bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Excited Codirectionally Magnetically Compensated Rotating Ranging Method
Authors: Wu Pan , Guo Zhiyong , Zhang Peng , Xu Wei , Liu Zhongxiang , Chen Wenhui , Wei Minghui
Published: March 2020

During the Steam-Assisted Gravity Drainage (SAGD) technology-based dual horizontal well drilling process, it is necessary to accurately monitor the well spacing in real time to ensure safe and parallel drilling. In this paper, a self-excited codirectionally magnetically compensated rotating ranging method and device is proposed. In the numerical calculation and simulation, both parallel and nonparallel drilling models are established. Based on the models and the magnetic dipole theory, the relation between the magnetization field and the well spacing is analyzed. Furthermore, the one-to-one correspondence between the peak-to-peak value of the magnetization field and the well spacing is revealed. Well spacing is then determined according to the measured peak-to-peak value. To achieve better results, the influence of magnetic source (the magnetic moment) and casing characteristics (magnetic susceptibility, dynamic magnetization length) on measured peak-to-peak value is analyzed. Finally, field tests are carried out, and the feasibility and effectiveness of the theory and device are proved. This study provides innovation for a new approach of magnetic guidance while drilling and has a great significance in the development, testing, and calibration of well-ranging instruments.
Astaxanthin Protects Ochratoxin A-Induced Oxidative Stress and Apoptosis in the Heart via the Nrf2 Pathway
Authors: Cui Gengyuan , Li Lin , Xu Weixiang , Wang Mingyang , Jiao Danyang , Yao Beibei , Xu Ketao , Chen Yueli , Yang Shuhua , Long Miao , Li Peng , Guo Yang
Published: March 2020

This study assessed the protective mechanism of astaxanthin (ASX) against ochratoxin A- (OTA-) induced cardiac injury in mice. Four groups of mice were established: control group (0.1 mL olive oil+0.1 mL NaHCO2), OTA group (0.1 mL OTA 5 mg/kg body weight), ASX group (0.1 mL ASX 100 mg/kg body weight), and ASX + OTA group (0.1 mL ASX 100 mg/kg body weight, 2 h later, 0.1 mL OTA 5 mg/kg body weight). The test period lasted for 27 days (7 days of dosing, 2 days of rest). Electrocardiogram, body weight, heart weight, tissue pathology, oxidative markers (malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH)), biochemical markers (creatine kinase (CK), creatine kinase isoenzyme (CK-MB), and lactate dehydrogenase (LDH)), electron microscopy, TUNEL, and Western blot tests were used to examine the effects of OTA on myocardial injury and ASX detoxification. The results showed that OTA exposure significantly decreased both body weight and heart weight. OTA induced a decrease in heart rate in mice and decreased tissue concentrations of SOD, CAT, and GSH, while increasing serum concentrations of cardiac enzymes (CK, CK-MB, and LDH) and tissue MDA. ASX improved heart rate, cardiac enzymes, and antioxidant levels in mice. The results of tissue pathology and TUNEL assay showed that ASX protects against OTA-induced myocardial injury. In addition, Western blot results showed that the OTA group upregulated Keap1, Bax, Caspase3, and Caspase9, while it downregulated Nrf2, HO-1, and Bcl-2 protein expression. ASX played a protective role by changing the expression of Keap1, Nrf2, HO-1, Bax, Bcl-2, Caspase3, and Caspase9 proteins. These results indicate that the protective mechanism of ASX on the myocardium works through the Keap1-Nrf2 signaling pathway and mitochondria-mediated apoptosis pathway. This study provides a molecular rationale for the mechanism underlying OTA-induced myocardial injury and the protective effect of ASX on the myocardium.
Prevalence of Methicillin-Resistant Staphylococcus aureus in Shrines
Authors: Arjyal Charu , KC Jyoti , Neupane Shreya
Published: March 2020

Methicillin-resistant Staphylococcus aureus (MRSA) infection in human beings and animals is concerning; it stands out as one of the leading agents causing nosocomial and community infections. Also, marginally increasing drug resistance in MRSA has limited therapeutic options. This study focuses on estimating the prevalence of MRSA in shrines, a place where human and animal interaction is frequent, sharing antibiotic-resistant bacteria, antibiotic-resistant genes, and diseases. A total of 120 environmental swabs were collected from targeted areas during the study period, March 2018 to May 2018. Staphylococcus aureus was identified by growth on mannitol salt agar (MSA), and MRSA by growth on mannitol salt agar containing 4 μg Oxacillin, Gram staining, and conventional biochemical test. Isolates of S. aureus were characterized by antibiotic susceptibility testing using the disc diffusion method. MRSA and methicillin-sensitive S. aureus (MSSA) proportion were 19% and 81%, respectively; a high rate of MRSA was observed in isolates from Thapathali (28.6%). MSSA isolates showed a high rate of resistance to erythromycin (64.7%). MRSA isolates were resistant to gentamicin (50%), cotrimoxazole (25%), erythromycin (50%), and ciprofloxacin (25%). The isolates were susceptible to linezolid (100%), clindamycin (100%), ciprofloxacin (75%), erythromycin (50%), tetracycline (100%), and cotrimoxazole (75%). Intermediate resistance was also found in gentamicin (50%). Of the 11 MSSA isolates that were erythromycin resistant and clindamycin sensitive, 6 (54.5%) showed the inducible clindamycin resistance (ICR) pattern and 2 MRSA isolates that were erythromycin resistant and clindamycin sensitive showed ICR pattern. Fifteen MSSA isolates were β-lactamase positive, whereas only two MRSA isolates showed β-lactamase production. There exists a minimal research work on infectious diseases that are shared between primates and animals. This study suggests the pervasiveness of MRSA/MSSA in the shrines, which may be a primary place for pathogen exchange between humans and primates.
Deep Learning on Computational-Resource-Limited Platforms: A Survey
Authors: Chen Chunlei , Zhang Peng , Zhang Huixiang , Dai Jiangyan , Yi Yugen , Zhang Huihui , Zhang Yonghui
Published: March 2020

Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger
Authors: Bonkaney Abdou Latif
Published: March 2020

This study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), solar radiation (SR), precipitation, and clearness index (K) are used. In addition, two heat indices, heat index (HI) and discomfort index (DI) are calculated to take into account the impacts of high humidity in conjunction with high ambient temperature. Hence, three different models were derived from the aforementioned variables. The three models have been tested using the k-folds cross-validation. Results show that the model with primitive variables such GDP per capita, Tmean, RH, SR, and WSP perform better than the other two models with a coefficient determination R2 equal to 0.87, 0.854, 0.833, and 0.551 for Niamey, Maradi, Zinder, and Agadez, respectively. According to the month considered, the mean absolute percentage error can give a small error for specific combinations of climate variables. The variables such as precipitation and clearness index are found to be not statistically significant.
Optimal Timing of Invasive Coronary Angiography following NSTEMI
Authors: Mahendiran Thabo , Nanchen David , Meier David , Gencer Baris , Klingenberg Roland , Räber Lorenz , Carballo David , Matter Christian M. , Lüscher Thomas F. , Windecker Stephan , Mach François , Rodondi Nicolas , Muller Olivier , Fournier Stephane
Published: March 2020

Objective. To obtain a real-world perspective of the optimal timing of angiography performed within 24 hours of admission with non-ST elevation myocardial infarction (NSTEMI). Background. Current guidelines recommend angiography within 24 hours of hospitalisation with NSTEMI. The recent VERDICT trial found that angiography within 12 hours of admission with NSTEMI was associated with improved cardiovascular outcomes among high-risk patients. We compared the outcomes of real-world NSTEMI patients undergoing angiography within 12 hours of admission with those of patients undergoing angiography 12 to 24 hours after admission. Methods. NSTEMI patients without life-threatening features who received angiography within 24 hours of admission were obtained from the SPUM-ACS registry, a cohort of consecutive patients admitted with acute coronary syndromes to four university hospitals in Switzerland. Cox models assessed for an association between door-to-catheter time and one-year major adverse cardiovascular events (MACE: cardiovascular mortality, myocardial infarction, and stroke). Results. Of 2672 NSTEMI patients, 1832 met the inclusion criteria. Among them, 1464 patients underwent angiography within 12 hours (12 h group) compared with 368 patients between 12 and 24 hours (12–24 h group). Multiple logistic regression identified out-of-hours admission as the only factor associated with delayed angiography. After 2 : 1 propensity score matching, 736 patients from the 12 h group and 368 patients from the 12–24 h group demonstrated no significant difference in rates of one-year MACE (7.7% vs. 7.3%, HR: 1.050, 95% CI 0.637–1.733, p=0.847). Stratification by GRACE score (>140 vs. ≤140) found no significant reduction in MACE among high-risk patients in the 12 h group (p for interaction = 0.601). Conclusions. In an unselected real-world cohort of NSTEMI patients, angiography within 12 hours of admission was not associated with improved one-year cardiovascular outcomes when compared with angiography 12 and 24 hours after admission, even among high-risk patients.
Autochthonous Acid-Producing Bacteria from Catfish (Clarias sp.) with Antibacterial Activity against Selected Fish Pathogens: A Preliminary Study
Authors: Prihanto Asep Awaludin , Nursyam Happy , Jatmiko Yoga Dwi , Hayati Royani L.
Published: March 2020

In this study, the application of an autochthonous microorganism as probiotic on catfish (Clarias sp.) was scarcely reported. This study aimed to obtain probiotic candidates from the digestive tract (intestinal and gastric) of catfish. A total of nine isolates were successfully isolated from the catfish. Almost all bacterial colonies were morphologically round, had flat edges, were yellow, and produced clear zones as a sign of producing acid during culture. The analysis showed that the three isolates had the best activity in inhibiting fish pathogen isolates. Furthermore, molecular analysis revealed that those three isolates were Bacillus velezensis UB-C1, Bacillus amyloliquifaciens UB-C5, and Bacillus cereus UB-C8. Interestingly, those three bacteria were non-lactic acid bacteria.
Discharge Hemoglobin Association with Long-Term Outcomes of ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention
Authors: Gao Ming , Zhang Xinying , Qin Ling , Zheng Yang , Zhang Zhiguo , Tong Qian , Li Hang
Published: March 2020

Background. Anemia following acute myocardial infarction (AMI) is associated with poor outcomes. While previous studies in patients with AMI have focused on anemia at admission, we hypothesized that hemoglobin (Hb) decline during hospitalization and lower discharge Hb would be associated with greater long-term mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). Methods. We analyzed records of 983 STEMI patients who were treated with primary PCI. The primary end point was all-cause mortality at 1 year and 2 years. The relationship between discharge Hb levels, decline in Hb levels, bleeding event classification, and all-cause mortality was determined. Results. Overall, 16.4% of patients had bleeding events, which were classified by the Thrombolysis in Myocardial Infarction (TIMI) score as 7% minimal, 8.6% minor, and 0.9% major. No significant gastrointestinal bleed and cerebral hemorrhage occurred in hospitals among these patients. The incidence rate of the 2-year all-cause mortality increased with severity of the bleeding event score (8.78% for no bleeding vs. 11.59% for minimal bleeding vs. 20.24% for minor bleeding vs. 55.56% for major bleeding, P<0.001). Discharge Hb was significantly associated with 2-year mortality in an unadjusted model (hazard ratio (HR) per 1 g/L decrease in discharge Hb = 1.020, 95% confidence interval (CI): 1.006–1.034, P=0.004) and in a confounder-adjusted model (HR per 1 g/L decrease in discharge Hb = 1.024, 95% CI: 1.011–1.037, P<0.001). The odds ratio (OR) for all-cause mortality at 2 years for participants with Hb below the twentieth percentile was 3.529 (95% CI: 1.976–6.302) and 2.968 (95% CI: 1.614–5.456) after adjustment for age and gender and 2.485 (95% CI: 1.310–4.715) after adjustment for all covariates. Conclusions. In this population of patients hospitalized for STEMI, all-cause mortality increased with lower discharge Hb, and discharge Hb was a significant predictor of mortality risk.
Performance of Sweet Potato’s Leaf-Derived Activated Carbon for Hydrogen Sulphide Removal from Biogas
Authors: Juma Geni , Machunda Revocatus , Pogrebnaya Tatiana
Published: March 2020

In this study, sweet potato leaf activated carbon (SpLAC) was prepared by the chemical activation method using KOH and applied as an adsorbent for H2S removal from biogas. The study focused on the understanding of the effect of carbonization temperature (Tc), varying KOH : C activation ratio, flow rate (FR) of biogas, and mass of SpLAC on sample adsorption capacity. The BET analysis was performed for both fresh and spent activated carbons as well as for carbonized samples, which were not activated; also, the activated carbon was characterized by XRF and CHNS techniques. The results showed that removal efficiency (RE) of the SpLAC increased with increase carbonization temperature from 600 to 800°C and the mass of sorbent from 0.4 g to 1.0 g. The optimal test conditions were determined: 1.0 g of sorbent with a KOH : C ratio of 1 : 1, Tc=800°C, and FR=0.02 m3/h which resulted in a sorption capacity of about 3.7 g S/100 g of the SpLAC. Our findings corroborated that H2S removal was contributed not only by the adsorption process with the pore available but also by the presence of iron in the sample that reacted with H2S. Therefore, upon successful H2S sorption, SpLAC is suggested as a viable adsorbent for H2S removal from biogas.
Role of Secreted Frizzled-Related Protein 1 and Tumor Necrosis Factor-α (TNF-α) in Bone Loss of Patients with Rheumatoid Arthritis
Authors: Ginting Andi Raga , Hidayat Rudy , Sumariyono Sumariyono , Koesnoe Sukamto
Published: March 2020

Bone loss is one of the emerging extra-articular manifestations of rheumatoid arthritis. TNF-α is the main inflammatory cytokine that can directly increase bone resorption. However, its role in bone formation is still unknown, especially related to secreted frizzled-related protein 1 (SFRP-1), an osteoblast inhibitor. This study examines the correlation between TNF-α and SFRP-1, with a bone turn over marker (CTX and P1NP). This is a cross-sectional study with 38 subjects of premenopausal female patients with RA. This study found that 60.6% of the patients were in remission or low disease activity. The median of TNF-α was 10.6 pg/mL, mean of SFRP-1 was 9.29 ng/mL, mean of CTX was 2.74 ng/mL, and the median of P1NP was 34.04 pg/ml. There is positive correlation between TNF-α and P1NP (r=0.363, p=0.026), also between SFRP-1 and P1NP (r=0.341; p=0.036). A low level of TNF-𝛼, high level of SFRP-1, high level of CTX, and low level of P1NP in this study indicate a high bone turn over process, with dominant resorption activity in premenopausal female patients with RA.
Metastatic Renal Cell Carcinoma to the Testis: A Clinicopathologic Analysis of Five Cases
Authors: Wang Gang , Zhou Chen , Villamil Carlos F. , So Alan , Yuan Ren , English John C. , Jones Edward C.
Published: March 2020

The testicular spread of renal cell carcinoma is extremely rare. Five cases of renal cell carcinoma metastatic to the testis are described. The patients ranged from 45 to 81 years of age. Four of the five patients had known renal cell carcinoma. The time intervals between the partial and radical nephrectomies for the primary kidney tumors and the occurrence of testicular metastases ranged from 29 to 34 months. In one patient, the testicular mass was the initial presentation leading to a diagnosis of renal cell carcinoma. There were three ipsilateral metastases, one contralateral metastasis, and one bilateral metastasis. The metastatic deposits ranged in size from 2.0 to 5.7 cm. One case had multiple metastatic tumor nodules. All of the metastatic tumors had clear cell histological features, microscopically concordant with the primary renal cell carcinoma subtype. Three patients died of the disease 17 to 42 months after orchiectomy. One patient is alive with additional metastatic lesions 13 months after orchiectomy. One patient had been free of disease at 87 months after orchiectomy but is now on targeted therapy for an additional metastasis at 93 months after orchiectomy. To date, this report is one of the largest single series of patients with renal cell carcinoma metastatic to the testis, and it has the longest follow-up and survival among all the reported cases.
Efficient ZrO(NO3)2.2H2O Catalyzed Synthesis of 1H-Indazolo[1,2-b] phthalazine-1,6,11(13H)-triones and Electronic Properties Analyses, Vibrational Frequencies, NMR Chemical Shift Analysis, MEP: A DFT Study
Authors: Piryaei Forozan , Shajari Nahid , Yahyaei Hooriye
Published: March 2020

The synthesis of 1H-indazolo[1,2-b]phthalazine-1,6,11(13H)-trione derivatives, using one-pot three-component condensation reaction of 3-nitrophthalic anhydride, hydrazine monohydrate, dimedone, and aromatic aldehydes in the presence of ZrO(NO3)2.2H2O as the novel catalyst and in reflux conditions in EtOH was reported. Quantum theoretical calculations for three structures of compounds (5a, 5b, and 5c) were performed using the Hartree–Fock (HF) and density functional theory (DFT). From the optimized structure, geometric parameters were obtained and experimental measurements were compared with the calculated data. The structures of the products were confirmed by IR, 1H NMR, 13C NMR, mass spectra, and elemental analyses. The IR spectra data and 1H NMR and 13C NMR chemical shift computations of the 1H-indazolo[1,2-b]phthalazine-1,6,11(13H)-trione derivatives in the ground state were calculated. Frontier molecular orbitals (FMOs), total density of states (DOS), thermodynamic parameters, and molecular electrostatic potential (MEP) of the title compounds were investigated by theoretical calculations. Molecular properties such as the ionization potential (I), electron affinity (A), chemical hardness (η), electronic chemical potential (µ), and electrophilicity (ω) were investigated for the structures. Thus, there was an excellent agreement between experimental and theoretical results.
Electricity Access, Community Healthcare Service Delivery, and Rural Development Nexus: Analysis of 3 Solar Electrified CHPS in Off-Grid Communities in Ghana
Authors: Opoku Richard , Adjei Eunice A. , Obeng George Y. , Severi Luc , Bawa Abdul-Rahim
Published: March 2020

Over 600 million people living in sub-Saharan Africa do not have access to electricity. Modern healthcare services, including vaccine refrigeration, which require electricity are therefore lacking in such energy-deprived communities. In this work, analysis has been conducted on how electricity access can help improve healthcare service delivery and rural development, with a case study on 3 different off-grid solar photovoltaic (PV) systems in community-based health planning and services (CHPS) in Ghana. Analysis from this study showed that for the 3.0 kWp solar PV systems installed at the various sites, the in-house electricity consumptions are between 4.30 and 7.58 kWh per day. It was found out that excess electricity generation of 148–304 kWh per month is available and can be used to provide other economic services including phone charging, torchlight battery charging, and small-sized cold storage services to generate income for the maintenance of the systems, which is critical for sustainability of solar PV installations in rural poor communities. The study results also showed that electrified health facilities which are able to provide basic healthcare services have potential impact on community health outcomes and rural development. Assessment conducted at the CHPS compounds revealed that, generally, there is improvement in healthcare service delivery resulting in time savings of 15-43 hours per month for the inhabitants which can potentially be used for productive work. The time savings were more significant in females and children than in males. In many rural agro-based communities in developing countries, female and children are usually the workforce engaged in various farming activities. This paper concludes that access to electricity in CHPS compounds helps to improve community health outcomes and increases time availability for women to engage in productive work that can potentially result in significant socioeconomic activities and rural development.
Phylogenetic Analysis and Antibiotics Resistance of Listeria Monocytogenes Contaminating Chicken Meat in Surabaya, Indonesia
Authors: Aksono Eduardus Bimo , Riwu Katty Hendriana Priscilla , Estoepangestie A. T. Soelih , Pertiwi Herinda
Published: March 2020

The objective of this study was to identify the phylogenetic analysis and antibiotic resistance of Listeria monocytogenes contaminating chicken meat in Surabaya. 60 chicken meat samples were collected from supermarkets, mobile vendors, and traditional markets in Surabaya. A selective medium is used for isolation and identification of Listeria monocytogenes by chopping 25 grams of the chicken meat and to put it into the sterilized Erlenmeyer flasks. Some methods were used for the identification procedures, such as biochemical and morphological tests, antibiotic resistance test, PCR, and sequencing; also a phylogenetic analysis was conducted by a neighbor-joining analysis using Genetix Mac ver 8.0 with hlyA genes of Listeria monocytogenes recorded in GenBank, such as Lineage I (KC808543), Lineage II (AY229462, AY229346, AY229499, and AY229404), Lineage III (KJ504139, HQ686043, KJ504116, and DQ988349), and Lineage IV (EU840690, EF030606). The result shows that the prevalence of L. monocytogenes in Surabaya contaminating the chicken meat samples from the supermarkets was 10% (2/20), from the mobile vendors was 0/20 (0%), and from the traditional markets was 5% (1/20). It was seen from the band at 456 bp fragment. Furthermore, three isolates found in Surabaya were included in the new lineages which were resistant to old-generation antibiotics such as sulfamethonazole-trimetophrim (SXT) and amoxyllin sulbactam (MAS), but they were still sensitive to new-generation antibiotics such as cefotaxime (CTX) and meropenem (MEM).
MEF2c-Dependent Downregulation of Myocilin Mediates Cancer-Induced Muscle Wasting and Associates with Cachexia in Patients with Cancer
Authors: Judge Sarah M. , Deyhle Michael R. , Neyroud Daria , Nosacka Rachel L. , D'Lugos Andrew C. , Cameron Miles E. , Vohra Ravneet S. , Smuder Ashley J. , Roberts Brandon M. , Callaway Chandler S. , Underwood Patrick W. , Chrzanowski Stephen M. , Batra Abhinandan , Murphy Meghan E. , Heaven Jonathan D. , Walter Glenn A. , Trevino Jose G. , Judge Andrew R.
Published: March 2020

Skeletal muscle wasting is a devastating consequence of cancer that contributes to increased complications and poor survival, but is not well understood at the molecular level. Herein, we investigated the role of Myocilin (Myoc), a skeletal muscle hypertrophy-promoting protein that we showed is downregulated in multiple mouse models of cancer cachexia. Loss of Myoc alone was sufficient to induce phenotypes identified in mouse models of cancer cachexia, including muscle fiber atrophy, sarcolemmal fragility, and impaired muscle regeneration. By 18 months of age, mice deficient in Myoc showed significant skeletal muscle remodeling, characterized by increased fat and collagen deposition compared with wild-type mice, thus also supporting Myoc as a regulator of muscle quality. In cancer cachexia models, maintaining skeletal muscle expression of Myoc significantly attenuated muscle loss, while mice lacking Myoc showed enhanced muscle wasting. Furthermore, we identified the myocyte enhancer factor 2 C (MEF2C) transcription factor as a key upstream activator of Myoc whose gain of function significantly deterred cancer-induced muscle wasting and dysfunction in a preclinical model of pancreatic ductal adenocarcinoma (PDAC). Finally, compared with noncancer control patients, MYOC was significantly reduced in skeletal muscle of patients with PDAC defined as cachectic and correlated with MEF2c. These data therefore identify disruptions in MEF2c-dependent transcription of Myoc as a novel mechanism of cancer-associated muscle wasting that is similarly disrupted in muscle of patients with cachectic cancer.

Significance:

This work identifies a novel transcriptional mechanism that mediates skeletal muscle wasting in murine models of cancer cachexia that is disrupted in skeletal muscle of patients with cancer exhibiting cachexia.
Field Application of Digital Technologies for Health Assessment in the 10,000 Families Study
Authors: Thyagarajan Bharat , Nelson Heather H. , Poynter Jenny N. , Prizment Anna E. , Roesler Michelle A. , Cassidy Erin , Putnam Sara , Amos Laura , Hickle Andrea , Reilly Cavan , Spector Logan G. , Lazovich DeAnn
Published: March 2020

Background:

We field tested new-to-market portable, digital applications to assess hearing, pulmonary, and cognitive function to determine the feasibility of implementing these applications across a range of age groups in the pilot phase of the 10,000 Families Study (10KFS), a new Minnesota family–based prospective cohort study.

Methods:

We followed manufacturer recommended protocols for audiometry (SHOEBOX Inc), spirometry (NuvoAir), and the digital clock drawing test (dCDT; Digital Cognition Technologies Inc).

Results:

These digital devices were low cost and readily implemented in a 2.5-hour health fair visit with minimal training (2–3 hours) of study staff. To date, we have performed these measurements on 197 eligible 10KFS participants during an in-person clinic visit. A total of 37 children (age 4–17 years), 107 adults (18–64 years), and 53 seniors (≥65 years) were eligible to undergo hearing and pulmonary assessments. Children were less likely to successfully complete the hearing test (76%) compared with adults (86%) and seniors (89%). However, successful completion of the pulmonary assessment was high across all groups: 100% of children and seniors and 98% of adults. The dCDT was performed among those over the age of 40, and completion rates were 92% for those aged 41–64 and 94% for those ≥65 years.

Conclusions:

Our field testing indicates these digital applications are easy and cost-effective to implement in epidemiologic studies.

Impact:

Digital applications provide exciting opportunities to collect data in population studies. Issues related to data privacy, data access, and reproducibility of measurements need to be addressed before deploying digital applications in epidemiologic studies.

See all articles in this CEBP Focus section, “Modernizing Population Science.”
Lossless 4D Medical Images Compression Using Adaptive Inter Slices Filtering
Authors: Belhadef Leila , Mekkakia Maaza Zoulikha
Published: March 2020

Recent lossless 4D medical images compression works are based on the application of techniques originated from video compression to efficiently eliminate redundancies in different dimensions of image. In this context we present a new approach of lossless 4D medical images compression which consists to application of 2D wavelet transform in spatial directions followed or not by either lifting transform or motion compensation in inter slices direction, the obtained slices are coded by 3D SPIHT. Our approach was compared with 3D SPIHT with/without motion compensation. The results show our approach offers better performance in lossless compression rate.
Statistical analysis of night radiance RH using VIIRS day/night band satellite time series data
Authors: Devkota Jyoti U.
Published: March 2020

Amount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept. 
Solar panel monitoring and energy prediction for smart solar system
Authors: Shirbhate Isha M. , Barve Sunita S.
Published: March 2020

Solar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation.
Adaptive real time traffic prediction using deep neural networks
Authors: Iyer Parinith R , Iyer Shrutheesh Raman , Ramesh Raghavendran , M.R. Anala , Subramanya K.N.
Published: March 2020

The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting using adaptive traffic light systems, instead of fixed-time traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model self-adjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.
An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets
Authors: Aik Lim Eng , Hong Tan Wei , Junoh Ahmad Kadri
Published: March 2020

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.
Suggestive GAN for supporting Dysgraphic drawing skills
Authors: Pallavi Smita
Published: March 2020

The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the action-motion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and  Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.
Hybrid Imperialistic Competitive Algorithm Incorporated with Hopfield Neural Network for Robust 3 Satisfiability Logic Programming
Authors: Kathirvel Vigneshwer , Mansor Mohd. Asyraf , Mohd Kasihmuddin Mohd Shareduwan , Sathasivam Saratha
Published: March 2020

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3-Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN-3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.
Artificial Bee Colony algorithm used for load balancing in cloud computing: Review
Authors: Ullah ARIF
Published: March 2020

Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed workload among various nodes equally in a situation where some nodes are under load and some are overload.  Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005.  It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This survey paper is a comprehensive study about load balancing in cloud computing using the ABC algorithm. It also defines some basic concept about swarm intelligent and its property.
Prediction of Bankruptcy using Big Data Analytics based on Fuzzy c-means Algorithm
Authors: Guha Arup
Published: March 2020

This paper has suggested an optimization approach of the cluster-based sampling using Fuzzy c means algorithm to the classifier in order to select the most appropriate instances of bankruptcy. This method was examined with the help of a clustering method and GA based artificial neural network in order to solve the existing data imbalance issue. The objective of this paper is to optimise the selected design model of GA-ANN by using Fuzzy C means algorithm to predict corporate bankruptcies by considering different financial ratios of companies across several industries within the period from 1994 to 2014. Effectiveness of this method was proved by comparing its accuracy rate with the results of existing method. From the performance result the accuracy rate of this method was found to be 78.2% and misclassification rate to be 0.2178.
Privileged Authenticity in Reconstruction of Digital Encrypted Shares
Authors: Dey Joydeep , Bhowmik Anirban , Sarkar Arindam , Karforma Sunil
Published: March 2020

Efficient message reconstruction mechanism depends on the entire partial shares received in random manner. This paper proposed a technique to ensure the authenticated accumulation of shares based on the privileged share.Threshold  number of received shares inclusive of the  privileged share, were being accumulated together to validate the original message. Although attaining threshold number of shares or more excluding the privileged share, it would not be possible to reconstruct the original message. Encryptional procedure has been put into the desired partial shares to confuse the evaesdroppers. Decisive parameter termed as hash tag has been extracted from the cumulative shares and bitwise checking procedure has been carried out. In first appearance of first mismatch, rests of the checking bits were ignored, as test case put under failure transaction. Different statistical tests namely floating frequency, entropy value have proved the robustness of the proposed technique. Thus, extensive experiments were conducted to evaluate the security and efficiency with better productivity.  
Sensitivity Analysis of a Species Conserving Genetic Algorithm's Parameters for Addressing the Niche Radius Problem
Authors: Brown Michael Scott
Published: March 2020

Niche Genetic Algorithms (NGA) are a special category of Genetic Algorithms (GA) that solve problems with multiple optima. These algorithms preserve genetic diversity and prevent the GA from converging on a single optima. Many NGAs suffer from the Niche Radius Problem (NRP), which is the problem of correctly setting a radius parameter for optimal results. While the selection of the radius value has been widely researched, the effects of other GA parameters on genetic diversity is not well known. This research is a parameter sensitivity analysis on the other parameters in a GA, namely mutation rate, number of individuals and number of generations.
Development of Path Planning Algorithm of Centipede Inspired Wheeled Robot in Presence of Static and Moving Obstacles Using ModifiedCritical-SnakeBug Algorithm
Authors: Das Subir Kumar , Dutta Ajoy Kumar , Debnath Subir Kumar
Published: March 2020

Path planning for a movable robot in real life situation has been widely cultivated and become research interest for last few decades. Biomimetic robots have increased attraction for their capability to develop various kind of walking in order to navigate in different environment. To meet this requirement of natural insect locomotion has enabled the development of composite tiny robots. Almost all insect-scale legged robots take motivation from stiff-body hexapods; though, a different distinctive organism we find in nature is centipede, distinguished by its numerous legs and pliable body. This uniqueness is anticipated to present performance benefits to build robot of the said type in terms of swiftness, steadiness, toughness, and adaptation ability.
This paper proposes a local path planning algorithm of multiple rake centipede inspired robot namely ModifiedCritical-SnakeBug(MCSB) algorithm. Algorithm tries to avoid static and dynamic obstacle both. The results demonstrate the capability of the algorithm.
Computational Intelligence based Lossless Regeneration (CILR) of Blocked Gingivitis Intraoral Image Transportation
Authors: Bhowmik Anirban , Dey Joydeep , Sarkar Arindam , Karforma Sunil
Published: March 2020

This paper presented that an intraoral image has been wrapped during wireless transportation with an encryption tool with an added essence of lossless regeneration property. Threshold based cryptographic transportation has provided the construction of reliable and robust medical data communication system. The accumulation of threshold shares only would result to the formation of the intraoral gingivitis image at the receivers’ end. The proposed technique dealt with the generation of n number of partial shares by creating a unique frame structure by the dentist / physician. Additional feature has been proposed on the computational lossless transportation.The existing techniques cause a high computational complexity. The proposed technique ensured the lossless regeneration property while blocked gingivitis image sharing. Filling of bits have been incorporated to ensure the static sized homogeneous blocks of intraoral gingivitis image. A graphical masking method had been deployed, followed by successive decryption procedure on minimum threshold shares that ensure lossless data regeneration. This can guide the dental treatment with enhanced accuracy. Different types of statistical testing like entropy analysis and histogram analysis confirms the exhibition of authenticity, confidentiality, and integrity of our proposed technique.
Extracting hidden patterns from dates' product data using a machine learning technique
Authors: Al-Hagery Mohammed Abdullah
Published: March 2020

Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits., furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.
Rice Grain Classification using Multi-class Support Vector Machine (SVM)
Authors: Ibrahim Shafaf , Amirah Zulkifli Nurul , Sabri Nurbaity , Amilah Shari Anis , Noordin Mohd Rahmat Mohd
Published: March 2020

Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.