Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary lipid quality, environment and the developing brain

Onderzoek toont aan dat borst gevoede kinderen in het voordeel zijn ten opzichte van kinderen die zuigelingenvoeding hebben gehad als het gaat om de ontwikkeling en functie van de hersenen. Een van de factoren die dit verschil mogelijk zou kunnen verklaren is het verschil in de kwaliteit van de lipiden in moedermelk versus die in zuigelingenvoeding. Dit proefschrift beschrijft effecten van de kwaliteit van lipiden in het postnatale dieet op de ontwikkeling en functie van de hersenen. We hebben gekeken naar twee aspecten van de lipide kwaliteit, namelijk de omega 3- en 6-meervoudig-onverzadigde vetzuur compositie en de manier waarop lipiden structureel georganiseerd zijn in melk (d.w.z. lipide druppel grootte en complexiteit van het oppervlakte). Met behulp van een muismodel evalueerden we de effecten van deze lipide kwaliteitsaspecten in voeding tijdens heb vroege leven op de vetzuuraccumulatie in de hersenen, neuronale circuitvorming (hiervoor hebben we gekeken naar het hypothalame circuit), cognitieve functie en gedrag. Deze uitkomsten worden ook onderzocht in relatie tot de effecten van de voedingsinterventies op de groei en metabole gezondheid. Daarnaast evalueerden we de effecten van een andere omgevingsfactor in knaagdierstudies, namelijk de sociale huisvestingssituatie van ratten en muizen in het laboratorium (dwz individueel gehuisvest versus sociaal gehuisvest), op de uitkomsten voor metabole gezondheid (dwz lichaamsgewicht, voedselopname en viscerale vetmassa) door middel van een systematische review en meta-analyse. De sociale huisvestingssituatie van dieren in het laboratorium kan een mogelijke moderator zijn van andere experimentele uitkomsten van dieet / drugs of andere interventies.
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification. These processes use neural networks with latent variable inputs to induce predictive distributions. However, the expressiveness of vanilla NPs is limited as they only use a global latent variable, while target specific local variation may be crucial sometimes. To address this challenge, we investigate NPs systematically and present a new variant of NP model that we call Doubly Stochastic Variational Neural Process (DSVNP). This model combines the global latent variable and local latent variables for prediction. We evaluate this model in several experiments, and our results demonstrate competitive prediction performance in multi-output regression and uncertainty estimation in classification.
Stathmin 1 expression is associated with aggressive phenotypes and cancer stem cell marker expression in breast cancer patients

Stathmin1 (STMN1) regulates progression in various cancers. The present study aimed to determine the relationship between STMN1 expression and several cancer-related markers in breast cancer. Using immunohistochemistry, we evaluated STMN1, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, epidermal growth factor receptor (EGFR), CK5/6, CD44, CD24, aldehyde dehydrogenase 1, E-cadherin, epithelial cell adhesion molecule, and vimentin in 237 breast cancer patients and the clinical significance of STMN1. STMN1 expression was evaluated in 51 breast cancer cell lines, and the prognostic value of STMN1 was calculated. Higher STMN1 expression was detected in cancer tissues and was predominantly localized in the cytoplasm. High STMN1 expression was associated with the triple negative subtype, nuclear grade progression, high expression of Ki-67, EGFR, CK5/6, E-cadherin and high CD44/low CD24. According to gene expression-based outcome for breast cancer online and the Kaplan-Meier plotter, STMN1 expression was higher in basal-type cell lines than in luminal-type cell lines, and overall survival and post-progression survival in the high STMN1 expression breast cancer patients were shorter than in low STMN1 expression patients. High STMN1 expression is a possible marker of breast cancer aggressiveness in association with proliferation, phenotype and cancer stem cell type.
Analysing the impact of learning inputs - Application to terrain traversability estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.
Genetic variability, flower morphology and physico- chemical characters and health benefits on jamun (Syzygium cuminii Skeels): A review

Jamun (Syzygium cuminii Skeels) belongs to myrtaceae family also known as black plum tree, Java plum, Indian blackberry is an important multifarious evergreen tree which can withstand the dry land conditions. It is a medium size tree of 10-30 m height with stout trunk of 40-100 cm in diameter, sometimes tree having 62.5 cm circumference have been recorded. Flowers are whitish to greenish in colour, slightly fragrant nearly stalk less consisting of many white or pinkish threadlike stamens (5 mm long) with inferior ovary. Fruits are ovoid-oblong, elliptic having a projected, or depressed or flat base. Initially the fruit are green in stage, turning pink and finally purple-black. Pulp is whitish or pinkish or dark purplish in colour. The seed in each berry is strongly astringent and slightly bitter and it is angular in shape which compressed together into a mass resembling a single seed. It is rich source of anti-oxidants which can combat many diseases and also improve the immune system. Eating Jamun is helpful in curing many diseases and disorders. The seed powder has anti-diabetic properties due to presence of an alkaloid jambolin or antimellin which halts the diastatic conversion of starch into sugar. Numerous researches revealed that the extracts of different parts of jambolin showed significant pharmacological action. Hence identification of such active ingredients is essential for producing the safer drugs in the various ailments including diabetes. Vast trees are found scattered throughout the tropical and subtropical regions of the country. Till date there is lack of organized orchard and no systematic collection and evaluation of jamun cultivars has been carried out in Chhattisgarh. Hence, based on this objective basically this paper jamun would get evaluation of genotypes, flower behavior and physic-chemical characters for crop improvement.
Communication-Efficient Distributed PCA by Riemannian Optimization

In this paper, we study the leading eigenvector problem in a statistically distributed setting and propose a communication-efficient algorithm based on Riemannian optimization, which trades local computation for global communication. Theoretical analysis shows that the proposed algorithm linearly converges to the centralized empirical risk minimization solution regarding the number of communication rounds. When the number of data points in local machines is sufficiently large, the proposed algorithm achieves a significant reduction of communication cost over existing distributed PCA algorithms. Superior performance in terms of communication cost of the proposed algorithm is verified on real-world and synthetic datasets.
Effect of solar tunnel drying on the instrumental texture parameters of dried beef

Drying causes many changes to the various quality properties of meat and meat products, including texture. Most of the textural changes in meat during drying are due to denaturation of meat proteins which results in different structural changes. The aim of this study was to determine the effect of solar tunnel drying on the different textural parameters of dried beef. The beef samples were dried in different sections of the solar tunnel dryer (A-D, from the collector end to the end of the drying chamber of the tunnel dryer, respectively) and in the open sun as the control. The textural parameters of the dried product were then determined using volodkevich bite jaws and texture profile analysis methods of the texture analyser as well as the puncturing method of the rheometer. The hardness and firmness values of the dried beef samples were used to assess the level of variability of the three texture-measuring instruments and other textural parameters evaluated by texture profile analysis (TPA). The puncture test hardness values were significantly lower (P ≤ 0.05) than the values found for all the test methods used and were approximately ten times lower than the volodkevich firmness values. The TPA test could only be done on rehydrated samples resulting in a lower force of compression compared to the force required to deform samples for the volodkevich test. However, there was a high level of variability of the results as given by the large values of standard deviation of the TPA samples. The difference between the other textural parameters of the sun and solar dried beef samples was mostly significant for the sample closest to the collector end of the solar drier (sample A). Its adhesiveness, springiness (%) and resilience texture parameters were higher compared to sun dried beef samples.
PROCUREMENT COST REDUCTION STRATEGY: IMPACT ON E-PROCUREMENT PERFORMANCE OF STATE PARASTATALS IN KENYA

: The objective of the study was to investigate the role of procurement cost reduction strategy on e-procurement performance of state Parastatals in Kenya. In doing this, the study adopted the transaction cost theory of (Bakker et al, 2008). The theory explains how the use of information technology (IT) has facilitated the reduction of coordination costs. The study used a cross-sectional survey research design. Both qualitative and quantitative research methodologies were used in the study. The population of this study comprised of the ICT and Procurement managers at all the 190 state parastatals in Kenya comprising of a total of 380 respondents. Since the population was highly heterogeneous, a cluster sampling was used to select 380 respondents from 190 state parastatals. Primary data was collected using a questionnaire covering the role of E-procurement in state corporation performance. The questionnaire contained both structured and unstructured questions. Secondary data was gathered from existing credible and recognized source. Quantitative data was analyzed by employing descriptive statistics and inferential analysis using statistical package for social science (SPSS) together with simple graphics analysis, descriptive statistics form the basis of virtually every quantitative analysis to data). Correlation analysis was used to establish the relationship between the independent and dependent variables. The positive (H1) hypothesis testing was done at 5% level of significance and SPSS was used for this purpose. The data was then presented using frequency distribution tables, bar charts and pie charts for easier understanding . Procurement performance in the state parastatals was regressed against the variable of the role cost reduction . The study established that, procurement cost reduction strategy had greater influence on e-procurement performance in state parastatals in Kenya. It also revealed that, procurement cost reduction strategy has a direct relationship with e-procurement strategy and finally improving the e-procurement performance of state parastatals in Kenya. The study recommended holding of the key success for delivering cost effective services hence e-procurement performance. And also, target cost contracting (TCC), to be accompanied by a gain-share/pain share arrangement serving as a cost incentive mechanism in state parastatals in Kenya. In line with this state corporation must view the embracement of e-procurement as a tool that will support procurement cost and processes as it generally contributes partner relationships, information sharing, and supply chain integrations as e-procurement contributes to supply chain performance .
Effectiveness of bed disinfectants on silkworm diseases and cocoon yield under tropical conditions of Kolar district, Karnataka

The impact of bed disinfectants for successful cocoon harvest and crop stability was successfully demonstrated in the farmer’s field during 2014-15. The study revealed that, application of Ankush vijetha green and slaked lime powder combination (T3) bed disinfectant was recorded low incidence of silkworm diseases like grasserie (0.10 – 0.08 %), flacherie (0.25 – 0.15%) and muscardine (0.05 – 0.35%) and lower defective cocoon percentage viz., stained cocoons (0.10 – 0.10 %), flimsy cocoon (0.30 – 0.04%) and uzi pierced cocoon (0.80 0.30 %) during post rainy and rabi seasons respectively. Further, single cocoon weight (1.821 g), shell weight (0.340 g), pupal weight (1.456 g), shell percentage (18.75 %), filament length (851.13 mtrs) and cocoon yield (81.10 kg/100 dfls) were recorded high in T3 compared to application of bundh powder (T1).
Modulation of the aerosol absorption and single-scattering albedo in Southwest Asia due to large scale and sea breeze circulations

Spectral aerosol particle absorption properties in the Arabian Gulf region were observed during the United Arab Emirates Unified Aerosol Experiment (UAE2) in August to September 2004. Measurements were taken at a coastal site located 60 km northeast of Abu Dhabi, the capital of the United Arab Emirates, allowing characterization of pollution and dust particle absorption properties in a highly heterogeneous environment. A large diurnal modulation was observed due to (a) strong seaand land-breeze and (b) changes in prevailing large scale flow. During the night, stagnating air resulted in gradual accumulation of pollution with maximum absorption in the early morning hours. The rising sun increased both the depth of the boundary layer and the temperature of the ___________________________________ corresponding author address: Krzysztof M. Markowicz, Warsaw Univ., Inst. Of Geophysics, 02-093 Warsaw, Poland, email:kmark@igf.fuw. edu.pl interior desert, resulting in strong and sudden sea-breeze onset, which ventilated the polluted air accumulated during the night. Our observations show that the onshore winds brought cleaner air resulting in decreasing values of the absorption coefficient and increasing values of the single-scattering albedo (ωo). The mean value of the absorption coefficient at 550 nm measured during the sea breeze was 10.2 Mm-1 while during the land breeze it was 13.8 Mm-1. Large scale transport also strongly influenced particle fine/coarse partition with “northern” flow bringing pollution particles and “southern” flow bringing more dust.
Statistically-anisotropic tensor bispectrum from inflation

We develop a possibility of generating tensor non-Gaussianity in a kind of anisotropic inflation, where a (1) gauge field is kinetically coupled to a spectator scalar field. Owing to this coupling, the coherent mode of the electric field appears and softly breaks the isotropy of the Universe. We compute the bispectrum of linearly-polarized tensor perturbations sourced by the gauge field and find that it is strongly red-tilted and has distinctive statistical anisotropies including higher-order multipole moments. Interestingly, the tensor bispectra with the specific combinations of linear polarization modes are dominant, and their amplitudes depend on the different sets of multipole moments. This new type of statistically-anisotropic tensor non-Gaussianity can be potentially testable with the upcoming cosmic microwave background B-mode polarization experiments.
Transistors: A Network Science-Based Historical Perspective

The development of modern electronics was to a large extent related to the advent and popularization of bipolar junction technology. The present work applies science of science concepts and methodologies in order to develop a relatively systematic, quantitative study of the development of electronics from a bipolar-junction-centered perspective. First, we searched the adopted dataset (Microsoft Academic Graph) for entries related to "bipolar junction transistor". Community detection was then applied in order to derive sub-areas, which were tentatively labeled into 10 overall groups. This modular graph was then studied from several perspectives, including topological measurements and time evolution. A number of interesting results are reported, including a good level of thematic coherence within each identified area, as well as the identification of distinct periods along the time evolution including the onset and coming of age of bipolater junction technology and related areas. A particularly surprising result was the verification of stable interrelationship between the identified areas along time.
Diagnosis of Autism in Children using Facial Analysis and Deep Learning

In this paper, we introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Although the disease is considered to be genetic, the highest rates of accurate diagnosis occur when the child is tested on behavioral characteristics and facial features. Patients have a common pattern of distinct facial deformities, allowing researchers to analyze only an image of the child to determine if the child has the disease. While there are other techniques and models used for facial analysis and autism classification on their own, our proposal bridges these two ideas allowing classification in a cheaper, more efficient method. Our deep learning model uses MobileNet and two dense layers in order to perform feature extraction and image classification. The model is trained and tested using 3,014 images, evenly split between children with autism and children without it. 90% of the data is used for training, and 10% is used for testing. Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture. Additionally, there may be other diseases that are similarly diagnosable.
Learned convex regularizers for inverse problems

We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional. The ICNN-based convex regularizer is trained adversarially to discern ground-truth images from unregularized reconstructions. Convexity of the regularizer is attractive since (i) one can establish analytical convergence guarantees for the corresponding variational reconstruction problem and (ii) devise efficient and provable algorithms for reconstruction. In particular, we show that the optimal solution to the variational problem converges to the ground-truth if the penalty parameter decays sub-linearly with respect to the norm of the noise. Further, we prove the existence of a subgradient-based algorithm that leads to monotonically decreasing error in the parameter space with iterations. To demonstrate the performance of our approach for solving inverse problems, we consider the tasks of deblurring natural images and reconstructing images in computed tomography (CT), and show that the proposed convex regularizer is at least competitive with and sometimes superior to state-of-the-art data-driven techniques for inverse problems.
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer

Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between manual contours and GTV-CNN was assessed with Dice-Sorensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal test-datasets by using whole-mount histology. Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.
Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process

An efficient condition-based predictive spare ordering approach is the key to guarantee safe operation, improve service quality, and reduce maintenance costs under a predefined lower availability threshold. In this paper, we propose a condition-based predictive order model (CBPO) for a mechanical component, whose degradation path is modeled as inverse Gaussian (IG) process with covariate effect. The CBPO is dependent on the remaining useful life (RUL), random lead-time, speed-up lead-time degree, and availability threshold. RUL estimation is obtained through the IG degradation process at each inspection time. Both regular lead-time and expedited lead-time considered in RUL-based spare ordering policy can be cost-effective and reduce losses caused by unexpected failure. Speed-up lead-time can meet the urgent needs for spare parts on site. The decision variable of CBPO is the spare ordering time. Based on the CBPO under the lower availability threshold constraint, the objective of this study is to determine the optimal spare ordering time such that the expected cost rate is minimized. Finally, a case study of the mechanical spindle is presented to illustrate the proposed model and sensitivity analysis on critical parameters is performed.