Germline NLRP1 Mutations Cause Skin Inflammatory and Cancer Susceptibility Syndromes via Inflammasome Activation.

Inflammasome complexes function as key innate immune effectors that trigger inflammation in response to pathogen- and danger-associated signals. Here, we report that germline mutations in the inflammasome sensor NLRP1 cause two overlapping skin disorders: multiple self-healing palmoplantar carcinoma (MSPC) and familial keratosis lichenoides chronica (FKLC). We find that NLRP1 is the most prominent inflammasome sensor in human skin, and all pathogenic NLRP1 mutations are gain-of-function alleles that predispose to inflammasome activation. Mechanistically, NLRP1 mutations lead to increased self-oligomerization by disrupting the PYD and LRR domains, which are essential in maintaining NLRP1 as an inactive monomer. Primary keratinocytes from patients experience spontaneous inflammasome activation and paracrine IL-1 signaling, which is sufficient to cause skin inflammation and epidermal hyperplasia. Our findings establish a group of non-fever inflammasome disorders, uncover an unexpected auto-inhibitory function for the pyrin domain, and provide the first genetic evidence linking NLRP1 to skin inflammatory syndromes and skin cancer predisposition.

Evaluation of consumer digital radon measurement devices: a comparative analysis.

Kansas State University (KSU) Engineering Extension conducted an abridged evaluation of eight consumer grade digital radon monitors. Using the KSU secondary radon chamber, these devices were exposed to three different radon concentrations for 7 d in average household temperature and relative humidity conditions. The three different radon concentration ranges used were: 12.8 pCi L-1to 15.5 pCi L-1(473.6 Bq m-3-573.5 Bq m-3), 27.7 pCi L-1to 29.4 pCi L-1(1024.9-10 857.8 Bq m-3), and ambient room level average radon concentration of 0.6 pCi L-1(22.2 Bq m-3). The American National Standards Institute/American Academy of Radon Scientists and Technologists Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (ANSI/AARST MS-PC) (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) minimum performance metrics were used to evaluate the accuracy and precision of each model type for each radon concentration tested. The eight different device models performed within the 0 ± 25% requirement for the individual percent error (IPE) for radon concentrations between 27.7 pCi L-1and 29.4 pCi L-1(1024.9-10 857.8 Bq m-3). For radon concentrations between 12.8 pCi L-1and 15.5 pCi L-1(444-592 Bq m-3) seven of the eight monitors fell within the IPE requirement and for ambient room radon concentrations six of the eight monitors fell within the IPE requirement for the ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) ranges. All eight device models fell within the ± 15% ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) coefficient of variation (CV) range for radon concentrations between 12.8 pCi L-1and 15.5 pCi L-1(444-592 Bq m-3) and for radon concentrations between 27.7 pCi L-1and 29.4 pCi L-1(1024.9-10 857.8 Bq m-3). In the future, evaluating the performance of these models over time to observe their long term accuracy and precision is anticipated.

Improving Hybrid CTC/Attention Architecture for Agglutinative Language Speech Recognition.

Unlike the traditional model, the end-to-end (E2E) ASR model does not require speech information such as a pronunciation dictionary, and its system is built through a single neural network and obtains performance comparable to that of traditional methods. However, the model requires massive amounts of training data. Recently, hybrid CTC/attention ASR systems have become more popular and have achieved good performance even under low-resource conditions, but they are rarely used in Central Asian languages such as Turkish and Uzbek. We extend the dataset by adding noise to the original audio and using speed perturbation. To develop the performance of an E2E agglutinative language speech recognition system, we propose a new feature extractor, MSPC, which uses different sizes of convolution kernels to extract and fuse features of different scales. The experimental results show that this structure is superior to VGGnet. In addition to this, the attention module is improved. By using the CTC objective function in training and the BERT model to initialize the language model in the decoding stage, the proposed method accelerates the convergence of the model and improves the accuracy of speech recognition. Compared with the baseline model, the character error rate (CER) and word error rate (WER) on the LibriSpeech test-other dataset increases by 2.42% and 2.96%, respectively. We apply the model structure to the Common Voice-Turkish (35 h) and Uzbek (78 h) datasets, and the WER is reduced by 7.07% and 7.08%, respectively. The results show that our method is close to the advanced E2E systems.

[Critical quality attribute assessment of big brand traditional Chinese medicine: quality control of Tongren Niuhuang Qingxin Pills in texture and sensory attributes].

Texture sensory attributes are the key items in quality control of Chinese medicinal honeyed pills. The purpose of this study is to develop a quality control method for assessing the texture sensory attributes of Chinese medicinal honeyed pills based on real-world Tongren Niuhuang Qingxin pilular masses and finished products. First, parameters of texture profile analysis(TPA) were optimized through single factor and central composite design(CCD) experiments to establish a detection method for texture sensory attri-butes of Tongren Niuhuang Qingxin Pills. The results showed that the established detection method was stable and reliable, with the optimal parameters set up as follows: deformation percentage of 70%, detection speed at 30 mm·min~(-1), and interval time of 15 s. Furthermore, 540 data points yielded form six texture sensory attributes of pills from 30 batches were subjected to multivariate statistical process control(MSPC) with Hotelling T~2 and squared prediction error(SPE) control charts to establish the quality control method of Tongren Niuhuang Qingxin Pills. This study is expected to provide a reference for improving the quality control system of Chinese medicinal honeyed pills.

The impact of COVID-19 infection on musculoskeletal pain and its associating factors: a cross-sectional study.

Objectives: Musculoskeletal pain after COVID-19 infection remains a concerning long-term complication of COVID-19. Here, our study aimed to investigate the prevalence of musculoskeletal pain associated with COVID-19 (MSPC) and healthcare-seeking behaviors, as well as the associating factors. Methods: A cross-sectional survey was conducted using convenience sampling and distributed to participants anonymously through the online platform Credamo. Demographic and characteristic data of the participants were collected and analyzed. Logistic regression analysis was employed to investigate potential factors associated with MSPC and healthcare-seeking tendencies. Results: A total of 1,510 participants responded to the survey, with 42.6% (643 individuals) exhibiting MSPC. Higher education level and a greater number of concomitant symptoms were significant risk factors for MSPC, while longer exercise duration and higher PSS-10 scores were protective factors. Additionally, higher income level, frequency and severity of pain, and greater PSS-10 scores increased healthcare-seeking intention. Conclusion: A significant proportion of individuals experience MSPC. Education level and concomitant symptoms were risk factors for MSPC, while exercise duration and PSS-10 score were potential protective factors. Income level, frequency and severity of pain, and PSS-10 score are significantly related to the willingness to seek medical treatment for MSPC.

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics.

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

Fusing NIR and Process Sensors Data for Polymer Production Monitoring.

Process analytical technology and multivariate process monitoring are nowadays the most effective approaches to achieve real-time quality monitoring/control in production. However, their use is not yet a common practice, and industries benefit much less than they could from the outcome of the hundreds of sensors that constantly monitor production in industrial plants. The huge amount of sensor data collected are still mostly used to produce univariate control charts, monitoring one compartment at a time, and the product quality variables are generally used to monitor production, despite their low frequency (offline measurements at analytical laboratory), which is not suitable for real-time monitoring. On the contrary, it would be extremely advantageous to benefit from predictive models that, based on online sensors, will be able to return quality parameters in real time. As a matter of fact, the plant setup influences the product quality, and process sensors (flow meters, thermocouples, etc.) implicitly register process variability, correlation trends, drift, etc. When the available spectroscopic sensors, reflecting chemical composition and structure, consent to monitor the intermediate products, coupling process, and spectroscopic sensor and extracting/fusing information by multivariate analysis from this data would enhance the evaluation of the produced material features allowing production quality to be estimated at a very early stage. The present work, at a pilot plant scale, applied multivariate statistical process control (MSPC) charts, obtained by data fusion of process sensor data and near-infrared (NIR) probes, on a continuous styrene-acrylonitrile (SAN) production process. Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN). The results show that the MSPC model was able to detect deviations from normal operative conditions, indicating the variables responsible for the deviation, be they spectral or process. Moreover, predictive regression models obtained using the fused data showed better results than models computed using single datasets in terms of both errors of prediction and R 2. Thus, the fusion of spectra and process data improved the real-time monitoring, allowing an easier visualization of the process ongoing, a faster understanding of possible faults, and real-time assessment of the final product quality.

A FT-NIR Process Analytical Technology Approach for Milk Renneting Control.

The study proposes a process analytical technology (PAT) approach for the control of milk coagulation through near infrared spectroscopy (NIRS), computing multivariate statistical process control (MSPC) charts, based on principal component analysis (PCA). Reconstituted skimmed milk and commercial pasteurized skimmed milk were mixed at two different ratios (60:40 and 40:60). Each mix ratio was prepared in six replicates and used for coagulation trials, monitored by fundamental rheology, as a reference method, and NIRS by inserting a probe directly in the coagulation vat and collecting spectra at two different acquisition times, i.e., 60 s or 10 s. Furthermore, three failure coagulation trials were performed, deliberately changing temperature or rennet and CaCl2 concentration. The comparison with fundamental rheology results confirmed the effectiveness of NIRS to monitor milk renneting. The reduced spectral acquisition time (10 s) showed data highly correlated (r > 0.99) to those acquired with longer acquisition time. The developed decision trees, based on PC1 scores and T2 MSPC charts, confirmed the suitability of the proposed approach for the prediction of coagulation times and for the detection of possible failures. In conclusion, the work provides a robust but simple PAT approach to assist cheesemakers in monitoring the coagulation step in real-time.

Current developments in drug delivery with thermosensitive liposomes.

Thermosensitive liposomes (TSLs) have been an important research area in the field of tumor targeted chemotherapy. Since the first TSLs appeared that using 1,2-dipalmitoyl-sn-glyce-ro-3-phosphocholine (DPPC) as the primary liposomal lipid, many studies have been done using this type of liposome from basic and practical aspects. While TSLs composed of DPPC enhance the cargo release near the phase transition temperature, it has been shown that many factors affect their temperature sensitivity. Thus numerous attempts have been undertaken to develop new TSLs for improving their thermal response performance. The main objective of this review is to introduce the development and recent update of innovative TSLs formulations, including combination of radiofrequency ablation (RFA), high-intensity focused ultrasound (HIFU), magnetic resonance imaging (MRI) and alternating magnetic field (AMF). In addition, various factors affecting the design of TSLs, such as lipid composition, surfactant, size and serum components are also discussed.

Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy.

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry.