Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of sex hormones on the size of intestinal lipoproteins

Larger intestinal lipoproteins are more likely to be retained longer in the intestinal wall, allowing more time for their fat to be hydrolyzed and subsequently taken up by the abdominal viscera. Since men generally accumulate more abdominal visceral fat than women, we sought to determine if males produce larger intestinal lipoproteins compared to females. Using the conscious lymph fistula mouse model, we discovered that the male mice indeed produced larger intestinal lipoproteins than the female mice when they were intraduodenally infused with lipid emulsion. We then employed our differentiated Caco-2 cell model with semipermeable membrane system to determine the effects of sex hormones on the size of intestinal lipoproteins. Lipoprotein size was quantitatively measured by calculating the ratio of triglycerides (TG)/Apolipoprotein B (ApoB) and by analyzing their transmission electron micrographs. Our studies showed that while there was no dose-dependent effect of estrogen and progesterone, testosterone significantly increased the size of lipoproteins. When these hormones were combined to resemble the physiological concentrations observed in males and the different ovarian cycle phases in premenopausal females, both the male and luteal groups had significantly larger lipoproteins than the ovulatory group; and the male group also had significantly larger lipoproteins than the follicular group. The ovulatory group secreted a significantly lower amount of TG than the male and luteal groups. ApoB was comparable among all these groups. These findings support our hypothesis that, through their testosterone effects, males are more likely to produce larger intestinal lipoproteins. Larger lipoproteins tend to remain longer in the intestinal wall and may facilitate fat uptake preferentially by the abdominal viscera. Our studies may partly explain why men are more prone to accumulating abdominal visceral fat, which is an independent predictor of mortality.
YOLO Network Optimization With a Single Circular Bounding Box for Detecting Defective Cigarettes

The manufacturing industry utilizes computing technology, robot technology, artificial intelligence, and IoT to improve production processes and quality. In particular, object detection technology is used in various industrial fields, and object detection methods based on deep learning are attracting attention. The tobacco processing industry requires automated production facilities, and quality control for defects in product appearance is essential. Mainly because tobacco products are sold at high prices, poor appearance is a significant issue in terms of consumer complaints and processing costs. Therefore, accurate cigarette detection is essential. We propose a modified network structure based on the YOLOv4-Tiny network, and use it to build a network optimized for cigarette detection. The modified network uses a single circular bounding box for learning and fast detection. It utilizes visual techniques, such as gradient-weighted Class Activation Mapping (Grad-CAM) to analyze the degree of activation of the network to construct an optimal network. This reduces the size of the network and increases processing speed, while maintaining detection accuracy. This paper is expected to play an important role in quality control and efficient production in the manufacturing industry.
Shake It Up Baby Now: The Changing Focus on TWIST1 and Epithelial to Mesenchymal Transition in Cancer and Other Diseases

TWIST1 is a transcription factor that is necessary for healthy neural crest migration, mesoderm development, and gastrulation. It functions as a key regulator of epithelial-to-mesenchymal transition (EMT), a process by which cells lose their polarity and gain the ability to migrate. EMT is often reactivated in cancers, where it is strongly associated with tumor cell invasion and metastasis. Early work on TWIST1 in adult tissues focused on its transcriptional targets and how EMT gave rise to metastatic cells. In recent years, the roles of TWIST1 and other EMT factors in cancer have expanded greatly as our understanding of tumor progression has advanced. TWIST1 and related factors are frequently tied to cancer cell stemness and changes in therapeutic responses and thus are now being viewed as attractive therapeutic targets. In this review, we highlight non-metastatic roles for TWIST1 and related EMT factors in cancer and other disorders, discuss recent findings in the areas of therapeutic resistance and stemness in cancer, and comment on the potential to target EMT for therapy. Further research into EMT will inform novel treatment combinations and strategies for advanced cancers and other diseases.
Regenerating Saffron (Crocus sativus L.) from Corm Lateral Buds via Indirect Somatic Embryogenesis

Saffron (Crocus sativus L.), being one of the distinguished commercial spice crops in the world, is in demand for its culinary, colorant, and pharmaceutical benefits. In this study, a novel indirect somatic embryogenesis (SE) system was, thus, established for the study of this plant. To this end, firstly, the lateral buds were cultured. Then, the cultures were transformed using Murashige and Skoog (MS) medium supplemented with either 6-benzyladenine (BA: 5 and 10 mg/L), naphthalene acetic acid (NAA: 0, 1, and 2 mg/L), or trans-zeatin (tZ: 0, 0.5, and 1.0 mg/L), before being classified into four structures: white globular (WG), yellow compact nodular (YCN), yellow-brown fragile (YBF), and dark-brown porous (DBP). As soon as BA (10 mg/L) and NAA (2 mg/L) were added, elevated percentages of white globular calli (56.8%) and white globular calli (31.5%) structures were induced. Additionally, 6-benzyladenine (5 mg/L) and naphthalene acetic acid (1 mg/L) allowed the formation of yellow-brown fragile structures, and the combination of 6-benzyladenine (10 mg/L) with trans-zeatin (1 mg/L) formed the DBP structures. After three months, the white globular calli were incubated using the MS basal medium, before being augmented with thidiazuron (TDZ: 1 mg/L) and picloram (PIC: 2 mg/L), from which 60% of the cases matured into shoots and, ultimately, cormlets. Morphoanatomical analyses also showed that the white globular calli cells were closely arranged, as they had a dense cytoplasm, a significant vascular differentiation, and embryoids. Furthermore, the yellow compact nodular structures were characterized by a strong differentiation capacity and contained many meristematic cells with high caryomitosis centers. We observed that the yellow-brown fragile calli had looser cell arrangements, with a vascular structure located on the protoderm edge, while there was no obvious cellular arrangement in the dark-brown porous structures. The induction of the adventitious buds in vivo on the MS medium that was supplemented with thidiazuron and picloram accordingly demonstrated the highest rates (60%) of white globular calli.
Analysis and Comparison of Fast Fourier and Wavelet Transform Techniques on Raw sEMG Data From Textile-Based Electrodes

Surface electromyography is an assessment tool that is frequently used in many areas of medical science today and is used to analyze muscle function. This measurement technique, which provides important information about the contraction of our body’s muscles, identifies which muscles are activated during which movements. Thanks to the development of technology and studies on smart textiles for physiological state monitoring, which are growing rapidly in the scientific and technological fields, real-time body monitoring during exercise and rehabilitation can be achieved. In this study, Fast Fourier Transform (FFT) and Wavelet Transform (WT) techniques were applied to the raw sEMG data obtained using textile-based electrodes, and the results were compared. The analysis of both techniques on the raw data was examined in detail. According to the results obtained, it has been seen that the Wavelet Transform method in signal processing offers a much more detailed analysis than the Fast Fourier technique.
Modulations for Terahertz Band Communications: Joint Analysis of Phase Noise Impact and PAPR Effects

The choice of modulation techniques for next-generation communications in the sub-terahertz and terahertz bands remains largely unresolved. A variety of traditional and new schemes show promise, but the question remains open on the illustrative comparison process for realistic terahertz systems. While there are some preliminary studies in this area, we emphasize that the peculiarities of terahertz hardware necessitate a scheme that (i) is resistant to system-wide phase noise (PN) and (ii) has a low peak-to-average power ratio (PAPR). Therefore, in this article, we present a comprehensive methodology to carefully model and jointly study the impacts of PN and PAPR on the performance of candidate modulations for terahertz links. We first deliver a mathematical model for sub-terahertz/terahertz phase noise impairments at 130 GHz, 225 GHz, and 1.02 THz based on measurements from actual terahertz hardware. We then introduce the PAPR penalty – an approach for fair comparison of bit error rate (BER) and spectral efficiency (SE) of modulation schemes with different PAPRs. We finally combine these two effects to comprehensively study the characteristics of single and multi-carrier modulation schemes for terahertz communications. Our study reveals that analyzing PAPR and PN jointly is paramount: accounting for only one leads to major deviations in the numerical results and misleading conclusions on best modulation choice(s). The delivered framework and evaluation should facilitate further studies, leading to a well-motivated selection of the most suitable modulation scheme(s) for prospective sub-terahertz and terahertz radio systems.
Flow Analysis of Vehicles on a Lane Using Deep Learning Techniques

—Increased volume of vehicles on the road everywhere in the world. It becomes difficult to regulate the traffic manually across all the traffic signals throughout the cities for the entire day. It is costly and tedious for manual traffic control at traffic signals. An Intelligent Transportation System (ITS) is proposed as a solution for traffic management and to address various challenges caused by increased vehicular density. Traffic analysis from real time camera images is the most adopted method for traffic state estimations which is a most important component in smart traffic management. The current traffic density estimators from videos estimate the volume of vehicles or percentage of area occupied in the lane. But for intelligent traffic management, more accurate traffic state estimation in terms of classification, density, speed, and flow rate for different categories of vehicles in different segments of the lane is needed. This work introduces a deep learning-based fine-grained vehicle flow analysis from traffic videos. A fine-grained traffic density distribution for different categories of vehicles over the entire coverage area of the lane with flow information is referred to as a dynamic traffic state map. A continuous traffic state map integrating vehicle categorization, lane density estimation, vehicle flow estimation, and orientation flow analysis. Vehicle categorization based on high and low-level features is proposed instead of area-based thresholding. A novel deep learning-based vehicle density estimation integrating coherence-based region segmentation with convolutional neural network and density estimate from the segment is proposed. The solution is able to provide an estimate of the traffic at a fine-grained level
Effects of Exercise Training on Patient-Specific Outcomes in Pancreatic Cancer Patients: A Scoping Review

Simple Summary Pancreatic cancer remains one of the most malignant solid tumours. Due to the disease’s rapid progression, pancreatic cancer patients often experience great distress and suffer from symptoms such as pain, cachexia, sarcopenia, and cancer-related fatigue. The importance of exercise interventions as a supportive strategy in cancer patients to address these and other symptoms is steadily increasing, and strong evidence of various beneficial effects is shown. However, the impact of exercise on pancreatic cancer patients is still poorly characterized. Therefore, this scoping review aimed to evaluate the impact of exercise on pancreatic cancer patients. The results of this review suggest that exercise can improve different patient-specific outcomes in this population. To confirm these findings, further randomized-controlled trials are needed. Abstract Background: International guidelines have already highlighted the beneficial effects of exercise in common cancer entities. However, specific recommendations for pancreatic cancer are still missing. This scoping review aimed to evaluate the impact of exercise training on patient-specific outcomes in pancreatic cancer patients. Methods: A literature search was undertaken using PubMed, Web of Science, and Cochrane Library. We included randomized controlled trials (RCTs) published before August 2023 with structured exercise interventions during or after pancreatic cancer treatment. Results: Seven articles that prescribed home-based or supervised exercise with aerobic or resistance training or both were reviewed. The results indicate that exercise is feasible and safe in pancreatic cancer patients. Furthermore, exercise was associated with improved quality of life, cancer-related fatigue, and muscle strength. Concerning other outcomes, heterogeneous results were reported. We identified a lack of evidence, particularly for patients with advanced pancreatic cancer. Conclusion: Exercise interventions in pancreatic cancer patients are feasible and can lead to improved quality of life, cancer-related fatigue, and muscle strength. However, further studies with larger sample sizes are needed to clarify the potential of exercise in pancreatic cancer, in particular for advanced stages.
Internal profitability determinants of organic production companies in Republika Srpska

This paper aims to investigate the value and influence of internal profitability determinants on profitability measured by return of assets of organic production companies in Srpska. For this purpose, abbreviated balance sheets of 21 companies engaged in this type of agricultural production in the period from 2014 to 2021 were provided and the regression model was defined. The results showed that the internal determinants of profitability, i.e. company age, liquidity ratio, debt measured by financial leverage, sales growth and capital turnover have a significant impact on profitability. While debt has a negative correlation, other determinants have a positive correlation. Research limitations refer to the sample because it does not include small-scale family farms, which are not required to submit financial reports.
Steady Decline of HBV DNA Load under NAs in Lymphoma Patients and a Higher Level of qAnti-HBc Predict HBV Reactivation

Background: Patients with lymphoma and chronic hepatitis B virus infection need to be treated with both chemotherapy and nucleotide analogue (NA) therapy. However, dynamic changes in HBV DNA loads with increasing chemotherapy cycles are lacking. It is unknown whether HBV replication markers, namely, the quantitative hepatitis B core antibody (qAnti-HBc), HBV RNA, and the hepatitis B virus core-related antigen (HBcrAg), are also markers for predicting HBV reactivation (HBVr). Methods: From 29 June 2010 to 6 December 2021, the data of patients with single-site diffuse large B-cell lymphoma and HBV infection (HBsAg+ and HBsAg−/anti-HBc+) were collected from a hospital medical record system, retrospectively. Serum HBV DNA loads (using real-time fluorescent quantitative PCR tests), qAnti-HBc levels (using a newly developed chemiluminescent particle immunoassay), HBV RNA levels (using the simultaneous amplification testing method based on real-time fluorescence detection), and HBcrAg levels (using a Lumipulse G HBcrAg assay) were tested, and factors related to HBVr were analyzed. Results: Under NAs, the HBV DNA loads of 69 HBsAg+ lymphoma patients declined from 3.15 (2.13–4.73) lg IU/mL to 1.00 (1.00–1.75) lg IU/mL, and further declined to 1.00 (1.00–1.04) lg IU/mL at the end of a 24-month follow-up. The qAnti-HBc levels decreased gradually during chemotherapy in HBsAg+ lymphoma patients (F = 7.090, p = 0.009). The HBV RNA and HBcrAg levels remained stable. A multivariate analysis revealed that higher qAnti-HBc levels (1.97 ± 1.20 vs. 1.12 ± 0.84 lg IU/mL, OR = 6.369, [95% CI: 1.523–26.641], p = 0.011) and higher HBV RNA levels (1.00 ± 1.13 vs. 0.37 ± 0.80 lg copies/mL, OR = 3.299, [95% CI: 1.229–8.854], p = 0.018) were related to HBVr in HBsAg−/anti-HBc+ lymphoma patients. Conclusions: HBV DNA loads declined under NAs during chemotherapy in lymphoma patients. In HBsAg−/anti-HBc+ lymphoma patients, a higher level of baseline serum qAnti-HBc and HBV RNA levels can predict the likelihood of HBVr during chemotherapy.
Surgically Resected Cardiac Angiosarcoma: Survival Analysis from the National Cancer Database

Angiosarcoma is a rare type of soft-tissue sarcoma arising from endothelial cells. It is considered ‘high-grade’ by definition, reflecting its aggressive behavior. We sought to investigate the role of surgery in cardiac angiosarcoma, identify late mortality predictors, and identify interactions with other modalities in its treatment using a national dataset. The 2004–2017 National Cancer Database was reviewed for patients with primary cardiac angiosarcoma. Late mortality predictors were evaluated with Kaplan–Meier curves and Cox regression analysis. Surgery in primary cardiac angiosarcoma was performed in 130 patients (median age 50.5 years; female sex 36.9%). The median follow up was 72.02 months, with a median overall survival (OS) of 14.32 months. In patients treated with surgery in combination with other modalities compared with those treated with surgery alone, median OSs were 17.28 and 2.88 months, respectively (log-rank = 0.018). Older patients (age > 57 years) experienced lower OS compared to those with an age < 57 (log-rank = 0.012). This may be partially explained by the difference in treatment strategies among age groups: those with increasing age, less surgery (p = 0.037), and less chemotherapy (p < 0.001) were chosen. With multivariable Cox regression analysis, age and race other than white or black were identified to be significant independent predictors of late mortality. Cardiac angiosarcoma has poor overall survival, and our findings should further encourage the use of surgery in combination with other therapeutic modalities in treating such an aggressive disease whenever possible.
COACHING FOR START-UP MANAGERS IN IMPROVING TEAM INNOVATION

The development of digital technology and information has triggered companies to have new ways of running their business. Technological advances make business activities easier, more efficient and effective. Companies must have a strategy that is capable of innovation and transformation in running their business and developing their human resources. There are several industrial sectors affected by the Covid-19 pandemic, one of which is the tourism industry. The number of foreign tourist visits to Indonesia in January 2021 was recorded to have decreased by 89.05 percent compared to January 2020, namely from 1.29 million visits to 141.26 thousand visits. Therefore, companies need leaders who are able to provide encouragement and motivation so that their members can continue to innovate amidst difficult situations. With coaching, managers can also facilitate self-learning, personal growth and how to improve performance using several interventions.
Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.
Formulation of Multiple Kernel Learning Using Composite Architectures

Multiple Kernel Learning (MKL) algorithms deal with learning the optimal kernel from training data along with learning the function that generates the data. Generally in MKL, the optimal kernel is defined as a combination of kernels under consideration (which are usually termed as base kernels). There are different approaches to learning the optimal combination of kernels. The main objective of the thesis is to develop models for finding the optimal combination of kernels suitable for different types of problems. This thesis describes the formulation of optimal kernel using four different approaches. The first approach is based on the selection of kernel suitable for the features (attributes) of the data. The features having similar characteristics are clustered together and a suitable kernel is found for each cluster. The optimal kernel is defined as a linear combination of the kernels defined over the cluster subspaces. We formulated a methodology for clustering the features and applying a separate kernel over each cluster. The second approach is based on Kumar et. al. (2012) [1] in which the problem of learning the kernel for a binary classification is designed as another binary classification problem and hence the data modeling problem involves the computation of two decision boundaries of which one is related with that of MKL and the other with that of input data. They used two different cost functions for finding the optimal function related with kernel learning and the classification task. We modified this work in such a way that in our approach, the optimal functions are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS). This global RKHS is defined as the direct sum of the RKHSs corresponding to the decision boundaries of MKL and that of input data. Hence the optimal function can be represented as the direct sum of the decision boundaries under consideration. Kumar et. al. (2012) framework has been extended to regression problems also. We also developed a nonlinear formulation, in which the optimal kernel is represented as a nonlinear combination of kernels. Such a combination of kernels results in an indefinite symmetric matrix and hence makes use of the concepts of Krein Space for finding the optimal functions. Finally, we formulated the MKL using composite kernel functions (MKLCKF). In this MKLCKF the optimal kernel is represented as a linear combination of composite kernel