Depression has been the leading cause of mental-health illness worldwide. Major depressive disorder (MDD), is a common mental health disorder that affects both psychologically as well as physically which could lead to loss of lives. Due to the lack of diagnostic tests and subjectivity involved in detecting depression, there is a growing interest in using behavioural cues to automate depression diagnosis and stage prediction. The absence of labelled behavioural datasets for such problems and the huge amount of variations possible in behaviour makes the problem more challenging. This paper presents a novel multi-level attention based network for multimodal depression prediction that fuses features from audio, video and text modalities while learning the intra and inter modality relevance. The multi-level attention reinforces overall learning by selecting the most influential features within each modality for the decision making. We perform exhaustive experimentation to create different regression models for audio, video and text modalities. Several fusions models with different configurations are constructed to understand the impact of each feature and modality. We outperform the current baseline by 17.52% in terms of root mean squared error.

Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have “missed” much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA’s estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA’s heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results’ qualitative interpretations, should be interpreted with great caution.

Freeform liquid three-dimensional printing (FL-3DP) is a promising new additive manufacturing process that uses a yield stress gel as a temporary support, enabling the processing of a broader class of inks into complex geometries, including those with low viscosities or long solidification kinetics that were previously not processable. However, the full exploitation of these advantages for the fabrication of complex multilateral structures has been hindered by difficulties in controlling the interfaces between inks and supports. In this work, an in-depth study of the rheological properties and interfacial stabilities between a nanoclay-modified support and silicone-based inks enabled a better understanding of the impact printing parameters have on the extruded filament morphology, and thus on printing resolutions. With these improvements, the fabrication of functional multimaterial pneumatic components applied to soft robotics could be demonstrated, exhibiting superior capabilities compared to casting or traditional extrusion-based additive manufacturing in terms of geometric freedom (overhanging and multimaterial structures), tunability of the component's functionality, and robustness between different phases. Overall, the full exploitation of FL-3DP advantages enables a broader design space for features and functionalities in soft robotic components that require complex and robust combinations of materials.

aIn our recent paper in PNAS (1), and subsequently (2), we have analyzed the mathematical model that is stated precisely by Yang et al. (3). As written, their model assumes that (i) the Genetic Relatedness Matrix (GRM) is known exactly, (ii) the phenotypic contributions of each of the P SNPs are independent identically distributed draws from the same normal distribution with mean 0 and variance σ 2 , and (iii) σ 2 is independent of P.The empirical facts are that (i) we do not know the GRM, but only have an estimate of it; (ii) the number of SNPs (P) used in the analysis depends on the technology; and (iii) there is no "empirical" evidence for the heritability estimates obtained using genome-wide complex trait analysis (GCTA) per se [notwithstanding the claims made by Yang et al. (4) in their response to our article (1)].We and others have shown that the singular value distribution is skewed, and hence it is unlikely that all of the singular values and singular vectors of the (unknown) true GRM can be reliably estimated.In a paper published elsewhere (2), we provide multiple examples of inconsistent σ 2 estimates published by the coauthors of the GCTA model (3). We show that the σ 2 estimates are unreliable when (i) N is fixed and P varies (ref. 2, p. 12), (ii) when P is fixed and N varies (ref. 2, p. 13), and (iii) when both N and P vary (ref. 2, p. 11). These authors claim that the estimate of σ 2 will decrease as P increases, but this specification is not a part of their model as we understand it.We do not understand the basis for the claim that "the GREML fits all of the SNPs jointly in a randomeffect model so that each SNP effect is fitted conditioning on the joint effects of all of the SNPs" (4). Although Yang and colleagues (4) insist on this fact, they do not provide any mathematical justification for this conclusion. We have responded in detail to all other critiques listed in this letter elsewhere (2).

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