Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Therapeutic Advances in Pituitary Carcinoma

Pituitary carcinoma (PC) is a rare, aggressive malignancy that comprises 0.1–0.2% of all pituitary tumors. PC is defined anatomically as a pituitary tumor that metastasizes outside the primary intrasellar location as noncontiguous lesions in the central nervous system or as metastases to other organs. Similar to pituitary adenoma, PC originates from various cell types of the pituitary gland and can be functioning or nonfunctioning, with the former constituting the majority of the cases. Compression of intricate skull-based structures, excessive hormonal secretion, impaired pituitary function from therapy, and systemic metastases lead to debilitating symptoms and a poor survival outcome in most cases. PC frequently recurs despite multimodality treatments, including surgical resection, radiotherapy, and biochemical and cytotoxic treatments. There is an unmet need to better understand the pathogenesis and molecular characterization of PC to improve therapeutic strategies. As our understanding of the role of signaling pathways in the tumorigenesis of and malignant transformation of PC evolves, efforts have focused on targeted therapy. In addition, recent advances in the use of immune checkpoint inhibitors to treat various solid cancers have led to an interest in exploring the role of immunotherapy for the treatment of aggressive refractory pituitary tumors. Here, we review our current understanding of the pathogenesis, molecular characterization, and treatment of PC. Particular attention is given to emerging treatment options, including targeted therapy, immunotherapy, and peptide receptor radionuclide therapy.
The Molecular Microscope Diagnostic System: Assessment of Rejection and Injury in Heart Transplant Biopsies.

This review describes the development of the Molecular Microscope Diagnostic System (MMDx) for heart transplant endomyocardial biopsies (EMBs). MMDx-Heart uses microarrays to measure biopsy-based gene expression and ensembles of machine learning algorithms to interpret the results and compare each new biopsy to a large reference set of earlier biopsies. MMDx assesses T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), recent parenchymal injury, and atrophy-fibrosis, continually "learning" from new biopsies. Rejection-associated transcripts mapped in kidney transplants and experimental systems were used to identify TCMR, AMR, and recent injury-induced inflammation. Rejection and injury emerged as gradients of intensity, rather than binary classes. AMR was one-third donor-specific antibody (DSA)-negative, and many EMBs first considered to have no rejection displayed minor AMR-like changes, with increased probability of DSA positivity and subtle inflammation. Rejection-associated transcript-based algorithms now classify EMBs as "Normal," "Minor AMR changes," "AMR," "possible AMR," "TCMR," "possible TCMR," and "recent injury. " Additionally, MMDx uses injury-associated transcript sets to assess the degree of parenchymal injury and atrophy-fibrosis in every biopsy and study the effect of rejection on the parenchyma. TCMR directly injures the parenchyma whereas AMR usually induces microcirculation stress but relatively little initial parenchymal damage, although slowly inducing parenchymal atrophy-fibrosis. Function (left ventricular ejection fraction) and short-term risk of failure are strongly determined by parenchymal injury. These discoveries can guide molecular diagnostic applications, either as a central MMDx system or adapted to other platforms. MMDx can also help calibrate noninvasive blood-based biomarkers to avoid unnecessary biopsies and monitor response to therapy.
Regenerative Circulatory Multiple-Beam Interferometry for the Study of Light-Propagation Effects

Novel circulatory and regenerative multiple-beam interferometry methods making use of optical maser oscillations of extreme monochromaticity based on stimulated emission present interesting possibilities for investigating with high-precision various effects of motion and of fields on the propagation of light. These methods inherently permit increasing considerably the accuracy of the historical relativistic experiments of Michelson, Sagnac, and others, and have also potential applications to studies of other radiation propagation effects in magnetic, electric, and gravitational fields, and in moving refractive media, and to the modulation of coherent radiation. These propagation effects can produce frequency splits of the optical-maser oscillations, and the frequencies of the resulting optical beats are a measure for those effects; frequency measurements can be accomplished with high accuracy by using primary frequency standards, e.g., atomic clocks. Contrary to the many proposed applications of optical masers which are directed towards utilizing the high-spatial coherence of the wave fields, the present subject is thus primarily concerned with the narrow-frequency bandwidths possible with these novel radiation sources. The high accuracies possible with the new interferometric methods described should lead to quantitative results which up to now were either impossible to achieve, or only by experiments on very large scales.
The variable-interval arithmetic coding using asymptotic deterministic randomness for data compression and encryption

Different from the traditional way which compresses the data first and then encrypts the compressed bit-stream later, a novel compression and encryption scheme using variable model arithmetic coding and coupled chaotic system can encrypt and compress the input plaintext synchronously. However, there will be a compromise between the amount of compression achieved and the amount of security incorporated, because the compression efficiency is determined by the key bit-stream. In this paper, an improved scheme using variable-interval arithmetic coding and asymptotic deterministic randomness has been proposed. The improved scheme is secure because the key bit-stream generated by the asymptotic deterministic randomness can resist previous attacks against chaotic encryption. In addition, the compression efficiency will not change with the key bit-stream, because the statistical model will no longer be changed. The results show that the new scheme can achieve high security and compression efficiency.
An impedance-matching technique for increasing the bandwidth of microstrip antennas

The nature of the inherent narrow bandwidth of conventional microstrip patch antennas is considered. It is observed that, except for single-feed circularly polarized elements, their bandwidth is limited only by the resonant behavior of the input impedance and not by radiation pattern or gain variations, which usually are negligible over a moderate 10 to 20% bandwidth. Therefore, broadband impedance matching is proposed as a natural to increase the bandwidth. The maximum obtainable bandwidth is calculated using Fano's broadband matching theory. It is found that by using an optimally designed impedance-matching network, the bandwidth can be increased by a factor of at least 3.9, the exact value depending on the degree of matching required. A transmission-line prototype for a proper matching network is developed. The translation of this prototype network into a practical structure (e.g. a microstrip or stripline circuit) is considered. Practical design examples and experimental results which clearly show the validity of the technique are given. >
IAES International Journal of Artificial Intelligence (IJ-AI)

Rico Kurniawan, Budi Utomo, Kemal N. Siregar, Kalamullah Ramli, Besral, Ruddy J. Suhatril, Okky Assetya Pratiwi Department of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Depok, Indonesia Faculty of Computer Science and Information Technology, Gunadarma University, Depok, Indonesia Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia
Conditional-Extreme-Point Based Fault-Tolerant Predictive Control of Five-Phase PMSM With Low Switching Frequency

The virtual voltage vector based fault-tolerant control has been widely concerned, but suffering from the increased switching frequency and the reduced bus voltage utilization at the faulty operation. Also, the harmonic space is uncontrollable. This article proposes a conditional-extreme-point based fault-tolerant model predictive control for a five-phase permanent magnet synchronous motor. The conditional extreme point is used to predict the optimal reference voltage vector. First, to suppress the harmonics, dual control sets are established without the sacrifice of bus voltage utilization. Then, the unconditional extreme point and y-axis voltage are predicted to select the appropriate control set, and two adjacent vectors are preselected for generating the optimal reference voltage vector. Subsequently, the conditional extreme point of the cost function is calculated to obtain the optimal reference voltage vector. Due to the unemployment of the null vector, the switching frequency is greatly reduced. Finally, the experimental results are presented to verify the proposed control scheme.
Cauldrons in the Cosmos: Nuclear Astrophysics

Nuclear astrophysics is, in essence, a science that attempts to understand and explain the physical universe beyond the Earth by studying its smallest particles. "Cauldrons in the Cosmos", by Claus E. Rolfs and William S. Rodney, serves as a basic introduction to these endeavors. From the major discoveries in the field to a discussion of the makeup of stars to an explanation of standard lab techniques, this text provides students and scientists alike a thorough and fascinating survey of the accomplishments, goals, and methods of nuclear astrophysics. A classic in its field, "Cauldrons in the Cosmos" will surely remain an important reference in nuclear astrophysics for years to come.
Analysis of The Diagnostic Parameters of Heart Diseases and Prediction of Heart Attacks

Medical data is made up of a huge number of heterogeneous variables gathered from various sources all of which provide a different perspective on a patient's condition. Machine Learning proves to be very effective method for the prediction of unstructured data. Algorithms such as SVC, K Nearest Neighbor, Random Forest Classifier, Naïve Bayes etc. can be used for the early detection for the disease. Data mining technique are used to gather data from health care databases and are used for making clinical decision of the disease at the preliminary level without the intervention of the medical experts. [1] Using the state-of-the-art wearable electronic equipment can also be used for collecting continuous data from the patients. The classification techniques in the area of Machine Learning in the medical field, with the goal to find similar patterns, thereby producing vital predictions, and being useful in early diagnosis of the disease is the focus of this research paper. The algorithm which fits the data and predicts with more accuracy is analyzed. The novelty in this research is predicting if a patient already with a heart disease will get a heart attack or not. Whereas, most of the researchers are interested only in predicting the presence of a heart disease. This paper focuses on the prediction of heart attacks in patients having a heart disease.
Autologous Platelet-Rich Plasma (PRP) for Treating Androgenetic Alopecia: A Novel Treatment Protocol Standardized on 2 Cases

Platelet-rich plasma (PRP) treatment has emerged in recent years as a valuable, effective, and affordable treatment for androgenetic alopecia. Androgenetic alopecia is the most common type of alopecia, affecting both men and women, and is characterized by diminished hair follicles mainly pronounced in the frontal region and vertex. A considerable variety of PRP treatment regimens have been described so far, but there is no consensus on the standardization of PRP preparation or administration protocol. Our study was conducted on two patients to test the efficacy of a new PRP application protocol of only two treatments by using a combination of a PRP collecting device and a conventional kit. Efficacy of treatment was assessed after a 6-month follow-up by artificial intelligence (AI)-driven software on microscopic images of treated regions. An average number of hairs, cumulative hair thickness, and the number of follicular units increased in the vertex region of both patients by 30/59%, 35/53%, and 14/48%, respectively. The novel treatment regimen showed significant effectiveness in only six months.
Using Digital Twin in a Shipbuilding Project

Three-dimensional modelling software tools enable the creation of a digital replica of the product—“Digital Twin”—a representative of “Virtual Reality” as one of the prominent trends of Industry 4.0. The development of the Digital Twin can start simultaneously with the development of the product, primarily for the purpose of selecting optimal technical and technological solutions prior to and during physical construction, and, ultimately, with the intention of managing the entire product life cycle. The Digital Twin, as one of the key technological achievements in the implementation of the business system transformation from traditional to smart, should also be recognized as the cornerstone of the “Shipyard 4.0” model, i.e., its “Cyber-Physical Space. ” This paper is based on statistical and empirical data of the observed shipyard with the aim to represent the significance of the Digital Twin ship in preserving and improving the competitiveness of the shipbuilding industry. Namely, with the emphasis this article places on the contribution of “advanced outfitting” in achieving savings in the shipbuilding process as well as its role in attaining high standards of environmental protection and workplace safety, the importance of its further improvement is an obvious conclusion—with Digital Twin being one of the recognized tools for this purpose.
Inverse Design of Energy‐Absorbing Metamaterials by Topology Optimization

Compared with the forward design method through the control of geometric parameters and material types, the inverse design method based on the target stress‐strain curve is helpful for the discovery of new structures. This study proposes an optimization strategy for mechanical metamaterials based on a genetic algorithm and establishes a topology optimization method for energy‐absorbing structures with the desired stress‐strain curves. A series of structural mutation algorithms and design‐domain‐independent mesh generation method are developed to improve the efficiency of finite element analysis and optimization iteration. The algorithm realizes the design of ideal energy‐absorbing structures, which are verified by additive manufacturing and experimental characterization. The error between the stress‐strain curve of the designed structure and the target curve is less than 5%, and the densification strain reaches 0.6. Furthermore, special attention is paid to passive pedestrian protection and occupant protection, and a reasonable solution is given through the design of a multiplatform energy‐absorbing structure. The proposed topology optimization framework provides a new solution path for the elastic‐plastic large deformation problem that is unable to be resolved by using classical gradient algorithms or genetic algorithms, and simplifies the design process of energy‐absorbing mechanical metamaterials.
Effect of Process Parameters on Stress Field of Laser Additive Manufacturing

In order to optimize the additive manufacturing process and find the process parameters affecting the mechanical properties of the parts, an additive manufacturing simulation model of Ti-6Al-4V titanium alloy was established, and the effects of ambient temperature, substrate thickness and wire temperature on the stress field and residual stress field were analyzed. The results show that the ambient temperature is inversely proportional to the residual stress of the cladding layer, while the substrate thickness and wire temperature are positively correlated to the residual stress of the cladding layer. When the ambient temperature increases from 0 °C to 600 °C, the maximum residual stress decreases by 36.0%, the maximum residual stress increases by 10.0% when the substrate thickness increases from 25 mm to 55 mm and the maximum residual stress increases by 7.48% when the temperature increases from 0 °C to 600 °C. The influence of the three parameters on the maximum residual stress is as follows: ambient temperature > substrate thickness > wire temperature. The research results can provide reference for stress control during actual manufacturing.
Black holes in X-ray binaries: Marginal existence and rotation reversals of accretion disks

We adopt the hypothesis that black holes in X-ray binaries are accreting gas from an accretion disk fed by a stellar wind emanating from the primary. The formation of such a disk is found to be marginal in the case of a steady, spherically symmetric stellar wind and may be controlled by random fluctuations in the wind parameters. Such fluctuations may cause reversals in the rotation direction of the disk. Reversals in a disk around a rapidly rotating black hole lead to two distinct intensity states and may be associated with the two states observed for Cyg X-1. (AIP)
Comparative Analysis of the Liver Transcriptome of Beijing You Chickens and Guang Ming Broilers under Salmonella enterica Serovar Typhimurium Infection

Salmonella enterica serovar Typhimurium (ST) is a food-borne pathogen that can infect animals and humans. It is currently the most common bacterial pathogen that negatively affects the poultry industry. Although different chicken breeds have been observed to exhibit diverse resistance to ST infection, the underlying genetic mechanisms remain unclear and the genes involved in this differential disease resistance need to be identified. To overcome this knowledge gap, we used a liver transcriptome analysis to screen differentially expressed genes (DEGs) in two different chicken breeds (local Beijing You (BY) and commercial Guang Ming No. 2 broiler line B (GM)) before and after ST infection. We also performed weighted gene co-expression network analysis (WGCNA) to detect hub genes, and employed selection signal analysis of candidate genes. Three promising genes (EGR1, JUN and FOS) were eventually identified, and were significantly and differentially expressed in the same breed under different conditions, and in the two breeds after ST infection. Hub genes, such as PPFIA4 and ZNF395, were identified using WGCNA, and were associated with the ratio of heterophils to lymphocytes (H/L), an indicator of disease resistance. the present study identified several genes and pathways associated with resistance to ST infection, and found that BY had greater resistance to ST infection than GM. The results obtained provide valuable resources for investigating the mechanisms of resistance to ST infection in different chicken breeds.
Bilinear Faster RCNN with ELA for Image Tampering Detection

With technological advances leading to an increase in mechanisms of image tampering, our fraud detection methods must continue to be upgraded to match their sophistication. One problem with current methods is that they require prior knowledge of the method of forgery in order to determine which features to extract from the image to localize the region of interest. When a machine learning algorithm is used to learn different types tampering from a large set of various image types, with a big enough database we can easily classify which images are tampered (by training on the entire image feature map for each image), but we still are left with the question of which features to train on, and how to localize the manipulation. To solve this, object detection networks such as Faster RCNN, which combine an RPN (Region Proposal Network) with a CNN have recently been adapted to fraud detection by utilizing their ability to propose bounding boxes for objects of interest to localize the tampering artifacts. In this work, an existing bilinear Faster RCNN model that was developed will be modified with the second stream having an input of the ELA (Error Level Analysis) JPEG compression level mask.
Screening, Diagnosis, and Treatment of Pediatric Ocular Diseases

Vision is an important aspect of a child’s quality of life and intellectual, social, and emotional development. Disruptions to vision during infancy and early childhood can cause lifelong vision impairment or blindness. However, early identification and treatment of eye disease can prevent loss of sight and its consequent long-term effects. Therefore, screening guidelines exist to guide physicians in detecting the most common threats to sight in the different stages of infancy and childhood. This review describes common causes of pediatric vision impairment, the recommended screening guidelines for diagnosing them, and current treatment modalities.
Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices

Automatic gym activity recognition on energy-and resource-constrained wearable devices removes the human-interaction requirement during intense gym sessions - like soft-touch tapping and swiping. This work presents a tiny and highly accurate residual convolutional neural network that runs in milliwatt microcontrollers for automatic workouts classification. We evaluated the inference performance of the deep model with quantization on three resource-constrained devices: two microcontrollers with ARM-Cortex M4 and M7 core from ST Microelectronics, and a GAP8 system on chip, which is an open-sourced, multi-core RISC-V computing platform from Green-Waves Technologies. Experimental results show an accuracy of up to 90.4% for eleven workouts recognition with full precision inference. The paper also presents the trade-off performance of the resource-constrained system. While keeping the recognition accuracy (88.1%) with minimal loss, each inference takes only 3.2 ms on GAP8, benefiting from the 8 RISC-V cluster cores. We measured that it features an execution time that is 18.9x and 6.5x faster than the Cortex-M4 and Cortex-M7 cores, showing the feasibility of real-time on-board workouts recognition based on the described data set with 20 Hz sampling rate. The energy consumed for each inference on GAP8 is 0.41 mJ compared to 5.17 mJ on Cortex-M4 and 8.07 mJ on Cortex-M7 with the maximum clock. It can lead to longer battery life when the system is battery-operated. We also introduced an open data set composed of fifty sessions of eleven gym workouts collected from ten subjects that is publicly available.