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Abstract Background Opioid use disorder (OUD) remains a significant public health issue as the number of opioid-related overdose deaths continues to reach new highs each year. Buprenorphine/Naloxone is a medication that has been shown to be highly effective for the treatment of OUD. However, the clinical management of patients on this medication is challenging as many patients discontinue treatment prematurely. We conducted a qualitative study focusing on experienced prescribers of buprenorphine to learn about what they believe are key challenges in prescribing this medication to patients with OUD and related strategies for improving treatment outcomes. Methods We conducted two rounds of interviews with 12 prescribers who were either trained as a primary care physician, nurse practitioner, or physician assistant. These prescribers were recruited from an academically-based treatment program, a community health center, and a commercial substance use disorder treatment facility. Interview data were coded and analyzed in accordance with a grounded theory approach. Results Key findings and related recommendations emerged for patient monitoring, integration of behavioral health with prescribing, patient volume requirements, and use of telehealth. Conclusion The interviews generated a number of recommendations for improving patient outcomes from buprenorphine treatment. Some of these recommendations can be implemented quite readily whereas others entail more substantial resources and time commitments.

The growing global demand for clean and sustainable energy sources has sparked interest in hybrid energy systems that combine multiple renewable energy technologies. This review paper explores the integration of cow dung biogas, solar thermal, and kinetic energy for power production. The synergistic utilization of these energy sources holds significant potential for addressing the energy challenges faced by various communities. This paper provides an overview of each technology, discusses the benefits and challenges of integration, and highlights successful case studies. Furthermore, it discusses this hybrid energy generation system's potential future developments and implications.

Abstract Background Hepatocellular carcinoma (HCC), a leading cause of cancer-related deaths globally, poses significant challenges in early detection. Improved diagnostic accuracy can drastically influence patient outcomes, emphasizing the need for innovative, non-invasive biomarkers. Methods: This study utilized a cohort of 402 participants, including healthy controls, chronic hepatitis patients, and HCC patients from Bangladesh, to evaluate DNA methylation signatures in peripheral blood mononuclear cells (PBMC). We performed targeted next-generation sequencing on selected genes previously identified to assess their methylation dynamics. The development of M8 and M4 scores was based on these dynamics, using Receiver Operating Characteristic (ROC) analysis to determine their effectiveness in detecting early-stage HCC alongside existing markers such as epiLiver and alpha-fetoprotein (AFP). Results: Integration of M8 and M4 scores with epiLiver and AFP significantly enhances diagnostic sensitivity for early-stage HCC. The M4+epiLiver score achieves a sensitivity of 79.4% in Stage A HCC, while combining M4 with AFP increases sensitivity to 88.2–95.7% across all stages, indicating a superior diagnostic performance compared to each marker used alone. Conclusions: Our study confirms that combining gene methylation profiles with established diagnostic markers substantially improves the sensitivity of detecting early-stage HCC. This integrated diagnostic approach holds promise for advancing non-invasive cancer diagnostics, potentially leading to earlier treatment interventions and improved survival rates for high-risk patients.

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

Abstract High-throughput tests for early cancer detection can revolutionize public health and reduce cancer morbidity and mortality. Here we show a DNA methylation signature for hepatocellular carcinoma (HCC) detection in liquid biopsies, distinct from normal tissues and blood profiles. We developed a classifier using four CpG sites, validated in TCGA HCC data. A single F12 gene CpG site effectively differentiates HCC samples from other blood samples, normal tissues, and non-HCC tumors in TCGA and GEO data repositories. The markers were validated in a separate plasma sample dataset from HCC patients and controls. We designed a high-throughput assay using next-generation sequencing and multiplexing techniques, analyzing plasma samples from 554 clinical study participants, including HCC patients, non-HCC cancers, chronic hepatitis B, and healthy controls. HCC detection sensitivity was 84.5% at 95% specificity and 0.94 AUC. Implementing this assay for high-risk individuals could significantly decrease HCC morbidity and mortality.

Species diversity and soil quality are deteriorating due to continuous disturbances in ecosystems caused by human interference. However, agroforestry is considered a good approach to minimizing environmental problems. Therefore, the objective of this study was to determine the impacts of participatory agroforestry on restoring soil fertility, farm productivity and biodiversity in the degraded Madhupur Sal forest of Bangladesh. The study purposefully selected 40 common agroforestry programs in Madhupur Sal forest for the collection of soil and plant data from 2020 to 2023. Agroforestry programs have improved soil organic matter, soil carbon, pH, and available N, P and K content to a substantial degree and protected soil degradation, enhancing yield. The soil improvement index represents the potentiality of agroforestry in restoring soil nutrients and carbon in the form of organic matter, which is an important indicator for carbon sequestration and mitigating the impacts of climate change. The resultant cost–benefit and land equivalent ratios were steadily higher, which corroborates the greater productivity and profitability of agroforestry compared to monoculture systems. In contrast, agroforestry restored 31 plant species, opening up opportunities for restoring plant species in the threatened forest ecosystem. Therefore, this study recommended selecting appropriate site-specific species for managing agroforestry and restoring ecosystems.

Abstract Background Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. Objective To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. Methods For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. Results A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value

In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017–18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06–2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child–the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05–2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.

Introduction: ACLF is characterized by acute deterioration of liver function in patients with chronic liver disease. HBV is one of the most important causes of both acute insult and underlying chronic liver disease in ACLF. Reactivation of HBV is one of the common causes of ACLF in our region. ACLF requires multiple organ support and is associated with high short and medium term mortality. This is the reason why early, rapid reduction of HBV DNA is essential in treating ACLF-B. Methods: Consecutive patients of ACLF-B due to spontaneous reactivation of HBV (ALT> 5xULN or >2 x baseline and HBV DNA >20,000 IU/ml) were randomized into tenofovir group (300mg/day) and telbivudine group (600mg/day) along with standard medical treatment. Clinical and laboratory parameters were evaluated at baseline, day-7, day-14, day-30 and day-90. HBV DNA was evaluated at baseline and after three months of therapy. Primary end point was survival or death at three months. Secondary end point was improvement of liver function assessed by Child-Turcotte Pugh score and MELD score at three months. Results: 30 patients were enrolled in the study and 15 of them received tenofovir and 15 patients received telbivudine. Most of the baseline parameters showed no difference except serum AST and serum creatinine level that showed statistically significant difference between two groups. After antiviral therapy both groups showed significant clinical improvement along with CTP and MELD scores. However statistically significant improvement between tenofovir and telbivudine groups was only seen with MELD score. Survival rate was 80% in tenofovir group and 60% in telbivudine group, but this was not statistically significant. Low serum albumin at baseline was predictor of mortality. Conclusion: In patients of ACLF-B, antiviral therapy with both tenofovir and telbivudine improve liver function, but there is no statistically significant difference in survival between tenofovir and telbivudine.

Introduction: HBV is major health problem globally due to complications, including ACLF, cirrhosis and hepa¬tocellular carcinoma. ACLF due to exacerbation of CHB is associate with 30%-70% mortality. Reduction of HBV-DNA is therefore a target of therapy in ACLF-B. Methods: Patients with spontaneous reactivation of HBV [(ALT >5×ULN or >2× baseline) and HBV-DNA >20,000 IU/ml] were randomized to Tenofovir mono therapy (300 mg/day) or Tenofovir plus Telbivudine (600 mg/day) dual therapy with standard care. Clinical and biochemical parameters were evaluated at baseline, 1 week, 4 weeks and at 3 months. Virological evaluation was done at baseline and at 3 months. Primary end points were reduction of HBV-DNA and resolution of ascites, as applied. Secondary end point was reduction of liver related complications, therapy related adverse effects and survival at 3 months. Results: 27 patients were enrolled. 15 received mono therapy with Tenofovir and 12 received dual therapy (Tenofovir plus Telbivudine). Baseline parameters in 2 groups had no significant difference. In both groups there was significant improvement of S. bilirubin, ALT, INR, CTP score and MELD score. Only MELD score showed significant improvement in patient with dual therapy at 3 months in comparison to mono therapy. 11 patients on Tenofovir mono therapy (n=15) showed undetected HBV-DNA (91.7%) at 3 months and one patient had detectable HBV-DNA (

Introduction: The aim of the study was to compare the safety and efficacy of tenofovir versus entecavir for treatment of naive acute on chronic liver failure (ACLF) due to hepatitis B virus (HBV) (ACLF-B). Methods: Thirty-two patients aged 14-65 years were enrolled in the study. Diagnosis of ACLF was confirmed by clinical condition, biochemical analysis, and virological data. The causes of both chronic liver damages and acute insult in all patients were HBV. They were expressing HBV DNA in the sera, positive for IgM anti-HBc, had increased levels of serum bilirubin, compromised prothrombin time; and more than 50% patients had encephalopathy. The standard dose of tenofovir and entecavir was given. Results: The antiviral effects of tenofovir and entecavir were evident as most patients became negative for HBV DNA in the sera after 90 days of therapy. Also, the levels of serum bilirubin, CTP (Child-Turcotte-Pugh) and MELD (model for end-stage liver disease) score exhibited significant improvement due to antiviral therapy. Although the improvement of liver functions, and liver damages were detected in patients receiving both tenofovir and entecavir, the survival of the patients was significantly higher in those receiving tenofovir compared to entecavir-treated patients. Conclusion: This prospective study with limited number patients provides a challenge to assess the real potential of tenofovir over entecavir as therapeutic option for ACLF-B by conducting a multicenter clinical trial enrolling patient of different races and background.

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola‐Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro‐controller based miniature electronic devices in terms of its run time complexity of O(n3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.

Opioid overdose epidemic is a national public health crisis in the US. Little is known about how large-scale data analytics can be leveraged to help physicians predict whether a prescription opioid user will develop opioid use disorder. To that end, we proposed a machine learning framework for identifying potential risk factors of opioid use disorder from a large-scale healthcare claims data. These risk factors identified by the proposed framework can be used to predict which patient will be at higher risk of opioid use disorder following an opioid prescription. We utilized clinical diagnosis and prescription histories from Massachusetts commercially insured individuals who were prescribed opioids. We performed several feature selection techniques on a class imbalanced analytic sample to identify patient-level demographic and clinical features that were influential predictors of opioid use disorder. We, then compared the predictive power of four well-known machine learning algorithms: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting to predict the patients’ risk of opioid use disorder. The study results showed that the Random Forest model achieved superior predictive performance in terms of AUC and recall. Alongside the higher predictive accuracy, the random forest model identified clinical features, some of which were fairly consistent with prior clinical findings. In addition, our proposed framework is capable of extracting some other clinical features, which are predictive of opioid use disorder and indicative as the proxies of patients’ health status. We anticipate that the findings of our study will potentially help reduce in-appropriate and over prescription of opioids.

In this paper, we define a new, special second order cone as a type-k second order cone. We focus on the case of k=2, which can be viewed as a second order conic optimization (SOCO) problem with an additional complicating variable. For this new problem, we develop the necessary prerequisites, based on previous work for traditional SOCO problem. We then develop a primal–dual interior point algorithm for solving a type-2 second order conic optimization problem, based on a family of kernel functions suitable for this type-2 SOCO. We finally derive a new iteration bound for our framework.

فکير لَلان‌شاه ‌‌ غزل‌سرایی نامدار و مشهور در زمينه‌ی موسیقی بنگلادشی است. درحدود ده‌هزار غزل از ترانه‌های عامیانه‌ی وی به‌طور شفاهی گردآوری شده‌اند که فقط هزاروصد ترانه تاکنون به‌دست ما رسیده است. استنباط‌های متفاوتی از دیدگاه‌های مذهبی لَلان‌شاه ‌‌وجود دارد؛ وی اشعاری درباره‌ی عشق، برابری و تصوف سروده است. زمانی‌که او در سرزمين بنگال چشم به جهان گشود، سراسر منطقه‌ی شبه‌قاره مستعمره‌ی امپرياليسم دولت انگليس بود. زبان فارسی در شبه‌قاره‌ی هند بيش‌از شش‌صد سال رواج داشت و به‌ويژه در دوره‌ی مغول به‌عنوان زبان رسمی استفاده می‌شد. از آن زمان، زبان‌وادبيات فارسی، فرهنگ‌ورسوم ايرانی با زبان‌وادبيات و فرهنگ‌و‌رسوم بنگالی مخلوط شد که تأثير شگرفی بر ادبيات بنگالی گذاشت. بسیاری از شاعران بنگلادشی از عناصر و واژه‌های فارسی در ادبیات خود بهره جستند. آن‌ها همچنین کتاب‌هایی به زبان فارسی چاپ کردند که نسخه‌های خطی آن‌ها را می‌توان امروزه در این منطقه پیدا کرد. به‌همین‌ترتیب، سروده‌های صوفیانه و ترانه‌های عامیانه‌ی لَلان‌شاه ‌‌در ایران نیز یافت می‌شوند و ایرانیان نیز از عناصر و واژه‌های فارسی اشعار او بهره‌مند شده‌اند. در این جستار، درباره‌ی تأثير زبان فارسی و تصوف ايرانی بر سروده‌های این شاعر بحث خواهیم کرد.

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

In case of non-alcoholic fatty liver disease, the ratio of serum aspartate aminotransferase (AST) level to platelet count index has been proposed as a non-invasive and readily available tool for the assessment of non-alcoholic steatohepatitis. The study was conducted on 50 non-alcoholic fatty liver disease patient (25 non-alcoholic steatohepatitis and 25 simple steatosis). The mean (± SD) serum AST level in the non-alcoholic steatohepatitis group was 55.2 ± 30.1 IU/L whereas in simple steatosis group it was 33.6 ± 20.0 IU/L. The mean platelet count in the non-alcoholic steatohepatitis group was 303.1 ± 68.7 x 109 /L whereas in the simple steatosis group it was 327.8 ± 66.8 x 109/L. The mean AST platelet ratio index (APRI) score in non-alcoholic steatohepatitis group was 0.5 ± 0.3 and in the simple steatosis group it was 0.3 ± 0.2. In conclusion, the APRI was significantly higher in the non-alcoholic steatohepatitis group than the simple steatosis group.

Landscape scale assessment of temporal variations in soil organic carbon (SOC) contents and soil pH and the implications for long-term agricultural sustainability was determined using legacy datasets collected over two periods separated by 20 years: the 1990s and 2010s. Soil data on SOC and pH were categorized according to the prevailing land types (based on inundation as highland (HL), medium highland (MHL), and medium lowland (MLL)), and physiographic types (i.e., Himalayan Piedmont plain, Tista Floodplain and Barind tract/Terrace) to determine which variable or combination of variables was more influential in spatial and temporal changes of these properties. SOC contents in the physiographic types were generally found to be low, varying between 8 to 12 g/kg. While, SOC contents were significantly higher in MHL and MLL compared with HL that experienced less inundation. The change in SOC contents over 20 years was significant with a 14.5% increase of SOC. There was a greater influence of land type compared with physiography on SOC contents over time. Inundation land types and associated cropping intensity were considered likely to influence SOC of soils under rice-based cropping systems. Furthermore, the levels of soil pH decreased by 0.5 units over 20 years with an approximately 50% increase in soils within a pH category of 4.6–5.5. The majority of soil pH results shift from slightly acidic to strongly acidic in the intervening 20-year period between samplings. Soil acidification is potentially a combination of inefficient and excess use of ammonium-based fertilizers with higher application rates and low input from residues. We conclude that acidification may continue with more intensive land use. However, trends in SOC contents over time under certain combinations of physiography and land type either increased slightly or showed a significant loss and in the latter, specifically, the role of land management is not clear. The legacy datasets would be useful for monitoring spatial and temporal soil quality trends at a regional scale, but has limited capacity to capture field level variations in soil properties as data on smallholder cropping practice and management were not collected. Therefore, future research examining the role of management in SOC and pH dynamics at the field-scale would guide the use of fertilizers, crop residue management, and amelioration of acidic soil, to improve the sustainability of rice-based cropping systems in Bangladesh.

Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP has a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies.

Background: Nonalcoholic steatohepatitis (NASH) and advanced fibrosis are the spectrum of nonalcoholic fatty liver disease (NAFLD) that may progress to cirrhosis. Objective: We aimed to determine the detecting capacity of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma glutamyl transpeptidase (GGT) for NASH and significant fibrosis. Methods: Demographic and laboratory data of 502 sonologically diagnosed NAFLD patients were retrospectively analysed. Area under receiver operating characteristics curve (AUROC) was performed for NASH and fibrosis score ?2 (significant fibrosis) with ALT, AST and GGT of 233 biopsied patients. Results: Of 502 patients ALT, AST and GGT was elevated in 252 (50.1 %), 184 (36.7%) and 138 (27.4%) respectively. There was no difference in histological activity and fibrosis score between normal and elevated ALT and AST. Forty two (40.2%) NASH and 23(20.2%) significant fibrosis had normal ALT level. GGT was differed in NASH and Non NASH (p< .005) and between significant fibrosis (p< .01) and insignificant fbrosis. To detect NASH AUROC curve ofGGT was 67.5%, whereas of ALT and AST was 55.2% and 55.7%. For significant fibrosis AUROC curve of ALT, AST and GGT was 44, 50 and 68.4 % respectively. GGT level of39.5 U/L could detect NASH with a 63% sensitivity and 65% specificity irrespective of sex. GGT 40.5U/L had 60% sensitivity and 59 % specificity to detect significant fibrosis. For fibrosis ?2 AUROC curve was 75.4% in male. Conclusion: No optimal ALT and AST level could detect NASH and fibrosis. GGT level of 40 U/L had a better detecting capacity for NASH and fibrosis especially in male.

p-cycle networks have attracted a considerable interest in the network survivability literature in recent years. However, most of the existing work assumes a known network topology upon which to apply p-cycle restoration. In the present work, we develop an incremental topology optimization ILP for p-cycle network design, where a known topology can be amended with new fibre links selected from a set of eligible spans. The ILP proves to be relatively easy to solve for small test case instances but becomes computationally intensive on larger networks. We then follow with a relaxation-based decomposition approach to overcome this challenge. The decomposition approach significantly reduces computational complexity of the problem, allowing the ILP to be solved in reasonable time with no statistically significant impact on solution optimality.