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This study examines the effects of blending virgin cotton fibres of selected lengths with a fixed percentage of recycled fibres on yarn quality. By choosing three different lengths of virgin cotton fibres and incorporating 20% recycled fibre, we investigate the resultant yarn properties through detailed statistical analysis. Our research identifies significant enhancements in yarn quality with increased virgin fibre length, offering new insights into effective fibre blending strategies. This investigation supports the textile industry in refining fibre blend ratios for improved yarn qualities while advocating for sustainable practices through the utilization of recycled fibres. It sets the stage for future exploration aimed at optimizing the balance between virgin and recycled fibres to achieve superior yarn characteristics.

Objective: A change in handwriting is an early sign of Parkinson’s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P

Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4–12 Hz, and the sum of power spectrum density over the entire spectrum of 2–74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Results: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ( $r^{2}$ = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Conclusion: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.

Abstract Adenoid cystic carcinoma (ACC) is an aggressive salivary gland malignancy with limited treatment options for recurrent or metastatic disease. Due to chemotherapy resistance and lack of targeted therapeutic approaches, current treatment options for the localized disease are limited to surgery and radiation, which fails to prevent locoregional recurrences and distant metastases in over 50% of patients. Approximately 20% of patients with ACC carry NOTCH-activating mutations that are associated with a distinct phenotype, aggressive disease, and poor prognosis. Given the role of NOTCH signaling in regulating tumor cell behavior, NOTCH inhibitors represent an attractive potential therapeutic strategy for this subset of ACC. AL101 (osugacestat) is a potent γ-secretase inhibitor that prevents activation of all four NOTCH receptors. While this investigational new drug has demonstrated antineoplastic activity in several preclinical cancer models and in patients with advanced solid malignancies, we are the first to study the therapeutic benefit of AL101 in ACC. Here, we describe the antitumor activity of AL101 using ACC cell lines, organoids, and patient-derived xenograft models. Specifically, we find that AL101 has potent antitumor effects in in vitro and in vivo models of ACC with activating NOTCH1 mutations and constitutively upregulated NOTCH signaling pathway, providing a strong rationale for evaluation of AL101 in clinical trials for patients with NOTCH-driven relapsed/refractory ACC.

Questo lavoro propone un’analisi dei dialetti veneti presenti sul quotidiano cattolico La Libertà, stampato prima della Staffetta Riograndense, sulla quale erano stati pubblicati i capitoli di Nanetto Pipetta, una pietra miliare letteraria nelle lingue dell’immigrazione dal nord Italia al Brasile. L’obiettivo è contestualizzare la formazione de La Libertà e mostrare i dialetti presenti in quel giornale, soprattutto nei testi relativi a religione, lavoro e famiglia, in cui emergono le rappresentazioni dei coloni italiani. La pluralità della presenza dei dialetti è segnata dal contatto linguistico tra l’italiano e il portoghese brasiliano. Proponiamo allora una presentazione della vitalità dei dialetti presenti nel periodico citato. In questo lavoro non utilizzeremo il termine Talian come riferimento al risultato della lingua di contatto originata giacché trattasi dell’analisi di un periodo storico in cui il termine Talian non era ancora stato coniato. Variety of the linguistic repertoire of Italian immigrants in the “Staffetta Riograndense” and the newspaper “La Libertà” (1909-1910) This paper proposes an analysis of the Venetian dialects present in the Catholic newspaper La Libertà, printed before the Staffetta Riograndense, on which were published the chapters of Nanetto Pipetta, a literary milestone in the languages of immigration from northern Italy to Brazil. The goal of this paper is to contextualize the formation of La Libertà and show the dialects present in it, especially in texts related to religion, work and family, in which representations of Italian settlers emerge. The plurality of the presence of dialects is marked by the linguistic contact between Italian and Brazilian Portuguese. We then propose a presentation of the vitality of the dialects present in the mentioned newspaper. We will not use the term Talian as a reference to the result of the contact language originated since this is the analysis of a historical period when the term Talian had not yet been coined.

The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.

Abstract Dysarthria is an early symptom of Parkinson’s disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan’s dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.

Dry reforming of methane (DRM) was extensively studied on Cu-doped LaNiO3 catalysts. The main findings of this work are as follows: (i) thermal switching of the catalyst phase between the parent perovskite and molecular-level nanocomposite of individual components formed in situ during DRM, (ii) reusability of the catalyst with enhanced activity, and (iii) regeneration of the catalyst phase at a lower temperature than that required for the formation of the parent perovskite. The present investigation provides an extensive analysis and understanding of the DRM reaction using Cu-doped LaNiO3 compared to the result reported by Moradi et al., (Chin. J. Catal., 2012, 33, 797–801) and hence provides new insights into its catalytic activity. Phase-pure LaNi1−xCuxO3 catalysts, specifically LaNi0.8Cu0.2O3, exhibited high catalytic activity towards the DRM reaction (97% CH4 and 99% CO2 conversion with an H2/CO ratio of ∼1.4–0.9). Remarkably, although the initial perovskite phase primarily decomposed into its component phases after DRM, its catalytic activity was barely affected and maintained even after 100 h. The regeneration of the initial perovskite from the disintegrated binary phases via annealing at temperatures even lower than the synthesis temperature together with the amazing retention of activity was very intriguing. The parallel activity of the pristine perovskite and its degraded binary mixtures makes it difficult to identify the actual components responsible for the DRM activity. Accordingly, we have explained the sustained activity of the degraded perovskite catalyst in the context of nanocomposite formation at the molecular level in the reforming atmosphere with the availability of Ni0 and NiO, as revealed by the thoroughly characterized samples in the as-prepared, aged, and regenerated forms. [ABSTRACT FROM AUTHOR]

Abstract Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant’s dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn–Tolosa–Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4–12 Hz to 0.5–4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r 2 = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Medicinal plants have been used to treat diseases from time immemorial. We aimed to examine the efficacy of the ethyl acetate fraction of Nymphaea nouchali flower extract (NNFE) against melanogenesis process, and the underlying mechanisms in vitro and in vivo. Paper spray ionisation mass spectroscopy and (+) mode electrospray ionisation revealed the presence of seven flavonoids, two spermidine alkaloids, 3,4,8,9,10-pentahydroxy-dibenzo[b,d]pyran-6-one, and shoyuflavone C in NNFE. NNFE (100 µg/mL) significantly inhibited the monophenolase and diphenolase activities of mushroom tyrosinase at 94.90 ± 0.003% and 93.034 ± 0.003%, respectively. NNFE significantly suppressed cellular tyrosinase activity and melanin synthesis in vitro in melan-a cells and in vivo in HRM2 hairless mice. Furthermore, NNFE inhibited tyrosinase (TYR), tyrosinase-related protein (TYRP)-1, TYRP-2, and microphthalmia-associated transcription factor (MITF) expression, thereby blocking melanin synthesis. In particular, NNFE suppressed cAMP production with subsequent downregulation of CREB phosphorylation. Additionally, it stimulated MAP kinase phosphorylation (p38, JNK, and ERK1/2) and the proteasomal debasement pathway, leading to degradation of tyrosinase and MITF and the suppression of melanin production. Moreover, selective inhibitors of ERK1/2, JNK, and p38 attenuated NNFE inhibitory effects on melanogenesis, and MG-132 (a proteasome inhibitor) prevented the NNFE-induced decline in tyrosinase protein levels. In conclusion, these findings indicate that NNFE is a potential therapy for hyperpigmentation.