Multiple Regions Research Articles

Durable improvements in atopic dermatitis in the head and neck and across other anatomic regions with rocatinlimab.

In a randomized phase 2b trial (NCT03703102) for adult patients with moderate-to-severe atopic dermatitis (AD), treatment with the T cell rebalancing anti-OX40 receptor antibody rocatinlimab (AMG 451/KHK4083) led to significant improvements in clinical measurements versus placebo including whole-body Eczema Area and Severity Index (EASI) score. AD manifestations can impact variable anatomic regions, and involvement of the head and neck, a sensitive, hard-to-treat area, can negatively impact quality of life. In this post hoc analysis, we investigated response to rocatinlimab treatment across anatomic regions, including the head and neck. Least squares mean change from baseline to Week 56 in EASI score was analyzed by anatomic region (head and neck, trunk, upper extremities, or lower extremities) for patients with baseline moderate-to-severe AD in the respective anatomic region, using mixed models for repeated measures. Rocatinlimab groups were compared with placebo at Week 16. The proportion of patients achieving at least 75% reduction from baseline in EASI (EASI-75) was calculated. Probability of relapsing in EASI-75 during the off-treatment follow-up period (Weeks 36-56) was estimated using a Kaplan - Meier approach. At Week 16, decrease from baseline in mean EASI score was greater with all rocatinlimab regimens versus placebo across all anatomic regions for patients with baseline moderate-to-severe AD in the respective region (all P < 0.001). EASI scores continued to improve on treatment after Week 16 and were maintained during the off-treatment period across all regions. Among patients with baseline moderate-to-severe AD in the head and neck (n = 219; rocatinlimab, n = 174; placebo, n = 45), mean difference (rocatinlimab vs placebo) at Week 16 in LS mean percent change in head and neck EASI score ranged from - 30.4% to - 42.6% across treatment regimens. In patients who received rocatinlimab from the start of the trial, 47% - 71% achieved EASI-75 in the head and neck at Week 36. Among EASI-75 responders at Week 36, the probability of relapsing in EASI-75 in any region was low (< 25% in the head and neck) 20weeks after treatment discontinuation until Week 56.Rocatinlimab treatment led to durable improvements in AD across multiple anatomic regions, including the sensitive head and neck region.

Multifunctional Neural Probes Enable Bidirectional Electrical, Optical, and Chemical Recording and Stimulation In Vivo.

Recording and modulation of neuronal activity enables the study of brain function in health and disease. While translational neuroscience relies on electrical recording and modulation techniques, mechanistic studies in rodent models leverage genetic precision of optical methods, such as optogenetics and fluorescent indicator imaging. In addition to electrical signal transduction, neurons produce and receive diverse chemical signals which motivate tools to probe and modulate neurochemistry. Although the past decade has delivered a wealth of technologies for electrophysiology, optogenetics, chemical sensing, and optical recording, combining these modalities within a single platform remains challenging. This work leverages materials selection and convergence fiber drawing to permit neural recording, electrical stimulation, optogenetics, fiber photometry, drug and gene delivery, and voltammetric recording of neurotransmitters within individual fibers. Composed of polymers and non-magnetic carbon-based conductors, these fibers are compatible with magnetic resonance imaging, enabling concurrent stimulation and whole-brain monitoring. Their utility is demonstrated in studies of the mesolimbic reward pathway by interfacing with the ventral tegmental area and nucleus accumbens in mice and characterizing the neurophysiological effects of a stimulant drug. This study highlights the potential of these fibers to probe electrical, optical, and chemical signaling across multiple brain regions in both mechanistic and translational studies.

Genetic overlap between major depressive disorder and obstructive sleep apnea

ObjectiveObservational studies have frequently shown a co-occurrence of psychiatric disorders and Obstructive sleep apnea (OSA), with major depressive disorder (MDD) being a prevalent psychiatric disorder. This study aims to investigate the genetic overlap between MDD and OSA to explore their underlying pathological mechanisms.MethodsLeveraging the extensive and recent GWAS for OSA and MDD, we conducted genetic correlation analyses utilizing Linkage disequilibrium score regression (LDSC), re-evaluated their pleiotropic Single-nucleotide polymorphisms (SNP) with Cross-Phenotype Association (CPASSOC) and Colocalization (COLOC), investigated the overlap at the gene level using physical annotations and Multi-marker Analysis of GenoMic Annotation (MAGMA), and finally employed Mendelian randomization (MR) to assess potential causal relationships between the two disorders.ResultsUpon our investigation, we established that MDD and OSA exhibit high heritability (h2MDD=0.02, h2OSA=0.04) alongside a significant genetic correlation (rg=0.31, P= 1.42E-23). Utilizing CPASSOC, we identified 397 pleiotropic SNPs, associable with 45 loci, two of which share common genetic fragments with a pleiotropic role. Furthermore, the MAGMA study uncovered a total of 154 pleiotropic genes capable of influencing multiple brain regions. Lastly, leveraging MR analysis, we concluded that MDD heightens the risk of developing OSA (P=3. 10E-04, OR (95%CI):1.28(1.12~ 1.47)).ConclusionIn summary, our study identified PCLO as a common gene between OSA and MDD and provided evidence that MDD causally contributes to the development of OSA. These insights enhance our understanding of the shared mechanisms underlying the comorbidity of these conditions.

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Screening and Identification Quality Markers for Antioxidant Activity of Tetrastigma hemsleyanum from Different Origins using Spectrum-effect Correlation Analysis

Background:: Tetrastigma hemsleyanum (THL) is a traditional medicinal plant with various biological activities, it has been validated that several THL extracts have strong pharmacological activities. Until now, there is no complete and systematic germplasm identification and quality evaluation standard. Objective:: Establishing fingerprint spectra of THL from different sources, evaluating their antioxidant activity and identifying quality markers. Methods:: In this paper, establishing fingerprints and identifying quality markers through UPLC-QTOF- MS/MS. The antioxidant capacity of forty-one batches of THL from multiple regions was determined by 1,1-Diphenyl-2-picrylhydrazyl radical (DPPH), 2,2'-Azinobis-(3-ethylbenzthiazoline-6- sulphonate) (ABTS), and Ferric reducing antioxidant power (FRAP) assays. Results:: The results indicated that THL methanol extracts showed good antioxidative activity and eight THL samples, including THL aerial parts, showed high antioxidative activity. Besides, Ultra Performance Liquid Chromatography (UPLC) fingerprints of THL from different places were established and chromatographic peaks probably correlated with antioxidant capacity were screened by principal component analysis (PCA) and partial least square regression (PLS) analysis. Conclusion:: The chemical constituents of these peaks were identified as possible quality markers for THL quality evaluation. In this study, twelve effective substances were preliminarily determined, such as isovitexin, L-epicatechin, procyanidin B, sitosterol, etc.

Intelligent Design Method and System of Tin Spraying Steel Mesh for Complex Combiner

ABSTRACT In the process of spraying tin into the combiner cavity, employing the Tin Spraying Steel Mesh (TSSM) is crucial. This operation is akin to covering a complex, maze-like area with multiple islands and branches. Manual operation is labor-intensive, time-consuming, and challenging to ensure high-quality results. Therefore, this paper presents an intelligent design method and system for the TSSM, based on graphic processing. Initially, isolated islands are used as nodes to establish the topology structure of complex paths. Then, an improved Delaunay triangulation method is utilized to extract intermediate paths for regions with branching paths, resulting in the division into multiple single path regions. Following this, the island regions undergo equal arc length segmentation, while the single path regions are subjected to random bidirectional segmentation to obtain the final TSSM. Finally, a TSSM intelligent segmentation system is developed by seamlessly integrating this method into the AutoCAD software platform using ObjectARX technology. The TSSM obtained by this method meets the quality requirements, demonstrating consistent segmentation direction and uniform gaps. Furthermore, the system can adjust segmentation parameters to cater to different combiner specifications, thereby significantly improving design efficiency and establishing itself as an indispensable auxiliary tool in the automated design of combiners.

Neuro-Immune Communication at the Core of Craving-Associated Brain Structural Network Reconfiguration in Methamphetamine Users

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Frontoparietal Structural Network Disconnections Correlate With Outcome After a Severe Stroke.

Structural disconnectome analyses have provided valuable insights into how a stroke lesion results in widespread network disturbances and how these relate to deficits, recovery patterns, and outcomes. Previous analyses have primarily focused on patients with relatively mild to moderate deficits. However, outcomes vary among survivors of severe strokes, and the mechanisms of recovery remain poorly understood. This study assesses the association between lesion-induced network disconnection and outcome after severe stroke. Thirty-eight ischaemic stroke patients underwent MRI brain imaging early after stroke and longitudinal clinical follow-up. Lesion information was integrated with normative connectome data to infer individual disconnectome profiles on a localized regional and region-to-region pathway level. Ordinal logistic regressions were computed to link disconnectome information to the modified Rankin Scale after 3-6 months. Disconnections of ipsilesional frontal, parietal, and temporal cortical brain areas were significantly associated with a worse motor outcome after a severe stroke, adjusted for the initial deficit, lesion volume, and age. The analysis of the underlying pathways mediating this association revealed location-specific results: For frontal, prefrontal, and temporal brain areas, the association was primarily driven by relatively sparse intrahemispheric disconnections. In contrast, the ipsilesional primary motor cortex, the dorsal premotor cortex, and various parietal brain regions showed a remarkable involvement of either frontoparietal intrahemispheric or additionally interhemispheric disconnections. These results indicate that localized disconnection of multiple regions embedded in the structural frontoparietal network correlates with worse outcomes after severe stroke. Specifically, primary motor and parietal cortices might gain particular importance as they structurally link frontoparietal networks of both hemispheres. These data shed novel light on the significance of distinct brain networks for recovery after a severe stroke.

A wide distribution of Beiji nairoviruses and related viruses in Ixodes ticks in Japan

Beiji nairovirus (BJNV), in the family Nairoviridae, the order Bunyavirales, was recently reported as a causative agent of an emerging tick-borne zoonotic infection in China. This study investigated the prevalence of BJNV in ticks in Japan. Screening of over 2,000 ticks from multiple regions revealed a widespread distribution of BJNV and BJNV-related viruses in Japan, particularly in the northern island, and in other high altitude areas with exclusive occurrence of Ixodes ticks. Phylogenetic analysis identified three distinct groups of nairoviruses in ticks in Japan: BJNV, Yichun nairovirus (YCNV) and a newly identified Mikuni nairovirus (MKNV). BJNV and YCNV variants identified in ticks in Japan exhibited high nucleotide sequence identities to those in China and Russia with evidence of non-monophyletic evolution among BJNVs, suggesting multiple cross-border transmission events of BJNV between the Eurasian continent and Japan. Whole genome sequencing of BJNV and MKNV revealed a unique GA-rich region in the S segment, the significance of which remains to be determined. In conclusion, the present study has shown a wide distribution and diversity of BJNV-related nairoviruses in Ixodes ticks in Japan and has identified unique genomic structures. The findings demonstrate the significance of BJNV as well as related viruses in Japan and highlight the necessity of monitoring emerging nairovirus infections and their potential risks to public health.

Probing the Low-velocity Regime of Nonradiative Shocks with Neutron Star Bow Shocks

Nonradiative shocks accelerate particles and heat astrophysical plasmas. While supernova remnants are the most well-studied example, neutron star (NS) bow shocks are also nonradiative and Balmer dominated. NS bow shocks are likely ubiquitous in the interstellar medium due to their large speeds imparted at birth, and they are thought to be a discrete source population contributing to the Galactic cosmic-ray spectrum. To date, nine NS bow shocks have been directly observed in Hα images. Most of these shocks have been characterized using narrowband Hα imaging and slit spectroscopy, which do not resolve the multicomponent velocity structure of the shocks and their spatial geometry. Here we present integral field spectroscopy of three NS bow shocks: J0742−2822, J1741−2054, and J2225+6535 (the Guitar Nebula). We measure the shock properties simultaneously in four dimensions: the 2D projected shock morphology, the radial velocity structure, and the Hα flux. The broad-to-narrow line ratio (I b/I n) is inferred from radial velocity profiles, and for J1741−2054, the narrow line is detected in multiple regions of the shock. The inferred line ratios and widths suggest that NS bow shocks represent a low-shock velocity regime (V ≲ 200 km s−1) in which I b/I n is high, distinct from the shock regime probed by supernova remnants. Our results illustrate a need for nonradiative shock models at velocities lower than previously considered, which will reveal the electron–ion temperature ratios and particle acceleration efficiencies of these bow shocks.

PARS: A Pattern-Aware Spatial Data Prefetcher Supporting Multiple Region Sizes

PARS: A Pattern-Aware Spatial Data Prefetcher Supporting Multiple Region Sizes

Global Overview of Suicidal Behavior and Risk Factors among General Population during the COVID-19 Pandemic: A Systematic Review and a Meta-Regression.

The COVID-19 pandemic has significantly impacted mental health worldwide, leading to increased rates of suicidal behavior. This systematic review and meta-regression aim to investigate the global prevalence and risk factors associated with suicidal behaviors in the general population during the pandemic. The study included 202 articles from January 1, 2019, to October 31, 2023, sourced from databases such as Embase, MEDLINE, CINAHL, Web of Science, and Cochrane Library. The meta-analysis revealed a prevalence of 13.5% for suicidal ideation, 10.4% for suicide attempts, and a death rate of 0.5%, translating to 4.52 per 100,000 person-years. Significant risk factors identified include being transgender, young adults (18-44years), unmarried status, low education, retirement, living alone, low social support, a history of suicide attempts, substance use, depression, anxiety, PTSD, sleep problems, poor perceived physical health, loneliness, quarantine, and residing in the Americas or multiple regions. The findings underscore the urgent need for targeted mental health interventions during pandemics, focusing on high-risk groups such as young adults, transgender individuals, those with low social support, and people with mental health issues. This comprehensive analysis provides valuable insights for policymakers and healthcare providers to develop effective strategies to mitigate the heightened risk of suicide during global health crises.

Data analytics driving net zero tracker for renewable energy

This research aims to assess the impact of renewable energy policies, investments, and emissions reductions toward achieving net-zero targets by 2050. We analysed key metrics using the Net Zero Tracker (NZT), including renewable energy capacity, policy strength, financial investment, and carbon emissions across multiple regions and industries. Our methodology involved data collection from 2020 to 2050, utilising predictive modeling to project trends in renewable energy adoption and emissions reduction. Key findings show that renewable energy capacity is expected to surpass 1000 GW by 2050, with an exponential increase around 2045. Policy Strength Index (PSI) will grow by 20 %, from 50 in 2020 to 60 in 2050, while investments in renewable energy will rise from $10 billion to $25 billion over the same period. Emissions are projected to steadily decrease to zero by 2050, which aligns with net-zero goals. The margin of error in the projections is ±5 %, considering potential policy implementation and technology development variations. These results underscore the critical role of enhanced policies, sustained investments, and international cooperation in accelerating the global transition to renewable energy. The research offers valuable insights for policymakers and stakeholders to guide future strategies for achieving a sustainable energy future.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

Air quality in Bulgaria—Monitoring data, perspectives, scientific research

&lt;p class="1"&gt;This paper addresses air quality in Bulgaria, a pressing environmental issue with monitoring data indicating persistent challenges in urban areas due to pollutants like PM10 and NO&lt;sub&gt;2&lt;/sub&gt;. Bulgaria’s government operates a comprehensive air quality monitoring network, offering real-time data to track pollutants across multiple regions. Scientific research in this area focuses on identifying pollution sources, assessing public health impacts, and developing models for pollution control. Efforts to improve air quality include implementing stricter regulations, investing in green technologies, and conducting public awareness campaigns. Future research emphasizes sustainable urban planning, the adoption of renewable energy, and advanced monitoring technologies to strengthen air quality management in Bulgaria.&lt;/p&gt;

Distinguishing fault offset from climatically modulated channel deflections: Insights from the Pearblossom slip-rate site, Mojave section of the San Andreas fault, California, USA

Fault slip histories are essential for understanding seismic hazard and regional fault system development but fundamentally depend on identifying, dating, and reconstructing displaced markers. Here, we use a case study of the Pearblossom site along the Mojave section of the San Andreas fault in California (USA) to show how pulses of sediment aggradation during wet periods can complicate such reconstructions by producing “imposter offsets”—landforms that develop with an initial deflection that is easily misread as tectonic displacement caused by fault slip. Specifically, we document two channels on the downstream side of the fault: a subtle one that we interpret to have been beheaded and displaced 24–49 m from a source channel on the upstream side of the fault, and a second and more prominent one that we interpret as an imposter offset of 36–88 m. Using optically stimulated luminescence dating, we determine that the source channel incised between 1.44 ± 0.43 ka and 1.27 ± 0.18 ka with a subsequent phase of alluvial fan aggradation at ~0.6 ka, when the channel with the imposter offset formed. Because the pulse of fan deposition coincides temporally with a wet period in Southern California precipitation records, we attribute formation of the imposter offset and the alluvial fan into which it incised to climatically modulated deposition at the site. Comparing precipitation records with charcoal ages compiled from multiple Mojave Desert region locations suggests that other slip-rate sites may be similarly affected. Although climatic effects can complicate slip-rate studies, we show that the morphology and upstream position of the deflected channel can indicate whether a site likely records useful information about fault slip.

Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients

Background: Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. Methods: A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. Results: OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p &lt; 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p &lt; 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p &lt; 0.001; female: 0.95 vs. 0.87, p &lt; 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p &lt; 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p &lt; 0.001; female: DSC 0.82 vs. 0.73, p &lt; 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. Conclusions: OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population.

Posterior Basolateral Amygdala is a Critical Amygdaloid Area for Temporal Lobe Epilepsy.

The amygdaloid complex consists of multiple nuclei and is a key node in controlling temporal lobe epilepsy (TLE) in both human and animal model studies. However, the specific nucleus in the amygdaloid complex and the neural circuitry governing seizures remain unknown. Here, it is discovered that activation of glutamatergic neurons in the posterior basolateral amygdala (pBLA) induces severe seizures and even mortality. The pBLA glutamatergic neurons project collateral connections to multiple brain regions, including the insular cortex (IC), bed nucleus of the stria terminalis (BNST), and central amygdala (CeA). Stimulation of pBLA-targeted IC neurons triggers seizures, whereas ablation of IC neurons suppresses seizures induced by activating pBLA glutamatergic neurons. GABAergic neurons in the BNST and CeA establish feedback inhibition on pBLA glutamatergic neurons. Deleting GABAergic neurons in the BNST or CeA leads to sporadic seizures, highlighting their role in balancing pBLA activity. Furthermore, pBLA neurons receive glutamatergic inputs from the ventral hippocampal CA1 (vCA1). Ablation of pBLA glutamatergic neurons mitigates both acute and chronic seizures in the intrahippocampal kainic acid-induced mouse model of TLE. Together, these findings identify the pBLA as a pivotal nucleus in the amygdaloid complex for regulating epileptic seizures in TLE.

Assessment of treatment effect heterogeneity for multiregional randomized clinical trials

Multiregional clinical trials (MRCTs) have become increasingly common in recent years. Detecting underlying regional heterogeneity is a critical issue for these trials. Existing methods for assessing treatment effect heterogeneity across regions have ignored the incomparability of baseline extrinsic risk factors of the randomized patients from different regions. In this paper, a calibration weighting method is proposed to calibrate the distribution of these extrinsic risk factors between multiple regions. We establish the consistency and the asymptotic normality of the calibration weighting estimator. Simulation studies confirm the finite sample properties of the proposed estimator as well as its superior performance over naive methods and the inverse probability weighting method. The proposed method is illustrated using a randomized clinical trial of adjuvant chemotherapy for resected non-small-cell lung cancer.

Mapping common and distinct brain correlates among cognitive flexibility tasks: concordant evidence from meta-analyses.

Cognitive flexibility allows individuals to switch between different tasks, strategies, or ideas; an ability that is important for everyday life. The Wisconsin card sorting test (WCST) and task switching paradigm (TSP) are popular measures of cognitive flexibility. Although both tasks require switching, the TSP requires participants to memorize switching rules and retrieve them when they view a cue (rule-retrieval), whereas the classic WCST requires participants to discover the switching rule via trial-and-error (rule-discovery). Many functional magnetic resonance imaging studies have examined brain responses to these tasks. Extant meta-analyses show concordance in activation in a widespread set of areas including frontal, parietal, and cingulate cortices. Critically, past meta-analyses have not specifically examined brain correlates associated with rule derivation (i.e., rule-discovery vs. rule-retrieval) in cognitive flexibility tasks. We examine for the first time common and distinct concordance in brain responses to rule-discovery (i.e., WCST) and rule-retrieval (i.e., TSP), as well as TSP subtypes using quantitative meta-analyses. We analyzed data from 69 eligible articles with a total of 1617 young-adult participants. Conjunction results show concordance in common fronto-parietal areas predominantly in the left hemisphere. Contrast analyses show that rule-discovery required increased involvement in multiple cortical and subcortical regions such as frontopolar (Brodmann Area 10), parietal, insular cortex, thalamus and caudate nucleus predominantly in the right hemisphere. No significant differences in concordance were observed among the three, task switching paradigm sub-types. We propose a neuroanatomical model of cognitive flexibility and discuss theoretical and practical applications.