Search

Abstract Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson’s correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.

Abstract The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis–NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis–NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis–NIR spectral data.

Abstract South Africa’s efforts toward eliminating malaria have positioned the country in the pre-elimination stage. Imported and sub-microscopic cases still contribute to the persistence of malaria in regions of low transmission as identified in this study where diagnostics is built largely on the use of Rapid Diagnostic Test (RDT). However, the presence of Pfhrp2/3 gene deletion is known to interfere with the accuracy of diagnosis with the use of RDT. Malaria elimination and detection of Pfhrp2/3 gene deletion in the pre-elimination setting requires accurate molecular surveillance. With the core objective of this study being the determination of the presence sub-microscopic malaria cases and deleted Pfhrp2/3 gene markers, a total of 354 samples were collected from five districts of KwaZulu Natal, South Africa. These samples were prepared for molecular analysis using primers and PCR conditions specific for amplification of 18S rRNA and msp-1gene. Positive amplicons were analysed for the presence of Pfhrp2/3 and flanking genes, along with Sanger sequencing and phylogenetic studies. Out of 354 samples collected 339 were tested negative with PfHRP2 based RDTs. Of these Pfhrp2 and Pfhrp3 gene deletions were confirmed in 94.7% (18/19) and 100% (19/19) respectively. High migration rate (75%) among the study participants was noted and phylogenetic analysis of sequenced isolates showed close evolutionary relatedness with India, United Kingdom, Iran, and Myanmar and China isolates. Molecular-based test is recommended as an essential surveillance tool for malaria management programs as the target focuses on elimination.

Abstract A composite of Zinc oxide loaded with 5-weight % silver decorated on carbon nanotubes (Ag-loaded ZnO: CNT) was synthesized using a simple refluxed chemical method. The influence of deviation in the weight % of carbon nanotube loading on photocatalytic dye degradation (methylene blue and rose bengal) and antibiotic (antimicrobial and antifungal) performance was investigated in this study. The light capture ability of Ag-loaded ZnO:CNT in the visible region was higher in photocatalytic activity than that of Ag-loaded ZnO and ZnO:CNT. The bandgap of the Ag-loaded ZnO: CNT was tuned owing to the surface plasmon resonance effect. The photocatalytic degradation investigations were optimized by varying the wt% in CNTs, pH of dye solution, concentration of the dye solution, and amount of catalytic dose. Around 100% photocatalytic efficiency in 2 min against MB dye was observed for Ag doped ZnO with 10 wt% CNT composite at pH 9, at a rate constant 1.48 min−1. Bipolaris sorokiniana fungus was first time tested against a composite material, which demonstrated optimum fungal inhibition efficiency of 48%. They were also tested against the bacterial strains Staphylococcus aureus, Bacillus cerius, Proteus vulgaris, and Salmonella typhimurium, which showed promising antibacterial activity compared to commercially available drugs. The composite of Ag doped ZnO with 5 wt% CNT has shown competitive zone inhibition efficacy of 21.66 ± 0.57, 15.66 ± 0.57, 13.66 ± 0.57 against bacterial strains Bacillus cerius, Proteus vulgaris, and Salmonella typhimurium which were tested for the first time against Ag-loaded ZnO:CNT.

Abstract Distance control in many-particle systems is a fundamental problem in nature. This becomes particularly relevant in systems of active agents, which can sense their environment and react by adjusting their direction of motion. We employ agent-based simulations to investigate the complex interplay between agent activity, characterized by Péclet number $$\hbox{Pe}$$ Pe , reorientation maneuverability $$\Omega$$ Ω , vision angle $$\theta$$ θ and vision range $$R_0$$ R 0 , and agent density, which determines agent distancing and dynamics. We focus on semi-dense crowds, where the vision range is much larger than the particle size. The minimal distance to the nearest neighbors, exposure time, and persistence of orientation direction are analyzed to characterize the behavior. With increasing particle speed at fixed maneuverability, particles approach each other more closely, and exhibit shorter exposure times. The temporal persistence of motion decreases with increasing $$\hbox{Pe}$$ Pe , reflecting the impact of activity and maneuverability on direction changes. For a vision angle $$\theta =\pi /4$$ θ = π / 4 , we observe the emergence of flocking aggregates with a band-like structure, somewhat reminiscent of the bands in the Vicsek model. Additionally, for vision angles $$\theta \ge \pi /2$$ θ ≥ π / 2 , several quantities are found to display a universal scaling behavior with scaling variable $$\hbox{Pe}^{3/2}/\Omega$$ Pe 3 / 2 / Ω . Our results are in good agreement with recent experiments of pedestrians in confined spaces.

Abstract Over the past century, the average surface temperature and recurrent heatwaves have been steadily rising, especially during the summer season, which is affecting the yield potential of most food crops. Hence, diversification in cropping systems with suitable fertilizer management is an urgent need to ensure high yield potential during the summer season. Since intercropping has emerged as an important strategy to increase food production, the present study comprises five intercropping systems in the main plot (sole cowpea, sole baby corn, cowpea + baby corn in 2:1, 3:1, and 4:1 row ratio), three levels of fertilizer viz. 100 (N20 P40), 125 (N25 P50), and 150% (N30 P60) recommended dose of fertilizer (RDF) in the subplot, along with two stress-mitigating chemicals (0.5% CaCl2 and 1% KNO3) in the sub-sub plots. A split-split plot system with four replications was established to carry out the field experiment. The effect of intercropping, fertilizer levels, and stress-mitigating chemicals on crop growth rate (CGR), relative growth rate (RGR), plant temperature, relative water content (RWC) and chlorophyll content of cowpea and baby corn, as well as cowpea equivalent yield (CEY), was investigated during the summer seasons of 2019 and 2020. The experiment was conducted at Agriculture University, Kota (Rajasthan), India. Results showed that CGR, RGR, RWC and chlorophyll content of both crops and CEY were maximum under intercropping of cowpea and baby corn in a 2:1 row ratio compared to other intercropping systems. However, the plant temperature of both crops was significantly lower under this system. CEY, CGR, RGR, and chlorophyll content were considerably greater in the subplots with a fertilizer application of 150% RDF compared to lower levels of fertilizer (100 and 125% RDF). Our findings further show that foliar application of CaCl2 0.5% at the flowering and pod-developing stages of cowpea dramatically boosted CEY, CGR, RGR, RWC, and chlorophyll content of both crops and lowered the plant temperature.

Abstract Chagas disease is a leading cause of non-ischemic cardiomyopathy in endemic regions of Central and South America. In Belize, Triatoma dimidiata sensu lato has been identified as the predominate taxon but vectorial transmission of Chagas disease is considered to be rare in the country. We recently identified an acute case of vector-borne Chagas disease in the northern region of Belize. Here we present a subsequent investigation of triatomines collected around the case-patient’s home. We identified yet undescribed species, closely related to Triatoma huehuetenanguensis vector by molecular systematics methods occurring in the peridomestic environment. The identification of a T. cruzi-positive, novel species of Triatoma in Belize indicates an increased risk of transmission to humans in the region and warrants expanded surveillance and further investigation.

Abstract Oviposition is essential in the life history of insects and is mainly mediated by chemical and tactile cues present on the plant surface. Oviposition deterrents or stimulants can modify insect oviposition and be employed in pest control. Relatively few gustatory oviposition stimuli have been described for tortricid moths. In this study the effect of NaCl, KCl, sucrose, fructose and neem oil on the number of eggs laid by Cydia pomonella (L.), Grapholita molesta (Busck) and Lobesia botrana (Dennis & Schifermüller) was tested in laboratory arenas containing filter papers loaded with 3 doses of a given stimulus and solvent control. In general, salts increased oviposition at the mid dose (102 M) and sugars reduced it at the highest dose (103 mM), but these effects depended on the species. Neem oil dramatically reduced the number of eggs laid as the dose increased, but the lowest neem oil dose (0.1% v/v) increased L. botrana oviposition relative to solvent control. Our study shows that ubiquitous plant chemicals modify tortricid moth oviposition under laboratory conditions, and that neem oil is a strong oviposition deterrent. The oviposition arena developed in this study is a convenient tool to test the effect of tastants on the oviposition behavior of tortricid moths.

Abstract The risk factors for breast cancer have been defined in several studies but there is deficient data for specific subtypes. We report here the pathological characteristics of a breast cancer cohort and risk factors for patients with triple-negative disease. In this case–control study, a prospective breast cancer cohort was evaluated for demographic, reproductive, obesity-related and other risk factors using a validated questionnaire. Tumors were characterized for routine pathological characteristics and immunohistochemical markers of basal-like breast cancer. Patients with triple-negative breast cancer (TNBC) constituted cases and those with non-TNBC were controls. Odds ratios (OR) were calculated for each risk factor and independent associations were tested in an unconditional logistic regression analysis. Between 2011 and 2014, 1146 patients were recruited, of whom 912 [TNBC 266 (29.1%), non-TNBC 646 (70.9%)] with sufficient pathology material were analysed. Reproductive factors of parity, breastfeeding, age-at-menarche, age at first full-term pregnancy and oral contraceptive use were not significantly associated with TNBC. Higher body mass index (BMI > 24.9 vs ≤ 24.9, OR 0.89, 95%CI 0.63–1.24, p = 0.49) was not significantly associated while lesser waist circumference (> 80 cm vs ≤ 80 cm, OR 0.64, 95%CI 0.45–0.9, p = 0.012) and lower waist-to-hip ratio were significantly associated (> 0.85 vs ≤ 0.85, OR 0.72, 95%CI 0.51–1.0, p = 0.056), with TNBC. History of tobacco use was not significantly associated while lower socio-economic status was borderline associated with TNBC (socio-economic category > 5 versus ≤ 5, OR 0.73, 95%CI 0.50–1.06, p = 0.106). No factor was significant after adjustment for covariates. Central obesity seems to be preferentially associated with non-TNBC, and lower socio-economic status with TNBC in India, while most other conventional risk factors of breast cancer show no significant association with TNBC versus non-TNBC.

Calcium regulates the response sensitivity, kinetics and adaptation in photoreceptors. In striped bass cones, this calcium feedback includes direct modulation of the transduction cyclic nucleotide-gated (CNG) channels by the calcium-binding protein CNG-modulin. However, the possible role of EML1, the mammalian homolog of CNG-modulin, in modulating phototransduction in mammalian photoreceptors has not been examined. Here, we used mice expressing mutant Eml1 to investigate its role in the development and function of mouse photoreceptors using immunostaining, in-vivo and ex-vivo retinal recordings, and single-cell suction recordings. We found that the mutation of Eml1 causes significant changes in the mouse retinal structure characterized by mislocalization of rods and cones in the inner retina. Consistent with the fraction of mislocalized photoreceptors, rod and cone-driven retina responses were reduced in the mutants. However, the Eml1 mutation had no effect on the dark-adapted responses of rods in the outer nuclear layer. Notably, we observed no changes in the cone sensitivity in the Eml1 mutant animals, either in darkness or during light adaptation, ruling out a role for EML1 in modulating cone CNG channels. Together, our results suggest that EML1 plays an important role in retina development but does not modulate phototransduction in mammalian rods and cones.

Abstract Following the reaction of biological membranes to external stimuli reveals fundamental insights into cellular function. Here, self-assembled lipid monolayers act as model membranes containing photoswitchable azobenzene glycolipids for investigating structural response during isomerization by combining Langmuir isotherms with X-ray scattering. Controlled in-situ trans/cis photoswitching of the azobenzene N = N double bond alters the DPPC monolayer structure, causing reproducible changes in surface pressure and layer thickness, indicating monolayer reorientation. Interestingly, for monolayers containing azobenzene glycolipids, along with the expected DPPC phase transitions an additional discontinuity is observed. The associated reorintation represents a crossover point, with the surface pressure and layer thickness changing in opposite directions above and below. This is evidence that the azobenzene glycolipids themselves change orientation within the monolayer. Such behaviour suggests that azobenzene glycolipids can act as a bidirectional switch in DPPC monolayers providing a tool to investigate membrane structure–function relationships in depth.

Abstract Indoor environments are major contributing locations where the respiratory virus transmission occurs. Higher air change rate (ACH) values (up to 12) have been recommended in hospital environments to reduce virus transmission. In the present study, the Large Eddy Simulation (LES) data of particle transport in a typical intensive care unit (ICU) is used to calculate the infection risk in close proximity interaction. Three different ACH (6, 9, 12) rates with face masks and one case with a healthy person wearing a face shield are considered. The average resident time of the droplets in the ICU is calculated to find the optimal ACH rate. Of the different types of masks analyzed in the present study, the triple-layer mask has shown the most resistance ( $$0 \%$$ 0 % probability of infection) to the penetration of virus-laden droplets, while the single-layer mask has shown the highest risk of infection (up to $$97 \%)$$ 97 % ) . The results show that the ACH rate has little effect on close proximity transmission. The ACH 9 case provided optimal value for the particle removal, while the ACH 12 has inferior performance to that of ACH 9. From an energy consumption view, our results recommend not using higher ACH in similar indoor environments. Inside indoor environments, it is advised to wear a three-layer face mask and face shield to reduce the risk of infection.

Abstract The present investigation was conducted to assess the nutritional diverseness and identify novel genetic resources to be utilized in chickpea breeding for macro and micro nutrients. The plants were grown in randomized block design. Nutritional and phytochemical properties of nine chickpea genotypes were estimated. The EST sequences from NCBI database were downloaded in FASTA format, clustered into contigs using CAP3, mined for novel SSRs using TROLL analysis and primer pairs were designed using Primer 3 software. Jaccard’s similarity coefficients were used to compare the nutritional and molecular indexes followed by dendrograms construction employing UPGMA approach. The genotypes PUSA-1103, K-850, PUSA-1108, PUSA-1053 and the EST-SSR markers including the 5 newly designed namely ICCeM0012, ICCeM0049, ICCeM0067, ICCeM0070, ICCeM0078, SVP55, SVP95, SVP96, SVP146, and SVP217 were found as potential donor/marker resources for the macro–micro nutrients. The genotypes differed (p

Abstract In the present work, we have developed a polymer based gas sensor. The polymer nanocomposites are synthesized by the chemical oxidative polymerization of aniline with ammonium persulfate and sulfuric acid. The fabricated sensor is able to achieve a sensing response of 4.56% for PANI/MMT-rGO at 2 ppm of hydrogen cyanide (HCN) gas. The sensitivity of the sensors PANI/MMT and PANI/MMT-rGO are 0.89 ppm−1 and 1.1174 ppm−1 respectively. The increase in the sensitivity of the sensor may be due to an increase in the surface area provided by MMT and rGO which provided more binding sites for the HCN gas. The sensing response of the sensor increases as the concentration of the gas exposed increases but saturates after 10 ppm. The sensor recovers automatically. The sensor is stable and can work for 8 months.

Abstract Failure to meet early childhood developmental milestones leads to difficulty in schooling and social functioning. Evidence on the inequality in the burden of developmental delays across population groups, and identification of potential risk factors for suspected developmental delays (SDD) among younger children, are essential for designing appropriate policies and programs. This study explored the level of socioeconomic and maternal education-based inequality in the prevalence of SDD among Nepalese children at subnational level and identified potential risk factors. Individual-level data from the 2019 Nepal Multiple Indicator Cluster Survey was used to estimate the prevalence of SDD among children aged 3–4 years. Regression-based slope index of inequality (SII) and relative index of inequality were used to measure the magnitude of inequality, in terms of household socioeconomic status (SES) and mother’s education, in the prevalence of SDD. In addition, a multilevel logistic regression model was used to identify potential risk factors for SDD. The national prevalence of SDD was found to be 34.8%, with relatively higher prevalence among children from rural areas (40.0%) and those from Karnali Province (45.0%) followed by Madhesh province (44.2%), and Sudhurpashchim Province (40.1%). The prevalence of SDD was 32 percentage points higher (SII: −0.32) among children from the poorest households compared to their rich counterparts at the national level. At the subnational level, such inequality was found to be highest in Lumbini Province (SII = −0.47) followed by Karnali Province (SII = −0.37), and Bagmati Province (SII = −0.37). The prevalence of SDD was 36 percentage points higher (SII: −0.36) among children whose mother had no formal education compared to children of higher educated mothers. The magnitude of education-based absolute inequality in SDD was highest in Lumbini Province (SII = −0.44). Multilevel logistic regression model identified lower levels of mother’s education, disadvantaged SES and childhood stunting as significant risk factors for SDD. One in each three children in Nepal may experience SDD, with relatively higher prevalence among children from rural areas. Subnational level variation in prevalence, and socioeconomic and education-based inequality in SDD highlight the urgent need for province-specific tailored interventions to promote early childhood development in Nepal.

Abstract Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients’ age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients’ survival in the short- and long-term.

Abstract The aim of this study was to investigate the sociodemographic characteristics of patients based on the poison chosen and different types of organophosphorus compounds. The data were collected to explore the sociodemographic characteristics of organophosphate (OP)-poisoned patients based on the source, site, and route of poisoning, education level, occupational status, and the purpose of poisoning. Furthermore, we estimated the serotonin and dopamine levels in the plasma samples of patients, and survival plots were also described. During the study of OP pesticide poisoning in 116 human subjects and 5 healthy volunteers, we observed, based on the survival plot, that75.9% of the patients were discharged, and the remaining patients died (24.1% of the patients) due to respiratory failure followed by cardiac arrest. Our findings suggest that the serotonin levels significantly (p

Abstract Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.

Abstract Nicotinamide N-methyltransferase (NNMT) is a metabolic regulator that catalyzes the methylation of nicotinamide (Nam) using the co-factor S-adenosyl-L-methionine to form 1-methyl-nicotinamide (MNA). Overexpression of NNMT and the presence of the active metabolite MNA is associated with a number of diseases including metabolic disorders. We conducted a high-throughput screening campaign that led to the identification of a tricyclic core as a potential NNMT small molecule inhibitor series. Elaborate medicinal chemistry efforts were undertaken and hundreds of analogs were synthesized to understand the structure activity relationship and structure property relationship of this tricyclic series. A lead molecule, JBSNF-000028, was identified that inhibits human and mouse NNMT activity, reduces MNA levels in mouse plasma, liver and adipose tissue, and drives insulin sensitization, glucose modulation and body weight reduction in a diet-induced obese mouse model of diabetes. The co-crystal structure showed that JBSNF-000028 binds below a hairpin structural motif at the nicotinamide pocket and stacks between Tyr-204 (from Hairpin) and Leu-164 (from central domain). JBSNF-000028 was inactive against a broad panel of targets related to metabolism and safety. Interestingly, the improvement in glucose tolerance upon treatment with JBSNF-000028 was also observed in NNMT knockout mice with diet-induced obesity, pointing towards the glucose-normalizing effect that may go beyond NNMT inhibition. JBSNF-000028 can be a potential therapeutic option for metabolic disorders and developmental studies are warranted.

Abstract This paper presents a microwave microfluidic biosensor for monitoring blood glucose levels. The glucose sensor is a triple ring microstrip patch antenna integrated with a biomimetic microfluidic device capable of measuring a fixed volume of glucose solution. The sensor was utilized to detect 50–500 mg/dL glucose solutions. The interaction of the glucose solution with the electromagnetic field on the patch's surface influences both the resonance frequency and the magnitude of reflection coefficient. The results indicate that the microfluidic device can reduce experimental error and enhance the correlation between glucose concentration, resonant frequency, and reflection coefficient. Finally, the microfluidic sensor had a sensitivity of 0.25 MHz/(mg/dL), a detection limit as low as 7.7 mg/dL, and correlation coefficients of resonance frequency and reflection coefficient with a glucose concentration of 0.996 and 0.984, respectively. The experiment on the sensor's stability verifies the sensor's excellent stability and rapid response (~ 150 ms). Consequently, the device can be used to differentiate the concentration of glucose solutions, as well as to detect blood glucose levels at an early stage.

Abstract Prostate cancer is one of the few malignancies that includes vaccination as a treatment modality. Elements of an effective cancer vaccine should include the ability to elicit a Type I T-cell response and target multiple antigenic proteins expressed early in the disease. Using existing gene datasets encompassing normal prostate tissue and tumors with Gleason Score ≤ 6 and ≥ 8, 10 genes were identified that were upregulated and conserved in prostate cancer regardless of the aggressiveness of disease. These genes encoded proteins also expressed in prostatic intraepithelial neoplasia. Putative Class II epitopes derived from these proteins were predicted by a combination of algorithms and, using human peripheral blood, epitopes which selectively elicited IFN-γ or IL-10 dominant antigen specific cytokine secretion were determined. Th1 selective epitopes were identified for eight antigens. Epitopes from three antigens elicited Th1 dominant immunity in mice; PSMA, HPN, and AMACR. Each single antigen vaccine demonstrated significant anti-tumor activity inhibiting growth of implanted Myc-Cap cells after immunization as compared to control. Immunization with the combination of antigens, however, was superior to each alone in controlling tumor growth. When vaccination occurred simultaneously to tumor implant, multiantigen immunized mice had significantly smaller tumors than controls (p = 0.002) and a significantly improved overall survival (p = 0.0006). This multiantigen vaccine shows anti-tumor activity in a murine model of prostate cancer.

Abstract Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.

Abstract Contamination of soil by antibiotics and heavy metals originating from hospital facilities has emerged as a major cause for the development of resistant microbes. We collected soil samples surrounding a hospital effluent and measured the resistance of bacterial isolates against multiple antibiotics and heavy metals. One strain BMCSI 3 was found to be sensitive to all tested antibiotics. However, it was resistant to many heavy metals and metalloids like cadmium, chromium, copper, mercury, arsenic, and others. This strain was motile and potentially spore-forming. Whole-genome shotgun assembly of BMCSI 3 produced 4.95 Mb genome with 4,638 protein-coding genes. The taxonomic and phylogenetic analysis revealed it, to be a Bordetella petrii strain. Multiple genomic islands carrying mobile genetic elements; coding for heavy metal resistant genes, response regulators or transcription factors, transporters, and multi-drug efflux pumps were identified from the genome. A comparative genomic analysis of BMCSI 3 with annotated genomes of other free-living B. petrii revealed the presence of multiple transposable elements and several genes involved in stress response and metabolism. This study provides insights into how genomic reorganization and plasticity results in evolution of heavy metals resistance by acquiring genes from its natural environment.