"How is your thesis going?"-Ph.D. students' perspectives on mental health and stress in academia.

Mental health issues among Ph.D. students are prevalent and on the rise, with multiple studies showing that Ph.D. students are more likely to experience symptoms of mental health-related issues than the general population. However, the data is still sparse. This study aims to investigate the mental health of 589 Ph.D. students at a public university in Germany using a mixed quantitative and qualitative approach. We administered a web-based self-report questionnaire to gather data on the mental health status, investigated mental illnesses such as depression and anxiety, and potential areas for improvement of the mental health and well-being of Ph.D. students. Our results revealed that one-third of the participants were above the cut-off for depression and that factors such as perceived stress and self-doubt were prominent predictors of the mental health status of Ph.D. students. Additionally, we found job insecurity and low job satisfaction to be predictors of stress and anxiety. Many participants in our study reported working more than full-time while being employed part-time. Importantly, deficient supervision was found to have a negative effect on Ph.D. students' mental health. The study's results are in line with those of earlier investigations of mental health in academia, which likewise reveal significant levels of depression and anxiety among Ph.D. students. Overall, the findings provide a greater knowledge of the underlying reasons and potential interventions required for advancing the mental health problems experienced by Ph.D. students. The results of this research can guide the development of effective strategies to support the mental health of Ph.D. students.

Distinct synaptic transfer functions in same-type photoreceptors.

Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous 'dual-colour' two-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates, and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single-neuron types there exist highly specialised mechanisms which are advantageous for the encoding of different visual features.

Ancestral circuits for vertebrate color vision emerge at the first retinal synapse.

For color vision, retinal circuits separate information about intensity and wavelength. In vertebrates that use the full complement of four "ancestral" cone types, the nature and implementation of this computation remain poorly understood. Here, we establish the complete circuit architecture of outer retinal circuits underlying color processing in larval zebrafish. We find that the synaptic outputs of red and green cones efficiently rotate the encoding of natural daylight in a principal components analysis­like manner to yield primary achromatic and spectrally opponent axes, respectively. Blue cones are tuned to capture most remaining variance when opposed to green cones, while UV cone present a UV achromatic axis for prey capture. We note that fruitflies use essentially the same strategy. Therefore, rotating color space into primary achromatic and chromatic axes at the eye's first synapse may thus be a fundamental principle of color vision when using more than two spectrally well-separated photoreceptor types.

Fovea-like Photoreceptor Specializations Underlie Single UV Cone Driven Prey-Capture Behavior in Zebrafish.

In the eye, the function of same-type photoreceptors must be regionally adjusted to process a highly asymmetrical natural visual world. Here, we show that UV cones in the larval zebrafish area temporalis are specifically tuned for UV-bright prey capture in their upper frontal visual field, which may use the signal from a single cone at a time. For this, UV-photon detection probability is regionally boosted more than 10-fold. Next, in vivo two-photon imaging, transcriptomics, and computational modeling reveal that these cones use an elevated baseline of synaptic calcium to facilitate the encoding of bright objects, which in turn results from expressional tuning of phototransduction genes. Moreover, the light-driven synaptic calcium signal is regionally slowed by interactions with horizontal cells and later accentuated at the level of glutamate release driving retinal networks. These regional differences tally with variations between peripheral and foveal cones in primates and hint at a common mechanistic origin.

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.

While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics.