Treatment for osteonecrosis of the femoral head (ONFH) in young individuals remains controversial. We developed a lantern-shaped screw, which was designed to provide mechanical support for the femoral head to prevent its collapse, for the treatment of ONFH. The purpose of this study was to investigate the efficacy and safety of the lantern-shaped screw loaded with autologous bone for the treatment of pre-collapse stages of ONFH.Thirty-two patients were randomly divided into two groups: the lantern-shaped screw group (core decompression and lantern-shaped screw loaded with autogenous bone) and the control group (core decompression and autogenous bone graft). During 36 months follow-up after surgery, treatment results in patients were assessed by X-ray and computed tomography (CT) scanning as well as functional recovery Harris hip score (HHS).p = 0.0325). Successful radiological results were achieved in 14 of 16 hips (88%) in the lantern-shaped screw group compared with 8 of 16 hips (50%) in the control group (P = 0.0221).Successful clinical results were achieved in 15 of 16 hips (94%) in the lantern-shaped screw group compared with 10 of 16 hips (63%) in the control group .The trial registration number:  Hip-preserving surgery has been variable in treating osteonecrosis of the femoral head (ONFH), but there is a lack of consensus on the effectiveness of joint preserving procedures for ONFH. It is undesirable that hip replacement has been undertaken for the young individuals in whom hip prostheses have a limited survival. It is better to preserve the femoral head than replace it . The exaIn the actual practice, an ideal implant should be guaranteed to contact with the bone around the tunnel of the core decompression, as well as buttressing the subchondral bone of the femoral head. Collapse of femoral head will be less likely to occur when the implant contacted with the subchondral bone maximally . Therefon = 10) were done on the Dynacell, which is the truly dynamic load cell, designed from the outset for measuring dynamic loads . The results showed that the deformation behavior of the unfolded titanium mesh would not be varied until the strength of vertical compression was increased to 256.7 N (range 248–272 N). By acting the force of 500 N on the titanium mesh, the deformation was 1.9 mm (range 1.5–2.4 mm). These data demonstrated that the lantern-shaped screws have the sufficient mechanical strength to allow for the weight-bearing activities.In collaboration with Weigao Orthopaedic Device Co., Ltd.  and Double Engine Medical Material Co., Ltd. , we have designed a lantern-shaped screw, for which we have obtained a patent (patent number: CN103445851B). This screw is made of titanium alloy and cannulated, which is able to be full of the autogenous bone. The titanium mesh installed outside the lantern-shaped screw can be unfolded into the lantern shape to support the subchondral bone as much as possible. Furthermore, the tail cap of angle plate is a locking screw and can block the lantern-shaped screw back off  scans of the hips were experienced at the same follow-up times. The primary results of this study were clinical and radiological failure. Clinical failure was defined as HHS < 75 points or a requirement for subsequent hip surgery. New occurrence of collapse or increased collapse of greater than 2 mm on X-ray during follow-up was defined as radiological failure , 33, 34.t-test or Mann-Whitney U test was used to compare metric data. All statistical assessments were two-sided and evaluated at the 0.05 level of statistical significance.SPSS 19.0  statistical software was used for statistical analysis. Baseline characteristics were assessed using descriptive statistics. The chi-square test was taken to compare nominal data. The p < 0.0001) and control group (P < 0.0001). HHS was significantly improved in the lantern-shaped screw group when compared to the control group (P = 0.0173). The proportion of successful clinical results was significantly higher in the lantern-shaped screw group compared with the control group. Successful clinical results were achieved in 15 of 16 hips (94%) in the lantern-shaped screw group . Successful radiological results were achieved in 14 of 16 hips (88%) in the lantern-shaped screw group compared with 8 of 16 hips (50%) in the control group (Table P = 0.0628; Fig. The control group required less operation time and blood loss, but was not statistically different from the lantern-shaped screw group Table . At 36 m16 hips 9% in the Our primary study utilizing our designed lantern-shaped screw for the treatment of pre-collapse stages of ONFH has generated promising effectiveness, with salvage of femoral head and improvement of the hip joint function at 36 months follow-up. A certain advantage of this screw is the achievement of surface at surface support between the weight-bearing area and upper surface of the lantern-shaped screw. Meanwhile, autogenous bone grafting promotes the bone regeneration and reconstruction.Preservation of the collapse of the femoral head is the great predominant principle to treat patients with pre-collapse stages of ONFH despite of the unestablished pathogenesis. The rareness of bone repair microcirculation will cause osteonecrosis, primarily occurring in weight-bearing region of the femoral head . HoweverFor many decades, surgical interventions for the hip-preserving have been controversial. Besides those techniques mentioned above, Papanagiotou et al.  reportedThe goal of this procedure was to provide mechanical support of the articular surface while promoting bone healing and remodeling, and delay or avoid total hip replacement. The lantern-shaped screw is made of titanium with quite great intensity. Actually, the device is designed predominantly based on one of the characteristics of titanium, that is, it has great plasticity with a ductility property of up to 50–60%. The intensity allows the device to give sufficient support assistance and the plasticity is responsible for the transformation from the shape of cylinder into lantern. Furthermore, the tail cap of angle plate is locking screw and can block the lantern-shaped screw back off. The whole surgical procedure via the support device involved core decompression by decreasing intraosseous pressure to prevent the ischaemia, reconstruction of necrotic bone beneath the weight-bearing region, mechanical support of the articular surface via the surface at surface contact achieved by the lantern-shaped screw. Therefore, the lantern-shaped screw can be used in pre-collapse stages of ONFH.This study has several limitations. Once the bone remodeling has occurred, the lantern-shaped screw has certain drawbacks in that it cannot be removed. Additionally, a small number of patients and short period of follow-up are the main limitations of this study. More studies with larger sample size and longer follow-up are required to accept the role of the lantern-shaped screw in ONFH.In summary, the biomechanical property and the bone remodeling regarding the lantern-shaped screw was verified, and the features of this device were also assessed. It is shown that the lantern-shaped screw loaded with autogenous bone may delay or avoid the progression of the collapse of the cartilage for the pre-collapse stages. It can not only provide appropriate circumstance to facilitate new bone remodeling but also strengthen the biomechanical structure for the weight-bearing region. We believe that the lantern-shaped screw could be used not only for autogenous bone graft but also for injection drugs, growth factor, mesenchymal stem cells and so on to be effective in early stages of ONFH. Therefore, we predict that the lantern-shaped screw could provide mechanical support and have considerable potential for medical application.


Fast-spiking parvalbumin-expressing interneurons (PVIs) and granule cells (GCs) of the dentate gyrus receive layer-specific dendritic inhibition. Its impact on PVI and GC excitability is, however, unknown. By applying whole-cell recordings, GABA uncaging and single-cell-modeling, we show that proximal dendritic inhibition in PVIs is less efficient in lowering perforant path-mediated subthreshold depolarization than distal inhibition but both are highly efficient in silencing PVIs. These inhibitory effects can be explained by proximal shunting and distal strong hyperpolarizing inhibition. In contrast, GC proximal but not distal inhibition is the primary regulator of their excitability and recruitment. In GCs inhibition is hyperpolarizing along the entire somato-dendritic axis with similar strength. Thus, dendritic inhibition differentially controls input-output transformations in PVIs and GCs. Dendritic inhibition in PVIs is suited to balance PVI discharges in dependence on global network activity thereby providing strong and tuned perisomatic inhibition that contributes to the sparse representation of information in GC assemblies. Fast-spiking parvalbumin-expressing interneurons (PVIs) and granule cells of the dentate gyrus receive layer-specific dendritic inhibition. The authors show that distal and proximal dendritic inhibition differentially control input-output transformations in PVIs and granule cells. Input−output transformations are controlled by dendritic inhibition provided by GABAergic inhibitory interneurons10. How dendritic inhibition influences integration of excitatory signals has been mainly studied in cortical and hippocampal pyramidal cells19; however, much less is known on this process in GABAergic interneurons and glutamatergic GCs of the dentate gyrus. This is an important question because these neurons display distinct morphological and physiological properties24 compared to neocortical and hippocampal pyramidal cells and their in vivo activity stays under tight inhibitory control26.Cognitive processes emerge from input−output transformations in cortical networks, which depend on the microcircuit connectivity and the dendritic tree as the biophysical substrate for synaptic input integration27. Neuronal activity in this region is sparse, with only few GCs being active when rodents explore the environment33, and is under the control of various GABAergic interneuron types30. PVIs provide perisomatic inhibition onto large GC populations35, while dendritic inhibition originates mainly from hilar commissural path-associated interneurons (HICAPs), with axon collaterals in the inner molecular layer, and somatostatin (SOM)-expressing hilar perforant path-associated interneurons (HIPPs), with axons localized in the outer half of the molecular layer39. The size of GC assemblies appears to be under control of dendrite-inhibiting SOM-positive interneurons40 (SOMIs), which form numerous synapses at distal GC dendrites41. PVIs are strongly recruited by layer-specific excitatory inputs and receive HIPP-mediated distal feedback and HICAP-mediated proximal feedforward and feedback dendritic inhibition38. Due to their steep current−frequency relationship, even small changes in excitatory input strength will influence PVI recruitment and in turn their impact on information processing in the dentate gyrus43. Thus, dendritic inhibition may serve as a regulator of excitability, synaptic plasticity and sparse activity in the dentate gyrus45.PVIs and GCs of the dentate gyrus receive information from the entorhinal cortex via the perforant path and from the contralateral hippocampus via the commissural path2+ signals in hippocampal and cortical principal cell dendrites evoked by back propagating action potentials46 and dendritic spike generation in vitro15 or sensory stimulation in vivo47, and showed that its impact is spatially and temporally confined. Much less is known regarding the effects of dendritic inhibition on the input−output transformations in interneurons and dentate gyrus principal cells. In this study we therefore asked: how does location, amplitude and timing of dendritic inhibition control excitatory input strength and action potential generation in PVIs compared to GCs in the dentate gyrus? By combining experimental and computational approaches, we show that in PVIs, off-path distal inhibition is more efficient than on-path proximal inhibition in controlling the amplitude of subthreshold excitatory postsynaptic signals (EPSPs) and similarly capable of silencing PVIs. These observations can be explained by a nonuniform distribution of the reversal potential of GABAA receptor (GABAARs)-mediated signals (EGABA), K-Cl cotransporters and the density of GABAAR-mediated conductances (GGABA) along the somato-dendritic axis, resulting in weak shunting on-path and strong hyperpolarizing off-path inhibition. In contrast, in GCs we observed hyperpolarizing inhibition along the entire somato-dendritic tree and no GGABA gradient, resulting in on-path inhibition being more efficient in silencing GCs than off-path inhibition.Previous computational and experimental investigations explored the interference of dendritic inhibition with CaRin = 101.5 ± 6.1 MΩ, mean ± s.e.m.) and axon collaterals restricted to the granule cell layer ). Inhibitory efficiency was the highest in PVIs when GABAergic signals were evoked on-site . Unexpectedly, inhibition was still highly efficient when evoked 50 or 100 µm off-path from the µEPSP induction location , suggesting that different chloride regulation mechanisms between dendritic compartment may underlie the observed EGABA gradient. Similar data were observed for adult rats . Therefore, in dependence on network activity, EPSP amplitudes will strongly fluctuate29. We examined the efficiency of on- and off-path-mediated GABAergic inputs in dependence on excitatory signal size, by evoking EPSPs of varying amplitude (1.1–25 mV) by extracellular stimulation of the middle molecular layer  and with EGABA values that linearly changed across the somato-dendritic axis, constrained by our experimental data , suggesting that dendritic filtering might be balanced by enriched GGABA at distal apical dendrites. Indeed, PVI models closely reproduced experimentally observed attenuations of dendritically evoked uncIPSCs under conditions of a linearly increasing somato-dendritic gradient of GGABA from 14 nS at the soma to ≥46 nS at distal dendrites , supporting our conclusion that GGABA gradually increases along apical dendrites of PVIs, while it remains uniform at the somato-dendritic axis of GCs.Dissimilarities in dendritic architecture and passive cable properties can induce cell-type-specific attenuations of distally evoked signalsABA Fig. . In GC mEGABA and GGABA distributions jointly account for the different dendritic inhibitory efficiencies in PVIs and GCs, we used model cells equipped with realistic Rm gradients and voltage-dependent conductances , the impact of inhibition diverged from the experimentally determined IE  resulted in different IE spatial profiles  was an efficient silencing mechanism  and systematically varied the latency of on- or off-path inhibitory signals evoked by RubiGABA uncaging (Δt: −200 to 20 ms) relative to EPSP onset  and increasing values to distal dendrites22 . The high distal Rm will boost the amplitude of local, synaptically evoked EPSPs and together with their low attenuation during dendritic propagation24 enhance their ability to induce somatic action potentials. In the case of inhibitory signals, our work shows that the combination of hyperpolarizing EGABA together with a high Rm22 and a high distal GGABA supports strong GABAergic IPSPs at outer dendritic compartments. Our immunohistochemical data indicate that EGABA gradients may be supported by different KCC2 densities across the somatodendritic axis of PVIs , indicating that high inhibitory efficiency can be particularly achieved when synaptic inputs are synchronized, such as during fast network oscillations49. Thus, PVIs are capable of sampling inhibitory and excitatory signals across the whole dendritic tree in a spatially unspecific manner.In vivo and in vitro investigations revealed that hippocampal fast-spiking interneurons upon recruitment by glutamatergic inputs generate action potentials at high temporal precisionent Fig. . HoweverVIs Fig. . The obsARs, our results are in agreement with studies recruiting specifically postsynaptic GABAARs using pair recordings18 or optogenetics46, showing a compartmentalized effect of dendritic inhibition on principal cell signaling.Our results imply a different functional role of distal dendritic inhibition in GCs, which has only a mild effect on GCs output and a lower influence on the amplitude of dendritically evoked EPSPs compared to PVIs . A small IE was also observed when excitatory inputs were close to GABAergic input sites . The somato-dendritic Rm gradient is opposite to the one in PVIs54. It is two-fold lower at distal dendrites than in PVIs resulting in smaller local EPSPs23. Moreover, dendritically evoked EPSPs undergo a stronger attenuation during their propagation to the soma23 (~4.5-fold larger at somato-dendritic distance of ~300 µm23). We provide evidence that GABAAR-mediated signal properties contribute to the low efficiency of distal inhibition. A nonuniform EGABA gradient at the somato-dendritic axis causes a stronger hyperpolarization at proximal than distal sites and acts synergistically with the uniform GGABA in generating smaller IPSPs at distal compared to proximal dendrites. Distally induced IPSPs are stronger attenuated during their propagation to the soma compared to PVIs  targeting GC spines in the outer molecular layer41, which largely originate from SOMIs37. Thus, distal inhibition may locally modulate dendritic synaptic signaling and plasticity.What mechanisms underlie the low impact of off-path inhibition in GCs? Similar to PVIs, GC dendrites express low levels of voltage-gated ion channels which confer linear integration of synaptic potentials71, shunting proximal inhibition will boost excitatory signals in PVIs and thereby support a basal PVI-mediated perisomatic inhibitory output onto GC populations. During this condition some GCs may overcome proximal inhibition and form new synchronously active cell associations. Repetitive exposure to the same environment will increase excitation levels71 and enhanced dendritic inhibition, which will in turn reduce PVI recruitment and modulate synaptic plasticity at GC distal dendrites. Such a mechanism may enable PVIs to provide phasic proximal inhibition onto GC populations balanced to the network activity state and allow subpopulations of GCs to form new coalitions representing new contextual information.In summary, our results indicate that PVIs and GCs show fundamentally different dendritic integration mechanisms. During low levels of perforant path-mediated excitatory drive to the dentate gyrus, such as during novel contextual exposure55 were performed using ACSF containing (in mM): 125 NaCl, 25 NaHCO3, 2.5 KCl, 1.25 NaH2PO4, 25 d-glucose, 2 CaCl2 and 1 MgCl2 (oxygenated with 95% O2/5% CO2) supplemented with 2 µM CGP55845 to block GABAB receptors. Whole-cell recordings (at 30–34 °C) were done using glass pipettes of 2–5 MΩ when filled with a solution containing (in mM): K-Gluconate 140, 132 or 127 for solutions with a calculated chloride equilibrium potential of −72.7, −63.3 or −54.2 mV respectively, KCl 4, 8 or 13, HEPES 10, MgCl2 2, Na2ATP 2, EGTA 10, 0.125 Alexa Fluor-488 and 0.15% biocytin . Whole-cell current-clamp recordings were obtained using an EPC 10 (HEKA) or a Multiclamp 700B (Molecular Devices) amplifier, filtered at 5 kHz and digitized at 40 kHz. Loaded recording pipettes had series resistances of 8–20 MΩ, which was compensated using the bridge balance in current-clamp mode and to 85% (time lag 5 µs) in voltage-clamp. For stimulus generation and data acquisition, we used custom-made programs (FPulse) written in Igor (Wavemetrics). Perforated-patch recordings were performed with an intracellular solution resulting in an equilibrium potential for chloride of −63.3 mV, supplemented with Gramicidin-A (Sigma) 50–60 µg/ml, freshly prepared from a DMSO stock. Recordings were performed in presence of AMPA and NMDA receptors blockers  after series resistance fell below 150 MΩ. Integrity of the perforated-patch was continuously tested and confirmed by the absence of Alexa Fluor-488 somatic fluorescence : Cs-Gluconate 130, TEACl 8, QX-314 1, HEPES 10, MgCl2 2, Na2ATP 2, EGTA 10, GTPNa3 0.5 and 0.15 % biocytin . Extracellular stimulation was obtained using a glass pipette (~1 MΩ) filled with a HEPES-buffered Na+-rich solution. For current-clamp experiments in PVIs and GCs, membrane potentials were kept at ~−65 and ~−70 mV, respectively, by constant current injection, thereby reproducing their in vivo membrane resting potential29. On-path GABAergic signals in Fig. GGABA  at the end of a −10 mV, 1 s voltage pulse. Discharge probability was defined as the number of action potentials evoked by extracellular stimulation divided by the number of stimulus trials.All experimental procedures were performed in accordance to national and institutional legislations . Transverse acute hippocampal slices  were obtained from Wistar rats (P19-28) of either sex. Slice preparation and whole-cell patch-clamp recordingsnce Fig. . For mea72. Infra-red Dodt-contrast was used to visually identify cells and subsequently two-photon laser  scanning microscopy  was used to visualize Alexa Fluor-488-filled dendrites of PVIs and GCs using ×20 or ×40 objectives . For microiontophoresis, glass pipettes (20–80 MΩ) were filled with 150 mM l-glutamic acid (pH 7.0) and 50 µM Alexa Fluor-488. The pipette tip was approximated to dendrites at a distance of ~150 µm to the soma until an increase in spontaneous activity was observed, indicating close proximity to the recorded cell dendrite (<2 µm distance between pipette tip and dendritic surface). Glutamate was then kept in the pipette using constant positive current (1–5 nA) and expelled using 0.1–0.4 ms negative current pulses (1 µA) with a microiontophoresis system . For GABA uncaging, slices were perfused with an extracellular solution containing RubiGABA  with a total volume of 8–15 ml (~32 °C). Experiments were performed in the dark to avoid unintentional uncaging of RubiGABA. Uncaging was induced using a 488 nm diode-pumped laser (Tectonics) coupled to the optical path of the two-photon laser and therefore positioned by the scanning mirrors and software of the Femtonics imaging system. To maintain stimulation intensity constant across dendritic compartments and cell types, duration (0.5 ms), laser power (0.5 mW) and RubiGABA concentration were kept constant throughout experiments and cell types. For experiments shown in Fig. z-axis distance between stimulated dendrites 35 µm). In this range of tissue depth, the amplitude of locally evoked IPSPs was not affected  of SOM-Cre mice (Ssttm2.1(cre)Zjh/J, Jackson Laboratory stock: 013044)39. The expression cassette of the rAAV contained tdTomato and Chr2 between two inverted loxP sites . Slices were prepared >14 d after injection. Full-field illumination  was applied 5 ms before extracellular stimulation of the perforant path at an intensity just sufficient to evoke action potentials. Twenty to sixty trials with and without light-mediated SOMI-activation were used for calculating the probability of action potential generation.Recombinant adeno-associated viruses (rAAVs) encoding Channelrhodopsin-2 (Chr2) and tdTomato were injected into the ventral dentate gyrus  and 0.025 M phosphate-buffered solution (PBS) at room temperature. For KCC2 immunohistochemistry, brains were dissected and incubated in 4% paraformaldehyde overnight and 50 µm floating sections were obtained using a vibratome (Leica VT1000S). Slices were incubated with primary antibodies against PV , calbindin  or KCC2  diluted in PBS containing 10% goat-serum and 0.3% triton X-100 for 24 h. Slices were afterwards incubated with Alexa Fluor-488 anti-mouse , Cy3-conjugated anti-rabbit , Alexa Fluor-647 anti-guinea pig or with streptavidin-conjugated Alexa Fluor-647  for 4–24 h (4 °C). Slices were counterstained with DAPI (5 min) and mounted in Mowiol. Confocal image stacks of labeled neurons were obtained with a laser scanning confocal microscope  using ×40 or ×63 magnification objectives (Zeiss Achroplan). After in vitro whole-cell recordings using two-photon or confocal microscopy, 44 cells were identified as basket and 20 as axo-axonic cells based on their characteristic axonal distributions73 using detailed passive cable models previously developed on the basis of five morphologically reconstructed PVIs55 and five GCs21. For PVIs, Ra was set to 170 Ω cm and the specific membrane capacitance (Cm)22 to 0.9 μF cm−2. Rm was nonuniformly distributed with somatic Rm set to 10 kΩ cm2 and the more distal tips of dendrites to 100 kΩ cm2 using a exponential somato-dendritic gradient. The exponential gradient in Rm had a constant (τ) of five, unless indicated, resulting in the best fit to the experimental data  with somato-dendritic distributions following previous experimental and single-cell modeling data of dentate gyrus PVIs74. In PVIs, density of gNa is high at the soma and declines with distance24 and was therefore set at the soma, axon and dendrites to 25, 25 and 1 mS cm−2 respectively. In Fig. gNa was set to 55, 85 and 0 mS cm−2 to reach action potential threshold values similar to those observed in vitro . gK and gIh were uniformly distributed with 20 mS cm−2 and 1 pS µm−2, respectively55 . Rm was nonuniform in GCs with 80 kΩ cm2 at the soma and perisomatic dendritic areas, and 50 kΩ cm2 at proximal and distal dendrites. We introduced a previously reported set of conductances that closely replicate the physiological phenotype of GCs54. Briefly, transient sodium channels (Na), L- T- and N-type calcium channels , fast and slow rectifiers , A-type potassium channels (AK), BK and SK calcium-dependent potassium channels , a slow after hyperpolarization conductance (sAHP) and a voltage-dependent cationic somatic current (Uc) were implemented with heterogeneous distribution over the somatodendritic domain54  onto one GC or BC56.For estimating the somato-dendritic distribution of els Fig. , we compG values of 0.25 and 5 nS and reversal potentials of 0 and −70 mV, representing glutamatergic and GABAergic inputs, respectively75.For testing the effect of inhibition on action potential generation in PVI and GC models Fig. , seven GAR-mediated signals . Statistical analysis was performed using Excel (Microsoft) with “Real Statistics Excel Resource Pack” or SigmaPlot v.13 (Systat). The average inhibitory effect (IE) was calculated on the basis of individual PSP sweeps in the presence and absence of GABAips Fig. . Analysit test for independent and related samples, respectively. Nonparametric two-tailed Wilcoxon rank sum or signed-ranks tests were employed if normality tests failed. Multiple comparisons were performed using one- or two-way ANOVA on Ranks and post-hoc Dunn’s or Holm−Sidak method for pairwise comparisons unless otherwise stated. Significance levels are indicated as p values. Data are presented as mean ± s.e.m.Differences between two samples were assessed with a two-tailed unpaired or paired Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Drosophila to the elaborate mammalian cortex, neurogenesis requires exceptional spatial and temporal precision to co-ordinate progenitor cell proliferation and subsequent differentiation to a diverse range of neurons and glia. A limited number of transiently expressed proneural basic-helix-loop-helix (bHLH) transcription factors, for exampleachaete-scute-complex (as-c) andatonal (ato) inDrosophila and the vertebrate homologues Ascl1 and Neurogenin2 (Ngn2), are able to orchestrate the onset of neuronal determination, context-dependent subtype selection and even influence later aspects of neuronal migration and maturation. Within the last decade, two models have emerged to explain how the temporal activity of proneural determination factors is regulated by phosphorylation at distinct sites. One model describes how cell-cycle associated phosphorylation on multiple sites in the N and C termini of vertebrate proneural proteins limits neuronal differentiation in cycling progenitor cells. A second model describes phosphorylation on a single site in the bHLH domain ofDrosophila atonal that acts as a binary switch, where phosphorylation terminates proneural activity. Here we combine activating mutations of phosphorylation sites in the N- and C- termini with an inhibitory phospho-mimetic mutation in the bHLH domain of Ascl1 and Ngn2 proteins, and test their functionsin vivo usingXenopus embryos to determine which mode of phospho-regulation dominates. Enhancing activity by preventing N- and C terminal phosphorylation cannot overcome the inhibitory effect of mimicking phosphorylation of the bHLH domain. Thus we have established a hierarchy between these two modes of proneural protein control and suggest a model of temporal regulation for proneural protein activity.From the relatively simple nervous system of Furthermore, these determination factors are able to influence relatively late aspects of neuronal migration and maturation despite having only a transient window of expression1. Proneural protein activity is exquisitely regulated both spatially and temporally3, and mechanisms are conserved fromDrosophila neurogenesis to the mammalian cortex.Development of the nervous system requires elaborate control to expand progenitor cells before subsequent differentiation into a diverse array of neuronal and glial subtypes. One of the most striking features is that a limited number of proneural basic-helix-loop-helix (bHLH) determinant factors, for example4. Within the last decade, models of phospho-regulation have emerged to explain the temporal control and/or context-dependent activity of proneural determination factors7. Modification of single regulatory phospho-sites may fine-tune activity of individual proneural proteins within a given context, for example GSK3β-mediated phosphorylation of Ngn2 to promote motor neuron formation8. Additionally, models have been described to regulate generic proneural activity in progenitor cells.The mechanisms that underlie the complexity of the developing nervous system have begun to be elucidated. For example there are multiple bi-directional links that intimately connect and influence the transition between progenitor cell proliferation and neuronal differentiation during neurogenesisXenopus embryos and mammalian cell culture10. Cyclin-dependent-kinase activity that promotes the cell cycle can additionally phosphorylate proneural proteins on multiple Serine-Proline sites located in the N and C termini either side of the basic helix-loop-helix domain that regulates DNA binding and dimerisation. This multi-site phosphorylation limits protein stability and chromatin association, and specifically reduces activation of genes associated with cell cycle exit and differentiation6. Correspondingly, Serine to Alanine phospho-mutant proneural proteins that cannot undergo this phosphorylation promote differentiation during development, and in cellular reprogramming assays9.Firstly, a multi-site phospho-regulatory model is described for vertebrate Ascl1 and Ngn2, based on data fromatonal inDrosophila retina and Ngn2 in mouse cortex; while the phospho-mutant versions behave as the wild-type proteins, the respective Serine/Threonine to Aspartic acid phospho-mimetics are inactive7. Phosphorylation of this single site inatonal acts a binary switch to terminate proneural activity in retinal R8 precursors7. Here we present a short study to determine the effect of combining the activating mutations blocking phosphorylation of the N and C-termini of mouse Ascl1 and Ngn2 with an inhibitory phospho-mimetic mutation in the bHLH domain, to determine whether one mode of phospho-regulation is dominant over the other.Secondly, a single regulatory phospho-site within the bHLH domain is described forAll work has been carried out under UK Home Office Licence and has passed an Institutional ethical review committee assessment at the University of Cambridge.Ascl1 (Genbank accession numberNM008553) and 6S-A phospho-mutant mouseAscl1 were published inNM009718) and 9S-A mouse Ngn2 were published inWild-type mousemAscl1_S150D_S = CGGCCAACAAGAAGATGGACAAGGTGGAGACGCTGC;AS = GCAGCGTCTCCACCTTGTCCATCTTCTTGTTGGCCG. mNgn2_T149D_S = CCGAGGATGCCAAGCTCGATAAGATCGAGACGCTGCG, AS = CGCAGCGTCTCGATCTTATCGAGCTTGGCATCCTCGG.X. laevis females are housed and cared for by a dedicated team of animal technicians operating under Home Office Licence. Each experiment requires eggs from 2 or 3 females (depending on N = 2 or 3) and females are used on rotation within the colony with at least a 3-month rest period after laying. A single male frog is sacrificed under humane conditions and Home Office Licence to provide testes for at least 16 experiments. Embryos obtained from fertilised eggs are used for the experiments and development is stopped 48 hours post fertilisation when embryos reach late neurula stage and prior to formation of tadpoles.All efforts are made to ameliorate suffering to any animal. For example, the colony of approximately 80X. laevis eggs were obtained by standard hormone methods of induction, and subsequently fertilisedin vitro. pCS2+ constructs were linearised and capped mRNA was transcribedin vitro using the SP6 mMessage mMachine® kit (Ambion). Embryos were injected unilaterally at the two cell stage with mRNA as indicated in the text, with GFP (for qPCR) or β-gal (ISH) as lineage tracers. Embryos were cultured at 18°C in Ficol solution and staged according toThus,X. laevis neural-β-tubulin13. Whole mount ISH was performed as described inDig-oxigenin-labelled anti-sense probes were synthesised from plasmid® Mini kit (Qiagen) and template cDNAs synthesised with the QuantiTect® Reverse Transcription Kit (Qiagen). qPCR was performed using the Quantifast® SYBR Green PCR kit (Qiagen) in a LightCycler® 480 (Roche). Thermal cycling conditions: 95°C for 5 minutes, then 45 cycles of 95°C for 10s, 60°C for 10s and 72°C for 20s. EF1α reference gene (Genbank accessionNM001087442): Forward, CACCATGAAGCCCTTACTGAG; Reverse, TGATAACCTGTGCGGTAAATG. N-β-Tubulin target gene (Genbank accessionNM001086064): Forward, TGGATTTGGAACCAGGCA; Reverse, GCTCAGCTCCTTCGGTGTA.GFP expression was used to confirm successful injection and samples of four embryos were snap frozen. Whole embryo RNA was extracted using the RNeasy™ TGX 18% gels in Tris-Glycine buffer. Primary antibodies were used at 1:2000 dilution for at least 1 hour at room temperature (tubulin) or at 4°C overnight (HA): Rat HRP-conjugated anti-HA clone 3F-10 antibody  and mouse anti-α-tubulin clone B-5-1-2 antibody . Anti-tubulin antibody was detected with a sheep HRP-conjugated anti-mouse antibody at 1:10000 dilution .For western blot analysis, 12 embryos were snap frozen at stage 11 and whole embryo protein was extracted as described inFor ISH data, embryos were scored for the extent and pattern of N-β-Tubulin expression on the injected side relative to uninjected side and to uninjected embryos. Scores were assigned as 0, no difference; +1, mild increase in expression within the neural tube with or without occasional ectopic expression on the injected side; +2, moderate increase with ectopic expression occurring in patches on the injected side and sometimes bilaterally; +3, substantial increase with extensive ectopic expression in a more homogenous pattern on the injected side and sometimes bilaterally. Experiments were conducted in independent duplicate and the N numbers refer to the range of total numbers of embryos in each injection category.For qPCR data, mRNA expression was normalised to expression of reference gene EF1α and mRNA levels in the injected embryos were calculated relative to stage-matched uninjected controls. Mean values are plotted and error bars show the standard error of the mean from two independent experiments. Statistical significance was calculated by a paired two-tailed student T test in Microsoft Excel 2010; NS = not significant; * = p< 0.05; ** = p< 0.025; *** = p< 0.0125. Western blot is representative from two independent experiments. For protein quantification, Image J software was used as described inXenopus, mouse and human proneural proteins12, with conservation of the model across germ layers to include bHLH proteins in other tissues such as MyoD in muscle/mesoderm14 and Ngn3 in pancreas/endoderm15. In each case, the regulatory phospho-sites are located in the N and C terminal domains, and preventing phosphorylation by mutating Serine/Threonine-Proline sites to Alanine-Proline results in enhanced protein stability and increased DNA binding affinity. This enables expression of slower responding differentiation genes that tend to be less epigenetically available; reviewed inExperiments supporting the regulatory model based on multi-site phosphorylation in the N- and C-termini of proneural proteins has been well documented inDrosophila atonal in the R8 photoreceptors of theDrosophila retina terminates proneural activity, and this mode of regulation appears to be conserved with Ngn2 in murine cortical progenitors7. This single conserved site at the junction between the loop region and helix 2 is predicted to lie in the near vicinity of negatively charged phosphates in the DNA backbone; phosphorylation of the site, possibly mediated by Protein Kinase A, reduces DNA binding to inactivateatonal upon R8 precursor selection7.In contrast, phosphorylation of a single site in the bHLH domain ofDrosophila as-c), nor has the effect of modulation been tested in theXenopus system. Hence, in the present study, the equivalent phospho-mimetic construct was made with substitution of Serine 150 to Aspartic acid (S150D) in wild-type (WT) mAscl1. To explore the effect of combining this potentially inactivating mutation with activating mutations that prevent phosphorylation on multiple N- and C-terminal sites, the same S150D phospho-mimetic substitution was made into 6S-A mAscl1, where six Serine-Proline (SP) sites in the N and C termini have been mutated to Alanine-Proline  sites in the N and C termini of mNgn2 have been mutated to Alanine-Proline5 . Thus, the introduction of the S150D phospho-mimetic into the WT or 6S-A Ascl1 constructs does not significantly affect the protein accumulation.Additionally, 6S-A Ascl1-based proteins accumulate more than the respective WT proteins, consistent with an increase in protein half-life of this phospho-mutantin situ hybridisation (ISH) for expression of N-β-tubulin .Data are available under the terms of the Drosophila wing disc, as well as Neurog2 in the developing mouse cortex can be inactivated by phosphorylation of a single conserved residue. This conserved residue is located in the bHLH domain and its phosphorylation disrupts DNA binding.The authors have previously shown that multi-site phosphorylation is a conserved mechanism that inhibits the neuronal-inducing activity of the proneural bHLH proteins. Correspondingly, phospho-null mutations in the N- and C-termini of Neurog2 and Ascl1 cumulatively increases the stability and proneural activity of these proteins. More recently , it has been shown that the proneural activity of Scute and Atonal in the X. laevisembryos. As shown using both qPCR analysis and whole mount in situ hybridization, the phospho-mimetic mutation in the bHLH domain of Ascl1 and Neurog2 abolished their ability to promote neuronal differentiation. The inhibitory phospho-mimetic mutation was also able to override the activating effect of the phospho-null mutations in the N- and C-termini of Ascl1. Neurog2, however, still maintained weak proneural activity. In this short research note, Hardwick and Philpott investigated which of the afore described bHLH protein phosphorylation mechanisms is dominant in the control of the proneural activity. The ability of the wild-type Ascl1 and Neurog2 to promote neuronal differentiation in comparison to versions of the proteins harboring phospho-null mutations in the N-and C-termini or a phospho-mimetic mutation in the bHLH domain alone, as well as in combination were studied. The proneural activity was assessed in gain of function assays using  In general, the report is well and succinctly written, the figures are clear and represent the authors conclusions. The report is of interest as it further demonstrates the conservation of phosphorylation in the bHLH domain as a dominant regulatory mechanism to inhibit proneural protein activity. Introduction of the bHLH phospho-mimetic mutation in the Neurog2 phospho-null N- and C-termini mutant (9S-A/T149D) was not sufficient to completely disrupt neuronal differentiation induced by this mutant. These findings complement that of Quan et al., which found that the Neurog2 bHLH phospho-mimetic mutant still maintains weak DNA binding activity. However, alternative mechanisms cannot be excluded. It will be of interest to study the phospho-null bHLH mutation in this system to further evaluate the consequences of persistent proneural activity.  Minor point: The authors state that the 9S-A/T149D Neurog2 mutant maintains residual activity, which is restricted to the neural ectoderm. However, for the embryo depicting this activity , it appears that the injected mRNA has not targeted the nonneural ectoderm .I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this research note, the authors attempt to reconcile two different mechanisms regulating the activity of pro-neural bHLH transcription factors. The authors have previously shown very convincingly that Neurogenin2 and Ascl1 are phosphorylated on multiple SP sites by Cyclin Dependant Kinase 2 (Cdk2), preventing their ability to drive neuronal differentiation. These phosphorylation sites are located in the N- and C-terminal regions of the proteins, but not in the DNA binding domain. Furthermore, they have previously shown that the multiple phosphorylation sites can act as a “rheostat” to regulate Ngn2/Ascl1 activity. In contrast, studies from Drosophila  and in the mouse cortex (Ngn2) pinpoints to an on/off mechanism by a single phosphorylation event in the DNA binding domain. Interestingly, this phosphorylation site is very well conserved in other pro-neuronal bHLH transcription factors such as  Ascl1. The authors have therefore decided to test the possibility that both mechanisms (rheostat and on/off switch) might co-exist to regulate the activity of pro-neural bHLH in vertebrates.  Overall the manuscript is very well written, the methods are well established in the lab and the assays used are very robust. The data support the conclusions of the study, i.e. that both mechanisms of regulation co-exist. However, I have one major issue that the authors could address easily to improve the validity of their conclusions. The statistical analyses of their data should be improved by doing the following: - mean and standard deviation (which is more appropriated than standard error of the mean) should be calculated on at least three repeats rather than two.  - within an experiment where multiple conditions are compared , a two-way ANOVA with post-hoc test should be applied rather than multiple t-tests. 2 should be performed. - when categorising data , a X Given the clear differences, I am sure that these analyses will not change the conclusion of the study but there is rightly a drive for more rigorous statistical analyses of the data that are reported.  One minor point that could be discussed by the authors, and maybe the subject of further studies: I would have expected the S150D / T149D mutants to have a dominant negative effect (i.e. less neurogenesis) but it seems that they are inert. Is it because they do not dimerise with Xenopus Ascl1 / Ngn2? Would the authors expect the same results should they use the Xenopus orthologues?I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


This study investigates the distribution of Antarctic minke whales (AMW) in relation to sea ice concentration and variations therein. Information on AMW densities in the sea ice‐covered parts of the Southern Ocean is required to contextualize abundance estimates obtained from circumpolar shipboard surveys in open waters, suggesting a 30% decline in AMW abundance. Conventional line‐transect shipboard surveys for density estimation are impossible in ice‐covered regions, therefore we used icebreaker‐supported helicopter surveys to obtain information on AMW densities along gradients of 0%–100% of ice concentration. We conducted five helicopter surveys in the Southern Ocean, between 2006 and 2013. Distance sampling data, satellite‐derived sea‐ice data, and bathymetric parameters were used in generalized additive models (GAMs) to produce predictions on how the density of AMWs varied over space and time, and with environmental covariates. Ice concentration, distance to the ice edge and distance from the shelf break were found to describe the distribution of AMWs. Highest densities were predicted at the ice edge and through to medium ice concentrations. Medium densities were found up to 500 km into the ice edge in all concentrations of ice. Very low numbers of AMWs were found in the ice‐free waters of the West Antarctic Peninsula (WAP). A consistent relationship between AMW distribution and sea ice concentration weakens the support for the hypothesis that varying numbers of AMWs in ice‐covered waters were responsible for observed changes in estimated abundance. The potential decline in AMW abundance stresses the need for conservation measures and further studies into the AMW population status. Very low numbers of AMWs recorded in the ice‐free waters along the WAP support the hypothesis that this species is strongly dependent on sea ice and that forecasted sea ice changes have the potential of heavily impacting AMWs. This may allow consideration whether the “moved‐into‐sea ice” hypothesis is at least tenable. Moreover, in the likely absence of absolute circumpolar abundance estimates in the near future, identifying dependencies and vulnerabilities of the species in their habitat seems to be a reasonable step toward helping to ensure protection, recognize threats, and mitigate impacts.Aerial surveys have been proven as a means to survey ice‐covered areas at constant survey speeds, with assumed minimal disturbance effects on animal distribution and effectively collecting cetacean sighting data in 0%–100% ice concentrations  were entered as assessed by the observers. Sightings were recorded noting species, distance to transect line , and group size. Declination angles were measured using inclinometers. If a sighting occurred and the species could not be identified or group size could not be determined immediately, the survey was halted in order to approach the sighting for closer inspection, a procedure known as “closing mode” , distance to shelf break, water depth, and bathymetric slope. Therefore, required data were obtained as described in the following.2.2.1https://seaice.uni-bremen.de/data/amsre/  and AMSR2 satellite sensors from the Institute of Environmental Physics, University of Bremen, 2.2.2Depth and bathymetric slope (rate of change in depth over distance) values were extracted from the GEBCO 2014 grid available from the British Oceanographic Data Centre (BODC). We calculated slope values using the ArcMap “Slope” tool  to the steeper upper continental slope (>1°). Globally, the shelf break generally is between 100 and 150 m water depth  for each data point, discriminating between positions on versus off the shelf.2.32.3.1B. bonaerensis, B. acuturostrata, and sightings that could not be clearly identified as either of them or that were defined as “minke‐like”. As the front observer had an unobstructed view of the track line, no left truncation was applied to the perpendicular distances going into the fit of the detection functions.We used distance sampling methods to estimate the probability of detection as a function of distance from the track line  = 1). Covariates tested in the MCDS component, in addition to perpendicular distance, included sighting condition , Beaufort sea state, group size and a measure of local sea ice concentration (ice concentration as judged by the observers), which was classified as 0%–9% ice coverage = “no ice” and 10%–100% = “ice”. Ice concentration was included as a factor to test for evidence that increasing complexity in the visual field may decrease the probability of detection. Perpendicular distances were truncated to exclude the furthest ~10% of detections, which is recommended to avoid overfitting the tail of the distribution  = 1 , water depth, bathymetric slope, distance to the shelf break, ASI sea ice concentration (corresponding to dates of particular survey effort), and distance to the ice edge (derived from the satellite data for each survey day as described above). Survey season was included in the density surface models as a factor term to account for any inter‐seasonal variations that may occur in AMW densities across the survey areas.The modeling for the density surfaces was undertaken in R , Weddell Sea (WS), East Antarctic Peninsula (EAP), and West Antarctic Peninsula (WAP) Figure . The delAll areas were visited by at least two expeditions thus providing some temporal coverage within each stratum. For each stratum, we produced a prediction grid. The spacing of the prediction grid was 6.25 km, representing the resolution of the coarsest environmental variable, i.e., the satellite‐based ASI ice concentration data). The prediction grids contained information on water depth, bathymetric slope, daily sea ice concentration, distance to ice edge, and distance to shelf break for each grid point, in addition to projected coordinates. We predicted AMW densities for each stratum for selected dates of the survey periods, meeting approximately the middle of the corresponding effort periods of the respective surveys that visited the stratum, to account for extant sea ice conditions.3In total, 40,695 km of track lines were covered by helicopter surveys Figure , Table 2B. bonaerensis) represented the majority, with 150 sightings of 275 individuals. One sighting of two individuals was identified as B. acuturostrata, one sighting of six individuals as either B. bonaerensis or B. acuturostrata, and three sightings of single animals were recorded as “minke‐like”  were eligible for detection function modeling .Based on the best model, we predicted AMW densities (uncorrected for availability) for separate strata and specific dates (to reflect respective ice conditions). No predictions using this model were possible for WAP, as no ice edge could be identified in this stratum for any year. Low ice concentrations in this area during all five survey years resulted in no MIZ; geographically, the closest ice edge was located east of and, thereby, “behind” the Antarctic Peninsula. We therefore considered distances to this ice edge as not ecologically meaningful for use in the model. The sample size  in WAP, however, was too small to create an independent GAM solely for WAP, based on the remaining variables. In order to obtain density predictions for this stratum in absence of a model‐based density estimate, we used conventional distance sampling , using the software package “Distance” Miller,  in R R . WAP pro2, in NM from 0.0024 (CV 0.33) to 0.0040 (CV 0.24) ind./km2 , including latitudes up to 58°S, we feel comfortable downgrading that as a potential explanation of the change from CPII to CPIII, at least for the longitudinal range covered during our surveys.Apart from the relationship of AMW densities and gradients in concentrations of sea ice and the consistency of this relationship over the years of the observation, our surveys provide further insights into potential sources of bias discussed in relation to the interpretation of the abundance comparisons between CPII and CPIII. Branch & Butterworth  identifiFurthermore, inter‐(summer) seasonal longitudinal shifts have been discussed as potential for bias in the evaluation of abundances estimated for CPII and CPIII. Survey effort of IDCR/SOWER large‐scale surveys could only be achieved in relatively limited longitudinal extents each summer season, resulting in circumpolar assessments taking several years to complete. In our surveys, we detected a longitudinal gradient of AMW distribution, with no indication for large longitudinal shifts between survey years of our study .2 . While this estimate is not directly comparable with the estimates for the other strata, it is valid to state that densities in WAP range in the order of a third of densities of all other strata (based on the assumption that availability bias for AMWs remains relatively constant throughout the strata). Our encounter rate  is also substantially lower than those reported by Thiele et al.  realized in WAP, only 16 sightings of AMWs were recorded here, equaling <10% of the total number of sightings. Predictions by the selected density surface model were not possible for WAP, because the model included distance to ice edge as a covariate, which could not be determined in WAP. Here, no ice edge according to the definition of a line bounding the 15% ice concentration margin could be defined for any of the survey years in WAP. Density estimation by conventional distance sampling instead revealed 0.0006 ind./kme et al.  and Friee et al. . This mae et al.  and Friee et al.  were base et al. . These we et al. . Warmer e et al. . With see et al. . Overalle et al. . The Ante et al. . The relB. acutorostrata occurred in WAP. High‐latitude records of B. acutorostrata are rare, with the species' center of distribution presumably located north of 60°S  during the surveys could be undertaken. The relatively high survey speed during aerial surveys tends to increase availability bias, because animals have less time to appear in the field‐of‐view of observers, compared to shipboard surveys. Another concern, that ice cover could increase complexity in the visual field and thus influence detection probability of whales, was controlled by testing ice cover as a factor during detection function modeling. Ice cover dropped out as an explanatory variable, thus providing no evidence for ice cover impairing or enhancing the detectability of whales to the observers during these surveys. Unfortunately, there is no evidence to indicate how availability differs across the entire study region. Differences in availability between open water and ice‐covered areas may also apply, but again, there is no strong evidence to indicate the directions and magnitudes of such bias. We therefore decided not to apply a generic correction factor, but instead to use minimum density estimates as a baseline to reduce additional sources of error and assumption‐based conclusions. Clearly, it is important to begin estimating availability bias across a range of sea ice and open‐water habitats, to allow for corrected abundance estimates from aerial surveys. Current studies on AMW ecology by means of time‐depth recording tags , are consistent with the hypothesis that this species is dependent on sea ice. The nature and magnitude of future responses of AMWs as a species are uncertain in novel environments as projected under climate change. An overall decrease of sea‐ice cover would narrow available habitat for AMWs. Our results suggest that the forecasted sea ice changes (Böning, Dispert, Visbeck, Rintoul, & Schwarzkopf, None declared.MS, KHK, HH, NK, RW, and US conceived the idea for this study. MS, HH, LSL, and US designed the study. MS, LSL, KHK, RW, SV, and HH collected the cetacean survey data. HH and SV processed the cetacean survey data. BD processed and prepared the bathymetric data. MH processed and prepared the sea‐ice data. HH, NK, and SV analyzed and interpreted the combined data sets. HH conducted the GIS analyses, NK conducted the statistical analyses. HH drafted the manuscript. All authors critically revised the manuscript and gave their final approval for publication.


Cdc2-like kinases (CLKs) play a crucial role in the alternative splicing of eukaryotic pre-mRNAs through the phosphorylation of serine/arginine-rich proteins (SR proteins). Dysregulation of this processes is linked with various diseases including cancers, neurodegenerative diseases, and many genetic diseases. Thus, CLKs have been regarded to have a potential as a therapeutic target and significant efforts have been exerted to discover an effective inhibitor. In particular, the small molecule CX-4945, originally identified as an inhibitor of casein kinase 2 (CK2), was further revealed to have a strong CLK-inhibitory activity. Four isoforms of CLKs  can be inhibited by CX-4945, with the highest inhibitory effect on CLK2. This study aimed to elucidate the structural basis of the selective inhibitory effect of CX-4945 on different isoforms of CLKs. We determined the crystal structures of CLK1, CLK2, and CLK3 in complex with CX-4945 at resolutions of 2.4 Å, 2.8 Å, and 2.6 Å, respectively. Comparative analysis revealed that CX-4945 was bound in the same active site pocket of the CLKs with similar interacting networks. Intriguingly, the active sites of CLK/CX-4945 complex structures had different sizes and electrostatic surface charge distributions. The active site of CLK1 was somewhat narrow and contained a negatively charged patch. CLK3 had a protruded Lys248 residue in the entrance of the active site pocket. In addition, Ala319, equivalent to Val324 (CLK1) and Val326 (CLK2), contributed to the weak hydrophobic interactions with the benzonaphthyridine ring of CX-4945. In contrast, the charge distribution pattern of CLK2 was the weakest, favoring its interactions with benzonaphthyridine ring. Thus, the relatively strong binding affinities of CX-4945 with CLK2 are consistent with its strong inhibitory effect defined in the previous study. These results may provide insights into structure-based drug discovery processes. SR proteins are generally composed of one or two RNA recognition motifs (RRMs) at the N-terminus, and two arginine/serine-rich domains (RS1 and RS2 domains) at the C-terminus . Th. Thα posIn the present study, we determined the structures of CLK1, CLK2, and CLK3 in complex with their small molecule inhibitor CX-4945. Overall structure of CLKs was similar to each other, but a close look into the active sites revealed the notable difference in pocket sizes and electrostatic surface charge distributions . First,


Snow depth and sea ice thickness in the Polar Regions are significant indicators of climate change and have been measured over several decades by ice-tethered buoys. However, sea ice temperature profiles measured by ice-tethered buoys are rarely used to infer snow depth and sea ice thickness owing to the lack of automatic discrimination algorithms, restricting the use of the data for sea ice thermodynamics studies. In this study, snow depth and sea ice thickness were retrieved through the measurements of sea ice temperature profiles using discrimination algorithms of the change point and the maximum likelihood detection methods. The data measured by 50 ice-tethered buoys were used to evaluate the accuracy of the results determined by the algorithm. Influences on the seasonal sea ice thermodynamic state, vertical interval of temperature sensors on the buoys, and initial ice thickness on the estimation errors were also evaluated. The performance of the discrimination algorithm for the data from the Arctic and Antarctic regions was also compared. There were no identifiable differences between the estimation errors from the Arctic and Antarctica. Increases in both the interval of the temperature sensors and the initial ice thickness enlarged the error for the estimation of ice thickness. A procedure developed in this study strengthens the potential application of measurements from the ice-tethered buoys only with the measurements of the vertical temperature profile of the layer of snow-covered ice, but not the measurements of ice basal and surface positions using acoustic sounding. Sea ice is a sensitive indicator of climate change in the Polar Regions. The thickness of Arctic sea ice has declined dramatically in recent decades ,2,3 and Numerical modeling is the most widely used and most effective research method to depict of the ocean–ice–atmosphere interactions in the Polar Regions . The accIn the Arctic, the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment from 1997 to 1998  and the Observations of snow depth and sea ice thickness using an IMB is completed by relatively expensive acoustic sounders. If the sonar sensors are damaged, the data of sea ice mass balance will not be obtained. A SIMBA can determinate the snow depth and sea ice thickness based on a heated operation mode, in which there is a significant difference in the threshold temperature of different media after heating because of the different specific heat capacities among air, snow, ice, and water. However, the automatic discrimination algorithms and the procedures of discrimination algorithm for snow depth and sea ice thickness using the measurement of vertical temperature profile of snow and ice have not been reported previously. In cases without the deployment of the acoustic sounding instruments, the vertical temperature profile through the air, snow, ice, and upper ocean can be used to discriminate snow depth and ice thickness because both the specific heats and the heat conduction coefficients are very different among air, snow, ice and water ,20. GeneIn these contexts, this paper focuses on exploring a discrimination algorithm and procedure to identify snow depth and sea ice thickness based on the methods of change point and maximum likelihood using the ice temperature profile data measured by IMBs, SIMBAs, and a TUT buoy. Three types of ice-tethered buoys were used in this study. The total number of ice-tethered buoys was 50, including 47 IMBs and one TUT buoy deployed in the Arctic, and two SIMBAs deployed in Antarctica . All theThe installations and schematic diagrams of three ice-tethered buoys are shown in The IMB can be considered as one of the most reliable and widely deployed observation instruments for sea ice mass balance. A potential deployment scheme for the IMB buoy can be described as follows. The IMBs are composed of a data logger, which is in charge of data acquisition, processing and remote transmission, a sea ice mass balance observation system and a thermistor string to measure sea ice temperature profiles. The IMB uses two acoustic sensors to measure sea ice thickness and snow depth. These two acoustic rangefinder sounders are fixed on a supporting structure, which is designed to be deployed through a 0.10 m diameter ice hole. The upward looking sonar whose accuracy is 0.01 m is fixed extending from the ice hole at 3.8 m under the snow-ice interface to measure the distance from the sonar to the bottom of the ice to invert ice bottom growth and ablation. Another sensor with an accuracy of 0.001 m is installed at 1.2 m from the snow surface to measure the distance from the sensor to the top of the snow to invert snow depth. The thermistor string with a length of 4.5 m consists of three sub–strings, each of which has a length of 1.5 m. There are 45 temperature probes evenly arranged on the temperature string at intervals of 0.01 m. And the IMB data are obtained from Ice Mass Balance (IMB) Buoy Program. Here, we used 47 IMB buoys deployed from 2002 to 2014. There were 11 buoys completing stable operation for more than one year and a buoy running for more than two years (25 months).SIMBAs are now widely deployed in the Arctic and Antarctica. The SIMBAs were assembled by the Scottish Association for Marine Science and modified by the Polar Research Institute of China. A SIMBA thermistor string had 240 temperature probes at intervals of 0.02 m. The data for the temperature profile of landfast ice in Antarctica near Zhongshan Station used in this paper were measured by two SIMBAs (SIMBA-2013A and SIMBA-2014A) deployed in 15 May 2013 and 13 May 2014, respectively. The observations of SIMBA-2013A and SIMBA-2014A continued until 27 November 2013 and 23 November 2014, respectively. The SIMBAs used in this study had no sonar, thus the snow depth and ice thickness of the SIMBAs were measured by drilled-hole ever 10 to 20 days during the operation of the buoys.The Polar Research Institute of China and Taiyuan University of Technology have started to develop and deploy a new type of ice-tethered buoy (TUT buoy) since CHINARE-2014 in the Arctic Ocean. The TUT buoy uses digital temperature sensor chips with a resolution of 0.0625 °C and an accuracy of 0.1 °C. A 4.5-m-long thermistor string is linked by 15 flexible printed circuit boards (FPCBs) with a length of 0.3 m. Two acoustic rangefinder sounders  were used to measure snow depth and ice thickness. Based on a 32-bit low-power microcontroller, we designed the data acquisition instrument and the iridium satellite data transmission system, which allowed data to be sent back to the lab computer in real time. The TUT buoy (TUT-2016A) used in this paper was deployed over multiyear ice in the Arctic on 9 August 2016 during the CHINARE-2016. The measurements, including snow depth, ice thickness, and sea ice temperature profiles of TUT-2016A, lasted from 9 August 2016 to 11 July 2017, with the data sampling interval of one hour.The thickness of sea ice and the depth of snow can be calculated by determination of the media interfaces: top interface of air-snow or air-ice (when the snow has melted completely), snow-ice interface and the lower interface of ice-water. The interfaces can be derived from the temperature profiles observed by the ice-tethered buoys. Through identifying changes in the positions of interfaces, snow depth and sea ice thickness can be obtained.The snow-ice interface is considered to be constant except for slush formation associated with flooding . This phThe interfaces between air and snow or ice, and between ice and water were identified using the temperature profiles, the daily amplitude, which are equal to the differences between the maximum and minimum values of the daily temperature profiles of each sensor, and the vertical temperature gradients. The temperature profile, daily amplitude of temperature profiles and the vertical gradients of the temperature were widely used to estimate the interface between air and snow (or ice) ,21,22. TThe evolution of snow depth over sea ice may be affected by synoptic processes phenomena such as storms, snowfall, and sleet, or due to melting caused by solar radiation. Thus, the temporal fluctuations of snow depth are much larger than for sea ice thickness. The daily amplitude of temperature profiles and the vertical gradients of temperature were examined because, in some cases, it was difficult to accurately distinguish the top interface by using the measurements of the temperature alone. From the data of the daily amplitude of temperature profiles and the vertical gradients of temperature, we checked the corresponding values one by one from the top of the temperature profiles to find the change points.In winter or sea ice growth period, sea ice was covered by deep snow, so the air-snow interface was considered as the top interface. As shown in D(i) is the daily amplitude of a certain point on the temperature profile; maxT(i) and minT(i) are the maximum temperature and minimum temperature at a certain time of a day, respectively. The measurements at the top of temperature profiles had significant daily amplitudes of about 4.5 °C, as shown in The temperature profile of sea ice at the end of the warming period was C-shaped. Correspondingly, the temperatures measured by the sensors exposed to the air were above 0 °C. The intersection of the top temperature profile with 0 °C was identified as air-ice interface d. The daG is the temperature gradient of the temperature profile; T is the temperature measured by buoys; z is the vertical coordinate. As shown in The temperature gradient of temperature profile can be described as follows ,21,22:(2To identify the lower ice interface (ice-ocean interface), temperature profiles for the lower ice layer were obtained from some thermistors near the bottom of the sea ice. The seawater temperature was determined using the lower five thermistors, which generally had a negligible temperature gradient from the bottom of the sea ice. The points where the temperature profile of the lower ice layer intersected the ocean temperature were regarded as the ice-ocean interface b. The icIn T1 is the temperature of air or snow; T2 is the temperature of sea ice; T3 is the temperature of ocean. In this study, the vertical gradients of temperature and daily amplitude of temperature profiles were also used to distinguish snow depth and ice thickness. For these estimations, T1, T2, T3 represented the vertical gradients of temperature or daily amplitude of temperature profiles, respectively. kS was the temperature value from the temperature profile. t1 was the location of the ice-snow interface, t2 was the location of the ice-ocean interface, and ke was the noise in the data of sea ice temperature profile. The detection of multiple change points can be accomplished with the single change point detection method, the intent of which is to estimate of the change points.An off-line change point detection method was used to obtain the optimal estimation of inflection points derived from data of ice-tethered buoys, which were used to identify the top and lower ice interfaces. We assumed that there were N points in the sea ice temperature profiles, so that the sea ice temperature profile is described as follows:topI is the estimate of the top interface and lowerI is the estimate of the lower interface. C stands for a point in the sequence number of data cluster. E1(m), E2(m), E3(m) and E4(m) are both segmented mean estimations of the data cluster:The principle of the least-squares change point estimation is to take a change point to minimize the sum of the squares of the segment errors between the two subgroups where the change point is the demarcation point. Thus, this method can be applied to the discrimination of the top and lower interfaces. The target formula can be described as follows:We introduced the least-absolute deviations in the estimation of the change point. The principle of this estimation method is to define a change point to minimize the absolute value of the segment error, for which the least-absolute deviations is more robust to outliers than the least-squares method on the handling of errors. The least-squares change point estimation brings in larger errors, which are often the outliers in the measurements and may lead to false results of change point estimation. The least-absolute deviations method can effectively reduce the effect. The least-absolute deviations estimation of change point can be described as follows:kS .Input: The sea ice temperature profile or the daily amplitude of temperature profiles or the vertical temperature gradients kS , we use the Equation (10) to get a change point C0 and two new profiles kS  and kS .Step 1: For the input parameters kS  to get a change point C11. Then a change point C12 detected by the Equation (11) based on kS . The error of change point estimation is E1.Step 2: We use the Equation (10) to process kS , we use the Equation (11) to get a change point C22. A new change point C21 detected by the Equation (10) based on kS . The error of change point estimation is E2.Step 3: For the profile E1 < E2, C11 and C12 are two change points representing the top and lower interfaces. Otherwise, C21 and C22 are two change points.Step 4: If Output: Two change points and calculated ice thickness and snow depth.In the change point detection method, we propose a selection algorithm for change point detection to improve the accuracy of inflection point detection. The selection algorithm of two change points is described as follows:kS was the temperature value from the temperature profile and t was the only change point of the profile:Here, we used an off-line mean change point detection method for a normal distribution based on the maximum likelihood method for the optimal estimation of the change point position. First, we discuss the situation of single change points, and then expand to the detection method of multiple points. The noise of this profile can be de described as follows:If the noise is Gaussian white noise, the likelihood function is described as follows:E1, E2, σ1 and σ2 are both uncertain. To acquire the likelihood maximum, we calculated the derivative of E1 and E2, respectively and made the derivative equals zero. Then we get a set of equations:The parameters We plugged the results of Equations (15)–(18) to simplify the Equation (14), and we can get a simplified equation as follows:The maximum of the Equation (19) is equivalent to the minimum value of the following log-likelihood, which is the maximum likelihood estimation of the change point of the normal distribution:After applying a logarithm to Equation (20), a new equation was obtained:t that makes the minimum value of the upper form is the maximum likelihood estimation of the position of the change point. However, in the calculation of sea ice thickness and snow depth, two change points are used. Here, kS are the temperature values from the temperature profile and have two change points of t1 and t2. A set of 4D vectors T  is introduced. The criterion of change point estimation by maximum likelihood method is that seeking out a group of T  to make the next equation have the minimum value of solution:The We combined the calculated top and lower interfaces to obtain the ice thickness and compared with “actual” ice thickness. The “actual” ice thickness of IMBs and TUT was measured by acoustic sounders and the “actual” ice thickness of SIMBAs was measured by drilled holes. The maximum of average error of sea ice thickness was 0.0948 m in IMB-2009D, whereas the minimum of average error of sea ice thickness was 0.0205 m in SIMBA-2013A. The estimation error of the entire samples of sea ice buoys is distributed in a relatively narrow range. We set the estimation error within 0–0.06 m as a small error, denoted as SE, which were 92% of the total samples. In detail, the errors in SE were mainly within 0.02–0.03 m (54%). There were 19 groups with errors ranging from 0.03–0.06 m, and there were five groups with sea ice thickness errors of 0.03–0.04 m. The number of buoys with errors of 0.04–0.05 m and 0.05–0.06 m were seven groups, respectively. The errors of sea ice thickness within 0.07–0.09 m were described as a medium error affected 6% of the 50 buoys, denoted as ME. The sea ice thickness errors of more than 0.09 m can be considered as a large error and was 6%, denoted as LE. The maximum error of snow depth was 0.092 m obtained for IMB-2011C. The minimum error of snow depth was 0.017 m in SIMBA-2013A. There were three groups with errors of 0.01–0.02 m, 23 groups with errors of 0.02–0.03 m, six groups with errors of 0.03–0.04 m, 10 groups with errors of 0.04–0.05 m, four groups with errors of 0.05–0.06 m, one group with errors of 0.06–0.07 m, 0.07–0.08 m, 0.08–0.09 m, and above 0.09 m, respectively.The change point detection method can be simple to determine the top interface using daily amplitude of temperature profiles. However, sea ice basal interface cannot reliably be estimated using the daily change of temperature profiles. The range of estimation error of snow depth was small. The maximum (minimum) average error of snow depth was 0.105 m (0.023 m) obtained for IMB-2011C (SIMBA-2013A).The calculated and observed ice thicknesses were consistent . For exaThe maximum error of sea ice thickness was 0.092 m in IMB-2009D. The minimum error of sea ice thickness was 0.017 m in SIMBA-2013A. There were two of the total buoys in ME, and one buoy in LE. The maximum (minimum) error of snow depth was 0.098 m (0.019 m) obtained for the IMB-2011C (SIMBA-2013A).Temperature profiles were used to calculate top and lower interfaces of sea ice through the maximum likelihood detection method . The floThe maximum (minimum) error of snow depth was 0.096 m (0.019 m) obtained for IMB-2011C (IMB-2003C). There were two groups with errors of 0.01–0.02 m, 17 groups with errors of 0.02–0.03 m, 12 groups with errors of 0.03–0.04 m, 10 groups with errors of 0.04–0.05 m, four groups with errors of 0.05–0.06 m, and one group with errors of 0.06–0.07 m, two groups with errors of 0.07–0.08 m, one group with errors of 0.08–0.09 m, one group with errors of above 0.09 m, respectively.The maximum error of snow depth using daily amplitude of temperature profiles was 0.093 m obtained for the IMB-2011C, and the minimum error of snow depth was 0.019 m obtained from two buoys of IMB-2003C in Arctic and SIMBA-2013A in the Antarctica. The range of estimation error of snow depth is small, which there were 46 buoys in SE, three buoys in ME, and one buoy in LE.The maximum error of sea ice thickness was 0.094 m in IMB-2009D. The minimum error of sea ice thickness was 0.014 m in IMB-2012J. There were two of the total buoys in ME, and one buoy in LE. The maximum error of snow depth was 0.097 m in IMB-2011C. The minimum error of snow depth was 0.016 m in SIMBA-2013A.In general, all the calculated average errors compared to the observed values of ice thickness obtained by the methods of change point and maximum likelihood are within a range of 0–0.11 m. After we used two methods to process the data of temperature profile, daily amplitude of temperature profile and vertical gradients of temperature profile, the maximum average errors of ice thickness occurred in IMB-2009D. The minimum average errors of ice thickness occurred in SIMBA-2013A (which appeared five times) and IMB-2012J (it appeared once). Correspondingly, all the calculated average errors of snow depth were 0.016–0.105 m. The maximum average errors of snow depth occurred in IMB-2011C. The minimum average errors of snow depth occurred in SIMBA-2013A (it appeared five times) and IMB-2003C (it appeared two times).In In The results of calculating ice thickness using the temperature profiles by two methods are shown in In The calculation of top interfaces using maximum likelihood method was closer to the observed values compared to that obtained using the change point method, with average errors of 0.038 m to 0.043 m, respectively, as shown in The results of calculating ice thickness using the vertical gradients of temperature by two methods are shown in The maximum and minimum errors of ice thickness and snow depth calculated by the two methods based on three parameters are shown in As shown in We showed 48 buoys with monthly ice thickness error in the Arctic and two buoys in Antarctica. The maximum value of average error was 0.026 m obtained in July and the minimum value of average error was 0.022 m obtained in January in the Arctic a. For SIIn summer, sea ice was at the melting period, the temperature of the upper ocean was colder than the ice cover, and a large number of bubbles and pores appeared in the middle layer of sea ice. At this time, sea ice temperatures may be in an isothermal state. And in such state, the calculated sea ice thickness will have larger error. In winter, when sea ice was in its freezing period, the sea ice internal temperature gradient was established very well, and the difference of temperature between ice and upper ocean was more obvious. Thus, the calculated sea ice thickness had a smaller error.When we used the discrimination algorithm to calculate the sea ice thickness, the temperature sensor intervals on the thermistor strings influenced the accuracy of sea ice thickness. The thermistor strings of IMBs have 45 temperature sensors equidistantly distributed with spacing of 0.10 m. The SIMBAs have 240 temperature sensors with 0.02 m interval on a flexible chain. TUT buoy has 150 temperature sensors and its temperature sensors interval is 0.03 m. We selected a TUT and two SIMBA buoys to analyze the effect of vertical spacing of temperature sensors on the calculation of ice thickness  and resaAs shown in Typically, ice floes in the Arctic and Antarctic with thicker initial ice thickness would have smaller growth rate and earlier onset of ice melt. The correlation between average error of sea ice thickness and initial ice thickness is clear. In the future, as the thinning of Arctic sea ice cover, our algorithm will become more reliable.Data of temperature profiles measured on two sets of SIMBA deployed in Antarctica near Zhongshan Station in 2013, 2014 were used to discern ice thickness with algorithms. Because of regional variations in sea ice physical properties or variations in the external environment, such as air temperature, snowfall, and salinity, the applicability of the discrimination algorithm needs to be reassessed. In the same ice season of freezing period, for example, vertical temperature gradients of sea ice were different between Arctic and Antarctica. In Antarctica, the sea ice where SIMBA-2013A and SIMBA-2014A were deployed was first year ice with very thin snow depth (<0.10 m). Thus, the internal temperature of sea ice may be easily affected by air temperature changes, resulting in larger estimation errors. More measurements from temperature sensors or interpolated temperature were fitted to linear profile to calculate the intersection to improve the accuracy of calculating the ice thickness. As shown in To efficiently use the snow depth and ice thickness discrimination algorithm, a procedure of discrimination  was builThe procedure to determine snow depth and ice thickness using the morphological temperature profile (linear or C–type) was according to the discussed determination modes. Information of ice season  were also considered. The top and lower interfaces were represented by change points to calculate snow depth and ice thickness. The discrimination of ice thickness was selected by maximum likelihood theory using the temperature profile. For the calculation of snow depth, change point theory was used to process temperature profile of sea ice.Data preprocessing. Firstly, measured results of temperature profile of sea ice are processed by threshold filtering and removing the outliers, such as 30 °C or −60 °C. In the second place, smoothing filter is used to filter out irrelevant data. In the third place, we use interpolation to handle missing values by creating an interpolation function to replace the null values of the temperature profile of sea ice.(1)Calculate the intersection of the temperature profile and 0 °C. If the intersection is lower than the initial snow-ice interface, the period of sea ice is the melting period.(2)If the intersection is higher than the initial snow-ice interface, the temperature profiles and the vertical gradients of temperature profiles are used to identify the period of sea ice. If the vertical temperature profile is linear, it is the ice growth period. If the temperature profile is C-type, it is the warming period. And in the growth and warming period, the vertical gradients of temperature profiles are different, thus the period can be identified as shown in Based on the temperature profiles, the vertical gradients of temperature profiles are calculated. Then the period of sea ice is determined.Use the change point theory and maximum likelihood theory to generate the output of the change points.Use the change points to obtain the top and lower interfaces as described in For any given temperature profiles of sea ice measured by ice-tethered buoys, the following discrimination algorithm procedure can be followed:This research demonstrated an approach for calculating snow depth and ice thickness in the Arctic Ocean and Antarctic from temperature profiles, daily amplitude and vertical gradients of temperature profile observed by the ice-tethered buoys. We transformed the problem of calculation of snow depth and ice thickness into a change point detection problem, and evaluated the applicability of theories of change point and maximum likelihood in the determination of snow depth and ice thickness. A procedure was developed to use data of temperature profiles of sea ice to calculate the top and lower interfaces of the layer of snow-covered ice applicable to various ice seasons, temperature sensors interval, initial ice thickness, deployment position. The results indicated that calculated snow depth and ice thickness maintain acceptable range of error. Estimation ice thickness errors in summer were significantly larger than in winter. The higher resolution of the temperature measurement can effectively reduce the error in actual calculation of ice thickness. By analyzing the effect of initial ice thickness, the algorithm and procedure are more adaptable to rapidly changing Arctic sea ice because the smaller the initial ice thickness could be easier to build sea ice temperature gradient thus have smaller estimation errors for ice thickness and snow depth. The practicality of this procedure was examined. Results also indicated that the application of discrimination algorithm and procedure can effectively improves the scientific value of the data of temperature profile through the snow-covered ice even without the joint deployment of acoustic sensors to measure the ice thickness and snow depth.The ice-tethered buoys used in this study are IMBs, SIMBAs and one TUT buoy. The SIMBAs have a heating mode that effectively distinguishes the snow-ice interface. Therefore, the combination of the data of heating mode of SIMBAs and our discrimination algorithm together could be further used to distinguish the snow-ice interface in the next step. In the current study, we don’t detail with the interface between snow and ice because the data measured by the SIMBAs is still limited. In addition, we plan to optimize observation instruments set up in the buoy. For instance, the TUT can be assembled with a thermistor string of 10 m or longer, which a sensor spacing is 0.02 m or smaller. The accuracy of the thermistor string can be increased to reach 0.01 °C by replacing the semiconductor temperature sensor with a more accurate platinum resistor to improve the accuracy of snow depth and ice thickness estimation. The sensor of conductivity and temperature (CT) can be assembled to measure the salinity and temperature of seawater just under the ice cover. In this study, 48 buoys deployed in the Arctic Ocean and two SIMBA buoys deployed in Antarctica were used. Therefore, in future work, we will strengthen the analysis of the data of the ice-tethered buoys deployed in Antarctica, especially for the ice zones further offshore, to enhance the applicability of our algorithm. Different ice types should be considered and analyzed when using our algorithm. For instance, estimated ice thickness should be combined with ice types such as thick or thin multiyear ice, hummock, ponded ice, deformed ice, and ice ridges to enhance applicability and reduce the uncertainties of the discrimination algorithm under different ice types.