Nonparametric Density Estimation Using Partially Rank-Ordered Set Samples With Application in Estimating the Distribution of Wheat Yield

We study nonparametric estimation of an unknown density function $f$ based on the ranked-based observations obtained from a partially rank-ordered set (PROS) sampling design. PROS sampling design has many applications in environmental, ecological and medical studies where the exact measurement of the variable of interest is costly but a small number of sampling units can be ordered with respect to the variable of interest by any means other than actual measurements and this can be done at low cost. PROS observations involve independent order statistics which are not identically distributed and most of the commonly used nonparametric techniques are not directly applicable to them. We first develop kernel density estimates of $f$ based on an imperfect PROS sampling procedure and study its theoretical properties. Then, we consider the problem when the underlying distribution is assumed to be symmetric and introduce some plug-in kernel density estimators of $f$. We use an EM type algorithm to estimate misplacement probabilities associated with an imperfect PROS design. Finally, we expand on various numerical illustrations of our results via several simulation studies and a case study to estimate the distribution of wheat yield using the total acreage of land which is planted in wheat as an easily obtained auxiliary information. Our results show that the PROS density estimate performs better than its SRS and RSS counterparts.Comment: 23 pages, 3 figures, 4 table

Improving nuclear medicine with deep learning and explainability: two real-world use cases in parkinsonian syndrome and safety dosimetry

Computer vision in the area of medical imaging has rapidly improved during recent years as a consequence of developments in deep learning and explainability algorithms. In addition, imaging in nuclear medicine is becoming increasingly sophisticated, with the emergence of targeted radiotherapies that enable treatment and imaging on a molecular level (“theranostics”) where radiolabeled targeted molecules are directly injected into the bloodstream. Based on our recent work, we present two use-cases in nuclear medicine as follows: first, the impact of automated organ segmentation required for personalized dosimetry in patients with neuroendocrine tumors and second, purely data-driven identification and verification of brain regions for diagnosis of Parkinson’s disease. Convolutional neural network was used for automated organ segmentation on computed tomography images. The segmented organs were used for calculation of the energy deposited into the organ-at-risk for patients treated with a radiopharmaceutical. Our method resulted in faster and cheaper dosimetry and only differed by 7% from dosimetry performed by two medical physicists. The identification of brain regions, however was analyzed on dopamine-transporter single positron emission tomography images using convolutional neural network and explainability, i.e., layer-wise relevance propagation algorithm. Our findings confirm that the extra-striatal brain regions, i.e., insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons contribute to the interpretation of images beyond the striatal regions. In current common diagnostic practice, however, only the striatum is the reference region, while extra-striatal regions are neglected. We further demonstrate that deep learning-based diagnosis combined with explainability algorithm can be recommended to support interpretation of this image modality in clinical routine for parkinsonian syndromes, with a total computation time of three seconds which is compatible with busy clinical workflow. Overall, this thesis shows for the first time that deep learning with explainability can achieve results competitive with human performance and generate novel hypotheses, thus paving the way towards improved diagnosis and treatment in nuclear medicine

Relativistic binary systems in scale-independent energy-momentum squared gravity

In this paper, we study the gravitational-wave (GW) radiation and radiative behavior of relativistic binary systems in the scale-independent energy-momentum squared gravity (EMSG). Using the post-Minkowskian gravity based on the Landau-Lifshitz formulation of the theory, the field equations of the scale-independent EMSG are solved approximately. The gravitational potential in the wave zone of a gravitational source is then obtained. Doing so, we derive the GW signals emitted from a binary system. The results are different from those obtained in general relativity (GR). It is shown that the relevant non-GR corrections modify the wave amplitude and leave the GW polarizations unchanged. In this case, the system loses energy to modified GWs. This leads to a change in the secular variation of the Keplerian parameters of the binary system. In this work, we investigate the non-GR effects on the radiative parameter, i.e., the first time derivative of the orbital period. Next, applying these results together with GW observations from the relativistic binary systems, we constrain/test the scale-independent EMSG theory in the strong-field regime. After assuming that GR is the valid gravity theory, as a priori expectation, we find that the free parameter of the theory is of the order $10^{-5}$ from the direct GW observation, the GW events GW190425 and GW170817, as well as the indirect GW observation, the double pulsar PSR J0737$-$3039A/B experiment.Comment: 20 pages. V2: changes and references have been added. Accepted for publication in MNRA

AAV-2 VECTOR INTEGRATE INTO THE OVINE MYOBLAST GENOME RANDOMLY AND PROMOTE DIFFERENTIATION AND PROLIFERATION VIA FOLLISTATIN OVER-EXPRESSION OF ERK1/2 AND AKT SIGNALING PATHWAYS

The aim of our study was to investigate effect of FST over-expression by using AAVserotype 2 (AAV 2) vector on ovine primary myoblast (OPM)differentiation andproliferation. Primary myoblast cultures were obtained from 60-day-old sheep fetuses.Western blot confirmed that AAV2 could successfully express FST protein intransduced primary myoblast cells. Southern blot results demonstrated that AAVvectors integrated at apparently random genomic sites and promoted the transgenicmyoblast proliferation and differentiation. The results suggested that the AAV systemcould be used to generate transgenic meat sheep in the futur

Investigating Burnout among Iranian EAP Teachers: A Comparison of ‎Content instructors and ELT Instructors

English for Academic Purposes (EAP) courses are currently well-established university programs. These courses are run independently by English Language Teaching (ELT) instructors and content instructors without any collaboration. However, ELT instructors and content instructors do not receive the same level of collegiality and social support from the organizations and students. This paper probed burnout among Iranian EAP teachers, including content instructors and ELT instructors in 28 state universities and its variations in relation to their demographic and organizational characteristics. To this aim, the Persian version of the Maslach Burnout Inventory (MBI) was administered to content instructors (N=185) and ELT instructors (N=86) in the state universities in Iran. The results of the study indicated that while most of EAP teachers, both content instructors and ELT instructors, had low burnout, a considerable number had mid-levels of emotional exhaustion and personal accomplishment. The findings of the study also indicated that the ELT instructors had higher emotional exhaustion than the content instructors, and it was also found that the content instructors with more than 13 years of experience and the ELT instructors with more than 20 years of experience in teaching such courses had the lowest burnout. Based on the findings of the study, educational administrators are suggested to take remedial and preventive actions against EAP teachers’ burnout and enhance ELT instructors’ occupational well-being. It also seems necessary to assist EAP teachers in adapting to the requirements of teaching EAP courses through pre/in-service teacher training courses to obviate the need for extensive experience for gaining expertise in it