Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Precoder and Combiner Design for MMSE Distributed Beamforming with Per-Antenna Power Constraints

We consider minimum mean square error (MMSE) joint precoder and combiner design for single and multi carrier distributed beam forming systems with nonuniform perantenna transmit power constraints. We show that, similar to the maximum-gain problem, an iterative Gauss-Seidel algorithm can be used for minimizing MSE which alternately optimizes the transmitter and receiver coefficients. In a single carrier system the optimum transmit coefficients are obtained by a simple projection of the effective MISO channel. In the multicarrier case with a sum-MSE objective, the Gauss-Seidel approach is once again applicable, but the transmit coefficients must be found by solving a quadratically constrained quadratic problem for which we apply a dual gradient algorithm. A numerical example is presented which shows improvement of 0.7 dB in carrier signal-to-noise ratio (SNR) relative to a projected eigenvector method for a multicarrier DBF system with Rayleigh-faded multipath channels.
Geo-Object-Based Soil Organic Matter Mapping Using Machine Learning Algorithms With Multi-Source Geo-Spatial Data

Soil is a complicated historical natural continuum that presents gradual changes in its properties and geographic area. Conventional soil survey and cartography methods on a macroscopic scale based on grids with a coarse resolution are inadequate for the rapid development of precision agriculture. The demand for soil mapping content and accuracy has increased as more convenient methods of acquiring multi-source geo-spatial data have been developed, and such data are commonly employed to extract basic mapping units and environmental variables in related algorithms. We employ geo-objects as basic units of soil property mapping, which are extracted from high-resolution remote sensing images using a convolutional neural network based learning algorithm. Multi-source geo-spatial data are transferred into each geo-object as environmental variables, and the relationships between soil properties and environmental variables are mined using powerful tree-based machine learning algorithms, including regressions with random forests and XGBoost. A data set that includes soil sample points and multi-source geo-spatial data is used to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the method allows for better soil organic matter mapping than state-of-the-art interpolation-based and linear-regression-based methods. The proposed procedure has potential to be a general method for mapping other soil properties. Its advantages are embodied in the modeling of relatively miscellaneous data with implicitly associated non-linear relationships between soil properties and environmental variables. The spatial scale and accuracy of the finer maps capture more detailed characteristics of the soil properties and are applicable to the micro-domain fields required for refined soil mapping with small variations.
Antiangiogenic therapy for Wilms tumor in an adult and literature review

Wilms tumor is extremely rare in adults. There is no standard treatment for Wilms tumor in adults, and the therapy protocols are based on those used in children. Here, we report a case of Wilms tumor in a 24-year-old woman who was effectively treated with apatinib, a small-molecule inhibitor of vascular endothelial growth factor receptor-2. The favorable outcome suggested that antiangiogenic therapy might be effective in treating adults with Wilms tumors. Starting with this case, we reviewed the features of Wilms tumors in adults and its treatment with antiangiogenic therapy. The information in this review could provide a novel approach to treating adults with Wilms tumors.
Molecular Docking Approach of Bryophyllum Pinnatum Compounds as Atherosclerosis Therapy By Targeting Adenosine Monophosphate-Activated Protein Kinase and Inducible Nitric Oxide Synthase

Background: Bryophyllum pinnatum is a herbal medicine from Indonesia which has an anti-inflammatory effect. Adenosine monophosphate-activated protein kinase (AMPK) and inducible nitric oxide synthase (iNOS) play a function in thickening and inflammation in atherosclerosis disease. Objective: This research aims conducted to know the potential of Bryophyllum pinnatum as a therapy for atherosclerosis by targeting AMPK and iNOS. Methods: We employed a molecular docking technique to interact active compounds of Bryohyllum pinnatum with AMPK and iNOS, which were retrieved on the protein databank. Molecular docking analysis utilizing tools such as Pyrx 9.5, Pymol, and Discovery Studio, to support the interaction between the compound and protein. Molecular Dynamic (MD) simulation also performed using CABS-FLEX 2.0 server to know the stability interaction. Results: Bryophillin B was an active compound that possesses significant binding to AMPK and iNOS. It had same binding pocket as native ligand, and Bryophyllin B has a stronger interaction with AMPK. Based on the RMSF, the interaction biding complex Bryophyllin B with AMPK and iNOS were stable Conclusion: Bryophillin B was predicted to have potential therapy for atherosclerosis disease.
Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review Protocol

Spine surgery patients with a history of organ transplantation are a complex population due to their unique anesthetic considerations, immunologic profiles, drug interactions, and potential organ dysfunction. It is common for these patients to develop neck/back pain and pathology that warrants surgical intervention. However, there is a relative dearth of literature examining their outcomes and clinical considerations. The purpose of this protocol is to investigate their clinical outcomes following spine surgery and medical management. We perform a systematic literature search using PRISMA-ScR guidelines to include case reports, cohort studies, and retrospective analyses. The search terms include kidney, liver, heart, pancreas, lung, and bone marrow for transplants of interest and contain an extensive list of terms covering spine surgery. The search is conducted using Medline, Embase, and the Cochrane Central Register of Controlled Trials. A thorough examination of titles and abstracts is performed followed by data extraction. The data points include patient demographics, past medical history, spine procedural information, and clinical outcomes. This systematic review will aid clinicians in identifying demographics, medical management, and clinical outcomes for spine surgery patients with a previous organ transplant. These findings will highlight the gaps in the knowledge of this complex population and stimulate further research.
Multiple HA substitutions in highly pathogenic avian influenza H5Nx viruses contributed to the change in the NA subtype preference

ABSTRACT Novel highly pathogenic avian influenza (HPAI) H5Nx viruses are predominantly circulating worldwide, with an increasing potential threat of an outbreak in humans. It remains largely unknown how the stably maintained HPAI H5N1 suddenly altered its neuraminidase (NA) to other NA subtypes, which resulted in the emergence and evolution of H5Nx viruses. Here, we found that a combination of four specific amino acid (AA) substitutions (S123P-T156A-D183N- S223 R) in the hemagglutinin (HA) protein consistently observed in the H5Nx markedly altered the NA preference of H5N1 viruses. These molecular changes in H5N1 impaired its fitness, particularly viral growth and the functional activities of the HA and NA proteins. Among the AA substitutions identified, the T156A substitution, which contributed to the NA shift, also dramatically altered the antigenicity of H5N1 viruses, suggesting an occurrence of antigenic drift triggered by selective pressure. Our study shows the importance of how HA and NA complement each other and that antigenic drift in HA can potentially cause a shift in the NA protein in influenza A virus evolution.
A Study on the Development Priority of Smart Shipping Items—Focusing on the Expert Survey

As smart technology in the shipping industry accelerates, demand for technology development, which aims to increase competitiveness by utilizing the latest smart technologies, is increasing. The shipping industry in each country is pushing for developing its smart technologies at a rapid pace, and it is urgent to prioritize the demand for smart technologies in the shipping industry for sustainable industry growth. The purpose of the present study is to verify the importance and urgency of smart technologies in the shipping industry, and the present study surveys 74 domestic and foreign shipping industry officials, covering shipping companies, brokers, governments, etc. The importance and urgency of each smart technology item in the shipping industry are questioned in a survey. It is necessary to focus investment on specific smart shipping items under a limited budget, and, since the priorities presented in this study were derived through comprehensive collection of opinions from 74 shipping experts, the priorities presented in this study are expected to be effective reference values when implementing sustainable shipping investment policies. According to an interview analysis on importance and urgency, both “autonomous ships” and “blockchain” items show high importance and urgency. Since both importance and urgency are high, strategic priorities are the highest, and it is expected to contribute effectively to enhancing the smartness of the shipping industry when it focuses its investment capabilities in those fields.
Clustering Enabled Classification using Ensemble Feature Selection for Intrusion Detection

Machine learning has been leveraged to increase the effectiveness of intrusion detection systems (IDSs). The focus of this approach, however, has largely be on detecting known attack patterns based on outdated datasets. In this paper, we propose an ensemble feature selection method along with an anomaly detection method that combines unsupervised and supervised machine learning techniques to classify network traffic to identify previously unseen attack patterns. To that end, three different feature selection techniques are used as part of an ensemble model that selects 8 common features. Moreover, k-Means clustering is used to first partition the training instances into k clusters using the Manhattan distance. A classification model is then built based on the resulting clusters, which represent a density region of normal or anomaly instances. This in turn helps determine the effectiveness of the clustering in detecting unknown attack patterns within the data. The performance of our classifier is evaluated using the Kyoto dataset, which was collected between 2006 and 2015. To our knowledge, no previous work proposed such a framework that combines unsupervised and supervised machine learning approaches using this dataset. Experimental results show the effectiveness of the proposed framework in detecting previously unseen attack patterns compared to the traditional classification approach.
Effects of 4-Br-A23187 on Bacillus subtilis cells and unilamellar vesicles reveal it to be a potent copper ionophore

Ionophores are small molecules or peptides that transport metal ions across biological membranes. Their transport capabilities are typically characterized in vitro using vesicles and single ion species. It is difficult to infer from these data which effects ionophores have on living cells in a complex environment (e.g., culture medium), since net ion movement is influenced by many factors including ion composition of the medium, concentration gradients, pH gradient, and protein‐mediated transport processes across the membrane. To gain insights into the antibacterial mechanism of action of the semisynthetic polyether ionophore 4‐Br‐A23187, known to efficiently transport zinc and manganese in vitro, we investigated its effects on the gram‐positive model organism Bacillus subtilis. In addition to monitoring cellular ion concentrations, the physiological impact of treatment was assessed on the proteome level. 4‐Br‐A23187 treatment resulted in an increase in intracellular copper levels, the extent of which depended on the copper concentration of the medium. Effects of copper accumulation mirrored by the proteomic response included oxidative stress, disturbance of proteostasis, metal and sulfur homeostasis. The antibiotic effect of 4‐Br‐A23187 is further aggravated by a decrease in intracellular manganese and magnesium. A liposome model confirmed that 4‐Br‐A23187 acts as copper ionophore in vitro.
‘There is power in the cry of a woman’: the approach of African women with disabilities to leadership

ABSTRACT African disability activists and leaders play a key role in addressing the pervasive stigma, discrimination, and exclusion that is encountered by people with disability. An understanding of the leadership influences, styles, and priorities of female disability leaders may encourage and support the African disability movement and women’s involvement in it. The current study undertook a series of in-depth interviews that explored the life stories and leadership experiences of seven female disability leaders from Tanzania, Ghana, and Kenya. Findings indicated that although the participants had experienced significant discrimination and pain as women with disability (WWD), they had been able to draw strength and conviction from their trauma and use their experiences to connect with other WWD. The participants described leadership as the service to others and they understood the importance of unity, hope, participatory leadership, and a commitment to a common goal and purpose. Through their leadership, they sought to empower individual WWD, as well as shape a united disability movement with a strong political voice. These findings demonstrate the importance of understanding the role women can play in disability leadership, serving as a hopeful reminder that trauma and marginalization can be reconceptualized as a source of strength and compassion.
Regulation of protein interactions by MOB1 phosphorylation

1 Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada, M5G 1X5 2 Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada, M5S 1A8 3 New address: The Institute of Cancer Research, Divisions of Structural Biology and Cancer Biology, London, UK, SW7 3RP 4 NE-CAT APS, Building 436E, Argonne National Lab, 9700 S.Cass Avenue, Argonne, Illinois 60439, USA. 5 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, M5S 1A8
Emerging respiratory infections threatening public health in the Asia‐Pacific region: A position paper of the Asian Pacific Society of Respirology†

In past decades, we have seen several epidemics of respiratory infections from newly emerging viruses, most of which originated in animals. These emerging infections, including severe acute respiratory syndrome coronavirus (SARS‐CoV), Middle East respiratory syndrome coronavirus (MERS‐CoV) and the pandemic influenza A(H1N1) and avian influenza (AI) viruses, have seriously threatened global health and the economy. In particular, MERS‐CoV and AI A(H7N9) are still causing infections in several areas, and some clustering of cases of A(H5N1) and A(H7N9) may imply future possible pandemics. Additionally, given the inappropriate use of antibiotics and international travel, the spread of carbapenem‐resistant Gram‐negative bacteria is also a significant concern. These infections with epidemic or pandemic potential present a persistent threat to public health and a huge burden on healthcare services in the Asia‐Pacific region. Therefore, to enable efficient infection prevention and control, more effective international surveillance and collaboration systems, in the context of the ‘One Health’ approach, are necessary.
Immune-Modulation by the Human Respiratory Syncytial Virus: Focus on Dendritic Cells

The human respiratory syncytial virus (hRSV) is the leading cause of pneumonia in infants and produces a significant burden in the elderly. It can also infect and produce disease in otherwise healthy adults and recurrently infect those previously exposed to the virus. Importantly, recurrent infections are not necessarily a consequence of antigenic variability, as described for other respiratory viruses, but most likely due to the capacity of this virus to interfere with the host's immune response and the establishment of a protective and long-lasting immunity. Although some genes encoded by hRSV are known to have a direct participation in immune evasion, it seems that repeated infection is mainly given by its capacity to modulate immune components in such a way to promote non-optimal antiviral responses in the host. Importantly, hRSV is known to interfere with dendritic cell (DC) function, which are key cells involved in establishing and regulating protective virus-specific immunity. Notably, hRSV infects DCs, alters their maturation, migration to lymph nodes and their capacity to activate virus-specific T cells, which likely impacts the host antiviral response against this virus. Here, we review and discuss the most important and recent findings related to DC modulation by hRSV, which might be at the basis of recurrent infections in previously infected individuals and hRSV-induced disease. A focus on the interaction between DCs and hRSV will likely contribute to the development of effective prophylactic and antiviral strategies against this virus.
Face Recognition Based on Facial Landmarks

These days exists a lot of different approaches for face recognition. In this paper presented one of the traditional approaches with steps which are basis for face recognition. Method used for face recognition includes four steps, which are face detection, head position estimation, features extraction and testing classifier. Automatic detection of face and facial landmarks is very crucial in this method because of the ratio distances between these landmarks used as features for classifier. All the paper long, one by one presented each of four steps. In experiments on a small face database, evaluated accuracy of the method.
Capturing the Fantastic Voyage of Monocytes Through Time and Space

Monocytes are a subset of cells that are categorized together with dendritic cells (DCs) and macrophages in the mononuclear phagocyte system (MPS). Despite sharing several phenotypic and functional characteristics with MPS cells, monocytes are unique cells with the ability to function as both precursor and effector cells in their own right. Before the development of hematopoietic stem cells (HSCs) in utero, monocytes are derived from erythro-myeloid precursors (EMPs) in the fetal liver that are important for populating the majority of tissue resident macrophages. After birth, monocytes arise from bone marrow (BM)-derived HSCs and are released into the circulation upon their maturation, where they survey peripheral tissues and maintain endothelial integrity. Upon sensing of microbial breaches or inflammatory stimuli, monocytes migrate into tissues where their plasticity allows them to differentiate into cells that resemble macrophages or DCs according to the environmental niche. Alternatively, they may also migrate into tissues in the absence of inflammation and remain in an undifferentiated state where they perform homeostatic roles. As monocytes are typically on the move, the availability of intravital imaging approaches has provided further insights into their trafficking patterns in distinct tissue compartments. In this review, we outline the importance of understanding their functional behavior in the context of tissue compartments, and how these studies may contribute towards improved vaccine and future therapeutic strategies.
Living at the Extremes: Extremophiles and the Limits of Life in a Planetary Context

Prokaryotic life has dominated most of the evolutionary history of our planet, evolving to occupy virtually all available environmental niches. Extremophiles, especially those thriving under multiple extremes, represent a key area of research for multiple disciplines, spanning from the study of adaptations to harsh conditions, to the biogeochemical cycling of elements. Extremophile research also has implications for origin of life studies and the search for life on other planetary and celestial bodies. In this article, we will review the current state of knowledge for the biospace in which life operates on Earth and will discuss it in a planetary context, highlighting knowledge gaps and areas of opportunity.
Enhancing Lifetime of Non Volatile Memory Caches by Write Aware Techniques

Traditional memory technologies, such as SRAM, suffers from limited package density and high leakage power. Applications are getting increasingly memory hungry in the age of big data. Non-volatile memories such as STTRAM, PCM, and ReRAM emerged as attractive contenders to replace traditional SRAM based memories. They have high density and zero leakage power. However, they have a limited write endurance. Non-uniform write patterns in applications can shorten the life of non-volatile memories. Traditional cache block replacement strategy like LRU leads some cache blocks to be accessed more frequently than others, accelerating the wear out of the cache. We present a Write Aware Last Level Non-Volatile Cache (WALL-NVC), which improves the life-time up to 5.84x for unicore, 3.34x for dual core and 4.11x for quad core system. It reduces intraset write variation in last-level caches upto 98.91%, 90.11%, and 94.12% for unicore, dual core, and quad-core systems, respectively using write distribution and NVM friendly replacement mechanism.
Deep Learning for Manipulator Visual Positioning

To solve the problem of robot visual servoing on unknown object, we apply deep learning to the robotics control and propose a manipulator visual positioning method based on deep learning. Firstly, we use a well-trained object detection network based on deep learning to recognize and detect objects in the first frame image, which is captured by the camera mounted on the robot; Then, user randomly selects object to be manipulated through human-computer interaction according to the result of recognition and detection. In the sequence of subsequent images, robotics control system can detect the object to be manipulated using this object class label information and well-trained object detection network; Finally, we can calculate the features error and design the visual sliding mode controller to control the manipulator to position the unknown object randomly selected by the user in the first frame image. We conduct two robotics experiments of visual positioning on unknown object in the complex nature scenes using a MOTOMAN-SV3X industrial manipulator. Experimental results show that our manipulator visual positioning method can position an unknown object without having to known any model information about the object prior to the positioning task execution, and it is an effective manipulator visual positioning on unknown object method.