Variance Stabilization Research Articles

Time stability and connectivity analysis with an intracortical 96-channel microelectrode array inserted in human visual cortex

Objective. Microstimulation via electrodes that penetrate the visual cortex creates visual perceptions called phosphenes. Besides providing electrical stimulation to induce perceptions, each electrode can be used to record the brain signals from the cortex region under the electrode which contains brain state information. Since the future visual prosthesis interfaces will be implanted chronically in the visual cortex of blind people, it is important to study the long-term stability of the signals acquired from the electrodes. Here, we studied the changes over time and the repercussions of electrical stimulation on the brain signals acquired with an intracortical 96-channel microelectrode array implanted in the visual cortex of a blind volunteer for 6 months. Approach. We used variance, power spectral density, correlation, coherence, and phase coherence to study the brain signals acquired in resting condition before and after the administration of electrical stimulation during a period of 6 months. Main results. Variance and power spectral density up to 750 Hz do not show any significant trend in the 6 months, but correlation coherence and phase coherence significantly decrease over the implantation time and increase after electrical stimulation. Significance. The stability of variance and power spectral density in time is important for long-term clinical applications based on the intracortical signals collected by the electrodes. The decreasing trends of correlation, coherence, and phase coherence might be related to plasticity changes in the visual cortex due to electrical microstimulation.

Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the “Normics” algorithm: Proteins are ranked based on their expression level–corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood.

Stability Variance and its Bayesian Estimation for Genotype × Environment Interaction in m × m Gracro-latin Square Layout

Background: In agricultural, the field experimenters have been bona fide way of verifying the test result when researchers have developed new cultivating method in order to improve the overall crop production. To meet this requirement experimenters have continuously be measured in thinking the new and/or modified techniques and methodologies. Methods: To estimate stability variance with two treatments through Graceo-latin square layout apart from prevailing Latin square layout with one treatment. The maximal data information prior and asymptotically locally invariant prior are also studied for the obtained stability variance in such layout. Result: The Graceo-latin square design layout with two different treatments has been proposed to study genotype × environment (G × E) interaction. Statistical model for the design and ANOVA is also developed and simultaneous methods are proposed to diagrammatize the respective stability variance.

In silico screening of some compounds derived from the desert medicinal plant Rhazya stricta for the potential treatment of COVID-19

The latest coronavirus pandemic (SARS-CoV-2) poses an exceptional threat to human health and society worldwide. The coronavirus (SARS-CoV-2) spike (S) protein, which is required for viral–host cell penetration, might be considered a promising and suitable target for treatment. In this study, we utilized the nonalkaloid fraction of the medicinal plant Rhazya stricta to computationally investigate its antiviral activity against SARS-CoV-2. Molecular docking and molecular dynamics simulations were the main tools used to examine the binding interactions of the compounds isolated by HPLC analysis. Ceftazidime was utilized as a reference control, which showed high potency against the SARS-CoV-2 receptor binding domain (RBD) in an in vitro study. The five compounds (CID:1, CID:2, CID:3, CID:4, and CID:5) exhibited remarkable binding affinities (CID:1, − 8.9; CID:2, − 8.7; and CID:3, 4, and 5, − 8.5 kcal/mol) compared to the control compound (− 6.2 kcal/mol). MD simulations over a period of 200 ns further corroborated that certain interactions occurred with the five compounds and the nonalkaloidal compounds retained their positions within the RBD active site. CID:2, CID:4, and CID:5 demonstrated high stability and less variance, while CID:1 and CID:3 were less stable than ceftazidime. The average number of hydrogen bonds formed per timeframe by CID:1, CID:2, CID:3, and CID:5 (0.914, 0.451, 1.566, and 1.755, respectively) were greater than that formed by ceftazidime (0.317). The total binding free energy calculations revealed that the five compounds interacted more strongly within RBD residues (CID:1 = − 68.8, CID:2 = − 71.6, CID:3 = − 74.9, CID:4 = − 75.4, CID:5 = − 60.9 kJ/mol) than ceftazidime (− 34.5 kJ/mol). The drug-like properties of the selected compounds were relatively similar to those of ceftazidime, and the toxicity predictions categorized these compounds into less toxic classes. Structural similarity and functional group analyses suggested that the presence of more H-acceptor atoms, electronegative atoms, acidic oxygen groups, and nitrogen atoms in amide or aromatic groups were common among the compounds with the lowest binding affinities. In conclusion, this in silico work predicts for the first time the potential of using five R. stricta nonalkaloid compounds as a treatment strategy to control SARS-CoV-2 viral entry.

ASSESSMENT OF WATER AVAILABILITY IN THE CASCADE RESERVOIR OF DURIANGKANG-MUKA KUNING FOR SUPPORTING THE INTEGRATED OPTIMIZATION

The Duriangkang reservoir was built in 1992–1995. It was the first estuary dam built in Indonesia and has the largest storage of any dam in Batam City. Its volume of effective storage is 107 million m3, and it is fulfilling the water need at a rate of about 2,500 l/s. The Muka Kuning reservoir was completed in 1990 and began operating in 1991. Initially, the IPA Muka Kuning reservoir had a water production rate of 310 l/s, which increased to 600 l/s in 2015 because of Batam City’s increasing water needs. This research is aimed at assessing the water availability in the Duriangkang–Muka Kuning cascade reservoir system. This evaluation was carried out to support the integrated management of optimization in cascade reservoirs. In this cascade system of the Muka Kuning and Duriangkang reservoirs, water is taken from downstream into the upstream reservoir, and a pump is used to create inflow into the downstream reservoir. This study’s methodology comprises rainfall data tests, trend absence tests, stationery tests, variance stability tests, and mean stability tests. Moreover, we conducted an analysis of dependable discharge for the wet discharge condition (35% probability), the normal discharge condition (50% probability), and the dry discharge condition (65% probability). To evaluate the water availability, we used the reservoir water balance. The results show that the water availability in Duriangkang is enough to fulfill the water need under all conditions (wet, dry, and normal); however, for Muka Kuning, the water availability can be fulfilled in the wet and normal conditions only, and under the dry condition, the water availability cannot fulfill the water need. Therefore, ensuring the integrated management of optimization is necessary.

Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network

With the growing popularity of smartphones, capturing high-quality images is of vital importance to smartphones. The cameras of smartphones have small apertures and small sensor cells, which lead to the noisy images in low light environment. Denoising based on a burst of multiple frames generally outperforms single frame denoising but with the larger compututional cost. In this paper, we propose an efficient yet effective burst denoising system. We adopt a three-stage design: noise prior integration, multi-frame alignment and multi-frame denoising. First, we integrate noise prior by pre-processing raw signals into a variance-stabilization space, which allows using a small-scale network to achieve competitive performance. Second, we observe that it is essential to adopt an explicit alignment for burst denoising, but it is not necessary to integrate an learning-based method to perform multi-frame alignment. Instead, we resort to a conventional and efficient alignment method and combine it with our multi-frame denoising network. At last, we propose a denoising strategy that processes multiple frames sequentially. Sequential denoising avoids filtering a large number of frames by decomposing multiple frames denoising into several efficient sub-network denoising. As for each sub-network, we propose an efficient multi-frequency denoising network to remove noise of different frequencies. Our three-stage design is efficient and shows strong performance on burst denoising. Experiments on synthetic and real raw datasets demonstrate that our method outperforms state-of-the-art methods, with less computational cost. Furthermore, the low complexity and high-quality performance make deployment on smartphones possible.

Abstract 2743: Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models

Abstract Successful oncology drug development hinges on proper use of preclinical xenograft and allograft mouse models that faithfully reflect histopathology and genomic features of patient tumors and exhibit clinically similar drug response. For in vivo pharmacology studies, tumor-bearing mice are treated by drugs and efficacy is monitored by continuingly measuring tumor volumes over weeks or even months, resulting in mouse-specific tumor growth data. In our previous study, we used linear mixed model (LMM) to fit tumor growth data and demonstrated its superiority to simple readouts, such as tumor growth inhibition (TGI), in discovering biomarkers and exploring mechanism of action for drugs. Here we extended the study to more parametric statistical models on an expanded dataset with over 100 thousand tumor growth curves. Six parametric models (exponential, exponential square, Gompertz, Logistic, Monomolecular, and von Bertalanffy) were fit to tumor growth curves within a single-treatment group, by the least square method after variance stabilization transformation. Best models were selected by Akaike information criterion (AIC)-based confidence set. In the Irinotecan case study, logarithmic transformation of tumor volume converts exponential or exponential square models to mixed-effects linear models that integrate random effect of mouse difference within a treatment group, and are used to identify predictive biomarkers for drug efficacy. We compared three variance stabilization transformations and found that log transformation of tumor volume is appropriate for majority of tumor growth curves. In log transformed data, exponential or exponential square model can satisfactorily fit nearly all tumor growth curves, as 96.1% of best model confidence sets contain exponential or exponential square model, followed by von Bertalanffy (92.5%), Gompertz (92.1%), Logistic (90.5%), and Monomolecular (58.9%). The percentage of studies, in which all treatment groups can be best fit by exponential or exponential square model, is 91.3% (82.4% for von Bertalanffy, 81.4% for Gompertz, 79.1% for Logistic, 37.3% for Monomolecular). Thus, exponential or exponential square model can be applied to most in vivo mouse studies, and should be preferred for their mathematical simplicity and interpretability. We further demonstrated the utility of the modeling method on a 16-PDX-model study for Irinotecan to identify biological processes and pathways related to its mechanism of action. Citation Format: Huajun Zhou, Binchen Mao, Sheng Guo. Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2743.

Abstract 4178: PAK4 inhibition improves PD1 blockade immunotherapy in prostate cancer

Abstract Prostate cancers have low immune infiltration and do not respond to PD1 blockade immunotherapy. The objective of this study was to examine the effect of PAK4 inhibition on anti-tumor immune response in preclinical prostate cancer models. C57BL/6J mice (male, 5-8 weeks old) were injected with wild type (WT) or PAK4 knockdown (KD) syngeneic Rm1 tumor cells (2X105 cell/mouse) and treated with or without αPD1 blockade (200μg, BioXCell). Tumor growth was monitored and analyzed using ANOVA of repeated measures (n=6). In a separate experiment, 1 cm RM1 tumors were collected for RNA sequencing (RNASeq) and immunohistochemistry (IHC). RNASeq data were normalized using the variance stabilizing transformation (VST) function as implemented within the DESeq2 package. IHC images were captured with Keyence BZ-X800 and positive staining was calculated using ImageJ. CD8+ lymphocytes depletion was achieved by CD8 antibody injection (200ug, BioXCell). PAK4 inhibitors PF-3758309 (PF) and KPT-9274 (KPT) were evaluated in murine models. PAK4 KD tumors treated with αPD1 antibody (αPD1) showed a significant reduction of tumor growth when compared to growth in WT (P<.05), WT αPD1 (P<.01), and KD (p<.05). Analysis of bulk tumor RNAseq showed that the combination of PAK4 KD with αPD1 was associated with increased CD8a, IFNγ, and PD-L1 expression. IHC with αCD8 verified that PAK4 KD increased CD8+ cell infiltration in mouse tumors. Further, RNAseq showed that PAK4 KD increased the expression of cytokines, chemokines, and genes related to the IFNγ response. These results suggest that PAK4 KD primes the tumor for immune infiltration and improves αPD1 treatment response. The anti-tumor effect of PAK4 KD with αPD1 in mice was CD8-dependent and could be eliminated by CD8+ cell depletion (p<.05). We tested two different PAK4 inhibitors in mice with WT Rm1 tumors. PF is a kinase inhibitor that blocks the function of PAK4. A combination of PF and αPD1 significantly reduced tumor growth when compared to WT (P<.05), WT αPD1 (P<.01), and PF (p<.05). KPT is a dual inhibitor of PAK4 and NAMPT, which reduces the cytosolic protein concentration of PAK4. A combination of KPT and αPD1 significantly reduced tumor growth when compared to WT (P<.01) and WT αPD1 (P<.01). In conclusion, PAK4 inhibition increased immune infiltration and improved αPD1 treatment response in preclinical mouse prostate cancer models. Citation Format: Shengchen Su, Sungyong You, Yanping Wang, Patrick Tamukong, Catherine S. Grasso, Hyung L. Kim. PAK4 inhibition improves PD1 blockade immunotherapy in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4178.

Denoising of scanning electron microscope images for biological ultrastructure enhancement.

Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. Source code is available on GitHub (https://github.com/VictorCSheng/VSID-Net).

Climatic indices as markers of table-grapes postharvest quality: A prediction exercise

Climatic conditions have been acknowledged to affect table-grapes production and postharvest quality. The correlation of the monitored environmental conditions such as rainfall and ambient temperature, with postharvest quality properties such as pedicel detachment force, pH, brix, grape volume, for three cultivation years 2015–2017 was analysed. The significance of the climatic variability on the postharvest quality was examined based on a hydrothermic index used in viticultural zoning namely Branas, Bernon, Levadoux (BBL) for two phenological stages the Budbreak-Flowering and Flowering-Veraison. Their analysis revealed correlations of good efficacy between the BudBreak-Flowering BBL and Flowering-Veraison BBL with the postharvest quality, adopting the Box-Cox transformation for variance stabilization and accuracy improvement of Pearson correlation between the analysed variables. The statistical analysis also revealed specific dependencies of the tested quality properties from the climatic indices and management zones that could be implemented in the postharvest management of table-grapes, according to the management zones delineation as these are determined in the context of the precision viticulture.

A new approach for reduction of the noise from microscopy images using Fourier decomposition

In this paper, an efficient method based on the Fourier decomposition method (FDM) is presented for noise removal of medical microscopic images. We propose an adaptive thresholding technique based FDM for denoising of heavily degraded images. An accurate image deconvolution is done with variance stabilization transformation and multi-scale Wiener filtering as a pre-processing step. The different series of frequency intrinsic band functions (FIBF’s) obtained with FDM which are further separated into noise and signal-significant FIBF’s based on cosine similarity index. The FDM adaptive thresholding technique is used to filter-out the unwanted frequency coefficients related to mixed Poisson-Gaussian noise (MPG). The thresholded FIBF’s and signal significant FIBF’s are combined to obtained reconstructed output. Finally, the optimization is done using mixed noise unbiased risk estimate (MNURE). To evaluate the effectiveness of proposed scheme, we have compared the results of the proposed scheme with six different state-of-the-art techniques. The simulation results verify, the effectiveness of proposed method. The proposed algorithm achieves better performance in terms of four quantitative evaluation measures by reducing the effect of noise.

Differences in Radiative Forcing, Not Sensitivity, Explain Differences in Summertime Land Temperature Variance Change Between CMIP5 and CMIP6

AbstractHow summertime temperature variability will change with warming has important implications for climate adaptation and mitigation. CMIP5 simulations indicate a compound risk of extreme hot temperatures in western Europe from both warming and increasing temperature variance. CMIP6 simulations, however, indicate only a moderate increase in temperature variance that does not covary with warming. To explore this intergenerational discrepancy in CMIP results, we decompose changes in monthly temperature variance into those arising from changes in sensitivity to forcing and changes in forcing variance. Across models, sensitivity increases with local warming in both CMIP5 and CMIP6 at an average rate of 5.7 ([3.7, 7.9]; 95% c.i.) × 10−3°C per W m−2 per °C warming. We use a simple model of moist surface energetics to explain increased sensitivity as a consequence of greater atmospheric demand (∼70%) and drier soil (∼40%) that is partially offset by the Planck feedback (∼−10%). Conversely, forcing variance is stable in CMIP5 but decreases with warming in CMIP6 at an average rate of −21 ([−28, −15]; 95% c.i.) W2 m−4 per °C warming. We examine scaling relationships with mean cloud fraction and find that mean forcing variance decreases with decreasing cloud fraction at twice the rate in CMIP6 than CMIP5. The stability of CMIP6 temperature variance is, thus, a consequence of offsetting changes in sensitivity and forcing variance. Further work to determine which models and generations of CMIP simulations better represent changes in cloud radiative forcing is important for assessing risks associated with increased temperature variance.

Characterizing Frequency Stability Measurements Having Multiple Data Gaps.

Time series measurements with data gaps (dead times) prevent accurate computations of frequency stability variances such as the Allan variance (AVAR) and its square-root the Allan deviation (ADEV). To extract frequency distributions, time-series data must be sequentially ordered and equally spaced. Data gaps, particularly large ones, make ADEV estimates unreliable. Gap imputation by interpolation, zero-padding, or adjoining live segments, all fail in various ways. We have devised an algorithm that fills gaps by imputing an extension of preceding live data and explaining its advantages. To demonstrate the effectiveness of the algorithm, we have implemented it on 513-length original datasets and have removed 30% (150 values). The resulting data is consistent with the original in all three major criteria: the noise characteristic, the distribution, and the ADEV levels and slopes. Of special importance is that all ADEV measurements on the imputed dataset lie within 90% confidence of the statistic for the original dataset.

Original image noise reconstruction for spatially-varying filtered driving scenes

Test drives for the development of camera-based automotivealgorithms like object detection or instance segmentation are veryexpensive and time-consuming. Therefore, the re-use of existingdatabases like COCO or Berkeley Deep Drive by intentionallyvarying the image quality in a post-processing step promises tosave time and money, while giving access to novel image qualityproperties. One possible variation we investigate is the sharpnessof the camera system, by applying spatially varying optical blurmodels as low-pass filters on the image data. Any such opera-tion significantly changes the amount and distribution of noise, acentral property of image quality, which in this context is an un-desired side-effect. In this article, a novel method is presentedto reconstruct the original camera sensor noise for the filteredimage. This is different from denoising. The method estimatesthe original camera sensor noise using the combination of princi-pal component analysis (PCA) and a variance-stabilizing trans-formation. The noise is then reconstructed for the filtered imagewith the PCA applied locally on small image sections, and an in-verse variance-stabilizing transformation. Although the resultingnoise distribution can slightly deviate from the original, this novelmethod does not introduce any image artifacts as denoising woulddo. We present the method as applied to synthetic and real driv-ing scenes at different noise levels and discuss the accuracy of thereconstruction visually and with statistical parameters.

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

Abstract The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p < 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p < 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

Factors Contributing to the Stability of the Farmer Producer Organisations: A Study in West Bengal

The study was conducted to assess the stability of Farmer Producer Organizations (FPOs) and the factors contributing to the stability of FPOs in West Bengal during 2020. Using random sampling procedure, data were collected from 120 farmer members from ten FPOs from four districts of the state namely Birbhum, Murshidabad, Purba Bardhaman and Nadia through personal interview method. For measuring the stability of FPOs, stability index was developed taking mutual trust, role clarity of the members, level of involvement of members in group works, satisfaction of the members, sense of attachment and conviction and sense of ownership as the indicators. The significant difference between the mean scores of stability index of high and low performing FPOs were observed with respect to the dimensions like satisfaction of the members, mutual trust, role clarity and sense of attachment and conviction. Attitude of members towards their FPO and cooperation were found to be significant contributor in developing better group stability within a high performing FPO. Attitude towards FPO, assimilation and competition were found to be the reliable predictors for the variance in group stability in low performing FPOs.

Convolutional neural network initialization approaches for image manipulation detection

Nowadays it is common to see image forgeries on almost every media in both professional and personal contexts. From visual retouches to deliberate fake scenes, the technology used to create image forgeries gets easier to use for all users. At the same time, different techniques to assess the authenticity of the content of an image have appeared by taking advantage of the deep learning paradigm. In this paper we propose two initialization approaches for Convolutional Neural Networks (CNNs) used for the detection of image manipulation operations. We focus on the variance stability for the output of a convolutional filter in CNN. Our first proposal is a scaling approach for first-layer convolutional kernels which can cope well with filters generated by different algorithms. Our second proposal is a random high-pass filter initialization approach for CNN's first convolutional layer. The first proposal explicitly computes simple statistical properties of the input signal, while the second approach incorporates the consideration of input statistics in the filter derivation without the need of carrying out explicit computation on the input. Experimental results show the utility of both approaches with improved performance in different image manipulation detection problems and on different CNN architectures.

Partner supportive dyadic coping and relationship stability among parents of children with autism spectrum disorder

Studies involving parents of children with autism spectrum disorder (ASD) have reported negative relationship outcomes for some couples and positive for others, indicating the need to determine the moderators of the link between stress and divorce. This study aims to examine the moderating effect of partner supportive dyadic coping, i.e., interpersonal coping that involves providing comfort, empathy, or practical advice to one’s partner, on the association between parental stress and relationship stability among parents of children with ASD. The study was conducted on a sample of parents (N=89) who met the criteria of being in a relationship and parenting at least one child diagnosed with ASD. The questionnaire included the Parental Stress Scale, a subscale of the Dyadic Coping Inventory, an item assessing the potential for divorce, and sociodemographic variables. The logistic regression analysis model explained a substantial amount of the variance of relationship stability. Higher probability of having a stable relationship was associated with lower levels of parental stress and higher levels of supportive dyadic coping. Additionally, supportive dyadic coping buffered the negative effect of parental stress: the effect of stress on relationship stability was evident only among participants whose partners showed low supportive dyadic coping. The results indicate that a relationship does not necessarily end in divorce when a couple experiences stressful circumstances, such as raising a child with ASD. The key factor could be supportive dyadic coping that prevents negative effects of stress on relationship maintenance. Support services should aim to enhance supportive dyadic coping skills among parents, and advocate for the fact that both stress and coping are joint processes of partners in a relationship.

Evaluation of collective samples of winter rapes for ecological plasticity and stability

Creation of high-yielding «00» varieties of winter rape, adapted to the conditions of the Forest-Steppe is one of the important problems in rape breeding. The aim of the study is to assess the ecological adaptability of collection samples of winter rapeseed by seed productivity (yield, weight of 1000 seeds), taking into account the specifics of the growing area and the instability of climatic conditions in the autumn-spring period. Collective samples of winter rape in the amount of 27 varieties of domestic and Ukrainian selection were used for research. Research methods: field and laboratory, mathematical and statistical. The results of the assessment of ecological adaptability of winter rapeseed collection samples of different ecological and geographical origin are given. The average yield of winter rape for the years of research ranged from 1.81 to 2.37 t/ha. The range of fluctuations of the coefficient of plasticity (bi) varied in the studied samples from 3.58 to 5.95. Of the 27 studied samples, the values of the regression coefficient (bi) had more than one variety: Bazhanyy (bi = 1.17), Amor (bi = 1.15), NPC 9800 (bi = 1.15), Atlant (bi = 1.11), Dema (bi = 1.11), Svitoch (bi = 1.08), Cheremosh (bi = 1.08), Antariya (bi = 1.06), Ranok Podillya (bi = 1.04). As a result of research it was found that the average weight of 1000 seeds of collection samples of winter rapeseed ranged from 3.73 to 4.73 g. The value of the stability variance (Si2) of the mass of 1000 seeds varied from 14.69 to 23.84), indicate that the empirical values differ from the theoretical ones. The lowest values of stability variance were obtained in the samples of Cheremosh and Dembo - (Si2) = 14.69 and 15.50 with a mass of 1000 seeds of 3.73 and 3.81 g. According to the results of four-year data, collection samples were selected, which showed stable indicators in terms of yield and are the most promising: Sveta (2,37 t/ha), Horyzont (2,36 t/ha), Elvys (2,32 t/ha), Halytskyi (2,27 t/ha). The study of the sign of the mass of 1000 seeds showed that the most stable indicator was found in varieties: Antariya, Vektra, Ranok Podillya, Votan, Demarka, Horyzont, Halytskyi. The obtained results can be used in further selection work to create competitive varieties with a high level of yield and stable manifestation of these characteristics.

Multiscale analysis of living benthic foraminiferal heterogeneity: Ecological advances from an intertidal mudflat (Loire estuary, France)

An unprecedented sampling effort on the Loire estuary allowed a multi scale approach to identify parameters controlling density variations of benthic foraminifera. Indeed, the distances between the samples analysed for this study vary from 1 cm to hundreds of kilometres. To catch this range of distance variations, a model called Scale Variance Analysis was build describing the participation of each scale to the total observed variance. The SVA model requires, for each scale, the stability of relative variance. A comparison with the Moran's Index and experimental variogram is proposed showing coherent conclusions with the SVA analysis. The analysis shows that in order to maximize information on foraminiferal density variation, sampling campaigns should be designed with stations distant from few meters to 1 km, with a particular focus on the hectometre scale. A range of scale too rarely investigated in the community of benthic foraminifera ecology. Next, based on two intertidal mudflat stations separated of few hundred meters, the present study shows that for Ammonia tepida, the scale dependant preponderant parameters is the Chl a concentration in the top first centimetre. Contrastingly, the indicators of food quality such as the lability index and the oxygen penetration depth do not seem to affect A. tepida densities. This high quantity, low quality diet is interpreted as an opportunistic behaviour that is indirectly confirmed by a kinetic approach. This approach compares the deep infaunal microhabitat density with the shallow infaunal microhabitat density. The identical ratio indicates quick saturation of the available resources.