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Ultrathin transparent conductive oxides (TCOs) are emerging candidates for next-generation transparent electronics. Indium oxide (In2O3) incorporated with post-transition-metal ions (e.g., Sn) has been widely studied due to their excellent optical transparency and electrical conductivity. However, their electron transport properties are deteriorated at the ultrathin two-dimensional (2D) morphology compared to that of intrinsic In2O3. Here, we explore the domain of transition-metal dopants in ultrathin In2O3 with the thicknesses down to the single-unit-cell limit, which is realized in a large area using a low-temperature liquid metal printing technique. Zn dopant is selected as a representative to incorporate into the In2O3 rhombohedral crystal framework, which results in the gradual transition of the host to quasimetallic. While the optical transmittance is maintained above 98%, an electron field-effect mobility of up to 87 cm2 V-1 s-1 and a considerable sub-kΩ-1 cm-1 ranged electrical conductivity are achieved when the Zn doping level is optimized, which are in a combination significantly improved compared to those of reported ultrathin TCOs. This work presents various opportunities for developing high-performance flexible transparent electronics based on emerging ultrathin TCO candidates.

On-chip optical switches have emerged as a new class of photonic components for high-performance optical communication networks and on-chip interconnects, in which the all-optical configuration without the incorporation of other control-means is highly desired. While two-dimensional (2D) ultrathin materials demonstrate their great potential in developing ultrafast all-optical switches owing to their unique light–matter interaction, such investigations have so far been limited to the fiber optic platform or free space. Here, we realize an all-optical on-chip switch from a silicon waveguide-based asymmetric Mach–Zehnder interferometer (MZI) structure enabled by 2D ultrathin Ga2S3. Upon the visible light excitation at 532 nm, excessive photocarriers in Ga2S3 cause a change of the refractive index and subsequently a phase variation between MZI arms at the 1550 nm operation wavelength, triggering on the optical switch. On the other hand, the switch is off without the visible light stimulation, as the phase variation is recovered due to the ultrafast photo-exciton relaxation behavior of Ga2S3. The Ga2S3-enabled all-optical switch is driven at an extremely small optical power density of 0.12 W cm−2 and exhibits a response and recovery time of 26.3 and 43.5 μs, respectively, which as a combination is superior to those of fiber optic-based all-optical switches enabled by 2D materials. This work may provide a viable approach to develop on-chip all-optical photonic components for practical integrated photonic chips.

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

There is a lack of large comparative study on the outcomes of reduced intensity conditioning (RIC) in acute myeloid leukemia (AML) transplantation using fludarabine/busulfan (FB) and fludarabine/melphalan (FM) regimens. Adult AML patients from Center for International Blood and Marrow Transplant Research who received first RIC allo-transplant between 2001 and 2015 were studied. Patients were excluded if they received cord blood or identical twin transplant, total body irradiation in conditioning, or graft-versus-host disease (GVHD) prophylaxis with in vitro T-cell depletion. Primary outcome was overall survival (OS), secondary end points were leukemia-free survival (LFS), nonrelapse mortality (NRM), relapse, and GVHD. Multivariate survival model was used with adjustment for patient, leukemia, and transplant-related factors. A total of 622 patients received FM and 791 received FB RIC. Compared with FB, the FM group had fewer transplant in complete remission (CR), fewer matched sibling donors, and less usage of anti-thymocyte globulin or alemtuzumab. More patients in the FM group received marrow grafts and had transplantation before 2005. OS was significantly lower within the first 3 months posttransplant in the FM group (hazard ratio [HR] = 1.82, P < .001), but was marginally superior beyond 3 months (HR = 0.87, P = .05). LFS was better with FM compared with FB (HR = 0.89, P = .05). NRM was significantly increased in the FM group during the first 3 months of posttransplant (HR = 3.85, P < .001). Long-term relapse was lower with FM (HR = 0.65, P < .001). Analysis restricted to patients with CR showed comparable results. In conclusion, compared with FB, the FM RIC showed a marginally superior long-term OS and LFS and a lower relapse rate. A lower OS early posttransplant within 3 months was largely the result of a higher early NRM.

Background The aim of this study was to integrate the available data published on radiation-induced sarcoma of the oral cavity into an analysis of its clinical features, treatment modalities and prognostic factors. Methods An electronic search was undertaken in September 2019. The eligibility criteria included publications that had enough clinical and histological information to confirm the diagnosis. Results Forty-two publications with 122 radiation-induced sarcoma of the oral cavities (RISOCs) were included. The mean latency period was 114 months and mean radiation total dose was 62.5 Gy. The tumors were more prevalent in males between 50 and 60 years old and the mandible was the most affected site. Osteosarcoma was the most prevalent histological type and patients were mostly treated with radical surgery. Conclusions RISOC showed a poor survival rate of 15.1% in 5-year follow-up. Gender and histological type were independently associated with survival.

Palmar-plantar erythrodysesthesia (PPE), more commonly known as hand-foot syndrome, is a dermatologic complication following chemotherapy with selective agents. In this article, we present the case of a young lady with stage III breast cancer who developed palmar-plantar erythrodysesthesia following treatment with Herceptin (trastuzumab), an unusual complication of this particular drug. From our review of literature, this is the second known occurrence of PPE secondary to trastuzumab monotherapy.

The use of cloth face coverings and face masks has become widespread in light of the COVID-19 pandemic. This paper presents a method of using low cost wirelessly connected carbon dioxide (CO2) sensors to measure the effects of properly and improperly worn face masks on the concentration distribution of exhaled breath around the face. Four types of face masks are used in two indoor environment scenarios. CO2 as a proxy for exhaled breath is being measured with the Sensirion SCD30 CO2 sensor, and data are being transferred wirelessly to a base station. The exhaled CO2 is measured in four directions at various distances from the head of the subject, and interpolated to create spatial heat maps of CO2 concentration. Statistical analysis using the Friedman’s analysis of variance (ANOVA) test is carried out to determine the validity of the null hypotheses (i.e., distribution of the CO2 is same) between different experiment conditions. Results suggest CO2 concentrations vary little with the type of mask used, however, improper use of the face mask results in statistically different CO2 spatial distribution of concentration. The use of low cost sensors with a visual interpolation tool could provide an effective method of demonstrating the importance of proper mask wearing to the public.

Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

Three-dimensional image modeling is essential in many scientific disciplines, including computer vision and precision agriculture. So far, various methods of creating three-dimensional (3D) models have been considered. However, the processing of transformation matrices of each input image data is not controlled. Site-specific crop mapping is essential because it helps farmers determine yield, biodiversity, energy, crop coverage, etc. Clifford Geometric Algebraic understanding of signaling and image processing has become increasingly important in recent years. Geometric Algebraic treats multi-dimensional signals in a holistic way to maintain relationship between side sizes and prevent loss of information. This article has used agricultural images acquired by unmanned aerial vehicles (UAVs) to construct three-dimensional models using Clifford geometric algebra. The qualitative and quantitative performance evaluation results show that Clifford geometric algebra can generate a three-dimensional geometric statistical model directly from drones' RGB images. Through peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual comparison, the proposed algorithm's performance is compared with latest algorithms. Experimental results show that proposed algorithm is better than other leading 3D modeling algorithms.

Background Identification of the cricothyroid membrane is frequently inaccurate in females because of less distinct anatomy compared with males. Supraglottic airway devices cause ventral displacement of the laryngeal structures. We investigated if this would impact on the accuracy of cricothyroid membrane identification using palpation. Methods We recruited 64 adult females who underwent assessment by volunteer participants with and without a sited i-gel® supraglottic airway device. The primary outcome was accuracy in identifying the cricothyroid membrane. Secondary outcomes included distance from participant estimate to actual cricothyroid membrane location and perceived difficulty using a visual analogue scale. Ultrasound images were analysed to determine the effect of the i-gel® on the anatomical structures relevant to cricothyroidotomy. Results The cricothyroid membrane was identified correctly in 42/64 subjects with the i-gel® in place (66%) vs 23/64 of controls (36%; P Conclusions The presence of the i-gel® improved accuracy of identifying the cricothyroid membrane using palpation in females. The cricoid cartilage was pushed ventrally by the i-gel® in the hypopharynx, creating a more palpable prominence. It may therefore be advantageous to retain a sited supraglottic airway, rather than remove it, before performing emergency cricothyroidotomy.

Smart Grid (SG) operation requires multi-way communication among power generation, transmission, distribution, and consumer facilities. The complete SG architecture involves multiple layers of networks stretching from energy control center to consumer facilities and multiple applications with diverse Quality-of-Service (QoS) requirements. To meet these requirements, Cognitive Radio (CR) which offers efficient spectrum utilization and sharing, has been considered as a favorable solution. Due to diverse requirements of latency, data rate and reliability, it is a great challenge to support various SG applications under spectrum sensing uncertainties, Signal-to-Noise Ratio (SNR) variations and availability of desired channel bandwidth. In this paper, we propose a dynamic channel selection algorithm based on Fuzzy Inference System (FIS) capable of selecting the most appropriate available idle channel with desired bandwidth, minimum required SNR and probability of miss detection Pmd. The proposed algorithm utilizes various modes of IEEE Standard 802.22, Wireless Regional Area Network (WRAN), at the physical layer. In case of unavailability of idle channels with required SNR and/or Pmd, we propose a novel approach to select the channel with closest possible parameters aiming to reduce the re-transmission probability and thus achieve the desired throughput, latency and reliability requirements. The performance is compared with existing algorithms to demonstrate the effectiveness and superiority of the proposed algorithm.

One of the reasons affecting job satisfaction is the leadership style that engages the employees on a daily basis. Although perceived leadership has been studied well in the past, but there are fewer studies focusing on the mediating effect of employee empowerment. The key objective of this research is to study the relationship of perceived leadership, employee empowerment and job satisfaction of the employees, and also to study the mediating role of employee empowerment on the relationship between perceived leadership, and job satisfaction of the employee. A sample of 200 professionals from different banks was selected for the purpose of this study. Moreover, the Smart PLS 3.0 software was used to analyze whether the model was fit by referring to the PLS-SEM. The results of this study revealed that the independent variable (perceived Leadership) has a significant and positive influence on job satisfaction. All the hypotheses were accepted, and there were none that could be rejected after the results were revealed. The study proves that a partial mediating effect of employee empowerment exists between perceived leadership and job satisfaction. This study can help managers in designing the right strategies for retaining their employees by empowering them according to the boundaries set by their organizations. The results can help organizations to identify the potential reasons for job satisfaction, which will eventually lead to higher productivity and profitability.

The world is moving rapidly from carbon-producing vehicles to green transportation systems. Electric vehicles (EV) are a big step towards a friendly mode of transport. With the constant rise in the number of electric vehicles, we need a widespread and seamless charging infrastructure that supports seamless charging and billing. Some users generate electricity using solar panels and charge their electric vehicles. In contrast, some use charging stations, and they pay for vehicle charging. This raises the question of trust and transparency. There are many countries where laws are not strictly enforced to prevent fraud in payment systems. One of the preeminent problems presently existing with any of the trading systems is the lack of transparency. The service provider can overcharge the customer. Blockchain is a modern-day solution that mitigates trust and privacy issues. We have proposed a peer-to-peer energy trading and charging payment system for electric vehicles based on blockchain technology. Users who have excess electricity which they can sell to the charging stations through smart contracts. Electric vehicle users can pay the charging bills through electronic wallets. We have developed the electric vehicle’s automatic-payment system using the open-source platform Hyperledger fabric. The proposed system will reduce human interaction and increase trust, transparency, and privacy among EV participants. We have analyzed the resource utilization and also performed average transaction latency and throughput evaluation. This system can be helpful for the policymakers of smart cities.

Odontogenic Keratocyst (OKC) is a benign, intraosseous, odontogenic cyst which originates from the basal cells of overlying epithelium or from the dental lamina remnants. Clinically, they are presented as asymptomatic swellings, although can sometimes be associated with pain. Growth of an OKC leads to expansion and destruction of bone as it infiltrates the tissue around it. It is commonly seen in males between the second and fourth decades of life. The aim of this study is to report on the clinicopathological characteristics of an odontogenic keratocyst in a 9-month-old female patient and posterior rehabilitation with a removable maxillary expander.

Mobile Ad-Hoc Networks (MANETs) are a collection of mobile nodes which are free to move from one place to another place without a central control entity. In MANETs the nodes are dependent on each other and the communication among mobile nodes is multi-hop due to which there are security issues in the MANETs protocols. Optimized Link State Routing (OLSR) and Dynamic Source Routing (DSR) protocols are mostly used as proactive and reactive routing protocols in MANETs. This research work analyzed the performance of the OLSR and DSR protocols in the presence and absence of black hole (BH) attack in terms of throughput, end-to-end delay, packet delivery ratio (PDR), and network load in various scenarios using OPNET Modeler 14.5 simulator. The results obtained in this research show that BH attack significantly degrades the performance of both DSR and OLSR protocols but due to the reactive nature of DSR routing protocol the performance is more degraded in DSR routing protocol as compared to OLSR routing protocol in the presence of BH attack.

The Internet of Things (IoT) has enabled communication anywhere between physical devices. Currently, concerns have been raised about suspicious transactions in IoT systems. Suspicious transactions may have a logical structure inconsistent with current information in the context of IoT. This article describes suspicious transactions in IoT systems and manages them using the blockchain as a service software plans. This study builds software-specific components for blockchain functions to implement in IoT networks. This study was conducted using Hyperledger Fabric as a blockchain service to test the software-defined blockchain components blockchain. The model was evaluated using average transaction throughput and latency. We observed that using blockchain as a software service system can provide excellent performance and security.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with numerous cardiac complications. We describe a case of type 1 ST-segment elevation myocardial infarction (STEMI) with focal myocarditis unmasked in a patient infected with SARS-CoV-2 with no previous cardiac history, in the hope of increasing understanding of the severity and possible complications of coronavirus disease 2019 (COVID-19) and improving its clinical management.

The industry of vehicles is now in the phase of a huge transition from fossil fuels to more clean and green energy fuels. Due to this huge transition towards clean energy, new technology has made its place in the market in the form of smart grids for electric vehicles and the charging points. As the quantity of electric vehicles (EVs) is growing fast, the need for the knowledge of costs occurs by these electric vehicles (EVs) has got huge attention. Recently the deep learning methods have received extensive attention for forecasting in various fields, and are been considered as vital for the future world. Although the forecasting for an individual user is highly challenging due to the high inconsistent behavior of the individual users, the forecasting of the aggregated values is quite promising. In this work, we proposed a LSTMs network based forecasting model in order to predict the short term cost incurred by the battery of a single electric vehicle. Then the proposed work is been tested by correlating it with another benchmark. It has been shown that our work shows better results than the other one for a single user.

The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system&rsquo, s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors&rsquo, data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV&rsquo, s stator temperature. We have also performed an informal analysis to demonstrate the proposed system&rsquo, s robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.

Persistent left superior vena cava (LSVC) is an asymptomatic congenital heart disease. It is usually found incidentally on imaging, during central line placements or while undergoing electrophysiological procedures. We present a case of a 91-year-old female who initially presented with seizures and was diagnosed with tachy-brady syndrome. She was planned to undergo dual-chamber permanent pacemaker placement. However, during the procedure, she was incidentally found to have an LSVC without a right superior vena cava. Due to challenging anatomy, her pacemaker was changed to a single-chamber atrial lead pacemaker. This case highlights the clinical implications of this unusual structural anomaly, technical difficulties that arise alongside and solutions on how to overcome these issues in the context of pacemaker implantation.

A sinus of Valsalva aneurysm (SOVA) is usually a silent entity until one of its complications arises, such as heart failure. SOVA itself is uncommon, but it is more frequently associated with a supracristal ventricular septal defect (SVSD). We present a 67-year-old man with a history of an asymptomatic SVSD who presented to the emergency department with signs and symptoms of heart failure. He was subsequently found to have a ruptured SOVA and underwent urgent surgical repair.

Image segmentation is widely used in the field of agriculture to improve the yields and protecting them from pests, herbs, shrubs, and weeds. Precision agriculture is also contributing to the inter and intra crop monitoring. Recently, unmanned aerial vehicles are used widely for acquiring images. In this paper, we purpose Clifford geometric algebra to enhance the segmented images acquired from the UAVs of different agricultural fields. The Clifford geometric algebra is also sometimes used as a collective term for the diverse range of mathematical fields, both classical and modern algebraic mathematics. Previous image segmentation approaches depend upon the intensity of red, green, and blue colors; but the complete perspective could not be obtained from these approaches. Geometric algebra overcomes this limitation and leads to a genuine color space image processing. It is mainly used in the processing of medical images. Subalgebra of the Clifford algebra is Quaternions. We have used this approach in agricultural images. The image segmentation of foreground and background is enhanced using Clifford geometric algebra; hence, the results obtained are fine-tuned segmented images. The anticipated result of our research would have a positive impact on the amelioration of the condition of the farmers and their livelihood.

The world is experiencing a cancer epidemic and an increase in the prevalence of the disease. Cancer remains a major killer, accounting for more than half a million deaths annually. There is a wide range of natural products that have the potential to treat this disease. One of these products is artemisinin; a natural product from Artemisia plant. The Nobel Prize for Medicine was awarded in 2015 for the discovery of artemisinin in recognition of the drug's efficacy. Artemisinin produces highly reactive free radicals by the breakdown of two oxygen atoms that kill cancerous cells. These cells sequester iron and accumulate as much as 1000 times in comparison with normal cells. Generally, chemotherapy is toxic to both cancerous cells and normal cells, while no significant cytotoxicity from artemisinin to normal cells has been found in more than 4000 case studies, which makes it far different than conventional chemotherapy. The pleiotropic response of artemisinin in cancer cells is responsible for growth inhibition by multiple ways including inhibition of angiogenesis, apoptosis, cell cycle arrest, disruption of cell migration, and modulation of nuclear receptor responsiveness. It is very encouraging that artemisinin and its derivatives are anticipated to be a novel class of broad-spectrum antitumor agents based on efficacy and safety. This review aims to highlight these achievements and propose potential strategies to develop artemisinin and its derivatives as a new class of cancer therapeutic agents.

To provide a continuous supply of electricity, renewable energy resources, and smart technological innovation must be exploited, aiming to provide a long-term solution to the existing energy crises faced by today’s world. The photovoltaic (PV) panels are deployed to receive solar energy from the sunlight and consequently convert it into electrical energy. The photovoltaic PV panel receives maximum solar radiation only when it is at 90° perpendicular to the sunlight, which is directly proportional efficiency of the system. If proper technique not used to collect the maximum amount of sunlight, solar PV generation can be a more expensive energy source as compared to conventional energy generation. The generated output power of PV panels depends on the quantity of solar energy they collect from sunlight, and this amount can be increased by exploiting tracking systems. The solar tracker tends to move PV panels perpendicular to sunlight radiations to collect the maximum amount of sunlight to increase the system’s efficiency.

Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.

Introduction: Ibrutinib works by inhibiting downstream signaling after the interaction between the mutated MYD88 (Leu265Pro) protein and BTK. In 2015, Ibrutinib, an inhibitor of Bruton's tyrosine kinase (BTK), was approved by the U.S Food and Drug Administration (FDA) for patients with symptomatic Waldenstrom Macroglobulinemia (WM). Methods: We performed a comprehensive literature search following PRISMA guidelines. Beginning with articles published after April 2015, we used databases like PubMed, Embase, Clinicaltrials.gov, Cochrane Library, and Web of Science. A total of 580 articles were identified initially, and after a detailed screening, we finalized 4 studies involving 248 WM patients. Results: The total number of patients who received Ibratunib based regimens were 248. The dose ranged from 140-560 mg. The overall response rate ranged from 90-92%, very good partial response ranged from 13-23%, and the partial response ranged from 36-75%. 1) Ibtrutunib +Rituximab: In a study by Dimopoulos et al., (N=150), the overall response rate (ORR) was 92% vs 47% in control vs placebo respectively. The progression-free survival (PFS) of 30 months was 82% vs 28% respectively. 2) Ibrutinib alone: In a study by Dimopoulos et al., with (N=31), ORR was 90%, a very good progression rate (VGPR) was 13 %, and the partial response (PR) was 58%. In a study by Treon et al., (N=63) ORR was observed in Treon et al. at 90.50%, VGPR was 15%, PR was 36%, no response (NR) was 57%, with overall survival (OS) was 24 months. In a study by Richard et al., (N=4), PR was noted at 75%. Conclusion: Ibtrutunib used for the treatment of WM showed an overall response rate of 92%. Infections, cytopenia, bleeding, hypertension and atrial fibrillation were major side effects reported. However, due to the recent FDA approval of the drug to be used for the patients of WM there is very limited studies on Ibrutinib. We recommend future randomized prospective trials to understand the efficacy and safety profile of Ibrutinib for WM treatment. Disclosures Anwer: Incyte, Seattle Genetics, Acetylon Pharmaceuticals, AbbVie Pharma, Astellas Pharma, Celegene, Millennium Pharmaceuticals.:Honoraria, Research Funding, Speakers Bureau.

Introduction: Waldenström's macroglobulinemia is a rare low-grade lymphoplasmacytic lymphoma characterized by CD20 expression on malignant cells and produces a monoclonal immunoglobulin M (IgM). Rituximab is a chimeric monoclonal antibody against CD20 antigen and the most widely used therapeutic agents in WM. Rituximab works by adhering to CD20, causes B-cell lysis mainly through antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity. Cyclophosphamide is an alkylating agent binds to DNA. Its cytotoxic effect is mainly due to cross-linking of strands of DNA and RNA, and to inhibition of protein synthesis. Our objective is to analyze and summarize the published literature for the efficacy of regimens containing both rituximab and cyclophosphamide for the treatment of Waldenström's macroglobulinemia (WM). Methods:We performed a comprehensive literature search on articles following PRISMA guidelines. Beginning with articles published after January 2005, we used databases like PubMed, Embase, Clinicaltrials.gov, Cochrane Library, and Web of Science. A total of 585 articles were identified initially, and after a detailed screening, we finalized 10 studies involving 425 WM patients. Results:The total number of patients were 425. The dose was 375 mg/m2. The complete response (CR) ranged from 3-19%, very good partial response (VGPR) ranged from 4-22%, and the partial response (PR) ranged from 8-82%. The overall response rate (ORR) ranged from 79-95%. Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP): In a retrospective study by Ioakimidis et al., included 23 WM patients were given R-CHOP. The ORR was 95%, with 69% achieving a major response, 43%, PR, and VGPR OF 9%. In a randomized control trial by Buske et al., N=23, the major response rate was 91%, and PR was 82%. Treon et al. (N=13) observed a major response rate of 27%. (Table 1). Fludarabine, Cyclophosphamide and Rituximab (FCR): In a retrospective study by Peinert et al., involving 29 patients receiving the FCR regimen, the ORR was 90%, with 79%, 3%, and 10% of patients achieving PR, CR, and NR, respectively. In a study by Tedeschi et al., involving 40 patients, ORR was 80%, and the major response rate was 80%. Souchet et al., N=82 reported an ORR of 84% and a PR of 46%. Progression-free survival (PFS) was 51.2 months for Tedeschi et al. Dexamethasone, Rituximab and Cyclophosphamide (DRC) In study Paludo et al., (N=50), receiving the DRC regimen, the highest ORR was 87%, PR 64%, and major response was 68%. In a phase II study by Dimopoulos et al., (N=72), the ORR of 82% and PR of 67% was observed. PFS was 24 months in this study. (Table 1) 4.Rituximab, Cyclophosphamide, Vincristine, and Prednisone (R-CVP) In study Ioakimidis et al, N=16, the ORR was 87%, the major response rate was 62%, and the PR was 43%. Rituximab, Cyclophosphamide, and Prednisone (RCP) Ioakimidis et al., reported 19 patients who were given RCP. The ORR, PR, and the major response rate was 94%, 73%, and 73%, respectively.Lenalidomide, Rituximab, Cyclophosphamide, and Dexamethasone (LR-CD) A study by Rosenthal et al., 15 patients were given LR-CD. ORR was 80%, with a major response rate in 80% and PR in 73% of patients. PFS was 38 months. Conclusion: Rituximab and cyclophosphamide, in combination regimens for the treatment of WM showed the overall response rate of 95%. Neutropenia was the dominant side effect reported in these regimens. There is a paucity of phase 3 randomized trials demonstrating a clinical benefit of anyone regimen over another. We recommend future randomized prospective trials better to understand the efficacy and safety profile of regimens containing both rituximab and cyclophosphamide base combinations. Disclosures Anwer: Incyte, Seattle Genetics, Acetylon Pharmaceuticals, AbbVie Pharma, Astellas Pharma, Celegene, Millennium Pharmaceuticals.:Honoraria, Research Funding, Speakers Bureau.

Background: Low-risk Myelodysplastic Syndromes (MDS) patients commonly present with anemia and may become blood transfusions dependent upon progression. Luspatercept, a targeted drug for an activin receptor ligand has emerged as new anemia treatment in MDS for patients with ring sideroblasts and the patients with SF3B1 mutation. This systemic review highlights the efficacy of luspatercept in MDS patients whom erythropoietin stimulating agents (ESA) are not effective. Methods: We conducted a comprehensive literature search using PubMed, Clinical trial.gov, Embase, Cochrane, and Web of science. Our search strategy included MeSH (Medical Subject Headings) terms and keywords for MDS and luspatercept including trade names and generic names from inception to 29 April 2020. Studies were selected according to PRISMA guidelines. The initial screening revealed 240 articles. After excluding review articles, duplicates, and non-relevant articles, finally we included two clinical trials, which reported transfusion independence (TI), an erythroid response (HI-E) in MDS patients with luspatercept. Proportions along with 95% Confidence Interval (CI) were extracted to compute pooled analysis using the 'meta' package by Schwarzer et al. in the R programming language (version 4.0.2) to report the efficacy of luspatercept. We pooled the results of the experimental arms of the two trials using the inverse variance method and logit transformation. Between studies, variance was calculated using DerSimonian-Laird Estimator. Results: A total of 287 patients were enrolled and evaluated in two phases II/III trials. Platzbecker et al and Fenaux et al reported Erythropoietin stimulating agents (ESA) with one median prior line of therapy (n= 148, n=46). Fenaux et al. also reported iron chelation therapy (n=71) as a prior line of therapy. Patients having ring sideroblast positive 500: n=57 for both studies. The baseline means hemoglobin (Hb) levels were eight before therapy. TI for more than eight weeks was observed in 38% of patients in both the MEDALIST trial and PACE trial. The erythroid response was 53% and 63% in both trials respectively. In a Phase II study, for LR-MDS patients, the overall erythroid response was higher among patients (n= 69%) having ringed sideroblast status (>15% ring sideroblast) and SF3B mutation (n=77%). The mean increase of Hb was observed in 29 out of 46 and 32 out of 41 pts in MEDALIST and PACE trial, respectively. Luspatercept proved to be efficacious in the pooled analysis i.e transfusion independence (TI): 38%, 95% CI 0.31-0.45; p =0.98, I2 = 0%), and erythroid response (HI-E): 54%, 95% CI 0.48-0.62; p=0.22, I2 = 32%) with an increase in mean Hb of 70% 95%: CI 0.59-0.78; I2 = 56%) (Figure 1). CONCLUSION: In patients with low risk MDS positive ringed sideroblast or SF3B1 mutation status shows good responses with luspatercept treatment, with reduced transfusion dependence, and higher erythroid response. Disclosures Anwer: Incyte Pharmaceuticals: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Speakers Bureau; Millennium Pharmaceuticals: Research Funding; Celgene: Research Funding; Astellas Pharma: Research Funding; Acetylon Pharmaceuticals: Research Funding; Seattle Genetics: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Speakers Bureau; AbbVie Pharmaceuticals: Research Funding.

Background: Conditioning regimen given before allogeneic hematopoietic stem cell transplantation (allo-HSCT) contributes significantly to the outcomes following transplant for myelodysplastic syndrome (MDS). Myeloablative conditioning (MAC) regimens are often associated with a lower risk of relapse; however, their use is often limited by toxicity and a higher risk of non-relapse mortality (NRM). Reduced-intensity conditioning (RIC) regimens are associated with a higher risk of relapse, a lower NRM, a lower incidence of graft versus host disease (GVHD), and hence is feasible in patients with advanced age or comorbidities. This study highlights the importance of the difference between the two interventions used in allo-HSCT for myelodysplastic syndrome (MDS). Methods: We conducted a comprehensive literature search using PubMed, Clinicaltrial.gov, Embase, Cochrane, and Web of Science on 5th May 2020 with no restriction of language or period. Initial research revealed 1698 articles. After excluding review articles, duplicates, and non-relevant articles, we included two randomized clinical trials and three cohort studies, which reported overall survival (OS), relapse-free survival (RFS), relapse incidence (RI), and incidence of acute and chronic (GVHD). Based on inclusion criteria, two randomized clinical trials (BMT CTN 0901 Trial Scott et al. 2017 & EBMT RICMAC Trial Kroger et al. 2017) and three cohort studies (BBMT 55226 Park et al. 2018, BBMT Orozco et al. 2019, BMT Alatrash et al. 2019) were included. Results: Among combined MDS and acute myeloid leukemia (AML) patients (n=1188) enrolled in 5 studies, 651 patients had MDS. Patients underwent MAC (n=484) or RIC (n=692) followed by either a matched related donor (MRD), n=236, or matched unrelated donor (MUD), n=236, allo-HCT. The median age range at the time of transplant was 50-54 years The use of RIC causes statistically non-significant trend of improved overall survival (OS) (OR 1.11: 95% CI: 0.74-1.67, p-value= 0.61) along with more risk of relapse incidence (RI) (R 1.34: 95% CI: 0.56-3.18, p value= 0.51), and reduced relapse free survival (RFS) (OR 0.88, 95% CI: 0.55-1.44: p value= 0.59). Differences were found in terms of safety of both conditioning regimens. MAC allo-HCT have more acute GVHD, grade 3 and 4 (OR 0.66: 95% CI: 0.29-1.52, p value= 0.33) and chronic GVHD, grade 3 and 4 (OR 0.72: 95% CI: 0.41-1.27: p value= 0.26) and treatment-related mortality (TRM) (OR 0.83, 95% CI: 0.37-1.86, p value= 0.65). The meta-analysis results are shown in figure-1. Conclusion: In adult MDS patients undergoing allo-HSCT, RIC is associated with improved overall survival, but at the cost of reduced RFS, and a higher risk of relapse. MAC, on the other hand, is associated with more treatment-related mortality and GVHD. Results of our analysis point out these trends but these are not statistically significant Figure 1 Disclosures Anwer: Incyte, Seattle Genetics, Acetylon Pharmaceuticals, AbbVie Pharma, Astellas Pharma, Celegene, Millennium Pharmaceuticals.: Honoraria, Research Funding, Speakers Bureau.

Data crawling, collection and analysis have become a popular pillar for the business intelligence of big data analysis which is the latest hot-topic among the research association. Numerous tools and techniques to solve and analyze the structured and unstructured datasets are developing very quickly. The previous studies show the different approaches in the identification of the strengths and weaknesses of multiple machine learning algorithms. But, most of the approaches demand more expert knowledge base information to understand the concepts of given data. In this paper, we modernize the machine learning methods for the effective prediction of heart disease. This work deliberates the detailed process of implementation of our proposed system. The goal of this work is to find a strong and effective machine learning algorithm for disease prediction for the problem; how can doctors get fast and better results for their diagnosis of heart disease. We design a new system for disease prediction using machine learning prediction algorithms (LR, ANN and SVC) by utilizing an effective approach of ETL, OLAP and data mining. The results showed that the best machine learning algorithm is SVC with 92% accuracy for the risk prediction model. We found that subjects at 56-64 years old have a high risk of heart disease, as well as men, have more heart disease rate than women. This proposed study can be favorable for the medical practitioners in the field of healthcare, supportive practice and precautions to the heart disease patients.

As with many other sectors, to improve the energy performance and energy neutrality requirements of individual buildings and groups of buildings, built environment is also making use of machine learning for improved energy demand predictions. The goal of achieving energy neutrality through maximized use of on-site produced renewable energy and attaining optimal level of energy performance at building-cluster level requires reliable short term (resolution shorter than one day) energy demand predictions. However, the prediction and analysis of the energy performance of buildings is still focused on the individual building level and not on small neighborhood scale or building clusters. In a smart grid context, to better understand electricity consumption at different spatial levels, prediction should be at both individual as well as at building-cluster levels, especially for neighborhoods with definite boundaries (such as universities, hospitals). Therefore, in this paper, using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals. Predicting at hourly granularity is important to understand short-term dynamics, yet most of the neighborhood scale studies are limited to yearly, monthly, weekly, or daily data resolutions. Two years of data were used in training the model and the prediction was performed using another year of untrained data. Learning algorithms such as; boosted-tree, random forest, SVM-linear, quadratic, cubic, fine-Gaussian as well as ANN were all analysed and tested for predicting the electricity demand of individual and groups of buildings. The results showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity when metrics such as computational time and error accuracy are compared.

The segmentation of brain tumor is a very crucial task for detecting the tumor in the early stage. Many methods of brain tumor segmentation are available in the literature but none of the method achieved satisfactory performance. Brain tumor has caused more deaths compared to other cases related to cancer. Due to the complex structure of the brain tumor and its merging with the normal tissues, the classification of tumor from normal tissues is a challenging task. In this article, we have proposed LIPC based methodology for brain tumor classification and segmentation. The main idea is to take the segmentation challenge as a multiclass classification task. MRI sequences of the brain have common characteristics (locality) and are sparse (sparsity). We have utilized both these properties in our proposed methodology. Firstly the image preprocessing techniques are used for noise removal and image enhancement. After that different textural features were calculated and reduced by PCA for better classification score. The dataset used is that of MICCAI 2013 containing High-grade tumor and Low-grade tumor for real patient data. Our method presented a Dice Score of 0.95 for complete tumor which is by far compared to the methods already available in terms of Dice Score and time complexity.

Hodgkin's lymphoma (HL) is a hematological disorder that has a high cure rate. It usually presents as asymptomatic lymphadenopathy or a mass on chest radiograph along with constitutional symptoms ("B" symptoms such as fever, night sweats, or unintended weight loss) in less than half the cases. Optic neuritis is a demyelinating condition that is rarely associated with HL. We present a case of HL that presented with optic neuritis as a paraneoplastic syndrome.

Smart Grid (SG)network comprises of wide variety of applications with diverse Quality of Service (QoS)requirements. Each SG application has its own requirements of latency, throughput and reliability. Cognitive Radio (CR), which offers better spectrum utilization through opportunistic spectrum access and spectrum sharing, is considered as a promising wireless technology for SG networks. Traffic scheduling and optimization is one of major challenges in CR-based SG communication network. In this paper, we propose a novel usage of mini-batch gradient descent method for the optimization of QoS-based differential scheduling in CR-based SG network. A 2-class based priority scheduling model with emergency and interrupt handling capability is used. It is shown through simulation results that mini-batch gradient descent method achieves better results as compared to gradient descent method in terms of the fast convergence and minimization of the overall cost function.

Background: Aerobic Exercise is a host of health benefits to reduce vigor risk and maintain body weight. The purpose of the present investigation is to determine the influence of aerobic exercises on body weight and Metabolic Equivalent of Task (MET) activity among cardiac rehabilitation phase II patients. The objective of the study is to investigate the impact of obesity on the efficacy of aerobic capacity. Methods: Fifteen obese (ten males, five females) and fifteen non-obese (eleven males, four females) participants of phase II cardiac rehabilitation were selected from a tertiary care hospital by their Body Mass Index (BMI). They were divided into two groups by simple random technique. Aerobic exercises were given for 12 weeks for post-CABG cardiac rehabilitation phase II obese and non-obese (healthy and overweight) patients. BMI and the Metabolic equivalent of task activity of cardiac rehabilitation phase II patients are measured by enrolling a patient in 12 weeks of aerobic exercises program. Results: The aerobic exercise shows a positive result in both obese and non-obese patients. Aerobic exercises improved metabolic equivalent of task in both obese(4.6667+_0.65134 ;< 0.05) and non-obese patients (4.6923+_.48038 ;< 0.05) significantly.But aerobic exercises were more effective in enhancing the efficacy of aerobic capacity in obese patients. Conclusion: It is evident that aerobic exercises are more effective for obese patients to maintain or reduces weight. Higher MET activity was observed in obese patients.

Key Clinical Message Blinatumomab, a bispecific T‐cell engager monoclonal antibody used to manage Philadelphia chromosome‐negative relapsed or refractory B‐cell precursor acute lymphoblastic leukemia (ALL) can be used to treat patients by inducing graft versus leukemia reaction post allogeneic hematopoietic stem cell transplantation, a feature which it was post allogeneic bone marrow transplantation, a feature which this drug was not aimed to do.

Key Clinical Message IgG4‐RD can also present in the skeletal muscle, mimicking several other diseases. It is unusual for this relatively new classification of diseases to present in the muscles and can be mistakenly diagnosed as other autoimmune diseases rendering a delay in the appropriate management and progression of the disease.

In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea&rsquo, s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.

Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT&ndash, blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies, it will also help the industry to make policies according to the predictions of ADL.

The use of leaded gasoline adversely affects cardiovascular, nervous, and immune systems. Study projects to rule out different variables of prognostic importance in lead-exposed subjects. A total of 317 traffic wardens with 5 years of outdoor experience and Hb levels

Silicon photonics has demonstrated great potential in ultrasensitive biochemical sensing. However, it is challenging for such sensors to detect small ions which are also of great importance in many biochemical processes. A silicon photonic ion sensor enabled by an ionic dopant-driven plasmonic material is introduced here. The sensor consists of a microring resonator (MRR) coupled with a 2D restacked layer of near-infrared plasmonic molybdenum oxide. When the 2D plasmonic layer interacts with ions from the environment, a strong change in the refractive index results in a shift in the MRR resonance wavelength and simultaneously the alteration of plasmonic absorption leads to the modulation of MRR transmission power, hence generating dual sensing outputs which is unique to other optical ion sensors. Proof-of-concept via a pH sensing model is demonstrated, showing up to 7 orders improvement in sensitivity per unit area across the range from 1 to 13 compared to those of other optical pH sensors. This platform offers the unique potential for ultrasensitive and robust measurement of changes in ionic environment, generating new modalities for on-chip chemical sensors in the micro/nanoscale.

Automatic Gain Control (AGC) are used on the receiver side of almost all the communication systems to adjust the power of the received signal. Software Defined Radios (SDR) mostly use Look-Up Table (LUT) based feedforward AGCs. The development of such AGC requires RF characterization of each SDR platform followed by piece-wise analysis and the subsequent operation algorithm. The process of RF characterization is a manual process which is both tedious and erroneous. In this paper, automation algorithm of the whole process is proposed as well as implemented on hardware. Through this research and development, the lengthy and tedious process has been reduced to an approximately 10 minutes single click process. The automated process has shown promising results as compared to the manual process with greater precision and reduced error. Through the proposed technology, the process of large scale assembly and production of SDRs for both military and commercial use can be eased.

Patients admitted with the presumed coingestion of beta-blockers (BBs) and calcium channel blockers (CCBs) should be initially managed in accordance with standardized resuscitation protocols (the airway, breathing, and circulation (ABC) approach). Additionally, more specific interventions should be promptly attempted. Intravenous glucagon and calcium salts have long been used in the treatment of BB and CCB toxicities. We present a case of a severe, concurrent BB and CCB toxicity resulting in cardiovascular collapse refractory to vasopressors. The administration of high-dose insulin (HDI) and lipid emulsion therapy (LET) resulted in a significant improvement in hemodynamics with an overall favorable outcome in the patient.