A 12 GHz satellite video receiver: Low noise, low cost prototype model for TV reception from broadcast satellites

A 12-channel synchronous phase lock video receiver consisting of an outdoor downconverter unit and an indoor demodulator unit was developed to provide both low noise performance and low cost in production quantities of 1000 units. The prototype receiver can be mass produced at a cost under $1540 without sacrificing system performance. The receiver also has the capability of selecting any of the twelve assigned satellite broadcast channels in the frequency range 11.7 to 12.2 GHz

A host receptor enables type 1 pilus-mediated pathogenesis of Escherichia coli pyelonephritis

Type 1 pili have long been considered the major virulence factor enabling colonization of the urinary bladder by uropathogenic Escherichia coli (UPEC). The molecular pathogenesis of pyelonephritis is less well characterized, due to previous limitations in preclinical modeling of kidney infection. Here, we demonstrate in a recently developed mouse model that beyond bladder infection, type 1 pili also are critical for establishment of ascending pyelonephritis. Bacterial mutants lacking the type 1 pilus adhesin (FimH) were unable to establish kidney infection in male C3H/HeN mice. We developed an in vitro model of FimH-dependent UPEC binding to renal collecting duct cells, and performed a CRISPR screen in these cells, identifying desmoglein-2 as a primary renal epithelial receptor for FimH. The mannosylated extracellular domain of human DSG2 bound directly to the lectin domain of FimH in vitro, and introduction of a mutation in the FimH mannose-binding pocket abolished binding to DSG2. In infected C3H/HeN mice, type 1-piliated UPEC and Dsg2 were co-localized within collecting ducts, and administration of mannoside FIM1033, a potent small-molecule inhibitor of FimH, significantly attenuated bacterial loads in pyelonephritis. Our results broaden the biological importance of FimH, specify the first renal FimH receptor, and indicate that FimH-targeted therapeutics will also have application in pyelonephritis

A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide Malleolus First Fixation.

OBJECTIVES: To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF. METHODS: Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs: (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool. RESULTS: Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.02, -0.06, and 0.106, respectively. This prediction model was deployed as an open-access online prediction tool. CONCLUSION: A ML-based prediction model accurately predicted the probability of a (occult) PMF in patients with a TSF based on patient- and fracture-specific characteristics. This prediction model can guide surgeons in their diagnostic workup and preoperative planning. Further research is required to externally validate the model before implementation in clinical practice. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence