DR-BERT: A protein language model to annotate disordered regions.

Despite their lack of a rigid structure, intrinsically disordered regions (IDRs) in proteins play important roles in cellular functions, including mediating protein-protein interactions. Therefore, it is important to computationally annotate IDRs with high accuracy. In this study, we present Disordered Region prediction using Bidirectional Encoder Representations from Transformers (DR-BERT), a compact protein language model. Unlike most popular tools, DR-BERT is pretrained on unannotated proteins and trained to predict IDRs without relying on explicit evolutionary or biophysical data. Despite this, DR-BERT demonstrates significant improvement over existing methods on the Critical Assessment of protein Intrinsic Disorder (CAID) evaluation dataset and outperforms competitors on two out of four test cases in the CAID 2 dataset, while maintaining competitiveness in the others. This performance is due to the information learned during pretraining and DR-BERT's ability to use contextual information.

Ablation of the integrin CD11b mac-1 limits deleterious responses to traumatic spinal cord injury and improves functional recovery in mice.

Background: Spinal cord injury (SCI) causes long-term sensorimotor deficits and posttraumatic neuropathic pain, with no effective treatment. In part, this reflects an incomplete understanding of the complex secondary pathobiological mechanisms involved. SCI triggers microglial/macrophage activation with distinct pro-inflammatory or inflammation-resolving phenotypes, which potentiate tissue damage or facilitate functional repair, respectively. The major integrin Mac-1 (CD11b/CD18, αMß2 or CR3), a heterodimer consisting of αM (CD11b) and ß2 (CD18) chains, is generally regarded as a pro-inflammatory receptor in neurotrauma. Multiple immune cells of the myeloid lineage express CD11b, including microglia, macrophages, and neutrophils. In the present study, we examined the effects of CD11b gene ablation on posttraumatic neuroinflammation and functional outcomes after SCI. Methods: Young adult age-matched female CD11b knockout (KO) mice and their wildtype (WT) littermates were subjected to moderate thoracic spinal cord contusion. Neuroinflammation in the injured spinal cord was assessed with qPCR, flow cytometry, NanoString, and RNAseq. Neurological function was evaluated with the Basso Mouse Scale (BMS), gait analysis, thermal hyperesthesia, and mechanical allodynia. Lesion volume was evaluated by GFAP-DAB immunohistochemistry, followed by analysis with unbiased stereology. Results: qPCR analysis showed a rapid and persistent upregulation of CD11b mRNA starting from 1d after injury, which persisted up to 28 days. At 1d post-injury, increased expression levels of genes that regulate inflammation-resolving processes were observed in CD11b KO mice. Flow cytometry analysis of CD45intLy6C-CX3CR1+ microglia, CD45hiLy6C+Ly6G- monocytes, and CD45hiLy6C+Ly6G+ neutrophils revealed significantly reduced cell counts as well as reactive oxygen production in CD11b KO mice at d3 post-injury. Further examination of the injured spinal cord with NanoString Mouse Neuroinflammation Panel and RNAseq showed upregulated expression of pro-inflammatory genes, but downregulated expression of the reactive oxygen species pathway. Importantly, CD11b KO mice exhibited significantly improved locomotor function, reduced cutaneous mechanical/thermal hypersensitivity, and limited tissue damage at 8 weeks post-injury. Conclusion: Collectively, our data suggest an important role for CD11b in regulating tissue inflammation and functional outcome following SCI. Thus, the integrin CD11b represents a potential target that may lead to novel therapeutic strategies for SCI.

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Lymphocyte-Related Immunomodulatory Therapy with Siponimod (BAF-312) Improves Outcomes in Mice with Acute Intracerebral Hemorrhage.

Modulators of the sphingosine-1-phosphate receptor (S1PR) have been proposed as a promising strategy for treating stroke. However, the detailed mechanisms and the potential translational value of S1PR modulators for intracerebral hemorrhage (ICH) therapy warrant exploration. Using collagenase VII-S-induced ICH in the left striatum of mice, we investigated the effects of siponimod on cellular and molecular immunoinflammatory responses in the hemorrhagic brain in the presence or absence of anti-CD3 monoclonal antibodies (Abs). We also assessed the severity of short- and long-term brain injury and evaluated the efficacy of siponimod in long-term neurologic function. Siponimod treatment significantly decreased brain lesion volume and brain water content on day 3 and the volume of the residual lesion and brain atrophy on day 28. It also inhibited neuronal degeneration on day 3 and improved long-term neurologic function. These protective effects may be associated with a reduction in the expression of lymphotactin (XCL1) and T-helper 1 (Th1)-type cytokines (interleukin 1ß and interferon-γ). It may also be associated with inhibition of neutrophil and lymphocyte infiltration and alleviation of T lymphocyte activation in perihematomal tissues on day 3. However, siponimod did not affect the infiltration of natural killer cells (NK) or the activation of CD3-negative immunocytes in perihematomal tissues. Furthermore, it did not influence the activation or proliferation of microglia or astrocytes around the hematoma on day 3. Siponimod appears to have a profound impact on infiltration and activation of T lymphocytes after ICH. The effects of neutralized anti-CD3 Abs-induced T-lymphocyte tolerance on siponimod immunomodulation further confirmed that siponimod alleviated the cellular and molecular Th1 response in the hemorrhagic brain. This study provides preclinical evidence that encourages future investigation of immunomodulators, including siponimod, which target the lymphocyte-related immunoinflammatory reaction in ICH therapy.

T-cell receptor signaling modulated by the co-receptors: Potential targets for stroke treatment.

Stroke is a severe and life-threatening disease, necessitating more research on new treatment strategies. Infiltrated T lymphocytes, an essential adaptive immune cell with extensive effector function, are crucially involved in post-stroke inflammation. Immediately after the initiation of the innate immune response triggered by microglia/macrophages, the adaptive immune response associated with T lymphocytes also participates in the complex pathophysiology of stroke and partially informs the outcome of stroke. Preclinical and clinical studies have revealed the conflicting roles of T cells in post-stroke inflammation and as potential therapeutic targets. Therefore, exploring the mechanisms that underlie the adaptive immune response associated with T lymphocytes in stroke is essential. The T-cell receptor (TCR) and its downstream signaling regulate T lymphocyte differentiation and activation. This review comprehensively summarizes the various molecules that regulate TCR signaling and the T-cell response. It covers both the co-stimulatory and co-inhibitory molecules and their roles in stroke. Because immunoregulatory therapies targeting TCR and its mediators have achieved great success in some proliferative diseases, this article also summarizes the advances in therapeutic strategies related to TCR signaling in lymphocytes after stroke, which can facilitate translation.

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks.

The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the-art approaches for protein family classification while being much more general than other architectures. Further, our method outperforms other approaches for protein interaction prediction for two out of three different scenarios that we generated. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.

A Systematic Review of Crohn's Disease Case Definitions in Administrative or Claims Databases.

BACKGROUND: We sought to review Crohn's disease (CD) case definitions that use diagnosis, procedure, and medication claims. METHODS: We searched PubMed and Embase from inception through January 31, 2022, using terms related to CD, inflammatory bowel disease, administrative claims, or validity. Each article was scrutinized by 2 authors independently screening and abstracting data. Collected data included participant characteristics, case definition characteristics, and case definition validity. When diagnostic accuracy was provided for multiple case definitions, we extracted the case definition selected by the authors. All diagnostic accuracy characteristics were captured. RESULTS: We identified 30 studies that evaluated a case definition using claims data to identify CD patients. The most common case definition included counts of diagnosis codes (57%) followed by a combination of diagnosis codes and medications (20%). All but 1 study validated the case definition with a medical chart review. In 2 studies, the patient's primary care provider completed a survey to confirm disease status. The positive predictive value of the case definitions ranged from 18% (≥1 code at a single U.S. health plan) to 100% (≥1 code plus a relevant prescription at a U.S. hospital). More complex case definitions (eg, ≥1 code + prescription or ≥2 codes) had lower variability in positive predictive value (≥80%) and specificity (≥85%) than the ≥1 code requirement. CONCLUSIONS: Health services researchers should validate case definitions in their research cohorts. When such validation cannot be performed, we recommend using a more complex case definition. Studies without a validated CD case definition should use sensitivity analyses to confirm the robustness of their results.

This systematic review of Crohn's disease (CD) case definitions identified that complex case definitions such as ≥1 diagnosis code + ≥1 prescription had desirable diagnostic accuracy properties.

Regulated necrosis in COVID-19: A double-edged sword.

COVID-19 caused by SARS-CoV-2 can cause various systemic diseases such as acute pneumonia with cytokine storm. Constituted of necroptosis, pyroptosis, and ferroptosis, regulated necrosis constitutes the cell death patterns under the low apoptosis condition commonly observed in COVID-19. Regulated necrosis is involved in the release of cytokines like TNF-α, IL-1 ß, and IL-6 and cell contents such as alarmins, PAMPs, and DAMPs, leading to more severe inflammation. Uncontrolled regulated necrosis may explain the poor prognosis and cytokine storm observed in COVID-19. In this review, the pathophysiology and mechanism of regulated necrosis with the double-edged sword effect in COVID-19 are thoroughly discussed in detail. Furthermore, this review also focuses on the biomarkers and potential therapeutic targets of the regulated necrosis pathway in COVID-19, providing practical guidance to judge the severity, prognosis, and clinical treatment of COVID-19 and guiding the development of clinical anti-SARS-CoV-2 drugs.

Molecular, Pathological, Clinical, and Therapeutic Aspects of Perihematomal Edema in Different Stages of Intracerebral Hemorrhage.

Acute intracerebral hemorrhage (ICH) is a devastating type of stroke worldwide. Neuronal destruction involved in the brain damage process caused by ICH includes a primary injury formed by the mass effect of the hematoma and a secondary injury induced by the degradation products of a blood clot. Additionally, factors in the coagulation cascade and complement activation process also contribute to secondary brain injury by promoting the disruption of the blood-brain barrier and neuronal cell degeneration by enhancing the inflammatory response, oxidative stress, etc. Although treatment options for direct damage are limited, various strategies have been proposed to treat secondary injury post-ICH. Perihematomal edema (PHE) is a potential surrogate marker for secondary injury and may contribute to poor outcomes after ICH. Therefore, it is essential to investigate the underlying pathological mechanism, evolution, and potential therapeutic strategies to treat PHE. Here, we review the pathophysiology and imaging characteristics of PHE at different stages after acute ICH. As illustrated in preclinical and clinical studies, we discussed the merits and limitations of varying PHE quantification protocols, including absolute PHE volume, relative PHE volume, and extension distance calculated with images and other techniques. Importantly, this review summarizes the factors that affect PHE by focusing on traditional variables, the cerebral venous drainage system, and the brain lymphatic drainage system. Finally, to facilitate translational research, we analyze why the relationship between PHE and the functional outcome of ICH is currently controversial. We also emphasize promising therapeutic approaches that modulate multiple targets to alleviate PHE and promote neurologic recovery after acute ICH.

Scoping review and classification of deep learning in medical genetics.

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes.

Background: In medical genetics, one application of neural networks is the diagnosis of genetic diseases based on images of patient faces. While these applications have been validated in the literature with primarily pediatric subjects, it is not known whether these applications can accurately diagnose patients across a lifespan. We aimed to extend previous works to determine whether age plays a factor in facial diagnosis as well as to explore other factors that may contribute to the overall diagnostic accuracy. Methods: To investigate this, we chose two relatively common conditions, Williams syndrome and 22q11.2 deletion syndrome. We built a neural network classifier trained on images of affected and unaffected individuals of different ages and compared classifier accuracy to clinical geneticists. We analyzed the results of saliency maps and the use of generative adversarial networks to boost accuracy. Results: Our classifier outperformed clinical geneticists at recognizing face images of these two conditions within each of the age groups (the performance varied between the age groups): 1) under 2 years old, 2) 2-9 years old, 3) 10-19 years old, 4) 20-34 years old, and 5) ≥35 years old. The overall accuracy improvement by our classifier over the clinical geneticists was 15.5 and 22.7% for Williams syndrome and 22q11.2 deletion syndrome, respectively. Additionally, comparison of saliency maps revealed that key facial features learned by the neural network differed with respect to age. Finally, joint training real images with multiple different types of fake images created by a generative adversarial network showed up to 3.25% accuracy gain in classification accuracy. Conclusion: The ability of clinical geneticists to diagnose these conditions is influenced by the age of the patient. Deep learning technologies such as our classifier can more accurately identify patients across the lifespan based on facial features. Saliency maps of computer vision reveal that the syndromic facial feature attributes change with the age of the patient. Modest improvements in the classifier accuracy were observed when joint training was carried out with both real and fake images. Our findings highlight the need for a greater focus on age as a confounder in facial diagnosis.

Risk factors for infections after endoscopic retrograde cholangiopancreatography (ERCP): a retrospective cohort analysis of US Medicare Fee-For-Service claims, 2015-2021.

OBJECTIVE: Contaminated reprocessed duodenoscopes pose a serious threat to patients in the endoscopy unit. Despite manufacturer changes to reprocessing guidelines, 20% of reprocessed duodenoscopes meet criteria for quarantine-level contamination based on microbiological or ATP testing. We aimed to examine risk factors for postendoscopic retrograde cholangiopancreatography (ERCP) infection. DESIGN: Retrospective cohort analysis. SETTING: US Medicare Fee-For-Service claims (2015-2021) and all-payer data (2017). PARTICIPANTS: In the Medicare data, 823 575 ERCP procedures were included. The all-payer five-state data, 16 609 procedures were included. INTERVENTIONS: ERCP was identified by Current Procedural Terminology and International Classification of Disease (ICD) procedure codes. We identified inpatient infections using ICD diagnosis codes. OUTCOME MEASURES: A logistic regression model predicted risk factors for infections occurring within 7-day and 30-day periods following ERCP. 7-day and 30-day all-cause hospitalisations and post-ERCP pancreatitis were also examined. RESULTS: Post-ERCP infection occurred within 3.5% of 7-day and 7.7% of 30-day periods in Medicare. Disposable duodenoscopes were billed in 711 procedures, with 1.4% (n=10, 7-day) and 3.5% (n=25, 30-day) post-ERCP infections. Urgent ERCPs were the strongest risk factor for infections in the 7-day period (OR 3.3, 95% CI 3.2 to 3.4). Chronic conditions, sex (male), age (older) and race (non-white) were also risk factors. In the all-payer five-state data, fewer infections (2.4%, 7 days) were observed. No difference arose between Medicare and other payers for 7-day period infections (OR 1.0, 95% CI 0.7 to 1.3). CONCLUSIONS: Urgent ERCPs, patient chronic conditions and patient demographics are post-ERCP infection risk factors. Patients with infection risk factors should be targeted for specialised infection control prevention measures, including disposable duodenoscopes.

Exome sequencing and analysis of 454,787 UK Biobank participants.

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System.

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

The voltage-gated proton channel Hv1 plays a detrimental role in contusion spinal cord injury via extracellular acidosis-mediated neuroinflammation.

Tissue acidosis is an important secondary injury process in the pathophysiology of traumatic spinal cord injury (SCI). To date, no studies have examined the role of proton extrusion as mechanism of pathological acidosis in SCI. In the present study, we hypothesized that the phagocyte-specific proton channel Hv1 mediates hydrogen proton extrusion after SCI, contributing to increased extracellular acidosis and poor long-term outcomes. Using a contusion model of SCI in adult female mice, we demonstrated that tissue pH levels are markedly lower during the first week after SCI. Acidosis was most evident at the injury site, but also extended into proximal regions of the cervical and lumbar cord. Tissue reactive oxygen species (ROS) levels and expression of Hv1 were significantly increased during the week of injury. Hv1 was exclusively expressed in microglia within the CNS, suggesting that microglia contribute to ROS production and proton extrusion during respiratory burst. Depletion of Hv1 significantly attenuated tissue acidosis, NADPH oxidase 2 (NOX2) expression, and ROS production at 3 d post-injury. Nanostring analysis revealed decreased gene expression of neuroinflammatory and cytokine signaling markers in Hv1 knockout (KO) mice. Furthermore, Hv1 deficiency reduced microglia proliferation, leukocyte infiltration, and phagocytic oxidative burst detected by flow cytometry. Importantly, Hv1 KO mice exhibited significantly improved locomotor function and reduced histopathology. Overall, these data suggest an important role for Hv1 in regulating tissue acidosis, NOX2-mediated ROS production, and functional outcome following SCI. Thus, the Hv1 proton channel represents a potential target that may lead to novel therapeutic strategies for SCI.

Delayed microglial depletion after spinal cord injury reduces chronic inflammation and neurodegeneration in the brain and improves neurological recovery in male mice.

Neuropsychological deficits, including impairments in learning and memory, occur after spinal cord injury (SCI). In experimental SCI models, we and others have reported that such changes reflect sustained microglia activation in the brain that is associated with progressive neurodegeneration. In the present study, we examined the effect of pharmacological depletion of microglia on posttraumatic cognition, depressive-like behavior, and brain pathology after SCI in mice. Methods: Young adult male C57BL/6 mice were subjected to moderate/severe thoracic spinal cord contusion. Microglial depletion was induced with the colony-stimulating factor 1 receptor (CSF1R) antagonist PLX5622 administered starting either 3 weeks before injury or one day post-injury and continuing through 6 weeks after SCI. Neuroinflammation in the injured spinal cord and brain was assessed using flow cytometry and NanoString technology. Neurological function was evaluated using a battery of neurobehavioral tests including motor function, cognition, and depression. Lesion volume and neuronal counts were quantified by unbiased stereology. Results: Flow cytometry analysis demonstrated that PLX5622 pre-treatment significantly reduced the number of microglia, as well as infiltrating monocytes and neutrophils, and decreased reactive oxygen species production in these cells from injured spinal cord at 2-days post-injury. Post-injury PLX5622 treatment reduced both CD45int microglia and CD45hi myeloid counts at 7-days. Following six weeks of PLX5622 treatment, there were substantial changes in the spinal cord and brain transcriptomes, including those involved in neuroinflammation. These alterations were associated with improved neuronal survival in the brain and neurological recovery. Conclusion: These findings indicate that pharmacological microglia-deletion reduces neuroinflammation in the injured spinal cord and brain, improving recovery of cognition, depressive-like behavior, and motor function.

Computer-Aided Theragnosis Based on Tumor Volumetric Information in Breast Cancer.

OBJECTIVE: A computer-assisted technology has recently been proposed for the assessment of therapeutic responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). The system, however, extracted features from individual scans in a tumor irrespective of its relation to the other scans of the same patient, ignoring the volumetric information. This study addresses this problem by introducing a novel engineered texton-based method in order to account for volumetric information in the design of textural descriptors to represent tumor scans. METHODS: A noninvasive computer-aided-theragnosis (CAT) system was developed by employing multiparametric QUS spectral and backscatter coefficient maps. The proceeding was composed of two subdictionaries: one built on the "pretreatment" and another on "week " scans, where was 1, 4, or 8. The learned dictionary of each patient was subsequently used to compute the model (histogram of textons) for each scan of the patient. Advanced machine learning techniques including a kernel-based dissimilarity measure to estimate the distances between "pretreatment" and "mid-treatment" scans as an indication of treatment effectiveness, learning from imbalanced data, and supervised learning were subsequently employed on the texton-based features. RESULTS: The performance of the CAT system was tested using statistical tests of significance and leave-one-subject-out (LOSO) classification on 56 LABC patients. The proposed texton-based CAT system indicated significant differences in changes between the responding and nonresponding patient populations and achieved high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. Specifically, the CAT system achieved the area under curve of 0.81, 0.83, and 0.85 on weeks 1, 4, and 8, respectively. CONCLUSION: The proposed texton-based CAT system accounted for the volumetric information in "pretreatment" and "mid-treatment" scans of each patient. It was demonstrated that this attribute of the CAT system could boost its performance compared to the cases that the features were extracted from solely individual scans.

The effect of pH on electrolyte detection of fingermarks on cartridge cases and subsequent microscopic examination.

Cartridge cases may contain deposited fingermarks when the firearm was loaded (http://www.nij.gov/pubs-sum/225320.htm, J Forensic Sci, 53, 2008 and 812). Cartridge cases can be individualized with microscopic examination. However, heat and friction degrades the deposited fingermark on the fired cartridge cases, if any on the surface. Also, unfired and fired cartridge cases are made of metal, which is a nonporous surface that does not retain fingermarks well (http://www.nij.gov/pubs-sum/225320.htm). This study tests the effects of pH level on fingermark clarity on brass fired and unfired cartridge cases and microscopic striation examination (MSE). Two trials were performed to determine the optimal pH in fingermark clarity for both types of cartridges. This was performed through immersion in six pH range solutions from dilutions of sulfuric acid and sodium hydroxide for 24 h for the purpose of enhancing the fingermarks on the metal. The use of the optimal neutral pH level is suggested because immersion of the cartridge cases in pH 1-3 and 3-5 affects MSE.

Measurement and dynamics of the spatial distribution of an electron localized at a metal-dielectric interface.

The ability of time- and angle-resolved two-photon photoemission to estimate the size distribution of electron localization in the plane of a metal-adsorbate interface is discussed. It is shown that the width of angular distribution of the photoelectric current is inversely proportional to the electron localization size within the most common approximations in the description of image potential states. The localization of the n=1 image potential state for two monolayers of butyronitrile on Ag(111) is used as an example. For the delocalized n=1 state, the shape of the signal amplitude as a function of momentum parallel to the surface changes rapidly with time, indicating efficient intraband relaxation on a 100 fs time scale. For the localized state, little change was observed. The latter is related to the constant size distribution of electron localization, which is estimated to be a Gaussian with a 15+/-4 A full width at half maximum in the plane of the interface. A simple model was used to study the effect of a weak localization potential on the overall width of the angular distribution of the photoemitted electrons, which exhibited little sensitivity to the details of the potential. This substantiates the validity of the localization size estimate.