Miracle-dz/UniFAR-FTUs huggingface dataset:
MotiQ: an open-source toolbox to quantify the cell motility and morphology of microglia

Microglia are the primary resident innate immune cells of the CNS. They possess branched, motile cell processes that are important for their cellular functions. To study the pathways that control microglial morphology and motility under physiological and disease conditions, it is necessary to quantify microglial morphology and motility precisely and reliably. Several image analysis approaches are available for the quantification of microglial morphology and motility. However, they are either not automated, not freely accessible, and/or limited in the number of morphology and motility parameters that can be assessed. Thus, we have developed MotiQ, an open-source, freely accessible software for automated quantification of microglial motility and morphology. MotiQ allows quantification of a diverse set of cellular motility and morphology parameters, including the parameters that have become the gold standard in the microglia field. We demonstrate that MotiQ can be applied to in vivo, ex vivo, and in vitro data from confocal, epifluorescence, or two-photon microscopy, and we compare its results to other analysis approaches. We suggest MotiQ as a versatile and customizable tool to study microglia.
Precision Efficacy Analysis for Regression.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a crossvalidity approach to select sample sizes such that models will predict as well as possible in future samples. Previous studies have shown the sample sizes suggested by the PEAR method to be superior to other methods in limited cross-validity shrinkage to acceptable a priori levels. A Monte Carlo study was conducted to verify the PEAR method further for the selection of regression sample sizes and to extend the analysis to include an investigation of the effects of multicollinearity on coefficient estimates obtained through multiple linear regression analysis. Appendixes show the derivation of the PEAR method for sample size selection, and give correlation matrices, stem-and-leaf plots, and histograms of cross-validity for the study. (Contains 10 tables, 4 figures, and 116 references.) (SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ********************************************************************************
Two-Dimensional Ultrathin Silica Films

Two-dimensional (2D) ultrathin silica films have the potential to reach technological importance in electronics and catalysis. Several well-defined 2D-silica structures have been synthesized so far. The silica bilayer represents a 2D material with SiO2 stoichiometry. It consists of precisely two layers of tetrahedral [SiO4] building blocks, corner connected via oxygen bridges, thus forming a self-saturated silicon dioxide sheet with a thickness of ∼0.5 nm. Inspired by recent successful preparations and characterizations of these 2D-silica model systems, scientists now can forge novel concepts for realistic systems, particularly by atomic-scale studies with the most powerful and advanced surface science techniques and density functional theory calculations. This Review provides a solid introduction to these recent developments, breakthroughs, and implications on ultrathin 2D-silica films, including their atomic/electronic structures, chemical modifications, atom/molecule adsorptions, and catalytic reactivity properties, which can help to stimulate further investigations and understandings of these fundamentally important 2D materials.
Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature

Supplementing investigator‐specified variables with large numbers of empirically identified features that collectively serve as ‘proxies’ for unspecified or unmeasured factors can often improve confounding control in studies utilizing administrative healthcare databases. Consequently, there has been a recent focus on the development of data‐driven methods for high‐dimensional proxy confounder adjustment in pharmacoepidemiologic research. In this paper, we survey current approaches and recent advancements for high‐dimensional proxy confounder adjustment in healthcare database studies.
Intraoperative complications in kidney tumor surgery: critical grading for the European Association of Urology intraoperative adverse incident classification

Abstract Introduction The European Association of Urology committee in 2020 suggested a new classification, intraoperative adverse incident classification (EAUiaiC), to grade intraoperative adverse events (IAE) in urology. Aims We applied and validated EAUiaiC, for kidney tumor surgery. Patients and methods A retrospective multicenter study was conducted based on chart review. The study group comprised 749 radical nephrectomies (RN) and 531 partial nephrectomies (PN) performed in 12 hospitals in Finland during 2016–2017. All IAEs were centrally graded for EAUiaiC. The classification was adapted to kidney tumor surgery by the inclusion of global bleeding as a transfusion of ≥3 units of blood (Grade 2) or as ≥5 units (Grade 3), and also by the exclusion of preemptive conversions. Results A total of 110 IAEs were recorded in 13.8% of patients undergoing RN, and 40 IAEs in 6.4% of patients with PN. Overall, bleeding injuries in major vessels, unspecified origin and parenchymal organs accounted for 29.3, 24.0, and 16.0% of all IEAs, respectively. Bowel (n = 10) and ureter (n = 3) injuries were rare. There was no intraoperative mortality. IAEs were associated with increased tumor size, tumor extent, age, comorbidity scores, surgical approach and indication, postoperative Clavien–Dindo (CD) complications and longer stay in hospital. 48% of conversions were reactive with more CD-complications after reactive than preemptive conversion (43 vs. 25%). Conclusions The associations between IAEs and preoperative variables and postoperative outcome indicate good construct validity for EAUiaiC. Bleeding is the most important IAE in kidney tumor surgery and the inclusion of transfusions could provide increased objectivity.
High tibial osteotomy effectively redistributes compressive knee loads during walking

The objectives of this study were to estimate pre‐ and postoperative lower limb kinematics and kinetics and knee intra‐articular forces during gait using musculoskeletal modeling in a cohort of patients with knee osteoarthritis (OA) undergoing high tibial osteotomy (HTO), compare these to controls, and determine correlations between changes in these parameters and Knee Injury and Osteoarthritis Outcome Score (KOOS) subscores after HTO. Sixteen patients with isolated, symptomatic medial compartment knee OA completed pre‐ and postoperative gait analysis (mean follow‐up time: 8.6 months). Sixteen age‐ and sex‐matched asymptomatic volunteers participated as controls. Musculoskeletal modeling was used to evaluate lower limb joint moments and knee contact forces during gait. While HTO had limited influence on sagittal plane kinematics and moments, significant changes in the load distribution at the knee after HTO were observed with a lower postoperative compressive load on the medial compartment during midstance and a higher compressive load on the lateral compartment during early and late stance. Moreover, the lateral shear force in midstance was significantly lower after HTO. Changes in the external knee adduction moment (KAM) did not always coincide with reductions in the knee compressive force in the medial compartment. Biomechanical changes did not correlate with improvements in KOOS subscores. Hence, HTO effectively unloaded the medial compartment by redistributing part of the overall compressive force to the lateral compartment during gait with limited influence on gait function. The KAM may not adequately describe compartmental load magnitude or changes induced by interventions at the compartment level. Clinical trial registration: ClinicalTrials. gov Identifier—NCT02622204. Clinical significance: This study provides important evidence for changes in joint level loads after corrective osteotomy as joint preserving surgery and emphasizes the need for additional biomechanical outcomes of such interventions.
Optimizing Two-Way Partial AUC With an End-to-End Framework

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with <inline-formula><tex-math notation="LaTeX">$\mathsf {TPR} \geq \alpha, \mathsf {FPR} \leq \beta$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">TPR</mml:mi><mml:mo>≥</mml:mo><mml:mi>α</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="sans-serif">FPR</mml:mi><mml:mo>≤</mml:mo><mml:mi>β</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-3185311.gif"/></alternatives></inline-formula> is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this article to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework.
Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit Information

In the current matrix factorization recommendation approaches, the item and the user latent factor vectors are with the same dimension. Thus, the linear dot product is used as the interactive function between the user and the item to predict the ratings. However, the relationship between real users and items is not entirely linear and the existing recommendation model of matrix factorization faces the challenge of data sparsity. To this end, we propose a kernelized deep neural network recommendation model in this article. First, we encode the explicit user—item rating matrix in the form of column vectors and project them to higher dimensions to facilitate the simulation of nonlinear user—item interaction for enhancing the connection between users and items. Second, the algorithm of association rules is used to mine the implicit relation between users and items, rather than simple feature extraction of users or items, for improving the recommendation performance when the datasets are sparse. Third, through the autoencoder and kernelized network processing, the implicit data are connected with the explicit data by the multilayer perceptron network for iterative training instead of doing simple linear weighted summation. Finally, the predicted rating is output through the hidden layer. Extensive experiments were conducted on four public datasets in comparison with several existing well-known methods. The experimental results indicated that our proposed method has obtained improved performance in data sparsity and prediction accuracy.
Cognitive, Sleep, and Autonomic Responses to Induction of a Ketogenic Diet in Military Personnel: A Pilot Study.

BACKGROUND: This pilot study examined the effect of a 2-wk ketogenic diet (KD) compared with a carbohydrate (CHO) diet in military personnel on cognitive performance, mood, sleep, and heart rate variability (HRV). METHODS: A randomized-controlled, cross-over trial was conducted with eight male military personnel (age, 36 ± 7 yr; body mass, 83.7 ± 9.2 kg; BMI, 26.0 ± 2.3 kg · m-2). Subjects ingested their habitual diet for 7 d (baseline), then an iso-energetic KD (∼25 g CHO/d) or CHO diet (∼285 g CHO/d) for 14 d (adaptation), separated by a 12-d washout. HRV, fasting capillary blood D-βHB, and glucose concentration, mood, and sleep were measured daily. Cognitive performance was measured on the 7th day of baseline and the 7th and 14th days of adaptation. Data were analyzed using a series of linear mixed models.RESULTS: Mean weekly D-βHB was higher (95% CI, +0.34 to +2.38 mmol · L-1) and glucose was lower (-0.45 to -0.21 mmol · L-1) in the KD compared with the CHO diet. Cognitive performance (Psychomotor Vigilance Task, 2-choice reaction time, and running memory continuous performance test) and mean weekly fatigue, vigor, and sleep (sleep duration, sleep efficiency, and sleep onset latency) were similar between diets. A diet × week interaction for HRV approached significance, with exploratory analyses suggesting HRV was lower compared with baseline during week-2 adapt (-27 to +4 ms) in the KD.DISCUSSION: A 2-wk KD induction in military personnel does not appear to affect cognitive performance, mood, or sleep, but may lower HRV, indicating increased physiological stress.Shaw DM, Henderson L, van den Berg M. Cognitive, sleep, and autonomic responses to induction of a ketogenic diet in military personnel: a pilot study. Aerosp Med Hum Perform. 2022; 93(6):507-516.
Bycatch and discards in the artisanal shrimp trawl fishery in Northern Peru

This study analyses the bycatch composition of an artisanal shrimp trawl fleet operating between Cabo Blanco and Máncora in Northern Peru between April 2019 and March 2020. A total of 300 hauls were analysed with respect to target catch and bycatch (consisting of other commercial species, discards, and macroalgae). A total of 277 species were recorded including 111 species of fish, 65 species of molluscs, 51 species of crustaceans, 22 species of algae, 12 species of cnidarian, 9 species of echinoderms, 4 species of Bryozoa and 3 species of polychaeta. Capture per unit effort (CPUE, kg.h-1) was highest for fish, followed by crustaceans, algae and molluscs. The target species Penaeus californiensis coffee shrimp constituted 17.8% of the overall catch,82.2% represented bycatch, and 50.6% represented discards. Coffee shrimps were more abundant in June and November 2019 and in January and February 2020. Highest bycatch CPUE occurred in May, June and December 2019. The most abundant species in the bycatch throughout the study period were sand perch (Diplectrum conceptione, 16% weight of the total catch), the macroalgae caulerpa (Caulerpa filiformis, 13%), sole flounder (Etropus ectenes, 6.4%), Pacific drum (Larimus pacificus, 5.7%), and lumptail searobin (Prionotus stephanophrys, 5.1%). Overall, the contribution of sand perch and flounder, exceeded the weight of coffee shrimp, therefore the interpretation that shrimp is the sole target species needs to be revised. The number of discarded species per month increased towards the spring months with the highest value in November. This study represents the first characterisation of bycatch in the artisanal trawling fishery in the Piura region in northern Peru and reveals a high proportion of bycatch in the fishery but also hints at potential temporal management measures that could be imposed to reduce the levels of bycatch. For example, the months of May and December had the greatest bycatch to shrimp ratios and the fishery could potentially be closed to avoid high bycatch risk, however, longer term information is needed to assess if the trends observed in bycatch are similar over longer periods of time. The species characterisation of bycatch also provides information for the design of modified nets which would target the reduction of small fish present in the catch.
Low Tumor Infiltrating Mast Cell Density Reveals Prognostic Benefit in Cervical Carcinoma

Objectives: Research on the role of mast cells (MCs) in cervical tumor immunity is more limited. Therefore, our study aimed to evaluate the prognostic value of MCs and their correlation with the immune microenvironment of cervical carcinoma (CC). Methods: The Cancer Genome Atlas (TCGA) data was utilized to obtain the degree of immune infiltration of MCs in CC. Meanwhile, this study retrospectively collected patient clinical characteristic data and tissue specimens to further verify the relevant conclusions. Mast cell density (MCD) was measured by the CIBERSORT algorithm in TCGA data and immunohistochemical staining of tryptase in CC tissues. Finally, differentially expressed genes (DEGs) of TCGA data were performed using “limma” packages and key gene modules were identified using the MCODE application in Cytoscape. Results: The results showed MCs were diffusely distributed in CC tissues. Moreover, we found that low tumor-infiltrating MCD was beneficial for overall survival (OS) in the TCGA cohort. Consistent conclusions were also obtained in a clinical cohort. In addition, a total of 305 DEGs were analyzed between the high tumor-infiltrating MCD and low tumor-infiltrating MCD group. Seven key modules, a total of 34 genes, were screened through the MCODE plug-in, which was mainly related to inflammatory response and immune response and closely correlated with cytokines including CSF2, CCL20, IL1A, IL1B, and CXCL8. Conclusion: In short, high tumor-infiltration MCs in CC tissue was associated with worse OS in patients. Furthermore, MCs were closely related to cytokines in the tumor microenvironment, suggesting that they collectively played a role in the immune response of the tumor. Therefore, MCD may be a potential prognostic indicator and immunotherapy target of CC.
Evaluation of different blood tests in dogs with extrahepatic portosystemic shunts to assess shunt closure after surgical treatment.

OBJECTIVE To determine the sensitivity and specificity of different individual and combined blood tests to assess extrahepatic portosystemic shunt (EHPSS) closure after gradual attenuation of EHPSS in dogs. STUDY DESIGN Clinical prospective study. ANIMALS Twenty client-owned dogs with EHPSS. METHODS Fasting ammonia (FA), preprandial, postprandial, and paired serum bile acids (SBA), the lidocaine/monoethylglycylxylidide (L/MEGX) test, and serum hyaluronic acid (SHA) were performed at diagnosis, and 1, 3, and 6 months postoperatively. Transsplenic portal scintigraphy was performed to determine EHPSS closure 3 months postoperatively. Their sensitivity and specificity in determining shunt closure postoperatively were calculated. RESULTS When assessing a single blood parameter, FA had the highest specificity (100%), whereas SHA and MEGX measured 15 min after lidocaine administration (T15) had the highest sensitivity (96.9% and 96.2%, respectively) for determining shunt closure postoperatively. The most promising blood test combinations were SHA (sensitivity 96.9%, specificity 81.8%), combined with the L/MEGX test (MEGX at T15: sensitivity 100%, specificity 72.4%) or the L/MEGX test (MEGX at T15) combined with either FA (sensitivity 100%, specificity 82.8%) or postprandial SBA (sensitivity 100%, specificity 81.5%). CONCLUSION Both SHA and the L/MEGX test were sensitive tests for determining shunt closure after gradual attenuation of EHPSS. Test performances could even be improved by combining these tests with each other or with traditional tests such as FA or postprandial SBA. CLINICAL SIGNIFICANCE Although SHA and the L/MEGX test are sensitive blood tests for determining EHPSS closure, especially when combined with traditional blood tests, imaging is still needed to confirm EHPSS closure.
The association of vasomotor symptoms during the menopausal transition and cognition in later life

Abstract Objective The majority of women experience vasomotor symptoms (VMS) during the menopausal transition. Whether self-reported VMS are associated with cognitive test performance later in life remains unclear. The goal of this study was to determine whether a greater burden of VMS is associated with poor later-life cognition. Methods The Wisconsin Longitudinal Study is a prospective study of randomly selected Wisconsin high school graduates of the class of 1957. At ages 65 and 72, a random subset of participants completed six cognitive tests, including similarities, letter and category fluency, immediate and delayed word recall, and digit ordering. Nested regression models were used to examine the association between extent of VMS, assessed at age 54, and baseline cognition at 65, adjusting for early-life socioeconomic status, women’s reproductive health variables, intelligence quotient, and midlife income. This series of models was also used to examine the association between VMS and change in cognition score from age 65 to 72. In sensitivity analyses, models were repeated in a sample using multiple imputation for missing covariates. Results Of the 5,326 women enrolled, 874 had data onVMS, covariates, and all cognitive tests. In an unadjusted model, higher VMS were associated with a lower similarities score (b = -0.09 95% CI -0.16 to -0.02) at age 65 but no other cognitive tests. In adjusted models, VMS were not related to cognition at age 65 or change in cognition. Results remained similar with multiple imputation. Conclusions Our study does not support a relationship between self-reported VMS and cognition later in life.
Valence, sensations and appraisals co-occurring with feeling moved: evidence on kama muta theory from intra-individually cross-correlated time series

ABSTRACT Emotional experiences typically labelled “being moved” or “feeling touched” may belong to one universal emotion. This emotion, which has been labelled “kama muta”, is hypothesised to have a positive valence, be elicited by sudden intensifications of social closeness, and be accompanied by warmth, goosebumps and tears. Initial evidence on correlations among the kama muta components has been collected with self-reports after or during the emotion. Continuous measures during the emotion seem particularly informative, but previous work allows only restricted inferences on intra-individual processes because time series were cross-correlated across samples. In the current studies, we instead use a within-subject design to replicate and extend prior work. We compute intra-individual cross-correlations between continuous self-reports on feeling moved and (1) positive and negative affect; (2) goosebumps and subjective warmth and (3) appraisals of closeness and morality. Results confirm the predictions of kama muta theory that feeling moved by intensified communal sharing cross-correlates with appraised closeness, positive affect, warmth and (less so) goosebumps, but not with negative affect. Contrary to predictions, appraised morality cross-correlated with feeling moved as much as appraised closeness did. We conclude that strong inferences on emotional processes are possible using continuous measures, replace earlier findings, and are largely in line with theorising.