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Immunological/inflammatory factors are implicated in the development of psychosis. Complement is a key driver of inflammation; however, it remains unknown which factor is better at predicting the onset of psychosis. This study aimed to compare the alteration and predictive performance of inflammation and complement in individuals at clinical high risk (CHR). We enrolled 49 individuals at CHR and 26 healthy controls (HCs). Twenty-five patients at CHR had converted to psychosis (converter) by the 3-year follow-up. Inflammatory cytokines, including interleukin (IL)-1β, 6, 8, 10, tumor necrosis factor-alpha (TNF-alpha), macrophage colony-stimulating factor levels, and complement proteins (C1q, C2, C3, C3b, C4, C4b, C5, C5a, factor B, D, I, H) were measured by enzyme-linked immunosorbent assay at baseline. Except for TNF- alpha, none of the inflammatory cytokines reached a significant level in either the comparison of CHR individuals and HC or between CHR-converters and non-converters. The C5, C3, D, I, and H levels were significantly lower (C5, p = 0.006; C3, p = 0.009; D, p = 0.026; I, p = 0.016; H, p = 0.019) in the CHR group than in the HC group. Compared to non-converters, converters had significantly lower levels of C5 (p = 0.012) and C5a (p = 0.007). None of the inflammatory factors, but many complement factors, showed significant correlations with changes in general function and symptoms. None of the inflammatory markers, except for C5a and C5, were significant in the discrimination of conversion outcomes in CHR individuals. Our results suggest that altered complement levels in the CHR population are more associated with conversion to psychosis than inflammatory factors. Therefore, an activated complement system may precede the first-episode of psychosis and contribute to neurological pathogenesis at the CHR stage.

Background Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. Methods A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. Results In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775–0.918] and the test set (AUC, 0.867; 95% CI, 0.732–949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. Conclusions The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

Novel porous nano-silicon/TiO2/rGO@carbon anodes with superior lifespan and desirable cycling stability are prepared by a step-wise synthetic procedure. The hybrid exhibits a high specific capacity of 1073.43 mAh g−1 at a current density of 500 mA g−1. Additionally, it delivers a reversible capacity of 724.08 mAh g−1 at 1000 mA g−1 even after 1000 long-term cycles. Simultaneously, a large average capacity is reinstated after cycling at high rates, such as 994.76, 743.33, and 599.70 mAh g−1 at 1000, 2000, and 3000 mA g−1, respectively. The greatly ameliorative electrochemical characteristics could be attributed to the abundant buffering space of hierarchical architecture, good separation of mechanically robust anatase-TiO2, sustainable confinement of elastic carbon skeletons, as well as improved electrical conductivity of rGO, which could suppress drastic volume variations and promote multiple Li+/electron transport without distinct pulverization.

We used data from the Michigan Urological Surgery Improvement Collaborative (MUSIC) to investigate the use of adjuvant and salvage radiotherapy (ART, SRT) among patients with high-risk pathology following radical prostatectomy (RP).For patients with pT3a disease or higher and/or positive surgical margins, we examined post-RP radiotherapy administration across MUSIC practices. We excluded patients with6 months follow-up, and those that failed to achieve a postoperative PSA nadir ⩽0.1. ART was defined as radiation administered within 1 year post RP, with all post-nadir PSA levels0.1 ng ml(-1). Radiation administered1 year post RP and/or after a post-nadir PSA ⩾0.1 ng ml(-1) was defined as SRT. We used claims data to externally validate radiation administration.Among 2337 patients undergoing RP, 668 (28.6%) were at high risk of recurrence. Of these, 52 (7.8%) received ART and 56 (8.4%) underwent SRT. Patients receiving ART were younger (P=0.027), more likely to have a greater surgical Gleason sum (P=0.009), higher pathologic stage (P0.001) and received treatment at the smallest and largest size practices (P=0.011). Utilization of both ART and SRT varied widely across MUSIC practices (P0.001 and P=0.046, respectively), but practice-level rates of ART and SRT administration were positively correlated (P=0.003) with lower ART practices also utilizing SRT less frequently. Of the 88 patients not receiving ART and experiencing a PSA recurrence ⩾0.2 ng ml(-1), 38 (43.2%) progressed to a PSA ⩾0.5 ng ml(-1) and 20 (22.7%) to a PSA ⩾1.0 ng ml(-1) without receiving prior SRT. There was excellent concordance between registry and claims data κ=0.98 (95% CI: 0.94-1.0).Utilization of ART and SRT is infrequent and variable across urology practices in Michigan. Although early SRT is an alternative to ART, it is not consistently utilized in the setting of post-RP biochemical recurrence. Quality improvement initiatives focused on current postoperative radiotherapy administration guidelines may yield significant gains for this high-risk population.

This paper describes various language modeling issues in a speech-to-speech translation system. These issues are addressed in the IBM speech-to-speech system we developed for the DARPA Babylon program in the context of two-way translation between English and Mandarin Chinese. First, the language models for the speech recognizer had to be adapted to the specific domain to improve the recognition performance for in-domain utterances, while keeping the domain coverage as broad as possible. This involved considerations of disfluencies and lack of punctuation, as well as domain-specific utterances. Second, we used a hybrid semantic/syntactic representation to minimize the data sparseness problem in a statistical natural language generation framework. Serious inflection and synonym issues arise when words in the target language are to be determined in the translation output. Instead of relying on tedious handcrafted grammar rules, we used N-gram models as a post-processing step to enhance the generation performance. When an interpolated language model was applied to a Chinese-to-English translation task, the translation performance, measured by an objective metric of BLEU, improved substantially to 0.514 from 0.318 when we used the correct transcription as input. Similarly, the BLEU score improved to 0.300 from 0.194 for the same task when the input was speech data.

We present MARS (Multilingual Automatic tRanslation System), a research prototype speech-to-speech translation system. MARS is aimed at two-way conversational spoken language translation between English and Mandarin Chinese for limited domains, such as air travel reservations. In MARS, machine translation is embedded within a complex speech processing task, and the translation performance is highly effected by the performance of other components, such as the recognizer and semantic parser, etc. All components in the proposed system are statistically trained using an appropriate training corpus. The speech signal is first recognized by an automatic speech recognizer (ASR). Next, the ASR-transcribed text is analyzed by a semantic parser, which uses a statistical decision-tree model that does not require hand-crafted grammars or rules. Furthermore, the parser provides semantic information that helps further re-scoring of the speech recognition hypotheses. The semantic content extracted by the parser is formatted into a language-independent tree structure, which is used for an interlingua based translation. A Maximum Entropy based sentence-level natural language generation (NLG) approach is used to generate sentences in the target language from the semantic tree representations. Finally, the generated target sentence is synthesized into speech by a speech synthesizer. Many new features and innovations have been incorporated into MARS: the translation is based on understanding the meaning of the sentence; the semantic parser uses a statistical model and is trained from a semantically annotated corpus; the output of the semantic parser is used to select a more specific language model to refine the speech recognition performance; the NLG component uses a statistical model and is also trained from the same annotated corpus. These features give MARS the advantages of robustness to speech disfluencies and recognition errors, tighter integration of semantic information into speech recognition, and portability to new languages and domains. These advantages are verified by our experimental results.