Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.
A Blockchain-Enabled Framework for mHealth Systems

Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients’ real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.
Replication-Competent ΔNS1 Influenza A Viruses Expressing Reporter Genes

The influenza A virus (IAV) is able to infect multiple mammalian and avian species, and in humans IAV is responsible for annual seasonal epidemics and occasional pandemics of respiratory disease with significant health and economic impacts. Studying IAV involves laborious secondary methodologies to identify infected cells. Therefore, to circumvent this requirement, in recent years, multiple replication-competent infectious IAV expressing traceable reporter genes have been developed. These IAVs have been very useful for in vitro and/or in vivo studies of viral replication, identification of neutralizing antibodies or antivirals, and in studies to evaluate vaccine efficacy, among others. In this report, we describe, for the first time, the generation and characterization of two replication-competent influenza A/Puerto Rico/8/1934 H1N1 (PR8) viruses where the viral non-structural protein 1 (NS1) was substituted by the monomeric (m)Cherry fluorescent or the NanoLuc luciferase (Nluc) proteins. The ΔNS1 mCherry was able to replicate in cultured cells and in Signal Transducer and Activator of Transcription 1 (STAT1) deficient mice, although at a lower extent than a wild-type (WT) PR8 virus expressing the same mCherry fluorescent protein (WT mCherry). Notably, expression of either reporter gene (mCherry or Nluc) was detected in infected cells by fluorescent microscopy or luciferase plate readers, respectively. ΔNS1 IAV expressing reporter genes provide a novel approach to better understand the biology and pathogenesis of IAV, and represent an excellent tool to develop new therapeutic approaches against IAV infections.
New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition †

For the effective application of thriving human-assistive technologies in healthcare services and human–robot collaborative tasks, computing devices must be aware of human movements. Developing a reliable real-time activity recognition method for the continuous and smooth operation of such smart devices is imperative. To achieve this, light and intelligent methods that use ubiquitous sensors are pivotal. In this study, with the correlation of time series data in mind, a new method of data structuring for deeper feature extraction is introduced herein. The activity data were collected using a smartphone with the help of an exclusively developed iOS application. Data from eight activities were shaped into single and double-channels to extract deep temporal and spatial features of the signals. In addition to the time domain, raw data were represented via the Fourier and wavelet domains. Among the several neural network models used to fit the deep-learning classification of the activities, a convolutional neural network with a double-channeled time-domain input performed well. This method was further evaluated using other public datasets, and better performance was obtained. The practicability of the trained model was finally tested on a computer and a smartphone in real-time, where it demonstrated promising results.
Genome-Wide Identification and Expression Patterns of the C2H2-Zinc Finger Gene Family Related to Stress Responses and Catechins Accumulation in Camellia sinensis [L.] O. Kuntze

The C2H2-zinc finger protein (C2H2-ZFP) is essential for the regulation of plant development and widely responsive to diverse stresses including drought, cold and salt stress, further affecting the late flavonoid accumulation in higher plants. Tea is known as a popular beverage worldwide and its quality is greatly dependent on the physiological status and growing environment of the tea plant. To date, the understanding of C2H2-ZFP gene family in Camellia sinensis [L. ] O. Kuntze is not yet available. In the present study, 134 CsC2H2-ZFP genes were identified and randomly distributed on 15 chromosomes. The CsC2H2-ZFP gene family was classified into four clades and gene structures and motif compositions of CsC2H2-ZFPs were similar within the same clade. Segmental duplication and negative selection were the main forces driving the expansion of the CsC2H2-ZFP gene family. Expression patterns suggested that CsC2H2-ZFPs were responsive to different stresses including drought, salt, cold and methyl jasmonate (MeJA) treatment. Specially, several C2H2-ZFPs showed a significant correlation with the catechins content and responded to the MeJA treatment, which might contribute to the tea quality and specialized astringent taste. This study will lay the foundations for further research of C2H2-type zinc finger proteins on the stress responses and quality-related metabolites accumulation in C. sinensis.
Reactivity in Cr- and Fe-MOF‑5

The metal nodes in metalorganic frameworks (MOFs) are known to act as Lewis acid catalysts, but few reports have explored their ability to mediate reactions that require electron transfer. The unique chemical environments at the nodes should facilitate unusual redox chemistry, but the difficulty in synthesizing MOFs with metal ions in reduced oxidation states has precluded such studies. Herein, we demonstrate that MZn3O(O2C�)6 clusters from Zn4O(1,4-benzenedicarboxylate)3 (MOF-5) serve as hosts for V 2+ and Ti 3+ ions and enable the synthesis of the first MOFs containing these reduced early metal ions, which can be accessed from MOF-5 by postsynthetic ion metathesis (PSIM). Additional MOF-5 analogues featuring Cr 2+ ,C r 3+ ,M n 2+ , and Fe 2+ at the metal nodes can be obtained by similar postsynthetic methods and are reported here for the first time. The inserted metal ions are coordinated within an unusual all-oxygen trigonal ligand field and are accessible to both inner- and outer-sphere oxidants: Cr 2+ - converts into Cr 3+ -substituted MOF-5, while Fe 2+ -MOF-5 activates NO to produce an unusual Fe-nitrosyl complex.
Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.
Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

Simple Summary This review discusses current techniques used to study post-translational protein modifications (PTMs) and their contribution to blood cancer research. Blood cancer is an umbrella term for cancers that affect blood cells and organs of the hematopoietic system, accounting for approximately 10% of all cancer diagnoses. PTMs are crucial for the normal functioning of cells via the regulation of protein structure, function, and localization. Mass spectrometry and antibody-based techniques are powerful analytical tools used for the detection and quantitation of PTMs such as phosphorylation. The application of these techniques in blood cancer research facilitates the identification of abnormal PTMs that contribute to cancer development and progression by promoting cancer cell growth, survival, and invasion. Many FDA-approved blood cancer treatments exert their anti-cancer effects by targeting protein modifications, thus emphasizing the importance of PTM-focused research in the identification of novel biomarkers and therapeutic targets to improve blood cancer survival outcomes. Abstract Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.
A Bio-Inspired Compliance Planning and Implementation Method for Hydraulically Actuated Quadruped Robots with Consideration of Ground Stiffness

There has been a rising interest in compliant legged locomotion to improve the adaptability and energy efficiency of robots. However, few approaches can be generalized to soft ground due to the lack of consideration of the ground surface. When a robot locomotes on soft ground, the elastic robot legs and compressible ground surface are connected in series. The combined compliance of the leg and surface determines the natural dynamics of the whole system and affects the stability and efficiency of the robot. This paper proposes a bio-inspired leg compliance planning and implementation method with consideration of the ground surface. The ground stiffness is estimated based on analysis of ground reaction forces in the frequency domain, and the leg compliance is actively regulated during locomotion, adapting them to achieve harmonic oscillation. The leg compliance is planned on the condition of resonant movement which agrees with natural dynamics and facilitates rhythmicity and efficiency. The proposed method has been implemented on a hydraulic quadruped robot. The simulations and experimental results verified the effectiveness of our method.
Melissa officinalis L. Aqueous Extract Exerts Antioxidant and Antiangiogenic Effects and Improves Physiological Skin Parameters

Melissa officinalis (MO) is a medicinal plant well-known for its multiple pharmacological effects, including anti-inflammatory, anticancer and beneficial effects on skin recovery. In this context, the present study was aimed to investigate the in vitro and in vivo safety profile of an MO aqueous extract by assessing cell viability on normal (HaCaT—human keratinocytes) and tumor (A375—human melanoma) cells and its impact on physiological skin parameters by a non-invasive method. In addition, the antioxidant activity and the antiangiogenic potential of the extract were verified. A selective cytotoxic effect was noted in A375 cells, while no toxicity was noticed in healthy cells. The MO aqueous extract safety profile after topical application was investigated on SKH-1 mice, and an enhanced skin hydration and decreased erythema and transepidermal water loss levels were observed. The in ovo CAM assay, performed to investigate the potential modulating effect on the angiogenesis process and the blood vessels impact, indicated that at concentrations of 100 and 500 µg/mL, MO aqueous extract induced a reduction of thin capillaries. No signs of vascular toxicity were recorded at concentrations as high as 1000 μg/mL. The aqueous extract of MO leaves can be considered a promising candidate for skin disorders with impaired physiological skin parameters.
Impact of the COVID-19 Pandemic on Estonian Elite Athletes: Survey on Mental Health Characteristics, Training Conditions, Competition Possibilities, and Perception of Supportiveness

Background: The postponement and cancellation of the competition season due to COVID-19 could cause significant mental health problems for an elite athlete. The aim of this study was to describe the mental health characteristics of Estonian elite athletes, their training conditions, competition possibilities, and the support they received during COVID-19. Methods: Athletes completed self-reported questionnaires (including Emotional State Questionnaire). The authors applied descriptive statistics, t-test, and χ2 test for comparison of study groups (p < 0.05). Results: Altogether 102 athletes (♂ = 44) were surveyed. The most disturbing issue for athletes was the closing of training centers (57.8%) and cancellation of competitions (50%); 64.7% of athletes reported a negative response from not being able to visit healthcare specialists. Fortunately, athletes could receive virtual coaching. Two-thirds of the athletes had some indication of distress (♀ > ♂): 25% of males and 39.7% of females had symptoms indicating depression; ♀ = 27.6%, ♂ = 13.6% anxiety; ♀ = 56.9%, ♂ = 31.8% fatigue (p = 0.021); ♀ = 55.1%, ♂ = 27.2% insomnia (p = 0.009); 27.5% thought about ending their career (frequency in high distress group compared with low: p = 0.022); and 2.9% were certain they would stop their training completely. Family members were the biggest emotional supporters; 16.7% did not get support from anyone. Conclusion: The Estonian sport community needs to adapt to life in a pandemic environment and help athletes to maintain training and competition activities and in turn, their mental health.
Rapid Detection of blaKPC-9 Allele from Clinical Isolates

The emergence of Klebsiella pneumoniae carbapenemase (KPC) nosocomial outbreaks related to specific blaKPC gene variants dictates the need for applicable diagnostic methods for allele discrimination. We report here a simple method of blaKPC-9 allele recognition based on a combination of endonuclease digestion analysis and PCR amplification using unique primers. K. pneumoniae isolates carrying the blaKPC gene were tested. Digestion with RsaI restriction endonuclease was found to efficiently differentiate the blaKPC-2 from the blaKPC-9 variants into two distinct groups of digestion patterns named KPC-2-like and KPC-9-like, respectively. An additional procedure, the amplification refractory mutation system (ARMS) method, was applied to identify the variant within the same group. The principles of this procedure could be developed to identify several blaKPC gene variants, as well as monitoring the spread and evolution of specific KPC variants within local geographical regions.
Development of a Lyophilization Process for Long-Term Storage of Albumin-Based Perfluorodecalin-Filled Artificial Oxygen Carriers

Every day, thousands of patients receive erythrocyte concentrates (ECs). They are indispensable for modern medicine, despite their limited resource. Artificial oxygen carriers (AOCs) represent a promising approach to reduce the need for ECs. One form of AOCs is perfluorodecalin-filled albumin-based nanocapsules. However, these AOCs are not storable and need to be applied directly after production. In this condition, they are not suitable as a medicinal product for practical use yet. Lyophilization (freeze drying) could provide the possibility of durable and applicable nanocapsules. In the present study, a suitable lyophilization process for perfluorodecalin-filled nanocapsules was developed. The nanocapsules were physicochemically characterized regarding capsule size, polydispersity, and oxygen capacity. Even though the perfluorodecalin-filled albumin-based nanocapsules showed a loss in oxygen capacity directly after lyophilization, they still provided a remarkable residual capacity. This capacity did not decline further for over two months of storage. Furthermore, the nanocapsule size remained unaltered for over one year. Therefore, the AOCs were still applicable and functional after long-term storage due to the successful lyophilization.
Changes in Alcohol Consumption during the COVID-19 Pandemic—Small Change in Total Consumption, but Increase in Proportion of Heavy Drinkers

Little is known about possible changes in alcohol consumption distribution during the COVID-19 pandemic. We estimated how individual changes in alcohol consumption during the pandemic translated into changes in: (i) mean consumption; (ii) dispersion of consumption distribution; and (iii) prevalence of heavy drinkers. We employed data from two independent web-surveys of Norwegian adults collected between April and July 2020 and limited to those reporting past year alcohol consumption (N1 = 15,267, N2 = 1195). Self-reports of changes in drinking behavior were quantified, assuming change being relative to baseline consumption level. During the pandemic, we found a small increase (Survey 1) or no change (Survey 2) in estimated mean alcohol consumption (which parallels to total consumption). However, in both surveys, the dispersion of the distribution increased significantly (p < 0.001). For most respondents, an average modest decline in consumption was found. However, the small fraction with the highest baseline consumption increased their consumption substantially, and in effect, the proportion of heavy drinkers increased markedly (p < 0.001). In conclusion, quantifications of reported changes in alcohol consumption during the pandemic suggest that the upper 5 to 10% of the drinkers increased their consumption and hence the prevalence of heavy drinkers increased, despite little or no change in total alcohol consumption.
Precision Phenomapping of Acute Coronary Syndromes to Improve Patient Outcomes

Acute coronary syndromes (ACS) are a global leading cause of death. These syndromes show heterogeneity in presentation, mechanisms, outcomes and responses to treatment. Precision medicine aims to identify and synthesize unique features in individuals, translating the acquired data into improved personalised interventions. Current precision treatments of ACS include immediate coronary revascularisation driven by ECG ST-segment elevation, early coronary angiography based on elevated blood cardiac troponins in patients without ST-segment elevation, and duration of intensified antithrombotic therapy according to bleeding risk scores. Phenotypically stratified analyses of multi-omic datasets are urgently needed to further refine and couple the diagnosis and treatment of these potentially life-threatening conditions. We provide definitions, examples and possible ways to advance precision treatments of ACS.