Miracle-dz/UniFAR-FTUs huggingface dataset:
Volumetric image-based supervised learning approaches for kidney cancer detection and analysis

Kidney cancers account for an estimated 140,000 global deaths annually. According to the Canadian Cancer Society, an estimated 6,600 Canadians were diagnosed with kidney cancer, and 1,900 Canadians died from it in 2017. Computed tomography (CT) imaging plays a vital role in kidney cancer detection, prognosis, and treatment response assessment. Automated CT-based cancer analysis is benefiting from unprecedented advancements in machine learning techniques and wide availability of high-performance computers. Typically, kidney cancer analysis requires a challenging pipeline of (a) kidney localization in the CT scan and general assessment of kidney functionality, (b) tumor detection within the kidney, and (c) cancer analysis. In this thesis, we developed deep learning techniques for automatic kidney localization, segmentation-free volume estimation, cancer detection, as well as CT features-based gene mutation detection, renal cell carcinoma (RCC) grading, and staging. Our convolutional neural network (CNN)-based kidney localization approach produces a kidney bounding box in CT, while our CNN-based direct kidney volume estimation approach skips the intermediate segmentation step that is often used for volume estimation at the cost of additional computational overhead. We also proposed a novel collage CNN technique to detect pathological kidneys, where we introduced a unique image augmentation procedure within a multiple instance learning framework. We further proposed a multiple instance decision aggregated CNN approach for automatic detection of gene mutations and a learnable image histogram-based deep neural network (ImHistNet) approach for RCC grading and staging. These approaches could be alternatives to renal biopsy-based whole-genome sequencing, RCC grading, and staging, respectively.
Emerging role of sentinel lymph node mapping for gynecologic oncology in the new era

Lymph node involvement is the most important prognostic factor in gynecologic malignancies and associated with the rate of recurrent and survival. Lymphadenectomy is one of standard staging and treatment in the current practice of selected cases of gynecological cancer even the rate of lymph node metastasis in early stage is low. This procedure increases operative time, blood loss, and associated with a long and short-term complication such as nerve injury, lymphocyst, hospital stay, and healthcare costa. Sentinel lymph node biopsy (SLNB) procedure has been studied to decrease morbidity without compromising oncological and surgical outcomes. Many studies in melanoma and breast cancer have shown that SLNB procedures accurately predict lymph node status and are associated with less morbidity [1, 2]. This procedure, therefore, has a place in gynecologic malignancy. It has been investigated in gynecological, vulvar, endometrial, cervical and ovarian cancer. Most medical centers now offer SLNB for early-stage vulvar cancer, endometrial cancer and cervical cancer. This technique was recently introduced and evaluated its feasibility in ovarian cancer in some centers. The objective of this review is to summarize and update the available data on the SLN concept in gynecological tumors.
Feedback Turbo Autoencoder

Designing channel codes is one of the core research areas for modern communication systems. Canonical channel codes asymptotically achieve near-capacity performance under large block length regime for additive white gaussian noise channels. However, this achieved success does not generalize to many channels. Channels with output feedback, proposed by Shannon, is one of such channels where practical codes have been unknown for several decades.Recently it has been demonstrated that deep learning based code outperforms the state-of-the-art codes for channels with output feedback. While the success is promising and inspiring, there are a few major challenges that need to be addressed. Firstly, the channel assumes a feedback with a unit step delay, which is not very practical. Second is the lack of generalization to larger block lengths. In this work, we propose Feedback Auto Turbo Encoder (FTAE) which harmoniously combines interleaver and iterative decoding with CNN architectures and demonstrate the blocklength gain and improved performance in the block feedback setting.
A comparative study of rice variety classification based on deep learning and hand-crafted features

Rice is vital to people all around the world. The demand for an efficient method in rice seed variety classification is one of the most essential tasks for quality inspection. Currently, this task is done by technicians based on experience by investigating the similarity of colour, shape and texture of rice. Therefore, we propose to find an appropriate process to develop an automation system for rice recognition. In this paper, several hand-crafted descriptors and Convolutional Neural Networks (CNN) methods are evaluated and compared. The experiment is simulated on the VNRICE dataset on which our method shows a significant result. The highest accuracy obtained is 99.04% by using DenNet21 framework.
Comparative study of single-phase phase-locked loops for grid-connected inverters under non-ideal grid conditions

In the renewable power generation systems, ensuring the synchronization of the inverter and the power grid is crucial for the stable operation of grid-connected inverters. Nowadays, the phase-locked loop (PLL) technology has become a widely used grid synchronization method because of its simple implementation and robustness under various grid conditions. Even though a lot of PLLs have been proposed, the overview and comparative analysis of multiple PLLs can be helpful for the practical applications. In addition, the weak grid condition is a great challenge for the system. Therefore, this study firstly presents an overview of the existing PLLs together with their general structures and basic working principles. Depending on the implementation of the phase detector, the PLL can be divided into three categories: power-based PLL (pPLL), orthogonal-signal-generator-based PLL (OSG-PLL) and adaptive-filter-based PLL (AF-PLL). Then, from the above classification, seven typical single-phase PLLs are selected for further study. Finally, some test results are given, and a comprehensive evaluation of the selected PLLs under different grid conditions is conducted.
Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

Decadal‐scale trends in aerosol and cloud properties provide important ways for understanding aerosol‐cloud interactions. In this paper, by using MODIS products (2003–2017), we analyze synergetic trends in aerosol properties and warm cloud properties over global ocean. Cloud droplet number concentration (CDNC) and aerosol parameters (aerosol optical depth, angstrom exponent, and aerosol index) show consistent decreasing trend over East Coast of the United States (EUS), west coast of Europe (WEU), and east coast of China (EC), and no significant trend in liquid water path is found over these regions during the period 2003–2017. Over regions with significant long‐term trends of aerosol loading and CDNC (e.g., EUS and WEU), the sensitivity of CDNC to aerosol loading based on the long‐term trend is closer to those derived from ground and aircraft observations and larger than those derived from instantaneous satellite observations, providing an alternative way for quantifying aerosol‐cloud interactions. A clear shift in the normalized probability density function of CDNC between the first 5 years (2003–2007) and the last 5 years (2013–2017) is found, with a decrease of around 50% in the occurrence frequency of high CDNC (>400 cm−3) over EUS and WEU. The relative variances of cloud droplet effective radius generally decrease with decreasing aerosol loading, providing large‐scale evidence for the effects of anthropogenic aerosols on the dispersion of cloud droplet size distribution. The long‐term satellite data sets provide great opportunities for quantifying aerosol‐cloud interactions and further confronting these interactions in climate models in the future.
Reproduction et échanges génétiques horizontaux chez les champignons mycorhiziens à arbuscules

Arbuscular Mycorrhizal Fungi (AMF) have a particular and complex genetic structure. Yet, many aspects of their genetics are still misunderstood and poorly documented. These organisms reproduce by asexual multinucleate spores. In this study, I investigated the mechanisms of genetic inheritance of nuclei through asexual reproduction in AMF. First, I determined the number of nuclei in mature and juveniles spores; I used statistical analysis to determine the relationship between the number of nuclei and the spore diameter. Four species from the genus Glomus were observed with a confocal microscope. The results showed that the number of nuclei has a significant positive relationship with spore diameter and more importantly, surprising heterogeneity in the number of nuclei among sister spores was found. To determine the number of nuclei in extraradical phases, three different structures from the mycelia were carefully examined with a confocal microscope. All the structures possessed more than one nucleus and showed that AMF probably lack a single-nucleus stage during their extraradical phases. To study the nuclei’s heritance, two different approaches were used: (i) Glomus irregulare was grown on medium complemented with aphidicolin to inhibit the mitosis. Observations with a confocal microscope permit to count the nuclei that come from the hyphae and not from the mitosis. The results showed that massive nuclear migration and mitosis are the mechanisms by which AMF spores are formed. (ii) The second approach confirm these results because with time-laps live cellular imaging of young spores of Glomus diaphanum it was possible to see many nuclei to get in the spores. In a second part of this thesis, I studied horizontal gene exchanges among AMF isolates through anastomoses. Thus, four isolates of the species G. irregulare were used in in vitro crossing experiments, in total six combinations using two isolates per crossing experiment. These crossing co-cultures were maintained over two years by subculturing. Spores of two different isolates were confronted in vitro prior to observation of anastomoses. 13% of spores formed anastomoses suggesting the occurrence of genetic exchange between two isolates. A mitochondrial molecular marker referred as Indel 5, was
Genotypes of the hepatitis B virus: basic aspects

Hepatitis B virus (HBV) infection is an important health problem worldwide. HBV has been classified into eight genotypes (A to H), which have been associated with different geographical areas: A with Europe and sub-Saharan Africa, B and C East Asia, D with the Mediterranean and Middle East region, E with Western Africa, F with the Amer- icas and G with France and United States. Recently, an additional genotype H has been observed in northern part of Latin America. The heterogeneity in disease manifestations and response to antiviral treatment among patients with chronic hepatitis B in different parts of the world may be attributed to differences in HBV genotypes. It has not been fully identified, however, whether there is a correlation between the HBV genotype and HBV replication, severity of liver disease or clinical outcome of treatment response. The role of HBV genotypes in liver disease deserves more study. This may lead to the identifica- tion of patient subgroups and the development of chronic hepatitis or hepatocellular car- cinoma. Testing for HBV genotypes is not yet clinically applicable. HBV genotypes and their clinical relevance should be thoroughly searched. The aim of this article is to sum- marize the entitled aspects of the HBV genotypes.
Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.
Effect of Long-Term Fertilizer Application on Performance of Wheat Crop and Soil Properties in a Vertisol

A field experiment was conducted in an ongoing Long-Term Fertilizer Experiment during 201718 and 2018-19 at the Research Farm, College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur to study the effect of long-term fertilizer application on performance of wheat crop and soil properties. The experiment was conducted in a randomized block design with ten treatments in four replications. The treatments comprised were 50% NPK, 100% NPK, 150% NPK, 100% NPK+Hand weeding, 100% NPK+Zn, 100% NP, 100% N, 100% NPK+FYM, 100% NPK-S and Control. The wheat crop (cv. GW-366) was grown with recommended nutrient dose of 120:80:40 (N: P2O5: K2O) in Rabi season. Field observations such as plant height and number of tillers were recorded at various growth stages and periodic intervals. The grain yield, straw yield and total biomass were recorded at crop harvest. The representative soil samples were collected from 0-15 cm depth and analyzed for different properties. The results revealed that, the wheat responded positively to the balanced nutrition as compared to no fertilization and imbalanced fertilization. Similarly, the application of balanced application of fertilizers showed higher soil organic carbon and nutrients as compared to the imbalanced nutrition.
Robotics Integration for Learning With Technology

This qualitative study examined how preservice elementary teachers integrated robotics into science, technology, engineering, and mathematics (STEM) lesson designs and why they designed their lessons in a particular way. Participants’ lesson designs were collected, and semistructured interviews were conducted. The authors analyzed lesson designs to examine how participants integrated robotics into their lesson designs and interviews to explore why they designed their lessons in a particular way. Our findings suggest that, in general, preservice elementary teachers designed lessons for student learning with technology. Only one lesson was for student learning from technology. The rest were for student learning with technology or applied a mixed approach that supported both student learning with and from technology. Preservice teachers’ lesson designs seemed to have been influenced by their pleasant struggles during robot design, collaboration experience, robotics integration knowledge, STEM content knowledge, and conception of STEM integration. Implications for teacher education are presented.
Control of culicid pests along low-land rivers of Austria: the attack-attract strategy.

In Austria the research activity for developing alternatives to insecticides controlling mosquito pests is closely related to Usutu-virus (Flaviviridae) epidemics found in 2001. The decision making of those Austrian communities mentioned in this paper is orientated on prevention of the urban inhabitants from mosquito-borne diseases and from mosquito invasions emerging in neighbouring flood areas. The comprehensive use of pesticides is forbidden by several laws. Accordingly, we have been attacking the larval development of mosquitoes by use of Bacillus sphaericus and Bacillus thuringiensis israelensis substances before they become adults. Using certain synergies of the landscape topography we are able to keep the invasions away from urban districts of the city of Tulln in six from seven flood events since 2005. The attract strategy is going to be realized as a substitute for the larvicid application. This new approach is based on a two-component attractant, which pulls female mosquitoes -mostly of the genus Aedestowards selected sites to deposit their egg clutches, where we can get rid on them by prepared traps and passively pushing. This method gave best results also with domestic mosquitoes (Culex) to diminish the population in gardens, at tourist areas and in urban parks. Experiments on the feasibility against blood-feeding pest of pastured cattle and around and inside stables are yet going to be done.
Modeling Cerebral Hemodynamics Using BOLD Magnetic Resonance Imaging and its Application in Mild Cognitive Impairment

This study develops a procedure and related analytical methods for deriving indices of cerebral hemodynamics in the magnetic resonance imaging (MRI) setting using resting state recordings of systemic blood pressure, pulse rate, and end-tidal CO2 synchronized with the MRI image acquisitions of blood oxygenation level dependent (BOLD) data as a measure of cerebral perfusion.We employed the concept of Principal Dynamic Modes (PDM) to model the effect of three determinants of cerebral perfusion: mean arterial blood pressure (MABP), end-tidal CO2 (PETCO2), and pulse rate (PR). The relation between these signals and the BOLD signal were used respectively to quantify cerebral autoregulation (CA), CO2 vasoreactivity (CVR), and pulse rate reactivity (PRR). Hemodynamic indices were obtained from 129 participants with normal cognition (NC) and mild cognitive impairment (MCI). CA was reduced in MCI compared to NC in the parietal lobe, CVR was reduced in MCI in the occipital and temporal lobes, and PRR was reduced in the frontal, parietal, occipital and temporal lobes. Reduced CVR and PRR were associated with worse cognitive scores including memory and executive function.Employed acquisition and analysis of MRI hemodynamic identified cerebral hemodynamic alterations in MCI, related to PR and ETCO2 changes.This modeling approach may offer a novel way to clinically assess cerebral hemodynamics during MRI.
Current Status of ProTherm: Thermodynamic Database for Proteins and Mutants

M. Michael Gromiha1 Hatsuho Uedaira1 gromiha@rtc.riken. go.jp uedaira@rtc.riken. go.jp Jianghong An2 Samuel Selvaraj1 jianghon@scripps.edu selvaraj@rtc.riken. go.jp Ponraj Prabakaran1 Akinori Sarai1 Praba@rtc.riken. go.jp sarai@rtc.riken. go.jp 1 RIKEN Tsukuba Institute, Institute of Physical and Chemical Research, 3-1-1 Koyadai,Tsukuba, Ibaraki 305-0074, Japan 2 Scripps Research Institute, La Jolla, California, USA
Effects of Sport Massage in Preventing Decreased Immunity After Sub-Maximal Physical Exercise

Physical exercise with heavy and tiring intensity can cause the formation of free radicals and the potential for oxidative stress. This oxidative stress due to strenuous and exhausting physical exercise is a physiological process that can cause the immune system to become depressed or decrease immune function. The purpose of this study was to determine the effect of sports massage to prevent decreased immunity after sub maximal physical exercise. The study design was an experimental pre and post test group design. The subjects were 20 students from the Department of Sport Science, Universitas Negeri Medan, randomly divided into 2 groups, group 1: given submaximal physical exercise and without sports massage; Group 2: sub-maximal physical exercise and given a sports massage. The results showed that the values of leukocytes and lymphocytes in the sub-maximal exercise group given sports massage were lower than the sub-maximal physical exercise group and without sports massage (p <0.05). Sub maximal physical exercise accompanied by sports massage can prevent a decrease in immunity.
SYNTHESIS OF NEW GENERATION CALIX[4]ARENE DERIVATIVES AS FLUORESCENCE CHEMOSENSOR AND ITS THEORETICAL CALCULATION

Calixarenes are among the commonly preferred molecules of the supramolecular chemistry since they are easily synthesizable and functionalizable.In this study, new types of calix[4]aren derivatives containing benzylisoquinoline groups were synthesized and the structures of the obtaining compounds were characterized by spectroscopic techniques such as 1H-NMR, 13C-NMR. Synthesized calix[4]aren derivatives towards some metal cations (Ag+, Al+3, Ba+, Ca+2, Co+2, Cs+2, Fe+3, Hg+2, Li+, Pb+2, Mg+2, Mn+2, Na+, Cu+2, Ba+2, Ni+2, Zn+2, Sr+2 showed fluorescence behaviors ve these behaviors were investigated utilizing UV-vis spectrophotometer and fluorescence spectroscopic techniques.It was found out according to the obtained results that it got complexed in 1:1 ratio as stoichiometric as a result of its activation with aqueous solutions of Cu+2 and Fe+2 ions and responded as an off-on sensor. In this regard, a highly selective and sensitive sensor was developed. Theoretical studies were also conducted in addition to the achieved experimental results. The bonding of metal ions was also calculated theoretically by using Gaussian 09 and GaussView 5.0. 8.
Target tracker with masked discriminative correlation filter

The discriminative correlation filter (DCF) method is widely used in target tracking due to its real-time performance. However, the computational efficiency of DCF results in boundary effect, which reduces the tracking accuracy in fast motion scene. Besides, background noise is always required to be carefully handled for they will cause trouble in scenes such as background clutter, occlusion, deformation etc. To address the two issues, this study proposes masked discriminative correlation filter, which uses mask to process DCF filter as well as target samples so as to suppress boundary effect and background noise. Experimental results on benchmark datasets show that the proposed tracker performs better than a series of benchmark trackers, and is superior to them in almost various scenes.
Biological specialization in Darluca filum.

The genus Darluca comprises a comparatively small group of parasitic fungi of the Family Sphaeropsidaceae of the Order Sphaeropsidales of the Class Fungi Imperfecti. It was named in honor of Michel Darluc, a French doctor of medicine. The members of the genus are well known as parasitizing the rust fungi, occurring on a variety of rusts scattered throughout the entire world. The present paper deals with the results of greenhouse cultures of Darlucacfilum (Biv.) Cast. on various rusts. The writer (Keener, 1933) has already reported the results of laboratory studies with pure cultures of this species of Darluca.