Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy.

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and ethics. This article presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates.

A Real-Time Adaptive Station Beamforming Strategy for Next Generation Phased Array Radio Telescopes.

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn't require hardware redesign but only modifies the firmware accordingly.

The Impact of Color Cues on the Learning Performance in Video Lectures.

This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information.

Sclerostin inhibition in rare bone diseases: Molecular understanding and therapeutic perspectives.

Sclerostin emerges as a novel target for bone anabolic therapy in bone diseases. Osteogenesis imperfecta (OI) and X-linked hypophosphatemia (XLH) are rare bone diseases in which therapeutic potential of sclerostin inhibition cannot be ignored. In OI, genetic/pharmacologic sclerostin inhibition promoted bone formation of mice, but responses varied by genotype and age.

Connective Tissue Growth Factor: Regulation, Diseases, and Drug Discovery.

In drug discovery, selecting targeted molecules is crucial as the target could directly affect drug efficacy and the treatment outcomes. As a member of the CCN family, CTGF (also known as CCN2) is an essential regulator in the progression of various diseases, including fibrosis, cancer, neurological disorders, and eye diseases. Understanding the regulatory mechanisms of CTGF in different diseases may contribute to the discovery of novel drug candidates.

Never-Ending Learning for Explainable Brain Computing.

Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture.

Biochar improves the humification process during pig manure composting: Insights into roles of the bacterial community and metabolic functions.

Biochar could promote humification in composting, nevertheless, its mechanism has not been fully explored from the perspective of the overall bacterial community and its metabolism. This study investigated the effects of bamboo charcoal (BC) and wheat straw biochar (WSB) on the humic acid (HA) and fulvic acid (FA) contents during pig manure composting. The results showed that BC enhanced humification more than WSB, and significantly increased the HA content and HA/FA ratio.

Interrelated feature selection from health surveys using domain knowledge graph.

Finding patterns among risk factors and chronic illness can suggest similar causes, provide guidance to improve healthy lifestyles, and give clues for possible treatments for outliers. Prior studies have typically isolated data challenges from single-disease datasets. However, the predictive power of multiple diseases is more helpful in establishing a healthy lifestyle than investigating one disease.

The role of sclerostin in lipid and glucose metabolism disorders.

Lipid and glucose metabolism are critical for human activities, and their disorders can cause diabetes and obesity, two prevalent metabolic diseases. Studies suggest that the bone involved in lipid and glucose metabolism is emerging as an endocrine organ that regulates systemic metabolism through bone-derived molecules. Sclerostin, a protein mainly produced by osteocytes, has been therapeutically targeted by antibodies for treating osteoporosis owing to its ability to inhibit bone formation.

Comparisons of oblique lumbar interbody fusion and transforaminal lumbar interbody fusion for degenerative spondylolisthesis: a prospective cohort study with a 2-year follow-up.

Objective: This study aimed to compare the clinical outcomes between oblique (OLIF) and transforaminal lumbar interbody fusion (TLIF) for patients with degenerative spondylolisthesis during a 2-year follow-up.

Methods: Patients with symptomatic degenerative spondylolisthesis who underwent OLIF (OLIF group) or TLIF (TLIF group) were prospectively enrolled in the authors' hospital and followed up for 2 years. The primary outcomes were treatment effects [changes in visual analog score (VAS) and Oswestry disability index (ODI) from baseline] at 2 years after surgery; these were compared between two groups.

Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022).

Objective: Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

Methods: Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers.

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression.

Biochar enhanced organic matter transformation during pig manure composting: Roles of the cellulase activity and fungal community.

Fungal degradation of cellulose is a key step in the conversion of organic matter in composting. This study investigated the effects of adding 10% biochar (including, prepared from corn stalk and rape stalk corresponding to CSB and RSB) on organic matter transformation in composting and determined the role of cellulase and fungal communities in the conversion of organic matter. The results showed that biochar could enhance the conversion of organic matter, especially in RSB treatment.

Genomic Profiling Reveals the Variant Landscape of Sporadic Parathyroid Adenomas in Chinese Population.

Objective: To define somatic variants of parathyroid adenoma (PA) and to provide novel insights into the underlying molecular mechanism of sporadic PA.

Methods: Basic clinical characteristics and biochemical indices of 73 patients with PA were collected. Whole-exome sequencing was performed on matched tumor-constitutional DNA pairs to detect somatic alterations.

Biochar reduces bioavailability of phosphorus during swine manure composting: Roles of phoD-harboring bacterial community.

The bioavailability of phosphorus is a vital index for evaluating the quality of compost products. This study examined the effects of adding wheat straw biochar (WSB) and bamboo charcoal (BC) on the transformation of various phosphorus fractions during composting, as well as analyzing the roles of the phoD-harboring bacterial community in the transformation of phosphorus fractions. Adding WSB and BC reduced the available phosphorus content in the compost products by 35.

Clinical and genetic characteristics of 29 Chinese patients with X-linked hypophosphatemia.

Objective: The aim of this study was to fully describe the clinical and genetic characteristics, including clinical manifestations, intact fibroblast growth factor 23 (iFGF23) levels, and presence of gene mutations, of 22 and 7 patients with familial and sporadic X-linked dominant hypophosphatemia (XLH), respectively.

Methods: Demographic data, clinical features, biochemical indicators, and imaging data of 29 patients were collected. All 22 exons and exon-intron boundaries of the gene were amplified by polymerase chain reaction (PCR) and directly sequenced.

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques.

Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC's updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training.

Clinical Characteristics and Pathogenic Gene Identification in Chinese Patients With Paget's Disease of Bone.

Objective: To evaluate the clinical features of sporadic Paget's disease of bone (PDB) in China and further explore the underlying genetic abnormalities of the disease.

Methods: Clinical characteristics, biochemical indices, bone turnover markers and radiographic examinations of the patients were collected. Genomic DNA was extracted from peripheral blood and whole-exome sequencing was carried out to identify the potential pathogenic genes.

Clinical characteristics and identification of a novel TGFB1 variant in three unrelated Chinese families with Camurati-Engelmann disease.

Background: To investigate the clinical characteristics and molecular diagnosis of Camurati-Engelmann disease (CAEND) in Chinese individuals.

Methods: We recruited six patients aged 14 to 45 years in three unrelated families with CAEND, including five females and one male. Clinical manifestations, biochemical tests, and radiographic examinations were analyzed.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions.