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Mounting evidence suggests a prominent role for alpha-synuclein (a-syn) in neuronal cell function. Alterations in the levels of cellular a-syn have been hypothesized to play a critical role in the development of Parkinson’s disease (PD); however, mechanisms that control expression of the gene for a-syn (SNCA) in cis and trans as well as turnover of a-syn are not well understood. We analyzed whether methyl-CpG binding protein 2 (MeCP2), a protein that specifically binds methylated DNA, thus regulating transcription, binds at predicted binding sites in intron 1 of the SNCA gene and regulates a-syn protein expression. Chromatin immunoprecipitation (ChIP) and electrophoretic mobility-shift assays (EMSA) were used to confirm binding of MeCP2 to regulatory regions of SNCA. Site-specific methylation and introduction of localized mutations by CRISPR/Cas9 were used to investigate the binding properties of MeCP2 in human SK-N-SH neuroblastoma cells. The significance of MeCP2 for SNCA regulation was further investigated by overexpressing MeCP2 and mutated variants of MeCP2 in MeCP2 knockout cells. We found that methylation-dependent binding of MeCP2 at a restricted region of intron 1 of SNCA had a significant impact on the production of a-syn. A single nucleotide substitution near to CpG1 strongly increased the binding of MeCP2 to intron 1 of SNCA and decreased a-syn protein expression by 60%. In contrast, deletion of a single nucleotide closed to CpG2 led to reduced binding of MeCP2 and significantly increased a-syn levels. In accordance, knockout of MeCP2 in SK-N-SH cells resulted in a significant increase in a-syn production, demonstrating that SNCA is a genomic target for MeCP2 regulation. In addition, the expression of two mutated MeCP2 variants found in Rett syndrome (RTT) showed a loss of their ability to reduce a-syn expression. This study demonstrates that methylation of CpGs and binding of MeCP2 to intron 1 of the SNCA gene plays an important role in the control of a

Introduction: A multitude of findings from cell cultures and animal studies are available to support the anti-cancer properties of cannabidiol (CBD). Since CBD acts on multiple molecular targets, its clinical adaptation, especially in combination with cancer immunotherapy regimen remains a serious concern. Methods: Considering this, we extensively studied the effect of CBD on the cytokine-induced killer (CIK) cell immunotherapy approach using multiple non-small cell lung cancer (NSCLC) cells harboring diverse genotypes. Results: Our analysis showed that, a) The Transient Receptor Potential Cation Channel Subfamily V Member 2 (TRPV2) channel was intracellularly expressed both in NSCLC cells and CIK cells. b) A synergistic effect of CIK combined with CBD, resulted in a significant increase in tumor lysis and Interferon gamma (IFN-g) production. c) CBD had a preference to elevate the CD25+CD69+ population and the CD62L_CD45RA+terminal effector memory (EMRA) population in NKT-CIK cells, suggesting early-stage activation and effector memory differentiation in CD3+CD56+ CIK cells. Of interest, we observed that CBD enhanced the calcium influx, which was mediated by the TRPV2 channel and elevated phosphor-Extracellular signal-Regulated Kinase (p-ERK) expression directly in CIK cells, whereas ERK selective inhibitor FR180204 inhibited the increasing cytotoxic CIK ability induced by CBD. Further examinations revealed that CBD induced DNA double-strand breaks via upregulation of histone H2AX phosphorylation in NSCLC cells and the migration and invasion ability of NSCLC cells suppressed by CBD were rescued using the TRPV2 antagonist (Tranilast) in the absence of CIK cells. We further investigated the epigenetic effects of this synergy and found that adding CBD to CIK cells decreased the Long Interspersed Nuclear Element-1 (LINE-1) mRNA expression and the global DNA methylation level in NSCLC cells carrying KRAS mutation. We further investigated the epigenetic effects of this sy

The effective management of brain tumors relies on precise typing, subtyping, and grading. This study advances patient care with findings from rigorous multiple instance learning experimentations across various feature extractors and aggregators in brain tumor histopathology. It establishes new performance benchmarks in glioma subtype classification across multiple datasets, including a novel dataset focused on the Indian demographic (IPD- Brain), providing a valuable resource for existing research. Using a ResNet-50, pretrained on histopathology datasets for feature extraction, combined with the Double-Tier Feature Distillation (DTFD) feature aggregator, our approach achieves state-of-the-art AUCs of 88.08 on IPD-Brain and 95.81 on the TCGA-Brain dataset, respectively, for three-way glioma subtype classification. Moreover, it establishes new benchmarks in grading and detecting IHC molecular biomarkers (IDH1R132H, TP53, ATRX, Ki-67) through H&E stained whole slide images for the IPD-Brain dataset. The work also highlights a significant correlation between the model decision-making processes and the diagnostic reasoning of pathologists, underscoring its capability to mimic professional diagnostic procedures.

Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance. However, most of these studies assume a fixed or no instruction provided in the prompt. We challenge this consensus by investigating the necessity of optimizing ICE when task-specific instructions are provided and find that there are many tasks for which it yields diminishing returns. In particular, using a diverse set of tasks and a systematically created instruction set with gradually added details, we find that as the prompt instruction becomes more detailed, the returns on ICE optimization diminish. To characterize this behavior, we introduce a task-specific metric called Normalized Invariability to Choice of Examples (NICE) that quantifies the learnability of tasks from a given instruction, and provides a heuristic to help decide whether to optimize instructions or ICE for a new task. Given a task, the proposed metric can reliably predict the utility of optimizing ICE compared to using random ICE. Our code is available at https://github.com/microsoft/nice-icl., Comment: Accepted as a full paper (9 pages) at ACL 2024 (Main)

This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance for chip design. We examine the TCO and performance metrics of a domain-adaptive LLM, ChipNeMo, against two leading LLMs, Claude 3 Opus and ChatGPT-4 Turbo, to assess their efficacy in chip design coding generation. Through a detailed evaluation of the accuracy of the model, training methodologies, and operational expenditures, this study aims to provide stakeholders with critical information to select the most economically viable and performance-efficient solutions for their specific needs. Our results underscore the benefits of employing domain-adapted models, such as ChipNeMo, that demonstrate improved performance at significantly reduced costs compared to their general-purpose counterparts. In particular, we reveal the potential of domain-adapted LLMs to decrease TCO by approximately 90%-95%, with the cost advantages becoming increasingly evident as the deployment scale expands. With expansion of deployment, the cost benefits of ChipNeMo become more pronounced, making domain-adaptive LLMs an attractive option for organizations with substantial coding needs supported by LLMs, Comment: Paper accepted in IEEE-ACM conference: 2024 IEEE LLM-Aided Design Workshop (LAD)

Plastics usage is rising daily because of increased population, modernization, and industrialization, which produces a lot of plastic garbage. Due to their various chemical structures, long chain polymeric compositions, and thermal/decomposition behavior, it is challenging to recycle these plastic wastes into hydrocarbon fuels. In the current work, domestic plastic waste was pyrolyzed at 473 to 973 K in a fixed bed reactor and compared with the three virgin plastics LDPE (low-density polyethylene), HDPE (high-density polyethylene), and PP (polypropylene), as well as a mixture of the three (virgin mixed plastics). The pyrolysis results showed that maximum liquid hydrocarbons obtained from HDPE, LDPE, PP, mixed plastic, and domestic waste were 64.6 wt.%, 62.2 wt.%, 63.1 wt.%, 68.6 wt.%, and 64.6 wt.% at 773 K, respectively. The composition of liquid fuels was characterized using FTIR and GC-MS, which showed a wide spectrum of hydrocarbons in the C8–C20 range. Furthermore, liquid fuel characteristics such as density, viscosity, fire and flash point, pour point, and calorific value were examined using ASTM standards, and the results were found to be satisfactory. This study provides an innovative method for recycling waste plastics into economical hydrocarbon fuel for use in transportation., Validerad;2023;Nivå 2;2023-02-08 (joosat);Funder: Science and Engineering Research Board (SERB), DST, New Delhi—110070 (Project file No. ECR/2017/000185).Licens fulltext: CC BY License

Municipal mixed plastic waste (MMPW) recycling is an innovative way to turn environmental waste into energy fuels. In the present study, a thermochemical process was applied to depolymerize MMPW to produce hydrocarbon fuels known as plasto-oil. The obtained plasto-oil was blended with conventional diesel to test the performance of the PCCI-mode single-cylinder, four-stroke, direct-injection diesel engine. The PCCI combustion mixture was tested with 15% and 30% fuel vapor to ensure homogeneity with and without exhaust gas recirculation. The modified engine findings were compared to a standard conventional engine. At higher loads, PCCI combustion showed reduced emission of carbon monoxide and nitrogen oxides. While the thermal braking efficiency was marginally reduced at all engine loads while using the blends. The results showed that with and without 10% exhaust gas recirculation, an increase in air mix reduced NOx emissions; however, in the case of smoke emissions, an opposite trend was observed. A blend of plasto-oils also decreased CO and unburned hydrocarbon (HC) emissions at higher loads. In conclusion, it was shown that plasto-oils combined with conventional diesel fuel outperformed diesel fuel alone., Validerad;2023;Nivå 2;2023-05-29 (joosat);Licens fulltext: CC BY LicenseFunder: Science and Engineering Research Board (SERB), DST, New Delhi (ECR/2017/000185)

Acute kidney injury (AKI) is a growing epidemic and is independently associated with increased risk of death, chronic kidney disease (CKD) and cardiovascular events. Randomized-controlled trials (RCTs) in this domain are notoriously challenging and many clinical studies in AKI have yielded inconclusive findings. Underlying this conundrum is the inherent heterogeneity of AKI in its etiology, presentation and course. AKI is best understood as a syndrome and identification of AKI subphenotypes is needed to elucidate the disease's myriad etiologies and to tailor effective prevention and treatment strategies. Conventional RCTs are logistically cumbersome and often feature highly selected patient populations that limit external generalizability and thus alternative trial designs should be considered when appropriate. In this narrative review of recent developments in AKI trials based on the Kidney Disease Clinical Trialists (KDCT) 2020 meeting, we discuss barriers to and strategies for improved design and implementation of clinical trials for AKI patients, including predictive and prognostic enrichment techniques, the use of pragmatic trials and adaptive trials.

In this work, the CuAgZr metallic glasses (MGs) are investigated, a promising material for biomedical applications due to their high strength, corrosion resistance, and antibacterial activity. Using an integrated approach of combinatorial synthesis, high-throughput characterization, and machine learning (ML), the mechanical properties of CuAgZr MGs are efficiently explored. The investigation find that post-deposition oxidation in inter-columnar regions with looser packing causes high oxygen content in Cu-rich regions, significantly affecting the alloys mechanical behavior. The study also reveals that nanoscale structural features greatly impact plastic yielding and flow in the alloys. ML algorithms are tested, and the multi-layer perceptron algorithm produced satisfactory predictions for the alloys hardness of untested alloys, providing valuable clues for future research. The work demonstrates the potential of using combinatorial synthesis, high-throughput characterization, and ML  techniques to facilitate the development of new MGs with improved strength and economic feasibility.

This study analyzes vibration modes in non-homogeneous orthotropic parallelogram plate with a one-dimensional circular thickness, focusing on SCCC edge condition, where C and S represent the clamped and simply supported edges of the plate, respectively. Circular Poisson’s ratio variation is considered, along with linear temperature changes. The study demonstrates the advantages of variable Poisson’s ratio over density parameter variation in obtaining shorter vibration time periods. Orthotropic parallelogram plate, thermal gradient, circular tapering, nonhomogeneity.

The objective of this study is to investigate the natural vibration of a rectangular plate made of orthotropic material with circular thickness (two dimensions) and temperature variation on the plate is parabolic (two dimensions) in nature. The solution to the problem is obtained by utilizing the Rayleigh-Ritz technique and the first four frequency modes are obtained under clamped edge conditions. The study aims to provide numerical data that demonstrate how circular variation in tapering parameters of plate can effectively control and optimized vibrational frequencies of the plate. Orthotropic rectangular plate, thermal gradient, circular tapering, aspect ratio.

In the current research, modes of frequency of isotropic tapered triangular plate having 1-D (one dimensional) circular thickness and 1-D (one dimensional) linear temperature profile for clamped boundary conditions are discussed. Authors implemented Rayleigh Ritz technique to solve the frequency equation of isotropic triangular plate and computed the first four modes with a distinct combination of plate parameters. Authors have performed the convergence study of modes of frequency of the isotropic triangular plate. Also, conducted comparative analysis of modes of frequency of the current study with available published papers and the results presented in tabular form. The aim of the present study is to show the impact of a one dimensional circular thickness and one dimensional linear temperature on modes of frequency of vibration of an isotropic tapered triangular plate.

The mutual fund sectors are one of the fastest growing sectors in Indian Economy and have awesome potential for sustained future growth. Mutual funds make saving and investing simple, accessible, and affordable. The advantages of mutual funds include professional management, diversification, variety, liquidity, affordability, convenience, and ease of recordkeeping—as well as strict government regulation and full disclosure. Financial markets are becoming more extensive with wide-ranging financial products trying innovations in designing mutual funds portfolio but these changes need unification in correspondence with investor’s expectations. Thus, it has become imperative to study mutual funds from a different angle, which is to focus on investor’s perception and expectations. This research paper focused attention on number of factors that highlights investors’ perception about mutual funds. It was found that mutual funds were not that much known to investors, still investor rely upon bank and post office deposits, most of the investor used to invest in mutual fund for not more than 3 years and they used to quit from the fund which were not giving desired results. Equity option and SIP mode of investment were on top priority in investors’ list. It was also found that maximum number of investors did not analyze risk in their investment and they were depend upon their broker and agent for this work.

Adaptation is the central feature and leading explanation for the evolutionary diversification of life. Adaptation is also notoriously difficult to study in nature, owing to its complexity and logistically prohibitive timescale. Here, we leverage extensive contemporary and historical collections of Ambrosia artemisiifolia-an aggressively invasive weed and primary cause of pollen-induced hayfever-to track the phenotypic and genetic causes of recent local adaptation across its native and invasive ranges in North America and Europe, respectively. Large haploblocks-indicative of chromosomal inversions-contain a disproportionate share (26%) of genomic regions conferring parallel adaptation to local climates between ranges, are associated with rapidly adapting traits, and exhibit dramatic frequency shifts over space and time. These results highlight the importance of large-effect standing variants in rapid adaptation, which have been critical to A. artemisiifolia's global spread across vast climatic gradients.

Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to various observed and unobserved confounding variables in the data generation process. In this paper, we formally analyze how confounding biases impact downstream classifiers and present a causal viewpoint to the solutions based on counterfactual data augmentation. We explore how removing confounding biases serves as a means to learn invariant features, ultimately aiding in generalization beyond the observed data distribution. Additionally, we present a straightforward yet powerful algorithm for generating counterfactual images, which effectively mitigates the influence of confounding effects on downstream classifiers. Through experiments on MNIST variants and the CelebA datasets, we demonstrate how our simple augmentation method helps existing state-of-the-art methods achieve good results.

To address the problem of NLP classifiers learning spurious correlations between training features and target labels, a common approach is to make the model's predictions invariant to these features. However, this can be counter-productive when the features have a non-zero causal effect on the target label and thus are important for prediction. Therefore, using methods from the causal inference literature, we propose an algorithm to regularize the learnt effect of the features on the model's prediction to the estimated effect of feature on label. This results in an automated augmentation method that leverages the estimated effect of a feature to appropriately change the labels for new augmented inputs. On toxicity and IMDB review datasets, the proposed algorithm minimises spurious correlations and improves the minority group (i.e., samples breaking spurious correlations) accuracy, while also improving the total accuracy compared to standard training., Comment: Accepted to ACL 2023

The causal capabilities of large language models (LLMs) is a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We further our understanding of LLMs and their causal implications, considering the distinctions between different types of causal reasoning tasks, as well as the entangled threats of construct and measurement validity. LLM-based methods establish new state-of-the-art accuracies on multiple causal benchmarks. Algorithms based on GPT-3.5 and 4 outperform existing algorithms on a pairwise causal discovery task (97%, 13 points gain), counterfactual reasoning task (92%, 20 points gain), and actual causality (86% accuracy in determining necessary and sufficient causes in vignettes). At the same time, LLMs exhibit unpredictable failure modes and we provide some techniques to interpret their robustness. Crucially, LLMs perform these causal tasks while relying on sources of knowledge and methods distinct from and complementary to non-LLM based approaches. Specifically, LLMs bring capabilities so far understood to be restricted to humans, such as using collected knowledge to generate causal graphs or identifying background causal context from natural language. We envision LLMs to be used alongside existing causal methods, as a proxy for human domain knowledge and to reduce human effort in setting up a causal analysis, one of the biggest impediments to the widespread adoption of causal methods. We also see existing causal methods as promising tools for LLMs to formalize, validate, and communicate their reasoning especially in high-stakes scenarios. In capturing common sense and domain knowledge about causal mechanisms and supporting translation between natural language and formal methods, LLMs open new frontiers for advancing the research, practice, and adoption of causality., Comment: 42 pages, 5 figures, working paper (minor changes in v2 to fix typos)

Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field. Despite notable advancements in predictive modeling, spanning core machine learning tasks to various scientific applications, challenges such as overlooked contextual factors, data-dependent decision-making, and unintentional re-use of test data have raised questions about the integrity of results. To address these issues, we propose adapting pre-registration practices from explanatory modeling to predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers to gain insight into the effectiveness of pre-registration in preventing biased estimates and promoting more reliable research outcomes. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.

Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-based retrieval models, recent work shows promising results on the zero-shot retrieval problem, i.e., no access to labeled data from the target domain. Two such popular paradigms are generation-augmented retrieval or GAR (generate additional context for the query and then retrieve), and retrieval-augmented generation or RAG (retrieve relevant documents as context and then generate answers). The success of these paradigms hinges on (i) high-recall retrieval models, which are difficult to obtain in the zero-shot setting, and (ii) high-precision (re-)ranking models which typically need a good initialization. In this work, we propose a novel GAR-meets-RAG recurrence formulation that overcomes the challenges of existing paradigms. Our method iteratively improves retrieval (via GAR) and rewrite (via RAG) stages in the zero-shot setting. A key design principle is that the rewrite-retrieval stages improve the recall of the system and a final re-ranking stage improves the precision. We conduct extensive experiments on zero-shot passage retrieval benchmarks, BEIR and TREC-DL. Our method establishes a new state-of-the-art in the BEIR benchmark, outperforming previous best results in Recall@100 and nDCG@10 metrics on 6 out of 8 datasets, with up to 17% relative gains over the previous best., Comment: preprint

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in the graph can propagate downstream to effect inference. In this work, we initially show that complete graph information is not necessary for causal effect inference; the topological order over graph variables (causal order) alone suffices. Further, given a node pair, causal order is easier to elicit from domain experts compared to graph edges since determining the existence of an edge can depend extensively on other variables. Interestingly, we find that the same principle holds for Large Language Models (LLMs) such as GPT-3.5-turbo and GPT-4, motivating an automated method to obtain causal order (and hence causal effect) with LLMs acting as virtual domain experts. To this end, we employ different prompting strategies and contextual cues to propose a robust technique of obtaining causal order from LLMs. Acknowledging LLMs' limitations, we also study possible techniques to integrate LLMs with established causal discovery algorithms, including constraint-based and score-based methods, to enhance their performance. Extensive experiments demonstrate that our approach significantly improves causal ordering accuracy as compared to discovery algorithms, highlighting the potential of LLMs to enhance causal inference across diverse fields.

Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often model-specific. In this paper, we address model-agnostic explanations, proposing two approaches for counterfactual (CF) approximation. The first approach is CF generation, where a large language model (LLM) is prompted to change a specific text concept while keeping confounding concepts unchanged. While this approach is demonstrated to be very effective, applying LLM at inference-time is costly. We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space. This space is faithful to a given causal graph and effectively serves to identify matches that approximate CFs. After showing theoretically that approximating CFs is required in order to construct faithful explanations, we benchmark our approaches and explain several models, including LLMs with billions of parameters. Our empirical results demonstrate the excellent performance of CF generation models as model-agnostic explainers. Moreover, our matching approach, which requires far less test-time resources, also provides effective explanations, surpassing many baselines. We also find that Top-K techniques universally improve every tested method. Finally, we showcase the potential of LLMs in constructing new benchmarks for model explanation and subsequently validate our conclusions. Our work illuminates new pathways for efficient and accurate approaches to interpreting NLP systems.

State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to failures on inputs from low-data regimes, such as domains that are not represented in training data. As an approximation to collecting ground-truth labels for the specific domain, we study the use of large language models (LLMs) for annotating inputs and improving the generalization of NLP models. Specifically, given a budget for LLM annotations, we present an algorithm for sampling the most informative inputs to annotate and retrain the NLP model. We find that popular active learning strategies such as uncertainty-based sampling do not work well. Instead, we propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model, utilizing the fact that most NLP models are finetuned from a base model. Experiments with classification (semantic similarity) and ranking (semantic search) tasks show that our sampling strategy leads to significant gains in accuracy for both the training and target domains.

In many classification datasets, the task labels are spuriously correlated with some input attributes. Classifiers trained on such datasets often rely on these attributes for prediction, especially when the spurious correlation is high, and thus fail to generalize whenever there is a shift in the attributes' correlation at deployment. If we assume that the spurious attributes are known a priori, several methods have been proposed to learn a classifier that is invariant to the specified attributes. However, in real-world data, information about spurious attributes is typically unavailable. Therefore, we propose a method to automatically identify spurious attributes by estimating their causal effect on the label and then use a regularization objective to mitigate the classifier's reliance on them. Compared to a recent method for identifying spurious attributes, we find that our method is more accurate in removing the attribute from the learned model, especially when spurious correlation is high. Specifically, across synthetic, semi-synthetic, and real-world datasets, our method shows significant improvement in a metric used to quantify the dependence of a classifier on spurious attributes ($\Delta$Prob), while obtaining better or similar accuracy. In addition, our method mitigates the reliance on spurious attributes even under noisy estimation of causal effects. To explain the empirical robustness of our method, we create a simple linear classification task with two sets of attributes: causal and spurious. We prove that our method only requires that the ranking of estimated causal effects is correct across attributes to select the correct classifier.

Counterfactual examples for an input - perturbations that change specific features but not others - have been shown to be useful for evaluating bias of machine learning models, e.g., against specific demographic groups. However, generating counterfactual examples for images is nontrivial due to the underlying causal structure on the various features of an image. To be meaningful, generated perturbations need to satisfy constraints implied by the causal model. We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI), that generates counterfactuals in accordance with the causal relationships between attributes of an image. Based on the generated counterfactuals, we show how to explain a pre-trained machine learning classifier, evaluate its bias, and mitigate the bias using a counterfactual regularizer. On the Morpho-MNIST dataset, our method generates counterfactuals comparable in quality to prior work on SCM-based counterfactuals (DeepSCM), while on the more complex CelebA dataset our method outperforms DeepSCM in generating high-quality valid counterfactuals. Moreover, generated counterfactuals are indistinguishable from reconstructed images in a human evaluation experiment and we subsequently use them to evaluate the fairness of a standard classifier trained on CelebA data. We show that the classifier is biased w.r.t. skin and hair color, and how counterfactual regularization can remove those biases. © 2022 IEEE.

Soil salinity is associated with the accumulation of soluble salts in higher concentration deteriorating soil health associated with unfavourable environment for plant growth. It is mostly confined to those regions where there is high temperature and low precipitation, mostly in arid and semi-arid regions. Major factors responsible for soil salinity can be categorised into primary and secondary factor affecting at the spatial and temporal scale. Higher concentration of soluble salts in soil increase the osmotic potential disrupting the movement of water from root to leaf. So, soil salinity is primarily associated with the water stress condition in plants which is a direct impact to plants. Indirectly it interferes with the nutrients absorption which is one of the most important factors for proper plant growth. Plants poses different mechanisms to avoid salt stress condition in soil but maximum of it are an active processes were additional energy must have to spend for it that can impact proper growth and production. The ions primarily responsible for both the soil and plant stress under soil salinity are Na+ and Cl- which concentration increases with certain primary and secondary soil salinization factors. So, primary aim to control the impact of soil salinity is to reduce the activity/concentration of both Na+ and Cl- from the soil. So, use of the essential nutrients (K+ and SO4-2) that has an antagonistic relationship with the salts is a new approach. Due to similar charge and physico chemical properties of K+ and SO4-2 with toxic ions Na+ and Cl- respectively, there lies an antagonistic relationship. Furthermore, SO4-2 of its less toxicity to plants and improve soil pH condition especially in arid and semi-arid region, the combination of K+ and SO4-2 salt is a good combination to ameliorate the Na+ and Cl- toxicity under saline soil.

SARS-CoV-2 is a highly infectious and deadly coronavirus that first appeared in late 2019 and has since created a pandemic of the acute respiratory disease known as "coronavirus disease 2019" (COVID-19). This literature aims to summarise current knowledge of COVID-19’s clinical, epidemiological, structural, and pathological aspects, as well as the timeline of spread and recent progress in India’s immunization effort. A literature review was conducted through the database of Google Scholar and the official website of the Ministry of Health and Family Welfare Government of India. All the latest studies regarding knowledge, prevention, and details related to COVID-19 were utilized. The outcome indicated that India is the world's second-most affecting coronavirus region. The adaptability of mutant virus and disobedience to CAB are the leading causes of the rising spread, therefore, having common symptoms of fever, sore throat, cough, and general weakness. This study reported that the number of COVID-19 cases has spread throughout the world, has caused an increase in morbidity and mortality in the population. The improvement of the immunization programme, on the other hand, has opposite impact on mortality rate.

5-methylcytosine (m5C) is one of the most prevalent modifications of RNA, playing important roles in RNA metabolism, nuclear export, and translation. However, the potential role of RNA m5C methylation in innate immunity remains elusive. Here, we show that depletion of NSUN2, an m5C methyltransferase, significantly inhibits the replication and gene expression of a wide range of RNA and DNA viruses. Notably, we found that this antiviral effect is largely driven by an enhanced type I interferon (IFN) response. The antiviral signaling pathway is dependent on the cytosolic RNA sensor RIG-I but not MDA5. Transcriptome-wide mapping of m5C following NSUN2 depletion in human A549 cells revealed a marked reduction in the m5C methylation of several abundant noncoding RNAs (ncRNAs). However, m5C methylation of viral RNA was not noticeably altered by NSUN2 depletion. In NSUN2-depleted cells, the host RNA polymerase (Pol) III transcribed ncRNAs, in particular RPPH1 and 7SL RNAs, were substantially up-regulated, leading to an increase of unshielded 7SL RNA in cytoplasm, which served as a direct ligand for the RIG-I–mediated IFN response. In NSUN2-depleted cells, inhibition of Pol III transcription or silencing of RPPH1 and 7SL RNA dampened IFN signaling, partially rescuing viral replication and gene expression. Finally, depletion of NSUN2 in an ex vivo human lung model and a mouse model inhibits viral replication and reduces pathogenesis, which is accompanied by enhanced type I IFN responses. Collectively, our data demonstrate that RNA m5C methylation controls antiviral innate immunity through modulating the m5C methylome of ncRNAs and their expression. Copyright © 2022 the Author(s). Published by PNAS.

A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For image classifiers, augmenting a training dataset using counterfactual examples has been empirically shown to break spurious correlations. However, the counterfactual generation task itself becomes more difficult as the level of confounding increases. Existing methods for counterfactual generation under confounding consider a fixed set of interventions (e.g., texture, rotation) and are not flexible enough to capture diverse data-generating processes. Given a causal generative process, we formally characterize the adverse effects of confounding on any downstream tasks and show that the correlation between generative factors (attributes) can be used to quantitatively measure confounding between generative factors. To minimize such correlation, we propose a counterfactual generation method that learns to modify the value of any attribute in an image and generate new images given a set of observed attributes, even when the dataset is highly confounded. These counterfactual images are then used to regularize the downstream classifier such that the learned representations are the same across various generative factors conditioned on the class label. Our method is computationally efficient, simple to implement, and works well for any number of generative factors and confounding variables. Our experimental results on both synthetic (MNIST variants) and real-world (CelebA) datasets show the usefulness of our approach.

Given a user's input text, text-matching recommender systems output relevant items by comparing the input text to available items' description, such as product-to-product recommendation on e-commerce platforms. As users' interests and item inventory are expected to change, it is important for a text-matching system to generalize to data shifts, a task known as out-of-distribution (OOD) generalization. However, we find that the popular approach of fine-tuning a large, base language model on paired item relevance data (e.g., user clicks) can be counter-productive for OOD generalization. For a product recommendation task, fine-tuning obtains worse accuracy than the base model when recommending items in a new category or for a future time period. To explain this generalization failure, we consider an intervention-based importance metric, which shows that a fine-tuned model captures spurious correlations and fails to learn the causal features that determine the relevance between any two text inputs. Moreover, standard methods for causal regularization do not apply in this setting, because unlike in images, there exist no universally spurious features in a text-matching task (the same token may be spurious or causal depending on the text it is being matched to). For OOD generalization on text inputs, therefore, we highlight a different goal: avoiding high importance scores for certain features. We do so using an intervention-based regularizer that constraints the causal effect of any token on the model's relevance score to be similar to the base model. Results on Amazon product and 3 question recommendation datasets show that our proposed regularizer improves generalization for both in-distribution and OOD evaluation, especially in difficult scenarios when the base model is not accurate., Comment: NeurIPS 2022 CML4Impact Workshop, NeurIPS 2022 DistShift Workshop

Given an unexpected change in the output metric of a large-scale system, it is important to answer why the change occurred: which inputs caused the change in metric? A key component of such an attribution question is estimating the counterfactual: the (hypothetical) change in the system metric due to a specified change in a single input. However, due to inherent stochasticity and complex interactions between parts of the system, it is difficult to model an output metric directly. We utilize the computational structure of a system to break up the modelling task into sub-parts, such that each sub-part corresponds to a more stable mechanism that can be modelled accurately over time. Using the system's structure also helps to view the metric as a computation over a structural causal model (SCM), thus providing a principled way to estimate counterfactuals. Specifically, we propose a method to estimate counterfactuals using time-series predictive models and construct an attribution score, CF-Shapley, that is consistent with desirable axioms for attributing an observed change in the output metric. Unlike past work on causal shapley values, our proposed method can attribute a single observed change in output (rather than a population-level effect) and thus provides more accurate attribution scores when evaluated on simulated datasets. As a real-world application, we analyze a query-ad matching system with the goal of attributing observed change in a metric for ad matching density. Attribution scores explain how query volume and ad demand from different query categories affect the ad matching density, leading to actionable insights and uncovering the role of external events (e.g., "Cheetah Day") in driving the matching density.

Neural network models trained on text data have been found to encode undesirable linguistic or sensitive concepts in their representation. Removing such concepts is non-trivial because of a complex relationship between the concept, text input, and the learnt representation. Recent work has proposed post-hoc and adversarial methods to remove such unwanted concepts from a model's representation. Through an extensive theoretical and empirical analysis, we show that these methods can be counter-productive: they are unable to remove the concepts entirely, and in the worst case may end up destroying all task-relevant features. The reason is the methods' reliance on a probing classifier as a proxy for the concept. Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc or adversarial methods will fail to remove the concept correctly. These theoretical implications are confirmed by experiments on models trained on synthetic, Multi-NLI, and Twitter datasets. For sensitive applications of concept removal such as fairness, we recommend caution against using these methods and propose a spuriousness metric to gauge the quality of the final classifier.

Cytokine-induced killer cells (CIK) in combination with dendritic cells (DCs) have shown favorable outcomes in renal cell carcinoma (RCC), yet some patients exhibit recurrence or no response to this therapy. In a broader perspective, enhancing the antitumor response of DC-CIK cells may help to address this issue. Considering this, herein, we investigated the effect of anti-CD40 and anti-CTLA-4 antibodies on the antitumor response of DC-CIK cells against RCC cell lines. Our analysis showed that, a) anti-CD40 antibody (G28.5) increased the CD3+CD56+ effector cells of CIK cells by promoting the maturation and activation of DCs, b) G28.5 also increased CTLA-4 expression in CIK cells via DCs, but the increase could be hindered by the CTLA-4 inhibitor (ipilimumab), c) adding ipilimumab was also able to significantly increase the proportion of CD3+CD56+ cells in DC-CIK cells, d) anti-CD40 antibodies predominated over anti-CTLA-4 antibodies for cytotoxicity, apoptotic effect and IFN-g secretion of DC-CIK cells against RCC cells, e) after ipilimumab treatment, the population of Tregs in CIK cells remained unaffected, but ipilimumab combined with G28.5 significantly reduced the expression of CD28 in CIK cells. Taken together, we suggest that the agonistic anti-CD40 antibody rather than CTLA-4 inhibitor may improve the antitumor response of DC-CIK cells, particularly in RCC. In addition, we pointed towards the yet to be known contribution of CD28 in the crosstalk between anti-CTLA-4 and CIK cells.

Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making. However, empirical studies have found mixed and even negative results. An open question, therefore, is under what conditions explanations can improve human understanding and in what way. Using adapted causal diagrams, we provide a formal characterization of the interplay between machine explanations and human understanding, and show how human intuitions play a central role in enabling human understanding. Specifically, we identify three core concepts of interest that cover all existing quantitative measures of understanding in the context of human-AI decision making: task decision boundary, model decision boundary, and model error. Our key result is that without assumptions about task-specific intuitions, explanations may potentially improve human understanding of model decision boundary, but they cannot improve human understanding of task decision boundary or model error. To achieve complementary human-AI performance, we articulate possible ways on how explanations need to work with human intuitions. For instance, human intuitions about the relevance of features (e.g., education is more important than age in predicting a person's income) can be critical in detecting model error. We validate the importance of human intuitions in shaping the outcome of machine explanations with empirical human-subject studies. Overall, our work provides a general framework along with actionable implications for future algorithmic development and empirical experiments of machine explanations., Comment: 30 pages, 15 figures

Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery has evolved separately from inference methods, preventing straight-forward combination of methods from both fields. In this work, we develop Deep End-to-end Causal Inference (DECI), a single flow-based non-linear additive noise model that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect (CATE) estimation. We provide a theoretical guarantee that DECI can recover the ground truth causal graph under standard causal discovery assumptions. Motivated by application impact, we extend this model to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Our results show the competitive performance of DECI when compared to relevant baselines for both causal discovery and (C)ATE estimation in over a thousand experiments on both synthetic datasets and causal machine learning benchmarks across data-types and levels of missingness.

Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algorithm performs consistently well on all shifts. Moreover, real-world data often has multiple distribution shifts over different attributes; hence we introduce multi-attribute distribution shift datasets and find that the accuracy of existing DG algorithms falls even further. To explain these results, we provide a formal characterization of generalization under multi-attribute shifts using a canonical causal graph. Based on the relationship between spurious attributes and the classification label, we obtain realizations of the canonical causal graph that characterize common distribution shifts and show that each shift entails different independence constraints over observed variables. As a result, we prove that any algorithm based on a single, fixed constraint cannot work well across all shifts, providing theoretical evidence for mixed empirical results on DG algorithms. Based on this insight, we develop Causally Adaptive Constraint Minimization (CACM), an algorithm that uses knowledge about the data-generating process to adaptively identify and apply the correct independence constraints for regularization. Results on fully synthetic, MNIST, small NORB, and Waterbirds datasets, covering binary and multi-valued attributes and labels, show that adaptive dataset-dependent constraints lead to the highest accuracy on unseen domains whereas incorrect constraints fail to do so. Our results demonstrate the importance of modeling the causal relationships inherent in the data-generating process., Comment: Published at ICLR 2023

Background: Cancer heterogeneity poses a serious challenge concerning the toxicity and adverse effects of therapeutic inhibitors, especially when it comes to combinatorial therapies that involve multiple targeted inhibitors. In particular, in non-small cell lung cancer (NSCLC), a number of studies have reported synergistic effects of drug combinations in the preclinical models, while they were only partially successful in the clinical setup, suggesting those alternative clinical strategies (with genetic background and immune response) should be considered. Herein, we investigated the antitumor effect of cytokine-induced killer (CIK) cells in combination with ALK and PD-1 inhibitors in vitro on genetically variable NSCLC cell lines. Methods: We co-cultured the three genetically different NSCLC cell lines NCI-H2228 (EML4-ALK), A549 (KRAS mutation), and HCC-78 (ROS1 rearrangement) with and without nivolumab (PD-1 inhibitor) and crizotinib (ALK inhibitor). Additionally, we profiled the variability of surface expression multiple immune checkpoints, the concentration of absolute dead cells, intracellular granzyme B on CIK cells using flow cytometry as well as RT-qPCR. ELISA and Western blot were performed to verify the activation of CIK cells. Results: Our analysis showed that (a) nivolumab significantly weakened PD-1 surface expression on CIK cells without impacting other immune checkpoints or PD-1 mRNA expression, (b) this combination strategy showed an effective response on cell viability, IFN-g production, and intracellular release of granzyme B in CD3+ CD56+ CIK cells, but solely in NCI-H2228, (c) the intrinsic expression of Fas ligand (FasL) as a T-cell activation marker in CIK cells was upregulated by this additive effect, and (d) nivolumab induced Foxp3 expression in CD4+CD25+ subpopulation of CIK cells significantly increased. Taken together, we could show that CIK cells in combination with crizotinib and nivolumab can enhance the anti-tumor immune response throu

This study aimed to assess COVID-19 awareness, attitude, practice, and post-infection effects, as well as vaccine acceptance, among general people in North India at the end of the first COVID-19 wave in India. A cross-sectional survey took place from 20 January to 28 February 2021 in Northern India, covering 8 states, including Haryana, Punjab, Himachal Pradesh, Uttar Pradesh, Uttarakhand, Jammu and Kashmir, Delhi, and Chandigarh to assess the KAP in the context of COVID-19. A validated questionnaire was distributed via social media (Instagram, WhatsApp), and responses were collected via a Google Form. The total number of participants was n=813, out of which more than half of the responders were male (54.2%). The majority of respondents belong to the age category 18 to 29 years (66.9%) and (85.8%) of respondents come from a nuclear family. Mean (percentage) scores for knowledge, attitude, and practice were 57.91%, 60.98%, and 89.79% respectively. The vaccine acceptance in participants was quite high (70.8%), (67.7%) have a positive belief in vaccine efficacy, and (42.7%) of respondents were willing to take the vaccine at a health centre/clinic. Out of the total participants (74.5%) believe that vaccines can control the spread of COVID-19. The prevalence of post-infection effects was more in females with a mean percentage of 52.94, like (94.1%) of females and (70.4%) of males were facing the loss of taste and smell even after recovering from COVID-19, similarly, (70.6%) of female and (38.9%) of male were facing sexual problems after recovering from COVID-19. Although the overall KAP regarding COVID-19 disease was average and balanced in some areas (69.56% overall in all categories of KAP) in our participants. Our result finding shows that there were certain gaps in knowledge, attitude, and practice of participants. Due to these gaps, India faced the second wave of COVID-19. These gaps can be decreased by efficient and tailor-made health education initiatives as the t

In this article, authors dealt with general solution of differential equation of orthotropic rectangular plate with clamped boundary conditions under bi-parabolic temperature variations. Rayleigh Ritz technique is adopted to solve the resultant equation and evaluate the frequencies for first four modes of vibration. The effect of circular thickness and density on frequencies of orthotropic plate are analyzed which is not done yet. The objective of the study is to optimize the frequency modes by choosing the appropriate variation in plate parameters. Th findings of the study complete the objective of the article. All the results are provided in tabular form.

HIV-1 does not persistently infect macaques due in part to restriction by several macaque host factors. This has been partially circumvented by generating chimeric SIV/HIV-1 viruses (SHIVs) that encode SIV antagonist of known restriction factors. However, most SHIVs replicate poorly in macaques unless they are further adapted in culture and/or macaques (adapted SHIVs). Therefore, development of SHIVs encoding HIV-1 sequences derived directly from infected humans without adaptation (unadapted SHIVs) has been challenging. In contrast to the adapted SHIVs, the unadapted SHIVs have lower replication kinetics in macaque lymphocytes and are sensitive to type-1 interferon (IFN). The HIV-1 Envelope (Env) in the chimeric virus determines both the reduced replication and the IFN-sensitivity differences. There is limited information on macaque restriction factors that specifically limit replication of the more biologically relevant, unadapted SHIV variants. In order to identify the IFN-induced host factor(s) that could contribute to the inhibition of SHIVs in macaque lymphocytes, we measured IFN-induced gene expression in immortalized pig-tailed macaque (Ptm) lymphocytes using RNA-Seq. We found 147 genes that were significantly upregulated upon IFN treatment in Ptm lymphocytes and 31/147 were identified as genes that encode transmembrane helices and thus are likely present in membranes where interaction with viral Env is plausible. Within this group of upregulated genes with putative membrane-localized proteins, we identified several interferon-induced transmembrane protein (IFITM) genes, including several previously uncharacterized Ptm IFITM3-related genes. An evolutionary genomic analysis of these genes suggests the genes are IFITM3 duplications not found in humans that are both within the IFITM locus and also dispersed elsewhere in the Ptm genome. We observed that Ptm IFITMs are generally packaged at higher levels in unadapted SHIVs when compared to adapted SHIVs. CRISPR/Ca

Cancer stem-like cells mediate tumor initiation, progression, and therapy resistance; however, their identification and selective eradication remain challenging. Herein, we analyze the metabolic dependencies of glioblastoma stem-like cells (GSCs) with high-resolution proton nuclear magnetic resonance (1H-NMR) spectroscopy. We stratify our in vitro GSC models into two subtypes primarily based on their relative amount of glutamine in relationship to glutamate (Gln/Glu). Gln/GluHigh GSCs were found to be resistant to glutamine deprivation, whereas Gln/GluLow GSCs respond with significantly decreased in vitro clonogenicity and impaired cell growth. The starvation resistance appeared to be mediated by an increased expression of the glutamate/cystine antiporter SLC7A11/xCT and efficient cellular clearance of reactive oxygen species (ROS). Moreover, we were able to directly correlate xCT-dependent starvation resistance and high Gln/Glu ratios with in vitro clonogenicity, since targeted differentiation of GSCs with bone morphogenic protein 4 (BMP4) impaired xCT expression, decreased the Gln/Glu ratio, and restored the sensitivity to glutamine starvation. Moreover, significantly reduced levels of the oncometabolites lactate (Lac), phosphocholine (PC), total choline (tCho), myo-inositol (Myo-I), and glycine (Gly) were observed in differentiated GSCs. Furthermore, we found a strong association between high Gln/Glu ratios and increased expression of Zinc finger E-box-binding homeobox 1 (ZEB1) and xCT in primary GBM tumor tissues. Our analyses suggest that the inhibition of xCT represents a potential therapeutic target in glioblastoma; thus, we could further extend its importance in GSC biology and stress responses. We also propose that monitoring of the intracellular Gln/Glu ratio can be used to predict nutrient stress resistance.

Photosynthetic reaction centres harvest the energy content of sunlight by transporting electrons across an energy-transducing biological membrane. Here we use time-resolved serial femtosecond crystallography(1) using an X-ray free-electron laser(2) to observe light-induced structural changes in the photosynthetic reaction centre of Blastochloris viridis on a timescale of picoseconds. Structural perturbations first occur at the special pair of chlorophyll molecules of the photosynthetic reaction centre that are photo-oxidized by light. Electron transfer to the menaquinone acceptor on the opposite side of the membrane induces a movement of this cofactor together with lower amplitude protein rearrangements. These observations reveal how proteins use conformational dynamics to stabilize the charge-separation steps of electron-transfer reactions. Time-resolved serial femtosecond crystallography is used to reveal the structural changes that stabilize the charge-separation steps of electron-transfer reactions in the photosynthetic reaction centre of Blastochloris viridis on a timescale of picoseconds.

We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts. Our theory applies to nonparametric models, while naturally allowing for (semi-)parametric restrictions (such as partial linearity) when such assumptions are made. We show how simple plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the magnitude of the bias, thus facilitating sensitivity analysis in otherwise complex, nonlinear models. Finally, we provide flexible and efficient statistical inference methods for the bounds, which can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple, and interpretable, tools. We demonstrate the utility of our approach with two empirical examples., Comment: This is an extended version of the paper was prepared for the NeurIPS-2021 Workshop "Causal Inference & Machine Learning: Why now?"; 55 pages; 10 figures

A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output. However, if training data contains both causal and correlational relationships, a model that optimizes prediction accuracy may not necessarily learn the true causal relationships between input and output variables. On the other hand, expert users often have prior knowledge of the causal relationship between certain input variables and output from domain knowledge. Therefore, we propose a regularization method that aligns the learned causal effects of a neural network with domain priors, including both direct and total causal effects. We show that this approach can generalize to different kinds of domain priors, including monotonicity of causal effect of an input variable on output or zero causal effect of a variable on output for purposes of fairness. Our experiments on twelve benchmark datasets show its utility in regularizing a neural network model to maintain desired causal effects, without compromising on accuracy. Importantly, we also show that a model thus trained is robust and gets improved accuracy on noisy inputs., Comment: Accepted at International Conference on Machine Learning (ICML'22)

We observe that many system policies that make threshold decisions involving a resource (e.g., time, memory, cores) naturally reveal additional, or implicit feedback. For example, if a system waits X min for an event to occur, then it automatically learns what would have happened if it waited

With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn "stable" feature representations whose effect on the output remains invariant across domains. Given the theoretical connection between generalization and privacy, we ask whether better OOD generalization leads to better privacy for machine learning models, where privacy is measured through robustness to membership inference (MI) attacks. In general, we find that the relationship does not hold. Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashion-MNIST, and Chest X-rays, we show that a lower OOD generalization gap does not imply better robustness to MI attacks. Instead, privacy benefits are based on the extent to which a model captures the stable features. A model that captures stable features is more robust to MI attacks than models that exhibit better OOD generalization but do not learn stable features. Further, for the same provable differential privacy guarantees, a model that learns stable features provides higher utility as compared to others. Our results offer the first extensive empirical study connecting stable features and privacy, and also have a takeaway for the domain generalization community; MI attack can be used as a complementary metric to measure model quality., Comment: Prior version accepted at Workshop on Privacy Preserving Machine Learning, NeurIPS 2020. Code: https://github.com/microsoft/robustdg

Cancer is a complex disease where resistance to therapies and relapses often pose a serious clinical challenge. The scenario is even more complicated when the cancer type itself is heterogeneous in nature, e.g., lymphoma, a cancer of the lymphocytes which constitutes more than 70 different subtypes. Indeed, the treatment options continue to expand in lymphomas. Herein, we provide insights into lymphoma-specific clinical trials based on cytokine-induced killer (CIK) cell therapy and other pre-clinical lymphoma models where CIK cells have been used along with other synergetic tumor-targeting immune modules to improve their therapeutic potential. From a broader perspective, we will highlight that CIK cell therapy has potential, and in this rapidly evolving landscape of cancer therapies its optimization (as a personalized therapeutic approach) will be beneficial in lymphomas.

Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed. Violation of any of these assumptions leads to significant error in the effect estimate. However, unlike cross-validation for predictive models, there is no global validator method for a causal estimate. As a result, expressing different causal assumptions formally and validating them (to the extent possible) becomes critical for any analysis. We present DoWhy, a framework that allows explicit declaration of assumptions through a causal graph and provides multiple validation tests to check a subset of these assumptions. Our experience with DoWhy highlights a number of open questions for future research: developing new ways beyond causal graphs to express assumptions, the role of causal discovery in learning relevant parts of the graph, and developing validation tests that can better detect errors, both for average and conditional treatment effects. DoWhy is available at https://github.com/microsoft/dowhy., Comment: Presented at ICML 2021 Workshop on the Neglected Assumptions in Causal Inference(NACI)

Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical properties of the original data preserves the privacy of the original data. To this end, prior works utilize differentially private data release mechanisms to provide formal privacy guarantees. However, such mechanisms have unacceptable privacy vs. utility trade-offs. We propose incorporating causal information into the training process to favorably modify the aforementioned trade-off. We theoretically prove that generative models trained with additional causal knowledge provide stronger differential privacy guarantees. Empirically, we evaluate our solution comparing different models based on variational auto-encoders (VAEs), and show that causal information improves resilience to membership inference, with improvements in downstream utility.