Fast kinetic experiments with dramatically improved time resolution have contributed significantly to understanding the fundamental processes in protein folding pathways involving the formation of a-helices and b-hairpin, contact formation, and overall collapse of the peptide chain. Interpretation of experimental results through application of a simple statistical mechanical model was key to this understanding. Atomistic description of all events observed in the experimental findings was challenging. Recent advancements in theory, more sophisticated algorithms, and a true long-term trajectory made way for an atomically detailed description of kinetics, examining folding pathways, validating experimental results, and reporting new findings for a wide range of molecular processes in biophysical chemistry. This review describes how optimum dimensionality reduction theory can construct a simplified coarse-grained model with low dimensionality involving a kinetic matrix that captures novel insights into folding pathways. A set of metastable states derived from molecular dynamics analysis generate an optimally reduced dimensionality rate matrix following transition pathway analysis. Analysis of the actual long-term simulation trajectory extracts a relaxation time directly comparable to the experimental results and confirms the validity of the combined approach. The application of the theory is discussed and illustrated using several examples of helix <==> coil transition pathways. This paper focuses primarily on a combined approach of time-resolved experiments and long-term molecular dynamics simulation from our ongoing work. Formation of the functional form of a protein from initiation of a primary sequence and propagation through secondary structural elements is one of the most fundamental biochemical processes in biological systems. Understanding the mechanism of protein structure formation from its amino acid sequence ,13,14,15Helices are the most common secondary structural elements in many functional proteins and have attracted much interest when examining the formation kinetics from a coil structure. Measurements describing the timescale to a helix from a coiled structure have been substantially improved with the availability of spectrophotometers with a dramatically improved time resolution. Early studies showed that helix–coil transitions occur in the 100-nanosecond range with a model helix near room temperature. Ultrafast laser temperature jump spectroscopy has been used to measure the relaxation dynamics of helix formation in 5- to 21-residue and longer helical peptides with picosecond to microsecond resolution ,44,45,46An extensive explanation of the approaches to modeling the helix–coil transition was described earlier . A descr+, often used in MSM, and the development of optimal dimensionality reduction (ODR) have been particularly insightful. ODR [Even as the state-of-the-art experimental techniques achieve faster time resolution, advanced algorithms and theoretical approaches are reaching a new limit, including milestoning ,70,71,72ful. ODR  was applful. ODR . The conAlthough these novel approaches have revealed previously unobserved processes in the field of protein folding, they have also recently uncovered significant new insights into folding mechanisms. These findings encompass a microscopic understanding of the folding pathways of secondary structure formation and the introduction of a measured true upper limit on the rate of protein folding (a “speed limit”), continuously downhill fast-folding kinetics, and direct observation of polypeptide collapse. One of the critical findings of this ODR modeling result is that both the helix and coil exist in a heterogeneous state, and it achieved structural characterization of multiple metastable folding intermediates. ODR also identified that helix folding occurs through multiple pathways, with the number of paths increasing with the model’s increased resolution.5 (A3RA)3-A-NH2), were performed with a nanosecond laser temperature jump apparatus [3H(A3RA)3-A-NH2, where the first turn of helix formation was monitored via quenching of tryptophan (W) fluorescence by a protonated histidine (H) residue four residues away [Helix–coil transitions have been studied experimentally and theoretically for nearly 60 years. In the late 1980s, many peptides comprising similar lengths and sequences found in proteins seemed to form stable helices in an aqueous environment. Since then, a large volume of equilibrium experiments and theoretical studies have been devoted to investigating the formation mechanism of a-helices in the steady state. Alanine-based peptides formed the backbone of these studies as they have a higher propensity to form helices. Kinetic studies, primarily with a 21-residue alanine-based peptide with an attached fluorescent probe at the N-terminus (X-(A)pparatus . The resues away . Laser tOver many decades, the fundamental question in the protein folding field has been that of whether secondary and tertiary structures of proteins fold following a predetermined sequential step or through multiple alternate pathways on a downhill free energy surface. Experimental observations, molecular dynamics simulations, and theoretical frameworks suggest that proteins fold by multiple pathways. It has been computationally demonstrated that at an atomistic level, no two trajectories are identical when progressing from an unfolded state to a folded state with the generation of a large number of detailed pathways to the folded state . Even atAtomistic molecular dynamics simulations are employed extensively to describe events that explicate the functions in biology. A significant challenge exists in expanding their scope due to biological systems’ vast spatial and temporal landscape. In recent years, substantial efforts have been made to develop advanced theories, sophisticated algorithms, and faster hardware to address these limitations. Here, we have applied optimal dimensionality reduction (ODR) theory combined with long-term simulation to describe multistate kinetics at an atomically detailed level for the most common secondary structural elements in proteins. Time-resolved experimental results are readily available to test the validity of this approach. ODR is a systematic approach to construct a reduced description of the dynamics of aggregated superstates obtained by clustering microstates. This is commonly applicable to kinetic events with discrete states and continuous-time evolution. It further encompasses continuous space events with discrete or continuous-time evolution. This reduced model maintains an optimal balance to recover short- and long-term dynamics.Recently, attempts have been made to describe helix–coil transitions by employing molecular dynamics simulations. The inference is made that a coil-to-helix transition occurs following multiple nucleation sites to fragmented helices. Description of helix folding rates and pathways from a pre-determined path is pursued with a milestoning approach. A complete description of helix–coil kinetics at an atomically detailed resolution was missing. Experimental results of fast kinetics are uncovering new details of the relaxation dynamics simultaneously with sophisticated theoretical approaches, which are being tested to characterize the measured pathways with Markov state modeling and milestoning. Recently developed ODR was able to describe a folding pathway, with two to four states, in a five-residue alanine homopolymer that can be compared to a master equation in a model based on molecular dynamics simulation. Interesting approaches to kinetic coarse-graining with PCCA+ are promising and open new important testing options. In our studies, a novel approach with simplified coarse-grained models of low dimensionality grounded upon a kinetic matrix was executed on the MD trajectory to capture microscopic insights into the pathway of the coil-to-helix transition. The conformations generated from analysis of the MD trajectory were clustered into a set of microstates, and a lifetime-based kinetic analysis was performed. Employing the PCCA+ algorithm ,76,79,80Regarding the coil-to-helix transition pathway, our analysis further indicated that helix–coil transition progresses through multiple pathways, and the number of paths increases relative to the increased resolution of the model. A brief summary of the results involving the application of this approach with new findings is presented here.cluster = 4.5 Å captured Nc = 194 clusters for WH210 and Nc = 199 clusters for WH21+. The lifetime-based kinetic analysis counting transitions between cluster cores and cluster residence times provided a kinetic matrix of order 194 for WH210 and 199 for WH21+. The kinetic matrix diagonalization generated eigenvalues λi and timescales ti = 1/λi. The reduced kinetic matrix for the dimensionality of N = 5 presented  relaxation time of 20 ns at 296 K were assigned to the correlated formation/breaking of neighboring hydrogen bonds. An analogous assignment was proposed for the non-protonated form of WH21 (WH210). Fitting to the average hydrogen bond fluctuation autocorrelation function  to a length-dependent helical homopeptide  providedn length A. Hydrogn length B,C. Coaring path .In contrast, A15 and A21 progress through intermediates well described in the transition pathways C,D. Simi10-helix structures in these systems, typically in the three-residue nuclei. π-Helices were not found in our trajectories.The longer alanine homopeptide intermediates primarily include partially folded helices at termini and turns, with a small population of partial hairpins. There is also a small probability of sampling 3+ exists with a 50% population of a-helical conformations [Generally, we refer to the model systems under study here as “helix-forming peptides”. It is clear from both experimental and computational studies that these small peptides do not form unique helical structures. Instead, they exist in dynamic equilibria of multiple conformations with a dominant helical population. For instance, at the melting temperature of 297 K, WH21rmations . It is wIt is important to examine the place of optimal dimensionality reduction (ODR) to milestoning and Markov state modeling (MSM) for an atomistic understanding of kinetics. MSM and milestoning are grounded on a more mature theoretical foundation as they have been present for longer. Milestoning can be used to describe processes with arbitrarily long timescales requiring large numbers of short trajectories between milestones, with the significant disadvantage of the technical difficulty of the method which has made it more challenging to automate. Both MSM and ODR explore conformational space with a smaller number of relatively long trajectories and apply only to molecular systems of moderate size. The advantage of MSM is the availability of relatively easy-to-use tools. Structural interpretation of MSM results is still a challenge. The use a relatively small number of microstates to discretize the conformational space and the ease of structural interpretation make our approach of ODR attractive. The current challenge with ODR is the incomplete exploration of the mathematical and physical properties of the method. Importantly, our investigation employing ODR uncovered the heterogeneity of the helical state and characterized novel folding intermediates on the coil-to-helix pathway. It should be noted that an essential advantage of ODR is that it generates low-dimensional kinetic models which reproduce the slowest timescales of the system dynamics, as well as generating a free energy landscape. Alternate approaches with reduced dimensionality are oriented explicitly to the landscape ,88,89.ODR reproduces the slowest timescales from MD trajectories significantly well, and a good agreement is present between calculated and experimental folding times for small–medium-sized peptides with the application of modern all-atom protein potentials. For instance, for WH210 at 300 K, relaxation times of about 300 ns were obtained with OPLS/AA , AMBER , and CHAAtomically detailed kinetics simulations are important descriptive tools for time-dependent events in biophysical chemistry, as life processes occur as a function of time. Fast kinetic experiments have significantly facilitated our understanding of the fundamental processes in folding pathways involving α-helices, β-hairpin, contact formation, and overall collapse of the peptide chain. The atomistic description of the observed events has been inadequate. The recent availability of advanced theories, robust algorithms, and true long-term trajectories will uncover microscopically detailed kinetics processes by examining folding pathways and through validation of experimental results with exciting new findings for a vast array of processes in molecular biophysics. We have discussed the successful application of optimum dimensionality reduction theory and the resultant new findings. We are taking advantage of this newly developed approach by progressively increasing the complexity of biomolecular systems from a simple dipeptide to small single-domain proteins through secondary structural elements. These approaches should gradually make possible the investigation of more complex cellular processes.


Understanding the mechanism of the brain via optical microscopy is one of the challenges in neuroimaging, considering the complex structures. Advanced neuroimaging techniques provide a more comprehensive insight into patho-mechanisms of brain disorders, which is useful in the early diagnosis of the pathological and physiological changes associated with various neurodegenerative diseases. Recent advances in optical microscopy techniques have evolved powerful tools to overcome scattering of light and provide improved in vivo neuroimaging with sub-cellular resolution, endogenous contrast specificity, pinhole less optical sectioning capability, high penetration depth, and so on. The following article reviews the developments in various optical imaging techniques including two-photon and three-photon fluorescence, second-harmonic generation, third-harmonic generation, coherent anti-Stokes Raman scattering, and stimulated Raman scattering in neuroimaging. We have outlined the potentials and drawbacks of these techniques and their possible applications in the investigation of neurodegenerative diseases. Neurodegenerative disorders (ND) affect as much as 30% of the elderly population over 85 years of age. An estimated 46 million people worldwide have been diagnosed with ND such as Alzheimer’s disease (AD) and other dementia, and is expected to surpass 135 million by 2050 , and simultaneously, the pump beam experiences intensity loss . Therefore, SRG and SRL do not contribute to the non-resonant background, which is a common issue in CARS microscopy. Fig The 2PF microscope uses an ultrashort pulse of near-infrared (NIR) wavelength for two-photon excitation. In 2PF, the excitation wavelength is chosen such that the fluorophore absorbs two photons simultaneously and emit a fluorescence signal with lower energy. Fig 2+ dynamics, it is also employed in in vivo studies to estimate the electrical activities of brain cells  and anionic forms have different fluorescence excitation spectra  with low excitation power. The study visualized the brain activity with 2PF and 3PF by varying one wavelength while keeping the other constant. The results suggested that the 3PF signal increased with an increase in the excitation wavelength  measurement of SR101. While the signal-to-background ratio (SBR) in 2PF is limited to just 910 μm, an imaging depth of 1340 μm was achieved using 3PF which may be limited due to signal depletion and SBR limit. Based on the proportionality dependence of wavelength-dependent σ3 and imaging depth Liu et al.  labeled blood vessels. The optimal excitation wavelength was selected based on the wavelength-dependent 3P action cross-section  generally affects the dopamine-producing cells in the central nervous system (CNS) ultimately affecting the movement of an individual. SHG microscopy was used to identify the conformation of α-synuclein, a protein that is known to form aggregates within the neuron in PD conditions  was used as a study model to identify the THG specificity for neuronal imaging , and CNP-GFP  were used, which expressed YFP in the cytoplasm under the promoter Thy1 and membrane attached GFP in myelinated cells via the CNP promoter respectively  throughout the plaque and its circumambient regions. The CARS images revealed the accumulation of lipids, primarily in areas of fibrillar Aβ plaques as shown in Fig. High-contrast imaging can be obtained using SHG and THG microscopy; however, obtaining a chemical contrast using these techniques is challenging , a condition leading to the demethylation of neurons in the brain and spinal cord. In this study, CARS microscopy was used in combination with other non-invasive techniques such as reflectance to reveal the changes in the lipid signals in myelinated and unmyelinated regions of the EAE mouse brain using thick living tissue sections  and proteins (30%)  was performed in frog muscle with a frequency-modulation, spectral-focusing SRS technique  phenomenon is one of the basic working principles of laser which was discovered in 1917 by Einstein. SE was first used by Hell and Wichmann  to achieels Fig. . Strong els Fig. . SRS imaThe recent developments in advanced optical imaging techniques have generated considerable excitement in brain imaging research. Neuroimaging techniques have widespread applications in the early detection of pathological and physiological changes associated with various neurological disorders. This review provides an overview of various neuroimaging microscopy techniques, with an emphasis on the technical aspects of the methods Table . Further


Soil moisture can significantly influence precipitation through soil moisture–precipitation feedback. Previous studies of soil moisture–precipitation feedback focused on the total precipitation, confounding the distinct roles of different storm types. Mesoscale convective systems (MCSs) are the largest form of deep convective storms, contributing 30 to 70% of warm-season rainfall in the central United States. Using unique datasets of MCS and non-MCS rains and soil moisture sourced from these rains, analyses revealed the dominant role of early warm-season (April to June) MCS rainfall in summer (July) MCS and non-MCS rainfall through positive and negative soil moisture–precipitation feedback, respectively. These results underscore the importance of understanding and modeling MCSs in the significant grain growing region of the central United States. Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs. Soil moisture can affect precipitation through many pathways. Wetter-than-normal soils can effectively increase the moist static energy in the planetary boundary layer (PBL) and favor moist convection –3. On th4Understanding soil moisture–precipitation feedback is important for improving understanding of subseasonal-to-seasonal predictability of precipitation in regions with strong land–atmosphere interactions –13. Loca1419Compared with non-MCS rainfall, which consists mainly of rainfall from isolated deep convection and stratiform rain, MCS rainfall is on average ∼7 times stronger in rain intensity, although it occurs less frequently in space and time . TherefoMaterials and Methods section. With these datasets, we spatially determine whether rain occurs preferentially over wetter/drier-than-surrounding soils and temporally determine whether rain occurs preferentially over wetter/drier-than-usual periods. More specifically, the spatial and temporal preferences of soil moisture anomalies for July afternoon  and nighttime  rainfall are analyzed using different methods .To reveal the roles of MCS and non-MCS rainfall in soil moisture–precipitation feedback in the central United States, we use a high-resolution (1/8° and hourly) database that associates the observed rainfall at each hour and grid location with MCS versus non-MCS rain events using an MCS tracking algorithm , 25, 30. methods , 10 to q methods . The staSI Appendix, Fig. S1), so we focus on the central United States in our analysis. Afternoon non-MCS rainfall, which accounts for 75% of the total afternoon rainfall, preferentially occurs over drier-than-surrounding (P < 0.05). The spatial preference is consistent with previous studies based on the total amount of afternoon rainfall . In contrast, with the limited poleward extent of the GPLLJ, the atmosphere in the Northern Great Plains (NGP) is less thermodynamically favorable for convection. With stronger westerly background winds in the NGP compared to the SGP, rainfall may preferentially occur over wetter soils . With the soil moisture dataset, the spatial and temporal preference of soil moisture for afternoon non-MCS rainfall is further analyzed for soil moisture sourced from early warm-season MCS and non-MCS rainfall separately  and early warm-season (April to June) rainfall. The latter is of particular interest given our goal to better understand soil moisture–precipitation feedback at the subseasonal timescale. Based on the tracer-enabled land surface model simulations, more than 40% of the July 1 soil moisture in the top first meter comes from early warm-season rainfall tagged by the tracers . In the SGP, the drier-than-usual soil moisture anomalies . Notably, a large fraction of MCS events in the central United States are initiated upwind near the Rocky Mountains Foothills. Subsequent convective aggregation generates MCSs that propagate eastward to the central United States .Similar analysis for afternoon MCS rainfall, which accounts for 25% of the total afternoon rainfall in July, shows much weaker drier-than-surrounding soil moisture anomalies but substantial wetter-than-usual soil moisture preferences  imply areas that receive more early warm-season MCS rainfall may also receive more MCS rainfall in July. Hence, the moistening effect of MCS- and non-MCS–sourced soil moisture on MCS rainfall is further examined through analysis of land–atmosphere coupling strengths.In total, 49% of nighttime rain is contributed by MCS rainfall, which preferentially falls over wet soils in the central United States A and B. Materials and Methods). These two coupling indices link the land surface states to the atmospheric responses  and is consistent with the role of antecedent MCS rainfall in these surface water components sourced from MCSs. In contrast, the maximum coupling strength between the non-MCS–sourced soil moisture and ET maximizes along a meridional band ∼100°W  due to other pathways  are limited, thus emphasizing the causal relationships between soil moisture and ET for each rainfall component.Pentad ET components sourced from preceding MCS and non-MCS rainfall are both strongly coupled with the initial soil wetness in the central United States . This isd ∼100°W D; this id ∼100°W . While Ed ∼100°W C and D. SI Appendix, Fig. S5), and there is no statistically significant autocorrelation of 5-d lagged pentad MCS rainfall  in areas of the central United States with strong coupling between ET and MCS rainfall . The difference between 5-d and 10-d lagged autocorrelations for MCS rainfall demonstrates a minimal role of rainfall persistence but a higher likelihood of land-driven feedback through ET .The atmospheric leg quantified by the lagged response of rainfall to ET 2 also shrainfall A. Importrough ET  that opeMaterials and Methods). To emphasize the role of ET in the process chain connecting soil moisture and rainfall, we compare the lagged correlation between initial soil moisture and pentad rainfall (Right of Left of Right and Left), confirming the role of ET in the process chain.Having demonstrated the connection between initial soil moisture and subsequent rainfall through ET using the two-legged coupling indices, we now examine the coupling strength between soil moisture and rainfall using lagged correlation . Consistent with the important role of ET in the soil moisture–precipitation relationship, the partial correlation with the effect of pentad ET removed shows larger values only in northern Texas  where ET and rainfall coupling is weak (Right), as tested against a null distribution with land-driven feedbacks removed . That is, removing the effect of pentad ET suppresses the significant correlation between soil moisture and pentad rainfall. Hence, the partial correlation analysis supports the important role of ET in connecting the initial soil moisture with the 5-d lagged pentad rainfall in areas with strong coupling strengths.To further support the role of ET in the process chain, we compare the correlation between initial soil moisture and 5-d lagged pentad rainfall Right of  and the  is weak B. Import is weak E and F c is weak , Right, Left of For MCS rainfall, the strong coupling between soil moisture and July rainfall over the central United States is supported by strong couplings between soil moisture and ET and between ET and precipitation, with ET sourced from both antecedent MCS and non-MCS rainfall Left of . ConsequAlthough MCS rainfall is strongly coupled to soil moisture contributed by both antecedent MCS and non-MCS rainfall E and F, In this study, we investigate the interactions between soil moisture and rainfall in the central United States from the unique perspective of how early warm-season (April to June) MCS and non-MCS rainfall affects July MCS and non-MCS rainfall through the distinct footprints of the two rainfall components on soil moisture. Besides differences in rain intensity and area, MCS and non-MCS rainfall also differ in their diurnal timing and the remote (Rocky Mountain Foothills) versus local  convective triggering, all of which play important roles in how MCS and non-MCS rainfall influence soil moisture–precipitation feedback. We find that MCS rainfall produces more spatially coherent soil moisture heterogeneity or mesoscale soil moisture gradient because of its larger intensity and area coverage compared to non-MCS rainfall . Hence, On the other hand, both early warm-season MCS and non-MCS rainfall provide a critical source of soil moisture that moistens the PBL and enhances July MCS rainfall, with a large fraction occurring during night hours. Such positive feedback between soil moisture and MCS rainfall extends beyond the subdaily scale, as evidenced by the strong positive coupling between soil moisture and MCS rainfall amount in the following pentad. However, soil moisture anomalies produced by antecedent non-MCS rainfall are confined closer to the surface due to the lighter rainfall intensity, so their impact on subsequent MCS and non-MCS rainfall tends to be shorter lived. This is in stark contrast to antecedent MCS rainfall that infiltrates deeper in the soil to produce a longer-lived soil moisture footprint and prolonged impact on summer rainfall . OverallOur analysis provides evidence that wet soils favor MCS rainfall during the nighttime in the central United States. This is consistent with results from an idealized model where the maximum value of convective available potential energy for continental convective storms scales with available surface moisture . In the Materials and Methods), which can affect the partitioning of total rainfall into its MCS and non-MCS components. Similar to previous observational studies of land–atmosphere interactions, our results are based on statistical analysis of soil moisture and precipitation, which could be prone to misinterpretation. For example, rainfall persistence may contribute to the correlation between initial soil moisture and 5-d lagged MCS rainfall. We note also that unequivocally excluding the role of rainfall persistence is extremely challenging, if not impossible. Still, our various analyses support, if not prove, the idea that we are seeing the effects of feedback. For example, we find no significant autocorrelation of 5-d lagged pentad MCS rainfall in the central United States where soil moisture–precipitation coupling is significant. In contrast, the autocorrelation of 10-d lagged pentad MCS rainfall is significant in that region, lending support to the presence of soil moisture–precipitation feedback, contributing to the rainfall persistence at longer timescales  exceeds 200 km and persists for at least 4 h .We use gridded precipitation data from the North American Land Data Assimilations System (NLDAS)  at 1/8° https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html). The NLDAS atmospheric forcing is interpolated to the land surface model domain at 4-km grid spacing as model input.The soil moisture data used in this study is obtained from 22 y of simulations (1997 to 2018) using the Noah land surface model with multiple physics parameterizations (Noah-MP) . For eacSI Appendix, Fig. S3 H and I). Such differences in soil moisture profiles can cause different responses to ET and different residence times in the soil that can affect soil moisture–precipitation feedback. The water tracer–enabled simulations are used in this study to understand the soil moisture–precipitation feedback associated with soil moisture derived from MCS or non-MCS rainfall.The land surface simulations use a unique water tracer–enabled version of Noah-MP (WT-Noah-MP)  to “tag”In using the offline land surface simulations and observed precipitation for analysis of soil moisture–precipitation feedback, it is assumed that the observed precipitation carries in it the signal of any actual soil moisture–precipitation feedbacks and the land model driven by the observationally based precipitation from NLDAS retains the soil moisture dynamics influenced by the feedbacks. Our observation-driven analysis has some advantages compared to using coupled land–atmosphere simulations that are subject to model errors and uncertainties in producing precipitation related to parameterizations of atmospheric processes .Our analysis primarily focuses on July, when the strongest interactions between soil moisture and rainfall are expected in the central United States , 49. We max) and the location(s) with minimum rainfall (Lmin) within a five grid point by five grid point (120 × 120 km) box centered at Lmax. For the soil moisture temporal preference analysis, the soil moisture anomaly at Lmax (SM’(Lmax)) is used to represent the wetness relative to its own seasonal cycle. Rainfall events at grid cells with terrain (topographic height within a 120 × 120 km box > 300 m) and water body features (water bodies cover >5% of the area within a 120 × 120 km box) are excluded from the analysis as their influence on precipitation may confound interpretation of soil moisture–precipitation feedback.We analyze the effect of soil wetness on afternoon and nighttime rainfall following the method used by refs. max)) and the soil moisture conditions of a control sample of days in nonevent years is compared against typical values to indicate its significance. To achieve adequate sample sizes, we pool together events and their corresponding control samples belonging to a fixed 5° × 5° box with their climatology removed to ensure comparability for different locations. For the 5° × 5° boxes with more than 25 events, the actual soil moisture anomaly difference between the events and the control samples e represents events and c represents control samples) are compared with typical values of the differences by pooling both events and control samples together in a 1,000 bootstrapping sampling of a size equal to the events. The percentile of the null distribution  against which the actual differences are compared quantifies the significance of the spatial/temporal soil preferences for rainfall events. We use soil moisture in the top 1 m to account for the root zone. While refs. The difference between the pre-event soil moisture conditions . By randomizing the rainfall time series, any land-driven feedbacks to rainfall are removed while the soil memory of preceding rainfall is retained. The 500 experiments provide the baseline for the null hypothesis against which the actual soil preferences are tested.The statistical significance of the spatial and temporal soil preferences  for rainfall events is further tested against a null hypothesis of no land-driven feedbacks. This null distribution is determined by using typical percentile values from 500 experiments produced by an analytical model representing soil moisture as a first-order Markov process. The analytical model is driven by permutation resampling of rainfall time series  or wet (>95%) preferences (not shown). To emphasize the role of MCS- or non-MCS–sourced soil moisture on the soil preferences, hatching is used to indicate areas where soil moisture sourced from MCS/non-MCS rain is significantly correlated with the soil preference, tested against the 500 baseline experiments with land-driven feedbacks removed (hatched areas in –F).Attributing the soil moisture preferences to soil moisture sourced from early warm-season (April to June) MCS and non-MCS rainfall is our primary interest. Therefore, we compute the soil moisture preferences for the same rainfall events using MCS- and non-MCS–contributed soil moisture components obtained from the WT-Noah-MP simulations described in SM is soil moisture at the beginning of each pentad in July, ET is pentad evapotranspiration, and PP is one pentad (5-d) lagged precipitation, The effects of soil moisture on rainfall are evaluated based on pentad rainfall to understand the roles of soil moisture on longer time scales. To quantify the variation of pentad rainfall in association with soil moisture through its influence on surface energy fluxes, we use the “two legged” coupling metric , 15 to eThe linkage between soil moisture and July rainfall is then examined by calculating their coupling strength using The soil moisture–precipitation feedback strength quantified by ss chain .The coupling strengths among soil moisture, ET, and rainfall associated with the MCS/non-MCS components are calculated by replacing each component in P values <0.05 . Their field significances are also tested against the null distribution generated by randomizing the time series of one variable in each leg and recalculating the correlations 100 times . To reveal areas significantly influenced by the MCS and non-MCS component, we also compare the differences in coupling strengths between the MCS and non-MCS contributed components. This is achieved by computing the coupling indices associated with the MCS and non-MCS components from daily data (instead of pentads) in July of each year and testing the significance of their coupling strength difference using the 22-y samples.For the two-legged coupling indices, their significances are indicated by the correlations between the two variables with We test the significance of the coupling between initial soil moisture and pentad rainfall against the 500 experiments from the analytical model that constitutes a null distribution with land-driven feedbacks removed. Local significance of coupling index is indicated by stippling in


Arc gene may be induced by a short behavioral event, resulting in synaptic activation. Arc mRNA is exported into the cytoplasm and can be trafficked into the dendrite of an activated synapse where it is docked and translated. The structure of Arc is similar to the viral GAG (group-specific antigen) protein, and phylogenic analysis suggests that Arc may originate from the family of Ty3/Gypsy retrotransposons. Therefore, Arc might evolve through “domestication” of retroviruses. Arc can form a capsid-like structure that encapsulates a retrovirus-like sentence in the 3′-UTR (untranslated region) of Arc mRNA. Such complex can be loaded into extracellular vesicles and transported to other neurons or muscle cells carrying not only genetic information but also regulatory signals within neuronal networks. Therefore, Arc mRNA inter- and intramolecular trafficking is essential for the modulation of synaptic activity required for memory consolidation and cognitive functions. Recent studies with single-molecule imaging in live neurons confirmed and extended the role of Arc mRNA trafficking in synaptic plasticity.Synaptic activity mediates information storage and memory consolidation in the brain and requires a fast de novo synthesis of mRNAs in the nucleus and proteins in synapses. Intracellular localization of a protein can be achieved by mRNA trafficking and localized translation. Activity-regulated cytoskeleton-associated protein (Arc) is a master regulator of synaptic plasticity and plays an important role in controlling large signaling networks implicated in learning, memory consolidation, and behavior. Transcription of the  Multicellular organisms use several pathways to coordinate cellular processes within specific organs and the whole organism. One of them is cell-to-cell communication over short and long distances. Although the flow of genetic information from DNA to RNA or reversely is restricted to a single cell, both DNA and RNA in their active forms are detected in extracellular fluids, raising the question about their role in cell-to-cell communication and intercellular flow of genetic information , 2. FurtPlasticity in the neuronal network is essential for the ability to learn and keep new information and is underlined by modification of neuronal synapses in response to electrical activity, a process called synaptic plasticity (reviewed in ). Long-tArc mRNA is transported to the dendrites that are enriched in Arc . Suc. Suc6]. γ-secretase and trafficking endosomes that processed the amyloid precursor protein to Aβ peptide, leading to amyloid deposition [Changes in Arc have been associated with several mental disorders, including major depressive disorder, psychosis, and schizophrenia –11. Althposition . Apart fposition . It is sposition .α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) [The majority of synapses in the central nervous system are glutamatergic, and they are important sites for environment-dependent plasticity, learning, and memory . Fast ex(AMPARs) . NMDARs (AMPARs) . At most(AMPARs) . Synapse(AMPARs) . These s(AMPARs) .Arc gene, the mechanisms of Arc transcription, and the pioneering works of Steward and his coworkers on the involvement of Arc mRNA trafficking in neurons [Arc mRNA trafficking. The latter is discussed in the context of retrovirus-like sequences in the human Arc gene and in the colonization of the human genome by such sequences and their domestication to adjust them to human development.In this manuscript, we present the general properties of the Arc protein and  neurons , 27–33 a neurons  and Past neurons  showing Messenger RNA is an intermediate in the expression of protein-coding genes. Transcription of such genes results in the synthesis of pre-mRNA, which is then spliced, edited, end-modified, and transported into the cytoplasm where it undergoes translation to form a polypeptide, which is then proceeded to become a functional or structural protein. However, current studies on the structure of human genes and the human transcriptome have made some modifications to this picture—mRNAs produced in transcription and posttranscriptional modifications are not homogeneously distributed in the cytoplasm, and this fact can be important for cellular functions and energy use.Subcellular localization of a protein may be reached by a mechanism dependent on the signal peptide  within this protein . The othcis-elements and localization elements  in the 3′-UTR (untranslated region) of mRNA [A general pathway of intracellular mRNA trafficking in somatic cells includes complexing of mRNA molecules with proteins in the nucleus, their export to the cytoplasm, and attaching to microtubules or microfilaments  Figure . mRNA tr of mRNA . Some ex of mRNA , 43. Bot of mRNA    [The 0, 2021) .Arc was discovered in searching for IEGs that responded to neuronal activity independently of protein synthesis. Arc is unique among other IEGs, as it is not a transcription factor and immediately moves through the dendritic arbor of the neuron in which it is activated—it was shown that after a single electroconvulsive shock, Arc was distributed in the molecular layer of the dentate gyrus, containing the dendrites of the dentate granule cells, while mRNAs of other IEGs were in the cell body .Arc is involved in the regulation of endocytosis of AMPARs –51 , BDNF tropomycin-receptor kinase (Trk) B, muscarinic acetylcholine receptors, and NMDARs [Arc has a major SRE-responsive region in its promoter located about 6.5 kb upstream of its transcription start site (TSS) [Arc transcription regulation has not been definitely confirmed [uine IEG . The traiewed in ). In norcription . The extd NMDARs . ERK phote (TSS) . The othonfirmed . A 6.9 Sonfirmed .Arc promoter have been identified or suggested to play a role in Arc response to neuronal activity, including a region 1.4 kb upstream of TSS containing a Zeste-like response element, a site binding to myocyte-enhancer factor-2  suggest its active transport [Arc mRNA. It is initiated through the MAP kinase-interacting kinase (MNK) and phosphorylating eukaryotic initiation factor 4E (eIF4E) [Before translation, ransport . The ERK (eIF4E) .Arc mRNA expression in the rat hippocampus and spatial learning in the Morris water maze was observed [Arc mRNA was measured in the cytoplasm, but not in the nucleus. Behavioral experience that activates the synapse may belong to a wide range of tasks, including those as diverse as sound exposure and complex reversal learning.Arc may be implicated in learning and memory as they both are underlined by synaptic plasticity (reviewed in ). A posiobserved . In addiArc expression were shown in the CA1 hippocampal neurons during fear memory formation in mice [Arc expression just after the stimulus was observed in the first phase and a second increase in Arc levels after 12 h, depending on BDNF and essential for memory consolidation. A similar delayed expression of Arc was observed during sleep-dependent consolidation of cortical plasticity in the cat visual cortex [Arc induction may occur in two-phase induction of expression during memory consolidation. In line with this suggestion are results with Arc-knockout mice showing that Arc might be needed for long-lasting memory for implicit and explicit learning tasks, despite intact short-term memory [Arc-knockout animals exhibited biphasic changes of hippocampal LTP in the dentate gyrus and the CA1 area with an increased early and absent late phase. Furthermore, LTD was considerably impaired in that experiment. These results confirm an essential role of Arc in the consolidation of enduring synaptic plasticity and memory storage, although the mechanism eliciting such delayed reactivation is still unclear.Two phases of  in mice . An initl cortex . Therefom memory . The ArcArc in rat cortical regions was observed after recalling a recent (24 h after training) memory relative to remote (1 month after training) memory [Arc expression in the striatum of rats that underwent training/pseudotraining on an operant lever-pressing task was observed [Arc mRNA in specific brain regions of newly trained rats and the rate of task acquisition [A more robust expression of ) memory . An incrobserved . Furtheruisition . This asuisition , 71.Arc mRNA levels in the rat hippocampus and behavioral performance during spatial reversal tasks [A role of Arc in cognitive flexibility was suggested based on results showing a strong positive correlation between al tasks .β), which is not bound to calmodulin [Arc was reported to act as a postsynaptic mediator of activity-dependent elimination of synapses in the developing cerebellum of rats by mediating the removal of excessive fiber synapses . Arc tarlmodulin . This inArc mRNA immunofluorescence signal in the dorsomedial stratum and the number of trials in rats, which underwent a reversal training, but not in rats that experienced continued-acquisition training [Arc mRNA in different populations of striatal efferent neurons.A relationship was observed between the overall training . This stArc expression may be regulated by large networks linked with learning and memory on the one hand, but on the other, Arc expression may form new and modify existing networks.Altogether, these results suggest a feedback-like relationship—Arc mRNA was selectively delivered to postsynaptic sites to mediate synapse-specific gene expression , which is also targeted by CArG box binding factor A (CBF-A), controlling Arc mRNA localization by a mechanism regulated by NMDARs and AMPARs [Arc mRNA localization as this process marked synapses for Arc mRNA to localize [Newly synthesized and matured et sites . Arc mRN complex . This cod AMPARs . It was localize .Arc mRNA migrated, contained accumulated Arc proteins [Arc mRNA was prevented by its capturing by active synapses. In LTP, Arc mRNA and Arc protein localized near active synapses but were also found in other dendritic locations [The newly made Arc protein is quickly localized near active synapses but is also distributed throughout dendrites . Activatproteins . Arc wasproteins . Furtherocations .Arc mRNA trafficking is supported by the specific elements in the 3′-UTR. Dynes and Steward showed that a fusion construct containing 3′-UTR of Arc mRNA bound by proteins of the coat of the MS2 phage, a single-stranded RNA virus, precisely localized in a microdomain at the base of dendritic spines [Arc mRNA was docked. The presence of a dedicated microdomain for Arc mRNA docking and translation at the entry to the spine confirms the suggestion that the newly synthesized proteins in dendrites may act preferentially or even exclusively at a single, neighboring synapse [As mentioned, c spines . That lo synapse .Arc mRNA at specific locations in synapse requires the presence of the ribosome and other components of the translation machinery in these sites. It was assumed that the translational apparatus was closely associated with psd as it synthesized the compounds needed for cotranslational assembly of psd/NRC [Arc mRNA in dendrites were not associated with increased levels of exon junction complex proteins [Translation of  psd/NRC . Howeverproteins .Arc transcription is induced by synaptic activation, Arc mRNA is transported to the active parts of dendrites where it is docked for translation. Therefore, two synaptic signals, for transcription and docking, may interplay, but the relationship between them is not fully known [Arc mRNAs may escape docking unless activation signal is kept on. The selectivity of Arc mRNA may be also supported by degeneration of the transcript in the nonactive sites, a process called activity-dependent Arc mRNA degradation. It was shown that Arc degradation occurred throughout dendrites and required the activation of NMDA receptor and active translation [Arc mRNA as another dendritic mRNA was not degraded in response to synaptic activation. It was shown in another study that such degradation might occur by nonsense-mediated mRNA decay (NMD) as Arc mRNA had a splice junction in the 3′-UTR, downstream of the stop codon [Arc mRNA. Knockdown of eIF4A3 increased synaptic strength and GLUR1 AMPA receptor load at synapses. Moreover, depletion of eIF4A3 increased Arc, so Arc mRNA was a natural target for NMD. Steward et al. using transgenic mice confirmed that Arc mRNA degradation occurred with features typical for NMD, and prior splicing of the 3′-UTR influenced fast dendritic delivery of newly synthesized Arc mRNA [When ly known . It is nnslation . Such acop codon . This spArc mRNA .Arc transcription after an inducing event, but dentate granule cells show a continuous transcription of the Arc gene [It is important to determine how long Arc transcription lasts after a stimulating signal. The forebrain neurons, including hippocampal pyramidal neurons, rapidly terminate Arc gene .Arc mRNA trafficking in synaptic plasticity [Arc gene tagged in 3′-UTR with stem loops binding to bacteriophage PP7 coat protein (PCP) was used in that work to visualize single mRNAs in real time. That model allowed following actual dynamics of Arc mRNA from transcription to degradation. A robust Arc transcriptional burst with prolonged ON state occurred after stimulation in cultured hippocampal neurons. Furthermore, transcription cycles continued after removal of initial stimulation. The correlation of stimulation with Arc transcription and mRNA transport in single neurons showed that stimulus-induced Ca2+ activity was needed but not sufficient for inducing Arc transcription and that inhibiting neuronal activity did not change the dendritic transport of newly produced Arc mRNAs.Recent technological developments allow studying mRNA localization and its local translation through the simultaneous detection of mRNAs and proteins in single cells through single-molecule imaging in real time (reviewed in ). This hasticity . A mouseArc mRNA was produced after synaptic activation [Arc mRNA. Na et al. showed that glutamate-induced Arc mRNA translation was associated with a reactivation of stalled polyribosomes [Arc open reading frame was necessary and sufficient for glutamate-induced mRNA Arc translation.Fujita et al. used reduction-triggered fluorescent probes combined with fluorescence correlation spectroscopy and observed in real time that tivation . Analysiibosomes . MoreoveArc mRNA trafficking in synaptic plasticity and opened new perspectives in neurosciences [In a more general context, Bauer et al. showed that the molecules of Rgs4  mRNA patrolled dendrites to transiently interact with multiple synapses, in agreement with the sushi-belt model , 89. Morsciences .Arc gene has a DNA sequence, which is normally found as its RNA counterpart in retroviruses, such as human immunodeficiency virus (HIV), enabling it to form virus-like particles [Myrum et al. demonstrated that Arc had two domains on either side of the central linker region . They alarticles . These tarticles , 35. Arcγ2  [The involvement of Arc in the intercellular sending of genetic information is closely associated with its molecular architecture also found in proteins of some retroviruses. Zhang et al. reported that the crystal structure of Arc formed a bilobar architecture and the N-terminal lobe bound to CaMKII and TARPargazin) . The domargazin) ). Therefargazin) .Arc is not completely clear. In retroviruses, the GAG proteins multimerize during replication to form a capsid that binds to viral RNA and undergoes envelopment by membranes before exiting the host cell in an EV [The role of the GAG-like sequences in in an EV , 96.Drosophila Arc1 protein formed capsid-like structures that bound to a specific retrovirus-like region in the 3′-UTR of its own transcript [Arc1 mRNA complex was loaded into EVs and transferred from neurons to muscles. It was not clear whether those vesicles contained multiple or single enveloped capsid-like structure(s). That transfer depended on a specific sequence in the 3′-UTR of Arc1 mRNA containing retrotransposon-like sequences as disruption of that sequence inhibited the transfer. Transfer of Arc1 and/or Arc1 mRNA was underlined by mechanisms similar to retroviruses and retrotransposons and was necessary for synaptic plasticity.Extracellular vesicles play a critical role in the communication between neurons, astrocytes, and microglia in normal and diseased brains . In a laanscript  That ArcArc mRNA trafficking [At the same time, Pastuzyn et al. showed in another groundbreaking paper the involvement of mammalian Arc in interneuron fficking . They laArc mRNA to target neurons, where it could undergo activity-dependent translation virology at the molecular level.As pointed out by Pastuzyn et al., many questions on intercellular he brain . Both As


For this theoretical scenario, the effect of reinforcing mandibular anchorage with temporary anchorage devices during correction of Class II malocclusion with fixed functional appliances will be analysed. In particular, the effect of anchorage reinforcement on reducing lower incisor inclination  will be assessed. The studies identified from two recently published systematic reviews  are poolIn the end, seven included papers remained , which compared a Class II group treated with skeletally anchored fixed functional appliances  and a Class II group treated with conventional fixed functional appliances . The results of these studies have been here re-analysed with conventional random-effects (REML) meta-analyses in terms of Mean Differences (MDs) with their 95% Confidence Intervals (CIs) using (a) the post-treatment measurements and (b) the treatment-induced changes (post minus pre). However, pre-treatment differences among the compared groups (i.e. if the two study groups are not adequately comparable at baseline) can influence the observed treatment effects. Therefore, meta-regression of pre-treatment differences on the observed treatment effects were performed. All analyses were done according to current guidelines with a P<0.05 considered significant for the meta-analyses and a P<0.10 considered significant for the meta-regressions.A.  The direction of each study’s effect  is the same in the analysis of Post-Pre increments and in the analysis of Post values.B.  The magnitude of the pooled treatment effect from the meta-analysis is the same in both analyses.C.  The P value for the pooled treatment effect from the meta-analysis is either statistically significant (P<0.05) or not (P≥0.05) in both analyses.D.  Any baseline discrepancies ) between the two compared groups will significantly impact only the analysis of final (Post) values, but not the analysis of treatment changes (Post-Pre).The results of the 2 analyses of the included studies (either using Post-Pre increments or the Post measurements) are illustrated in The results of the two meta-analyses are given apart from Looking at the results of In order to assess the effect of any baseline discrepancies on the measured outcome, meta-regressions of the baseline (Pre) incisor inclination difference on the treatment effects (MDs) of skeletal reinforcement were run and are seen in Finally, an interesting observation can be made if one considers the design of the included studies in conjunction with their results. Theoretically, one might expect that in the analysis of randomised trials, the two analysis methods  would yield similar results, since baseline similarity  between experimental – control groups is expected. For the first randomised trial, from These issues highlight the complexity one often faces when designing and analysing the results of clinical trials that was raised previously  and migh


It is known that tomato (Lycopersicon esculentum) contains bioactive compounds to combat type-2 diabetes. Therefore, it is of interest to document data from the molecular docking analysis of compounds from Lycopersicon esculentum with the insulin receptors tocombat type-2 diabetes. We report the binding features of cinnamic acid, chlorogenic acid, gallic acid & glucoside with insulin receptors for further consideration. Type-2 diabetes is one of the greatest global health emergencies of the twenty-first century. Insulin resistance is believed to be the key event contributing to the progression of the disease in type-2 diabetes. Insulin resistance results in reduced glucoseabsorption by peripheral tissues, as well as impairment in lipid homeostasis. Several tissues have a crucial role to play in the development of insulin resistance and type-2 diabetes. Insulin Receptor (IR) is a tetrameric protein composed of two extracellularalpha subunits and two transmembrane beta subunits . The binIt is known that tomato (Lycopersicon esculentum) contains bioactive compounds to combat type-2 diabetes ,8. ThereThe high-resolution structure of Insulin receptor was downloaded from PDB (PDB Id: 1IRK)  Figure .12 compounds identified from the tomato plant were selected from the literature. The structures of these compounds have been retrieved from the PubChem Compound Database in the Spatial Data File (.SDF) file format and converted to the PDB file format usingthe Online Smile Translator. Energy minimization of ligands was performed using ChemBio 3D Ultra 12.0, based on the method stated.The Patchdock server was used to determine the interaction between the insulin receptor and the selected compounds. PatchDock identified the top candidate solutions based on the complementary form of soft molecular surfaces. The clustering RMSD was set to4.0 Å as proposed by the software developer for larger molecules and the complex type was set to default. The PatchDock algorithm divides the Connolly dot surface representation of the molecules into concave, convex and smooth regions. Complementary patchesare then coordinated to create the candidate transformations of the docked complex (the candidate transformations are docked complexes of the specified receptor and ligand molecule based on the patchdock theory). The result has been retrieved from the e-mailaddress given and downloaded ,11.Docking was performed using the PatchDock software between the lead compounds present in Tomato Plants targeting 1IRK to determine the binding efficiency in the form of Atomic Contact Energy (ACE) as shown in Table 1 (see PDF). ACE is a good parameter/indicatorto know the docking efficiency of target molecules with lead compounds under the Patch Dock system. All of the selected compounds showed a good interaction with the target insulin protein receptorBased on the score parameters and the H-bond information; we selectedthe best four compounds. Among them, chlorogenicacid has an ACE value of-141.17 kcal / mol. In order to analysis the docking the H - bond details, docked structure was visualized using PYMOL software. This study showed that the selected four compounds had morethan two H-bond interactions with the target insulin receptor protein. Vijayalakshmi et al. 2015  stated tCinnamic acid compound interacts with the insulin receptor molecule in a reasonable manner with a strong ACE value of-277.46 kcal / mol, rendering it the second most active product. Four H-bond interactions were established via THR-1203, PRO-1104, LEU-1106 andASN-1233 . GallicaWe report the binding features of cinnamic acid, chlorogenic acid, gallic acid & glucoside with insulin receptors for further consideration.


To evaluate a cross-shaped incision technique for thick-gingiva and thin-gingiva patients treated with implant-supported fixed prosthesis.Total 55 patients receiving cross-shaped incision were assigned into thick-gingiva group (29 cases) and thin-gingiva group (26 cases). Follow-up was performed at 3 and 12-month after final restoration.Mesial and distal papilla height was significantly greater in thick-gingiva group than thin-gingiva group at 3 and 12 months, while periodontal depth and crestal marginal bone level around implant had no significant difference between the two groups during follow-up. No case of recession of buccal marginal gingiva was observed in thick-gingiva group. However, the recession of marginal gingiva of buccal aspect of the crown was found in 5 patients (19.2%) with thin-gingiva.The cross-shaped incision may be applied to reconstruct gingival papillae and avoid the gingival recession in patients with thick-gingiva phenotype.Trial registration This study was registered at ClinicalTrials.gov . Dental implant has been successfully used to restore missing teeth. However, food impaction at the implant site is considered as a common complication in patients with implant prostheses. Food impaction is the phenomenon in the chewing when the food dregs or fibers are pushed into the clearance by occlusal force or owing to the gingival shrinkage . Food imThe loss of interproximal contact between fixed implant prostheses and adjacent teeth is the main issue of vertical food impaction, which can be solved through occlusal adjustment, re-making of the prosthesis, inlay-crown or full crown of adjacent teeth . HorizonVarious treatment plans and techniques have been proposed to restore the deficient papilla. Hard tissue augmentation during implant placement was suggested as an effective method to obtain desired inter-dental/inter-implant papillae . OrthodoThis study aimed to investigate whether different gingival phenotypes have the same ability to recover from surgical injury, and we evaluated a cross-shaped incision technique for thick-gingiva and thin-gingiva patients treated with implant-supported fixed prosthesis.https://clinicaltrials.gov/ct2/show/NCT04706078). All the patients signed informed consent before the implant surgery.The present study was performed in accordance with the World Medical Association Declaration of Helsinki and was approved by Ethics Committee of West China Hospital of Stomatology, Sichuan University, China . The study adheres to CONSORT guidelines and is registered in the ClinicalTrials.gov  and at least 2 mm of keratinized tissue width around the implant.Active periodontal infections.Heavy smoking (> 10 cigarettes per day).Fifty-five subjects were selected from the patients who need treatment with a cross-shaped incision technique of the Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University in China between June 2018 and June 2020.Patients were divided into two groups according to the gingival phenotype determined by a periodontal probe. After the insertion of the probe into the facial aspect of the sulcus through the gingival margin, the simple visual method is based on the transparency of the periodontal probe through the gingival margin while probing the buccal sulcus at the midfacial aspect of the tooth. When the outline of the underlying periodontal probe can be seen through the gingival, the gingival phenotype is considered thin. The gingival phenotype is thick in the other case. When the crown shape protrusion is not obvious, the main direction of the probe is parallel to the long axis of the tooth. The peri-implant phenotype was categorized as thin-gingiva  or thick-gingiva  Fig.  13].Fig.Fig13].FThe bone-level implants were placed 1 mm below the alveolar bone level according to the manuals. The trial started at 3–6 months after the one-stage surgery. Impression was taken for the fabrication of definitive crown after 3-6 months of healing. Titanium abutments with the lowest gingival height (1–1.5 mm) were selected, and all of the crowns were made of zirconia. Platform switching which referred to the use of a smaller diameter abutment on a larger diameter implant collar was used in all the implants.# blade. The cross-shaped incisions went directly to the bone surface. The length of the cross-shaped incisions was 1–2 mm in keratinaized gingiva : plaque accumulation around the marginal peri-implant tissue was assessed by the criteria of mPI .Modified Sulcus Bleeding Index (mBI): the bleeding tendency of the marginal peri-implant tissue was evaluated using mBI .Probing Depth : PD was assessed at the mid-buccal, mid-oral, mesial and distal aspects of the buccal surfaces of each implant with a standard periodontal probe, and final value was determined by the average of four aspects.Gingival margin level (GML): gingival margin level was assessed by calculating the vertical distance between the most apical point of gingival margin at the buccal aspect of the crown and line connecting the peak of the adjacent mesial and distal natural teeth (PMD) . The lenAll patients in this study accepted oral hygiene instruction at each visit. All patients were examined at 3-month and 12-month after final restoration  and implant shoulder (IS) were used for measurements. fBIC-IS was defined as the vertical distance the first bone-implant contact to implant shoulder, and the distance was assessed at the mesial and distal aspect of implant, respectively. When the marginal crestal bone was located coronal to the IS, a positive (+) value was given, where a negative (–) value when located apically to the IS, the value was deemed as zero when IS and fBIC coincided. The crestal bone level at the time of impression taking was regarded as baseline Fig. . The knoThe statistical analysis was performed using GraphPad Prism 6.0 program. The data were presented as means ± standard deviations (SD), the differences between thin-gingiva group and thick-gingiva group were compared using the paired t test. A difference was considered significant if the P value was < 0.05.During the study period, a total of 55 patients were included. Patients were grouped into 2 groups according to gingival phenotype. Detailed demographic data and sample characteristics were presented in Table The Jemt parameters of soft tissue were listed in Table The GML on the buccal aspect of the crown was assessed by the distance from TBF to PMD. Table The change of peri-implant hard tissue was shown in Table 2 abutments [The purpose of this study was to evaluate clinical and radiographic manifestations of a cross-shaped incision technique for thick-gingiva and thin-gingiva patients treated with implant-supported fixed prosthesis. Our study demonstrated that papillae filled more than half of the proximal space, in good harmony with the adjacent papillae in both thin-gingiva and thick-gingiva groups. In our study, zirconia crowns were fabricated and cemented to abutments with the lowest gingival height. Welander et al. demonstrated an apical shift of the barrier epithelium and the marginal bone around AuPt-alloy, where the soft tissue dimensions remained stable around Ti and ZrObutments . Kajiwarbutments . And we Moreover, securing a rich blood flow in soft tissues around implants is considered to be advantageous for the maintenance of immune function , which wThe importance of possessing an adequate width and thickness of keratinized mucosa seems to be crucial both for natural teeth and dental implants. A deficiency of  keratinized mucosa around implants has been shown to hinder patient oral hygiene, leading to soft tissue inflammation, mucosal recession, and attachment loss . Having Marginal crestal bone in both thin-gingiva and thick-gingiva groups was located coronal to the IS at the beginning, then decreased slightly during the following year, in accordance with the results of other studies , 22. TheBased on these results, the zirconia crown is beneficial for the reconstruction of gingival papillae by promoting microcirculatory dynamics in soft tissue around implant. The gingival margin remained stable in patients with thick phenotype gingiva after the cross-shaped incision of gingival sulcus around the crown. The cross-shaped incision has several advantages. It is visible to check the proper placement of crowns and easy to clean excessive cement with the slight lift of incised gingiva. The limitation for cross-shaped incision is that the recession of marginal gingiva where the incision is made may happen in thin-gingiva group. The patients included must have at least 2 mm of keratinized tissue width around the implant. The lack of negative controls was another limitation of this study.The cross-shaped incision may be applied to reconstruct gingival papillae and avoid the gingival recession in patients with thick-gingiva phenotype. For the patients with thin-gingival phenotype, a modified method aimed to reconstruct gingival papillae and avoid the gingival recession need further study.


In 2016, the Centers for Medicare & Medicaid Services (CMS) implemented the Service Intensity Add-On (SIA) payment, which incentivized skilled nurse and medical social worker (SN/MSW) visits in the last seven days of life. Little is known about the impact of this initiative. Using 100% Medicare hospice claims, we identified a 10% random sample of Medicare hospice beneficiaries utilizing routine home care service during calendar years 2012-2018. We compared the provision of SN/MSW visits on service dates before and after the SIA’s implementation relative to beneficiaries’ date of death. We also determined hospice providers’ success in providing SN/MSW visits in the last days of life and categorized all providers into quintiles according to the average rate of these visits in the period prior to the SIA’s implementation. Cumulative over the last seven days of life, we calculated an increase of 15.7 SN/MSW minutes  per beneficiary after the SIA was implemented. The per-minute increase was greatest on days nearer to death . There was no detectable visit increase on days which were ineligible for the SIA. Additionally, those providers in the quintile providing the lowest rate of SN/MSW visits pre-SIA exhibited a 14-percentage point increase in rates of these visits, the third, fourth, and fifth quintiles exhibited little change over time. Further monitoring is needed to ensure beneficiaries receive adequate end-of-life care.


To date, ovarian cancer screening in asymptomatic women has not shown a mortality benefit. The aim of this simulation study was to outline the impact of different histological subtypes on a potential stage-shift, achieved by screening.Real-world data were derived in the period of 2000–2017 from the Klinischen Tumorregister Austria. We estimated five-year overall survival (OS) of patients with ovarian cancer regarding different histological subtypes and FIGO stages. A theoretical model was generated predicting the trend of OS mediated by an eventual down-shifting of ovarian cancer from FIGO stage III/IV to FIGO stage I/II by screening, considering the influence of different histological subtypes.3458 ovarian cancer patients were subdivided according to histological subtypes and FIGO classification. Major difference in distribution of histological types was found between FIGO stage I/II and III/IV. A theoretical down-shift of tumors from high to low FIGO stages based on our registry calculations showed that the five-year OS would increase from 50% to nearly 80% by perfect screening.In our simulation study, we showed that down-shifting ovarian cancers by successful screening might increase OS by 30 percentage point. Our results underscore the importance to recognize ovarian cancer as a heterogenous disease with distinct epidemiologic, molecular and clinical features. The individual characteristic of each histotype is of utmost impact on the definition of screening aims and may influence early detection and stage-shift. Efficacy of screening is mainly dependent on detection of high-risk cancer types and not the slow growing low-grade types. Screening as an option to detect cancer early to improve OS is a desirable medical tool. Although screening represents a laudable strategy to detect a potential cancer in an early stage and to ensure a subsequent therapy, it also contains the risk of over-diagnosis –5. To guThe primary aim of this best-case scenario study was to elucidate the effect of a potential stage-shift on OS in ovarian cancer screening, when the distinct impact of the various histological subtypes is taken into consideration.Cases were identified retrospectively by computer search in the database of the Klinisches Tumorregister Austria, a cancer registry organized by the Austrian Association for Gynecologic Oncology (AGO). The outcome of the procedure was documented prospectively in patients’ record forms. Histology was taken from the local pathology and no central review has been performed..All clinical data were evaluated from the database. Clinical data included the FIGO stage of the diagnosed carcinoma as well as its histological subtype. Based on the survival rates of the individual FIGO stages (FIGO stage I/II and FIGO stage III/IV), the overall survival (OS) across all stages for our real-world data was calculatedCarcinoma subtypes were classified according to their different clinical and biological behavior into high-grade serous carcinomas (HGSOC), high-grade endometrioid carcinomas (HGEOC), low-grade serous carcinomas (LGSOC), low-grade endometrioid carcinomas (LGEOC), mucinous carcinomas (MOC), clear cell carcinomas (CCOC) according to the recommendation of the fifth ovarian consensus conference [20|. Furthermore, the FIGO classification 2014 was used to subdivide the different histological subtypes into two groups: FIGO I/II and FIGO III/IV . χ 2 test was used to determine the differences in FIGO stage and histology. In this retrospective study, p values should be viewed with caution due to the fact that in large samples even minuscule effects can become statistically significant [Potential differences in five-year OS by histological subtypes  and FIGO stages (FIGO stage I/II and FIGO stage III/IV) were analyzed using the product limit method of Kaplan–Meier and the log-rank test. Kaplan–Meier curves were generated using Stata V15/IC .nificant .p < 0001). Proportion of HGSOC was 40% and 83% in FIGO stage I/II and III/IV, respectively.A total of 4922 ovarian cancer patients diagnosed and treated in different Austrian clinical centers Table.  between As shown in the Kaplan–Meier curves, OS was significantly different when comparing the various histological subtypes in FIGO stage I/II (p < 0.0001)   (p < 0.0001)   and high-grade carcinomas (HGOC) of the main two histological subtypes (serous and endometrioid) were combined to form two large groups and both groups were further sub-classified with regard to their FIGO stages Table .Table 2HTo test for an improvement of five-year OS in women successfully screened for ovarian carcinoma, a diagram was elaborated simulating the effect of a potential stage-shift. For this purpose, five-year OS (x-axis) is plotted against the potential percentage of stage-shift (y-axis) Fig. . In our We assume that probability for early detection of LGOC is much higher because those tumors take longer to proceed from an early to an advanced stage. This is also supported by the higher proportion of LGOC diagnosed in FIGO stage I/II. If screening-mediated stage-shift would affect mainly LGOC, the beneficial effect would be minor. The three-colored curve therefore initially showed a very flat slope when plotting five-year OS against stage-shift for low-grade carcinomas  as compared to the very favorable prognosis in early stages (83%). In total, the effect of early detection of MOC on the OS was shown in the last 2.5% of the curve . These crude effects from stage-shift are caused by the respective incidence of the various histological subtypes and their real-world distribution in between the FIGO stages.One strength of this study was that the consideration of the histological subtype was complemented by integrating the grade of malignancy. Into agreement with the World Health Organization (WHO) classification in the guidelines for female reproductive tumors in 2014, we classified the endometrioid cancer further into LGEOC and HGEOC . This gamalignant neoplasms” of the ovary, without differentiation between different histological entities, their resulting different biologic behavior and their different probabilities to be detected by screening [The PLCO study did not report any significant reduction of mortality by screening women at average risk . In a sucreening .“stage down-shifting” as a proper tool to improve OS. Needless to say, that further research is required to explore the predictive power of screening methods especially regarding the different subtypes of ovarian cancer.In conclusion, we created a theoretical scenario, where we assumed every ovarian cancer can be early detected using not otherwise specified screening methods. In this set-up based on real-world data, we simulated the impact of a down-shift from FIGO stage III/IV to FIGO stage I/II on clinical outcome. Even though the stage-shift of every histologic subtype would increase the survival, early detection of HGOC and CCOC would have the biggest impact on OS benefit. Regarding screening endeavors in general, our data underscore the considerable influence of the “stage of ovarian cancer” on patients’ survival and highlights


The efficacy of first-and second-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in NSCLC patients with the EGFR L861Q mutation has been studied previously. However, there is little evidence on the efficacy of osimertinib in NSCLC patients with uncommon mutations. Here, we report the case of a 68-year-old man with advanced NSCLC with concurrent EGFR L861Q mutation as well as TP53 and RB1 mutations. The patient was treated with osimertinib as first-line therapy and achieved a remarkable progression-free survival of 15 months. His symptoms were significantly alleviated and the dose was well tolerated. The findings of the present study indicate that osimertinib might be a good treatment option for NSCLC patients with the L861Q mutation. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have revolutionized the therapeutic paradigm for advanced non-small cell lung cancer (NSCLC) with EGFR mutations. Common EGFR mutations account for approximately 75% to 80% of EGFR-mutant NSCLC cases, including exon 19 deletion and the L858R mutation, which has been reported to improve the efficacy of EGFR-TKIs in clinical trials. The remaining EGFR-mutant cases are a highly heterogeneous group of genomic alterations within EGFR exons 18–21  revealed a fluoro-2-deoxy-d-glucose (FDG)-positive lesion in the left middle lung lobe and metastases in multiple bones  by NGS was performed. The AF of the L861Q mutation increased to 73.2%, with TP53 and RB1 mutations and absence of EGFR T790M . Concurrent mutations in EGFR-mutant lung cancers may contribute to tumor heterogeneous outcomes and associate with resistance to EGFR-TKI treatment .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques  are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient . Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%,  and 98.1%,  for PDAC, 96.5%, , and 99.7%,  for CPP, and 98.9%,  and 98.3%,  for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time. The diagnosis of pancreatic cancer has a grim prognosis, with a 5-year survival rate less than 10%, so there is an urgent need for better early detection and treatment options , in partThe important improvements of deep learning and other machine learning techniques are expected to produce a big impact in medical images diagnosis, however these techniques are currently underdeveloped . Among tIn the current study, we used two deep learning techniques, the Convolution Neural Network (CNN) and Long Short-term Memory (LSTM) models to detect the focal pancreatic masses in four EUS imaging modalities .Data from 65 patients with focal pancreatic masses were included in the study, with a total of 20 images selected for each patient from the movies stored on the embedded HDD of the ultrasound system, 5 images each of EUS: gray-scale (B-mode), low-MI contrast-enhancement (CHI) , high-MI color Doppler (CDI), real-time elastography (RTE). The study protocol was approved by the Research and Ethics Committee of the University of Medicine and Pharmacy of Craiova and carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All patients received and signed a written informed consent.The final diagnosis has been confirmed through cytopathological analysis of EUS-FNA/FNB samples, with a follow-up of over 12 months for the patients and categorized as follows: chronic pseudotumoral pancreatitis (CPP), pancreatic neuroendocrine tumor (PNET) and pancreatic ductal adenocarcinoma (PDAC).The imaging equipment consisted of a linear EUS  coupled with a high-end ultrasound system (Hitachi Preirus). Each lesion was assessed using:Gray-scale (B-mode);Contrast-enhancement (Sonovue/Lumason 4.8 mL): pre-contrast (low-MI), post-contrast (10s and 20s) for the arterial phase, and post-contrast (30s and 40s) for the venous phase;Color Doppler, post-contrast (high-MI);Real-time elastography.EUS images of CPP were characterized by hypoechoic masses in gray-scale; hyper-enhanced with low-MI CHI in arterial phase and venous phase; hyper-enhanced with CDI and intermediate stiffness with RTE .The PNET masses had the following features on EUS: hypoechoic mass in gray-scale; hyper-enhanced with low-MI CHI in arterial phase and venous phase (wash-out); hyper-enhanced with high-MI CDI and high stiffness with RTE .PDAC images were characterized by the following patterns: hypoechoic mass in gray-scale; hypo-enhanced with low-MI CHI in the arterial and venous phase; hypo-enhanced with high-MI CDI and high stiffness with RTE .We developed a pancreatic diagnosis prediction method which combines CNN with LSTM to automatically analyze the sequential and multistate pancreatic images. A CNN-LSTM was designed for sequence diagnosis problems with spatial inputs, like pancreatic images and videos. Our CNN-LSTM model could effectively encode spatio-temporal information and extract high-level representations. CNN assume that all inputs and outputs are independent of each other, while the basic assumption of LSTM is that there is an interaction between the input sequences . Subsequgray scale, contrast harmonic sequential images taken at 0, 10, 20, 30, 40 seconds, color Doppler, and respectively real-time elastography imaging.We used the CNN model to extract the spatial features of images from four types of pancreatic imaging modalities: We applied the LSTM network to extract the temporal information of the sequential images of the contrast harmonic imaging taken at 0, 10, 20, 30, 40 seconds.softmax function were used for the pancreatic diagnosis prediction.We used a concatenation layer to integrate the feature vectors. After merging the features, a fully connected layer (FC3) and a softmax activation. The convolutional layers filter the images by detecting patterns at different locations in the image. A pooling layers follows a convolution layer to down-sample the features from the previous convolution layer, such that every feature map contained in a pooling layer is connected with a feature map in the convolution layer. LSTM network models are a type of recurrent neural network that are able to learn and remember over long sequences of input data; it detects and locates patterns inside images sequences and extracts the temporal information [For each type of imaging modality, we have developed a CNN with 4 convolutional layers with a feature map of size (3 x 3), 3 max-pooling layers with a pooling window of size (2 x 2), 2 dropout layers and 2 fully connected (dense) layers. The final dense layer has 3 outputs and a ormation .The parameters of the model are listed in We have applied different techniques in order to reduce the overfitting. Rectified linear unit  was applThe initial set of 1300 images was augmented as described below resulting in a final dataset of 3360 images. Of the total set of 3360 focal pancreatic masses images, 2688 images were used for training and 672 images for testing .The initial large size of the pancreatic mass images (1024 x 768px) contained the clinical region of interest in the middle and adjacent tissue on every side. Therefore, by selecting up to four 500x500px sub-areas from the original image which contained the area of interest and some of the adjacent tissue, we could increase the image data set while maintaining relevant clinical data in each sub-areas. More precisely, the transformations we applied to the image dataset were:Automatic cropping of the image border which contained information about equipment and patient’s identification.Manual selection of either one (PDAC) or between 2–4 (PNET and CPP diagnosis classes) 500x500px images from each initial one of the 1300 1024x768px original images. For the PNET and CPP diagnosis classes, the 500x500px images were selected such that each included the region of interest and a variable area of adjacent tissue on the right, top or bottom of the region of interest.The final dataset of 3360 500x500px images was obtained by performing several additional automatic transformations which are practiced routinely in CNN studies: width_shift and height_shift to randomly translate pictures vertically or horizontally, shear_range to randomly apply shearing transformations, and zoom_range to randomly zoom in images .The CNN datasets were developed using the train-validation-test pattern . The valThe network was trained using the RMSProp optimization algorithm  on a sinTo analyze the performance of our CNN-LSTM method, we used the following medical diagnosis metrics: specificity (Sp) (1), sensitivity (Se) (2), negative predictive value (NPV) (3), positive predictive value (PPV) (4), the test accuracy (5), the area under curve (AUC) and precision recall curves. In addition, we computed the 95% confidence intervals (CI) for NPV and PPV .Another important diagnostic tool we used for the analysis of probabilistic prediction of multi-class classification was the receiver operating characteristic (ROC) curves. The ROC Curves summarize the trade-off between the true positive rate and false positive rate for a predictive model using different probability thresholds. This means that the top left corner of the plot is the ideal point with a false positive rate of zero, and a true positive rate of one. We computed the micro and macro averaging to evaluate the overall performance across all classes. In micro averaging, we computed the performance from the individual true positives, true negatives, false positives, and false negatives of each diagnosis class. In macro averaging we computed the average performance of each diagnosis classes.We used the Precision-Recall as another metrics of the model’s prediction . PrecisiThe algorithm was trained for 50 epochs (iterations over the whole training and testing datasets). The results of the convolution operation are obtained from the 128x128px versions of the 500x500px images. This is an established method for balancing the algorithm’s ability to analyze relevant anatomical features in every image, and the computational burden (GPU memory and running speed) . The algThe overall sensitivity of the model for the diagnosis of focal pancreatic masses was 98.60%%, with a specificity of 97.40%. As shown in The ROC curves of the CNN-LSTM method are illustrated in In the current pilot study, we propose a novel diagnosis classification method CNN-LSTM to characterize the focal pancreatic masses during the EUS procedure: the mixture between the CNN in order to better utilize spatial and configuration information of 2D images and the LSTM for the analysis of the contrast harmonic images to take into consideration the dependency relationships between the successive frames and extract sequential dynamic information which improved the accuracy of the overall result.Other machine learning methods were developed before to assist endoscopists in the EUS evaluation of pancreatic lesions with similar results as our method. In a study by Zhu et al. , the supThe important improvements of deep learning over other machine learning techniques had a big impact in medical image diagnosis. Kuwahara et al.  evaluateIn our study we used CNNs to extract the visual features of focal pancreatic masses because they can be adapted to their intrinsic structure, while the recurrent neural networks (RNNs), particularly LSTM was used to exploit the temporal information contained in EUS temporal images. The combination of CNNs and RNNs generated a model with an overall high accuracy of 98.26%. Although this represents an acceptable accuracy rate in the field, the algorithm will be further tested and improved on a larger patient database during future studies to address the current limited sample size and the lack of an independent test cohort.In the current pilot study, we have used an endoscopic ultrasound imaging data set to train the CNN-LSTM algorithm to generate the automatic, real-time diagnosis. A CNN model was developed in order to differentiate the visual features of chronic pseudotumoral pancreatitis, neuroendocrine tumor and ductal adenocarcinoma images. Furthermore, a LSTM network model was developed to capture the dynamics of the physical features over time. As a clinical decision supporting system, deep learning models could improve the differential diagnostic ability of pancreatic masses. Based on these promising preliminary results and further testing on a larger dataset, our method could become an important tool for the computer-aided diagnosis of focal pancreatic masses.S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


A 61-year-old man with highly symptomatic palpitations presented 13 months after undergoing pulmonary vein isolation for paroxysmal atrial fibrillation. A 12-lead electrocardiogram revealed atrial tachycardia, and the patient was scheduled for mapping, which revealed two regions of reconnection along the posterior part of the region of the left inferior pulmonary vein. The Advisor™ HD Grid Mapping Catheter, Sensor Enabled™ catheter  was used to map the AT. The voltage map revealed two regions of reconnection along the posterior part of the region of the left inferior pulmonary vein (LIPV) . The activation mapping of the LIPV gap-related AT in the posterior part of the left atrium (LA) is shown in Video 1.A 61-year-old man presented with highly symptomatic palpitations 13 months following pulmonary vein isolation (PVI) for paroxysmal atrial fibrillation (AF). A 12-lead electrocardiogram revealed atrial tachycardia (AT), and the patient was scheduled for mapping and further ablation in our electrophysiology laboratory  edit1  and 2B. Two seconds of ablation in this low-voltage region  at a power setting of 30 W resulted in termination of the tachycardia. Further ablation was performed along the posterior part of the LIPV–LA junction. During this procedure, 21,000 electrical points were automatically collected over a period of 12 minutes and a procedure time of 150 minutes. No atrial arrhythmia (AF or AT) could be induced at the end of the procedure.Entrainment mapping confirmed the involvement of the posterior part of the LIPV during tachycardia. Post-pacing interval minus the total cycle length in the posterior part of the LIPV was <20 ms in the two regions with a distance of >2 cm. Local electrograms recorded from the distal electrodes on the Advisor™ HD Grid catheter at the antrum LIPV–LA junction delineated mid-diastolic potentials, which indicated slow conduction through a critical isthmus. The combination of activation, entrainment, and voltage mapping confirmed the diagnosis of an endocardial gap-related reentry in the posterior part of the LA, close to the LIPV 1 In addition to providing rapid voltage and activation maps, this catheter can be positioned in such a way so as to rapidly localize gaps and provide information on the direction of conduction. In particular, omnipolar mapping will allow for accurate directionality as well as a more detailed substrate mapping in the presence of changes in the direction of conduction. The unique shape and predictable electrode location allow for the rapid mapping of connections between the LA and the pulmonary veins as well as the diagnosis of complex post-PVI ATs.1High-resolution mapping of the atria can be rapidly and accurately performed using the Advisor™ HD Grid catheter for the ablation of AF, redo AF ablations, and post-PVI ATs.2 We used the Advisor™ HD Grid mapping catheter to create local activation timing maps and voltage maps. Zones with a peak-to-peak bipolar voltage of <0.05 mV and 0.05–0.5 mV were defined as scar ultra–low-voltage zones and low-voltage zone, respectively, via the EnSite Precision™ three-dimensional mapping system (Abbott). Both bipolar  and unipolar  filters were switched on. The HD Wave Solution algorithm on the EnSite Precision™ mapping system assigns the highest peak-to-peak voltage to anatomical points collected by the Advisor™ HD Grid catheter, thus reducing the possibility of missing viable tissues within the low-voltage areas (“scars”) in the LA. This allows us to identify and delineate circuits and the critical isthmus in AT post-AF ablation. Areas with a voltage 0.5–1.5 mV were defined as abnormal low-voltage areas. However, these thresholds may be dependent on the electrode spacing and the angle between the bipolar direction and the activation direction. Electrograms can vary depending on multiple factors, such as electrode size, interelectrode spacing, and direction of the activation wavefront.5 Bipolar voltage and electrogram characteristics can be further affected by variations in electrode size and/or interelectrode distance of the mapping catheter, direction of the activation wavefront, and wall thickness. Mapping of scar-related AT can be challenging even with the use of a high-density mapping catheter. The best duplicate algorithm of the EnSite Precision™ mapping system uses orthogonal bipoles to compare signal amplitude for collocated mapping data to more accurately display the highest amplitude signal. The Advisor™ HD Grid catheter has 16 equidistant electrodes, allowing for the assessment of conduction directionality as well as omnipolar electrography. This allows for the differentiation of far-field and near-field signals using a single catheter, which can be helpful for the determination of an endpoint in PVI. The Advisor™ HD Grid is a directional high-density mapping catheter which not only identifies local electrical signal but, more importantly, also captures the direction of wavefront propagation, especially in low-voltage zones. The unique shape and predictable electrode location allow for the rapid mapping of connections between the LA and the pulmonary veins as well as the diagnosis of complex post-PVI ATs. This enhances the accuracy of mapping in atrial arrhythmias. This catheter can also be easily used to assess the bidirectional conduction block along a line of ablation.The Advisor™ HD Grid mapping catheter has a paddle shape composed of a 2.5-French 4 × 4 grid  with equidistant 3-mm spacing between the electrodes. The catheter design allows the simultaneous recording of bipolar electrograms in orthogonal directions , which can be used to visualize two different sets of maps. When a couple of orthogonal bipoles are acquired at the same location, the best duplicate algorithm automatically selects the one with the highest peak-to-peak voltage .