Search

Heavy metal ions can be introduced into the water through several point and non-point sources including leather industry, coal mining, agriculture activity and domestic waste. Regrettably, these toxic heavy metals may pose a threat to both humans and animals, particularly when they infiltrate water and soil. Heavy metal poisoning can lead to many health complications, such as liver and renal dysfunction, dermatological difficulties, and potentially even malignancies. To mitigate the risk of heavy metal ion exposure to humans and animals, it is imperative to extract them from places that have been polluted. Several conventional methods such as ion exchange, reverse osmosis, ultrafiltration, membrane filtration and chemical precipitation have been used for the removal of heavy metal ions. However, these methods have high operation costs and generate secondary pollutants during water treatment. Biosorption is an alternative approach to eliminating heavy metals from water that involves employing eco-friendly and cost-effective biomass. This review is focused on the heavy metal ions contamination in the water, biosorption methods for heavy metal removal and mathematical modeling to explain the behaviour of heavy metal adsorption. This review can be helpful to the researchers to design wastewater treatment plants for sustainable wastewater treatment. [ABSTRACT FROM AUTHOR]

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance. [ABSTRACT FROM AUTHOR]

Heavy metal ions can be introduced into the water through several point and non-point sources including leather industry, coal mining, agriculture activity and domestic waste. Regrettably, these toxic heavy metals may pose a threat to both humans and animals, particularly when they infiltrate water and soil. Heavy metal poisoning can lead to many health complications, such as liver and renal dysfunction, dermatological difficulties, and potentially even malignancies. To mitigate the risk of heavy metal ion exposure to humans and animals, it is imperative to extract them from places that have been polluted. Several conventional methods such as ion exchange, reverse osmosis, ultrafiltration, membrane filtration and chemical precipitation have been used for the removal of heavy metal ions. However, these methods have high operation costs and generate secondary pollutants during water treatment. Biosorption is an alternative approach to eliminating heavy metals from water that involves employing eco-friendly and cost-effective biomass. This review is focused on the heavy metal ions contamination in the water, biosorption methods for heavy metal removal and mathematical modeling to explain the behaviour of heavy metal adsorption. This review can be helpful to the researchers to design wastewater treatment plants for sustainable wastewater treatment. [ABSTRACT FROM AUTHOR]

A large body of evidence has shown a direct link between arsenic exposure and drug resistance to Leishmania parasites against antimonial preparations in visceral leishmaniasis (VL) hyper-endemic regions, especially in India and its sub-continent. However, the implicated roles of arsenic on the VL host, pathophysiological changes, and immune function have not yet been clarified, particularly at the reported concentration of arsenic in the VL hyper-endemic area of Bihar, India. Herein, we exposed the mouse VL model to arsenic (0.5 mg/L to 2 mg/L) through their drinking water and analyzed its effect on T cells proliferation, Th1/Th2-mediators, MAPK signaling cascade, and parasite load in preclinical models. Coherently, the parasite count in Giemsa stained spleen imprint has been investigated and found significant positive associations with levels of arsenic exposure. The liver and kidney function tests (AST, ALT, ALP, BUN, Creatinine, Urea, etc.) are apparent to hepatonephric toxicity in arsenic exposed VL mice compared to unexposed. This observation appears to be consistent with the up-regulated expression of immune regulatory Th2 mediators (IL-4, IL-10, TGF-β) and down-regulated expression of Th1 mediators (IL-12, IFN-γ, TNF-α) with a suppressed leishmanicidal function of macrophage (ROS, NO, iNOS). We also established that arsenic exposure modulated the host ERK-1/2 and p38 MAPK signaling cascade, limited T lymphocyte proliferation, and a lower IgG2a/IgG1 ratio to favor the Leishmania parasite survival inside the host. This study suggests that the contorted Th1-subtype and exacerbated Th2-subtype immune responses are involved in the increased susceptibility and pathogenesis of Leishmania parasite among subjects/individuals regularly exposed to arsenic. [ABSTRACT FROM AUTHOR]

Therapeutics, based on small interfering RNA (siRNA), have demonstrated tremendous potential for treating cancer. However, issues such as non-specific targeting, premature degradation, and the intrinsic toxicity of the siRNA, have to be solved before they are ready for use in translational medicines. To address these challenges, nanotechnology-based tools might help to shield siRNA and ensure its specific delivery to the target site. Besides playing a crucial role in prostaglandin synthesis, the cyclo-oxygenase-2 (COX-2) enzyme has been reported to mediate carcinogenesis in various types of cancer, including hepatocellular carcinoma (HCC). We encapsulated COX-2-specific siRNA in Bacillus subtilis membrane lipid-based liposomes (subtilosomes) and evaluated their potential in the treatment of diethylnitrosamine (DEN)-induced hepatocellular carcinoma. Our findings suggested that the subtilosome-based formulation was stable, releasing COX-2 siRNA in a sustained manner, and has the potential to abruptly release encapsulated material at acidic pH. The fusogenic property of subtilosomes was revealed by FRET, fluorescence dequenching, content-mixing assay, etc. The subtilosome-based siRNA formulation was successful in inhibiting TNF-α expression in the experimental animals. The apoptosis study indicated that the subtilosomized siRNA inhibits DEN-induced carcinogenesis more effectively than free siRNA. The as-developed formulation also suppressed COX-2 expression, which in turn up-regulated the expression of wild-type p53 and Bax on one hand and down-regulated Bcl-2 expression on the other. The survival data established the increased efficacy of subtilosome-encapsulated COX-2 siRNA against hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

The healthcare budget is increasing day-by-day as the population of the world increases. The same is the case regarding the workload of health care workers, that is, doctors and other practitioners. Under such a scenario where workload and cost are increasing drastically, there is a dire need of integrating recent technological enhancements with the said domain. Since the last decade, a lot of work is in the process considering the said integration bringing revolutionary changes. For remote monitoring, existing systems use different types of Internet of things devices that measure different health parameters. One of the major problems in such a system is to find an optimum routing approach that can resolve energy and thermal issues that are taking the limelight in the research arena. In this article, a dynamic routing technique is proposed which is keen to connect multiple in vivo/ex vivo Internet of things devices and a sink (focusing thermal and energy problem) and then forwarding data from sink to remote location for monitoring. Performance parameters are kept energy efficiency and thermal awareness and analytical results show that the proposed protocol supersedes existing approaches in said metrics. [ABSTRACT FROM AUTHOR]

Amyand's hernia is an inguinal hernia with trapped appendix; its frequency is approximately 1%. Acute appendicitis is a rare clinical presentation (0.1%) of Amyand's hernia. It was first described by Claudius Amyand in 1735. We report a patient of Amyand's hernia, three years of age, presented with right inguinoscrotal hernia. Right side groin discomfort was present. Herniotomy was performed revealing anon-inflamed appendix within the inguinal canal for which a classic appendectomy was done. [ABSTRACT FROM AUTHOR]

Unlike many other Internet of Things applications, in addition to the limited power of Internet of Medical Things devices, the safety aspect and reliability are the major concerns while designing wireless body area network routing solutions. In fact, the heat dissipation from Internet of Medical Things devices and changes in body posture cause tissue damage and frequent wireless links breakage, respectively. Hence, designing an efficient routing solution is extremely challenging in wireless body area network. Considering the safety aspects of human body, the power constraint of Internet of Medical Things devices, and topological variations in wireless body area network environment, this article presents a thermal-aware, energy-efficient, and reliable routing protocol named thermal and energy aware routing. In the given perspective, thermal and energy aware routing considers the weighted average of three costs while selecting the routing path: energy consumption, heat dissipation, and link quality (between communicating nodes). The proposed protocol is validated by a comparison with a state-of-the-art wireless body area network routing protocol. Simulation results demonstrate that the proposed protocol is efficient in terms of energy consumption, thermal impact, and packet reception rate. [ABSTRACT FROM AUTHOR]

The arsenic contamination of ground water in visceral leishmaniasis (VL) endemic areas in Bihar, India leads to human exposure through drinking water. Possibly, the consumed arsenic (As) accumulates in the tissues of VL patients, who subsequently internalize intracellular amastigotes to confer resistance against chemotherapy to the parasite, leading to modulation in the host’s immune response. This hypothesis appears to be consistent with the in vitro findings that in arsenic-exposed parasites, the mitochondrial membrane potential became depolarized, whereas the reduced thiol and lactate production was overexpressed with enhanced glucose consumption; therefore, the reduced thiol possibly supports an immunosuppressive state in the host cells. This observation was well supported by the down-regulated expression of pro-inflammatory cytokines (IL-2, IL-12, IFN-γ, and TNF-α) with a suppressed anti-leishmanial function of macrophage (NO, ROS). In contrast, the pathophysiological mechanism of VL has received ample support by the promotion of Th2 cytokines (IL-4 and IL-10) in the presence of arsenic-exposed Leishmania parasites (LdAS). Dysfunction of mitochondria and the overexpression of lactate production raise the possibility of the Warburg effect being operative through the up-regulation of glucose consumption by parasites to enhance the energy production, possibly augmenting virulence. Therefore, we surmise from our data that arsenic exposure to Leishmania donovani modulates the immune response and infection pattern by impairing parasite function, which may affect the anti-leishmanial effect in VL. [ABSTRACT FROM AUTHOR]

Abstract Visceral leishmaniasis (VL) is a disease caused by protozoan species of the genus Leishmania and is transmitted through bites from the Phlebotomus sand fly; it is associated with considerable morbidity and mortality in many parts of world, including India. Reports on the protective role played by saliva proteins of Lutozomyia longipalpis, Phlebotomus papatasi and Phlebotomus duboscqi. are available. However, no studies have explored the salivary proteins of P. argentipes , which is the known proven vector for the transmission of VL in the Indian sub-continent. Herein we revealed the presence of two proteins of 14.2 and one protein of 13.6 kDa in Indian strain P. argentipes which is absolute identical to previously reported protein of SP15 family (PagSP01, PagSP02 and PagSP07) of P. argentipes of NIH colony, USA. In an experimental study on P. argentipes from Bihar, India, we demonstrated that a strong humoral and cellular immune response was triggered to reduce the concomitant Leishmania load in groups of immunized mice. The immunized group produced a considerable amount of IgG antibodies, and their splenocytes generated TH1 cytokines (IL-12, IFN-γ) with the support of delayed-type hypersensitivity (DTH) reactivity in such mice at the challenged site. We summarize from our data that some identical proteins to previous from SP15 family protein of 14.2 and 13.6 kDa molecular size, derived from Indian P. argentipes and reported its first time, can also be significant in resolution of VL infection after modulation of host protective T cell response in VL. [ABSTRACT FROM AUTHOR]

Transmission power control is the strategy in which a sensor node transmits at a lowest possible power level to maintain an error-free transmission being in dynamic multi-path fading environment, ensuring energy efficiency and interference minimization. In this paper, we propose a novel technique for power control while considering a realistic radio fading environment. The proposed technique is based on Kalman filter that iteratively estimates and predicts the upcoming received radio signal strength indicator (RSSI) values. Theoretical analysis and simulation results are given that show the better performance of the proposed methodology in comparison to existing power control strategies in time-varying fading propagation environment. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective: To estimate the frequency of gallbladder perforation with conventional Monopolar Diathermy versus Harmonic scalpel in laparoscopic cholecystectomy. Study Design: Quasiexperimental study. Place and Duration of Study: Department of Surgery, Combined Military Hospital, Quetta Pakistan, from Sep 2018 to Sep 2019. Methodology: A total of 160 patients undergoing laparoscopic cholecystectomy for symptomatic gallstones were included in the study and were randomly allocated into two groups based on the surgical technique of dissecting gall bladder from the liver bed. In Group-A, Monopolar Diathermy was used, and in Group-B Harmonic scalpel was used. Frequency of gall bladder perforation and demographic details were recorded on predefined proforma. Results: In our study, mean age was 48.21±8.94 years and 49.14±9.31 years in Group-A and Group-B respectively. Comparison of Gallbladder perforation in both groups showed 17(21.25%) cases in Group-A and 5(6.25%) cases in Group-B, (p-value <0.001). Conclusion: In laparoscopic cholecystectomy, the frequency of gallbladder perforation with conventional monopolar diathermy is significantly higher compared to the harmonic scalpel. [ABSTRACT FROM AUTHOR]

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students' comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity. [ABSTRACT FROM AUTHOR]

News reports in media contain news about society's social and political conditions. With the help of publicly available digital datasets of events, it is possible to study a complex network of mass violations, i.e., Mass Killings. Multiple approaches have been applied to bring essential insights into the events and involved actors. Power law distribution behavior finds in the tail of actor mention, co-actor mention, and actor degree tells us about the dominant behavior of influential actors that grows their network with time. The United States, France, Israel, and a few other countries have been identified as major players in the propagation of Mass Killing throughout the past 20 years. It is demonstrated that targeting the removal of influential actors may stop the spreading of such conflicting events and help policymakers and organizations. This paper aims to identify and formulate the conflicts with the actor's perspective at a global level for a period of time. This process is a generalization to be applied to any level of news, i.e., it is not restricted to only the global level. [ABSTRACT FROM AUTHOR]

Objective: To determine the diagnostic accuracy of FAST scans in patients with blunt abdominal trauma keeping exploratory laparotomy as a gold standard. Study Design: Cross-sectional validation study. Place and Duration of Study: Surgical Unit of Combined Military Hospital Kohat from Jan 2019 to June 2019. Methodology: Trauma patients reporting to the emergency department had a FAST scan using a portable ultrasound machine. The radiologist performed and interpreted the scans within one hour of the patient's arrival. The four standard views were obtained. Those who had positive FAST scans underwent a laparotomy without further evaluation. Patients with negative FAST scans did not have laparotomy unless unstable. Only those patients who underwent laparotomy were included in our study. Results: Out of 70 cases, comparison of the FAST scans in patients with blunt abdominal trauma keeping exploratory laparotomy as the gold standard to diagnose intra-abdominal injury was recorded as 88.88%, 82.35%, 94.00%, 70.00% and 87.14% for sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy respectively. Conclusion: Diagnostic accuracy of FAST scan in diagnosing intra-abdominal injury is higher, so it can be used routinely to diagnose intra-abdominal injury as a result of blunt abdominal trauma. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are in the spotlight of researchers and engineering industries due to many applications. Remote health monitoring for general as well as military purposes where tiny sensors are attached or implanted inside the skin of the body to sense the required attribute is particularly prominent. To seamlessly accomplish this procedure, there are various challenges, out of which temperature control to reduce thermal effects and optimum power consumption to reduce energy wastage are placed at the highest priority. Regular and consistent operation of a sensor node for a long-time result in a rising of the temperature of respective tissues, where it is attached or implanted. This temperature rise has harmful effects on human tissues, which may lead to the tissue damage. In this paper, a Temperate Aware and Energy Optimized (TAEO) routing protocol is proposed that not only deals with the thermal aspects and hot spot problem, but also extends the stability and lifetime of a network. Analytical simulations are conducted, and the results depict better performance in terms of the network lifetime, throughput, energy preservation, and temperature control with respect to state of the art WBAN protocols. [ABSTRACT FROM AUTHOR]

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic. [ABSTRACT FROM AUTHOR]

Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%. [ABSTRACT FROM AUTHOR]

Internet of things have emerged enough due to its applications in a wide range of fields such as governance, industry, healthcare, and smart environments (home, smart, cities, and so on). Internet of things–based networks connect smart devices ubiquitously. In such scenario, the role of wireless sensor networks becomes vital in order to enhance the ubiquity of the Internet of things devices with lower cost and easy deployment. The sensor nodes are limited in terms of energy storage, processing, and data storage capabilities, while their radio frequencies are very sensitive to noise and interference. These factors consequently threaten the energy consumption, lifetime, and throughput of network. One way to cope with energy consumption issue is energy harvesting techniques used in wireless sensor network–based Internet of things. However, some recent studies addressed the problems of clustering and routing in energy harvesting wireless sensor networks which either concentrate on energy efficiency or quality of service. There is a need of an adequate approach that can perform efficiently in terms of energy utilization as well as to ensure the quality of service. In this article, a novel protocol named energy-efficient multi-attribute-based clustering scheme (E2-MACH) is proposed which addresses the energy efficiency and communication reliability. It uses selection criteria of reliable cluster head based on a weighted function defined by multiple attributes such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes. The consideration of such parameters in cluster head selection helps to preserve the node's energy and reduce its consumption by sending data over links possessing better signal-to-noise ratio and hence ensure minimum packet loss. The minimized packet loss ratio contributes toward enhanced network throughput, energy consumption, and lifetime with better service availability for Internet of things applications. A set of experiments using network simulator 2 revealed that our proposed approach outperforms the state-of-the-art low-energy adaptive clustering hierarchy and other recent protocols in terms of first-node death, overall energy consumption, and network throughput. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4 standard is widely used as a communication protocol by low-powered devices, including Internet of Medical Things–enabled technologies. These low-powered technologies have the ability to integrate several communicating and interacting devices for the purpose of wide range applications such as Smart homes and healthcare applications. However, the fixed superframe structure of IEEE 802.15.4 results in the unequal resource utilization among these low-powered heterogeneous Internet of Things devices. The low resource utilization degrades the network performance. In order to improve the network performance and optimum resource utilization, dynamic superframe approach is recommended. This article presents a content-based dynamic superframe adaptation algorithm for the low-powered Internet of Medical Things devices to address the resource utilization challenges. In the content-based dynamic superframe adaptation, the network coordinator dynamically adjusts the superframe along with the backoff exponent. It uses five Quality of Service metrics simultaneously: the application defined data traffic, receive ratio, Personal Area Network source nodes in numbers, number of collisions, and observed delay to achieve the optimal solution. A detailed analysis of the simulations shows that the content-based dynamic superframe adaptation is able to behave more intelligently to adjust superframe dynamic allocations according to the application's content requirements. Moreover, it outperforms the other existing schemes in achieving the better resource utilization in IEEE 802.15.4 framework. [ABSTRACT FROM AUTHOR]

Here, we report a simple, economic and autoclavable monophasic LGPY medium supplemented with 10% fetal bovine serum (FBS), for routine maintenance of Leishmania donovani promastigotes for laboratory use. In comparison to commercially available M199 and RPMI-1640 media, LGPY has shown approximately seven fold more cell growth. The parasite has been observed to survive in the medium for at least 15 days post-inoculation. The medium also supports long-term sub-passaging of the promastigotes and can also be stored at 4 °C or room temperature for 14 months and 45 days, respectively. [ABSTRACT FROM AUTHOR]

Objective: To compare the frequency of seroma formation in harmonic scalpel vsunipolar electrocautery in post modified radical mastectomy (MRM). Study Design: Quasi experimental study. Place and Duration of Study: General Surgery Department, Pak Emirates Military Hospital Rawalpindi, from Jul to Dec 2017. Methodology: Eighty women with breast carcinoma planned for modified radical mastectomy were included in the study with inclusion and exclusion criteria and divided in equal group A (Harmonic) and group B (Unipolar cautery). Randomization was performed by lottery method for both groups. A standard level III clearance was performed. All patients were followed up on weekly basis and seroma formation was evaluated in both groups. Results: Mean age in group A and B was 51.36 ± 11.04 years vs 52 ± 11.19 years (p>0.05). Mean duration of complain in group A and B was 1.675 ± 0.47 months vs 1.775 ± 0.42 (p>0.05). Mean time of procedure in group A and B was 110.00 ± 4.71 mins vs 100.875 ± 5.14 (p>0.05). Mean body mass index in group A and B was 30.217 ± 4.99 kg/m2 vs 30.210 ± 5.31 kg/m2 (p>0.05). Seroma formation in group A and group B patients were found to be 5 (12.5%) vs 10 (25%) with (p>0.05). Conclusion: Frequency of seroma formation in harmonic scalpel is lower than electrocautery in post modified radical mastectomy, so it can be safely performed along with harmonic scalpel with reduced duration of drainage and seroma formation. [ABSTRACT FROM AUTHOR]

Currently the main concerns regarding control of visceral leishmaniasis (VL) caused by L. donovani are immunosuppression, relating toxicity of anti-leishmanial drug and little development in appropriate vaccine and vector ( P. argentipes ) control. Reports available from ex-vivo studies reflect significance of vector salivary gland homogenate (SGH) in reverting immunosuppression of infected VL subjects and as such the immunogenic nature of SGH can be a strategy to modulate immune system and anti-leishmanial function to enable immune response to control the disease. Several related studies also identified a better utility of vector anti-saliva antibodies in achieving such effects by an adoptive transfer approach instead of direct stimulation with SGH protein. However, conclusive evidences on VL cases are far beyond satisfactory to suggest role of SGH into modulation of host immune response in VL subjects in India. This study was under taken to make comparison on change in cytokines (TH 1 and TH 2 ) response pattern and anti-leishmanial macrophage (Mϕ) function following stimulation of their PBMC S with SGH protein derived from P. argentipes sand fly vector for VL or anti SGH antibodies raised in rabbit. This study reports for the first time that L. donovani sensitized healthy subject demonstrates an up-regulated Interferon-γ (TH 1 ) and down regulate Interleukin-10 (TH 2 ) production following stimulation of their PBMCs by P. argentipes anti-saliva antibodies accompanied with an improvement in anti-leishmanial Mϕ function for nitric oxide (NO) production. Subsequent experiments suggest that P. argentipes based anti-SGH antibodies when used to stimulate LD infected PBMCs in healthy subjects resulted in better clearance of Leishmania amastigotes load compare to SGH protein. Possibly the immunogenic components of anti-saliva an antibody maintains the level of protective cytokine (INF-γ) and seems to restrict the infection by host protection by vector saliva. [ABSTRACT FROM AUTHOR]

In this article, we propose a scheduling based temperature and power-aware heuristic for multi-core systems. Instead of using a fixed value of temperature threshold in load balancing techniques, the proposed heuristic suggests adjusting the value of threshold temperature as a function of the workload. As the workload reduces, the value of the threshold is lowered accordingly and vice versa. Lowering the value of threshold temperature decreases the temperature peaks, temperature spatial, and temporal gradients. Furthermore, it also reduces the total power consumed by the processing unit. The effectiveness of the approach is evaluated in a simulation-based environment that includes a scheduling and a thermal modeling tool. For evaluation, the heuristic is integrated with a state of the art load balancing technique based on global scheduling and a comparison is performed with the popular thermal-aware techniques. The results analysis shows that the use of proposed heuristic lowers the temperature peak by up to 7 °C, temperature spatial gradients by up to 35%, temporal gradient by 65%, and power utilization by up to 5.5% when compared with the state-of-the-art thermal balancing techniques and predictive thermal-aware models. [ABSTRACT FROM AUTHOR]

With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service. [ABSTRACT FROM AUTHOR]

Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method strongly relies on the strength of extracted features and the ability to deal with low-quality face images. The proficiency to learn robust features from raw face images makes deep convolutional neural networks (DCNNs) attractive for face recognition. The DCNNs use softmax for quantifying model confidence of a class for an input face image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with available training examples. The primary goal of this paper is to improve the efficacy of face recognition systems by dealing with false positives through employing model uncertainty. Results of experimentations on open-source datasets show that 3-4% of accuracy is improved with model uncertainty over the DCNNs and conventional machine learning techniques. [ABSTRACT FROM AUTHOR]