Bidirectional communication between presynaptic and postsynaptic components contribute to the homeostasis of the synapse. In the neuromuscular synapse, the arrival of the nerve impulse at the presynaptic terminal triggers the molecular mechanisms associated with ACh release, which can be retrogradely regulated by the resulting muscle contraction. This retrograde regulation, however, has been poorly studied. At the neuromuscular junction (NMJ), protein kinase A (PKA) enhances neurotransmitter release, and the phosphorylation of the molecules of the release machinery including synaptosomal associated protein of 25 kDa (SNAP-25) and Synapsin-1 could be involved.Accordingly, to study the effect of synaptic retrograde regulation of the PKA subunits and its activity, we stimulated the rat phrenic nerve  resulting or not in contraction (abolished by µ-conotoxin GIIIB). Changes in protein levels and phosphorylation were detected by western blotting and cytosol/membrane translocation by subcellular fractionation. Synapsin-1 was localized in the levator auris longus (LAL) muscle by immunohistochemistry.Here we show that synaptic PKA Cβ subunit regulated by RIIβ or RIIα subunits controls activity-dependent phosphorylation of SNAP-25 and Synapsin-1, respectively. Muscle contraction retrogradely downregulates presynaptic activity-induced pSynapsin-1 S9 while that enhances pSNAP-25 T138. Both actions could coordinately contribute to decreasing the neurotransmitter release at the NMJ.This provides a molecular mechanism of the bidirectional communication between nerve terminals and muscle cells to balance the accurate process of ACh release, which could be important to characterize molecules as a therapy for neuromuscular diseases in which neuromuscular crosstalk is impaired.The online version contains supplementary material available at 10.1186/s11658-023-00431-2. The neuromuscular system is a complex and interconnected network that links the nervous system with musculature, and their interaction is fundamental for their health and correct functionality. This crosstalk starts at the neuromuscular junction (NMJ) and is mediated by pre- and postsynaptic signals that are regulated by the nerve stimulus and the resulting muscle contraction. This bidirectional regulation is transduced into the synaptic cells and confluent on protein kinases that trigger intracellular pathways, which modulate activity-dependent mechanisms such as neurotransmission and synaptic plasticity. One of these kinases is the protein kinase A (PKA), a cAMP-dependent serine–threonine protein kinase controlling general cellular mechanisms but also synaptic-induced transduction pathways , 2 at maPKA is composed of two catalytic (C) and two regulatory (R) subunits, constituting an inactive tetrameric structure that is activated when cAMP binds to the R subunits and the C subunits are liberated –10. RegaActivation of PKA catalytic function depends on the presence of cAMP. This second messenger can bind to the cyclic nucleotide-binding domain (CNB domain) of regulatory subunits, which promotes the release and activation of PKA catalytic subunits. Conversely, in the absence of cAMP, the two regulatory and two catalytic subunits remain together, forming an inactive tetramer . PKA actWith the aim to investigate the influence of the pre- and postsynaptic activity on the neurotransmission modulation at the presynaptic component of the NMJ, we have chosen two PKA targets, synaptosomal associated protein of 25 kDa (SNAP-25) and Synapsin-1, in accordance with their implication with acetylcholine release and their presynaptic location , 31. SNATherefore, here we hypothesize that presynaptic stimulation and the induced muscle contraction differently modulate PKA catalytic and regulatory subunits to balance synaptic function and regulate kinase functions on several substrates from the synaptic exocytotic machinery. This study demonstrates that presynaptic activity and the resulting muscle contraction coordinately regulate the PKA signaling pathway at the NMJ and their phosphorylating activity on its relevant synaptic substrates pSynapsin-1 and pSNAP-25 T138. These findings add to our knowledge of the bidirectional communication between nerve terminals and muscle fibers to enhance acetylcholine release through PKA-phosphorylation targets at the NMJ.n ≥ 3) were used as a biological iteration. All animal work was approved by the Ethics Committee of Animal Experimentation of the Universitat Rovira i Virgili (reference number 10760).Male and female adult Sprague Dawley rats  were cared for in accordance with the European Community Council Directive guidelines for the humane treatment of laboratory animals. For samples of tissue harvest, the animals were euthanized with a lethal dose of 4% tribromoethanol (Sigma-Aldrich). For each type of experimental condition, at least three animals ].To block muscle stimulation μ-conotoxin GIIIB  was used. This toxin inhibits sarcolemmal voltage-dependent sodium channels (VSDCs) without affecting synaptic ACh release or ACh signaling . It was N-[2-((p-Bromocinnamyl)amino)ethyl]-5-isoquinolinesulfonamide dihydrochloride . H-89 was made as 10 mM stock and used at 10 μM diluted in Ringer’s solution with DMSO.PKA activity was blocked with All chemicals were diluted in Ringer’s solution, and both control and drug-containing solutions contained 0.1% dimethyl sulfoxide (DMSO) as the vehicle.As a typical model to study the development and function of the NMJ –45, diapPresynaptic stimulation (Ctrl versus ES): to show the impact of the synaptic activity, we compared presynaptically stimulated muscles whose contraction was blocked by μ-CgTx-GIIIB with nonstimulated muscles also incubated with μ-CgTx-GIIIB to control for nonspecific effects of the blocker.Contraction (ES versus ES + C): to estimate the effect of nerve-induced muscle contraction, we compared stimulated/contracting muscles with stimulated/noncontracting muscles whose contraction was blocked by μ-CgTx-GIII. By comparing the presynaptic stimulation with or without postsynaptic activity, we separate the effect of contraction. However, one should consider that postsynaptic contraction experiments also contain presynaptic activity.One hemidiaphragm was used as a treatment, and the other served as its paired untreated control. All treatments were performed ex vivo. Muscles were stimulated through the phrenic nerve at 1 Hz, which allows the maintenance of different tonic functions without depleting synaptic vesicles, for 30 min using the A-M Systems 2100 isolated pulse generator (A-M System) as in previous studies –40. We dIn the experiments that needed only stimulation without contraction, μ-CgTx-GIIIB was used see “”. NevertTo estimate the effect of PKA inhibition under synaptic activity, we compared presynaptically stimulated muscles whose contraction was blocked by μ-CgTx-GIIIB with and without H-89: ES versus ES + H-89.To show the impact of the PKA inhibition under muscle contraction, we compared stimulating and contracting muscles with and without H-89: (ES + C) versus (ES + C) + H-89.To demonstrate if degradation or redistribution along the axon is involved, the diaphragm muscle was dissected with special care to preserve phrenic nerve connectivity. We compared stimulating and contracting muscles with and without protease inhibitor (Prot.Inh.) cocktail 1% : (ES + C) versus (ES + C) + Prot.Inh.Furthermore, to assess the effect of PKA blocking, three different experiments have been performed:Whole-cell lysate samples were immediately frozen after treatment completion. Membrane/cytosol fractionated lysates were processed immediately after the treatment without a freezing step. Further details of homogenization and western blotting technique can be consulted in Cilleros-Mañé et al. .The densitometry of the bands was obtained with ImageJ software. The integrated optical density of the bands was normalized with respect to: (1) the background values; and to (2) the total protein transferred on PVDF membranes, measured by total protein analysis . The reRattus norvegicus databases  and contaminants database (247 entries), both searches assuming trypsin digestion. The false discovery rate (FDR) and protein probabilities were calculated by Percolator. For protein quantification, the ratios between each TMT label against 126-TMT label were used and quantification results were normalized on the basis of protein median. The results are a ratio of reporter ion abundance, and they are dimensionless.Diaphragm preparations were collected and processed to obtain proteome data. Protein identification, filtering, and quantification were performed by a specialized Centre for Omic Sciences at the Universitat Rovira i Virgili. In brief, protein identification and quantification was performed on Proteome Discoverer software v.1.4.0.288  by Multidimensional Protein Identification Technology (MudPIT) combining the eight raw data files obtained from each fraction of the sample. For protein identification, all MS and MS/MS spectra were analyzed using Mascot search engine (v.2.5). The workflow was set up using two different Mascot node combing n = 3).Synapsin-1 was localized in the levator auris longus (LAL) muscle by immunohistochemistry (IHC) in accordance with the protocol presented in Cilleros-Mañé et al. . LAL musAs a control, primary antibodies were omitted from some muscles during the immunohistochemical procedures. These control muscles never exhibited positive staining. In double-staining protocols, omitting either one of the two primary antibodies completely abolished the corresponding staining and there was no cross-reaction with the other primary antibody. At least three muscles were used as negative controls.r) was used for quantitative analysis of colocalization. This statistic coefficient provides the overall association of two probes in an image. Images were assembled using Adobe Photoshop software , and neither the contrast nor brightness was modified.A laser-scanning confocal microscope (Nikon TE2000-E) was used to study immunolabeled NMJs from the whole-mount muscles . FIJI (In ≥ 3) were used as biological replicates for every experiment detailed previously. Thus, all experiments were carried out at least in three biological replicates, and each one of these was assessed in three technical replicates. Sample size to optimize the number of used animals was calculated using the previously established criteria [t-test or its nonparametric alternative Wilcoxon test . Each dot in the bars of the graphs represents the mean result of one animal. The significance threshold was *p < 0.05, **p < 0.01, and ***p < 0.001.At least three animals  and regulatory (R) subunits, we selected the antibodies that showed high specificity for the corresponding protein at the predicted molecular weight (in kDa): Cα 40, Cβ 40, RIα 48, RIβ 51, RIIα 50, and RIIβ 53 in the rat diaphragm Fig. A 52, 53. To estiWith the anti-PKA antibodies validated, we studied whether the presynaptic activity and the resulting muscle contraction regulate the protein levels of C and R subunits. The PKA catalytic subunits Cα and Cβ reacted in different ways to synaptic activity and muscle contraction Fig. C. PKA CαThe PKA regulatory subunits also responded differently to presynaptic stimulus Fig. D. RIα reIn summary, all C and R subunits can have their own response to neuromuscular synapse activity events, and a modulatory influence of the postsynaptic activity on the PKA activity can be inferred. Figure Because the subcellular distribution of the PKA subunits contributes to the regulation of PKA action, we next examined how pre- and postsynaptic activities redistribute the PKA subunits between the membrane and cytosol fraction. Figure The Cα Fig. C and Cβ When regulatory subunits are analyzed after presynaptic stimulation, we found that presynaptic stimulus (Ctrl vs ES) did not change the cytosolic levels of any of the R subunits except for RIIβ, which increased, which would contribute to maintaining the holoenzyme in the cytosol and negatively regulating the catalytical action of the PKA Fig. E–H. In aAnalyzing regulatory II subunits in the contraction condition (ES versus ES + C), we found that RIIα and RIIβ Fig. E, F wereFinally, we found that regulatory I subunits are regulated only during nerve-induced muscle contraction, although RIβ also decreased in the membrane as a result of the presynaptic stimulation, which would oppose the catalytic action of PKA Fig. G, H. RIαIn summary: (i) electrical stimulation results in a moderate increase of total Cβ and a redistribution of both RIIα (less expression and displacement to the membrane) and RIIβ ; and (ii) after the nerve-induced muscle contraction, there is a total reduction of the Cβ both in membrane and cytosol as well as an inversion of the RIIα and RIIβ protein level and subcellular distribution that suggests a balance between both regulatory isoforms to control PKA activity.After determining that neuromuscular activity regulates the association of PKA with the cytosol and membrane compartments, we studied whether the anchor protein AKAP150 could be involved because it participates in the neuromuscular muscarinic-induced PKA signaling . We usedTo further explore the neuromuscular activity regulation on the PKA signaling and focus on the presynaptic effects, we evaluated the phosphorylation of the PKA substrates SNAP-25 and Synapsin-1 , and that of Synapsin-1 versus nAChRs showed a poor association (r = 0.56 ± 0.11) . As expected, PKA downregulation with H-89 abolished the muscle contraction effect on pSNAP-25  pathway –40. MuscThe arrival of the nerve impulse at the presynaptic terminal of the NMJ triggers the release of ACh and results in muscle contraction. Several molecular pathways carefully regulate neurotransmission, including PKA signaling , 36, 62.The activity of the C and R subunits of PKA, besides cAMP availability, is controlled by transcriptional or posttranscriptional changes in the synthesis or degradation –26. At NHere we found that PKA catalytic subunits were differently affected by synaptic activity. PKA Cα levels did not vary in any treatment, so it could be considered as constitutive in the skeletal muscle and not regulated by synaptic activity. However, PKA Cβ increased in presynaptic stimulus conditions and decreased during nerve-induced muscle contraction. The decrease of Cβ levels during muscle contraction (ES versus ES + C) was induced following proteasomal activity and activity-induced degradation of the subunit. Therefore, it seems that the nerve-induced PKA Cβ levels are retrogradely downregulated by the contraction effect. This retrograde regulation might be due to BDNF/TrkB, as our preliminary data show that the effects of presynaptic stimulus and muscle contraction on Cβ can be enhanced with exogenous BDNF treatment.2 muscarinic ACh receptors (mAChRs), which downregulate ACh release at the NMJ [2 muscarinic receptors are G-protein-coupled receptors (GPCRs) that use G proteins to inhibit adenylyl cyclase and PKA. Furthermore, PKA Cβ is inhibited by M2 mAChR in basal conditions at the NMJ [2 mAChR while presynaptic stimulus enhances it. Alternatively, we cannot discard an activity-induced degradation of PKA Cβ [Furthermore, the contraction-induced downregulation of PKA Cβ, detected at the membrane and cytosolic fractions, could be related with the M the NMJ . The M2  the NMJ  like conf PKA Cβ  caused bPKA regulatory subunits are directly involved in the regulation of the activity of the catalytic ones. Therefore, the study of their regulation is crucial to understanding the PKA signaling. Here we show that PKA regulatory subunits were regulated differently by synaptic activity. Thus, RIα seems to be regulated only by the postsynaptic contraction, suggesting its involvement in a contraction-dependent postsynaptic mechanism. RIβ is regulated by a presynaptic stimulus-dependent mechanism. Interestingly, RIIα and RIIβ follow opposed dynamics. RIIα decreased in Ctrl versus ES but increased in ES versus ES + C condition. RIIβ, however, increased by presynaptic activity (Ctrl versus ES) but decreased by nerve-induced muscle contraction (ES versus ES + C). Therefore, RIIα downregulation in presynaptic stimulus condition would permit catalytic subunits (maybe Cβ) to phosphorylate targets involved in ACh secretion. On the other hand, during the muscle contraction condition, RIIα would act oppositely by binding to catalytic subunits (maybe Cβ) and inhibiting their activity and ACh liberation. This is in concordance with the dynamic of RIIα in the membrane and explains the effect of presynaptic and muscle contraction activities over pSynapsin-1 levels (see below). On the contrary, presynaptic stimulus condition would increase RIIβ and prevent catalytic subunits (maybe Cβ) from phosphorylating targets, whereas muscle contraction condition would decrease RIIβ and allow the activity of catalytic subunits. This is in concordance with the dynamic of RIIβ in the cytosol and would be closely related with SNAP-25 T138 phosphorylation as this phosphorylation is promoted in the opposite way by pre- and postsynaptic activities (see below). In summary, each PKA regulatory subunit has its own precisely adjusted regulation mechanism. Although the regulation of RIα and RIβ remains unclear, RIIα and RIIβ are oppositely regulated by pre- and postsynaptic activities and can inversely regulate the activity of Cβ subunit. Together, the present results indicate that several activity-dependent mechanisms can balance the final catalytic activity of Cβ at the NMJ.Targeting of PKA to specific intracellular sites is achieved by AKAPs , 67. AsiN-ethylmaleimide-sensitive factor attachment protein alpha (α-SNAP) [We observed the phosphorylation of targets to understand the regulation of kinase activity. Some PKA targets involved in transmitter release are (α-SNAP) , cystein(α-SNAP) , Synapsi(α-SNAP) , snapin (α-SNAP) , syntaph(α-SNAP) , rabphil(α-SNAP) , Rab3 in(α-SNAP) , and SNA(α-SNAP) , 34. SNA(α-SNAP) , 77. SNA(α-SNAP) . Nonphos(α-SNAP) . Once ph(α-SNAP) , 30, 79.Here we found that SNAP-25 and pSNAP-25 T138 were differently regulated by synaptic activity. Coincident with our previous study at the NMJ , SNAP-25Recent results indicated that Cβ is enriched in the muscle synaptic areas of the diaphragm while Cα is almost equally distributed between synaptic and extrasynaptic regions . Accordi1, which decreases ACh release [The results show that phosphorylation of SNAP-25 at T138 is not modified by the presynaptic stimulus, indicating that a change of this phosphorylation is not crucial to the ACh release at the NMJ, unlike the phosphorylation of SNAP-25 at S187 or Munc18-1 , among o release , does no release . This an2 inhibition in basal condition (which increases the ACh release) induces an important increase in SNAP-25 T138 phosphorylation, which can be abolished by a previous incubation with H-89. This finding demonstrates that mAChR-M2 inhibits the phosphorylation of SNAP-25 at T138, which is modulated by PKA activity [2, Cβ subunit, and SNAP-25 T138 phosphorylation. Here we found that the contraction condition has the same effect as the mAChR-M2 inhibition on RIIβ subunit, AKAP150, and SNAP-25 T138 phosphorylation. Thus, we could suggest that contraction downregulates mAChR-M2 and therefore the final phosphorylation of SNAP-25 T138 will increase, as we found here. Further experiments are needed to confirm this hypothesis.Recently, it has been shown that mAChR-Mactivity . These r2 muscarinic AChR and A1 adenosine receptor [Interestingly, the activity-induced dynamic of Synapsin-1 phosphorylation is different from that of SNAP-25 T138 phosphorylation. pSynapsin-1 level increases during presynaptic stimulation, and nerve-induced muscle contraction downregulates it. Both conditions downregulated pSynapsin-1 level during previous incubation with H-89, demonstrating that the Synapsin-1 phosphorylation involves PKA activity at the NMJ. Levels of pSynapsin-1 and Cβ subunit change accordingly, suggesting a direct relationship. Furthermore, the results also show that RIIα may contribute to the regulation of Cβ subunit to phosphorylate Synapsin-1. Synapsin-1 phosphorylation induced by presynaptic activity would contribute to ACh release as synapsins regulate vesicle clustering and therefore neurotransmitter release , 80–88. receptor –94. Howereceptor  Further Here we found that the total protein of Synapsin-1 changes in parallel to pSynapsin-1 in response to pre- and postsynaptic activities. A previous incubation with H-89 affects Synapsin-1 and pSynapsin-1 levels differently, suggesting different PKA involvement. H-89 preincubation in the presynaptic activity conditions did not change Synapsin-1 levels. Thus, PKA is not involved in the increase of Synapsin-1 during presynaptic stimulus. However, Synapsin-1 levels decrease when H-89 is preincubated during contraction, indicating that PKA activity contributes to maintaining Synapsin-1 levels in the nerve terminal, in opposition to the contraction effect. In addition, contraction could downregulate PKA levels, resulting in the Cβ subunit decrease observed here. The results indicate that contraction triggers the PKA positive regulation over Synapsin-1. Regarding Synapsin-1 phosphorylation, H-89 preincubation reduces pSynapsin-1 levels during presynaptic activity and muscle contraction. Thus, PKA activity phosphorylates Synapsin-1 during both activity conditions. Because muscle contraction induces downregulation of both Synapsin-1 and pSynapsin-1 and PKA opposes both downregulations (ratio pSynapsin-1/Synapsin-1 remains unchanged), the decrease in pSynapsin-1 could be a direct consequence of the decrease in total protein.Several subunits of PKA have been located at the NMJ –23. In pAs there are postsynaptic PKA substrates, we cannot discard that the activity-dependent effect of PKA on these targets could be involved in the retrograde signaling pathway here described. Indeed, it is well known that a balance of PKA and PKC regulates the stability of AChRs at the NMJ, both during development –101 and The results provide evidence that nerve terminals need both pre- and postsynaptic activities to modulate SNAP-25 and Synapsin-1 phosphorylation and ensure an accurate neurotransmission process. It is known that muscle contraction further enhances the nerve-induced BDNF levels , 55, andIt is known that PKA activity enhances ACh release at the NMJ. In the present work, we determined the activity-dependent dynamics of PKA subunits at the NMJ, which are related with phosphorylation of targets involved in synaptic vesicle exocytosis (SNAP-25 and Synapsin-1). Figure The results provide a molecular mechanism of the bidirectional communication between nerve terminals and muscle cells that contribute to balancing the optimal process of ACh release, which could be important to provide molecules as a therapy for neuromuscular diseases in which the neuromuscular crosstalk is impaired.Additional file 1. Original blots. The western blotting pictures were cropped from the original pictures with different conditions but not modified. Each dot in the bars of the graphs is representing the mean result of one animal.


Numerous complications of lumbar fusion surgery have been reported, with adjacent segment disease (ASD) being one of the most important. Few studies describe the effect of sagittal, horizontal screw angles and distance of pedicle screw apex to superior endplate on the incidence of ASD in lumbar spine. The purpose of this retrospective study is to evaluate the hypothesis that unsatisfactory pedicle screw insertion positions would increase the likelihood of ASD.t test was performed to compare data between two groups; Spearman analysis was performed to analyze the relationship between two continuous variables. Multivariate binary logistic models were performed to identify the independent risk factors of ASD. The receiver operating characteristic (ROC) curve was performed to measure model discrimination and Hosmer–Lemeshow (H–L) test was used to measure calibration. ROC curve evaluated the discrimination ability of sagittal screw angle and distance in predicting incidence of ASD.Outpatients with lumbar spinal stenosis underwent posterolateral lumbar fusion at L4-S1 with a least 2-year follow-up were studied. ASD at L3–L4 was defined as a condition in which intervertebral disk narrowing, posterior vertebral opening, and vertebral slippage progress at the last follow-up in comparison with the postoperative. Independent p < 0.05). VAS, ODI at the last follow-up were positively correlated with Pfirrmann grade of L3–4 disk and sagittal screw angle, while negatively correlated with distance of screw apex to superior endplate (p < 0.05). Multivariate binary logistic model indicated that follow-up time , distance of screw apex to superior endplate , sagittal screw angle  were statistically significant. The models showed great discrimination and calibration. The area under the curve of ASD identified by sagittal angle and distance was 0.895 and the cut-off values were 5.500° and 6.250 mm, respectively.Patients in ASD group exhibit higher incidence of osteoporosis, higher Visual analogue scale (VAS), Oswestry disability index (ODI), bigger sagittal screw angle, shorter distance of pedicle screw apex to superior endplate than those in non-ASD group (Sagittal screw angle and distance of screw apex to superior endplate were significantly associated with the risk of ASD. Various complications and problems related to PLF have been reported. Among these complications, the incidence of radiographical ASD has been reported to be higher than 8%, attributing to the fact that constrained mobility and loads of the instrumented segments is compensated by the adjacent segment , 2. MostThis study was conducted based on the principle of voluntary participation and was in accordance with the protocols proposed by the Ethic committees of Shanghai East Hospital. All the patients signed corresponding informed consents and verbally informed prior to the study and understood the purpose for which their data will be used. Four-hundred and forty-three patients who underwent PLF at L4-S1 combined with or without bone cement screw at L5 by one surgeon in our hospital and underwent outpatient follow-up between September 2015 and June 2022. Finally, 189 patients with at least 2 years of postsurgical follow-up were included in our study. The inclusion criteria were the presence of preoperative lumbar spinal stenosis at L4-S1 levels, chronic and persistent radiculopathy despite conservative treatment, progressive neurological deficits, persistent and unremitting low back pain for more than 6 months, loss of quality of life because of neurological claudication. The exclusion criteria were as follows: spondylolisthesis, concomitant scoliosis of more than 15°, violation of adjacent facet during surgery, patients with prior spinal surgery, postoperative infection and patients who underwent implant removal during the follow-up period.In all patients, PLF at L4-S1 were performed. After a midline surgical exposure, the soft tissues were dissected to the lateral tips of the transverse processes. Exposure for the superior facet joint required detachment of muscle from facet capsule, but care was taken to preserve the capsule itself. During surgery, pedicle screw and rod instrumentation were performed without violating the facet joint.Preoperative demographic data including gender, age, body mass index (BMI), osteoporosis, years of follow-up were collected. Clinical outcomes including Oswestry Disability Index (ODI), low back visual analogue scale (VAS) at the last follow-up were recorded. Radiological evaluation including spinopelvic parameters, pedicle screw related, and ASD diagnosis-related parameters were measured by lateral radiographs. Spinopelvic parameters including sacral slope (SS), pelvic tilt (PT), lumbar lordosis (LL), pelvic incidence minus lumbar lordosis (PI-LL) were measured preoperatively  Fig. 1AA1A. PediT value >  − 2.5 as negative and T value ≤  − 2.5 as osteoporosis. Two orthopedic spine surgeons, who were not involved with the operation and blinded to all clinical information, performed radiological measurement using picture archiving and communication system. The measurements were performed twice for each parameter with an adequate time interval to prevent bias from distorting the results. To minimize the interobserver error, average values were used for statistical analyses.Radiographical ASD was diagnosed at the last follow-up when plain radiographs demonstrated one or more of the following lesions at the segment adjacent to the fused segments relative to postoperative: narrowing of disk height (> 3 mm), anterior or posterior slippage (> 3 mm), posterior opening between adjacent vertebral bodies (> 5°)  Fig. 2)2). Preopp value of less than 0.05 was accepted as significant.Statistical analysis was performed using SPSS 17.0 statistical package . The descriptive results were expressed as mean and standard deviation (SD) for continuous variables with an approximately normal distribution. The independent t test was performed to compare demographic, radiological, and clinical data between the two groups, Spearman analysis was performed for correlation between clinical evaluation and characteristics of patients. Two multivariable logistic regression models were obtained for continuous/two categorical indicators  with a binary indicator of ASD as the dependent variable and clinical characteristics as the candidate independent variables. Hosmer–Lemeshow goodness of fit test was used to assess the model fit (Hosmer–Lemeshow statistic ≥ 0.05). Sagittal screw angle and distance were tested to discriminate ASD using receiver operating characteristic (ROC) curves. Youden is defined as the sum of specificity + sensitivity − 1. p > 0.05). ASD group had a significantly higher low back VAS , higher ODI , bigger sagittal screw angle , higher incidence of osteoporosis , while ASD group had a significantly shorter distance of pedicle screw apex to superior endplate  compared with non-ASD group . No major surgery-related complications occurred, including wound infection, additional neurological dysfunction, or hardware failure. The gender, age, BMI, years of follow-up, Pfirrmann degree at L3–4, horizontal screw angle and spinopelvic parameters were comparable between two groups (p < 0.001), bigger posterior opening at L3–L4 vertebral  and more severe posterior slippage  of L3 at the last follow-up. While intervertebral disk height, opening angle at L3-L4 vertebral and vertebral slippage of L3 showed no significant differences postoperatively between groups , Pfirrmann grade  and sagittal screw angle , while negatively correlated with distance  and SS . Besides, ODI was positively correlated with Pfirrmann grade  and sagittal screw angle , while negatively correlated with distance .To reveal the influencing factors of clinical outcome, correlation between clinical outcome and patient characteristics are correlated in Table p = 0.684 for Hosmer and Lemeshow goodness of fit test. The observed vs predicted risk of ASD in model 1 showed a good fit (Fig. p = 0.742 for Hosmer and Lemeshow goodness of fit test. The observed vs predicted risk of ASD in model 2 also showed a good fit (Fig. Table Although advances in surgical techniques have made spinal surgeries for patients safer and more extensive and complex, some scholars even think that it may be an effective way to prevent ASD to pull out the screw for patients with unsatisfactory pedicle screw implantation , identifThere are also several limitations of this study. First, the muscles, skin, and other soft tissues are not included in the model. There are many other important factors that affect ASD, including age, BMI, length of fusion, type of instrumentation, sagittal parameters , and howOur results indicate that incidence of ASD increase with the prolongation of time after surgery, shorter distance of pedicle screw apex to the superior endplate and bigger sagittal screw angle between pedicle screw and superior endplate, especially with a distance < 6.250 mm or sagittal screw angle > 5.500°. Spinal surgeons should be aware of the placement of pedicle screws during surgery, and it may be an effective way to prevent ASD to pull out the screw for patients with unsatisfactory pedicle screw implantation.


To study the optimum entry point and trajectory for anterior transpedicular root screw (ATPRS) placement into the lower cervical spine (LCS), so as to provide a basis for clinical application. 3-7 and the left P line  was equally divided into 9 zones (a-i). The ideal entry point and path of cervical ATPRS were designed and recorded. Additionally, 7 cadaveric specimens were selected, and the screw placement parameters were regenerated according to the above methods for screw placement.  A retrospective analysis of cervical CT images of patients who underwent cervical CT examination in the Spinal Surgery of Ningbo No. 6 Hospital from January 2020 to August 2021 was conducted. The data were obtained and modeled. On the coronal plane, the vertebral body (VB) between the anterior midline of cervical vertebral segments C3-7 in zones a, d, and g, zones b, e, and h, and zones c, f, and i showed a gradually decreasing trend. The sagittal angle range of C3-7 in all patients showed a gradually increasing trend in zones a-c, d-f, and g-i. The distance from the anterior midline of C3-7 to the P line increased in all patients, and the distance was longer in males than in females, with statistical significance. Pedicle screws were successfully inserted in all the 7 cadaveric specimens.  Zone i of each segment, with the longest screw length, was the best area for screw placement. In all patients, the horizontal angles of vertebrae C ATPRS placement can be used for LCS internal fixation, and the precise screw placement parameters can be simulated by the software, which provides theoretical basis for its future clinical application. The change of lifestyle in modern society has driven the continuously rising incidence of cervical spine injury (CSI), with degenerative diseases, trauma, and infection of the lower cervical spine (LCS) frequently occurring in the anterior column of the vertebral body (VB) , 2. ClinThere are two anterior cervical internal fixation methods: vertebral screws, which are widely used in clinical practice, and pedicle screws, which are limited by difficulty in screw placement , 11. TheTherefore, precise screw insertion is the key to successful clinical application of ATPS. Under this circumstance, we proposed a novel anterior screw technique called anterior transpedicular root screws (ATPRSs) for anterior cervical arch foundation, which is also the novelty of this study. At the same time, due to ethnic anatomical differences, there are limited anatomical and imaging studies related to this technique in China. Thus, the motivation of this paper is to study and measure the applied anatomy and CT of the lower cervical vertebra from the anterior approach, in order to provide anatomical and imaging-related parameters for ATPRS implantation in the LCS.Cervical CT images of patients undergoing cervical CT examination in the Spinal Surgery of Ningbo No. 6 Hospital from January 2020 to August 2021 were collected. Inclusion criteria: Adult patients who visited Spinal Surgery and underwent cervical CT examination, with no obvious LCS deformity or history of cervical surgery , and complete imaging data were enrolled. Exclusion criteria: Patients with fracture, deformity, tumor, and obvious degeneration or previous cervical surgery history were excluded, as well as those aged under 25 or over 70. According to the inclusion and exclusion criteria, 39 patients were finally enrolled, including 22 males and 17 females, with an age range of 28-66 years (mean: 39.3 ± 8.9). This study was approved by the hospital Ethics Committee (Ethics Number: NBU-2021-021). Because of the retrospective nature of this study, the requirement for informed consent was waived.3-7 successively through multilayer mask processing. After smoothing and triangular patch (TriPatch) trimming, the complete 3D images of vertebrae C3-7 were obtained and saved.The original CS CT data of 39 patients in Dicom format were imported into the Mimic 17.0 software  for analysis. After reading CT sequence images, the threshold value of CS was selected to obtain the original mask. Then, three-dimensional (3D) reconstruction was performed to establish 3D images of vertebrae CIn order to avoid measurement biases caused by subjective factors of the gauger, all the following data were repeatedly measured by two researchers skilled in the operation of mimics software, and the measured data results were averaged as the final data.3-7 levels were displayed with the highest transparency to establish contour lines. On the coronal plane, a line parallel to the anterior midline of the VB at the intersection of the anterior edge of the Luschka's joint  and the superior endplate was made, which is called the P line, and the VB between the anterior midline of the C3-7 levels to the left P line was evenly divided into 9 zones (a-i), by referring to our previous study [The retrieved 3D images of Cus study . Then, aα), sagittal plane angle (β) of the anterior pedicle screw, and the distance from the midline of anterior edge of VB to the P line were measured. Related data of each VB was recorded using the Excel software.Through the measurement module of the Mimics software, the measurement file was established to determine the position and length of the screw. The horizontal plane angle . After eliminating bone defects, deformities, and obvious degeneration by CT examination, the soft tissue in front of the cervical VB was gradually and thoroughly removed, the prevertebral fascia was incised, and the VB was exposed, with the strip width to the bilateral UVJs. The prepared specimens were refrigerated at -20°C and taken out 24 hours before the formal experiment.Seven CS specimens were randomly numbered and scanned by CT, and the related parameters were measured by the Mimics software in the same way as adult CS CT images. After thawing the specimen at room temperature and fully exposing the VB and other bone structures, the screws were gradually inserted under the guidance of C-arm X-ray machine and Kirschner wires, and ATPRS were placed under X-ray fluoroscopy. Subsequently, the screw trajectory was observed to determine whether the screw was located in the bone. After confirming that the upper wall of the channel and the left and right lateral walls were not damaged, the safety of screw placement was reconfirmed by anteroposterial-lateral fluoroscopy. Following screw placement, a CT scan was performed to determine the position of the screw and whether the screw was inserted safe and effective. Also, the specimen was dissected along the screw with a pendulum saw to check whether the screw penetrated the bone surface or damaged the surrounding structure, and the screw trajectory was observed.t-test was performed. For the intragroup comparison of screw placement zones, a paired sample t-test was used. A significance level of two-tailed α = 0.05 was used in all analyses.Statistical analysis was made by SPSS 22.0 . Mean ± SD was used to indicate the measurement data of normal distribution, and an independent samples 3-7 in all patients. The optimal areas were screw lengths over 22 mm for males and 20 mm for females, as shown in After software simulation of screw placement, it was found that the APSs could be placed at the cervical levels CP < 0.05). However, no significant difference was present in angles between zones a, d, and g of C3-7 segments in all patients (P > 0.05). Nor was there any obvious difference in the horizontal angle between zones b, e, and h and zones c, f, and i (P > 0.05), all of which showed a decreasing trend in the angle.The screw horizontal angles of all patients are shown in P < 0.05). However, there was no significant difference in the angle between zones a, b, and c of C3-7 segments in all patients (P > 0.05). Also, zones d, e, and f were not evidently different from zones g, h, and i in the sagittal angle (P > 0.05), all of which showed an increasing trend . Subsequently, screw placement was carried out on cadaveric specimens according to simulated nail placement parameters, which proved the feasibility of nail placement, as shown in As shown in At present, anterior CS plate fixation is widely used, in which 13-15 mm vertebral mono- or bicortical screws are mostly used as the fixation screws, with better fixation strength for patients undergoing single-level surgery . However3-7 on the same sagittal plane. But the horizontal angle of different sagittal planes showed statistically significant differences with a gradually decreasing trend, among which the angles of zones a, d, and g were the largest. Therefore, when nailing in these areas, the inclination angle should be the largest. The sagittal angle at the same level had no statistically significant difference, but the sagittal angle at different levels showed a statistically significant difference with a gradually increasing trend, which is similar to our previous findings [3-7 to the anterior edge of the UVJ gradually increased. Finally, we further applied this method to cadaveric specimens and confirmed its feasibility. However, this study mainly used fluoroscopy-guided free-hand screw placement, and despite successful placement in cadaveric specimens, the study demonstrated a 21.7% incidence of critical pedicle rupture in the axial plane [In this study, the Mimics software is used to carry out 3D reconstruction with CT images to realize simulation operation and 3D measurement. The simulation of screw placement can be repeated in the software, which is helpful to the study of the best entry area and trajectory of screw placement. The basic research of anterior pedicle screw placement using the Mimics software involves the question of reliability in comparison with the real thing. The samples included in the research were 3D models reconstructed by CT scanning. Although there is a certain difference between the surface treatment and the real object, there is no distortion in the angle; moreover, this technology is a CT scan orientation, so the distribution of the spatial structure of the model is consistent with the real object. At present, a large number of studies have used the Mimics software to build cervical VB for finite element analysis and clinical surgical procedures –29. In tfindings . The metfindings  and was al plane . Patton al plane  found thal plane , 32.This study still shows room for improvement. First of all, the data collected are all from CT reconstruction images, which inevitably leads to human errors. Besides, in the actual clinical operation, it is relatively difficult to find the optimum entry point in clinical screw placement because most patients requiring surgery had CS degeneration of varying degrees. And in view of the accuracy of this screw placement method, it is necessary to strictly study and analyze the patients with surgical indications. Moreover, the sample size of this study is small, so it is necessary to further study the biomechanical stability and accuracy of this screw placement method. In a word, ATPRS placement into the LCS is a feasible internal fixation technique, but its clinical application value needs further systematic analysis and research.In summary, anatomical and radiographic measurements demonstrate that ATPRS can be used as a means of internal fixation of the lower CS in clinical practice. Due to the accuracy of this operation, it should not be used as a routine operation, and patients with surgical indications should be strictly selected. But we think that as research continues and technology advances, ATPRS will be further promoted and applied.


Salmonella ssp. The viability of Lactobacillus ssp., Streptococcus spp. and Bifidobacterium ssp. was higher than 10 log CFU/g during the whole storage period. The GS and GK1 formulations had the lowest scores, while GK ice cream was preferred. The formulations showed distinct sensory profiles in the CATA, and the ice cream with 100% kefir was associated with desirable attributes. The ice creams exhibited microbiological and sensory characteristics that meet the expectations of the product’s target audience.Considering the need for functional foods and the use of by-products of the food industry, a potentially functional ice cream was developed, using soy extract, soy kefir and dehydrated jaboticaba peel. Five ice creams were produced using soy kefir (K) and soy extract (S): (1) GS—100% S; (2) GK1-75% S/25% K; (3) GK2-50% S/50% K; (4) GK3-25% S/75% K and (5) GK-100% K; The products were evaluated by physicochemical, microbiological and sensory  analyses. The addition of kefir was found to increase the acidity of the products. The concentrations of total phenolic compounds in the formulations with kefir were approximately ten times higher than the GS formulation. All products presented concentrations of thermotolerant coliforms <3 NMP/g and absence of  Consumer demand for a healthy and balanced diet has driven the food industry to seek alternatives that can meet this demand. Among the products developed focusing on this market are those with reduced trans-fat and sodium content, whole foods and organic, vegan and functional foods, including probiotics .Lactobacillus kefir, species of the genera Leuconostoc spp., Lactococcus spp. and Acetobacter spp. and lactose-positive and/or lactose-negative yeasts, which grow synergistically” [Kefir is defined as a “fermented milk produced by inoculating kefir grains or starter culture, composed of tically” . This betically” ,4,5,6.Cow’s milk kefir is the most popular beverage, but its consumption is limited for lactose-intolerant, dairy-allergic and vegan people, indicating the need to adapt the culture to non-dairy substrates, especially those obtained from cereals and legumes .The water-soluble soy extract can be used as a substitute for cow’s milk, due to its good nutritional profile and lactose-free characteristic, for use in products intended for consumers that are lactose-intolerant or allergic to milk protein . The soyIce cream is a frozen product obtained from an emulsion of fats and proteins or a mixture of water and sugar, allowing the addition of other ingredients, which does not de-characterize the product. This frozen dessert is widely accepted by consumers, with a worldwide consumption per capita of 5.29 L ,11.Due to changes in the population’s eating habits, the market for vegetable-based ice creams has expanded, with the incorporation of fruits with functional components, such as carotenoids, fibers and phenolic compounds.Among Brazilian fruits, the jaboticaba stands out for having a sweet and slightly spicy flavor and dark purple peel, rich in minerals, vitamin C, soluble fiber and phenolic compounds . Such compounds can be used as a natural dye and have antioxidant properties, with potential beneficial effects on health, such as modulation of the lipid profile and anti-inflammatory and anti-atherogenic properties .The modification of the raw material and ingredients used in the preparation of ice cream can affect the textural properties, flavor, color and product acceptance. Thus, the aim of this work was to develop a potentially functional ice cream, from soy extract, soy kefir and dehydrated jaboticaba peel, which can be consumed by lactose-intolerant, milk-protein-allergic and vegan individuals.The soy extract was obtained from the Soy Derivatives Production and Development Unit (Universoja—FCF—UNESP Araraquara) . The soyThe ripe fruits were purchased from local producers of Araraquara-SP in August and October 2018, the jaboticaba’s harvest period. The fruits were selected, sanitized in an aqueous sodium chloride solution (100 mg/L) for 30 min and rinsed in running water. The residue was dehydrated in ovens equipped with air circulation at 60 °C to constant moisture content (12.05 ± 0.27 g/100 g). Subsequently, the dehydrated residue was ground in a food processor, placed in glass jars covered with aluminum foil and stored at room temperature until use.Five ice creams were produced, with different concentrations of soy kefir and soy extract The formulations were processed on two separate occasions and evaluated during a 90-day storage period at −24 ± 2 °C. The proximate composition of the ice creams was determined in the freshly processed product, and the microbiological safety and viability of the potentially probiotic microorganisms were monitored at 15-day intervals. The other parameters  were evaluated immediately after preparation (T0) and at the end of the storage period (T90). The physicochemical and microbiological analyses were performed in triplicate.−8 (25 g of ice cream in 225 mL of sterile 0.1% peptone water). Microbiological quality was evaluated by the enumeration of coliforms at 45 °C, using the most probable number (MPN) assay [Salmonella spp. [Lactobacillus spp.—Lactobacilli Man Rogosa Sharpe ágar—MRS , Streptococcus spp.—M17 ágar and Bifidobacterium spp.—BIM-25 , respectively—using the microdroplet technique [Serial decimal dilutions were performed up to 10N) assay  and detella spp. . The popechnique . MRS andThe moisture, ash, protein and lipid contents were determined according to the Analytical Standards of the Adolfo Lutz Institute . Total cThe ice cream sample for the determination of phenolic compounds, antioxidant activity and anthocyanin were freeze-dried to preserve their characteristics in the evaluated time periods. The extracts for the determination of total phenolic compounds and antioxidant activity were obtained according to the procedure described by Karaaslan et al. . The qua®/T25 digital, Staufen, Germany) and incubation for 60 min. The extract was centrifuged  and the supernatant was stored in an amber flask. Then, the operation was repeated using 70% acetone. In a place without light, the obtained extracts were diluted, and a 100 μL aliquot of each dilution was mixed with 1.0 mL of the ABTS radical. After homogenization, the samples were left to stand for six minutes, and an absorbance reading was performed at 734 nm in a spectrophotometer (Shimadzu UV-1800). The analyses were performed in triplicate and the results are expressed in μmol Trolox/g.The antioxidant capacity was determined by capturing the ABTS free radical reaction according to the application used by Rufino et al. . For the®) [®, Series 1100, Santa Clara, CA, USA) equipped with a quaternary pump system and UV-visible detector. A C18 Shim-pak CLC-ODS reverse-phase column  was used for the separation of anthocyanins, and the mobile phase consisted of a linear elution gradient of formic acid (5%) and methanol, respecting the ratio of 85:15 (v/v) to 20:80 over 25 min, maintained for 15 min. The flow rate of the mobile phase, the injected volume and the column temperature used were 1.0 mL·min−1, 5 μL and 29 °C, respectively. The chromatograms were processed at 520 nm, and standard curves—constructed from standards of cyanidin 3-glycoside and delphinidin 3-glycoside —were used for the quantification.For the extraction of anthocyanins, 0.5 g of freeze-dried ice cream samples were diluted (1% HCL methanolic solution), homogenized (Ultra-Turrax®)  and vacu®) . The ext®) , using aThe color parameters of the samples were determined in a Konica Minolta portable colorimeter (CR-410) using illuminant D65 and 10° visual angle—using the CIEL L * a * b * system—whereas the chroma and hue values were calculated based on parameters a * and b * .A total of 115 consumers  participated in the acceptance  and ChecThe attributes of appearance, aroma and flavor were evaluated in the acceptance test using a nine-point structured hedonic scale, ranging from “I liked it a lot” to “I disliked it a lot” . In the Consumers were instructed to fill out a CATA questionnaire, containing attributes or phrases describing the ice cream samples . Such att test, (p < 0.05). The acceptance test results were analyzed by analysis of variance, considering the sample and consumers as sources of variation, and Tukey’s test of means (p < 0.05). For the CATA, the frequency of indication of each term was determined, and the Cochran Q test was applied to identify significant differences among samples for each of the sensory attributes. Furthermore, Multiple Correspondence Analysis (MCA) was performed on the frequency table, containing the samples in rows and the CATA questionnaire terms in columns.The data from microbiological and physicochemical tests were evaluated by analysis of variance and Tukey’s test of means or a Salmonella spp. and had coliform counts at 45 °C < 3 NMP/g during the storage period (90 days), being considered safe for human consumption [Bifidobacterium spp., Lactobacillus spp. and Streptococcus spp. was higher than 10 log CFU/g in all formulations throughout the storage period. Conventionally, kefir is prepared with cow’s milk, an ideal medium for lactic fermentation. In this study, replacing cow’s milk with soy extract did not reduce the viability of potentially probiotic bacteria, indicating an adaptation of the starter culture to the ice cream ingredients and processing conditions  to produce kefir.GS, GK1, GK2, GK3 and GK ice creams showed the absence of sumption . Regardinditions . Santos r et al.  verifiedIce cream’s processing steps, such as homogenization, churning and the consequent incorporation of oxygen, as well as low storage temperatures, may result in decreasing the population of beneficial microorganisms. Temperature fluctuations during storage, resulting in the formation of ice crystals, can also reduce the survival of strains . HoweverThere is no consensus about an appropriate dose of probiotic organisms required to achieve beneficial effects. According to the International Scientific Association for Probiotics and Prebiotics (ISAPP), all developed ice creams should be designated as a product “containing live and active cultures”. These products could not be considered a probiotic since they have a diverse microbial community that is not completely defined, in terms of the composition and stability of the strains, and without proof of beneficial effects. All ice cream also showed a minimum of 9 log CFU per serving, as suggested by ISAPP .The results of the physicochemical analyses are presented in w/v) to the soy extract used to obtain the soy kefir. The GK and GS formulations have the highest and lowest caloric values, with 139.52 and 98.02 Kcal/100 g, respectively, because of their carbohydrate contents. The protein and lipid contents were similar among all formulations.Kefir also caused a significant decrease in the moisture content of the ice cream (moisture GK = 69.61 ± 0.33 g/100 g and GS = 80.25% ± 0.14 g/100 g), and this result was expected because kefir has a lower moisture content than soy extract . The GK formulation (100% kefir) showed the highest carbohydrate content (21.59 ± 0.12 g/100 g), differing from the other formulations produced with a mixture of kefir and soy extract. This behavior is explained by the addition of sugar (10% p < 0.05) while GK had the highest average of such compounds . Higher stability in total phenolic content was observed for GK3, GK and GK2 formulations with a reduction of only 6.94%, 7.73% and 8.29%, respectively, after 90 days of storage. However, the opposite effect was observed in the GS formulation with a reduction of 52.26%   and jaboThe ice creams processed with kefir also exhibited the highest antioxidant capacity, without differing from each other. However, after 90 days of storage, only the sample without kefir (GS) did not show a significant reduction in this parameter. This result cannot be attributed to the concentration of total phenolic compounds because the formulations with kefir showed a lower reduction in these compounds at the end of the storage period. The antioxidant activity of a food is related to the concentration and chemical structure of bioactive compounds , which may change during storage. In addition, synergy among compounds may result in increased antioxidant activity ,44 and eThe concentration of anthocyanins was expressed as cyanidin 3-glucoside and delphinidin 3-glucoside , since tThe concentration of anthocyanins in ice cream formulations is related to the amount of peel added (0.02%), which was identical for all formulations. Delphinidin was the most abundant anthocyanin, contrary to the data obtained by Reynertson et al.  and InadOne of the challenges of using anthocyanins as natural dyes in foods is their instability in the face of pH variations, which affect the color conferred by these compounds. While a low pH is associated with greater stability of the compound, an increase in the pH results in a reduction in intensity and a change in the color pattern conferred . The pH The GK and GK3 formulations were the lightest samples showing the highest averages for parameter L * at T0 and T90. The values of a * were positive for all formulations, indicating a predominance of red color, and only GK1 showed a significant reduction in this parameter at the end of the storage time. The b * value was also positive for all formulations, with a tendency of a yellow color.Chroma (C *) is a quantitative parameter related to the color intensity perceived by the human eye. The GK1, GK3 and GK samples have the highest averages for C * at T0, and the formulation with 50% kefir (GK2) exhibited the greatest reduction in this parameter at the end of 90 days. The results of the hue angle  indicate that GK (T0 and T90), GK3 (T0) and GK2 (T90) are characterized by a reddish color, while in the other samples, there was a predominance of yellow.The characteristic coloration of the anthocyanin pigment is variable and can give the product a pink, red or blue color, depending on the source. Although equal concentrations of dehydrated jaboticaba peel were used, the addition of different concentrations of kefir may interfere with the color of the product, as well as with its stability. In summary, the results indicate that the ice creams are characterized as dark, with a color ranging from red to yellow and with low saturation. GK1 and GK2 exhibited the lowest color stability during storage, and the one prepared with 100% kefir (GK) showed a tendency toward the most pronounced red color.3.6. Sensory Analysis.p > 0.05) among the ice creams in appearance, color and aroma. After 90 days of storage, GK showed the highest hedonic averages for the sensory attributes evaluated, differing from all formulations regarding flavor. It is also noteworthy that the sample with 100% kefir (GK) was the only one that did not show a reduction in color acceptance during the storage time  at T0  and T90  . On the A total of 24 attributes were previously selected by the trained assessors to characterize the ice cream by the CATA method. The frequencies of the attributes and the results of Cochran’s Q-test for the freshly processed product (T0) are reported in p < 0.05) in 14 of the attributes analyzed , product acceptance and emotional responses . In thisThe results indicated that these ice creams, especially the one prepared with 100% kefir (GK), are suitable vehicles for potential probiotic strains. The jaboticaba peels imparted a purple color and improved the antioxidant capacity and anthocyanin profile of the products. The use of alternative raw materials and ingredients made it possible to obtain a product with functional potential that can be ingested by individuals who adopt a diet with the restriction of lactose, milk protein or foods of animal origin.


Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. This is because ‘what happens in the Arctic does not remain in the Arctic’ but rather is connected to areas at lower latitudes6. Changes in the Arctic will have profound effects politically, economically, ecologically, and climatologically on Earth. The Arctic is experiencing the largest temperature increases on our planet7 due to global warming. This process is attributed to Arctic Amplification10 and is driving the rapid changes in the Arctic. The most striking change is the decline in Arctic sea ice extent since the late 1970s12. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and volume, and transitioned to a predominantly seasonal ice cover14 compared to a perennial ice cover in the 1980–1990s17. From 2000 onward, observations suggest that the Arctic has become warmer and wetter18, evaporation and turbulent fluxes from the ice-free ocean has increased20, the surface albedo has darkened21, and cloud cover has also increased23. This era with these large changes observed in the Arctic climate system has been coined the ‘New Arctic’.Drastic changes occurring in the Arctic sea ice cover in recent years6. Understanding of the magnitude and nature of the reshuffling of the Arctic surface energy budget (SEB) and the feedbacks therein remains limited. This knowledge gap is illustrated by the large spread in climate model projections of the changes in surface turbulent fluxes, near surface temperatures, and hence lower tropospheric stability25. The temperature structure of the lower atmosphere and changes in the SEB (induced by the changes in the sea ice pack) are leading drivers of Arctic Amplification26. Therefore, synthesizing observations to better understand the evolution of the lower tropospheric temperature structure and its influence on the SEB is critical for improving model predictive capabilities of Arctic Amplification and future sea ice change.The shift to thinner, seasonal ice in the ‘New Arctic’ is accompanied by a reshuffling of energy flows at the surface28. Studies have recently shown that the accumulation of radiative energy at the surface in early summer  is a good predictor of September sea ice extent 29. Sea ice thickness has been shown to be important for predicting September sea ice area up to 6 months in advance30 owing to both thickness and extent being influenced by the same thermodynamic and dynamic processes. Thus, understanding what drives the year-to-year variability of sea ice thickness and extent, through winter preconditioning and melt season evolution, can help elucidate the drivers behind different projected trends in Arctic sea ice loss.Sea ice growth and melt are driven by changes in the SEB, which is influenced by atmospheric forcing and climate variability33. However, given sea ice mobility, this is a serious limitation to process-oriented understanding by inhibiting the ability to track cumulative effects of atmospheric processes on the SEB and sea-ice mass balance. Studying sea ice from a Lagrangian framework has been used for tracing biogeochemical transport35, ice volume flux38, snow distribution39, and for developing a numerical sea ice model40. Lagrangian tracking of coincident sea ice and atmospheric conditions has also been done41, which we expand on here with higher temporal resolution (daily) and more complete atmospheric conditions including terms for calculating the SEB. In this work, we present the creation of a database to monitor the memory of the sea ice parcels using a Lagrangian framework, tracking their daily motion, characteristics such as thickness and concentration, SEB, and associated atmospheric conditions as they undergo seasonal evolution and drift through the Arctic Ocean between October 2002 and September 2020. The database starts in 2002 as this highlights conditions in the ‘New Arctic’ and due to availability of important satellite data. This framework will enable the scientific community to effectively monitor and analyze the evolution of the sea ice and SEB over a variety of atmospheric and sea ice conditions. This effort uniquely unifies a wide variety of satellite and reanalysis data and can provide crucial knowledge of how the ‘New Arctic’ sea ice couples with the atmosphere, and also how a range of atmospheric forcings and episodic weather events influence the SEB, sea-ice mass balance, and hence seasonal evolution of Arctic sea ice.A quantitative understanding of the interaction between sea ice and the atmosphere is important for describing the coupled Arctic climate system and is necessary for improving model physics, which, in turn, can improve seasonal forecasts and climate projections of the fate of the sea ice in the ‘New Arctic’. Previous studies have examined and quantified sea ice-atmospheric interactions using an Eulerian framework41, the location of each sea-ice parcel is tracked daily using the weekly Simulated 12-month Ice Parcel Tracks from Gridded Sea Ice Motion Version 142 on the 25-km Equal-Area Scalable Earth (EASE) Grid. These weekly ice motion data are linearly interpolated to daily vectors. Weekly ice motion data are used to reduce uncertainties associated with assimilating daily buoy motion in the product . If sea ice concentrations fall below 15%, a parcel’s tracking is ceased. If more than 15% sea ice concentration materializes in open water grid cells, a new sea ice parcel is identified and tracked. It was found that the sea ice motion product can produce accurate tracking of parcels over time with little cumulative errors due to largely unbiased motion evaluations43. When comparing drift tracks to the drift of the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp, an error of 27 km over 293 days was found41. One study found that the standard deviation of the sampling error in ice motion to be 5 km to 12 km per day by comparing ERS-1 synthetic aperture radar (SAR) and drifting buoy motion to Lagrangian parcel tracks44. A separate simulation is run for each year, running from the beginning of October to the end of September of the following year . Each year, new sea ice parcels are identified at the beginning of October. Sea ice parcels that did not melt out by the end of September are “linked” with the sea ice parcels identified the following October and are flagged as multiyear ice (see “Outputs from Database” section below for more details). At the time of writing, the database includes data through September 2020.Sea ice parcels are tracked in a Lagrangian framework to investigate how sea ice characteristics and their SEB co-evolve and respond to atmospheric conditions. Beginning on 1 October 2002, sea ice parcels are identified in 25-km grid cells where sea ice concentrations are > 15% and given a unique identification number. Adapting the Lagrangian approach46. Along with these data layers, flags are given to denote the presence/absence of episodic weather events. Specifically, if a cyclone is present at the location of the parcel, the parcel is flagged with unique system identifications along with distance from the center of the system, the cyclone area, and several metrics of cyclone intensity. By incorporating synoptic event information, users can pinpoint perturbations that may result in propagating effects on the SEB and sea ice mass balance. Currently only closed system cyclones are flagged, however future work plans to include other types of episodic weather events.At each time-step, daily averaged variables of interest are incorporated as individual data layers for each sea ice parcel including those characterizing sea ice conditions, the SEB terms between the parcel and atmosphere, and atmospheric conditions (see Table 47. A group exists for each data source that has been combined with the sea ice parcel (Atmospheric Infrared Sounder (AIRS), Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS), Modern-Era Retrospective Reanalysis Version 2 (MERRA-2), Cyclones, etc.). Every group contains a dataset for each individual variable, with multiple columns denoting pressure levels when applicable. The files are grouped by year (“year” here is from October 1 to the following September 30) and for each year a metadata file exists summarizing key characteristics of each sea ice parcel which can be used to filter sea ice parcels that meet certain criteria. Because the tracking algorithm is restarted each year at the beginning of October, parcel ID numbers do not carry over from September into October. Instead, an additional metadata file is included linking all parcels that survived summer melt  with the nearest parcel at the beginning of the following October so that it is possible to track parcels across multiple years.The database is stored in HDF5 file format where an individual file exists for each unique sea ice parcel Fig. . The top48  and the Sea Ice Index product housed at the NSIDC49 (https://nsidc.org/data/G02135/versions/3). Both datasets are derived from two sources: (1) the Near-Real-Time Daily Polar Gridded Sea Ice Concentrations (NRTSI) from the Special Sensor Microwave Imager/Sounder (SSMI/S) on board the Defense Meteorological Satellite Program (DMSP) satellites50 and (2) the DMSP Special Sensor Microwave/Imager , and the Special Sensor Microwave Imager/Sounder . The Sea Ice Index uses the NASA Team Algorithm51 for sea ice concentration estimates, while the CDR is a rule-based combination of the NASA Team Algorithm and the NASA Bootstrap algorithm52. Sea ice concentrations derived from the NASA Team algorithm are used to determine when to start/stop the Lagrangian tracking method described above. Multiple sources of sea ice concentration are included as different algorithms perform better than others in certain conditions , although the trends in sea ice area and extent tend to agree53.Two sea ice concentration data products are included in this database, the Sea Ice Concentration Climate Data Record (CDR)54 . PIOMAS is driven by National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data and is formulated in a generalized orthogonal curvilinear coordinate system which is used for bilinear interpolation with every sea ice parcel location. This grid has a mean horizontal resolution of 22 km for the Arctic Ocean. Sea ice thickness values produced by PIOMAS compare favorably with Ice, Cloud, and land Elevation Satellite (ICESat) measurements, with a correlation of 0.83 and root mean squared error of 0.61 m for spring (February-March), and a correlation of 0.65 and root mean squared error of 0.76 m for autumn (October-November)55.Continuous, daily sea ice thickness estimates from observations are lacking for 2002–2020. Therefore, daily sea ice thickness is obtained from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS), a coupled ocean and sea ice model that focuses on the Arctic Ocean39 (https://nsidc.org/data/NSIDC-0758/versions/1). The model is forced with NASA’s Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalysis products, providing two individual sets of snow properties. By performing full surface and internal energy balances and mass balances within a multilayer snowpack evolution system, SnowModel-LG accounts for rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The redistribution of snow particles due to wind is not included in SnowModel-LG as the sea ice parcel sizes (14 × 14 km in SnowModel-LG) are too large to simulate snow erosion and deposition. Other possibly important processes that are not incorporated in SnowModel-LG include snow blowing into leads and snow-ice formation (when seawater floods the snowpack and refreezes due to a heavy snow load that submerges the ice surface below sea level). SnowModel-LG outputs have shown reasonable agreement with ice mass balance (IMB) buoys and measurements from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment and the Norwegian young sea ICE (N-ICE2015) measurements56.As with sea ice thickness, continuous, daily snow depth estimates from observations are not available for incorporation into this product. Daily, pan‐Arctic snow depth and density on a 25-km × 25‐km grid are obtained from the Lagrangian snow-evolution model (SnowModel-LG)57  and ECMWF Reanalysis 5th Generation (ERA5)58  variables provides more flexibility in research applications, such as relating the database to output from models forced by different reanalyses. For instance, SnowModel-LG produces two sets of snow characteristics, one forced with MERRA-2 and one forced with ERA5. Inclusion of atmospheric variables from each reanalysis model enables consistency for snow-atmosphere comparisons.Inclusion of both Modern Era Retrospective Analysis for Research and Applications (MERRA-2)in situ measurements taken during the N-ICE2015 campaign, ERA5 was found to have a large positive bias in 2 m temperature in winter and spring59.ERA5 was produced using 4D-Var data assimilation in CY41R2 of ECMWF’s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure (model) levels in the vertical, with the top level at 0.01 hPa. Values have a spatial resolution of 0.25° latitude by 0.25° longitude. Surface values used here include total precipitation, snowfall, skin temperature, surface pressure, 2-meter air temperature, total column water vapor, and 10-meter wind speed and direction. Specific humidity, air temperature, and wind speed and direction at four pressure levels  are also available. Although atmospheric data from ERA5 correlates well with in situ measurements during the N-ICE2015 campaign, MERRA-2 was found to have large biases in the total column water vapor in spring and summer59.MERRA-2 uses the Goddard Earth Observing System, Version 5.12.4 (GEOS-5) atmospheric model and Global Statistical Interpolation (GSI) analysis scheme and has an approximate spatial resolution of 0.5° latitude by 0.625° longitude. Surface values used here include total precipitation, snowfall, skin temperature, 2-meter air temperature, surface pressure, total column water vapor, 10-meter wind speed and direction, total precipitable water, and total precipitable snow. Specific humidity and air temperature at two pressure levels (850hPa & 500hPa) and wind speed and direction at three pressure levels  are also available. Although atmospheric variables from MERRA-2 correlate well with 61 (https://airs.jpl.nasa.gov/data/get-data/standard-data/), which is important in the Arctic where data are sparse and clouds are prevalent. AIRS Version 6 temperatures and humidity products have been compared with a variety of in-situ data and have shown to have modest errors in skin temperature (+/−2.3 K), 2 m air temperature (+/−3.41 K) and specific humidity (+/−0.55 g/kg)25. Version 7, AIRS-only atmospheric variables are used and include single level values of skin temperature, surface air temperature, and total column precipitable water as well as air temperature, geopotential height, and relative and specific humidity at 6 pressure levels . Some of these variables are also provided by MERRA-2 and ERA5, but AIRS provides an observational perspective to complement the model derived variables.NASA’s AIRS onboard the Aqua satellite was launched in May 2002 and has been collecting twice daily, global data ever since. AIRS has 2378 infrared channels and a 13.5 km spatial resolution. The AIRS instrument was designed to produce highly accurate temperature and humidity profiles globally62 is used for identifying closed cyclone systems due to its consistency in capturing cyclone events, its broad agreement in results with other cyclone tracking algorithms64, and the availability of methodology from Webster et al.65. To describe the tracker briefly, sea level pressure (SLP) fields from ERA5 reanalysis are regridded and smoothed to 1-degree resolution on the polar stereographic grid as described by Murray and Simmonds66. The Laplacian (LP) of the SLP fields is then calculated to determine the local maxima of LP relative to eight neighboring grid cells. Once these local maxima are identified, a set of criteria are imposed: (a) the second derivative of the SLP in the x- and y-directions must be positive, and (b) the mean LP in the immediate vicinity of the maxima must meet the “concavity criterion” where LP is equal to or greater than 0.2 hPa per degree latitude squared. At every 6-hourly time-step, the cyclone centers are determined through an iterative approach that finds the minimum first derivatives (in x and y) within the local area of a center candidate, identifying both open and closed systems. The cyclone area is determined by fitting an ellipse to the near-zero LP values in eight opposing directions from the cyclone center. For the purpose of the ice parcel database, only closed systems are included and given unique identifiers representing individual systems. All points within the cyclone area are flagged as the same cyclone event, and if multiple cyclones overlap in a given area, those points are flagged with each cyclone ID. Along with the cyclone ID, the minimum SLP, maximum LP, maximum wind speed, distance to cyclone center, and cyclone area information are included in the ice parcel database as a group.The Melbourne University cyclone tracking scheme67. Clear-sky/all-sky surface and top-of-atmosphere radiative fluxes are obtained from the CERES CERES-SYN1DEG product69 (https://ceres.larc.nasa.gov/data/#syn1deg-level-3).The CERES instrument is used by the Radiation Budget Science Project at NASA Langley to produce surface, atmosphere, and top-of-atmosphere radiative fluxes. These data products range from the instantaneous fluxes for each ~20 km CERES footprint to monthly, gridded radiative fluxes. This product incorporates CERES radiances, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud properties, surface albedo retrievals, and meteorological information from the Global Modeling and Assimilation Office (GMAO) to produce 1-hourly resolved surface, atmosphere, and top-of-atmosphere radiative fluxes. Comparisons between the CERES longwave (LW) and shortwave (SW) surface fluxes and surface radiometer observations show uncertainties ~6% in the longwave and 23% in the shortwave at the hourly, regional time scale over global ocean and landhttps://airs.jpl.nasa.gov/data/get-data/standard-data/), MERRA-2 10 m wind speed and passive microwave sea ice concentration produced using the NASA Team algorithm70. Turbulent fluxes are estimated using the bulk aerodynamic method with the Monin-Obukhov Similarity Theory and an iterative calculation based on Launiainen & Vihma71 on the 25km2 polar stereographic grid. These fluxes are derived with a few modifications that were tailored specifically to capture the unique conditions of the boundary layer and roughness of the Arctic sea ice (see for more information73). The Arctic sea ice specific changes made to this algorithm have not been adapted or included in any other climate models or reanalysis products and are better suited to simulate turbulent fluxes from the Arctic Ocean. In fact, when compared with in situ data from the N-ICE2015 campaign, AIRS LH and SH fluxes had errors of 0.74 W/m2 and 5.32 W/m2, respectively25. Overall, these comparisons produce an error of ~20% in the AIRS-derived surface turbulent fluxes, but provide the most complete picture of Arctic surface turbulent fluxes over a 20-year period, in the absence of in situ data.The turbulent flux terms of sensible (SH) and latent (LH) heat are produced using AIRS Version 7 Level 3 data products of skin temperature, 925 and 1000 hPa air temperature, relative humidity, and geopotential height  and ends on September 30 of the year (end year). In each of these directories there is one file for each sea ice parcel in HDF5 format. Each file name is ‘TrajD_SYYY-EYYY_XXXXX.hf’, where SYYY is the start year and EYYY is the end year, and XXXXX is the unique trajectory number. Each trajectory file has information of the parcel’s latitude and longitude location at each day of its lifetime. Each file also contains sub folders for AIRS, CERES, ERA5, MERRA-2, PIOMAS, sea ice concentration, snow, and cyclone information for that particular parcel at each day of the parcel’s lifetime. Information about each variable in each subfolder is contained in the files, with long name and unit descriptors. There is also information on each parcel in the ICE-PARCEL subfolder which contains information on the duration, start and end regions and dates, and if it survives the summer melt or not.The dataset is available at the National Snow and Ice Data Center (NSIDC) 10.5067/NJRT1HKVTFAQ75. These buoys were deployed in various regions throughout the Arctic Ocean and recorded sea ice thickness, snow depth, and air temperature and pressure along with GPS locations. IMB buoys provide 4-hourly data that are aggregated here to daily means. The ice mass balance buoy data are incorporated into the ice motion data42 of the ice parcel database and do not provide independent validation of drift location. However, the drift location of the buoys is useful for evaluating the derived Langragian sea ice parcel tracks. Additionally, comparing sea ice parcels with these buoys provides independent validation of key sea ice/snow/atmospheric variables.Data from sea ice mass balance (IMB) buoys is obtained from the CRREL-Dartmouth Mass Balance Buoy Program75. Although IMB buoys are not an independent validation of the sea ice parcel drift locations, they are a useful tool for evaluating the results of the Lagrangian methodology. After removing buoys that do not match the time frame of our database and those that contain erroneous location data, we are left with 74 buoys that are used for comparison. Figure The modeled sea ice parcel trajectories are assessed by comparing them to sea ice mass balance buoys that have been deployed throughout the Arctic Ocean76.The buoy/parcel pairs originating in the Central Arctic and drifting south through Fram Strait show the largest discrepancy in ending locations, with the buoy traveling further south than the sea ice parcel (the date of the end point locations are the same for buoys and sea ice parcels as buoy drifts tend to last longer than that of the ice parcels). While many trajectories remain within 100 km of the associated buoy Fig. , those bin situ point sources (buoys) are expected. Using NASA’s Operation IceBridge77 as a reference, the standard deviation of sea ice thickness within a 25 km by 25 km grid cell on a given day ranges from 0.01–5.67 m, with a mean of 1.44 m and median of 1.43 m . Smaller differences in air temperature and pressure are expected as these values have less spatial variability.Comparisons of the properties observed by sea ice mass balance buoys and those derived from the sea ice parcel Lagrangian framework are shown in Fig. Comparing the sea ice parcel values with buoy data Fig.  providesin-situ observations from the buoys themselves (Bobs). Figure To evaluate how much of the discrepancies in the variables between the buoys and the dataset are due to errors in the Lagrangian tracking differences, due to deviating trajectories, are compared to differences arising from modeling/retrievals/sampling scale. This is done using the same methodology used for the Lagrangian tracked ice parcel database where common parameters from the input datasets are interpolated to the locations of the IMB buoys and averaged over the parcel area. This results in parameters produced in our Lagrangian tracks database (Ldata), parameters produced with the same methodology but with the buoy locations (Bdata), and the 75 by identifying the closest ice parcel to the deployment location. Results show ice parcels generally remain within 100 km of the corresponding buoy  per year and 153 parcels  per year in May and June, respectively, are indicative of an earlier open water season in recent years83. Similarly, a negative trend in freezing sea ice parcels in October along with increasing trends in freezing in November  and 92.8  parcels per year, respectively) are representative of a later end to the open water season79.To examine ice parcel freezing/melting events further, Fig. With the shift from a predominantly multiyear ice (MYI) pack to a predominantly first year ice (FYI) pack in the ‘New Arctic’, the survivability of each of these ice classifications is of keen interest and can be observed as in Fig. 80. The SEB is calculated primarily with NASA remotely sensed observations of the radiative component from CERES and the turbulent flux component derived from AIRS:The inclusion of CERES and AIRS data with these sea ice parcel trajectories provides opportunities to examine connections between the SEB and the fate of sea ice. As mentioned earlier, studies have shown that the SEB in June, July, and August can be a good predictor of September sea ice extentTime-series of select variables relating to the SEB are shown in Fig. 75 (buoy ID 2004D) and the nearest sea ice parcel at time of deployment (sea ice parcel ID 2003–2004_19893) where vertical lines indicate the presence of cyclones. As buoys/sea ice parcels can be influenced by multiple cyclones on the same day, only the nearest cyclone (distance from buoy/ice parcel to cyclone center) is shown. Because the buoy and sea ice parcel locations differ slightly they often experience different cyclone events  and a transition to a seasonal sea ice cover. The survivability of sea ice parcels is linked to the June, July, and August summed SEB, where parcels that experience a larger flux of energy at the surface are less likely to survive the summer melt season. This can be expanded upon in future work by exploring the impact of individual components contributing to the SEB and the influence of SEB on autumn ice growth.81, this dataset includes drivers of surface energy fluxes from which the SEB can be calculated. Additionally, flags have been included to identify when sea ice parcels are potentially influenced by synoptic events such as cyclones. The dataset records distance from the center of the system as well as cyclone intensity. This dataset allows users to track the movement and evolution of sea ice parcels and the associated atmospheric state as they advect throughout the Arctic Ocean.A ‘New Arctic’ sea ice parcel database has been presented which combines satellite observations and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This novel dataset contains daily sea ice parcel locations, sea ice and snow conditions, and atmospheric states and fluxes from 2002–2019. Building on previous workCurrent work is underway to include additional data layers to the Lagrangian ice parcel database for enabling studies of atmosphere-ice interactions; these data layers include: a daily surface melt and freeze product, indicators for extreme moisture and warm/cold air intrusions, and flags for polar low systems. Additional data sets may be added in the future where applicable, and the database will be updated yearly with current data.83. Throughout the summer melt season, air temperatures tend to oscillate around the freezing point and there are multiple melt and refreezing events that occur at the surface84. A new daily melt onset data product (under development) can be incorporated to assess the amount of melt/freeze events that occur for individual sea ice parcels along with atmospheric and snow/sea ice conditions.The timing of melt onset and freeze up throughout the melt season is important for sea ice survivability throughout the year and in understanding the Arctic climate systemet al.85. and Woods & Caballero86 will be developed. This same criterion will also be used to identify warm and cold air outbreaks.Future work will involve creating a database of extreme moisture and temperature intrusions and polar lows, and then applying these flags to the sea ice parcel Lagrangian database using a similar methodology to that used for cyclone events laid out in this paper. Extreme temperature and moisture intrusions will be identified using ERA5 and MERRA-2 atmospheric variables. Moisture intrusion events that enter the Arctic (crossing 70° N) are of interest as that energy can have significant impacts on the Arctic surface energy budget of the tracked sea ice parcels. To identify these events, a modified version of the methodology in Woods 65. Polar lows are intense, mesoscale cyclones that are associated with fast propagation speeds; strong winds; high intensity precipitation as snowfall, hail, and/or rainfall; high waves; and freezing sea-spray88. Interactions between polar lows and the sea ice/ocean surface remain poorly understood and these events may have important implications for sea ice mass balance. Similar to the cyclone database, polar low flags will be added as a data layer to the sea ice parcels if they coincide with an event.A database of polar lows will be created using ERA5 data and an adaptation of the cyclone tracking algorithm89, CryoSat-290, and the Ice, Cloud and land Elevation Satellite-2 (ICESat-2)91 thickness products to supplement the PIOMAS thickness data.As satellite data of sea ice thickness have become more reliable, some of these datasets could also be incorporated into this data base. These include but are not limited to: the Soil Ocean and Ocean Salinity (SMOS)-CryoSat2in situ Lagrangian data to validate and interpret the snow depth and sea ice thickness results from the remotely-sensed ice parcel database presented here. MOSAiC was a year-long field campaign in the central Arctic where the R/V Polarstern was frozen into the pack ice92. The overarching objective of the MOSAiC expedition was to collect process-oriented, continuous field observations of the Arctic climate system year-round to advance understanding centered on Arctic system science in the ‘New Arctic’. The field experiments encompassed nested spatial scales up to 50 km and continuously drifted with the wind and ocean currents between Oct. 2019 – Oct. 2020.Once data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition has been quality checked and processed, MOSAiC datasets will present a unique opportunity to use The results shown here are just the ‘tip of the iceberg’ in the amount of new research and scientific results that this database will enable. This database has vast applications for the wider scientific community to utilize and to better understand sea ice-atmospheric interactions in the ‘New Arctic’ and to explore what atmospheric factors and their timing might hinder or aid in the survivability of sea ice throughout the year. It also enables process-oriented research when compared to previous Eulerian based investigations. This database could also be used to assess climate model simulations of Arctic variables and processes to evaluate and improve model physics. Currently, this dataset is expected to be hosted by the National Snow and Ice Data Center, where it will be available for public download.