Human complete NFAT1 deficiency causes a triad of joint contractures, osteochondromas, and B-cell malignancy.

The discovery of humans with monogenic disorders has a rich history of generating new insights into biology. Here we report the first human identified with complete deficiency of nuclear factor of activated T cells 1 (NFAT1). NFAT1, encoded by NFATC2, mediates calcium-calcineurin signals that drive cell activation, proliferation, and survival. The patient is homozygous for a damaging germline NFATC2 variant (c.2023_2026delTACC; p.Tyr675Thrfs∗18) and presented with joint contractures, osteochondromas, and recurrent B-cell lymphoma. Absence of NFAT1 protein in chondrocytes caused enrichment in prosurvival and inflammatory genes. Systematic single-cell-omic analyses in PBMCs revealed an environment that promotes lymphomagenesis with accumulation of naïve B cells (enriched for oncogenic signatures MYC and JAK1), exhausted CD4+ T cells, impaired T follicular helper cells, and aberrant CD8+ T cells. This work highlights the pleiotropic role of human NFAT1, will empower the diagnosis of additional patients with NFAT1 deficiency, and further defines the detrimental effects associated with long-term use of calcineurin inhibitors.

Dominant negative variants in IKZF2 cause ICHAD syndrome, a new disorder characterised by immunodysregulation, craniofacial anomalies, hearing impairment, athelia and developmental delay.

BACKGROUND: Helios (encoded by IKZF2), a member of the Ikaros family of transcription factors, is a zinc finger protein involved in embryogenesis and immune function. Although predominantly recognised for its role in the development and function of T lymphocytes, particularly the CD4+ regulatory T cells (Tregs), the expression and function of Helios extends beyond the immune system. During embryogenesis, Helios is expressed in a wide range of tissues, making genetic variants that disrupt the function of Helios strong candidates for causing widespread immune-related and developmental abnormalities in humans. METHODS: We performed detailed phenotypic, genomic and functional investigations on two unrelated individuals with a phenotype of immune dysregulation combined with syndromic features including craniofacial differences, sensorineural hearing loss and congenital abnormalities. RESULTS: Genome sequencing revealed de novo heterozygous variants that alter the critical DNA-binding zinc fingers (ZFs) of Helios. Proband 1 had a tandem duplication of ZFs 2 and 3 in the DNA-binding domain of Helios (p.Gly136_Ser191dup) and Proband 2 had a missense variant impacting one of the key residues for specific base recognition and DNA interaction in ZF2 of Helios (p.Gly153Arg). Functional studies confirmed that both these variant proteins are expressed and that they interfere with the ability of the wild-type Helios protein to perform its canonical function-repressing IL2 transcription activity-in a dominant negative manner. CONCLUSION: This study is the first to describe dominant negative IKZF2 variants. These variants cause a novel genetic syndrome characterised by immunodysregulation, craniofacial anomalies, hearing impairment, athelia and developmental delay.

Axial length, more than aging, decreases the thickness and superficial vessel density of retinal nerve fiber layer in non-glaucomatous eyes.

PURPOSE: This study seeks to build a normative database for the vessel density of the superficial retina (SVD) and evaluate how changes and trends in the retinal microvasculature may be influenced by age and axial length (AL) in non-glaucomatous eyes, as measured with optical coherence tomography angiography (OCTA). METHODS: We included 500 eyes of 290 healthy subjects visiting a county hospital. Each participant underwent comprehensive ophthalmological examinations and OCTA to measure the SVD and thickness of the macular and peripapillary areas. To analyze correlations between SVD and age or AL, multivariable linear regression models with generalized estimating equations were applied. RESULTS: Age was negatively correlated with the SVD of the superior, central, and inferior macular areas and the superior peripapillary area, with a decrease rate of 1.06%, 1.36%, 0.84%, and 0.66% per decade, respectively. However, inferior peripapillary SVD showed no significant correlation with age. AL was negatively correlated with the SVD of the inferior macular area and the superior and inferior peripapillary areas, with coefficients of -0.522%/mm, -0.733%/mm, and -0.664%/mm, respectively. AL was also negatively correlated with the thickness of the retinal nerve fiber layer and inferior ganglion cell complex (p = 0.004). CONCLUSION: Age and AL were the two main factors affecting changes in SVD. Furthermore, AL, a relative term to represent the degree of myopia, had a greater effect than age and showed a more significant effect on thickness than on SVD. This relationship has important implications because myopia is a significant issue in modern cities.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE: In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS: To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS: Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.

Inborn errors of immunity manifesting as atopic disorders.

Inborn errors of immunity are traditionally best known for enhancing susceptibility to infections. However, allergic inflammation, among other types of immune dysregulation, occurs frequently in patients with inborn errors of immunity. As such, the term primary atopic disorders (PADs) was recently coined to describe the group of heritable monogenic allergic disorders. It is becoming increasingly important for clinicians to recognize that allergic diseases such as food allergy, atopic dermatitis, and allergic asthma are expressions of misdirected immunity, and in patients who present with severe, early-onset, or coexisting allergic conditions, these can be indications of an underlying PAD. Identifying monogenic allergic disease through next-generation sequencing can dramatically improve outcomes by allowing the use of precision-based therapy targeting the patient's underlying molecular defect. It is therefore imperative that clinicians recognize PADs to be able to provide informed therapeutic options and improve patient outcomes. Here, we summarize the clinical features commonly seen with each of the currently known PADs, identify clinical warning signs that warrant assessment for PADs, and lastly, discuss the benefits of timely diagnosis and management of these conditions.

Mechanistic understanding of the combined immunodeficiency in complete human CARD11 deficiency.

BACKGROUND: Germline pathogenic variants impairing the caspase recruitment domain family member 11 (CARD11)-B cell chronic lymphocytic leukemia/lymphoma 10 (BCL10)-MALT1 paracaspase (MALT1) (CBM) complex are associated with diverse human diseases including combined immunodeficiency (CID), atopy, and lymphoproliferation. However, the impact of CARD11 deficiency on human B-cell development, signaling, and function is incompletely understood. OBJECTIVES: This study sought to determine the cellular, immunological, and biochemical basis of disease for 2 unrelated patients who presented with profound CID associated with viral and fungal respiratory infections, interstitial lung disease, and severe colitis. METHODS: Patients underwent next-generation sequencing, immunophenotyping by flow cytometry, signaling assays by immunoblot, and transcriptome profiling by RNA-sequencing. RESULTS: Both patients carried identical novel pathogenic biallelic loss-of-function variants in CARD11 (c.2509C>T; p.Arg837∗) leading to undetectable protein expression. This variant prevented CBM complex formation, severely impairing the activation of nuclear factor-κB, c-Jun N-terminal kinase, and MALT1 paracaspase activity in B and T cells. This functional defect resulted in a developmental block in B cells at the naive and type 1 transitional B-cell stage and impaired circulating T follicular helper cell (cTFH) development, which was associated with impaired antibody responses and absent germinal center structures on lymph node histology. Transcriptomics indicated that CARD11-dependent signaling is essential for immune signaling pathways involved in the development of these cells. Both patients underwent hematopoietic stem cell transplantations, which led to functional normalization. CONCLUSIONS: Complete human CARD11 deficiency causes profound CID by impairing naive/type 1 B-cell and cTFH cell development and abolishing activation of MALT1 paracaspase, NF-κB, and JNK activity. Hematopoietic stem cell transplantation functionally restores impaired signaling pathways.

Idiopathic splenomegaly in childhood and the spectrum of RAS-associated lymphoproliferative disease: a case report.

BACKGROUND: KRAS (KRAS proto-oncogene, GTPase; OMIM: 190,070) encodes one of three small guanosine triphosphatase proteins belonging to the RAS family. This group of proteins is responsible for cell proliferation, differentiation and inhibition of apoptosis. Gain-of-function variants in KRAS are commonly found in human cancers. Non-malignant somatic KRAS variants underlie a subset of RAS-associated autoimmune leukoproliferative disorders (RALD). RALD is characterized by splenomegaly, persistent monocytosis, hypergammaglobulinemia and cytopenia, but can also include autoimmune features and lymphadenopathy. In this report, we describe a non-malignant somatic variant in KRAS with prominent clinical features of massive splenomegaly, thrombocytopenia and lymphopenia. CASE PRESENTATION: A now-11-year-old girl presented in early childhood with easy bruising and bleeding, but had an otherwise unremarkable medical history. After consulting for the first time at 5 years of age, she was discovered to have massive splenomegaly. Clinical follow-up revealed thrombocytopenia, lymphopenia and increased polyclonal immunoglobulins and C-reactive protein. The patient had an unremarkable bone marrow biopsy, flow cytometry showed no indication of expanded double negative T-cells, while malignancy and storage disorders were also excluded. When the patient was 8 years old, whole exome sequencing performed on DNA derived from whole blood revealed a heterozygous gain-of-function variant in KRAS (NM_004985.5:c.37G > T; (p.G13C)). The variant was absent from DNA derived from a buccal swab and was thus determined to be somatic. CONCLUSIONS: This case of idiopathic splenomegaly in childhood due to a somatic variant in KRAS expands our understanding of the clinical spectrum of RAS-associated autoimmune leukoproliferative disorder and emphasizes the value of securing a molecular diagnosis in children with unusual early-onset presentations with a suspected monogenic origin.

Can a Deep-learning Model for the Automated Detection of Vertebral Fractures Approach the Performance Level of Human Subspecialists?

BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. QUESTIONS/PURPOSES: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients' clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? METHODS: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. RESULTS: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. CONCLUSION: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. LEVEL OF EVIDENCE: Level II, diagnostic study.

Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi-Dirac Correction Functions.

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi-Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi-Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi-Dirac correction function exhibits better capabilities of image augmentation and segmentation.

A Germline Mutation in the C2 Domain of PLCγ2 Associated with Gain-of-Function Expands the Phenotype for PLCG2-Related Diseases.

We report three new cases of a germline heterozygous gain-of-function missense (p.(Met1141Lys)) mutation in the C2 domain of phospholipase C gamma 2 (PLCG2) associated with symptoms consistent with previously described auto-inflammation and phospholipase Cγ2 (PLCγ2)-associated antibody deficiency and immune dysregulation (APLAID) syndrome and pediatric common variable immunodeficiency (CVID). Functional evaluation showed platelet hyper-reactivity, increased B cell receptor-triggered calcium influx and ERK phosphorylation. Expression of the altered p.(Met1141Lys) variant in a PLCγ2-knockout DT40 cell line showed clearly enhanced BCR-triggered influx of external calcium when compared to control-transfected cells. Our results further expand the molecular basis of pediatric CVID and phenotypic spectrum of PLCγ2-related defects.

Germline CBM-opathies: From immunodeficiency to atopy.

Caspase recruitment domain (CARD) protein-B cell CLL/lymphoma 10 (BCL10)-MALT1 paracaspase (MALT1) [CBM] complexes are critical signaling adaptors that facilitate immune and inflammatory responses downstream of both cell surface and intracellular receptors. Germline mutations that alter the function of members of this complex (termed CBM-opathies) cause a broad array of clinical phenotypes, ranging from profound combined immunodeficiency to B-cell lymphocytosis. With an increasing number of patients being described in recent years, the clinical spectrum of diseases associated with CBM-opathies is rapidly expanding and becoming unexpectedly heterogeneous. Here we review major discoveries that have shaped our understanding of CBM complex biology, and we provide an overview of the clinical presentation, diagnostic approach, and treatment options for those carrying germline mutations affecting CARD9, CARD11, CARD14, BCL10, and MALT1.

Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.

BACKGROUND & AIMS: Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. METHODS: We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. RESULTS: In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. CONCLUSIONS: We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images.

Knowledge of medications among patients with glaucoma in Taiwan.

BACKGROUND/PURPOSE: To investigate the knowledge and learning ability of glaucoma patients regarding their anti-glaucoma topical medications. METHODS: Patients on regular follow-up at the Glaucoma Clinic at Hsin-Chu General Hospital were recruited. After detailed ocular examinations, the participants were asked to recall and identify their glaucoma eye drops. The same test was repeated 3 months later. The results of both tests, the learning ability of patients regarding their glaucoma drugs, and the relationship between learning ability and demographic variables were evaluated. RESULTS: Two hundred eighty-seven glaucoma patients participated in this study. Of the study population, 25.8% and 57.1% could recall their topical mediation at the first and second tests, whereas 72.1% and 88.5% could identify their prescribed eye drops at the first and second tests, respectively. Approximately 34% of the participants showed improved knowledge at the repeat test, whereas 40% of the participants showed no improvement. Participants with a better learning ability were more likely to be younger, with a higher level of education, and with less visual field impairment. CONCLUSION: The knowledge of glaucoma patients regarding their prescribed medication was deficient in Taiwan. Physician effort could improve knowledge on the prescribed drugs. Patient-centered education should be considered, targeting elderly individuals, illiterate individuals, and those with loss of visual function to increase compliance with glaucoma medication.

A gene profiling deconvolution approach to estimating immune cell composition from complex tissues.

BACKGROUND: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. RESULTS: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. CONCLUSIONS: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a .

Sufficient dimension reduction with additional information.

Sufficient dimension reduction is widely applied to help model building between the response [Formula: see text] and covariate [Formula: see text] In some situations, we also collect additional covariate [Formula: see text] that has better performance in predicting [Formula: see text], but has a higher obtaining cost, than [Formula: see text] While constructing a predictive model for [Formula: see text] based on [Formula: see text] is straightforward, this strategy is not applicable since [Formula: see text] is not available for future observations in which the constructed model is to be applied. As a result, the aim of the study is to build a predictive model for [Formula: see text] based on [Formula: see text] only, where the available data is [Formula: see text] A naive method is to conduct analysis using [Formula: see text] directly, but ignoring [Formula: see text] can cause the problem of inefficiency. On the other hand, it is not trivial to utilize the information of [Formula: see text] to infer [Formula: see text], either. In this article, we propose a two-stage dimension reduction method for [Formula: see text] that is able to utilize the information of [Formula: see text] In the breast cancer data, the risk score constructed from the two-stage method can well separate patients with different survival experiences. In the Pima data, the two-stage method requires fewer components to infer the diabetes status, while achieving higher classification accuracy than the conventional method.

Dynamic change of surface microbiota with different environmental cleaning methods between two wards in a hospital.

Terminal disinfection and daily cleaning have been performed in hospitals in Taiwan for many years to reduce the risks of healthcare-associated infections. However, the effectiveness of these cleaning approaches and dynamic changes of surface microbiota upon cleaning remain unclear. Here, we report the surface changes of bacterial communities with terminal disinfection and daily cleaning in a medical intensive care unit (MICU) and only terminal disinfection in a respiratory care center (RCC) using 16s ribosomal RNA (rRNA) metagenomics. A total of 36 samples, including 9 samples per sampling time, from each ward were analysed. The clinical isolates were recorded during the sampling time. A large amount of microbial diversity was detected, and human skin microbiota (HSM) was predominant in both wards. In addition, the colonization rate of the HSM in the MICU was higher than that in the RCC, especially for Moraxellaceae. A higher alpha-diversity (p = 0.005519) and a lower UniFrac distance was shown in the RCC due to the lack of daily cleaning. Moreover, a significantly higher abundance among Acinetobacter sp., Streptococcus sp. and Pseudomonas sp. was shown in the RCC compared to the MICU using the paired t test. We concluded that cleaning changes might contribute to the difference in diversity between two wards.

Phylogenetic tree construction using trinucleotide usage profile (TUP).

BACKGROUND: It has been a challenging task to build a genome-wide phylogenetic tree for a large group of species containing a large number of genes with long nucleotides sequences. The most popular method, called feature frequency profile (FFP-k), finds the frequency distribution for all words of certain length k over the whole genome sequence using (overlapping) windows of the same length. For a satisfactory result, the recommended word length (k) ranges from 6 to 15 and it may not be a multiple of 3 (codon length). The total number of possible words needed for FFP-k can range from 46=4096 to 415. RESULTS: We propose a simple improvement over the popular FFP method using only a typical word length of 3. A new method, called Trinucleotide Usage Profile (TUP), is proposed based only on the (relative) frequency distribution using non-overlapping windows of length 3. The total number of possible words needed for TUP is 43=64, which is much less than the total count for the recommended optimal "resolution" for FFP. To build a phylogenetic tree, we propose first representing each of the species by a TUP vector and then using an appropriate distance measure between pairs of the TUP vectors for the tree construction. In particular, we propose summarizing a DNA sequence by a matrix of three rows corresponding to three reading frames, recording the frequency distribution of the non-overlapping words of length 3 in each of the reading frame. We also provide a numerical measure for comparing trees constructed with various methods. CONCLUSIONS: Compared to the FFP method, our empirical study showed that the proposed TUP method is more capable of building phylogenetic trees with a stronger biological support. We further provide some justifications on this from the information theory viewpoint. Unlike the FFP method, the TUP method takes the advantage that the starting of the first reading frame is (usually) known. Without this information, the FFP method could only rely on the frequency distribution of overlapping words, which is the average (or mixture) of the frequency distributions of three possible reading frames. Consequently, we show (from the entropy viewpoint) that the FFP procedure could dilute important gene information and therefore provides less accurate classification.

Real-time 3D stabilization of a super-resolution microscope using an electrically tunable lens.

Single-molecule localization microscopy (SMLM) has become an essential tool for examining a wide variety of biological structures and processes. However, the relatively long acquisition time makes SMLM prone to drift-induced artifacts. Here we report an optical design with an electrically tunable lens (ETL) that actively stabilizes a SMLM in three dimensions and nearly eliminates the mechanical drift (RMS ~0.7 nm lateral and ~2.7 nm axial). The bifocal design that employed fiducial markers on the coverslip was able to stabilize the sample regardless of the imaging depth. The effectiveness of the ETL was demonstrated by imaging endosomal transferrin receptors near the apical surface of B-lymphocytes at a depth of 8 µm. The drift-free images obtained with the stabilization system showed that the transferrin receptors were present in distinct but heterogeneous clusters with a bimodal size distribution. In contrast, the images obtained without the stabilization system showed a broader unimodal size distribution. Thus, this stabilization system enables a more accurate analysis of cluster topology. Additionally, this ETL-based stabilization system is cost-effective and can be integrated into existing microscopy systems.

Climate data for building simulations with urban heat island effects and nature-based solutions.

As cities face a changing climate, buildings will be subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under climate change is essential for maintaining sustainable and resilient communities. To better prepare building simulation climate data with urban effects, a computationally efficient approach is used to generate "urbanized" data, where the city's unique signature is obtained through the dynamic Weather Research and Forecasting model for the Ottawa, Canada region. We demonstrate this process using existing climate data and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. The data consists of several 31-year time series of climate variables such as temperature, humidity, wind speed and direction, pressure, cloud cover, and precipitation over different global warming thresholds. Such a dataset allows building practitioners to evaluate building performance under both historical and future climate conditions, as well as to evaluate the impacts of nature-based solutions to mitigate future climate change risks.