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Microtubule affinity-regulating kinase 2 (MARK2) contributes to establishing neuronal polarity and developing dendritic spines. Although large-scale sequencing studies have associated MARK2 variants with autism spectrum disorder (ASD), the clinical features and variant spectrum in affected individuals with MARK2 variants, early developmental phenotypes in mutant human neurons, and the pathogenic mechanism underlying effects on neuronal development have remained unclear. Here, we report 31 individuals with MARK2 variants and presenting with ASD, other neurodevelopmental disorders, and distinctive facial features. Loss-of-function (LoF) variants predominate (81%) in affected individuals, while computational analysis and in vitro expression assay of missense variants supported the effect of MARK2 loss. Using proband-derived and CRISPR-engineered isogenic induced pluripotent stem cells (iPSCs), we show that MARK2 loss leads to early neuronal developmental and functional deficits, including anomalous polarity and dis-organization in neural rosettes, as well as imbalanced proliferation and differentiation in neural progenitor cells (NPCs). Mark2 +/− mice showed abnormal cortical formation and partition and ASD-like behavior. Through the use of RNA sequencing (RNA-seq) and lithium treatment, we link MARK2 loss to downregulation of the WNT/β-catenin signaling pathway and identify lithium as a potential drug for treating MARK2 -associated ASD. [Display omitted] MARK2 plays essential roles in the central nervous system. Here, we demonstrate the association between MARK2 variants, downregulated WNT/β-catenin signaling, and autism spectrum disorder (ASD) through the study of individuals with ASD and Mark2 +/− mice. [ABSTRACT FROM AUTHOR]

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling. [ABSTRACT FROM AUTHOR]

In an effort to solve the P availability issue in Inceptisols, fermentation is one method used to enhance the quality of liqiuid organic fertilizer (LOF) made from Moringa (Moringa oleifera). The type and concentration determine the effectiveness of LOF on nutrient uptake and plant yields. This study aims to evaluate the impact of LOF types, concentrations, and their interactions on P uptake and rice grain yield in Inceptisols. The research was conducted in a greenhouse using a completely random design (CRD) with two factors. The first factor is the type of LOF, consisting of two levels, i.e., fresh and fermented Moringa extract. The second factor is the LOF concentration, with four levels (i.e., 0, 20, 40, 60, and 80 ml/l) and three replications. Data analysis using ANOVA, DMRT, and correlation. The study revealed that the interaction between LOF types and concentrations affects P uptake. The highest P uptake shown by fermented Moringa with a concentration of 60 ml/l was 20.02 mg/plant and 40 ml/l was 18.73 mg/plants., or 1.5 times higher than the control. Grain yield was not affected by type, LOF concentration, or interaction. Fermented Moringa has good potential as LOF, while the effect on grain yield needs further research.

CTNND2 encodes δ‐catenin, a component of an adherens junction complex, and plays an important role in neuronal structure and function. To date, only heterozygous loss‐of‐function CTNND2 variants have been associated with mild neurodevelopmental delay and behavioral anomalies, a condition, which we named Rauch‐Azzarello syndrome. Here, we report three siblings of a consanguineous family of Syrian descent with a homozygous deletion encompassing the last 19 exons of CTNND2 predicted to disrupt the transcript. All presented with severe neurodevelopmental delay with absent speech, profound motor delay, stereotypic behavior, microcephaly, short stature, muscular hypotonia with lower limb hypertonia, and variable eye anomalies. The parents and the fourth sibling were heterozygous carriers of the deletion and exhibited mild neurodevelopmental impairment resembling that of the previously described heterozygous individuals. The present study unveils a severe manifestation of CTNND2‐associated Rauch‐Azzarello syndrome attributed to biallelic loss‐of‐function aberrations, clinically distinct from the already described mild presentation of heterozygous individuals. Furthermore, we demonstrate novel clinical features in homozygous individuals that have not been reported in heterozygous cases to date. [ABSTRACT FROM AUTHOR]

This paper compares three unsupervised machine-learning algorithms – local outlier factor (LOF), Isolation Forest (iForest) and one-class support vector machine (OCSVM) – for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains several Au, Cu and Pb–Zn mineral occurrences. The methodology incorporates single-element geochemistry, multivariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co–Cr–Ni–V–Sn association indicating a lithological influence; PC2 shows a Au–Bi–Cu–W association suggesting epithermal Au mineralization; PC3 shows variability in Zn–V–Co–Sb–Cu–Cr; PC4 shows a Au–Cu–Ba–Sr–Ag association indicating Au and polymetallic mineralization; PC5 reflects Zn–Ag–Ni–Pb related to hydrothermal mineralization; and PC6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better than LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF outperformed iForest and OCSVM in identifying all four Pb–Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associated with Au–Pb mineralization, while iForest detected anomalies associated with Pb–Zn–Cu occurrences and neighbouring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE and west by detecting anomalies associated with two Cu occurrences and a Pb–Cu occurrence. This study examined the influence of contamination fraction on detection of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases from 5 to 10%. However, even with a 35% contamination fraction, some Cu occurrences remained outside the anomaly category, indicating potentially overlooked geochemical signals from mineral occurrences due to sampling schemes. [ABSTRACT FROM AUTHOR]

Hydroponic lettuce cultivation using AB-mix nutrients has several weaknesses, one of which is that the price of nutrients is relatively expensive and difficult to obtain in small towns. Rice bran liquid organic fertilizer (LOF) has the potential to be a substitute or supplementary nutrient in hydroponic cultivation; it has a fairly high nitrogen content (2.08%). This research aims to examine the effect of rice bran POC on the yield of lettuce plants cultivated hydroponically. The research used a non-factorial Randomized Group Design (RGD), consisting of 3 treatments, namely P1 (rice bran LOF 5 ml/l), P2 (AB-mix 1500 ppm), and P3 (AB-mix 1500 ppm + rice bran LOF 5 ml/l), which were repeated 10 times to obtain 30 experimental units. The observation variables in this research were plant height, number of leaves, wet weight, and dry weight of harvest. Data analysis was carried out using analysis of variance; if there was a real effect, a 5% BNT test was carried out. The results of the research showed that rice bran POC (5 ml/l) was not able to be used as the sole nutrient in hydroponic lettuce cultivation, AB-mix 1500 ppm nutrition treatment + 5 ml/l rice bran LOF gave the best and highest results in plant height, number of leaves, wet weight, and lettuce dry weight. [ABSTRACT FROM AUTHOR]

Pertumbuhan data yang terjadi saat ini berpengaruh terhadap analisis data di berbagai bidang, seperti astronomi, bisnis, kedokteran, pendidikan, dan finansial. Data yang terkumpul dan tersimpan mengandung nilai ekstrem atau nilai pengamatan yang berbeda dari kebanyakan nilai hasil pengamatan lain. Nilai ekstrem tersebut disebut dengan outlier. Outlier pada sebagian data sering kali memiliki nilai yang mengandung informasi penting, sehingga perlu dikaji agar dapat diambil keputusan untuk menghapus atau menggunakan data tersebut sebelum diterapkan dalam penambangan data. Deteksi outlier dapat dilakukan sebagai prapemrosesan data dengan menggunakan teknik analisis outlier. Beberapa teknik analisis outlier yang banyak diterapkan antara lain metode berbasis jarak (distance), metode berbasis kepadatan (density), dan metode local outlier factor (LOF). K-nearest neighbors (KNN) merupakan salah satu algoritma penambangan data yang sangat sensitif terhadap outlier karena cara kerjanya yang bergantung pada nilai k. Oleh karena itu, perlu penanganan tepat saat KNN bekerja pada dataset yang mengandung outlier. Metode eksperimen dipilih dalam menerapkan metode usulan, dengan tujuan untuk mengoptimasi algoritma KNN berdasarkan perbandingan analisis outlier (KNN-distance, KNN-density, dan KNN-LOF). Hasil penelitian menunjukkan bahwa KNN-kepadatan unggul sebanyak tiga kali: pada Wisconsin Breast Cancer dengan nilai rata-rata akurasi sebesar 99,34% pada k=3 dan k=5; pada Glass dengan nilai rata-rata akurasi sebesar 85,25% pada k=7; dan pada Lymphography dengan nilai rata-rata akurasi sebesar 85,45% pada k=5. Selanjutnya, berdasarkan hasil uji Friedman dan uji Nemenyi, juga terbukti bahwa ada perbedaan yang signifikan antara KNN-kepadatan dengan KNN-LOF.

In an effort to solve the P availability issue in Inceptisols, fermentation is one method used to enhance the quality of liqiuid organic fertilizer (LOF) made from Moringa (Moringa oleifera). The type and concentration determine the effectiveness of LOF on nutrient uptake and plant yields. This study aims to evaluate the impact of LOF types, concentrations, and their interactions on P uptake and rice grain yield in Inceptisols. The research was conducted in a greenhouse using a completely random design (CRD) with two factors. The first factor is the type of LOF, consisting of two levels, i.e., fresh and fermented Moringa extract. The second factor is the LOF concentration, with four levels (i.e., 0, 20, 40, 60, and 80 ml/l) and three replications. Data analysis using ANOVA, DMRT, and correlation. The study revealed that the interaction between LOF types and concentrations affects P uptake. The highest P uptake shown by fermented Moringa with a concentration of 60 ml/l was 20.02 mg/plant and 40 ml/l was 18.73 mg/plants or 1.5 times higher than the control. Grain yield was not affected by type, LOF concentration, or their interaction. Fermented Moringa has good potential as LOF, while the effect on grain yield needs further research. [ABSTRACT FROM AUTHOR]

Objectives: The mining of building cluster features in topographic maps is of great significance to realize automatic cartographic synthesis and spatial knowledge mining, but it is difficult to identify building polygon communities with different distribution densities and morphological characteristics in cities. Methods: A clustering method combining local outlier factor (LOF) is proposed. Based on the adjacency map of buildings, feature vectors are constructed according to the differences of form factors and proximity distances between adjacent buildings. LOF algorithm is used to dynamically calculate the anomaly degree of feature vectors and eliminate the abnormal vectors. And the building cluster is obtained. Results: By adjusting the upper limit of LOF local anomaly factor and proximity number, the final clustering results are obtained. The experimental results show that the proposed method can effectively identify and distinguish densely distributed building groups in the city. Conclusions: We provide a new way to solve the clustering problem of urban dense buildings, verify the importance of Gestalt criteria, and the clustering results are consistent with human visual cognition. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the data quality, the big data cleaning method for distribution networks is studied in this paper. First, the Local Outlier Factor (LOF) algorithm based on DBSCAN clustering is used to detect outliers. However, due to the difficulty in determining the LOF threshold, a method of dynamically calculating the threshold based on the transformer districts and time is proposed. In addition, the LOF algorithm combines the statistical distribution method to reduce the misjudgment rate. Aiming at the diversity and complexity of data missing forms in power big data, this paper has improved the Random Forest imputation algorithm, which can be applied to various forms of missing data, especially the blocked missing data and even some completely missing horizontal or vertical data. The data in this paper are from real data of 44 transformer districts of a certain 10 kV line in a distribution network. Experimental results show that outlier detection is accurate and suitable for any shape and multidimensional power big data. The improved Random Forest imputation algorithm is suitable for all missing forms, with higher imputation accuracy and better model stability. By comparing the network loss prediction between the data using this data cleaning method and the data removing outliers and missing values, it can be found that the accuracy of network loss prediction has improved by nearly 4 % using the data cleaning method identified in this paper. Additionally, as the proportion of bad data increased, the difference between the prediction accuracy of cleaned data and that of uncleaned data is more significant.

Given the basic data anomalies and significant redundancy in line loss management within distribution networks， a technique for identifying and correcting abnormal line loss data based on segmented regions is proposed. In view of the redundancy of terminal data， a Kalman filter algorithm is employed to fuse terminal redundant data. Then， by traversing the distribution transformers of various line nodes in the distribution network and using local outlier factor （LOF） algorithm， the operational data are detected. Based on the topological relationship of the distribution networks， the Girvan-Newman （GN） algorithm is used to segment the abnormal nodes. By analyzing the neighboring node measurement data and the imbalance index of the segmented regions， the boundary of the regions is dynamically adjusted until the segmented regions meet the estimated observability conditions. The final division result of segmented regions is obtained， and abnormal data are solved using the measurement model， constraint model， and estimation model within the regions. Finally， an example of the 10 kV Shixin line and Shijin line in a province in Northwest China is used to validate the proposed method. The results demonstrate that the proposed method can identify and correct abnormal line loss data within distribution networks.

The missing and abnormal data in power transformer operation and monitoring greatly affect the accuracy of fault diagnosis and thus threaten the stable operation of power systems. To conduct outlier detection and improve data quality for safety warning, this paper proposes a transformer operation data preprocessing method based on KNN (K-nearest neighbor) and LOF (local outlier factor) for power transformer operation data classification. Firstly, this paper analyzes the characteristics of transformer operation data. Secondly, the local reachable density of the input data is calculated by LOF algorithm. The local outlier factor score of the data is derived according to the local reachable density, and the abnormal data is output according to the abnormal score. Then, KNN algorithm is utilized to classify the relevant data around the abnormal value and missing value of the transformer. The data are filled or corrected according to the classification results. Thirdly, the elbow method is used to determine the optimal K value and cluster operation data by K-Means algorithm. Finally, the proposed method is applied and verified with real transformer operation data in case study. The results show the method can effectively detect and correct the abnormal and missing data, conduct transformer data cleaning and preprocessing and provide accurate and effective data samples for transformer fault diagnosis.

Scallion is one type of vegetable plant that is widely used as a flavouring or seasoning for dishes and other vegetable mixtures in several types of cuisine in Indonesia. For this reason, one of the efforts that can be made to increase the production of scallions in meeting the consumption needs of the community is by means of technical cultivation through the application of liquid organic fertiliser (LOF). The purpose of this study was to analyse the need for the right dose of LOF for scallions. This study used a non-factorial Randomised Group Design method by testing 6 treatment of LOF dose, namely: J1 = 1 ml/L water, J2 = 1.5 ml/L, J3 = 2 ml/L, J4 = 2.5 ml/L, J5 = 3 ml/L and J6 = 3.5 ml/L. Each treatment was repeated as many as 4 replications. The results from analysis of variance (ANOVA) showed that doses of LOF affected very significantly on the parameters of root length, root weight, wet stalk weight, and fresh shoot yield with the LOF dose of 1.5 ml/L water (J2) giving the best results.

Summary: Heterozygous missense variants and in-frame indels in SMC3 are a cause of Cornelia de Lange syndrome (CdLS), marked by intellectual disability, growth deficiency, and dysmorphism, via an apparent dominant-negative mechanism. However, the spectrum of manifestations associated with SMC3 loss-of-function variants has not been reported, leading to hypotheses of alternative phenotypes or even developmental lethality. We used matchmaking servers, patient registries, and other resources to identify individuals with heterozygous, predicted loss-of-function (pLoF) variants in SMC3, and analyzed population databases to characterize mutational intolerance in this gene. Here, we show that SMC3 behaves as an archetypal haploinsufficient gene: it is highly constrained against pLoF variants, strongly depleted for missense variants, and pLoF variants are associated with a range of developmental phenotypes. Among 14 individuals with SMC3 pLoF variants, phenotypes were variable but coalesced on low growth parameters, developmental delay/intellectual disability, and dysmorphism, reminiscent of atypical CdLS. Comparisons to individuals with SMC3 missense/in-frame indel variants demonstrated an overall milder presentation in pLoF carriers. Furthermore, several individuals harboring pLoF variants in SMC3 were nonpenetrant for growth, developmental, and/or dysmorphic features, and some had alternative symptomatologies with rational biological links to SMC3. Analyses of tumor and model system transcriptomic data and epigenetic data in a subset of cases suggest that SMC3 pLoF variants reduce SMC3 expression but do not strongly support clustering with functional genomic signatures of typical CdLS. Our finding of substantial population-scale LoF intolerance in concert with variable growth and developmental features in subjects with SMC3 pLoF variants expands the scope of cohesinopathies, informs on their allelic architecture, and suggests the existence of additional clearly LoF-constrained genes whose disease links will be confirmed only by multilayered genomic data paired with careful phenotyping.

Summary: Ebstein’s anomaly, a rare congenital heart disease, is distinguished by the failure of embryological delamination of the tricuspid valve leaflets from the underlying primitive right ventricle myocardium. Gaining insight into the genetic basis of Ebstein’s anomaly allows a more precise definition of its pathogenesis. In this study, two distinct cohorts from the Chinese Han population were included: a case-control cohort consisting of 82 unrelated cases and 125 controls without cardiac phenotypes and a trio cohort comprising 36 parent-offspring trios. Whole-exome sequencing data from all 315 participants were utilized to identify qualifying variants, encompassing rare (minor allele frequency

Complex networks are a diverse set of networks found in various fields, such as social, technological, and biological networks. One important task in complex network analysis is link prediction, which involves detecting missing links or predicting future link formation. Many methods based on network structure analysis have been developed for link prediction, including network representation learning (NRL) models that represent nodes in a low-dimensional space. Fusion-based attributed NRL methods are particularly effective, as they capture both content and structure information. However, NRL models for link prediction are binary classification models, which face challenges in identifying negative links and prioritizing predicted links. To address these challenges, we propose a novel approach that treats link prediction as a novelty detection problem. Our approach uses the Local Outlier Factor (LOF) algorithm to quantify the novelty of non-existent links based on the representations of existing links. Our experimental results show that our proposed approach outperforms existing methods, particularly when used with fusion-based attributed NRL models. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lithium-ion batteries may lead to fire and other accidents when working under overcharge， high temperature， and external short circuits. The faults can be prevented from escalating to thermal runaway through early fault diagnosis and fault location of lithium-ion batteries and corresponding measures in time. To this end， based on the operational data of the lithium-ion battery energy storage system， the local outlier factor （LOF） algorithm is used for fault diagnosis and analysis. By calculation of the single-day and multi-day voltage operation data， the specific location of the faulty battery is determined， and the abnormal condition of the battery is analyzed. The research results verify the effectiveness of the LOF algorithm applied to the fault diagnosis of the lithium-ion battery energy storage system.

The use of agrochemicals is still an option in terms of providing fast nutrition and increasing plant growth more efficiently to a certain extent. However, its continuous use results in a decrease in soil quality and soil fertility, which in turn causes the accumulation of heavy metal ions in plant tissues, and affects nutritional yields and food safety. The community service activities were carried out by the SBF (Soil Biodiversity and Fertility) team of the Jember University Soil Science study program in Karangrejo Village, Gumukmas District, Jember Regency. Field visits were conducted on farmers' fields to review, evaluate and discuss qualitative characteristics of soil properties. This service activity aims to increase understanding of sustainable soil fertility management and training in making liquid organic fertilizer based on a local bacterial consortium. The results of the activity showed that farmers' knowledge of the parameters of soil properties, especially pH, nutrients, and soil organic matter, was still minimal. The level of acidity (pH) of the soil determines the factors of production through the ease with which nutrients are absorbed by plants as well as the possibility of the presence of toxic elements that can interfere with plant growth. Knowledge of soil pH is needed in relation to nutrient management and liming. A deeper understanding of several parameters of soil properties, especially soil pH values, needs to be emphasized and followed up through mentoring activities. Mentoring activities as follow-up activities for farmer groups are carried out with the target output of liquid organic fertilizer products. Entrepreneurship training for farmer groups members also needs to be carried out to build an entrepreneurial spirit and added value from livestock products by farmers so that the farmer's household economy can sustainable.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Our research deals with intelligent decision support systems based on rule-based knowledge bases. Decision support systems use rules "If a condition, then a decision" as a form of knowledge representation. In the process of inference, which mirrors the process of human reasoning, we look for rules that confirm the facts and thus generate new knowledge. Such rule-based knowledge bases can (and often do) contain outlier rules. Our goal is to find such unusual rules. Thanks to this, we can influence the completeness of the knowledge base by finding unusual rules and asking domain experts to supplement knowledge in a rare area. To enhance the effectiveness of decision support systems, we conducted separate investigations into two distinct methods. The first method involved the utilisation of the Local Outlier Factor (LOF) algorithm in detecting rule outliers, while the second method employed the Self-Organizing Maps (SOM) algorithm for the same purpose. Our experiments not only confirmed the effectiveness of both the LOF and SOM algorithms but also involved comparing the results obtained from both methods. The discovery of outlier rules can aid knowledge engineers and domain experts in knowledge exploration and enhance the completeness of the knowledge base, which is crucial for decision support systems. [ABSTRACT FROM AUTHOR]

Familial hypercholesterolaemia (FH) is an autosomal dominant dyslipidaemia, characterised by elevated LDL cholesterol (LDL-C) levels in the blood. Three main genes are involved in FH diagnosis: LDL receptor (LDLr), Apolipoprotein B (APOB) and Protein convertase subtilisin/kexin type 9 (PCSK9) with genetic mutations that led to reduced plasma LDL-C clearance. To date, several PCSK9 gain-of-function (GOF) variants causing FH have been described based on their increased ability to degrade LDLr. On the other hand, mutations that reduce the activity of PCSK9 on LDLr degradation have been described as loss-of-function (LOF) variants. It is therefore important to functionally characterise PCSK9 variants in order to support the genetic diagnosis of FH. The aim of this work is to functionally characterise the p.(Arg160Gln) PCSK9 variant found in a subject suspected to have FH. Different techniques have been combined to determine efficiency of the autocatalytic cleavage, protein expression, effect of the variant on LDLr activity and affinity of the PCSK9 variant for the LDLr. Expression and processing of the p.(Arg160Gln) variant had a result similar to that of WT PCSK9. The effect of p.(Arg160Gln) PCSK9 on LDLr activity is lower than WT PCSK9, with higher values of LDL internalisation (13%) and p.(Arg160Gln) PCSK9 affinity for the LDLr is lower than WT, EC50 8.6 ± 0.8 and 25.9 ± 0.7, respectively. The p.(Arg160Gln) PCSK9 variant is a LOF PCSK9 whose loss of activity is caused by a displacement of the PCSK9 P' helix, which reduces the stability of the LDLr-PCSK9 complex. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper's contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time. [ABSTRACT FROM AUTHOR]

Application of organic matter (compost Sago Ela palm waste and Liquid Organic fertilizers (LOF)) to the soil could have major benefits, such as to improve the soil physical condition (soil structure, water retention), and soil chemical properties (binding and providing nutrients, increasing CEC). The purpose of this study are (1) to improve the soil chemical properties of Ultisols, and (2) to increase plant uptake of N, P, and K, and the growth and yield of corn (Zea mays L.). The research was conducted in the field, namely in Telaga Kodok Village, Leihitu Sub District, and Central Maluku District. The experiment was designed in a factorial, and arranged in a randomized completed block design (RCBD). The first factor was the provision of compost Sago Ela palm waste (K) and the second factor was the provision of liquid fertilizer (C). The results showed that the compost combined with LOF could improve pH, Al-exchangeable, Total-N, P-available soil, Uptake-P, Uptake-K, and the dry weight of seed corn. While the treatment of sago Ela palm waste compost and LOF can independently raise the K-available soil, N-uptake, and affect corn’s plant growth (height and trunk diameter). The treatment doses of sago Ela palm waste compost of 60 Mg ha-1 and LOF of 20 mL L-1 solution can increase the exchangeable Al, Total-N, and P-available soil, respectively 0.56 cmol(+)kg-1, 0.21%, and 31.00 mg kg-1; also able to increase the uptake of P, K, and weight dry seed corn respectively 0.21% and 1.26%, and 121.33 g plant-1. The treatment doses of Sago Ela palm waste compost of 60 ton ha-1 and liquid organic fertilizer of 10 mL L-1 solution can increase the soil pH by 5.70.

The number of diabetic patients is increasing yearly worldwide, requiring the need for a quick intervention to help these people. Mortality rates are higher for diabetic patients with other serious health complications. Thus, early prediction for such diseases positively impacts healthcare quality and can prevent serious health complications later. This paper constructs an efficient prediction system for predicting diabetes in its early stage. The proposed system starts with a Local Outlier Factor (LOF)-based outlier detection technique to detect outlier data. A Balanced Bagging Classifier (BBC) technique is used to balance data distribution. Finally, integration between association rules and classification algorithms is used to develop a prediction model based on real data. Four classification algorithms were utilized in addition to an a priori algorithm that discovered relationships between various factors. The named algorithms are Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machines (SVM), and K Nearest Neighbor (KNN) for data classification. Results revealed that KNN provided the highest accuracy of 97.36% compared to the other applied algorithms. An a priori algorithm extracted association rules based on the Lift matrix. Four association rules from 12 attributes with the highest correlation and information gain scores relative to the class attribute were produced. [ABSTRACT FROM AUTHOR]

Protein kinase C (PKC) has historically been considered an oncoprotein. This stems in large part from the discovery in the early 1980s that PKC is directly activated by tumor-promoting phorbol esters. Yet three decades of clinical trials using PKC inhibitors in cancer therapies not only failed, but in some cases worsened patient outcome. Why has targeting PKC in cancer eluded successful therapies? Recent studies looking at the disease for insight provide an explanation: cancer-associated mutations in PKC are generally loss-of-function (LOF), supporting an unexpected function as tumor suppressors. And, contrasting with LOF mutations in cancer, germline mutations that enhance the activity of some PKC isozymes are associated with degenerative diseases such as Alzheimer's disease. This review provides a background on the diverse mechanisms that ensure PKC is only active when, where, and for the appropriate duration needed and summarizes recent findings converging on a paradigm reversal: PKC family members generally function by suppressing, rather than promoting, survival signaling.

The discovery in the 1980s that protein kinase C (PKC) is a receptor for the tumor-promoting phorbol esters fueled the dogma that PKC is an oncoprotein. Yet 30+ years of clinical trials for cancer using PKC inhibitors not only failed, but in some instances worsened patient outcome. The recent analysis of cancer-associated mutations, from diverse cancers and throughout the PKC family, revealed that PKC isozymes are generally inactivated in cancer, supporting a tumor suppressive function. In keeping with a bona fide tumor suppressive role, germline causal loss-of-function (LOF) mutations in one isozyme have recently been identified in lymphoproliferative disorders. Thus, strategies in cancer treatment should focus on restoring rather than inhibiting PKC.

The application of a wed-based liquid organic fertilizer can increase the production of black soybeans (Glycine Max L. Merril). The study aimed to identify the best source and dose of liquid organic fertilizer (LOF) for black soybean growth and yield. The researchers used a three-times-repeated Completely Randomized Design (CRD) using a factorial layout. The first factor was the source of LOF, which included Siam weed (Chromolaena odorata, L.), Goat weed (Ageratum conyzoides L.), and yellow creeping daisy (Wedelia trilobata L.). The second factor was the concentration of weed-based LOF, consisted of water (control treatment); 12 ml/L; 16 ml/L; 20 ml/L. The results of the study show, weed-based LOF, namely LOF Yellow creeping daisy, Goat weed, and Siam weed, resulted in no significant difference in the growth and yield of the black soybean. Except for the variables of root fresh weight and number of pods per plant, the variation in concentration of weed-based liquid organic fertilizer had no significant effect on plant growth and yield.

Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO2 series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R2 = 76.135%, the correlation between the actual and predicted values (rap) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, rap = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R2 = 0.996), whereas, on the test set, the results were not very good (R2 = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O3. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.

With the booming development of the cyber-physical system, human society is much more dependent on information technology. Unfortunately, like software, hardware is not trusted at all, due to so many third parties involved in the separated integrated circuit's (IC) design and manufacturing stages for the high profit. The malicious circuits (named Hardware Trojans) can be implanted during any stage of the ICs' design and manufacturing process. However, the existing pre-silicon approaches based on machine learning theory have good performance, they all belong to supervised learning methods, which have a key prerequisite that is numerous already known information. Meanwhile, hardware Trojans are even more unimaginable because today's ICs are becoming more complicated. The known information is even harder to gain. Furthermore, the training process for supervised learning methods tends to be time-consuming and generally requires a huge amount of balanced training data. Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD. We firstly visualize the distribution features of normal nets and Trojan nets, and then reveal the differences between the two types of nets to reduce the dimension of the feature set. According to the outliers of each net, the abnormal nets are selected and verified by professionals later to confirm whether it is a true Trojan relative to the host circuit to realize the detection. The experiments show that the proposed method can detect hardware Trojans effectively and reduce the cost of manual secondary detection. For the Trust-HUB benchmarks, the PL-HTD achieves up to 73.08% TPR and 97.52% average TNR, moreover, it achieves average 96.00% accuracy, which shows the feasibility and efficiency of hardware Trojans detecting by employing a method without the guidance of class label information.

The article concerns the detection of outliers in rule-based knowledge bases containing data on Covid 19 cases. The authors move from the automatic generation of a rule-based knowledge base from source data by clustering rules in the knowledge base to optimize inference processes and to detecting unusual rules allowing for the optimal structure of rule groups. The paper presents a two-phase procedure, wherein in the first phase, we look for the optimal structure of rule clusters when there are outlier rules in the knowledge base. In the second phase, we detect outliers in the rules using the LOF (Local Outlier Factor) algorithm. Then we eliminate the unusual rules from the database and check whether the selected cluster quality measures are responded positively to the elimination of outliers, which would indicate that the rules were rightly considered outliers. The performed experiments confirmed the effectiveness of the LOF algorithm and selected cluster quality measures in the context of detecting atypical rules. The detection of such rules can support knowledge engineers or domain experts in knowledge mining to improve the completeness of the knowledge base, which is usually the basis of the decision support system. [ABSTRACT FROM AUTHOR]