Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors

Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.

Modelling the spatial and temporal constrains of the GABAergic influence on neuronal excitability

GABA (γ-amino butyric acid) is an inhibitory neurotransmitter in the adult brain that can mediate depolarizing responses during development or after neuropathological insults. Under which conditions GABAergic membrane depolarizations are sufficient to impose excitatory effects is hard to predict, as shunting inhibition and GABAergic effects on spatiotemporal filtering of excitatory inputs must be considered. To evaluate at which reversal potential a net excitatory effect was imposed by GABA (EGABAThr), we performed a detailed in-silico study using simple neuronal topologies and distinct spatiotemporal relations between GABAergic and glutamatergic inputs. These simulations revealed for GABAergic synapses located at the soma an EGABAThr close to action potential threshold (EAPThr), while with increasing dendritic distance EGABAThr shifted to positive values. The impact of GABA on AMPA-mediated inputs revealed a complex temporal and spatial dependency. EGABAThr depends on the temporal relation between GABA and AMPA inputs, with a striking negative shift in EGABAThr for AMPA inputs appearing after the GABA input. The spatial dependency between GABA and AMPA inputs revealed a complex profile, with EGABAThr being shifted to values negative to EAPThr for AMPA synapses located proximally to the GABA input, while for distally located AMPA synapses the dendritic distance had only a minor effect on EGABAThr. For tonic GABAergic conductances EGABAThr was negative to EAPThr over a wide range of gGABAtonic values. In summary, these results demonstrate that for several physiologically relevant situations EGABAThr is negative to EAPThr, suggesting that depolarizing GABAergic responses can mediate excitatory effects even if EGABA did not reach EAPThr.

On Nonparametric Conditional Quantile Estimation for Non-stationary Random

A kernel conditional quantile estimate of a real-valued non-stationary spatial process is proposed for a prediction goal at a non-observed location of the underlying process. The originality is based on the ability to take into account some local spatial dependency. Large sample properties based on almost complete and \(L^q\)-consistencies of the estimator are established. A numerical study is given in order to illustrate the performance of our methodology.

Applying spatial mutual information to AIS data

This paper examines a new interpretation for spatial mutual information based on the mutual information between an attribute value and a spatial random variable. This new interpretation permits the measurement of variations in spatial mutual information over the domain, not only answering the question of whether a spatial dependency exists and the strength of that dependency, but also allowing the identification of where such dependencies exist. Using simulated and real vessel reporting data, the properties of this new interpretation of spatial mutual information are explored. The utility of the technique in detecting spatial boundaries between regions of data having different statistical properties is examined. The technique is shown to successfully identify vessel traffic boundaries, crossing points between traffic lanes, and transitions between regions having differing vessel movement patterns.

Analyzing Spatial Dependency of the 2016–2017 Korean HPAI Outbreak to Determine the Effective Culling Radius

Highly pathogenic avian influenza (HPAI) outbreaks are a threat to human health and cause extremely large financial losses to the poultry industry due to containment measures. Determining the most effective control measures, especially the culling radius, to minimize economic impacts yet contain the spread of HPAI is of great importance. This study examines the factors influencing the probability of a farm being infected with HPAI during the 2016–2017 HPAI outbreak in Korea. Using a spatial random effects logistic model, only a few factors commonly associated with a higher risk of HPAI infection were significant. Interestingly, most density-related factors, poultry and farm, were not significantly associated with a higher risk of HPAI infection. The effective culling radius was determined to be two ranges: 0.5–2.2 km and 2.7–3.0 km. This suggests that the spatial heterogeneity, due to local characteristics and/or the characteristics of the HPAI virus(es) involved, should be considered to determine the most effective culling radius in each region. These findings will help strengthen biosecurity control measures at the farm level and enable authorities to quickly respond to HPAI outbreaks with effective countermeasures to suppress the spread of HPAI.

THE RESPONSE OF WHEAT PRODUCTIVITY TO CLIMATE PATTERNS: EVIDENCE FROM PAKISTANI DISTRICTS

The study examines the impact of climate variables on wheat production in 10 major wheat-producing districts of Pakistan. In doing so, we apply the Driscoll–Kraay approach to estimate the panel data from 1981 to 2019. Our empirical analysis reveals that climate variables, including temperature, rainfall and humidity, follow a common correlation across districts. We find that wheat productivity and temperature, as well as rainfall, follow an inverted U-shaped relation. The response of the wheat productivity is quadratic rather than linear towards average temperature and rainfall during the specific time of cultivation, including planting, flowering and harvesting. Besides, fertilizer use promotes and humidity impedes wheat productivity. Our findings are robust considering heterogeneity, serial correlation and spatial dependency.