Advanced crop yield prediction using machine learning and deep learning: a comprehensive review

The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present

Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now

Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images, prequalified to fool simple, signal-based classifiers into believing they are real. We then show that prequalified generated images can be identified reliably by classifiers that only look at geometric properties. We use three such classifiers. All three classifiers are denied access to image pixels, and look only at derived geometric features. The first classifier looks at the perspective field of the image, the second looks at lines detected in the image, and the third looks at relations between detected objects and shadows. Our procedure detects generated images more reliably than SOTA local signal based detectors, for images from a number of distinct generators. Saliency maps suggest that the classifiers can identify geometric problems reliably. We conclude that current generators cannot reliably reproduce geometric properties of real images.Comment: Project Page: https://projective-geometry.github.io | First three authors contributed equall

Deep brain stimulation of the hypothalamic region: a systematic review

Background: Deep brain stimulation (DBS) has been successfully used for the treatment of circuitopathies including movement, anxiety, and behavioral disorders. The hypothalamus is a crucial integration center for many peripheral and central pathways relating to cardiovascular, metabolic, and behavioral functions and constitutes a potential target for neuromodulation in treatment-refractory conditions. To conduct a systematic review, investigating hypothalamic targets in DBS, their indications, and the primary clinical findings. Methods: PubMed, Scopus, and Web of Science databases were searched in accordance with the PRISMA guideline to identify papers published in English studying DBS of the hypothalamus in humans. Results: After screening 3,148 papers, 34 studies consisting of 412 patients published over two decades were included in the final review. Hypothalamic DBS was indicated in refractory headaches (n = 238, 57.8%), aggressive behavior (n = 100, 24.3%), mild Alzheimer’s disease (n = 58, 14.1%), trigeminal neuralgia in multiple sclerosis (n = 5, 1.2%), Prader-Willi syndrome (n = 4, 0.97%), and atypical facial pain (n = 3, 0.73%). The posterior hypothalamus was the most common DBS target site across 30 studies (88.2%). 262 (63.6%) participants were males, and 110 (26.7%) were females. 303 (73.5%) patients were adults whereas 33 (8.0%) were pediatrics. The lowest mean age of participants was 15.25 ± 4.6 years for chronic refractory aggressiveness, and the highest was 68.5 ± 7.9 years in Alzheimer’s disease patients. The mean duration of the disease ranged from 2.2 ± 1.7 (mild Alzheimer’s disease) to 19.8 ± 10.1 years (refractory headaches). 213 (51.7%) patients across 29 studies (85.3%) reported symptom improvements which ranged from 23.1% to 100%. 25 (73.5%) studies reported complications, most of which were associated with higher voltage stimulations. Conclusions: DBS of the hypothalamus is feasible in selected patients with various refractory conditions ranging from headaches to aggression in both pediatric and adult populations. Future large-scale studies with long-term follow-up are required to validate the safety and efficacy data and extend these findings

Facial Emotion Recognition

Emotion is a complex conscious that humans experience as a result of interactions with the environment. The project basically takes in the image, recognises the emotion by fragmenting the image with the deep learning technique using CNN. The tools and framework used here are keras and TensorFlow respectively. Here the facial feeling analysis refers to computing system that makes an attempt to mechanically analyse and recognise facial feeling and facial feature changes from visual data. The image being pre-processed helps for aiming a better quality of image and hence the emotion can be detected in a better way. This project can be used in many fields and one such field is mental health care centre. Patients with bipolar disorders should be treated by adhering the emotional behavior of patient and our project helps in doing the same

Reprogramming of bivalent chromatin states in NRAS mutant melanoma suggests PRC2 inhibition as a therapeutic strategy.

The dynamic evolution of chromatin state patterns during metastasis, their relationship with bona fide genetic drivers, and their therapeutic vulnerabilities are not completely understood. Combinatorial chromatin state profiling of 46 melanoma samples reveals an association of NRAS mutants with bivalent histone H3 lysine 27 trimethylation (H3K27me3) and Polycomb repressive complex 2. Reprogramming of bivalent domains during metastasis occurs on master transcription factors of a mesenchymal phenotype, including ZEB1, TWIST1, and CDH1. Resolution of bivalency using pharmacological inhibition of EZH2 decreases invasive capacity of melanoma cells and markedly reduces tumor burden in vivo, specifically in NRAS mutants. Coincident with bivalent reprogramming, the increased expression of pro-metastatic and melanocyte-specific cell-identity genes is associated with exceptionally wide H3K4me3 domains, suggesting a role for this epigenetic element. Overall, we demonstrate that reprogramming of bivalent and broad domains represents key epigenetic alterations in metastatic melanoma and that EZH2 plus MEK inhibition may provide a promising therapeutic strategy for NRAS mutant melanoma patients

Chromatin State Dynamics Confers Specific Therapeutic Strategies in Enhancer Subtypes of Colorectal Cancer

Objective: Enhancer aberrations are beginning to emerge as a key epigenetic feature of colorectal cancers (CRC), however, a comprehensive knowledge of chromatin state patterns in tumour progression, heterogeneity of these patterns and imparted therapeutic opportunities remain poorly described. Design: We performed comprehensive epigenomic characterisation by mapping 222 chromatin profiles from 69 samples (33 colorectal adenocarcinomas, 4 adenomas, 21 matched normal tissues and 11 colon cancer cell lines) for six histone modification marks: H3K4me3 for Pol II-bound and CpG-rich promoters, H3K4me1 for poised enhancers, H3K27ac for enhancers and transcriptionally active promoters, H3K79me2 for transcribed regions, H3K27me3 for polycomb repressed regions and H3K9me3 for heterochromatin. Results: We demonstrate that H3K27ac-marked active enhancer state could distinguish between different stages of CRC progression. By epigenomic editing, we present evidence that gains of tumour-specific enhancers for crucial oncogenes, such as ASCL2 and FZD10, was required for excessive proliferation. Consistently, combination of MEK plus bromodomain inhibition was found to have synergistic effects in CRC patient-derived xenograft models. Probing intertumour heterogeneity, we identified four distinct enhancer subtypes (EPIgenome-based Classification, EpiC), three of which correlate well with previously defined transcriptomic subtypes (consensus molecular subtypes, CMSs). Importantly, CMS2 can be divided into two EpiC subgroups with significant survival differences. Leveraging such correlation, we devised a combinatorial therapeutic strategy of enhancer-blocking bromodomain inhibitors with pathway-specific inhibitors (PARPi, EGFRi, TGFβi, mTORi and SRCi) for EpiC groups. Conclusion: Our data suggest that the dynamics of active enhancer underlies CRC progression and the patient-specific enhancer patterns can be leveraged for precision combination therapy

Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma.

Histone methyltransferase KMT2D harbors frequent loss-of-function somatic point mutations in several tumor types, including melanoma. Here, we identify KMT2D as a potent tumor suppressor in melanoma through an in vivo epigenome-focused pooled RNAi screen and confirm the finding by using a genetically engineered mouse model (GEMM) based on conditional and melanocyte-specific deletion of KMT2D. KMT2D-deficient tumors show substantial reprogramming of key metabolic pathways, including glycolysis. KMT2D deficiency aberrantly upregulates glycolysis enzymes, intermediate metabolites, and glucose consumption rates. Mechanistically, KMT2D loss causes genome-wide reduction of H3K4me1-marked active enhancer chromatin states. Enhancer loss and subsequent repression of IGFBP5 activates IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. Pharmacological inhibition of glycolysis and insulin growth factor (IGF) signaling reduce proliferation and tumorigenesis preferentially in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating an increased use of the glycolysis pathway for enhanced biomass needs via enhancer reprogramming, thus presenting an opportunity for therapeutic intervention through glycolysis or IGF pathway inhibitors

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe