Geochemistry and 14C dating of guano deposits in the Karaftu Cave, Kurdistan, Iran: implication for paleoenvironment.

Karaftu Cave in the northwest of Divandareh includes four floors plus an underground area. The bat hall and its underground area are covered with guano deposits. 14C dating indicates the onset of guano deposition is about 14,260±50 BP years, and its average accumulation rate is about 4.1 mm/yr for depths of 360 to 205 cm. Bacterial and fungal metabolisms decay guano, release acids, and disperse large amounts of microorganisms inside the cave. Interactions between acids and guano caused leaching, dissolution, change in the distribution, and abundance of elements, which leads to the formation of secondary minerals in guano. These variations in minerals and elements also depend on the local climatic conditions. Distribution of elements in the Ce/Ce* versus Pr/Pr* diagram and the correlation coefficient between Ce and Mn display three different paleoclimate conditions (dry, wet, and dry) during the accumulation of the guano. Also, dolomite, phosphate, and iron oxide minerals have been formed during the passage of water through bedrock beneath the guano. This water is unsuitable for drinking and harmful to the organisms in the region. Effect of these acids on the substrate also leads to the collapse of the cave floor, generation of a new underground, fall in the groundwater level, change in the groundwater drainage system, drying of springs around the cave, loss of green cover, and a negative impact on the ecosystem in the region. To keep the cave environment clean and remove these problems, it is better to harvest guano and use it as fertilizer.

Gene expression profile of placentomes and clinical parameters in the cows with retained placenta.

BACKGROUND: Retained placenta (RP) is a prevalent disorder in cattle with many health-related and economic costs for the farm owners. Its etiology has not been clarified yet and there is no definite therapy for this disorder. In this study we conducted RNA-seq, hematologic and histologic experiments to survey the causes of RP development. METHODS: Blood samples were collected from 4 RP and 3 healthy cows during periparturtion period for hematological assessments followed by placentome sampling within 30 min after parturition. Cows were grouped as RP and control in case the placenta was retained or otherwise expelled, respectively. Total RNA was extracted from placentome samples followed by RNA-sequencing. RESULTS: We showed 240 differentially expressed genes (DEGs) between the RP and control groups. Enrichment analyzes indicated immune system and lipid metabolism as prominent over- and under-represented pathways in RP cows, respectively. Hormonal assessments showed that estradiol-17ß (E2) was lower and cortisol tended to be higher in RP cows compared to controls at the day of parturition. Furthermore, histologic experiment showed that villi-crypt junctions remain tighter in RP cows compared to controls and the crypts layer seemed thicker in the placentome of RP cows. Complete blood cell (CBC) parameters were not significantly different between the two groups. CONCLUSION: Overall, DEGs derived from expression profiling and these genes contributed to enrichment of immune and lipid metabolism pathways. We suggested that E2 could be involved in development of RP and the concentrations of P4 and CBC counts periparturition might not be a determining factor.

Transient ischemic dilation or transient RV visualization in patients with normal SPECT stress myocardial perfusion imaging: Correlation with CT coronary artery calcium scoring and coronary angiography.

BACKGROUND: Ancillary findings on MPI, such as transient ischemic dilation (TID) and transient right ventricular visualization (TRV), are recognized as markers of extensive CAD and predictive of adverse outcomes. They usually occur in association with stress-induced regional MPI abnormalities. However, the clinical significance of these ancillary markers in the presence of normal stress MPI is incompletely understood. METHODS: From a cohort of 564 consecutive patients referred for clinical SPECT stress MPI, 44 patients had normal stress SPECT MPI and either TID (n = 28) or TRV (n = 16). These imaging findings were correlated with CT coronary calcium (CAC), CT coronary angiography (CTA), and invasive coronary angiography (ICA) in patients with severe CAC ≥ 1000 HU. TID and TRV were quantified as stress/rest ratios. Severe CAD was defined as > 70% luminal stenosis on CTA or ICA. RESULTS: The median TID ratio was 1.23, with a range of 1.13-1.48; the median TRV ratio was 1.30, with a range of 1.20-1.48. Of 44 patients with TID or TRV, only 9 patients (20.5%) had severe obstructive > 70% CAD by angiography (6 of 28 patients (21.5%) with TID and 3 of 16 patients (19%) with TRV). Severe multi-vessel CAD occurred in only 2 of 44 patients (4.5%). In contrast, of 9 patients with CAC > 1000 HU, 6 (67%) had severe obstructive CAD. CONCLUSION: In patients with normal stress SPECT MPI and TID or TRV, the incidence of severe obstructive CAD was relatively low and predominantly single-vessel CAD. These findings do not support the concept that TID or TRV with normal stress MPI is predictive of high-risk CAD.

Genetic association study of promoter variation rs3761549 in the FOXP3 gene of Iranian patients diagnosed with brain tumour.

Forkhead box P3 (FOXP3) gene (Gene ID: 50943, Xp11.23) is an X-linked gene that encodes FOXP3 protein, an essential transcription factor in CD4+ CD25+ FOXP3 regulatory T (Treg) cells. FOXP3 mutation has been linked with the pathogenesis of several tumours; however, little is known about the role of single-nucleotide polymorphism (SNP) in its promoter region and its correlation with brain tumour. In the present study, we have investigated the association between SNPs in the promoter region of FOXP3 gene, a promoter SNP, -2383 C/T (rs3761549) with susceptibility to brain cancer in a population of Iran. The distribution of case, control, age and sex was balanced and with rs3761549 C/T allele frequencies distribution also falling in Hardy-Weinberg equilibrium (P = 0.053 and 0.062). The allele C of rs3761549 is lower in the brain tumour cases when compared with the controls (364 vs 392, P = 0.005). The frequency of combined T variant genotype (TT + CT) was significantly higher in the brain cancer cases compared with the controls (28 vs 8, P = 0.001), which was consistent with the T allele distribution. When we used the CC genotype as a reference, we found that both CT and TT genotypes were associated with a higher risk of developing brain tumour (odds ratio [OR], 0.3583; 95% confidence interval [CI], 0.164-0.8197 and OR, 0; 95% CI, 0-0.4118, respectively).

Expression profile of FGF receptors in preimplantation ovine embryos and the effect of FGF2 and PD173074.

Fibroblast growth factors (FGFs) and their receptors (FGFRs) are increasingly recognized as important regulators of embryo development in mammals. This study investigated the importance of FGF signaling during in vitro development of ovine embryo. The mRNAs of four FGFR subtypes were detected throughout preimplantation development of in vitro fertilized (IVF) embryos, peaked in abundance at the morula stage, and decreased significantly at the blastocyst stage. To gain insight into the role of these mRNAs in embryo development, IVF embryos were cultured in the presence of FGF2 (100 or 500 ng/ml: beginning from days 1 or 4 to 7) or PD173074 (1 µM: beginning from days 1 to 7) as usual treatments for activation or inhibition of FGFRs, respectively. FGF2-supplementation did not affect the percentage of embryos that developed to the blastocyst, blastocyst cell count and the proportion of cells allocated in inner cell mass (ICM) and trophectoderm (TE) compared to control (p > 0.05). Also, increasing the dosage or duration of FGF2 treatment did not significantly alter blastocyst yield or differential cell count (p > 0.05). PD173074-mediated inhibition of FGFRs did not significantly affect blastocyst yield (p > 0.05). Assessment of expression profiles of lineage-associated markers revealed that FGF2 (500 ng/ml) supplementation: (i) significantly increased expression of putative hypoblast marker (GATA4), (ii) significantly decreased expression of putative epiblast (EPI) marker (NANOG) and (iii) did not change TE markers (CDX2 and IFNT) and pluripotency makers (OCT4, SOX2 and REX1). In summary, FGF2-mediated activation of FGFRs may promote a switch in transcriptional profile of ovine ICM from EPI- to hypoblast-associated gene expression.

Catheter ablation of an accessory pathway within the marshall vein in a patient with mechanical mitral valve.

A 37-year-old man, known case of mitral valve replacement, was referred for ablation. Meticulous mapping showed the earliest signal in the distal coronary sinus (CS). Endocardial ablation via transseptal approach failed to permanently ablate the accessory pathway. Similarly, ablation within the CS was unsuccessful. Finally, the mapping catheter was introduced into the vein of Marshall (VoM) and a very early signal was obtained. In the first 3 seconds of the radiofrequency energy application within the VoM, delta wave disappeared. During 1-year follow-up, the patient had no recurrence of the palpitations and electrocardiogram showed no delta wave.

A soy lecithin nanoparticles-based extender effectively cryopreserves Holstein bull sperm.

Plant-based semen extenders, typically derived from soybean lecithin, are easier to modulate more and consistent in their composition than animal-based extenders. As large lecithin particles can, however, reduce effectiveness and solubility in bull semen extenders, sonication was used to create nano-lecithin (NL) particles of soybean lecithin. The objective was to determine the effects of lecithin type and concentration on the quality of frozen-thawed bovine sperm. We hypothesized that reducing the size of lecithin improves its interactions with the sperm and enhances the parameters that favor its motility, viability and fertility. Semen was collected from six mature Holstein bulls and ejaculates meeting minimum standards were pooled. Eight Tris-based extenders that contained 1, 2, 3, or 4 % of either conventional lecithin (L1-L4) or NL (NL1-NL4), plus two control extenders (one animal-based extender containing 20 % egg yolk [EY] and a commercial lecithin-based extender [BioXcell®]) were compared. Among soybean lecithin-based extenders, NL3 had the highest total and progressive sperm motility, and average path, straight-line and curvilinear sperm velocity, and was comparable to EY. Additionally, sperm mitochondrial activity was the highest in NL3, whereas sperm viability was highest in EY, NL3, and L4. Following in vitro fertilization of in vitro-matured bovine oocyes, NL3 had cleavage and hatching rates comparable to BioXcell®, but a lower blastocyst rate than EY. Overall, NL3 performed better than the other extenders for most end points, with efficiency comparable to EY. We, therefore, concluded that reducing lecithin particle size to a nano level improves sperm cryopreservation with optimal performance with 3 % NL.

Multiparametric MRI maps for detection and grading of dominant prostate tumors.

PURPOSE: To develop an image-based technique capable of detection and grading of prostate cancer, which combines features extracted from multiparametric MRI into a single parameter map of cancer probability. MATERIALS AND METHODS: A combination of features extracted from diffusion tensor MRI and dynamic contrast enhanced MRI was used to characterize biopsy samples from 29 patients. Support vector machines were used to separate the cancerous samples from normal biopsy samples and to compute a measure of cancer probability, presented in the form of a cancer colormap. The classification results were compared with the biopsy results and the classifier was tuned to provide the largest area under the receiver operating characteristic (ROC) curve. Based solely on the tuning of the classifier on the biopsy data, cancer colormaps were also created for whole-mount histopathology slices from four radical prostatectomy patients. RESULTS: An area under ROC curve of 0.96 was obtained on the biopsy dataset and was validated by a "leave-one-patient-out" procedure. The proposed measure of cancer probability shows a positive correlation with Gleason score. The cancer colormaps created for the histopathology patients do display the dominant tumors. The colormap accuracy increases with measured tumor area and Gleason score. CONCLUSION: Dynamic contrast enhanced imaging and diffusion tensor imaging, when used within the framework of supervised classification, can play a role in characterizing prostate cancer.

Electrocardiographic, electrophysiologic, and anatomical features of ventricular tachycardia originating from noncoronary cusp.

In this article, we report 2 young patients (a 15-year-old adolescent girl and a 25-year-old man) with drug-refractory palpitations. Admission electrocardiograms showed runs of ventricular tachycardia with left bundle-branch block morphology, left inferior axis, early precordial QRS transition, and positive QRS complex in lead I. In right ventricular mapping, the earliest activation site was found in the His bundle region. Aortic root mapping showed a very early fractionated ventricular signal with large atrial potential and no His potential in the noncoronary cusp region. Radiofrequency energy application in this region resulted in tachycardia termination within 5 to 10 seconds. During a 3- to 6-month follow-up period, the patients remained asymptomatic, and the electrocardiogram showed no ventricular arrhythmias.

Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms.

Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient. We report a machine learning model based on over 100 clinical and imaging features that predict the outcome of wide-neck bifurcation aneurysm treatment with an intrasaccular embolization device. We combine clinical features with a diverse set of common and novel imaging measurements within a random forest model. We also develop neural network segmentation algorithms in 2D and 3D to contour the sac in angiographic images and automatically calculate the imaging features. These deliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive model classifies complete vs. partial occlusion outcomes with an accuracy of 75.31%, and weighted F1-score of 0.74.

Prostate implant reconstruction from C-arm images with motion-compensated tomosynthesis.

PURPOSE: Accurate localization of prostate implants from several C-arm images is necessary for ultrasound-fluoroscopy fusion and intraoperative dosimetry. The authors propose a computational motion compensation method for tomosynthesis-based reconstruction that enables 3D localization of prostate implants from C-arm images despite C-arm oscillation and sagging. METHODS: Five C-arm images are captured by rotating the C-arm around its primary axis, while measuring its rotation angle using a protractor or the C-arm joint encoder. The C-arm images are processed to obtain binary seed-only images from which a volume of interest is reconstructed. The motion compensation algorithm, iteratively, compensates for 2D translational motion of the C-arm by maximizing the number of voxels that project on a seed projection in all of the images. This obviates the need for C-arm full pose tracking traditionally implemented using radio-opaque fiducials or external trackers. The proposed reconstruction method is tested in simulations, in a phantom study and on ten patient data sets. RESULTS: In a phantom implanted with 136 dummy seeds, the seed detection rate was 100% with a localization error of 0.86 ± 0.44 mm (Mean ± STD) compared to CT. For patient data sets, a detection rate of 99.5% was achieved in approximately 1 min per patient. The reconstruction results for patient data sets were compared against an available matching-based reconstruction method and showed relative localization difference of 0.5 ± 0.4 mm. CONCLUSIONS: The motion compensation method can successfully compensate for large C-arm motion without using radio-opaque fiducial or external trackers. Considering the efficacy of the algorithm, its successful reconstruction rate and low computational burden, the algorithm is feasible for clinical use.

A deep community based approach for large scale content based X-ray image retrieval.

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist's dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

Tissue typing using ultrasound RF time series: experiments with animal tissue samples.

PURPOSE: This article provides experimental evidence to show that the time series of radiofrequency (RF) ultrasound data can be used for tissue typing. It also explores the tissue typing information in RF time series. Clinical and high-frequency ultrasound are studied. METHODS: Bovine liver, pig liver, bovine muscle, and chicken breast were used in the experiments as the animal tissue types. In the proposed approach, the authors record RF echo signals backscattered from tissue, while the imaging probe and the tissue are stationary. This sequence of recorded RF data generates a time series of RF echoes for each spatial sample of the RF signal. The authors use spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with feedforward neural networks for tissue typing. The experiments are repeated at ultrasound frequency of 6.6 and also 55 MHz. The effects of increasing power and frame rate are studied. RESULTS: The methodology yielded an average two-class classification accuracy of 95.1% when ultrasound data were acquired at 6.6 MHz and 98.1% when data were collected with a high-frequency probe operating at 55 MHz. In four-class classification experiments, the recorded accuracies were 78.6% and 86.5% for low and high-frequency ultrasound data, respectively. A set of 12 texture features extracted from the B-mode image equivalents of the RF data yields an accuracy of only 77.5% in typing the analyzed tissues. An increase in acoustic power and the frame rate of ultrasound results in an improvement in classification results. CONCLUSIONS: The results of this study demonstrate that RF time series can be used for ultrasound-based tissue typing. Further investigation of the underlying physical mechanisms is necessary.

Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection.

The application of deep learning algorithms in medical imaging analysis is a steadily growing research area. While deep learning methods are thriving in the medical domain, they seldom utilize the rich knowledge associated with connected radiology reports. The knowledge derived from these reports can be utilized to enhance the performance of deep learning models. In this work, we used a comprehensive chest X-ray findings vocabulary to automatically annotate an extensive collection of chest X-rays using associated radiology reports and a vocabulary-driven concept annotation algorithm. The annotated X-rays are used to train a deep neural network classifier for finding detection. Finally, we developed a knowledge-driven reasoning algorithm that leverages knowledge learned from X-ray reports to improve upon the deep learning module's performance on finding detection. Our results suggest that combining deep learning and knowledge from radiology reports in a hybrid framework can significantly enhance overall performance in the CXR finding detection.

AI Accelerated Human-in-the-loop Structuring of Radiology Reports.

Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image. AI techniques can accelerate the concept expansion and phrasal grouping tasks to efficiently create a domain specific lexicon ontology for structuring reports. Using Chest X-ray (CXR) reports as an example, we demonstrate that with robust vocabulary, even a simple NLP pipeline can extract 83 directly mentioned abnormalities (Ave. recall=93.83%, precision=94.87%) and 47 abnormality/normality descriptions of key anatomies. The richer vocabulary enables identification of additional label mentions in 10 out of 13 labels (compared to baseline methods). Furthermore, it captures expert insight into critical differences between observed and inferred descriptions, and image quality issues in reports. Finally, we show how the CXR ontology can be used to anatomically structure labeled output.

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.

Importance: Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. Objective: To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. Design, Setting, and Participants: This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. Main Outcomes and Measures: The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. Results: The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001). Conclusions and Relevance: These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.

Building medical image classifiers with very limited data using segmentation networks.

Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem, constrained image sizes and model complexities can lead to unnecessary increase in computational cost and decrease in performance. As many common morphological features are usually shared by different classification tasks of an organ, it is greatly beneficial if we can extract such features to improve classification with limited samples. Therefore, inspired by the idea of curriculum learning, we propose a strategy for building medical image classifiers using features from segmentation networks. By using a segmentation network pre-trained on similar data as the classification task, the machine can first learn the simpler shape and structural concepts before tackling the actual classification problem which usually involves more complicated concepts. Using our proposed framework on a 3D three-class brain tumor type classification problem, we achieved 82% accuracy on 191 testing samples with 91 training samples. When applying to a 2D nine-class cardiac semantic level classification problem, we achieved 86% accuracy on 263 testing samples with 108 training samples. Comparisons with ImageNet pre-trained classifiers and classifiers trained from scratch are presented.

Comparison of energy consumption of wheat production in conservation and conventional agriculture using DEA.

Energy is one of the essential resources for human life and mainly classified as non-renewable resources. Since huge amounts of energy are consumed in the agriculture sector, an energy audit is an essential strategy in countries. Conservation agriculture as a tool for sustainable development can lead to saving agricultural resources. In the current investigation, energy audit for wheat conservation and conventional production systems was performed. For this purpose, 48 farms were selected randomly in 2016, and their energy performance was evaluated and compared. The data were analyzed to calculate energy parameters. Also, data envelopment analysis technique was used to identify the possible ways to achieve higher efficiency in farms. To this end, current and optimum situations and saving energy in different cultivation systems were determined using Charnes, Cooper, and Rhodes (CCR) model. The research results showed that the average energy ratio, net energy gain, specific energy, and energy productivity for conservation farms were 4.31, 137,656 MJ ha-1, 5.56 MJ kg-1, and 0.18 kg MJ-1, respectively. Corresponded values for conventional farms were measured to be 3.03, 90,101 MJ ha-1, 7.69 MJ kg-1, and 0.13 kg MJ-1, respectively. Data envelopment analysis results revealed that the highest saving energy in conventional system belongs to diesel fuel and irrigation inputs, and the least amount of energy saving was seen in human labor input. While for the conservation system, the highest and the least amount of energy saving belongs to nitrogen and human labor, respectively.

Computer-aided diagnosis of prostate cancer with emphasis on ultrasound-based approaches: a review.

This paper reviews the state of the art in computer-aided diagnosis of prostate cancer and focuses, in particular, on ultrasound-based techniques for detection of cancer in prostate tissue. The current standard procedure for diagnosis of prostate cancer, i.e., ultrasound-guided biopsy followed by histopathological analysis of tissue samples, is invasive and produces a high rate of false negatives resulting in the need for repeated trials. It is against these backdrops that the search for new methods to diagnose prostate cancer continues. Image-based approaches (such as MRI, ultrasound and elastography) represent a major research trend for diagnosis of prostate cancer. Due to the integration of ultrasound imaging in the current clinical procedure for detection of prostate cancer, we specifically provide a more detailed review of methodologies that use ultrasound RF-spectrum parameters, B-scan texture features and Doppler measures for prostate tissue characterization. We present current and future directions of research aimed at computer-aided detection of prostate cancer and conclude that ultrasound is likely to play an important role in the field.