Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of a Monobromobimane (MBB) Derivatization and RP-HPLC-FLD Detection Method for Sulfur Species Measurement in Human Serum after Sulfur Inhalation Treatment

(1) Background: Hydrogen sulfide (H2S) is a widely recognized gasotransmitter, with key roles in physiological and pathological processes. The accurate quantification of H2S and reactive sulfur species (RSS) may hold important implications for the diagnosis and prognosis of diseases. However, H2S species quantification in biological matrices is still a challenge. Among the sulfide detection methods, monobromobimane (MBB) derivatization coupled with reversed phase high-performance liquid chromatography (RP-HPLC) is one of the most reported. However, it is characterized by a complex preparation and time-consuming process, which may alter the actual H2S level; moreover, a quantitative validation has still not been described. (2) Methods: We developed and validated an improved analytical protocol for the MBB RP-HPLC method. MBB concentration, temperature and sample handling were optimized, and the calibration method was validated using leave-one-out cross-validation and tested in a clinical setting. (3) Results: The method shows high sensitivity and allows the quantification of H2S species, with a limit of detection of 0.5 µM. Finally, it can be successfully applied in measurements of H2S levels in the serum of patients subjected to inhalation with vapors rich in H2S. (4) Conclusions: These data demonstrate that the proposed method is precise and reliable for measuring H2S species in biological matrices and can be used to provide key insights into the etiopathogenesis of several diseases and sulfur-based treatments.
Gene Multimerization in Expression Vector: A Potential Strategy for Enhanced Protein Expression in E. coli

Protein production in any expression system can be enhanced either by optimizing the culturing parameters or targeting the genetic factors that enhance protein production. One of those genetic factors is gene dosage, which can be increased by increasing the vector copy number. However, increased number of expression vector poses some metabolic burdens on bacterial cells. The current study provides gene multimerization strategy as an alternate way to amplify the gene dosage and explores its effect on the expression of human epidermal growth factor (hEGF) by constructing three types of expression vectors i.e., pET28-EGF-C1, pET28-EGF-C2 and pET28-EGF-C3, each containing single, double and triple expression cassettes, respectively. These vectors were transformed in E. coli strain Rosetta-gami 2 (DE3) to develop three different bacterial populations. Expression of human epidermal growth factor was analyzed by SDS-PAGE and subsequent densitometric analysis was done using ImageJ software. The difference in protein expression of all three populations was analyzed by using one-way ANOVA. Clones with double and triple copies of genes showed an increased expression up to 3.8 and 3.1 folds as compare to the clones having single copy of gene. Gene multimerization strategy can be used to enhance production of recombinant proteins in bacterial expression system.
Power System Harmonic Estimation Based on Park Transform

This paper presents a novel method for power system harmonic estimation based on the Park transform. The proposed method firstly extends the signal to a group of three-phase signals in a-b-c coordinate. Then, a linear fitting based method is adopted to estimate the fundamental frequency. Afterwards, the Park transform is utilized to convert the three-phase signals from a-b-c coordinate to d-q- 0 coordinate. Finally, the amplitude and phase of a harmonic component of interest can be calculated using the d-axis and q-axis components obtained. Simulation studies have been conducted using matrix laboratory (MATLAB) and power system computer aided design/electromagnetic transients including direct current (PSCAD/EMTDC). Simulation studies in MATLAB have considered three scenarios, i.e., no-frequency-deviation scenario, frequency-deviation scenario and the scenario in the presence of inter-harminics. The results have demonstrated that the proposed method achieves very high accuracy in frequency, phase and amplitude estimation under noisy conditions, and suffers little influence of the inter-harmonics. Moreover, comparison studies have proved that the proposed method is superior to FFT and Interpolated FFT with the Hanning Window (IpFFTHW). Finally, a popular case in PSCAD/EMTDC has been employed to further verify the effectiveness of the proposed method.
Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

Simple Summary Uterine cervical cancer (CC) is a leading cause of cancer-related deaths in women worldwide. Pelvic magnetic resonance imaging (MRI) allows the assessment of local tumor extent and guides the choice of primary treatment. MRI tumor segmentation enables whole-volume radiomic tumor profiling, which is potentially useful for prognostication and individualization of therapy in CC. Manual tumor segmentation is, however, labor intensive and thus not part of routine clinical workflow. In the current work, we trained a deep learning (DL) algorithm to automatically segment the primary tumor in CC patients. Although the achieved segmentation performance of the trained DL algorithm is slightly lower than that for human experts, it is still relatively good. This study suggests that automated MRI primary tumor segmentations by DL algorithms without any human interaction is possible in patients with CC. Abstract Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.
Introducing the smart education framework: core elements for successful learning in a digital world

The purpose of this article is to identify crucial elements that are prerequisites for successful experience in a technology-supported environment. Technological advances as well as interdisciplinary insights demand a new perspective on education. This article reflects on these developments and based on this proposes a theoretical framework of smart education for evaluating learning, as well as for guiding educators to design learning activities in technology-supported environments. The proposed framework identifies three core elements, respectively teacher presence, learner presence and technology presence. On the individual level, these elements are subdivided and specified. Teachers presence incorporates student-centred, personalised, and collaborative pedagogical models to design learning in technology-facilitated environments, facilitating the learning process by promoting.
Exposure to Food Marketing via Social Media and Obesity among University Students in Saudi Arabia

This study investigated the associations between obesity and unhealthy food/drink intake with both the frequency of social media platform usage and food/drink marketing exposure on social media. Data were obtained from 316 university students aged 18–29 years at two universities in Jeddah, Saudi Arabia. These participants completed online questionnaires with sections on demographics, anthropometric measurements, social media platform usage, food marketing exposure via social media, and unhealthy food consumption. All of the participants, 20.3% and 13.6% were overweight and obese, respectively. Snapchat was the most popular application (85.8%), followed by Instagram (75%), YouTube (61%), Twitter (51%), and TikTok (50%). The obese participants were more likely to purchase foods/drinks after watching relevant social media advertisements than their non-obese counterparts (p < 0.04). Moreover, those who purchased foods/drinks more frequently after watching such advertisements consumed higher amounts of potato chips (p < 0.01) and fast foods (p < 0.03). Finally, those who used Snapchat, TikTok, and Instagram tended to have higher consumption rates for potato chips (p < 0.02), fast foods (p < 0.01), sweets (p < 0.02), and sugary drinks (p < 0.04). Public health policymakers in Saudi Arabia should consider regulating unhealthy food and drink advertisements on social media platforms, especially those targeted at younger generations.
External Validation of Two Established Clinical Risk Scores Predicting Outcome after Local Treatment of Colorectal Liver Metastases in a Nationwide Cohort

Simple Summary Patients with colorectal liver metastases (CRLM) are able to achieve long-term survival when they receive local treatment of CRLM (resection or tumor ablation). Existing clinical risk scores (CRSs) predicting prognosis of patients after resection of colorectal liver metastases were developed in highly specialized centers and thus may not function in the general population. We validated the Fong and GAME CRSs in a large population-based cohort, including two important subgroups: young/elderly and with/without perioperative chemotherapy. Both CRSs showed predictive ability. However, they were not able to discriminate preoperative risk sufficiently for clinical decision-making and, thus, require improvement. Abstract Optimized surgical techniques and systemic therapy have increased the number of patients with colorectal liver metastases (CRLM) eligible for local treatment. To increase postoperative survival, we need to stratify patients to customize therapy. Most clinical risk scores (CRSs) which predict prognosis after CRLM resection were based on the outcome of studies in specialized centers, and this may hamper the generalizability of these CRSs in unselected populations and underrepresented subgroups. We aimed to externally validate two CRSs in a population-based cohort of patients with CRLM. A total of 1105 patients with local treatment of CRLM, diagnosed in 2015/2016, were included from a nationwide population-based database. Survival outcomes were analyzed. The Fong and more recently developed GAME CRS were externally validated, including in pre-specified subgroups (≤70/>70 years and with/without perioperative systemic therapy). The three-year DFS was 22.8%, and the median OS in the GAME risk groups (high/moderate/low) was 32.4, 46.7, and 68.1 months, respectively (p < 0.005). The median OS for patients with versus without perioperative therapy was 47.6 (95%CI [39.8, 56.2]) and 54.9 months (95%CI [48.8, 63.7]), respectively (p = 0.152), and for below/above 70 years, it was 54.9 (95%CI [49.3–64.1]) and 44.2 months (95%CI [37.1–54.3]), respectively (p < 0.005). The discriminative ability for OS of Fong CRS was 0.577 (95%CI [0.554, 0.601]), and for GAME, it was 0.596 (95%CI [0.572, 0.621]), and was comparable in the subgroups. In conclusion, both CRSs showed predictive ability in a population-based cohort and in predefined subgroups. However, the limited discriminative ability of these CRSs results in insufficient preoperative risk stratification for clinical decision-making.
Should Corporation Care about Negative Brand Publicity? Understanding the Impact of Negative Brand Publicity on Employees' Corporate Brand Pride

This article examines the effects of perceived negative brand publicity on employee emotions, attitudes and brand supporting behaviors. Drawing on Affective-Events-Theory (AET), it attempts to identify underlying affective and cognitive processes leading to behavioral change. Using data gathered from a large-scale survey of employees in Germany, our results show that perceived negative brand publicity affects emotional and attitudinal corporate brand pride of employees. In addition, higher levels of perceived negative brand publicity were negatively associated with brand-supporting behavior, such as employee referrals and word- of-mouth (WOM). We show that corporate brand experience through internal communications can be an effective tool in mitigating harmful effects of perceived negative brand publicity.
A new genus of the family Nesticidae (Arachnida: Aranei) from the Caucasus

фия биотопа . Обсуждается структура пальпы самца у Nesticini, предлагается новая терминология , ос нованная на гомологии различных склеритов . specimens of Nesticidae whose morphology do not ABSTRACT. A new genus, Daginesticus gen.n. with the type species, D. dzhamirzoevi sp.n. ( #$ ) is described from the Caucasus based on morphological and molecular evidences. Three new combinations are proposed: Daginesticus mamajevae (Marusik, 1987) comb.n. , Aituaria borutzkyi (Reimoser, 1930) comb.n. and Aituaria eriashvilii (Marusik, 1987) comb.n. (all ex. Carpathonesticus ). The new genus is closely related to Aituaria Esyunin et Efimik, 1998 and has unre-duced eyes and well-developed body pigmentation. Description, figures, diagnosis and photographs of the habitat are provided. The conformation of the male palp in Nesticini is discussed and new terms are suggested based on homology of different sclerites.
Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Enterprise financial analysis has a far-reaching impact on modern enterprise management decision-making and plays a role that cannot be ignored. Financial status is related to the life and death of an enterprise and is the lifeline of an enterprise. Fast and efficient financial analysis can provide reliable and accurate decision-making information support for enterprise investors, operators, creditors, and other organizations and individuals to understand and evaluate the enterprise status and future development potential. With the development of intelligent methods such as associative memory neural network, the research of financial analysis decision support based on artificial intelligence has been paid more and more attention by academia and management, and has made new progress. Efficient and accurate financial risk prediction can help enterprises predict the possible financial risks in the future earlier, facilitate the early detection of problems, and take effective measures to avoid risks or minimize losses. However, most of the existing mature financial risk prediction studies are based on balanced data sets. The research on the classification of unbalanced data sets is not mature and perfect and needs to be further studied. Generally speaking, this paper mainly adopts the research method of cross integration of various disciplines and organically integrates the key theories, methods, and technologies such as default risk management theory, financial index analysis theory, data mining principle, prediction and decision theory, computer technology, multiclassifier integration technology, a variety of enterprise financial risk early warning technology and statistical sampling, carrying out systematic research on enterprise financial risk prediction. This review constructs the enterprise financial risk prediction method system based on heterogeneous data mining technology, mainly including data preprocessing layer, improved nearest neighbor delta increment layer, heterogeneous nearest neighbor extraction layer, and case-based reasoning prediction layer, so as to improve the traditional financial risk prediction method and obtain a new risk classification prediction model. The case-based method has some significant advantages in risk prediction performance, and it also helps to reduce the probability of financial risk.
Multi-objective Design Optimization of a Novel Switched Reluctance Motor with Unequal Alternating Stator Yoke Segments

Switched reluctance motors (SRMs) have attracted considerable attention for energy conversion within industrial applications. To realize the high-quality operation status, optimization for SRMs is one of the most essential steps in the design stage. In this paper, a multi-objective design optimization method is proposed for a novel SRM with unequal alternating stator yoke segments. First, the asymmetric flux distribution and the inconsistent current phenomenon of the conventional six-phase SRM are introduced. The unequal yoke SRM (UYSRM) is developed to improve the current profiles and reduce the unnecessary stator mass. Then, an optimization method considering the slot fill factor is proposed and performed on the proposed UYSRM. In the optimization process, the sensitivity analysis is conducted based on the orthogonal array, and the sensitive degrees of different variables are calculated. To take full advantage of the stator slot area, the number of parallel strands of phase windings is adjusted according to the number of turns and the variables of the stator. The performances of the UYSRM are evaluated to validate the proposed SRM and optimization method. Finally, a prototype motor is manufactured and tested. Both the simulation and test results verify the effectiveness and reasonability of the proposed design optimization method for the UYSRM. Besides, the proposed method offers a new research orientation for electric machines.
A novel tool wear suppression method for polycrystalline diamond by applying magnetic field in turning of ferrous materials

Ferrous materials are widely used in the mould industry because of their excellent properties. However, ferrous materials are regarded as difficult-to-cut materials for diamond turning due to their affinity for diamond. The resulting catastrophic tool wear leads to high machining costs in ultraprecision turning. The unpaired d-electrons in the workpiece are regarded as the dominant element causing catastrophic diamond wear during the turning. However, an insightful method for unpaired d-electrons in existing assisted machining schemes is still lacking. As a type of diamond tool, polycrystalline diamond (PCD) tools are inexpensive to appropriately carry out wear experiments. Therefore, in this study, a novel assisted method based on a magnetic field (MF) was applied to suppress the PCD tool wear. Experimental results demonstrated that the MF-assisted machining can reduce the flank wear of PCD tools by 30.6%. The spin polarisation of d-electrons by the MF was the dominant mechanism inhibiting the chemical reaction and graphitisation, and thus suppressing the PCD tool wear. Simultaneously, the suppression of machining system vibration by the Lorentz force also had a positive effect on the suppression of the PCD tool wear. The innovative method provides an assisted machining scheme to inhibit the catastrophic wear of PCD tools.
In Situ Diagnosis of Multichip IGBT Module Wire Bonding Faults Based on Collector Voltage Undershoot

Condition monitoring of insulated gate bipolar transistor (IGBT) modules is an effective way to improve the transient performance and reliability of modular multilevel converters (MMC). This article proposes a novel bond wire failure monitoring method for the multichip IGBT modules in the MMC half-bridge submodule (SM) structure. The collector voltage undershoot VCA(np) of the complementary IGBT switch is measured during the turn-off switching transition of the controlled IGBT switch. VCA(np) is sensitive to the induced voltage over the stray inductance of the IGBT bond wires and provides high sensitivity as a health indicator. The non-intrusive measurement technique is demonstrated using a half-bridge circuit during the turn-off transition of the controlled IGBT, while its complementary switch (i.e., the device under test) is in the off state. Theoretical analysis and experimental results show that the parametric drift due to wire bonding failures can be effectively monitored with high specific relative sensitivity and granularity. To ensure its effectiveness in practical applications, the influence of load current, SM capacitor voltage, and junction temperature is discussed. In addition, a readout circuit is designed featuring the integrated desaturation detection and voltage peak detection, which offers both the short-term overcurrent fault protection and long-term bond wire aging monitoring functions.
Transsynaptic cerebellin 4–neogenin 1 signaling mediates LTP in the mouse dentate gyrus

Significance Synapses are controlled by transsynaptic adhesion complexes that mediate bidirectional signaling between pre- and postsynaptic compartments. Long-term potentiation (LTP) of synaptic transmission is thought to enable synaptic modifications during memory formation, but the signaling mechanisms involved remain poorly understood. We show that binding of cerebellin-4 (Cbln4), a secreted ligand of presynaptic neurexin adhesion molecules, to neogenin-1, a postsynaptic surface protein known as a developmental netrin receptor, is essential for normal LTP at entorhinal cortex→dentate gyrus synapses in mice. Cbln4 and neogenin-1 are dispensable for basal synaptic transmission and not involved in establishing synaptic connections as such. Our data identify a netrin receptor as a postsynaptic organizer of synaptic plasticity that collaborates specifically with the presynaptic neurexin–ligand Cbln4.
FDI Attack Detection at the Edge of Smart Grids Based on Classification of Predicted Residuals

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.