Electrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention , but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially. Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid and paired juxtacellular/extracellular recordings . We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curation of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

The uncertainty in time of particle detection within a scintillator detector, characterised by the coincidence time resolution (CTR), is explored with respect to the interaction position within the scintillator crystal itself. Electronic collimation between two scintillator detectors is utilised to determine the CTR with depth of interaction (DOI) for different materials, geometries and wrappings. Significantly, no relationship between the CTR and DOI is observed within experimental error. Confinement of the interaction position is seen to degrade the CTR in long scintillator crystals by 10%.

But facts are chiels that winna ding An downa be disputed – from _A Dream_ by Robert Burns (1786) (But facts are fellows that will not be overturned, And cannot be disputed) _Wave separation, wave intensity, the reservoir-wave concept, and the instantaneous wave-free ratio (2015) N Westerhof, P Segers and BE Westerhof, Hypertension, DOI: 10.1161/HYPERTENSIONAHA.115.05567_ Hereinafter referred to as [WSW]. This paper by three distinguished workers in the field of cardiovascular mechanics, concludes that both the reservoir pressure and instantaneous wave-free ratio are ’... both physically incorrect, and should be abandoned’. These are very strong conclusions which, if they were opinions could only be debated. Reading the paper in detail, however, reveals that it contains numerous factual errors in their discussion of these two entities. Since facts are different from opinions, we believe that it is essential that these errors be corrected before they gain credence by repetition. False facts are highly injurious to the progress of science, for they often endure long; but false views, if supported by some evidence, do little harm, for every one takes a salutary pleasure in proving their falseness. – Charles Darwin (1871) Because we are naturally prejudiced about the validity of both the reservoir pressure (Pres) and instantaneous wave-free ratio (iFR), having been involved in the conception and development of both ideas, we will try to present our arguments as transparently and fairly as possible. As far as possible we will demonstrate the errors by direct quotations from the paper. The whole paper¹ is available from the Hypertension web site and should be consulted directly if there are any questions about our treatment of the text. Approximately two thirds of the paper is taken up with a discussion of wave separation and wave intensity from the point of view of the more usual Fourier-based methods of analysing cardiovascular mechanics, frequently called the impedance method. This part of the paper is, as far as we can see, both insightful and free of major errors. We found some of the discussion about wave intensity analysis thought-provoking and agree with most of their conclusions. We recommend the first two-thirds of this paper to anyone interested in arterial mechanics. In contrast, the last third of the paper, starting with the final sentence of the section ’Summary of Wave Separation and WIA’ is riddled with errors of interpretation and, more importantly, contains a number of mistakes (or in Darwin’s terms ’false statements of fact’) that need to be corrected. Instead of dealing with these errors chronologically, we will point out the fundamental errors first and then deal with their sequelae.

This manuscript is aimed at discussing our thoughts on the use of fitted EOFs for climate studies. Fitted EOF analysis is an extension of traditional EOFs that attempts to extract EOFs and associated PCs encapsulating predictors and response relationship making use of multivariate regression. Fitted EOFs of ENSO and volcanic aerosols are identified by estimating the impact of these factors on grid of surface temperature anomalies in the Tropics. We mapped influence of ENSO and volcanoes on temperature and removed these impacts to provide adjusted reconstructed grid of surface temperature (1856 - 2011). Spatial gaps are filled with ordinary kriging as gappy data are bottleneck for EOF analysis. ENSO accounts for more variability in surface temperature than volcanoes. Adjusted annual average temperature time series indicates warming as does the unadjusted version. However, it plateaus prominently after 2000. (As it stands, I’ve just taken a LaTeXtemplate for the Journal Frontiers here, but wouldn’t anticipate that we would ever submit it there. I’ve also added in some section outlines. As a primary goal, the abstract should render the general significance and conceptual advance of the work clearly accessible to a broad readership. References should not be cited in the abstract. Refer to http://www.frontiersin.org/ or TABLE [TAB:01] for abstract requirement and length according to article type.) KEYWORDS: EOFs, ENSO, volcanic aerosols, warming, Tropics, surface temperature All article types: you may provide up to 8 keywords; at least 5 are mandatory.

A method is described for estimating the wave speed of a coronary artery from simultaneous measurements of pressure and velocity. It involves finding the wave speed that minimises the distance between the probability density functions of the measured pressure and velocity and the probability density functions of the pressure and velocity obtained from the separation of the waves into their forward and backward components using the assumed wave speed.

ABSTRACT A highly versatile EIT system, nicknamed the ScouseTom, has been developed. The system allows control over current amplitude, frequency, number of electrodes, injection protocol and data processing. A Keithley 6221 current source is used, along with a 24-bit EEG system for voltage recording. Custom PCBs interface with a PC to control the measurement process, electrode addressing and triggering external stimuli. The performance of the system has been characterised using resistor phantoms in experiments representative of human scalp recordings, with an overall SNR of 77.5 dB (n=343), stable across a four hour recording and across frequencies from 20 Hz to 20 kHz. The ScouseTom was used successfully in four modes of operation: time difference, triggered averaging, multi-frequency EIT and impedance spectrum measurements, in experiments investigating stroke and evoked potentials in both rat and human recordings. The experimental procedure is controlled by software and is readily adaptable to new paradigms. Where possible, commercial or open-source components have been used, to minimise the cost and complexity in reproduction. All of the hardware designs and software for the system have been released under an open source licence, encouraging contributions and allowing for rapid replication.

ABSTRACT Electrical Impedance Tomography (EIT) could be used as a rapid and non-invasive technique to image/diagnose ischaemic or haemorrhagic stroke. However, there are currently no suitable imaging/classification methods which which can be applied to human stroke data. In part this is due to the complexity of the problem itself, but it is also affected by a lack of available data on which to evaluate different techniques. Multi-frequency EIT data (alongside MRI/CT) has been collected on 23 stroke patients, and 10 healthy volunteers, as part of a clinical trial in collaboration with the Hyper Acute Stroke Unit (HASU) at University College London Hospital (UCLH). Data was collected at 17 frequencies between 5Hz and 2kHz, with 31 current injections, yielding 930 measurements at each frequency. The raw data, collected simultaneous on all channel using an EEG amplifier sampling at 16kHz, is also made available. BIT MORE MAYBE

ABSTRACT Three models of arterial pressure during diastole; a single exponential with zero asymptote, a single exponential with a non-zero asymptote, and a double exponential with a zero asymptote; are fitted to measurements of pressure in the ascending aorta. The results indicate that the single exponential with zero asymptote fits the data relatively poorly. Both the single exponent with a non-zero asymptote and the double exponent fit the measured pressure during diastole remarkably well. These two models, however, diverge significantly at longer times commensurate with extended diastole due to missing or ectopic beats. We conclude that the best choice of model can only be ascertained by looking at the arterial pressure during abnormal extended diastoles.

There is, to date, no derivation of reservoir pressure \(P_r\) from first principles, but this does not necessarily negate its usefulness. From its inception the reservoir-excess pressure concept has been recognised as an heuristic hypothesis whose validity must be judged by its utility.

INTRODUCTION Bubbles mediate turbulent fluxes at the air-sea interface. Aerosol fluxes have an intimate dependency on bubbles surfacing and, by bursting, releasing carriers into the atmospheric boundary layer. Whitecaps on the open ocean are the product of turbulence at the sea surface, because of wave-breaking events. As we will see later in the paper, the injected turbulence that produces whitecaps is distributed vertically as well. Whitecaps have the visual aspect of foam patches. For a complete definition, we should relate whitecaps with bubbles by considering whitecaps as clusters of surface bubbles. The bubble size distributions within whitecaps may differ, of course, but a larger size distribution of bubbles is prevalent at the sea surface. Knowledge of whitecapping variability can improve the calculation of aerosol fluxes between ocean and atmosphere , therefore one should consider whitecaps as a primary source for aerosol production. In order to have a proper correlation between aerosol production and whitecaps, one should introduce the concept of whitecap fraction or coverage. The whitecap size area measures the magnitude of turbulence injected by the atmosphere into the ocean. Recent whitecap coverage studies show the roles that wave development, wave-wave interaction, wave-current interaction and wind history (the cause of swells) play in influencing the variations of whitecap coverage in addition to wind speed , , . In this sense, whitecaps strongly relate to energy dissipation of waves, the least known process of wave evolution . There is a direct source for surface foam generation. We are referring at a phenomenon which hasn’t been observed on the open ocean accurate enough. It is described by bubble subsurface dynamics in relation with whitecap dynamics. These bubbles organize in bubble plumes, which feed foam at the sea surface. Bubble plumes variability is crucial in assesing other indirect sources for whitecap generation. In this category are included wind speed at sea surface, wave frequency spectrum and wave amplitude, which can all describe the rate of wave energy dissipation. In this research, we correlate the rate of wave energy dissipation, by considering the parameters just mentioned, with bubble plumes dynamics. Wave energy dissipation can be depicted as a reaction of wave-breaking. It can be quantified at the sea surface, analyzing the size area of whitecap fractions. Wave energy dissipation can be rated at the subsurface level by observing how deep bubbles are injected. Therefore, wave energy dissipation has a tight relation with the range of bubble plumes depth. This study should be an intrusive insight on having a complete picture of the causes that influence the variability of whitecaps, which has a direct effect on aerosol production. In the next sections, some details on why bubbles are important in gas fluxes and aerosol production, existing parametrizations, the work done to realize the observations and data collecting in a high winds regime, and further research ideas are presented. By going right to the direct source and analyzing the dynamics of bubble plumes will help us understand with deep accuracy the mechanisms that drive whitecap variability. It has already been proven that, considering whitecap fraction as function of wind speed only, is not enough for our current understanding. That is why the details in the physics of wave energy dissipation will give us a much clearer view on the sensitiviy in the variations of whitecap fractions. THE IMPORTANTCE OF BUBBLES IN GAS FLUXES AND AEROSOL PRODUCTION Bubbles encapsulate water vapors and other gases. We are interested in analyzing bubbles, because they are a direct cause for transfer velocity of different gases. Aerosols are produced as sea spray particles by breaking the surface tension of individual bubbles. The bubble film layers are fragmentated into small droplets (film droplets). Bubbles usually cary salt and organic particles so they are called “dirty” bubbles. The more “loaded” bubbles are, the more effective they are in generating cloud condensation nucleis. The difficulty is that loaded bubbles should, theoretically, have a higher dragging effect, which comes with a decrease in vertical velocity (upward motion) of the bubbles. Coarser salt or organic particles carried by bubbles have a higher inertia, so the “popping” momentum in launching aerosols into the atmospheric boundary layer is inhibited. Molecular diffusion for subsurface bubbles is an important feature for gas transfer, because it actually reduces the transfer velocity of gases. If the molecular diffusion through bubble walls is higher, then the number of available bubbles at the surface, which can organize in foam patches, is smaller. If the foam patches contain gases with low solubility, they become void fractions. There is a relation between void fractions and interstitial water, which involves the transfer velocity. Interstitial water is the area, at sea surface, unaffected by the disruption of surface tension. Its characteristic si that it surrounds the bubbles, and it has an influence on the total gas transfer rate. For highly insoluble gases, the capacity of the interstitial water may be smaller than that of the bubbles even for small void fractions . This is called restriction of gas transfer in a dense isolated plume “suffocation”. High subsurface bubbles molecular diffusion inhibits the buoyancy of bubbles and, hence, the vertical upward velocity of bubbles. In this sense, the Wolf model (2006) shows that there is a higher gas transfer for low Schmidt number, Sc, therefore subsurface lower molecular diffusion, D, generates more gas available at the surface, hence a higher transfer rate. Schmidt number describes the capacity of molecular difussion through the bubble’s wall. According to Wolf (2006), the Schmidt number is equivalent to molecular difussion D, and it can be found in the following mathematical relation: {K}_{b} = {Sc}^{-x}{\alpha}^{-y}, where Kb is a contribution to the transfer velocity between the main air or gas and water reservoirs and it only depends on the partial equilibrium of a bubble during its lifetime, and x and y are positive constants. Low Schmidt number means higher upward vertical velocity for subsurface bubbles (because of low molecular difussion), therefore more bubbles are available for surfacing. Wolf model assumes two cases for the transfer velocity: one without void fraction and the other with 20 % void fraction. It can be noticed that the transfer velocity is reduced at low Schmidt number with 20 % void fraction present. Correlation between observed gases with different solubilities and bubble size distribution would give an insight on what type of fluxes, from the chemical point of view, are more likely to be enhanced. The presence of void fractions assumes that gas is trapped at the surface, therefore the transfer velocity is suppressed. Considering this phenomenon, one can correlate void fraction with whitecap time decay and, therefore, with transfer velocity in low winds, because in high winds condition, this phenomenon might not be so relevant. The reason is because, during high winds regime, high wind stress fragments the bubbles, so gas transfer is enhanced. PARAMETERIZATIONS AND CORRELATIONS Our interest is to go right to the source of whitecap variability and, therefore, of aerosol fluxes. Other fluxes, such as momentum fluxes especially, have no or little dependency on whitecap variability. One should dynamically couple whitecaps and bubble plumes characteristics in order to explicitly quantify the potential of aerosol production. A range of several parameterizations for whitecap coverage have been performed. Whitecap fraction as a function of wind speed only is the most common, and it takes the form of a power-law relation between W and U₁₀, W = a{U}^b_{10}, with coefficients a and b derived from a set of observations. Because it was clear that W cannot vary with wind speed in a linear way, some autors tried to link W with the cubed wind speed, W = a({U}_{10} + b)^3. In the literature of Monahan and Lu, in 1990, there are two stages in the evolution of whitecaps. In stage A the foam is generated by actively breaking waves, where bubbles are entrained and fragmented inside the breaking wave crest, and is also refered as the acoustically active phase . In stage B whitecaps are subjects to the decaying foam patches, where bubble creation processes cease, therefore the newly formed bubble plume becomes acoustically quiescent and evolves under the influence of turbulent diffusion . During the quiescent phase, one can quantify the decay time of whitecap foam by using a simple exponential model implemented by Callaghan et al. in 2012: A(t) = {A}_{0}\exp\left({\tau} \right), where A(t) is time evolving area of whitecap foam during the decay phase, t is time where t = 0 occurs at the time when the foam patch area is at its maximum value, A₀, and τ is a constant called whitecap foam decay time. According to Philips, in 1985, to obtain the whitecap fraction, one can refer to an average bubble persistence time τbub to obtain W: W = ^{\infty}\tau _{bub}c\Lambda(c)dc, where τbub is the bubble persistence time, Λ(c) is the Philips parameter and c is the forward speed of the beaking wave crest. The Philips parameter is a characterization of breaking wave dynamics in terms of breaking wave speed, and is defined such that Λ(c)dc is the meters of breaking crest per square meter of ocean surface . Another proposal of whitecap parameterization comes from Zhao and Toba, in 2001, namely the non-dimensional Reynolds number RH, which is optimized for wind waves with velocity and length scales represented by u* and wave height Hs, respectively, {R}_{H} = _{*}{H}_{s}}{{\nu}_{w}}, where νw is the kinematic viscosity of water, because the Reynolds number is applied on the ocean. The Reynolds number relates the inertial forces of the fluid to fluid viscosity. If the fluid viscosity is low, the Reynolds number is high, therefore the fluid is more susceptible to turbulence. This shows how much wind stress is needed, for the given kinematic viscosity of water, to generate waves with a critical amplitude at which they can break and produce whitecaps. In this sense, Wolf, in 2005, suggested that whitecap coverage, W, can be related with wind speed and wave height, so that can lead to a direct correlation of whitecape coverage with Reynolds number: W \propto {R}_{H}. Considering the dependency of whitecap fractions on sea state as well, not only on wind speed, is essential in order assess their variability. Swells are are particular sea states that inhibit whitecap production. Because swells move faster than the environmental wind speed, they don’t loose energy through wave energy dissipation (decisive in whitecap production), resulting in attenuated wave-breaking occurence. Swells generate a low level wave-driven supergeostrophic jet that acts to disorganize eddies within turbulent flow at sea surface. This translates into removing friction caused by turbulent stress at sea surface, phenomenon responsible for foam cell rupture within whitecaps , which decreases foam time decay. The amplitude of the swell, according to observations realized by Callaghan in 2012, can be calculated by integrating the wave spectrum between frequencies f₁ and f₂: a = 2_{1}}^{{f}_{2}}S(f)df}, where S(f) is the wave spectral density. Geostrophy relates to a horizontal wind flow, aligned with the isobars (in this case, along the vertical), having no contribution on the vertical component of the wind. Usually, a purely geostrophic horizontal wind at sea surface cannot inject enough turbulence to generate wave breaking. Therefore, a downward ageostrophic component or a shift from the purley geostrophy in the wind at sea surface should be present. The wave-driven supergeostrophic jet subdues the downward ageostrophic component (and enhances the upward ageostrophic component or the upward momentum flux), thus it reduces turbulent injection from atmosphere to ocean. The surface value of this wave-driven jet or stress can be described by integrating the wave-induced stress going into each wave component: \left(0 \right) = ^{\infty}\omega\beta\phi(\omega)d\omega, where ω is the wave angular frequency, ϕ(ω) is the frequency spectrum, and β is the dimensionless wave growth parameter. OBSERVATIONAL MECHANISMS We observed the sea state and fluxes in a high winds regime, during the HIWINGS experiment project. Our region of interest was the North Atlantic ocean, south of Greenland, in the path of strong and frequent cyclones. There was an acute positive NAO (North Atlantic Oscillation) index during that period, namely from the beginning of October to middle November, 2013. Because of this phenomenon, a powerfull jet stream and intense horizontal pressure gradients were present, hence the repetitive cyclones. We succeeded to perform the measurements during winds of over 20 m/s, which makes the data and the future results more valuable, because it is something that probably hasn’t been realized in the past. A notable variety of instruments that measured the sea state and fluxes were used during this field experiment. The scientific crew was partitioned in different teams having specific goals regarding their interest for the data, but a unique goal when it comes to understand the big picture of the air-sea coupled system dynamics. Our specific scientifc goal is to understand the mechanisms of primary source for aerosol production, and hence aerosol turbulent fluxes. We already made the consideration that aerosol turbulent fluxes occur via bubbles, whitecaps, and bubble plumes dynamics. Our main focus is on bubble plume and whitecap physics, but on individual bubble physics as well. Insights about the relation between whitecap coverage variability and bubble plume dynamics will be presented in detail in the next section. Regarding the physics of individual bubbles, our interest is, by using the two acoustical resonators, to obtain the bubble size distribution as a function of depth at a wide range of wind speeds . We will be able to compare the bubble size distribution obtained by using this method with an optical bubble size distribution using the subsurface bubble camera. In order to have a deep understanding of this natural process in a high winds regime, we need to harmonize and correlate the measurements performed using this rich variety of instruments. We used a foam camera to detect whitecap fractions, a submerged bubble camera to explore the optical properties of bubbles within bubble plumes, two resonators to assess the acoustical properties of bubbles within bubble plumes, and a sonar to detect the bubble plumes dynamics. All instruments were set in place along a 11 meters spar buoy. Just under the foam camera, we had wires that could measure the sea state. More detailed informations on the sea state can be generated by the Wave Rider, which was separated from the spar buoy. Other instruments took measurements from the ship’s board, including measurements of aerosol and gas fluxes. RESEARCH APPROACHES AND FURTHER STUDY In this section, our scientific objectives are presented. We remind, as well, the specific instruments which generated the data that we will process and analyze. The first basic objective is to detect the temporal behaviour of bubble plumes. This implies analyzing change of bubble plumes penetration depth in time, as well as bubble plumes duration. In this way, a correlation of bubble plumes duration with whitecap fractions variability and with whitecap time decay, as well, would be an interesting completion at our overall understanding. Thus, we will make a time evolution of bubble plumes per each wave-breaking event, and produce a mean over all observed wave-breaking events. The sonar data will be used to perform these calculations. Next, we will assess how variability in bubble plumes depth affects the whitecap time decay. For instance, we will be able to check if a fast decrease in bubble plumes depth influences the longevity of whitecap fractions. To support this correlation between whitecap fractions time decay and bubble plumes dynamics we will use data collected by the sonar and the foam camera. There was already mentioned that the primary source for whitecap production and variabily is the existence of bubble plumes, therefore our intention is to make direct correlations between bubble plumes and other factors like wind speed and sea state. If we can verify and understand these direct correlations, than we will check the validity of a dynamical relation between bubble plumes and whitecap production. Considering this fact, we want to see how the depth of bubble plumes change with sea state. Thus, we will correlate these changes in bubble plumes depth with different wave states, like wave frequency spectrum and wave amplitude. We want to analyze this bubble plumes dynamics during wind driven waves, which are slower than the environmental wind speed, have higher frequencies and lower amplitudes. For comparison, we will make the analysis during swells as well, which are affected by wind history, are faster than the environmental wind speed, have lower frequencies and higher amplitudes. As a result, we will be able to determine how swells, because of low magnitude wave energy dissipation, shape whitecap coverage variability. Conditions that maximize the effect of swells are weak to moderate wind at sea surface, waves (swells) usually coming from the south , and low frequency waves (around 0.1 Hz). A good way to estimate the effect of swells would be to observe them after the storm passes, during post-storm lower winds. We can perform this by using the wind speed ship data and Wave Rider data. As discussed above, wave energy dissipation is a determinant factor for whitecap production. Going to the direct relation between whitecap coverage and bubble plumes, wave energy dissipation can be considered as a function of bubble plume depth. Significant bubble plume depth translates into large magnitude wave energy dissipation. We must include the whole environmental set when it comes to wave energy dissipation. Therefore, we must consider wind speed, wave frequency and wave height. On the open ocean, winds faster than waves extract energy from the waves by reducing their height, enlarging their slope (having higher frequency), and, thus, leading to wave energy dissipation. The presence of this phenomena sustaines a strong correlation between bubble plumes depth and wave state. Accounting for the fact that wave energy dissipation is proportional to wind speed and bubble plume depth, and inversely proportional to wave amplitude and wave speed (wave phase velocity), we can infer that whitecaps have the same properties. We can show this complex relation of wave energy dissipation with the other parameters through the following equation: $$\epsilon = _{\ast}{d}_{bp}}{{a}_{w} c},$$ where u* is wind speed (wind stress), dbp is bubble plume depth, aw is wave amplitude, and c is wave phase speed. This mathematical relation shows that high wind speed and a deep bubble plume relative to reduced wave amplitude (high wave frequency) and low wave phase speed results in a large wave energy dissipation magnitude. A specific method of estimating the bubble plume depth can be realized by calculating the total air fraction on the vertical. In order to perform this, all bubble sizes, for a given volume, must be integrated. Another interesting and, we think, relevant approach in quantifying whitecap coverage variability is by looking at bubble plumes advections. We want to see if we can grasp differences in the general pattern of bubble plumes advections due to a certain turbulent phenomenon called Stokes drift velocity. This Stokes flow is a turbulent factor within Langmuir circulation and it stands out in the form of horizontal vorticity advection, which is differential, meaning that it changes with depth (in this case, it has a lower magnitude with depth). The Langmuir circulation is the manifestation of relatively shallow turbulence in the upper ocean, and it consists in counter-rotating vortices within a shallow water layer. If the differential vorticity advection due to Stokes flow adds up to general flow pattern of bubble plumes advection, then there is a net shift in the position of bubble plumes relative to the position of whitecaps. Our intention is to observe a sensitivity in whitecap fractions variability due to this shift. This will allow us to find, through the anticorrelation between whitecaps and bubble plumes due to Stokes drift, a direct source of whitecap coverage variability.

---------- -------- -------- ------- ------- ------- ------- -------------- ------- Features Spikes Seed Perp. Iter. T.B. L.E. L.E. / Iter. Total 384 128820 128820 1000 2000 0.13 8.37 0.004 10.71 384 128820 128820 700 2000 0.12 6.94 0.003 8.14 384 128820 128820 400 2000 0.12 4.91 0.002 5.49 384 128820 128820 100 2000 0.12 3.75 0.002 4.04 384 96600 96600 100 2000 0.06 2.90 0.001 3.08 384 64400 64400 100 2000 0.03 1.45 0.0007 1.54 384 128820 50000 100 2000 0.01 0.96 0.0004 1.13 384 32200 32200 100 2000 0.006 0.53 0.0003 0.56 256 466000 466000 100 2000 24.62 20.29 0.0101 45.48 768 466000 466000 100 2000 66.77 19.15 0.0091 86.45 ---------- -------- -------- ------- ------- ------- ------- -------------- ------- : Time (in hours) that different parts of the t-sne algorithm require to run for different combinations of features, spikes, perplexity and number of iterations. The machine was a gaming desktop with an i7 CPU, 76GB RAM and a Titan X GPU with 12GB of memory. Per.: Perplexity, T.B. : Tree Building, L.E. : Learn Embedding

I have been working in Neuroscience for seven years already, but my PhD, in a slightly irrelevant way, was in molecular biophysics / protein mechanics. It was about figuring out the folding dynamics of protein molecules so small nobody knew how to measure appropriately yet. Soon after I started in it, I realized I had landed myself in an interesting situation. Although most biophysicists / physical chemists work in a molecular biology / biochemistry lab I ended up working in the Molecular and Nanoscale Physics group of the Department of Physics and Astronomy of my university. My project demanded a strong collaboration with the biochemists and the molecular biologists across the road from us (someone had to to mutate and purify the proteins I was all too happy to blast with very strong lasers beams) but I also spent large chunks of my time interacting with the engineers and physicists in my group. It was maybe three years into my studies when I started piecing together the reasons I often felt like suffering a bit from bipolar or split personality syndromes. I especially remember the monthly lab meetings were the physicists would visit the molecular biologists’ offices (or vice versa) and would all sit down to discuss the progress of a common project (mine being one of those). We all seemed to share the same overarching goal, i.e. to understand how proteins fold and how structure leads to function. Everybody agreed that this was how we would be able to prevent and correct misfoldings in the cell and create artificial proteins with novel functionalities. Yet, the question of how to go on about that goal was what appeared to divide us at a level that went all the way down to our most basic and assumed notions of how to do science, or even what science is in the first place. Over the course of my degree it dawn on me that there was this oft used word which appeared to posses two very different meanings according to which group you asked. The magic word, and in my view, the cause of many a misunderstanding was ’model’. To the molecular biologist, to model a protein was to collect all the data there was to collect for it (structural, dynamic, thermodynamic and what have you), maybe draw some qualitative and descriptive (i.e language based) assumptions about how that protein did what it did and then collate it all together in a nice publication. For the physicist, modeling a protein meant to collect the least amount of data from it possible and use these to create, expand, tweak, or disprove one of the caricaturish but quantitative descriptions of how all proteins under all conditions folded and functioned. And there you had it; model as an exhaustive collection of true information, collated in an as easy to use look up table (written in English or another language) or model as a summarizing group of principles (written in maths) that are general but always a bit wrong. Or to put it differently, models driven by data, or data driven by models.

Renal phenotypic variability in Townes-Brocks syndrome. Case report of family with biopsy proven focal segmental glomerulosclerosis.