Miracle-dz/UniFAR-FTUs huggingface dataset:
(2020). Resolution Enhancement of VCO-based ADCs by Passive Interpolation and Phase Injection. 2019 XXXIV Conference on Design of Circuits and Integrated Systems (DCIS), 20-22

—This work describes a simple way to improve the resolution of low-pass voltage-controlled-oscillators based analog-to-digital converters (VCO-based ADCs) implemented with ring-oscillators. We propose to insert a passive resistive network into the differential delay cells of the oscillator to get additional interpolated phases. These interpolated phases are then injected to other similar oscillators. By increasing the number of phases coming from all the oscillators, the effective gain of the system is higher resulting in a resolution enhancement of the converter. To validate the idea, a prototype of an open-loop VCO-based ADC was built in VerilogA language with the ring-oscillators designed making use of a 65-nm CMOS process. The results of transient simulations were compared to the results of a behavioral ideal model of the system built in MATLAB to verify the correct performance. As expected, the SNR was improved in concordance with the expected increase in the number of phases. Finally, it was checked that the proposed circuit used to extract and inject the interpolated phases did not penalize the total power consumption. The proposed circuit structure is particularly suitable for high-bandwidth applications, where the oversampling ratio (OSR) is strongly restricted and the gain cannot be increased to the full-scale range due to non-linearity issues. Due to the highly digital nature of the VCO-based ADC structures, this solution may be of special interest to be implemented in new deep-submicron CMOS processes.
Oral manifestations and blood proﬁle in patients with thalassemia trait

Background/Purpose: Patients with thalassemia trait (TT) may have anemia. This study evaluated whether TT patients had speciﬁc oral manifestations and a particular blood proﬁle compared with normal individuals. Methods: The oral manifestations and mean red blood cell count, corpuscular cell volume, red blood cell distribution width, Mentzer index, and Green and King index as well as bloodconcentra-tions of hemoglobin, iron, total iron binding capacity, vitamin B12, folic acid, and homocysteine in 65 TT patients and in 130 age-and sex-matched healthy controls were measured and compared. Results: TT patients had signiﬁcantly higher frequencies of all oral manifestations than healthy controls ( p < 0.001 for all), in which burning sensation of oral mucosa (90.8%), lingual varicosity (90.8%), dry mouth (72.3%), atrophic glossitis (32.3%), and numbness of the oral mucosa (30.8%) were the ﬁve leading oral manifestations for TT patients. Moreover, TT patients had signiﬁcantly lower mean hemoglobin level, corpuscular cell volume, Mentzer index, and Green and King index ( p < 0.001 for all) as well
Interfacial adaptation and presence of gaps of NeoMTA Plus, BioRoot RCS and MTA in root-end cavities: a micro-CT study

Aim: This study aimed to use the micro-computed tomography to evaluate the interfacial adaptation and the presence of gaps of NeoMTA Plus, BioRoot RCS, and MTA in the root-end cavities. Methodology: Thirty standardized bovine roots measuring 15 mm in length were selected. Chemical-mechanical preparation was performed up to instrument #80 and obturation with the cold lateral compaction technique with cement based on zinc oxide and eugenol. The roots were kept at 37º C for seven days. Afterward, apicectomy of the apical 3mm and a root-end ﬁlling cavity was performed at 3mm depth. Micro-computed tomography (micro-CT) was performed to measure the volume of the retroactivity. The roots were divided by stratiﬁed randomization into three groups according to the retro-end ﬁlling material: NeoMTA Plus, BioRoot RCS, and MTA. A new micro-CT was performed to assess the presence of voids in the root-end ﬁlling material and between it and the canal wall. One-way ANOVA and Tukey tests were performed using the BioEstat 4.0 program. Results: There was no diﬀerence in the initial volume values of the root-end cavities (P > 0.05). After the insertion of root-end ﬁlling materials, the most signiﬁcant volumes of voids were observed in the NeoMTA Plus group (P < 0.05), with no diﬀerence for the BioRoot RCS and MTA Angelus groups (P > 0.05). Conclusion: Micro-computed tomography showed that MTA and BioRoot RCS have better interfacial adaptation and presented fewer number of gaps than NeoMTA Plus when used as root-end ﬁlling materials.
GENERALIZED METHOD OF SITUATIONAL DECOMPOSITION AND MULTIFUNCTIONAL INTELLIGENT CONTROL SYSTEMS

A fundamentally new type of automated optimal control systems, characterized as multifunctional intelligent systems, is considered. Their distinguishing feature is the ability to implement various control tasks depending on the circumstances that determine the required behavior of the control object. Such systems have a high degree of autonomy, including the ability to operate in a fully automatic mode, which increases their reliability and survivability. The proposed generalized method of situational decomposition forms the mathematical basis of the considered control principle. Its essence lies in the fact that in order to develop control decisions in the control system, a finite set of different tasks adapted to certain situations is used. In the process of functioning, the control system independently evaluates the situations that arise and sets the necessary control task for execution. The proposed developments seem to be extremely important and interesting from the point of view of building self-tuning control systems with increased autonomy. Such systems are relevant for complex technological processes
REAL AND SIMULATED MASKED FACE RECOGNITION WITH A PRE-TRAINED MODEL

Facial recognition has currently become indispensable owing to the efficacy of precise identification verification. Because of the distinctiveness of human biometrics, face recognition enables humans to communicate with technology while maintaining their privacy. Advancements in pre-trained models such as FaceNet have enabled improvement in identification accuracy in face recognition technology. Response to the Covid-19 pandemic has led to the replacement of conventional face recognition with masked face recognition. This change has encouraged the use of collaboration to resolve the related issues, which has resulted in the development of algorithms for face occlusion, collection of data on masked and unmasked faces and improvement of pre-trained models. Current research has utilised custom datasets or a specially produced dataset for masked face recognition. To increase the amount of data available for modelling, some studies have implemented mask simulation in facial photos. In this study, FaceNet is evaluated on two datasets: the real-masked face recognition dataset and the simulated masked face recognition dataset. Particularly, we highlight the performance of FaceNet on simulated masked faces. Using simulated masks achieved 67% accuracy, while the use of real masks achieved 84.3%. Results from the two datasets are compared with each other and with other studies using different pre-trained models with similar datasets. This study reveals that simulated masked faces perform less effectively than real masked faces, as corroborated by various other studies.
A displaced lower mantle source of the Hainan plume in South China revealed by receiver function imaging of the CEArray

We analyzed 49,592 teleseismic receiver functions recorded by 278 CEArray stations to image the mantle transition zone (MTZ) beneath the South China Block to understand origins of deep velocity anomalies and their potential links to subduction and intraplate volcanism. We employed a fast-marching method and a high-resolution 3-D velocity model (FWEA18) derived from full waveform inversion in computing P-to-S conversion times to better image the 410-km and 660-km discontinuities. Our results indicate that the common-conversion-point stacking of receiver functions using 3-D conversion times yielded better migration images of the two discontinuities. The images revealed a slightly depressed 410-km with a few small uplifted patches, and showed that the 660-km beneath the western Yangtze Craton is depressed by 10-25 km, which is likely caused by the stagnant Paleo-Paciﬁc slab. The 660-km beneath the southern Cathaysia Block has a 5-15 km high plateau with a topographic low at its central part. The lateral dimension of the topographic low is ˜150 km and located beneath the central Pearl River Mount Basin near Hong Kong. We speculate the topographic low occurs within the Hainan plume with a temperature excess of ˜300-400 K and is caused by the garnet phase transition. The displaced deep plume enters the MTZ and spreads nearly horizontally at the base. The plume evolves into two channels with a minor one toward the northeast and a major one toward the southwest, which keep moving upward to the 410-km. The southwest channel is likely the source that feeds the Hainan volcanoes.
Object Tracking with an Event Camera Object Tracking with an Event Camera

Abstract In this project, an event-based camera was used to develop a functional proof of concept for a procedural 3-D cube tracking system. Hough space shape detection was used to track the position of cube edges in the image space, and an optimization-based algorithm was used in conjunction for finding the pose of the cube. Additionally, a ghosting effect was seen following the trailing edge of events. A sensor model was developed for simulating and investigating this phenomenon.
MIT Open Access Articles MicroRNA miR-125b causes leukemia

MicroRNA miR-125b has been implicated in several kinds of leukemia. The chromosomal translocation t(2;11)(p21;q23) found in patients with myelodysplasia and acute myeloid leukemia leads to an overexpression of miR-125b of up to 90-fold normal. Moreover, miR-125b is also up-regulated in patients with B-cell acute lymphoblastic leukemia carrying the t(11;14)(q24;q32) translocation. To decipher the presumed oncogenic mechanism of miR-125b, we used transplantation experiments in mice. All mice transplanted with fetal liver cells ectopically expressing miR-125b showed an increase inwhitebloodcellcount,inparticularinneutrophilsand monocytes, associated with a macrocytic anemia. Among these mice, half died of B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia, or a myeloproliferative neoplasm, suggesting an important role for miR-125b in early hematopoiesis. Furthermore, coexpression of miR-125b and the BCR-ABL fusion gene in transplanted cells accelerated the development of leukemia in mice, compared with control mice expressing only BCR-ABL, suggesting that miR-125b confers a proliferative advantage to the leukemic cells. Thus, we show that overexpression of miR-125b is suf ﬁ cient both to shorten the latency of BCR-ABL – induced leukemia and to independently induce leukemia in a mouse model.
OPTIMAL DEEP FEATURE MODEL SELECTION WITH WEIGHTED MULTI-SIMILARITY-BASED CONTENT-BASED IMAGE RETRIEVAL FRAMEWORK

The scheme of Content-Based Image Retrieval (CBIR) is developed for the process of retrieving of images on the basis of the visual elements defining the image content. Even though, it has certain complications of huge calculation problems such as scoring of the image similarity and feature extraction. The conventional feature extraction models only aim toward the high or low-level features and utilize few manmade features to minimize these gaps. It is required to establish a framework for feature extraction to minimize the gap without utilizing the manmade features from joining low and high-level features. The deep learning is considered as a very powerful tool for feature indication that can represent high and low-level information entirely. In this paper, new optimal deep learning model-based feature extractions for CBIR model are performed for retrieving the accurate images. In the training phase, diverse images are gathered from the standard database, which is used into Optimal Deep Feature Selection Model (ODFSM)by the deep learning models with VGG16, VGG19, Inception, Xception, Resnet50, Resnet101, Resnet 152, and Densenet for extracting the features. Here, the optimization in deep learning models takes place with the help of hybrid optimization algorithm known as Probability-based Coyote-Forest Optimization Algorithm (P-CFOA) for getting the efficient features. In the testing phase, the query images are given into optimal deep feature selection model for extracting the features of the query image. Then, the weighted multi-similarity is performed between the extracted features of database images and query image, in which the weights are optimized using the same P-CFOA. The database images with less weighted multi-similarity when correlated with extracted features of query image are retrieved finally. The simulation analyses are made to demonstrate the better efficiency of image retrieval through the optimal deep feature extractors.
DESCRIPTIVE EPIDEMIOLOGICAL AND CLINICOPATHOLOGICAL STUDY OF GLIOMAS IN A TERTIARY CARE HOSPITAL

Background: Tumours of the central nervous system (CNS) are difficult to treat, and the survival time for patients with high-grade tumours is short. This study aims to conduct a clinicopathological investigation of gliomas to understand their characteristics, prevalence, and clinical manifestations. Materials and Methods: This retrospective descriptive clinicopathological study was conducted at the Department of Neuropathology, Madras Medical College and Rajiv Gandhi Government General Hospital, Chennai, from 2008 to 2013. Seven hundred fourteen cases of glioma specimens were received during this study period. Result: Gliomas were highest in the age group of 51 to 60 years, and Glioblastoma multiforme was the most common form of glioma, with 213 cases (29.8%). Grade IV tumours were common among males, while grade II tumours were more common in women. The cerebrum was the most common location for glioma involving the frontal lobe (22.41%). HPE evaluation revealed glioblastoma multiforme as the most common, followed by diffuse astrocytoma in 176 cases (24.6%). Pilocytic astrocytoma was found to commonly affect the age group of 0 to 20 years with a relative percentage of 44.5%. Diffuse astrocytoma was most common in the age group 21 to 40 years. GBM was seen most commonly in the age group of 41 to 60. Conclusion: The data in this study represents the clinical manifestations and prevalence of gliomas in Chennai, including all age groups. The evidence generated from this study can be beneficial in making informed decisions for funding further research needed for this tumour.
A MACHINE LEARNING-BASED CLASSIFIER FOR ANTICIPATING RISK FACTORS ASSOCIATED WITH CERVICAL CANCER

Cervical cancer ranks as the second most prevalent form of cancer among women, but it is also highly preventable. Numerous studies have underscored the widespread lack of knowledge surrounding cervical cancer and its prevention. In developing countries, medical students represent the future healthcare professionals who can play a pivotal role in increasing public awareness and assessing knowledge about symptoms and risk factors. The present research focused on investigating the causes of cervical cancer and preventive methods for women who have already been diagnosed with this life-threatening disease. Each year, a significant number of women receive a diagnosis of cervical cancer, resulting in a substantial loss of lives worldwide. The Human papillomavirus (HPV) has been recognized as a significant risk factor for cervical cancer, and fortunately, effective prevention measures are available to a large extent. However, most cervical cancer cases are detected in economically disadvantaged countries where organized HPV screening or vaccination programs are lacking. High-income countries that have implemented comprehensive screening programs have successfully reduced the prevalence and mortality rates of cervical cancer by 50% over the past three decades. Fertility-preserving surgical approaches are now considered the standard treatment for women diagnosed with initial-stage, mild-risk cervical cancer. As cervical cancer remains a significant health concern for women, implementing a comprehensive strategy for prevention and control is paramount in our efforts to eliminate this disease. This research aims to introduce an ensemble-based classifier that enhances the early detection and prediction of cervical cancer. Through comparative analysis with other base classifiers, our proposed classifier demonstrates superior accuracy and performance.
Group Therapy with Peer Support Provider Participation in an Acute Psychiatric Ward: 1-Year Analysis

(1) Background: Group psychotherapy improves therapeutic process, fosters identification with others, and increases illness awareness; (2) Methods: In 40 weekly group sessions held in an acute psychiatric ward during one year, we retrospectively evaluated the inpatients’ participation and the demographic and clinical variables of the individuals hospitalized in the ward, the group type according to Bion’s assumptions, the main narrative themes expressed, and the mentalization processes by using the Mentalization-Based Therapy-Group Adherence and Quality Scale (MBT-G-AQS); (3) Results: The “working” group was the prevailing one, and the most represented narrative theme was “treatment programs”; statistically significant correlations were found between the group types according to Bion’s assumptions and the main narrative themes (Fisher’s exact, p = 0.007); at our multivariate linear regression, the MBT-G-AQS overall occurrence score (dependent variable) was positively correlated with the number of group participants (coef. = 14.87; p = 0.011) and negatively with the number of participants speaking in groups (coef. = −16.87, p = 0.025); (4) Conclusion: our study suggests that the group shows consistent defense mechanisms, relationships, mentalization, and narrative themes, which can also maintain a therapeutic function in an acute ward.
on ‘ Nutrition dynamics in Africa: opportunities and challenges for meeting the sustainable development goals ’ Using stable isotope techniques in nutrition assessments and tracking of global targets post-2015

Stable isotopes are non-radioactive, safe and are applied for various purposes in human health assessment in trace amounts that minimally disturb normal physiology. The International Atomic Energy Agency supports the use of stable isotope techniques to design and evaluate interventions addressing malnutrition in all its forms with focus on infant and young child feeding; maternal and adolescent nutrition; diet quality; prevention and control of non-communicable diseases; healthy ageing and gut function. These techniques can be used to objectively measure: (1) amount of human milk consumed and whether an infant is exclusively breastfed; (2) body composition in the context of re-feeding programmes for moderate and severe acute malnutrition and as an indicator of the risk for obesity; (3) bio-availability and bioconversion of pro-vitamin A and vitamin A body stores following vitamin A intervention programmes; (4) absorption and retention of iron, zinc and protein; (5) total energy expenditure for validation of physical activity measurement and dietary assessment tools and (6) diagnosis of Helicobacter pylori . Stable isotope techniques will be invaluable in the tracking of global targets on exclusive breast-feeding childhood obesity and anaemia among women. Efforts are underway to make nuclear techniques more affordable, ﬁ eld-friendly and less invasive, and to develop less sophisticated but precise equipment. Advocacy for the wide adoption of the techniques is needed.
Pulmonary parenchymal segmentation based on CT images of lungs in a highland population

： In response to the more complex characterization of lung CT image data in the highland population, the problem of left and right lung adhesions, gross contours and blurred borders that are very likely to arise during lung parenchymal segmentation, we propose a new network structure based on U-Net for lung parenchyma segmentation in highland population. First, we introduce residual block, which replaced ordinary convolution with residual convolution in the encoding stage to improve the speed of network convergence and accuracy; Then, the Augmented Attention Module (AAM) is introduced in the skip connection part to enhance the feature of the lung parenchyma contour information; Finally, considering the processing efficiency of the network, we reduce the depth of the network model to 4 layers. After that, we verify the effectiveness of our method on the public dataset LUNA16, and the segmentation results show that the selected evaluation indicators are improved to varying degrees. This shows that our proposed method has a good effect on the segmentation of lung parenchyma. Finally, applying our method to lung CT image segmentation in a highland population gives some advantages over other existing methods.
Identiﬁcation of presented SARS-CoV-2 HLA class I and HLA class II peptides using HLA peptidomics

SUMMARY The human leukocyte antigen (HLA)-bound viral antigens serve as an immunological signature that can be selectively recognized by T cells. As viruses evolve by acquiring mutations, it is essential to identify a range of presented viral antigens. Using HLA peptidomics, we are able to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived peptides presented by highly prevalent HLA class I (HLA-I) molecules by using infected cells as well as overexpression of SARS-CoV-2 genes. We ﬁnd 26 HLA-I peptides and 36 HLA class II (HLA-II) peptides. Among the identiﬁed peptides, some are shared between different cells and some are derived from out-of-frame open reading frames (ORFs). Seven of these peptides were previously shown to be immunogenic, and we identify two additional immunoreactive peptides by using HLA multimer staining. These results may aid the development of the next generation of SARS-CoV-2 vaccines based on presented viral-speciﬁc antigens that span several of the viral genes.