Simple Summary Our study centers on refining the diagnosis of small-cell lung cancer (SCLC), a unique subtype with distinct therapeutic implications compared to other lung cancers. Our primary goal is the identification of specific differentially expressed proteins in SCLC as opposed to healthy lung tissue. Additionally, we aim to discern the protein expression of SCLC from large cell neuroendocrine carcinoma (LCNEC), a closely related entity. This research has the potential to enhance our understanding of these intricate lung cancers, potentially transforming the landscape of detection and tailored treatment strategies. Abstract The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Biflavonoids are dimeric forms of flavonoids that have recently gained importance as an effective new scaffold for drug discovery. In particular, 3′-8″-biflavones exhibit antiviral and antimicrobial activity and are promising molecules for the treatment of neurodegenerative and metabolic diseases as well as cancer therapies. In the present study, we directly compared 3′-8″-biflavones (amentoflavone, bilobetin, ginkgetin, isoginkgetin, and sciadopitysin) and their monomeric subunits (apigenin, genkwanin, and acacetin) and evaluated their radical scavenging activity (with DPPH), antifungal activity against mycotoxigenic fungi (Alternaria alternata, Aspergillus flavus, Aspergillus ochraceus, Fusarium graminearum, and Fusarium verticillioides), and inhibitory activity on enzymes (acetylcholinesterase, tyrosinase, α-amylase, and α-glucosidase). All the tested compounds showed weak radical scavenging activity, while antifungal activity strongly depended on the tested concentration and fungal species. Biflavonoids, especially ginkgetin and isoginkgetin, proved to be potent acetylcholinesterase inhibitors, whereas monomeric flavonoids showed higher tyrosinase inhibitory activity than the tested 3′-8″-biflavones. Amentoflavone proved to be a potent α-amylase and α-glucosidase inhibitor, and in general, 3′-8″-biflavones showed a stronger inhibitory potential on these enzymes than their monomeric subunits. Thus, we can conclude that 3′-8″-dimerization enhanced acetylcholinesterase, α-amylase, and α-glucosidase activities, but the activity also depends on the number of hydroxyl and methoxy groups in the structure of the compound.

Thanks to the climatic and geographical conditions, the area of the Northwestern part of Bosnia and Herzegovina has a long tradition of producing honey and other bee products. However, there is little or no literature data on the physico-chemical properties and biological activity of different types of honey and other bee products from Bosnia and Herzegovina. Five different types of honey were analyzed: monofloral honey (acacia, chestnut, linden), meadow honey and forest honey. Physico-chemical parameters, sensory analysis, color of honey, antioxidant activity, and content of total phenols were analyzed in five types off collected honey samples. The analyzes performed showed that chestnut honey contains the highest and acacia honey has the lowest content oftotal phenolic compounds. The forest honey showed the best antioxidant activity. The color of the honey was measured according to the CIELab system and the estimated L, a, bparameters show that all types of honey from this area can be characterized asdark types of honey (L50) with the presence of a yellow color. The obtained results show that the analyzed samples of five different types of honey are rich in polyphenolic components and represent a good source of antioxidants in the human diet.KEYWORDS:honey,physico-chemical parameters, color, antioxidant activity, total phenols

The review and analysis of a timeline work and stoppage/failure of transportational complex on separation in SC coal mine „Gračanica“ LLC Gornji Vakuf – Uskoplje has been given in this work. The work is based on collecting and analysing data. Collecting data lasted for one year and it is analysed and shown in this work. Rightfully determined the state of work and stoppage/failure, allows precautions and choice of strategy for the next period. Conclusions about which stoppage/failure affected the stoppage of transportational system and separation in full are derived from the research, and based on those conclusions, suggestions about activities which would minimize these stoppages on acceptable value are given. Key words: mine, coal, effective work, stoppage, failure, transportational complex, separation, belt conveyor, scraper.

We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally around an equilibrium under mild assumptions. The proposed strategy does not require solving the Hamilton–Jacobi–Bellman equation, i.e., a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the Hamilton–Jacobi–Bellman equation is replaced with an easy-solvable state-dependent Lyapunov matrix equation. We exploit a linear-like factorization of the underlying nonlinear system and a policy-iteration algorithm to yield a linear-like policy-iteration for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an asymptotically exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via four examples.

Abstract Congenital malformations are defined as structural or functional anomalies that occur in utero or at birth and can be detected at an early age. They are also known as birth defects, disabilities or congenital malformations. Congenital malformations are accompanied by hereditary or developmental disabilities or disease. From the establishment of the registry in early 2019 until the end of 2021, the total number of reported congenital malformations is 449. According to available data from EUROCAT (European network of population-based registries for the epidemiological surveillance of congenital anomalies), the average rate of congenital malformations in the countries of the European Union (EU) is 262/per 10,000 live births, while the registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. In the Federation of Bosnia and Herzegovina, the highest incidence rate was registered in Sarajevo Canton (175 cases with a rate of 416/10,000 live births) and Tuzla Canton (122 cases with a rate of 356/10,000 live births). The most common congenital malformations are heart defects, cleft lip and palate, musculoskeletal deformities and Down syndrome. In the Federation of Bosnia and Herzegovina (FBiH) in 2020, 135 children under the age of 5 died, among which 18 children (13.3%) died from congenital malformations, deformations and chromosomal abnormalities (Q00-Q99). Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age. Congenital malformations represent a significant public health problem, given that they lead to disability, incapacity and pressure on the health system, as well as the problem of social integration of patients. Key messages • The registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. • Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age.

We provide observational evidence that suggests the presence of a molten silicate layer above the core of Mars, which is overlain by a partially molten layer, indicating that the core of Mars is smaller than previously thought. The detection of deep reflected S waves on Mars inferred a core size of 1,830 ± 40 km (ref. ^ 1 ), requiring light-element contents that are incompatible with experimental petrological constraints. This estimate assumes a compositionally homogeneous Martian mantle, at odds with recent measurements of anomalously slow propagating P waves diffracted along the core–mantle boundary^ 2 . An alternative hypothesis is that Mars’s mantle is heterogeneous as a consequence of an early magma ocean that solidified to form a basal layer enriched in iron and heat-producing elements. Such enrichment results in the formation of a molten silicate layer above the core, overlain by a partially molten layer^ 3 . Here we show that this structure is compatible with all geophysical data, notably (1) deep reflected and diffracted mantle seismic phases, (2) weak shear attenuation at seismic frequency and (3) Mars’s dissipative nature at Phobos tides. The core size in this scenario is 1,650 ± 20 km, implying a density of 6.5 g cm^−3, 5–8% larger than previous seismic estimates, and can be explained by fewer, and less abundant, alloying light elements than previously required, in amounts compatible with experimental and cosmochemical constraints. Finally, the layered mantle structure requires external sources to generate the magnetic signatures recorded in Mars’s crust.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.