Intelligent devices for assessing essential tremor: a comprehensive review.

Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.

Application of proteomics to determine the mechanism of ozone on sweet cherries (Prunus avium L.) by time-series analysis.

This research investigated the mechanism of ozone treatment on sweet cherry (Prunus avium L.) by Lable-free quantification proteomics and physiological traits. The results showed that 4557 master proteins were identified in all the samples, and 3149 proteins were common to all groups. Mfuzz analyses revealed 3149 candidate proteins. KEGG annotation and enrichment analysis showed proteins related to carbohydrate and energy metabolism, protein, amino acids, and nucleotide sugar biosynthesis and degradation, and fruit parameters were characterized and quantified. The conclusions were supported by the fact that the qRT-PCR results agreed with the proteomics results. For the first time, this study reveals the mechanism of cherry in response to ozone treatment at a proteome level.

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning.

Lymph node metastasis examined by the resected lymph nodes is considered one of the most important prognostic factors for colorectal cancer (CRC). However, it requires careful and comprehensive inspection by expert pathologists. To relieve the pathologists' burden and speed up the diagnostic process, in this paper, we develop a deep learning system with the binary positive/negative labels of the lymph nodes to solve the CRC lymph node classification task. The multi-instance learning (MIL) framework is adopted in our method to handle the whole slide images (WSIs) of gigapixels in size at once and get rid of the labor-intensive and time-consuming detailed annotations. First, a transformer-based MIL model, DT-DSMIL, is proposed in this paper based on the deformable transformer backbone and the dual-stream MIL (DSMIL) framework. The local-level image features are extracted and aggregated with the deformable transformer, and the global-level image features are obtained with the DSMIL aggregator. The final classification decision is made based on both the local and the global-level features. After the effectiveness of our proposed DT-DSMIL model is demonstrated by comparing its performance with its predecessors, a diagnostic system is developed to detect, crop, and finally identify the single lymph nodes within the slides based on the DT-DSMIL and the Faster R-CNN model. The developed diagnostic model is trained and tested on a clinically collected CRC lymph node metastasis dataset composed of 843 slides (864 metastasis lymph nodes and 1415 non-metastatic lymph nodes), achieving the accuracy of 95.3% and the area under the receiver operating characteristic curve (AUC) of 0.9762 (95% confidence interval [CI]: 0.9607-0.9891) for the single lymph node classification. As for the lymph nodes with micro-metastasis and macro-metastasis, our diagnostic system achieves the AUC of 0.9816 (95% CI: 0.9659-0.9935) and 0.9902 (95% CI: 0.9787-0.9983), respectively. Moreover, the system shows reliable diagnostic region localizing performance: the model can always identify the most likely metastases, no matter the model's predictions or manual labels, showing great potential in avoiding false negatives and discovering incorrectly labeled slides in actual clinical use.

Effects of Atmospheric Cold Plasma Treatment on the Storage Quality and Chlorophyll Metabolism of Postharvest Tomato.

Atmospheric cold plasma (ACP) is a potential green preservation technology, but its preservation mechanism is still unclear, and the effects of different plasma intensities on postharvest tomatoes are little studied. In this study, the effects of different ACP treatments (0 kV, 40 kV, 60 kV, and 80 kV) on the sensory quality, physiological indexes, key enzyme activities, and gene expression related to the chlorophyll metabolism of postharvest tomatoes were investigated during the storage time. The results showed that compared with the control group, the tomatoes in the plasma treatment group had a higher hardness and total soluble solid (TSS) and titratable acid (TA) contents, a lower respiratory intensity and weight loss rate, a higher brightness, and a lower red transformation rate, especially in the 60 kV treatment group. In addition, chlorophyll degradation, carotenoid accumulation, and chlorophyllase and pheophorbide a mono-oxygenase (PAO) enzyme activities in the postharvest tomatoes were inhibited in the 60 kV treatment group, and the expressions of three key genes related to chlorophyll metabolism, chlorophyll (CLH1), pheophytinase (PPH), and red chlorophyll catabolic reductase (RCCR) were down-regulated. The results of the correlation analysis also confirmed that the enzyme activity and gene expression of the chlorophyll metabolism were regulated by the ACP treatment, aiming to maintain the greenness of postharvest tomatoes.

Pseudogene AK4P1 promotes pancreatic ductal adenocarcinoma progression through relieving miR-375-mediated YAP1 degradation.

Pseudogenes have been reported to play oncogenic or tumor-suppressive roles in cancer progression. However, the molecular mechanism of most pseudogenes in pancreatic ductal adenocarcinoma (PDAC) remains unknown. Herein, we characterized a novel pseudogene-miRNA-mRNA network associated with PDAC progression using bioinformatics analysis. After screening by dreamBase and GEPIA, 12 up-regulated and 7 down-regulated differentially expressed pseudogenes (DEPs) were identified. According to survival analysis, only elevated AK4P1 indicated a poor prognosis for PDAC patients. Moreover, we found that AK4 acts as a cognate gene of AK4P1 and also predicts worse survival for PDAC patients. Furthermore, 32 miRNAs were predicted to bind to AK4P1 by starBase, among which miR-375 was identified as the most potential binding miRNA of AK4P1. A total of 477 potential target genes of miR-375 were obtained by miRNet, in which 49 hub genes with node degree ≥ 20 were identified by STRING. Subsequent analysis for hub genes demonstrated that YAP1 may be a functional downstream target of AK4P1. To confirmed the above findings, microarray, and qRT-PCR assay revealed that YAP1 was dramatically upregulated in both PDAC cells and tissues. Functional experiments showed that knockdown of YAP1 significantly suppressed PDAC cells growth, increased apoptosis, and decreased the ability of invasion. In conclusion, amplification of AK4P1 may fuel the onset and development of PDAC by targeting YAP1 through competitively binding to miR-375, and serve as a promising biomarker and therapeutic target for PDAC.

Characterization and Antimicrobial Activity of Silver Nanoparticles Synthesized with the Peel Extract of Mango.

The green synthesis of silver nanoparticles (AgNPs) from biological waste, as well as their excellent antibacterial properties, is currently attracting significant research attention. This study synthesized AgNPs from different mango peel extract concentrations while investigating their characteristics and antibacterial properties. The results showed that the AgNPs were irregular with rod-like, spherical shapes and were detected in a range of 25 nm to 75 nm. The AgNPs displayed antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), showing a more significant impact when synthesized with 0.20 g/mL of mango peel extract. Therefore, the antibacterial effect of different diluted AgNP concentrations on the growth kinetic curves of E. coli and S. aureus after synthesis with 0.20 g/mL mango peel extract was analyzed. The results indicated that the AgNP antibacterial activity was higher against S. aureus than against E. coli, while the AgNP IC50 in these two strains was approximately 1.557 mg/mL and 2.335 mg/L, respectively. This research provides new insights regarding the use of postharvest mango byproducts and the potential for developing additional AgNP composite antibacterial materials for fruit and vegetable preservation.

Antifungal Effect of Chitosan/Nano-TiO2 Composite Coatings against Colletotrichum gloeosporioides, Cladosporium oxysporum and Penicillium steckii.

Postharvest pathogens such as C. gloeosporioides (MA), C.oxysporum (ME) and P. steckii (MF) are the causal agents of disease in mangoes. This paper presents an in vitro investigation into the antifungal effect of a chitosan (CTS)/nano-titanium dioxide (TiO2) composite coating against MA, ME and MF. The results indicated that, the rates of MA, ME and MF mortality following the single chitosan treatment were 63.3%, 84.8% and 43.5%, respectively, while the rates of mycelial inhibition were 84.0%, 100% and 25.8%, respectively. However, following the addition of 0.5% nano-TiO2 into the CTS, both the mortality and mycelial inhibition rates for MA and ME reached 100%, and the mortality and mycelial inhibition rate for MF also increased significantly, reaching 75.4% and 57.3%, respectively. In the MA, the dry weight of mycelia after the CTS/0.5% nano-TiO2 treatment decreased by 36.3% in comparison with the untreated group, while the conductivity value was about 1.7 times that of the untreated group, and the protein dissolution rate and extravasation degree of nucleic acids also increased significantly. Thus, this research revealed the potential of CTS/nano-TiO2 composite coatings in the development of new antimicrobial materials.

Effects of ozone concentration on the postharvest quality and microbial diversity of Muscat Hamburg grapes.

Grapevines are widely planted around the world. Although grapes have high nutritional value, they are highly perishable. To explore the effect of ozone concentration on the postharvest quality of Muscat Hamburg grapes, the ethylene production rate, respiratory intensity, soluble solids, titratable acidity, firmness, threshing rate, total yeast and mold counts, and the activities of superoxide dismutase, peroxidase, catalase, polyphenol oxidase and phenylalanine ammonia lyase were determined, and the fungal metagenome on the grape surface was analyzed. Among the ozone treatment groups, 14.98 mg m-3 ozone showed a positive effect on grape preservation. After 80 days of storage, the contents of soluble solids and titratable acidity increased by 3.1% and 0.03%, respectively, compared with the control group. Over the same period, firmness increased by 4.22 N and the threshing rate decreased by 0.5%. During storage, the activity of polyphenol oxidase was inhibited and the activities of superoxide dismutase, peroxidase, catalase, and phenylalanine ammonia lyase were maintained, which delayed the senescence of grapes and maintained freshness. Ozone can reduce the number of fungi on the grape surface, change the colony structure, and reduce the occurrence of diseases. An ozone concentration of 14.98 mg m-3 can delay the senescence of Muscat Hamburg grapes and improve storage quality.

Effects of ozone treatment on SOD activity and genes in postharvest cantaloupe.

Ozone has been shown to play a positive role in the storage and preservation of agricultural products. However, there is little research on the cantaloupe preservation mechanism of ozone treatment (OT), especially the effect on superoxide dismutase (SOD) and the mechanism of scavenging superoxide anion In this study, xizhoumi 25 was used as a typical cantaloupe material to detect content, hydrogen peroxide (H2O2) and SOD enzyme activity in the pericarp and pulp, respectively, and transcriptomics and qRT-RCR were used for cantaloupe SOD family gene expression. The results showed that the rate of and H2O2 content were inhibited and SOD activity was higher in the treatment group compared with the control (CK) group in the pericarp and pulp; SOD was more active in the pericarp and was higher than that in the pulp. The transcription level of Cu/Zn-SOD, identified as the most abundant component of the cantaloupe SOD gene family, was promoted in the OT group, especially the key gene Cu/Zn-SOD-1. The expression level of the Fe-SOD gene was promoted in the pericarp but regulated in the pulp, while the expression of the Mn-SOD gene was down-regulated in the OT group in both pericarp and pulp. In addition, the results of qRT-PCR were consistent with the transcriptome results. Correlation analysis showed that OT not only enhanced the positive correlation between and H2O2 in the whole cantaloupe and the negative correlation between and SOD activity in the pericarp but also altered the correlation between SOD genes and The mechanism of regulation in postharvest cantaloupe treated with ozone may be through stimulating the SOD activity and altering the expression of related genes in the pericarp and pulp.

Bi-Normal Filtering for Mesh Denoising.

Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.