lung cancer (NSCLC). There were only four single-arm trials with a total of 357 patients. As other trials have provided new and controversial data, we performed this updated meta-analysis to test the hypothesis. PATIENTS AND METHODS: Systematic reviews in Medline, Cochrane, and Science Direct up to December 2015 identified publications exploring the efficacy of PORT in resectable stage IIIA-N2 NSCLC. RESULTS: Overall, 16 trials comprising 3278 patients were included. There was a significant benefit in favor of PORT regarding OS [hazard ratio (HR): 0.73, 95% confidence interval (CI): 0.58-0.92, P=0.008; absolute benefit at 5 years=8%], disease-free survival (HR: 0.70, 95% CI: 0.60-0.83, P<0.0001), and locoregional recurrence-free survival (HR: 0.37, 95% CI: 0.24-0.58, P<0.0001). Restriction of the analysis to trials with induction and/or adjuvant chemotherapy led to similar results. PORT significantly decreased the risk of local recurrence (risk ratio: 0.64, 95% CI: 0.50-0.82, P=0.0006) and DM (risk ratio: 0.74, 95% CI: 0.62-0.88, P=0.0005), and the absolute risk differences were 13 and 14%, respectively. CONCLUSION: The addition of PORT, with or without chemotherapy, significantly improves local control and survival in patients with resectable stage IIIA-N2 NSCLC.

MiR-744 functions as a proto-oncogene in nasopharyngeal carcinoma progression and metastasis via transcriptional control of ARHGAP5.

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastasis-prone epithelial cancer. The paucity of effective treatment strategies for recurrent and metastatic NPC is the major cause for stagnating survival rate of NPC. Therefore, it's urgent to understand the molecular mechanisms underlying NPC progression and identify novel avenues for targeted therapy. It has emerged recently that microRNAs are potential pro-tumorigenic or tumor-suppressive factors that participate in oncogenesis. In this study, we found that miR-744 expression was upregulated in NPC specimens compared to nasopharyngeal epithelium (NPE) tissue, and miR- 744 upregulation was significantly associated with TNM stage, tumorigenesis and metastasis. Functional studies revealed that miR-744 acts as a novel tumor promotor in NPC. Moreover, we determined that miR-744 targets ARHGAP5 (Rho GTPase activating protein 5), a protumorigenic gene, by directly interacting with its promoter and thereby regulating its expression at transcriptional level. Reintroduction of ARHGAP5 resembled the effects of miR-744 and silencing of ARHGAP5 clearly abrogated miR-744-induced enhancement of cell migration and invasion. High level of ARHGAP5 was positively correlated with that of miR-744 and with advanced stages of NPC, as well as with lymph node metastasis. Taken together, these data reveal for the first time that miR-744 exerts its proto-oncogenic function by directly targeting ARHGAP5 promoter. This newly identified miR-744/ARHGAP5 pathway provides further insight into the progression and metastasis of NPC and indicates potential novel therapeutic targets for NPC.