Improved method for SNR prediction in machine-learning-based test

This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach, the dynamic parameters can be predicted by using the signature results. However, it can only estimate the SNR accurately within a certain range. In order to overcome this limitation, an improved method based on work is applied in this work. It is validated on the Labview model of a 12-bit 80 Ms/s pipelined ADC with a pulse- wave input signal of 3 LSB noise and 7-bit nonlinear rising and falling edges

Two improved methods for testing ADC parametric faults by digital input signals

In this paper, two improved methods are presented extending our previous work. The first one improves the results by adjusting the voltage levels of the input pulse wave stimulus. Compared with the sine wave input stimulus, the four-level pulse wave can detect even more faulty cases with the offset faults. The second one improves the results by calculating the similarity of the output spectra between the golden devices and the DUTs. Compared with the previous method [10], it is less sensitive to the jitter and the change of the rise/fall time of the input pulse wave stimulus. In these two methods, a number of golden devices are tested at first to obtain the fault-free range. At last, a signature result is obtained from both methods. It can filter out the faulty devices in a quick way before testing the specific values of the conventional dynamic and static parameters

The test ability of an adaptive pulse wave for ADC testing

In the conventional ADC production test method, a high-quality analogue sine wave is applied to the Analogue-to-Digital Converter (ADC), which is expensive to generate. Nowadays, an increasing number of ADCs are integrated into a system-on-chip (SoC) platform design, which usually contains a digital embedded processor. In such a platform, a digital pulse wave is obviously less expensive to generate than an accurate analogue sine wave. As a result, the usage of a digital pulse wave has been investigated to test ADCs as the test stimulus. In this paper, the ability of a digital adaptive pulse wave for ADC testing is presented via the measurement results. Instead of the conventional FFT analysis, a time-domain analysis is exploited for post-processing, from which a signature result can be obtained. This signature can distinguish between faulty devices and the fault-free devices. It is also used in the machine-learning-based test method to predict the dynamic specifications of the ADC. The experimental results of a 12-bit 80 M/s pipelined ADC are shown to evaluate the sensitivity and accuracy of using a pulse wave to test an ADC

redicting dynamic specifications of ADCs with a low-quality digital input signal

A new method is presented to test dynamic parameters of Analogue-to-Digital Converters (ADC). A noisy and nonlinear pulse is applied as the test stimulus, which is suitable for a multi-site test environment. The dynamic parameters are predicted using a machine-learning-based approach. A training step is required in order to build the mapping function using alternate signatures and the conventional test parameters, all measured on a set of converters. As a result, for industrial testing, only a simple signature-based test is performed on the Devices-Under-Test (DUTs). The signature measurements are provided to the mapping function that is used to predict the conventional dynamic parameters. The method is validated by simulation on a 12-bit 80 Ms/s pipelined ADC with a pulse wave input signal of 3 LSB noise and 7-bit nonlinear rising and falling edges. The final results show that the estimated mean error is less than 4% of the full range of the dynamic specifications

CaHSL1 Acts as a Positive Regulator of Pepper Thermotolerance Under High Humidity and Is Transcriptionally Modulated by CaWRKY40

Pepper (Capsicum annuum) is an economically important vegetable and heat stress can severely impair pepper growth, development, and productivity. The molecular mechanisms underlying pepper thermotolerance are therefore important to understand but remain elusive. In the present study, we characterized the function of CaHSL1, encoding a HAESA-LIKE (HSL) receptor-like protein kinase (RLK), during the response of pepper to high temperature and high humidity (HTHH). CaHSL1 exhibits the typical structural features of an arginine-aspartate RLK. Transient overexpression of CaHSL1 in the mesophyll cells of Nicotiana benthamiana showed that CaHSL1 localizes throughout the cell, including the plasma membrane, cytoplasm, and the nucleus. CaHSL1 was significantly upregulated by HTHH or the exogenous application of abscisic acid but not by R. solanacearum inoculation. However, CaHSL1 was downregulated by exogenously applied salicylic acid, methyl jasmonate, or ethephon. Silencing of CaHSL1 by virus-induced gene silencing significantly was reduced tolerance to HTHH and downregulated transcript levels of an associated gene CaHSP24. In contrast, transient overexpression of CaHSL1 enhanced the transcript abundance of CaHSP24 and increased tolerance to HTHH, as manifested by enhanced optimal/maximal photochemical efficiency of photosystem II in the dark (Fv/Fm) and actual photochemical efficiency of photosystem II in the light. In addition, CaWRKY40 targeted the promoter of CaHSL1 and induced transcription during HTHH but not in response to R. solanacearum. All of these results suggest that CaHSL1 is directly modulated at the transcriptional level by CaWRKY40 and functions as a positive regulator in the response of pepper to HTHH

Cálculo estructural del ala de una aeronave no tripulada

Trabajo Final (IA)--FCEFN-UNC, 2007Desarrolla un análisis estructural del ala de la aeronave no tripulada fabricada con material compuesto. Verifica el comportamiento estructural, a partir de la evaluación del coeficiente de seguridad a primera falla y la carga crítica al pandeo. Se comprueba que estos resultados cumplan con los valores de carga establecidos por normas aeronáuticas. Por otra parte, se calcula de deflexión de la puntera del ala, la cual puede emplearse posteriormente para validar el modelo de análisis con ensayos a escala real

STING activation in TET2-mutated hematopoietic stem/progenitor cells contributes to the increased self-renewal and neoplastic transformation

Somatic loss-of-function mutations of the dioxygenase Ten-eleven translocation-2 (TET2) occur frequently in individuals with clonal hematopoiesis (CH) and acute myeloid leukemia (AML). These common hematopoietic disorders can be recapitulated in mouse models. However, the underlying mechanisms by which the deficiency in TET2 promotes these disorders remain unclear. Here we show that the cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS)-stimulator of interferon genes (STING) pathway is activated to mediate the effect of TET2 deficiency in dysregulated hematopoiesis in mouse models. DNA damage arising in Tet2-deficient hematopoietic stem/progenitor cells (HSPCs) leads to activation of the cGAS-STING pathway which in turn promotes the enhanced self-renewal and development of CH. Notably, both pharmacological inhibition and genetic deletion of STING suppresses Tet2 mutation-induced aberrant hematopoiesis. In patient-derived xenograft (PDX) models, STING inhibition specifically attenuates the proliferation of leukemia cells from TET2-mutated individuals. These observations suggest that the development of CH associated with TET2 mutations is powered through chronic inflammation dependent on the activated cGAS-STING pathway and that STING may represent a potential target for intervention of relevant hematopoietic diseases

SRSF1 modulates PTPMT1 alternative splicing to regulate lung cancer cell radioresistance

Background Radioresistance is the major cause of cancer treatment failure. Additionally, splicing dysregulation plays critical roles in tumorigenesis. However, the involvement of alternative splicing in resistance of cancer cells to radiotherapy remains elusive. We sought to investigate the key role of the splicing factor SRSF1 in the radioresistance in lung cancer. Methods Lung cancer cell lines, xenograft mice models, and RNA-seq were employed to study the detailed mechanisms of SRSF1 in lung cancer radioresistance. Clinical tumor tissues and TCGA dataset were utilized to determine the expression levels of distinct SRSF1-regulated splicing isoforms. KM-plotter was applied to analyze the survival of cancer patients with various levels of SRSF1-regulated splicing isoforms. Findings Splicing factors were screened to identify their roles in radioresistance, and SRSF1 was found to be involved in radioresistance in cancer cells. The level of SRSF1 is elevated in irradiation treated lung cancer cells, whereas knockdown of SRSF1 sensitizes cancer cells to irradiation. Mechanistically, SRSF1 modulates various cancer-related splicing events, particularly the splicing of PTPMT1, a PTEN-like mitochondrial phosphatase. Reduced SRSF1 favors the production of short isoforms of PTPMT1 upon irradiation, which in turn promotes phosphorylation of AMPK, thereby inducing DNA double-strand break to sensitize cancer cells to irradiation. Additionally, the level of the short isoform of PTPMT1 is decreased in cancer samples, which is correlated to cancer patients' survival. Conclusions Our study provides mechanistic analyses of aberrant splicing in radioresistance in lung cancer cells, and establishes SRSF1 as a potential therapeutic target for sensitization of patients to radiotherapy

Advances and Challenges in Protein-Ligand Docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100