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Introduction Globally, nine million people are injured due to fire, heat and hot substances every year with a mortality rate of 1.34%. Research shows conflicting reports regarding sex as a risk factor for mortality in patient with burns. We analysed whether sex is a risk factor for in-hospital mortality in patients admitted with burns in urban India. Methods A subgroup analysis of patients with burns as mechanism of injury was done from a multi-centre prospective observational study ‘Towards Improved Trauma Care Outcomes (TITCO)’ in India conducted in October 2013 to December 2015. Results 1209 patients were identified as having suffered burns from the TITCO study. The mean age was 23 years with 58.9% females. The overall 30 day in-hospital mortality was 574 (48.5%). Among patients who succumbed, 72% were females and 27% were males. We did a multivariable logistic regression and found that females were at a significantly higher odds of death compared with males (OR=1.73 95%CI=1.06-2.80, p value=0.02). After performing a sex and age stratified analysis, females in the age group of 15-44 years had a significantly higher number of deaths compared to males in that group. Conclusion In this multi-center study from urban Indian university hospitals, sex was identified as an independent risk factor of mortality in patients admitted with burns. Females have an increased odds of mortality from burns.

Purpose of review This review provides an overview of the current state of research around improving healthcare delivery for patients with cirrhosis in the outpatient, inpatient, and transitional care settings. Recent findings Recent studies have broadly employed changes to the model of care delivery, team composition, and technology to improve cirrhosis care. In the outpatient setting, approaches have included engaging caregivers, patient navigators, and non-physicians and using virtual care, smartphone applications, and wearables. Inpatient care approaches have focused on the role of interdisciplinary teams, education interventions, and changes to the medical record system, while post-discharge interventions have included day hospitals and care coordinator interventions. This review also describes the Veterans Health Administration’s novel, population-level approach to delivery of cirrhosis care, and addressed how the pandemic has impacted the delivery of cirrhosis care. Summary Comprehensive, evidence-based approaches to delivering high-quality cirrhosis care continue to evolve to meet the needs of a growing population in an ever-changing healthcare environment.

Wound healing requires well-coordinated events including hemostasis, inflammation, proliferation, and remodeling. Delays in any of these stages leads to chronic wounds, infections, and hypertrophic scarring. Burn wounds are particularly problematic, and may require intervention to ensure timely progression to reduce morbidity and mortality. To accelerate burn wound healing, Platelet-Rich Plasma (PRP)1 can be of value, since platelets release growth factor proteins and inorganic polyphosphates (polyP) that may be integral to wound healing. We used polyP-depleted keratinocyte (HaCaT) and fibroblast cell culture models to determine cell proliferation and scratch-wound repair to determine if polyP, platelet lysate, or combined treatment could accelerate wound healing. While polyP and PRP significantly reduced the open scratch-wound area in fibroblasts and keratinocytes, polyP had no effect on keratinocyte or fibroblast proliferation. PRP was also evaluated as a treatment in a murine model of full thickness wound healing in vivo, including a treatment in which PRP was supplemented with purified polyP. PRP induced significantly more rapid re-epithelialization by Day 3. Pure polyP enhanced the effects of PRP on epithelial tongues, which were significantly elongated in the PRP + high-dose polyP treatment groups compared to PRP alone. Thus, PRP and polyP may serve as an effective therapeutic combination for treating wounds.

A deep learning approach is presented to detect safe landing locations using LIDAR scans of the Lunar surface. Semantic Segmentation is used to classify hazardous and safe locations from a LIDAR scan during the landing phase. Digital Elevation Maps from the Lunar Reconnaissance Orbiter mission are used to generate the training, validation, and testing dataset. The ground truth is generated using geometric techniques by evaluating the surface roughness, slope, and other hazard avoidance specifications. In order to train a robust model, artificially generated training data is augmented to the training dataset. A UNet-like neural network structure learns a lower dimensional representation of LIDAR scan to retain essential information regarding safety of the landing locations. A softmax activation layer at the bottom of the network ensures that the network outputs a probability of a safe landing spot. The network is also trained with a cost function that prioritizes the false safes to achieve a sub 1% false safes value. The results presented show the effectiveness of the technique for hazard detection. Future work on electing one landing spot based on proximity to the intended landing spot and the size of safety region around it is motivated.

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications’ components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.

Background: Pancreatic trauma occurs in 0.2-2% of patients with blunt trauma and 1–12% of patients with penetrating trauma. The mortality and morbidity rates range from 9-34% and 30-60% respectively. We aimed to review the management of pancreatic trauma in a multicenter database from India.Methods: We analyzed all patients who suffered a pancreatic injury and who were included in the multicenter prospective observational study ‘Towards Improved Trauma Care Outcomes (TITCO)’.Results: Of the 16047 trauma cases, 1134 (7.1%) patients suffered abdominal trauma. Of all those with abdominal trauma 55 patients (4.9%) had injury to the pancreas. 28 patients (50.9%) with pancreatic trauma were managed conservatively. 27 patients (49.1%) underwent surgical exploration in the form of laparotomies. 11 procedures were undertaken for pancreas. A total of 45 (82%) patients had associated injuries along with pancreatic injury. Thorax (19) (including injuries to lung, pleura and ribs), liver (17), bowel (14) and spleen (13) were the most common associated injuries.Conclusion: Conservative management was as common as operative management in patients with pancreatic injuries. Most (80%) grade III/IV underwent operative treatment. Many patients (82%) had associated injuries. Level of evidence: III

Inorganic-organic hybrid perovskite materials have been a topic of interest for the last few years due to their superior optoelectronic properties. However, the optical properties of perovskite materials are strongly dependent on the film morphology. A textured film morphology is expected to have higher light absorption as well as light out-coupling efficiency compared to a smooth film. There have been numerous methods for controlling and optimizing the film morphology to achieve high efficiency in solar cells and light emitting diodes. Here we have demonstrated that controlled anti-solvent treatment at low temperature can lead to Stranski-Krastanov growth in CH3NH3PbBr3 thin films with superior optical and electronic properties for light emitting diode applications. We have studied their photoluminescence properties at the micro- to nano-scale via fluorescence microscopy, hyper-spectral imaging and scanning near-field optical microscopy. We have demonstrated that the nanostructured micro-islands are highly emissive because of large quasi-Fermi level splitting (QFLS) due to the localization of free charges in the smaller crystals. We have shown that the photoluminescence as well as electroluminescence can be improved by at least seven-fold due to the presence of micro-islands on a smooth background film enhancing light out-coupling. Photo-induced photoluminescence enhancement is also observed in smooth films while micro-islands show photo-degradation.

Background & Aims Chronic liver disease (CLD) represents a major global health burden. We undertook this study to identify the factors associated with adverse outcomes in patients with CLD who acquire the novel coronavirus-2019 (COVID-19). Methods We conducted a multi-center, observational cohort study across 21 institutions in the United States (US) of adult patients with CLD and laboratory-confirmed diagnosis of COVID-19 between March 1, 2020 and May 30, 2020. We performed survival analysis to identify independent predictors of all-cause mortality and COVID-19 related mortality, and multivariate logistic regression to determine the risk of severe COVID-19 in patients with CLD. Results Of the 978 patients in our cohort, 867 patients (mean age 56.9 ± 14.5 years, 55% male) met inclusion criteria. The overall all-cause mortality was 14.0% (n = 121), and 61.7% (n = 535) had severe COVID-19. Patients presenting with diarrhea or nausea/vomiting were more likely to have severe COVID-19. The liver-specific factors associated with independent risk of higher overall mortality were alcohol-related liver disease (ALD) (hazard ratio [HR] 2.42, 95% confidence interval [CI] 1.29–4.55), decompensated cirrhosis (HR 2.91 [1.70–5.00]) and hepatocellular carcinoma (HCC) (HR 3.31 [1.53–7.16]). Other factors were increasing age, diabetes, hypertension, chronic obstructive pulmonary disease and current smoker. Hispanic ethnicity (odds ratio [OR] 2.33 [1.47–3.70]) and decompensated cirrhosis (OR 2.50 [1.20–5.21]) were independently associated with risk for severe COVID-19. Conclusions The risk factors which predict higher overall mortality among patients with CLD and COVID-19 are ALD, decompensated cirrhosis and HCC. Hispanic ethnicity and decompensated cirrhosis are associated with severe COVID-19. Our results will enable risk stratification and personalization of the management of patients with CLD and COVID-19. Clinicaltrials.gov number NCT04439084

Failure of conventional anti-inflammatory therapies in osteoarthritis (OA) underlines the insufficient knowledge about inflammatory mechanisms, patterns and their relationship with cartilage degradation. Considering non-linear nature of cartilage loss in OA, a better understanding of inflammatory milieu and MMP status at different stages of OA is required to design early-stage therapies or personalized disease management. For this, an investigation based on a synovium-synovial fluid (SF) axis was planned to study OA associated changes in synovium and SF along the progressive grades of OA. Gene expressions in synovial-biopsies from different grades OA patients (N = 26) revealed a peak of IL-1β, IL-15, PGE2 and NGF in early OA (Kellgren–Lawrence (KL) grade-I and II), the highest MMP levels were found in advanced stages (KL grade-III and IV). MMPs (MMP-1, 13, 2 and 9) abundance and FALGPA activity estimated in forty SFs of progressive grades showed the maximum protein levels and activity in KL grade-II and III. In an SF challenge test, SW982 and THP1 cells were treated with progressive grade SFs to study the dynamics of MMPs modulation in inflammatory microenvironment, the test yielded a result pattern, which matched with FALGPA and the protein-levels estimation. Inflammatory mediators in SFs served as steering factor for MMP up-regulation. A correlation-matrix of IL-1β and MMPs revealed expressional negative correlation.

There is an acute shortage of deceased donor organs for liver transplantation in the United States. Nearly a third of patients either die or become too sick for transplant while on the transplant waitlist. Living donor liver transplantation (LDLT) bridges the gap between demand and supply of organs for liver transplantation. This article reviews current living donor selection criteria, and avenues for expansion of criteria with novel surgical techniques and ongoing outcomes research. Ways in which institutions can establish and expand LDLT programs using the Living Donor Champion model are discussed. Efforts to expand recipient indications for LDLT are described.

BackgroundLate-stage diagnosis of ovarian cancer, a disease that originates in the ovaries and spreads to the peritoneal cavity, lowers 5-year survival rate from 90% to 30%. Early screening tools that can: i) detect with high specificity and sensitivity before conventional tools such as transvaginal ultrasound and CA-125, ii) use non-invasive sampling methods and iii) longitudinally significantly increase survival rates in ovarian cancer are needed. Studies that employ blood-based screening tools using circulating tumor-cells, -DNA, and most recently tumor-derived small extracellular vesicles (sEVs) have shown promise in non-invasive detection of cancer before standard of care. Our findings in this study show the promise of a sEV-derived signature as a non-invasive longitudinal screening tool in ovarian cancer.MethodsHuman serum samples as well as plasma and ascites from a mouse model of ovarian cancer were collected at various disease stages. Small extracellular vesicles (sEVs) were extracted using a commercially available kit. RNA was isolated from lysed sEVs, and quantitative RT-PCR was performed to identify specific metastatic gene expression.ConclusionThis paper highlights the potential of sEVs in monitoring ovarian cancer progression and metastatic development. We identified a 7-gene panel in sEVs derived from plasma, serum, and ascites that overlapped with an established metastatic ovarian carcinoma signature. We found the 7-gene panel to be differentially expressed with tumor development and metastatic spread in a mouse model of ovarian cancer. The most notable finding was a significant change in the ascites-derived sEV gene signature that overlapped with that of the plasma-derived sEV signature at varying stages of disease progression. While there were quantifiable changes in genes from the 7-gene panel in serum-derived sEVs from ovarian cancer patients, we were unable to establish a definitive signature due to low sample number. Taken together our findings show that differential expression of metastatic genes derived from circulating sEVs present a minimally invasive screening tool for ovarian cancer detection and longitudinal monitoring of molecular changes associated with progression and metastatic spread.

Without any uncertainty, the COVID-19 pandemic has set the world to a stop. The infection is spreading rapidly and could be a peril to the human race. Seeing the prerequisite of the hour, one needs to continuously make certain safeguards of that one being wearing a mask in any way at all times. To make a secure environment that leads to open safety, we propose a proficient learning and computer vision based approach concentrated on the real-time robotized observation of individuals to find unmasked faces in open areas. Subsequently, the recommended solution favors the society by sparing time and helps in bringing down the spread of coronavirus. It can be implemented successfully when lockdown is lifted completely bringing about people in open get-togethers, shopping centers, etc.. Automated inspection will reduce manpower to oversee the public and can also be used in any place.

Introduction: Working with The International Society of Nephrology (ISN), Kim Solez and J Stewart Cameron created the Video Legacy Project (VLP) in 1990 to document the lives of the key figures that founded Kidney Medicine and highlight the history of Kidney Medicine within the medical community These recordings were made on VHS tapes, but it became obvious that digitization of the material was essential, and this also was undertaken as the 21st century began Transplantation and transplant pathology were important parts of the project It is hard to imagine a medical subspecialty without any video history at all, but that fate almost befell kidney medicine when funding was removed from the ISN Video Legacy Project in 2007 During the ISN council meeting of 2006, the idea of making the interviews freely available on the Internet was proposed but this did not actually happen A major feature of the project was not to impose a standard format but allow subjects to talk in an unscripted manner and for as long they wanted For a time, 3 of the 45 video interviews were accessible on the ISN website, but then the site was restructured and for 8 years none of the videos were accessible We need to be reminded of the inconsistency of a rolling present through understanding of how present hypotheses, concepts and treatment recommendations came about Despite a strong movement amongst professional historians to downgrade the role of “great men” in the progress of ideas, these ideas in sum came from an endless series of talented individuals Hearing directly on video from this series of such men and women, almost all of whom have now passed away, not only gives a vivid idea of their characters but also an insight into how their minds worked Methods: The videotapes recorded between 1990 and 2007 were kept in temperature-controlled storage until the difficult Canadian winter of 2017-18 when they languished in the senior author's unheated garage Conventional wisdom held that the information on the tapes was fugitive and that by this time they would all be unreadable On a whim, we submitted them for conversion to mp4 video files in April-June 2018 and every tape was readable! We combined the footage with the Pioneers of European Nephrology series obtained by ERA-EDTA and today there is a collection of 177 videos on the Nephrology Video Legacy playlist and 82 videos on the Nephrology Pioneers playlist both of which can be found on the YouTube Channel of Kim Solez Some videos recorded in 2019 were reduced to audio-only with the devastating effects of the COVID-19 pandemic on Europe in 2020 http://www youtube com/user/kimsolez Results: Top videos included: Homer Smith Comical Film About Function of the Kidney from 1950s, Wilhelm Kolff Video Legacy Interview 1995, Donald W Seldin Video Legacy Interview 1997, Resurrecting Video Legacy Project Apr 19 2018, Robert H Heptinstall Video Legacy Interview October 1995 Conclusions: Many of the people interviewed such as Homer Smith, Robert Heptinstall, J Stewart Cameron, John Dirks, and Gabriel Richet had an important influence on the senior author’s direction of the Banff Transplant Pathology Meetings and the spirit and philosophy of those meetings George Orwell wrote “The most effective way to destroy people is to deny and obliterate their own understanding of their history ” It is comforting that in the end this did not happen to nephrology No conflict of interest

Introduction: The story began in January 2009 when General Internal Medicine Resident 1 expressed an interest in kidney medicine and began working on Nephrology research tasks with the last author as an undergraduate student This all emerged unintentionally, not conceived of as an experiment until the present first author began doing similar Nephrology research tasks in January 2018 as a premed student and decided to test this immersion approach to learning kidney medicine and kidney pathology before medical school, after receiving the required privacy training This method stands out as distinct from the usual stepwise acquisition of knowledge in classical medical education in medical school because it allows the student to be fully “immersed” into the world of nephrology without yet having adequate background in medicine in general Methods: Just like other immersive education, nephrology immersion was established by the inclusion of premedical students in a renal pathology and nephrology lab The student had the opportunity to work with pathologists and clinicians on a daily basis, gaining knowledge of the specialty by attending weekly nephrology/pathology rounds, regularly studying renal biopsies from the hospital, and completing online and in-person curricula meant for nephrology trainees (eg the American Society of Nephrology Kidney Week Early Program ‘Fundamentals of Renal Pathology’) Results: The student demonstrated a good understanding of kidney pathology and nephrology material in several ways: through successful completion of standardized curricula, weekly preparation and presentation of kidney biopsy cases at pathology/nephrology rounds, and training new pathology fellows Conclusions: A necessary component of the nephrology immersion program is that the students must suspend belief that they have truly mastered the subject until they are in medical school and learn the surrounding context of related subjects necessary to fully understand nephrology However, once that happens the students are very likely to choose a career in Nephrology because they have already been exposed to the specialized knowledge in the field Standardized curricula make it easy to assess the knowledge of such immersion students and they perform at a level comparable to current nephrology fellows, despite lacking full “medical” understanding of the subjects Immersion is an effective education technique in many other areas of human endeavor and represents an additional innovative approach to kidney medicine education, which is something the International Society of Nephrology (ISN) is seeking in several of its programs In the pandemic era it may make sense to delay entry into medical school until such time as more face to face experiences are available Meanwhile we are proving it is possible to learn selected aspects of medicine before medical school We would emphasize that we are not advocating a return to the apprenticeship approach to medical training which preceded the Flexner Report Mastery of the information and skills of medicine is key, and some of that is only attainable through medical school No conflict of interest

Delivery time estimates are an important factor for online food delivery platforms. These platforms also depend on batching - delivering two orders together - to increase efficiency and reduce cost. In this paper we propose a novel system for enhanced delivery time estimates for batched orders. The system is based on multiple machine learning algorithms that work together to make the predictions. We observe that the system leads to an increase in the number of times the food is delivered within the estimated delivery times by about 6%.

Segmentation of characters is a major step in the OCR system, and recognition accuracy will highly depend on good segmentation. The segmentation in Devanagari script is more cumbersome than Latin script because of the presence of a large character set which includes modifiers, compound characters, complex connected characters, and shirorekha. Shirorekha creates the major problem in Devanagari character segmentation, and it reduces segmentation accuracy as compared to Latin script. This paper gives a comparative study on various segmentation techniques used for Devanagari script and finds an efficient approach from these techniques.

In many computer vision tasks, dehazing is an important preprocessing step such as in various application areas of tracking and object recognition. Dehazing is the task of removing haze content or haze effect from the image under consideration. Nowadays, learning-based methods are used widely for dehazing. Among them, a convolutional neural network is one such method. After the successful use of CNN in different fields of image processing, it is being applied with modifications in various algorithms for dehazing as well. Deep CNNs are good at obtaining feature information automatically in a data-driven manner. In this paper, we have discussed various methods that use CNN for dehazing and their performance is measured by PSNR and SSIM.

Internet of things with its seamless interoperation with information communication networks has rewarded our lives making it simple. Its foray into medical domain is a boon for disease diagnosis, patient care and in reducing load of caregivers/doctors. It is becoming the new age technology for assessment of alarming situations, remote monitoring and to overcome human limitations. Due to anytime, anywhere ubiquitous capabilities, it is deemed to help in the well-being of people. In the field of medicine, a number of cases regarding vital disease diagnosis remain unfolded due to limitations of human prediction techniques. So to assist in such cases, there is a need to use specific computing technologies like Internet of things (IoT). There is also a need to correct discovery of disease stage to save patient’s life. Similarly, in many other areas of medical domain, the game changer IoT can be used for smart medical service. IoT has got much attention in this decade due to its close assistance in dealing with many chronic diseases which are on the rise. This new age intelligence can resolve human limitation to a great extent in dealing with patients and with anytime, anywhere, always available ubiquitous capabilities help to avoid negligence. This paper surveys various trends of how standard healthcare systems can be replaced by IoT healthcare systems with effective and more promising solutions, puts forth the inherent challenges and their comparative analysis.

Accurate X-ray screening systems are of paramount importance in the present day. Most existing systems predict only on the basis of a single image, which could lead to false positives and false negatives due to limited information present. We implemented several approaches using single, two and multiple X-Ray views to make a reliable and practical model with varying levels of success in threat object detection. These approaches include long-established methods such as Bag of Visual Words (BOVW), 3D Object Recognition, Adaptive Implicit Shape Model and Deep Neural Networks. The approaches took in dual inputs to make more informed predictions. Varying levels of success are obtained in these methods ranging from 73% using BOVW to 87% using Deep CNN. It was observed that, when two views of an object are considered, an improvement of 5% to 15% in performance took place (considering various approaches) compared to a single view.

Background and aims Coronavirus disease 2019 (COVID-19) is associated with liver injury, but the prevalence and patterns of liver injury in liver transplantation (LT) recipients with COVID-19 are open for study. Approach and results We conducted a multicenter study in the United States of 112 adult LT recipients with COVID-19. Median age was 61 years (interquartile range, 20), 54.5% (n = 61) were male, and 39.3% (n = 44) Hispanic. Mortality rate was 22.3% (n = 25); 72.3% (n = 81) were hospitalized and 26.8% (n = 30) admitted to the intensive care unit (ICU). Analysis of peak values of alanine aminotransferase (ALT) during COVID-19 showed moderate liver injury (ALT 2-5× upper limit of normal [ULN]) in 22.2% (n = 18) and severe liver injury (ALT > 5× ULN) in 12.3% (n = 10). Compared to age- and sex-matched nontransplant patients with chronic liver disease and COVID-19 (n = 375), incidence of acute liver injury was lower in LT recipients (47.5% vs. 34.6%; P = 0.037). Variables associated with liver injury in LT recipients were younger age (P = 0.009; odds ratio [OR], 2.06; 95% confidence interval [CI], 1.20-3.54), Hispanic ethnicity (P = 0.011; OR, 6.01; 95% CI, 1.51-23.9), metabolic syndrome (P = 0.016; OR, 5.87; 95% CI, 1.38-24.99), vasopressor use (P = 0.018; OR, 7.34; 95% CI, 1.39-38.52), and antibiotic use (P = 0.046; OR, 6.93; 95% CI, 1.04-46.26). Reduction in immunosuppression (49.4%) was not associated with liver injury (P = 0.156) or mortality (P = 0.084). Liver injury during COVID-19 was significantly associated with mortality (P = 0.007; OR, 6.91; 95% CI, 1.68-28.48) and ICU admission (P = 0.007; OR, 7.93; 95% CI, 1.75-35.69) in LT recipients. Conclusions Liver injury is associated with higher mortality and ICU admission in LT recipients with COVID-19. Hence, monitoring liver enzymes closely can help in early identification of patients at risk for adverse outcomes. Reduction of immunosuppression during COVID-19 did not increase risk for mortality or graft failure.

Light Fidelity (LiFi) is a wireless visible light communication technology, which answers the challenges of 5G. This paper explains the basic need of LiFi over WiFi. It describes the working methodology of LiFi and briefly discusses the various modulation techniques used in LiFi technology for data transmission. It also gives an overview of journey of visible light communication technology from ancient time to LiFi era. In this paper we have described the applications of LiFi in different domains which will help us to magnify the new research area in this technology.

Distribution grid with high solar penetration can cause huge voltage fluctuations due to sudden change of solar generation. This causes power quality issues, customer dissatisfaction and risks customer equipment. As a result, voltage control becomes extremely important since utilities have to keep the service voltage between 114V and 126V. Conventional electromechanical voltage control devices like Load Tap Changers (LTCs) and Switched Capacitor Banks (SCBs) do not act fast enough to mitigate the voltage fluctuations cause by solar. New methods are being suggested to use smart inverters going forward, but most inverters are still legacy inverters in the field. This paper introduces a novel approach by using Dynamic V Ar Controllers (DVC) to regulate the voltage by injecting reactive power dynamically. A pilot project at Hawaiian Electric has shown that with 30 DVCs distributed on a feeder with 37% (1.03 MW PV ) PV penetration, DVCs reduce feeder voltage fluctuations up to 1.54%(1.85V) which could increase the PV hosting capacity of the circuit from 37% (1.03 MW PV ) to 99% (2.7 MW PV ).

Adoption of renewable energies is on the rise all over the globe. Among renewable energy resources, solar Photovoltaic (PV) resources have gained a significant momentum in multiple parts of the world. Beside all the benefits renewable energy resources provide, they have also introduced new challenges, which must be addressed as soon as possible. This paper focuses on the mitigation of voltage issues caused by high penetration of PV resources. In this work, new Volt- V Ar Control (VVC) devices such as smart inverters and Dynamic VAr Controllers (DVCs) are selected and the interplay between them is investigated. Furthermore, the impact of these new VVC technologies on reducing voltage volatility and consequently increasing PV hosting capacity of distribution systems is assessed. To achieve those objectives, advanced Quasi-Static Time-Series (QSTS) simulations are performed in OpenDSS and controlled by Python. These powerful tools provide the opportunity to model new VVC devices, to implement advanced VVC schemes and smart functions, and to investigate the interaction between new VVC devices. Simulation results for select feeders show that by employing smart inverters and DVCs, existing PV hosting capacity of the considered Hawaiian distribution system can increase by 15% and 45% respectively compared to existing installed capacity. However, when both technologies are utilized, PV hosting increases by 60% for the select feeders, indicating that DVCs complement smart inverters capabilities.

Therapeutic drug monitoring (TDM) in cancer, while imperative, has been challenging due to inter-patient variability in drug pharmacokinetics. Additionally, most pharmacokinetic monitoring is done by assessments of the drugs in plasma, which is not an accurate gauge for drug concentrations in target tumor tissue. There exists a critical need for therapy monitoring tools that can provide real-time feedback on drug efficacy at target site to enable alteration in treatment regimens early during cancer therapy. Here, we report on theranostic optical imaging probes based on shortwave infrared (SWIR)-emitting rare earth-doped nanoparticles encapsulated with human serum albumin (abbreviated as ReANCs) that have demonstrated superior surveillance capability for detecting micro-lesions at depths of 1 cm in a mouse model of breast cancer metastasis. Most notably, ReANCs previously deployed for detection of multi-organ metastases resolved bone lesions earlier than contrast-enhanced magnetic resonance imaging (MRI). We engineered tumor-targeted ReANCs carrying a therapeutic payload as a potential theranostic for evaluating drug efficacy at the tumor site. In vitro results demonstrated efficacy of ReANCs carrying doxorubicin (Dox), providing sustained release of Dox while maintaining cytotoxic effects comparable to free Dox. Significantly, in a murine model of breast cancer lung metastasis, we demonstrated the ability for therapy monitoring based on measurements of SWIR fluorescence from tumor-targeted ReANCs. These findings correlated with a reduction in lung metastatic burden as quantified via MRI-based volumetric analysis over the course of four weeks. Future studies will address the potential of this novel class of theranostics as a preclinical pharmacological screening tool.

In this paper, a fifth-generation microstrip patch antenna has proposed. The proposed antenna design is working on 10.04 GHz with a return loss of −56.65 dB. The patch antenna has a compact structure of 18 mm × 18 mm with a FR4 glass epoxy substrate of 1.6 mm thickness. The results are simulated using Computer Simulation Technology Microwave Studio.

Advances in nanotechnology have enabled the design of nanotherapeutic platforms that could address the challenges of targeted delivery of active therapeutic agents to the central nervous system (CNS). While the majority of previous research studies on CNS nanotherapeutics have focused on neurons and endothelial cells, the predominant resident immune cells of the CNS, microglia, are also emerging as a promising cellular target for neurodegeneration considering their prominent role in neuroinflammation. Under normal physiological conditions, microglia protect neurons by removing pathological agents. However, long-term exposure of microglia to stimulants will cause sustained activation and lead to neuronal damage due to the release of pro-inflammatory agents, resulting in neuroinflammation and neurodegeneration. This Perspective highlights criteria to be considered when designing microglia-targeting nanotherapeutics for the treatment of neurodegenerative disorders. These criteria include conjugating specific microglial receptor-targeting ligands or peptides to the nanoparticle surface to achieve targeted delivery, leveraging microglial phagocytic properties, and utilizing biocompatible and biodegradable nanomaterials with low immune reactivity and neurotoxicity. In addition, certain therapeutic agents for the controlled inhibition of toxic protein aggregation and for modulation of microglial activation pathways can also be incorporated within the nanoparticle structure without compromising stability. Overall, considering the multifaceted disease mechanisms of neurodegeneration, microglia-targeted nanodrugs and nanotherapeutic particles may have the potential to resolve multiple pathological determinants of the disease and to guide a shift in the microglial phenotype spectrum toward a more neuroprotective state.

Twitter, a microblogging and social networking service, gives us access to a large scale social data. In this report, we try to obtain sentiments of the tweets in context of the #metoo movement. We try to derive a model that classifies tweets into categories of different linguistic aspects like hate, sarcasm, allegations and support/opposition. This helps analyze the tweets and flag them according to relevance. Here we explore elementary machine learning algorithms, multinomial naive Bayes and random forest classifier with ratio selection classification to improve stance classification at greater efficiency than baseline models.

Megatrends such as rapid adoption of Distributed Energy Resources (DER) such as roof-top solar PV, lithium ion battery storage technology and electric vehicles is changing the paradigm of utility infrastructure. In addition to operating an aging infrastructure and/or load demand increase, utilities need to find new philosophies and methodologies for managing their conventional precepts of maintaining reliability, resilience and good quality of supply to consumers in addition to supporting these new technical challenges that impact the grid head on. Sheer reliance on medium voltage (MV), slow, electromechanical solutions such as load tap changers, switched capacitor banks, line voltage regulators, or line upgrades will not help utilities to manage all problems associated with these fast-moving trends. In recent times, a new distributed grid-edge power electronics technology has shown promise for utilities called the dynamic var compensator (DVC). This technology resides on the secondary side (Low Voltage LV) of service transformers and can help utilities manage fast voltage fluctuations under changing load trends and help with voltage optimization in addition to preserving the fundamental requirements demanded from the grid. This grid edge technology can be used by the utilities as a cost-effective Non-Wires Alternative (NWA) to achieve deterministic flexible control over their system without the need for consumer participation. This paper through several case studies and field experiences highlights the use of this grid edge technology as a new resource in every utility’s toolkit.

Background: Shoulder subluxation is the common problem as it causes shoulder pain and hinders activity . In flaccid stage, propreoceptive impairment, lack of tone and muscle paralysis reduce the support and normal stirring action of rotator cuff muscles particularly supraspinatus. Methods: Ethical clearance was obtained from Institutional Ethical Comittee, KIMSDU, Karad. An Experimental study was conducted with 50 subjects .Which were divided into two groups using consecutive sampling with random allocation with computer generated table. Group A was given conventional treatment for reducing shoulder subluxation in stroke survivors Group B was treated with the Therapeutic shoulder sling and proximal control exercises .The outcome measures were AP lat X-ray, VAS, Functional outcome measurement By Motor assessment scale :Upper arm function. Results: Within the group comparison for the X-ray was done with the pair t test which was very significant for group A with P value of 0.0301 and Extremely significant for group B with P value ?0.0001 . within the group comparison for visual analogue scale for both the groups is extremely significant with the p value of ?0.0001 .within the group comparison for motor assessment scale for both the groups is extremely significant with the p value of ?0.0001 Conclusions: Therapeutic shoulder sling with proximal control exercises shows significant reduction of subluxation in shoulder x-ray as well as it reduces the pain and improves ADL activity.

Perovskite light-emitting diodes have almost reached the threshold for potential commercialization within a few years of research. However, there are still some unsolved puzzles such as large ideality factor and the presence of large negative capacitance especially at the low-frequency regime yet to be addressed. Here, we have fabricated a methylammonium lead tri-bromide perovskite n-i-p structure for light-emitting diodes from a smooth and textured emissive layer and demonstrated for the first time that these two factors are strongly dependent on the perovskite film morphology. Bias-dependent capacitance measurement also reveals the transition between negative to positive capacitance in textured films at the low-frequency regime. We have observed an anomalous capacitive behavior at the mid-frequency regime in smooth perovskite films but not in textured films. The relatively large ideality factor and anomalous capacitive behavior observed in perovskite light-emitting diodes are due to the presence of strong coupling between ions and electrons near the electrode interface. Therefore, the ideality factor and anomalous capacitance at the mid-frequency regime can be decreased by minimizing electronic-ionic coupling in textured perovskite films, while light outcoupling can be improved significantly.

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

GSK2245035, a small molecule Toll-like Receptor 7 (TLR7) agonist developed for immunomodulatory treatment for allergic airways disease, aimed to reduce Th2 and enhance Th1/Treg responses to aeroallergens via the local induction of type I interferons (IFNs). GSK2245035 demonstrated selectivity for potent release of type I IFNs compared to TNF-α and IL-6, with dose dependent increases in the interferon inducible chemokine, IP-10, in the nasal compartment. Implantation and parturition require pro-inflammatory processes including IFNs, Interferon Stimulated Genes, TNFα and IP-10 while pregnancy requires immune regulation to maintain maternal fetal immune tolerance, and recombinant type I IFNs induced abortions in monkeys. Due to its mechanism of action, GSK2245035 was studied at pharmacologically and clinically relevant doses in a monkey pregnancy model. Monkeys received 0, 3 or 30 ng/kg/week GSK2245035 intranasally once weekly, from Day 20 postcoitum through Day 63 postpartum. Although systemic IFN-α and IP-10 levels were approximately 14.8 or 40 -fold (respectively) above predose levels at 3 or 30 ng/kg/week, respectively, there were no effects on pregnancy and infant outcome. Non-adverse effects included increased incidence of nasal discharge, increased maternal body temperature at 30 ng/kg/week and dose-dependent increases in maternal IP-10 and IFN-α and decreased infant anti-KLH IgM and IgG titers following KLH immunization at ≥3 ng/kg/week, relative to controls. Potentially, lower IFN-α and IP-10 levels as well as once-weekly intranasal dosing vs daily subcutaneous or intramuscular dosing with recombinant type I IFNs could explain the lack of pregnancy effects; however, there was an undesired impact on offspring immune function.

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23‐27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of “digital pathology” were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made., The Banff Digital Pathology Working Group assesses the current state of digital pathology and presents next steps for digital transplantation pathology, including utilization of whole slide imaging and artificial intelligence and other algorithm types.

Mumps, a vaccine-preventable disease, cause inflammation of salivary glands and may cause severe complications, such as encephalitis, meningitis, deafness, and orchitis/oophoritis. In India, mumps vaccine is not included in the universal immunization program and during 2009 to 2014, 72 outbreaks with greater than 1500 cases were reported. In August 2016, a suspected mumps outbreak was reported in Jaisalmer block, Rajasthan. We investigated to confirm the etiology, describe the epidemiology, and recommend prevention and control measures. We defined a case as swelling in the parotid region in a Jaisalmer block resident between 23 June 2016 and 10 September 2016. We searched for cases in health facilities and house-to-house in affected villages and hamlets. We tested blood samples of cases for mumps immunoglobulin M (IgM) enzyme-linked immunosorbent assay (ELISA). We found 162 cases (60% males) with a median age of 9.4 years (range: 7 month-38 years) and 65 (40%) were females. Symptoms included fever (70%) and bilateral swelling in neck (65%). None of them were vaccinated against mumps. Most (84%) cases were school-going children (3-16 years old). The overall attack rate was 2%. Village A, with two hamlets, had the highest attack rate (hamlet 1 = 13% and hamlet 2 = 12%). School A of village A, hamlet 1, which accommodated 200 children in two classrooms, had an attack rate of 55%. Of 18 blood samples from cases, 11 tested positive for mumps IgM ELISA. This was a confirmed mumps outbreak in Jaisalmer block that disproportionately affected school-going children. We recommended continued surveillance, 5-day absence from school, and vaccination.

The identification and molecular profiling of early metastases remains a major challenge in cancer diagnostics and therapy. Most in vivo imaging methods fail to detect small cancerous lesions, a problem that is compounded by the distinct physical and biological barriers associated with different metastatic niches. Here, we show that intravenously injected rare-earth-doped albumin-encapsulated nanoparticles emitting short-wave infrared light (SWIR) can detect targeted metastatic lesions in vivo, allowing for the longitudinal tracking of multi-organ metastases. In a murine model of basal human breast cancer, the nanoprobes enabled whole-body SWIR detection of adrenal gland microlesions and bone lesions that were undetectable via contrast-enhanced magnetic resonance imaging (CE-MRI) as early as, respectively, three weeks and five weeks post-inoculation. Whole-body SWIR imaging of nanoprobes functionalized to differentially target distinct metastatic sites and administered to a biomimetic murine model of human breast cancer resolved multi-organ metastases that showed varied molecular profiles at the lungs, adrenal glands and bones. Real-time surveillance of lesions in multiple organs should facilitate pre-therapy and post-therapy monitoring in preclinical settings.

Online video consumption is one of the most popular internet activities worldwide. However unstable network and frequent buffering usually degrades the user experience. Streaming media is multimedia that is constantly received and then presented to an end-user. Among various protocols supporting video streaming HTTP Live Streaming (HLS) has become one of the most commercially used protocols. In this paper, we implement video streaming using HTTP streaming. This paper uses a fast forward moving pictures expert group (FFmpeg) as a video transcoding tool. To reduce the manual intervention, the automation tool Ansible (DevOps tool) is being used to store the video in the AWS S3 bucket for reducing the load on the server. It is also observed that decreasing the size of the .ts files results in reducing the time after which the first frame of video starts playing.

We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task is to generate the next response based on background knowledge about the movie such as the plot, review, Reddit comments etc. This requires capturing structural, sequential and semantic information from the conversation context and the background resources. This is a new task and has not received much attention from the community. We propose a new architecture that uses the ability of BERT to capture deep contextualized representations in conjunction with explicit structure and sequence information. More specifically, we use (i) Graph Convolutional Networks (GCNs) to capture structural information, (ii) LSTMs to capture sequential information and (iii) BERT for the deep contextualized representations that capture semantic information. We analyze the proposed architecture extensively. To this end, we propose a plug-and-play Semantics-Sequences-Structures (SSS) framework which allows us to effectively combine such linguistic information. Through a series of experiments we make some interesting observations. First, we observe that the popular adaptation of the GCN model for NLP tasks where structural information (GCNs) was added on top of sequential information (LSTMs) performs poorly on our task. This leads us to explore interesting ways of combining semantic and structural information to improve the performance. Second, we observe that while BERT already outperforms other deep contextualized representations such as ELMo, it still benefits from the additional structural information explicitly added using GCNs. This is a bit surprising given the recent claims that BERT already captures structural information. Lastly, the proposed SSS framework gives an improvement of 7.95% over the baseline.

BackgroundWe examined the association between sarcopenia and post-transplant mortality in acutely ill inpatients with cirrhosis who underwent urgent liver transplantation.MethodsIncluded were inpatients at 4 centers who were urgently listed as nonstatus 1 and transplanted from 2005 to 2017 with an abdominal computed tomography scan 48 cm/m experienced higher rates of death at 1 year (86% versus 95%) and 3 years (73% versus 95%) (Log-rank P = 0.01). In MELD-adjusted analysis, sarcopenia was strongly associated with post-transplant mortality (HR = 4.39, 95% CI 1.49-12.97). Among women, 35% died. Median SMI was similar in women who died versus survived (45 versus 44 cm/m). SMI was not associated with post-transplant mortality (HR = 1.02, 95% CI 0.96-1.09). Optimal search did not identify any SMI cutoff that predicted post-transplant mortality.ConclusionsAmong patients who underwent urgent inpatient evaluation and liver transplantation, we identified an SMI cutoff value of 48 cm/m to predict post-transplant mortality in men. Our data support the use of SMI as a tool to capture the impact of muscle depletion on post-transplant mortality in acutely ill men with cirrhosis undergoing urgent liver transplantation.

Adoption of solar Photovoltaic (PV) is on the rise in different parts of the world. Till date, most solutions proposed to mitigate voltage problems caused by high PV penetration on distribution network have hovered around smart inverters. As new control technologies at the grid-edge such as Secondary VAr Controllers (SVCs) become ubiquitous, the need to understand the interplay between smart inverters and SVCs is felt. This paper addresses the interaction between smart inverters and SVCs through QSTS simulations. The field results collected from a pilot project performed at Hawaiian electric are taken as the basis for conducting these simulations and creating future scenarios. Using SVCs, Smart Inverters and optimized settings of the LTC, it is demonstrated that the PV hosting capacity of the distribution system can be doubled from (4.8MW PV to 9.6 MW PV ) without causing voltage problems. Further, using a dynamic coordinated control of the LTC with SVCs, it is shown that the ramp rate of real power flow at the substation during evening time (duck curve effect) can be reduced by 10.8%. Finally, it is conjectured that the dynamic control of LTC can eliminate the need for extensive DER interconnection studies by naturally achieving optimized settings under varying operating conditions.

High residential PV penetration in the distribution grid can cause large voltage variations due to sudden changes in PV generation. This ultimately degrades power quality, customer satisfaction, risks utility infrastructure, customer equipment and utility employees working in the field. As a result, controlling voltage becomes imperative given that most utilities have to maintain voltage within a service range of 114V to 126V. Conventional assets, such as LTCs and Cap Banks, do not respond fast enough to mitigate these voltage variations. Lack of fast distributed control limits further PV penetration. Newer approaches of using smart inverters moving forward are being proposed, however, most inverters in the field are still legacy inverters. This paper presents a novel approach of using Secondary VAr Controllers (SVC) to control voltage by injecting VArs dynamically. Through a pilot project on a system with 77% (4.8 MW PV ) PV penetration at Hawaiian Electric, it is demonstrated that 61 SVCs distributed on two feeders can tighten the feeder-wide voltage profile by up to 4% (4.8V), which translates to nearly doubling (146% or 9.23 MW PV ) of the PV hosting capacity.