Genotypic and Phenotypic Spectrum of Foveal Hypoplasia: A Multicenter Study.

PURPOSE: To characterize the genotypic and phenotypic spectrum of foveal hypoplasia (FH). DESIGN: Multicenter, observational study. PARTICIPANTS: A total of 907 patients with a confirmed molecular diagnosis of albinism, PAX6, SLC38A8, FRMD7, AHR, or achromatopsia from 12 centers in 9 countries (n = 523) or extracted from publicly available datasets from previously reported literature (n = 384). METHODS: Individuals with a confirmed molecular diagnosis and availability of foveal OCT scans were identified from 12 centers or from the literature between January 2011 and March 2021. A genetic diagnosis was confirmed by sequence analysis. Grading of FH was derived from OCT scans. MAIN OUTCOME MEASURES: Grade of FH, presence or absence of photoreceptor specialization (PRS+ vs. PRS-), molecular diagnosis, and visual acuity (VA). RESULTS: The most common genetic etiology for typical FH in our cohort was albinism (67.5%), followed by PAX6 (21.8%), SLC38A8 (6.8%), and FRMD7 (3.5%) variants. AHR variants were rare (0.4%). Atypical FH was seen in 67.4% of achromatopsia cases. Atypical FH in achromatopsia had significantly worse VA than typical FH (P < 0.0001). There was a significant difference in the spectrum of FH grades based on the molecular diagnosis (chi-square = 60.4, P < 0.0001). All SLC38A8 cases were PRS- (P = 0.003), whereas all FRMD7 cases were PRS+ (P < 0.0001). Analysis of albinism subtypes revealed a significant difference in the grade of FH (chi-square = 31.4, P < 0.0001) and VA (P = 0.0003) between oculocutaneous albinism (OCA) compared with ocular albinism (OA) and Hermansky-Pudlak syndrome (HPS). Ocular albinism and HPS demonstrated higher grades of FH and worse VA than OCA. There was a significant difference (P < 0.0001) in VA between FRMD7 variants compared with other diagnoses associated with FH. CONCLUSIONS: We characterized the phenotypic and genotypic spectrum of FH. Atypical FH is associated with a worse prognosis than all other forms of FH. In typical FH, our data suggest that arrested retinal development occurs earlier in SLC38A8, OA, HPS, and AHR variants and later in FRMD7 variants. The defined time period of foveal developmental arrest for OCA and PAX6 variants seems to demonstrate more variability. Our findings provide mechanistic insight into disorders associated with FH and have significant prognostic and diagnostic value.

Deep learning enabled inorganic material generator.

Recent years have witnessed utilization of modern machine learning approaches for predicting the properties of materials using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based on conditional variational autoencoders (CVAEs) and the predictor module consists of three deep neural networks trained for predicting the enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one-hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules.

Adaptive capability as indicated by behavioral and physiological responses, plasma HSP70 level, and PBMC HSP70 mRNA expression in Osmanabadi goats subjected to combined (heat and nutritional) stressors.

A study was conducted to assess the impact of heat and nutritional stress simultaneously on the adaptive capability as indicated by behavioral and physiological responses, plasma heat shock protein 70 (HSP70) level, and peripheral blood mononuclear cells (PBMC) HSP70 gene expression in goats. Twenty-four adult Osmanabadi bucks (average body weight (BW) 16.0 kg) were used in the present study. The bucks were divided into four groups viz., C (n = 6; control), HS (n = 6; heat stress), NS (n = 6; nutritional stress), and CS (n = 6; combined stress). The study was conducted for a period of 45 days. C and HS bucks had ad libitum access to their feed while NS and CS bucks were under restricted feed (30 % intake of C bucks) to induce nutritional stress. The HS and CS bucks were exposed to solar radiation for 6 h a day between 10:00 a.m. and 4:00 p.m. to induce heat stress. The data was analyzed using repeated measures analysis of variance. The standing time differed significantly (P < 0.01) between ad libitum fed groups (C and HS) and restricted feeding groups (NS and CS). The highest (P < 0.01) lying time was recorded in the CS group while the lowest in the C and HS groups. The highest (P < 0.01) drinking frequency was also recorded in the CS group. Water intake recorded was significantly (P < 0.01) higher in both the HS and CS groups. The highest respiration rate (RR), pulse rate (PR), and rectal temperature (RT) during the afternoon were also recorded in the CS group. Further, skin temperature of the head, flank, and scrotum during the afternoon was also higher (P < 0.01) in the CS group. In addition, both plasma HSP70 concentration and PBMC HSP70 messenger RNA (mRNA) transcript expression were also significantly (P < 0.01) higher in the CS group. It can be concluded from this study that when two stressors occur simultaneously, they may have severe impact on adaptive capabilities of Osmanabadi bucks as compared to that would occur individually. Further, the study indicated that lying time, drinking frequency, RR, RT, plasma HSP70, and PBMC HSP70 gene expression may act as ideal biological markers for assessing the impact of CS on adaptive capabilities in bucks.

Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines.

The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.

Physarum can compute shortest paths.

Physarum polycephalum is a slime mold that is apparently able to solve shortest path problems. A mathematical model has been proposed by Tero et al. (Journal of Theoretical Biology, 244, 2007, pp. 553-564) to describe the feedback mechanism used by the slime mold to adapt its tubular channels while foraging two food sources s(0) and s(1). We prove that, under this model, the mass of the mold will eventually converge to the shortest s(0)-s(1) path of the network that the mold lies on, independently of the structure of the network or of the initial mass distribution. This matches the experimental observations by Tero et al. and can be seen as an example of a "natural algorithm", that is, an algorithm developed by evolution over millions of years.