Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Revisiting the impact of genomic hot spots: C12orf35 locus as a hot spot and engineering target.

Traditional Chinese hamster ovary (CHO) cell line development is based on random integration (RI) of transgene that causes clonal variation and subsequent large-scale clone screening. Therefore, site-specific integration (SSI) of transgenes into genomic hot spots has recently emerged as an alternative method for cell line development. However, the specific mechanisms underlying hot spot site formation remain unclear. In this study, we aimed to generate landing pad (LP) cell lines via the RI of transgenes encoding fluorescent reporter proteins flanked by recombination sites to facilitate recombinase-mediated cassette exchange. The RI-based LP cell line expressing high reporter levels with spontaneous C12orf35 locus deletion exhibited similar reporter fluorescent protein levels compared to targeted integrants with an identical reporter LP construct at the CHO genome hot spot, the C12orf35 locus. Additionally, Resf1, a C12orf35 locus gene, knockout (KO) in the RI-based LP cell line with conserved C12orf35 increased reporter expression levels, comparable to those in cell lines with C12orf35 locus disruption. These results indicate that the effect of SSI into the C12orf35 locus, a genomic hot spot, on high-level transgene expression was caused by C12orf35 disruption. In contrast to C12orf35 KO, KO at other well-known hot spot sites at specific loci of genes, including Fer1L4, Hprt1, Adgrl4, Clcc1, Dop1b, and Ddc, did not increase transgene expression. Overall, our findings suggest that C12orf35 is a promising engineering target and a hot spot for SSI-based cell line development.

Mapping malaria transmission foci in Northeast Thailand from 2011 to 2021: approaching elimination in a hypoendemic area.

BACKGROUND: Thailand is approaching local elimination of malaria in the eastern provinces. It has successfully reduced the number of cases over the past decade, but there are persistent transmission hot spots in and around forests. This study aimed to use data from the malaria surveillance system to describe the spatiotemporal trends of malaria in Northeast Thailand and fine-scale patterns in locally transmitted cases between 2011 and 2021. METHODS: Case data was stratified based on likely location of infection and parasite species. Annual Parasite Index per 1000 population (API) was calculated for different categories. Time series decomposition was performed to identify trends and seasonal patterns. Statistically significant clusters of high (hot spots) and low (cold spots) API were identified using the Getis-Ord Gi* statistic. The stability of those hot spots and the absolute change in the proportion of API density from baseline were compared by case type. RESULTS: The total number of confirmed cases experienced a non-linear decline by 96.6%, from 1061 in 2011 to 36 in 2021. There has been a decline in both Plasmodium vivax and Plasmodium falciparum case numbers, with only four confirmed P. falciparum cases over the last two years-a 98.89% drop from 180 in 2011. API was generally higher in Si Sa Ket province, which had peaks every 2-3 years. There was a large outbreak in Ubon Ratchathani in 2014-2016 which had a high proportion of P. falciparum reported. The proportion of cases classified increased over the study period, and the proportion of cases classed as indigenous to the village of residence increased from 0.2% to 33.3%. There were stable hot spots of indigenous and imported cases in the south of Si Sa Ket and southeast of Ubon Ratchathani. Plasmodium vivax hot spots were observed into recent years, while those of P. falciparum decreased to zero in Ubon in 2020 and emerged in the eastern part in 2021, the same year that P. falciparum hot spots in Si Sa Ket reached zero. CONCLUSIONS: There has been a large, non-linear decline in the number of malaria cases reported and an increasing proportion of cases are classed as indigenous to the patient's village of residence. Stable hot spots of ongoing transmission in the forested border areas were identified, with transmission likely persisting because of remote location and high-risk forest-going behaviours. Future efforts should include cross-border collaboration and continued targeting of high-risk behaviours to reduce the risk of imported cases seeding local transmission.

Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface.

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

Human serum albumin as a potential drug delivery system for N-methylated hot spot insulin analogs inhibiting hormone aggregation.

The purpose of this study was to investigate whether Human Serum Albumin (HSA) can bind N-methylated analogs of hot spots of native insulin. Three N-methylated derivatives of the A13-A19 fragment of native insulin were used: L(N-Me)YQLENY (1), LYQ(N-Me)LENY (2), and L(N-Me)YQ(N-Me)LENY (3). The studied N-methylated insulin fragments possess inhibiting potential against hormone aggregation. A variety of research techniques, including spectroscopic methods and microscopy assays, were used to study the interaction of HSA with the N-methylated insulin fragments. Based on spectroscopic measurements with Congo Red and Thioflavin T, all the analyzed N-methylated peptides were able to interact with the HSA surface. The CD spectrum registered for HSA in the presence of L(N-Me)YQLENY showed the smallest content of α-helix conformation, indicating the most compact HSA structure. Based on the results of MST, the dissociation constants (Kd) for complexes of HSA and peptides 1-3 were 19.2 nM (complex 1), 15.6 nM (complex 2), and 8.07 nM (complex 3). Microscopy assays, dynamic light scattering measurements as well as computer simulation of protein-ligand interaction also confirmed the possibility of docking the N-methylated inhibitors within HSA.

Mapping pollen allergenicity from urban trees in Valencia: A tool for green infrastructure planning.

Urban trees provide many benefits to citizens but also have associated disservices such as pollen allergenicity. Pollen allergies affect 40% of the European population, a problem that will be exacerbated with climate change by lengthening the pollen season. The allergenic characteristics of the urban trees and urban parks of the city of Valencia (Spain) have been studied. The Value of Potential Allergenicity (VPA) was calculated for all species. The most abundant allergenic trees with a very high VPA were the cypresses, followed by Platanus x hispanica and species of genera Morus, Acer and Fraxinus, with a high VPA. On the contrary, Citrus x aurantium, Melia azedarach, Washingtonia spp., Brachychiton spp. and Jacaranda mimosifolia were among the most abundant low allergenic trees. VPA was mapped for the city and a hot spot analysis was applied to identify areas of clustering of high and low VPA values. This geostatistical analysis provides a comprehensive representation of the VPA patterns which is very useful for urban green infrastructure planning. The Index of Urban Green Zone Allergenicity (IUGZA) was calculated for the main parks of the city. The subtropical and tropical flora component included many entomophilous species and the lowest share of high and very high allergenic trees in comparison with the Mediterranean and Temperate components. Overall, a diversification of tree species avoiding clusters of high VPA trees, and the prioritization of species with low VPA are good strategies to minimize allergy-related impacts of urban trees on human health.

Target-induced hot spot construction for sensitive and selective surface-enhanced Raman scattering detection of matrix metalloproteinase MMP-9.

Studies have found that matrix metalloproteinase-9 (MMP-9) plays a significant role in cancer cell invasion, metastasis, and tumor growth. But it is a challenge to go for highly sensitive and selective detection and targeting of MMP-9 due to the similar structure and function of the MMP proteins family. Herein, a novel surface-enhanced Raman scattering (SERS) sensing strategy was developed based on the aptamer-induced SERS "hot spot" formation for the extremely sensitive and selective determination of MMP-9. To develop the nanosensor, one group of gold nanospheres was modified with MMP-9 aptamer and its complementary strand DNA1, while DNA2 (complementary to DNA1) and the probe molecule 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB) were grafted on the surface of the other group of gold nanospheres. In the absence of MMP-9, DTNB located on the 13-nm gold nanospheres has only generated a very weak SERS signal. However, when MMP-9 is present, the aptamer preferentially binds to the MMP-9 to construct MMP-9-aptamer complex. The bare DNA1 can recognize and bind to DNA2, which causes them to move in close proximity and create a SERS hot spot effect. Due to this action, the SERS signal of DTNB located at the nanoparticle gap is greatly enhanced, achieving highly sensitive detection of MMP-9. Since the hot spot effect is caused by the aptamer that specifically recognizes MMP-9, the approach exhibits excellent selectivity for MMP-9 detection. Based on the benefits of both high sensitivity and excellent selectivity, this method was used to distinguish the difference in MMP-9 levels between normal and cancer cells as well as the expression of MMP-9 from cancer cells with different degrees of metastasis. In addition, this strategy can accurately reflect the dynamic changes in intracellular MMP-9 levels, stimulated by the MMP-9 activator and inhibitor. This strategy is expected to be transformed into a new technique for diagnosis of specific cancers related to MMP-9 and assessing the extent of cancer occurrence, development and metastasis.

A novel approach to identify priority areas for optimal nutrient management in mixed land-use watersheds through nutrient budget assessment.

Excessive nutrient supply in agricultural regions has led to various environmental issues, thereby requiring concentrated management owing to its persistent upward trend. Nutrient budgets (NBs), a vital agricultural environmental indicator, are employed for nutrient management in agricultural areas, using data surveyed by administrative agencies. However, the spatial extent of nutrient data for nutrient budgeting is limited by administrative boundaries according to the surveying organization, posing challenges in interpreting spatial patterns at the watershed level. In this study, a novel approach was developed to identify priority nutrient management areas by applying hot spot spatial analysis to watershed-level NBs, considering hydrological characteristics. This method was applied to approximately 850 subwatersheds across the Republic of Korea, where land cover characteristics are complex. Reassessing nutrient budgets at the watershed scale, accounting for overlapping administrative boundary areas and crop cultivation ratios, indicated similar levels between the two methods. Hot spot analysis revealed that watersheds with elevated NBs mirrored the spatial patterns of livestock excreta and cropland. The spatial distribution characteristics of watersheds with high nutrient levels in rivers corresponded with the concentration characteristics of industrial and commercial areas. Therefore, applying watershed-level NBs based on land cover ratios that consider nutrient input characteristics in agricultural regions is deemed appropriate for selecting priority nutrient management areas. Collectively, this study presents a method for selecting nutrient management priority areas by simultaneously considering the spatial characteristics of various environmental factors, such as land cover, livestock excreta, river water quality, and land area-based watershed-specific NBs. The proposed approach, considering mixed land cover characteristics, is anticipated to be valuable for selecting priority management areas in watersheds with diverse pollution sources. Future research is needed to explore nutrient budgets within watersheds, the influence of land use on pollution sources, and their correlation with water quality.

Spatial variations in the associations of mental distress with sleep insufficiency in the United States: a county-level spatial analysis.

In this research, we conducted hierarchical multiple regression and complex sample general linear model (CSGLM) to expand knowledge on factors contributing to mental distress, particularly from a geographic perspective. Based on the Getis-Ord G* hot-spot analysis, geographic distribution of both FMD and insufficient sleep showed several contiguous hotspots in southeast regions. Moreover, in the hierarchical regression, even after accounting for potential covariates and multicollinearity, a significant association between FMD and insufficient sleep was found, explaining that mental distress increases with increasing insufficient sleep (R2 = 0.835). In the CSGLM, a R2 value of 0.782 indicated that the CSGLM procedure provided concrete evidence that FMD was significantly related to sleep insufficiency even after taking complex sample designs and weighting adjustments in the BRFSS into account. This geographic association between FMD and insufficient sleep based on cross-county study has not been reported before in the literature. These findings suggest a need for further investigation on geographic disparity on mental distress and insufficient sleep and have novel implications in our understanding of the etiology of mental distress.

Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform.

BACKGROUND: Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features. RESULTS: In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. CONCLUSIONS: Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .

Functional inhibition of c-Myc using novel inhibitors identified through "hot spot" targeting.

Protein-protein interactions drive various biological processes in healthy as well as disease states. The transcription factor c-Myc plays a crucial role in maintaining cellular homeostasis, and its deregulated expression is linked to various human cancers; therefore, it can be considered a viable target for cancer therapeutics. However, the structural heterogeneity of c-Myc due to its disordered nature poses a major challenge to drug discovery. In the present study, we used an in silico alanine scanning mutagenesis approach to identify "hot spot" residues within the c-Myc/Myc-associated factor X interface, which is highly disordered and has not yet been systematically analyzed for potential small molecule binding sites. We then used the information gained from this analysis to screen potential inhibitors using a conformation ensemble approach. The fluorescence-based biophysical experiments showed that the identified hit molecules displayed noncovalent interactions with these hot spot residues, and further cell-based experiments showed substantial in vitro potency against diverse c-Myc-expressing cancer/stem cells by deregulating c-Myc activity. These biophysical and computational studies demonstrated stable binding of the hit compounds with the disordered c-Myc protein. Collectively, our data indicated effective drug targeting of the disordered c-Myc protein via the determination of hot spot residues in the c-Myc/Myc-associated factor X heterodimer.

A Comparative Analysis of NOX4 Protein Expression in Malignant and Non-Malignant Thyroid Tumors.

The comparative analysis of the expression of the reactive oxygen species-generating NADPH oxidase NOX4 from TCGA data shows that the NOX4 transcript is upregulated in papillary thyroid carcinomas (PTC)-BRAFV600E tumors compared to PTC-BRAFwt tumors. However, a comparative analysis of NOX4 at the protein level in malignant and non-malignant tumors is missing. We explored NOX4 protein expression by immunohistochemistry staining in malignant tumors (28 classical forms of PTC (C-PTC), 17 follicular variants of PTC (F-PTC), and three anaplastic thyroid carcinomas (ATCs)) and in non-malignant tumors (six lymphocytic thyroiditis, four Graves' disease, ten goiters, and 20 hyperplasias). We detected the BRAFV600E mutation by Sanger sequencing and digital droplet PCR. The results show that NOX4 was found to be higher (score ≥ 2) in C-PTC (92.9%) compared to F-PTC (52.9%) and ATC (33.3%) concerning malignant tumors. Interestingly, all C-PTC-BRAFV600E expressed a high score for NOX4 at the protein level, strengthening the positive correlation between the BRAFV600E mutation and NOX4 expression. In addition, independent of the mutational status of BRAF, we observed that 90% of C-PTC infiltrating tumors showed high NOX4 expression, suggesting that NOX4 may be considered a complementary biomarker in PTC aggressiveness. Interestingly, NOX4 was highly expressed in non-malignant thyroid diseases with different subcellular localizations.

Hot Spot Engineering in Hierarchical Plasmonic Nanostructures.

The controllable nanogap structures offer an effective way to obtain strong and tunable localized surface plasmon resonance (LSPR). A novel hierarchical plasmonic nanostructure (HPN) is created by incorporating a rotating coordinate system into colloidal lithography. In this nanostructure, the hot spot density is increased drastically by the long-range ordered morphology with discrete metal islands filled in the structural units. Based on the Volmer-Weber growth theory, the precise HPN growth model is established, which guides the hot spot engineering for improved LSPR tunability and strong field enhancement. The hot spot engineering strategy is examined by the application of HPNs as the surface-enhanced Raman spectroscopy (SERS) substrate. It is universally suitable for various SERS characterization excited at different wavelengths. Based on the HPN and hot spot engineering strategy, single-molecule level detection and long-range mapping can be realized simultaneously. In that sense, it offers a great platform and guides the future design for various LSPR applications like surface-enhanced spectra, biosensing, and photocatalysis.

Different Mutational Landscapes in Human Papillomavirus-Induced and Human Papillomavirus-Independent Invasive Penile Squamous Cell Cancers.

Penile squamous cell carcinomas (SCC) are rare cancers that arise after transforming human papillomavirus (HPV) infections or independent of HPV in the background of chronic dermatoses. Limited knowledge about genetic alterations driving penile carcinogenesis comes from studies of mainly small cohorts of typically mixed etiology. In this comparative genetic study of HPV-induced and HPV-independent invasive penile SCC of 156 patients from a single institution in a low-incidence country, hotspots of 50 cancer-relevant genes were analyzed with targeted next-generation sequencing. Seventy-nine of 156 SCC were classified as HPV induced, and 77 of 156 SCC arose independent of HPV. Only 28 (35%) of 79 HPV-induced penile SCC, but 69 (90%) of 77 HPV-independent SCC carried somatic gene mutations. PIK3CA, FGFR3, and FBXW7 mutations occurred in both groups in similar numbers as seen in other human cancers. In contrast, mutations in TP53 (44/77; 57%), CDKN2A (35/77; 45%), and HRAS (13/77; 17%) genes occurred with one exception of a HIV positive patient exclusively in HPV-independent SCC with a frequent co-occurrence of TP53 and CDKN2A mutations (28/77; 42%). Mutations in multiple genes occurred in 9 (11%) of 79 HPV-induced SCC versus 47 (62%) of 77 HPV-independent SCC (χ2; P < .001). More than one mutation per gene (multi hits) was characteristic for HPV-independent SCC in 14 (18%) of 77 compared with only 3 (4%) of 79 HPV-induced SCC (χ2; P < .001). The total number of mutations in HPV-induced penile SCC (47 mutations) was significantly lower than that in HPV-independent SCC (143 mutations; Welsh test; P < .001). The presence of somatic driver gene mutations did not correlate with the age of patients, histology, or tumor stage of the primary SCC in either etiologic group, suggesting that acquisition of driver gene mutations is an early event after invasion. This large cohort analysis identified characteristic differences in mutational landscapes for the 2 etiologies. While genetic mutations in tumor suppressor genes drive HPV-independent penile carcinogenesis, oncogenic action of E6 and E7 substitute for mutations in HPV-induced SCC. A subgroup of patients with advanced SCC may be candidates for targeted therapy and clinical trials, although the majority of advanced penile SCC remain a therapeutic challenge.

SARS-CoV-2 hot-spot mutations are significantly enriched within inverted repeats and CpG island loci.

SARS-CoV-2 is an intensively investigated virus from the order Nidovirales (Coronaviridae family) that causes COVID-19 disease in humans. Through enormous scientific effort, thousands of viral strains have been sequenced to date, thereby creating a strong background for deep bioinformatics studies of the SARS-CoV-2 genome. In this study, we inspected high-frequency mutations of SARS-CoV-2 and carried out systematic analyses of their overlay with inverted repeat (IR) loci and CpG islands. The main conclusion of our study is that SARS-CoV-2 hot-spot mutations are significantly enriched within both IRs and CpG island loci. This points to their role in genomic instability and may predict further mutational drive of the SARS-CoV-2 genome. Moreover, CpG islands are strongly enriched upstream from viral ORFs and thus could play important roles in transcription and the viral life cycle. We hypothesize that hypermethylation of these loci will decrease the transcription of viral ORFs and could therefore limit the progression of the disease.

Translocation detection from Hi-C data via scan statistics.

Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver-operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi-C data.

An overview of plutonium isotopes in soils, China: Distribution, spatial patterns, and sources.

Plutonium (Pu) is an anthropogenic radionuclide which has drawn significant attentions due to its radiotoxicity, and the sources of plutonium linked with nuclear accidents and contaminations. The 240Pu/239Pu atom ratio is source dependent and can be used as a fingerprint to determine the sources of radioactive contaminant. However, the distribution and sources of plutonium in soils of China have not yet been systematically studied at a national scale up to date. The distribution, spatial patterns, and sources of plutonium in soils of China were discussed in this work. The concentrations of 239,240Pu are in the range of 0.002-4.824 mBq/g with a large variation, and the 239,240Pu concentrations in surface soils increase with the increasing latitude, which affects by multi-factors such as organic matter and particle size, etc. The inventories of 239,240Pu are in the range of 7.31-554 Bq/m2. The weighted average of 240Pu/239Pu atom ratios (0.180 ± 0.004) in all surface samples is good agreement with the ratio of global fallout (0.180 ± 0.014) of the nuclear weapons tests, this indicate that the major source of plutonium in China is global fallout. However, among some sites, distinctly lower 240Pu/239Pu atom ratio compared to the global fallout values were observed in the northwest China, indicating a significant contribution from other source besides the global fallout. Furthermore, the spatial clustering patterns of hot spots (high values) and cold spots (low values) for plutonium showing the clear associations with nuclear tests, especially the Chinese Lop Nor nuclear weapons tests (CNTs) and the Semipalatinsk nuclear weapons tests (STS). Radioactive material including plutonium from the STS or CNTs was transported by the prevailing westerlies to the northwest China. This review about the fingerprints and distribution of plutonium in soils of China will help researchers to establish a reference database for future radiation risk assessment and environmental radioactive management.

Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study.

The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot.

Label-free detection of vitamin B by two-step enhanced Raman technique using dynamic borohydride-reduced silver nanoparticles.

A creatively designed novel two-step enhancement technique is presented in which B vitamin molecules are dynamically adsorbed onto the surface of silver nanoparticles by sodium borohydride, followed by local plasmon resonance in the presence of cations (calcium ions), ultimately achieving synergistic chemical and physical enhancement on the same molecule and constructing a "surface hot spots" two-step enhancement platform for vitamin detection. The Raman signal of the promoted vitamin molecule is enhanced by nine orders of magnitude. In a subsequent study it was observed that the vitamin B2 molecules were in a near-vertical image on the surface of the silver nanoparticles, which may also contribute to the Raman signal enhancement. Combined with deep learning techniques, the method has been successfully applied to the detection of B vitamins in body fluids. As an accurate, rapid, reproducible, non-invasive, and versatile assay platform, it holds great promise for the intelligent identification of trace B molecules in food, pharmaceuticals, and the human body.

Redundant Fault Diagnosis for Photovoltaic Systems Based on an IRT Low-Cost Sensor.

In large solar farms, supervision is an exhaustive task, often carried out manually by field technicians. Over time, automated or semi-automated fault detection and prevention methods in large photovoltaic plants are becoming increasingly common. The same does not apply when talking about small or medium-sized installations, where the cost of supervision at such level would mean total economic infeasibility. Although there are prevention protocols by suppliers, periodic inspections of the facilities by technicians do not ensure that faults such as the appearance of hot-spots are detected in time. That is why, nowadays, the only way of continuous supervision of a small or medium installation is often carried out by unqualified people and in a purely visual way. In this work, the development of a low-cost system prototype is proposed for the supervision of a medium or small photovoltaic installation based on the acquisition and treatment of thermographic images, with the aim of investigating the feasibility of an actual implementation. The work focuses on the system's ability to detect hot-spots in supervised panels and successfully report detected faults. To achieve this goal, a low-cost thermal imaging camera is used for development, applying common image processing techniques, operating with OpenCV and MATLAB R2021b libraries. In this way, it is possible to demonstrate that it is achievable to successfully detect the hottest points of a photovoltaic (PV) installation with a much cheaper camera than the cameras used in today's thermographic inspections, opening up the possibilities of creating a fully developed low-cost thermographic surveillance system.