Miracle-dz/UniFAR-FTUs huggingface dataset:
a nondeterministic lattice of information

In 1993 Landauer and Redmond [2] defined a “lattice of information,” where a partition over the type of secret’s possible values could express the security resilience of a sequential, deterministic program: values within the same cell of the partition are those that the programs does not allow an attacker to distinguish. That simple, compelling model provided not only a refinement order for deterministic security (inverse refinement of set-partitions) but, since it is a lattice, allowed the construction of the “least-secure deterministic program more secure than these other deterministic programs”, and its obvious dual. But Landauer treated neither demonic nor probabilistic choice. Later work of our own, and independently of others, suggested a probabilistic generalisation of Landauer’s lattice [1, 3] — although it turned out that the generalisation is only a partial order, not a lattice [5]. This talk looks between the two structures above: I will combine earlier qualitatitve ideas [6] with very recent quantitative results [4] in order to explore – What an appropriate purely demonic lattice of information might be, the “meat in the sandwich” that lies between Landauer’s deterministic, qualitative lattice and our probabilistic partial order. – The importance of compositionality in determining its structure. – That it is indeed a lattice, that it generalises [2] and that it is generalised by [1, 3]. – Its operational significance and, of course, – Thoughts on how it might help with constructing (secure) programs.
Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. Further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we combine efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in fast label-free analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40-min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average of 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in a modest 10% decrease in proteome coverage. We also compared the performance of WWA with DIA. DIA underperformed WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.
Deep characterization of cancer drugs mechanism of action by integrating large-scale genetic and drug screens

Knowing a drug’s mechanism of action (MOA) is essential for its clinical success by selecting the best indications, likely responders, and combinations. Yet knowledge of many drugs’ MOA remains lacking. Here we present DeepTarget, a computational tool for deep characterization of cancer drugs’ MOA by integrating existing large-scale genetic and drug screens. Spanning ∼1500 drugs across ∼18K possible target genes, DeepTarget predicts: (1) a drug’s primary target(s), (2) whether it specifically targets the wild-type or mutated target forms, and (3) the secondary target(s) that mediate its response when the primary target is not expressed. We first tested and successfully validated DeepTarget in a total of eleven unseen gold-standard datasets, with an average AUC of 0.82, 0.77, and 0.92 for the above three prediction abilities, respectively. We then proceed to use it in a wide range of applications: First, we find that DeepTarget’s predicted specificity of a drug to its target is strongly associated with its success in clinical trials and is higher in its FDA-approved indications. Second, DeepTarget predicts candidate drugs for targeting currently undruggable cancer oncogenes and their mutant forms. Finally, DeepTarget predicts new targets for drugs with unknown MOA and new secondary targets of approved drugs. Taken together, DeepTarget is a new computational framework for accelerating drug prioritization and its target discovery by leveraging large-scale genetic and drug screens.
Two forms of asynchronous release with distinctive spatiotemporal dynamics in central synapses

Asynchronous release is a ubiquitous form of neurotransmitter release that persists for tens to hundreds of milliseconds after an action potential (AP). How asynchronous release is organized and regulated at the synaptic active zone (AZ) remains debatable. Using nanoscale-precision imaging of individual release events in rat hippocampal synapses, we observed two spatially distinct subpopulations of asynchronous events, ~75% of which occurred inside the AZ and with a bias towards the AZ center, while ~25% occurred outside of the functionally defined AZ, i.e., ectopically. The two subpopulations also differed markedly in temporal properties, with ectopic events occurring at significantly longer time intervals from synchronous events. Both forms of asynchronous release did not, to a large extent, utilize the same release sites as synchronous events. Both asynchronous event subpopulations also differ from synchronous events in some aspects of exo-endocytosis coupling. Specifically, for synchronous but not asynchronous events, coupling with the fast calcium-dependent endocytosis had a gradient decreasing from the AZ center towards its periphery. These results identify two distinct subpopulations of asynchronous release events with distinctive spatiotemporal organization and coupling to endocytic mechanisms.
Dual Noncanonical Amino Acid Incorporation Enabling Chemoselective Protein Modification at Two Distinct Sites in Yeast

Incorporation of more than one non-canonical amino acid (ncAA) within a single protein endows the resulting construct with multiple useful features such as augmented molecular recognition or covalent crosslinking capabilities. Herein, for the first time, we demonstrate the incorporation of two chemically distinct ncAAs into proteins biosynthesized in Saccharomyces cerevisiae. To complement ncAA incorporation in response to the amber (TAG) stop codon in yeast, we evaluated opal (TGA) stop codon suppression using three distinct orthogonal translation systems. We observed selective TGA readthrough without detectable cross-reactivity from host translation components. Readthrough efficiency at TGA was modulated by factors including the local nucleotide environment, gene deletions related to the translation process, and the identity of the suppressor tRNA. These observations facilitated systematic investigation of dual ncAA incorporation in both intracellular and yeast-displayed protein constructs, where we observed efficiencies up to 6% of wildtype protein controls. The successful display of doubly-substituted proteins enabled the exploration of two critical applications on the yeast surface - A) antigen-binding functionality; and B) chemoselective modification with two distinct chemical probes through sequential application of two bioorthogonal click chemistry reactions. Lastly, by utilizing a soluble form of a doubly-substituted construct, we validated the dual incorporation system using mass spectrometry and demonstrated the feasibility conducting selective labeling of the two ncAAs sequentially using a ”single-pot” approach. Overall, our work facilitates the addition of a 22nd amino acid to the genetic code of yeast and expands the scope of applications of ncAAs for basic biological research and drug discovery. Graphical Abstract Herein we report the establishment of dual noncanonical amino acid incorporation in yeast to support expression and site-selective labelling of doubly substituted proteins in solution and on the yeast surface.
Cardiolipin synthesis in Pseudomonas fluorescens UM270 plays a relevant role in stimulating plant growth under salt stress

Membrane cardiolipin (CL) phospholipids play a fundamental role in the adaptation of bacteria to various environmental conditions, including saline stress. Here, we constructed deletion mutants of two CL synthetase genes, clsA and clsB, in the rhizobacterium Pseudomonas fluorescens UM270, and evaluated their role in plant growth promotion under salt stress. P. fluorescens UM270 ΔclsA and ΔclsB mutants showed a significant reduction in CL synthesis compared to the UM270 wild-type strain (58% ΔclsA and 53% ΔclsB), and their growth rate was not affected, except when grown at 100 and 200 mM NaCl. Additionally, the root colonization capacity of both mutant strains was impaired compared with that of the wild type. Concomitant with the deletion of clsA and clsB, some physiological changes were observed in the UM270 ΔclsA and ΔclsB mutants, such as a reduction in indole acetic acid and biofilm production. By contrast, an increase in siderophore biosynthesis was observed. Further, inoculation of the UM270 wild-type strain in tomato plants (Lycopersicon esculentum Saladette) grown under salt stress conditions (100 and 200 mM NaCl) resulted in an increase in root and shoot length, chlorophyll content, and dry weight. On the contrary, when each of the mutants (ΔclsA and ΔclsB) were inoculated in tomato plants, a reduction in root length was observed when grown at 200 mM NaCl, but the shoot length, chlorophyll content, and total plant dry weight parameters were significantly reduced under normal or saline conditions (100 and 200 mM NaCl), compared to UM270 wild-type-inoculated plants. In conclusion, these results suggest that CL synthesis in P. fluorescens UM270 plays an important role in the promotion of tomato plant growth under normal conditions, but to a greater extent, under salt-stress conditions.
Prioritizing Annotated miRNAs: Only a Small Percentage are Candidates for Biological Regulation

The potential for miRNAs to regulate gene expression remains controversial. DROSHA initiates the biogenesis of miRNAs while Argonaute (AGO) and TNRC6 proteins form complexes with miRNAs that recognize RNA. Here we investigate the fate of miRNAs in the absence of critical RNAi protein factors. Knockout of DROSHA expression reduced levels of some miRNAs, but not others. Knocking out AGO proteins, which directly contact the mature miRNA, decreased expression of miRNAs. Quantitative analysis indicates compensation to maintain the overall pool of AGO after knockout of AGO variants. Evaluation of miRNA binding to AGO proteins revealed that association between AGO and miRNAs was similar for AGO1 - 4. Contrary to the assumptions underlying many peer-reviewed reports, not all annotated miRNAs have equal potential as biological regulators. Cellular abundance, DROSHA dependence, and physical association with AGO must be considered when forming hypotheses related to their function. Our data prioritize sixty miRNAs – under two percent of the overall annotated miRNA repertoire – as being most likely to function as robust gene regulators. Our approach will facilitate identifying biologically active miRNAs.
Cryogenic Comparator Characterization and Modeling for a Cryo-CMOS 7b 1-GSa/s SAR ADC

This paper reports the experimental characterization and modelling of a stand-alone StrongARM comparator at both room temperature (RT) and cryogenic temperature (4.2 K). The observed 6-dB improvement in the comparator input noise at 4.2 K is attributed to the reduction of the thermal noise and to the suppressed shot noise in the MOS transistors becoming dominant at cryogenic temperature. The proposed model is employed in the design of a loop-unrolled $2\times$ time-interleaved 1-GSa/s 7b SAR ADC for spin-qubit readout. As predicted by the comparator model, the ADC is noise-limited at RT to a SNDR of 38.2 dB at Nyquist input, while this improves to 41.1 dB at 4.2 K, now limited by distortion, thus resulting in the state-of-the-art FoMw for cryo-CMOS ADC of 20.9 fJ/conv-step.
Factors Affecting Intention to Use a New Human Resource Information System at One of the Small and Medium Enterprises in Indonesia

This study aims to analyze factors affecting the intention to use a new human resource information system at PT. XYZ. PT. XYZ is a small-medium enterprise located in Jakarta, Indonesia. Due to the Covid-19 pandemic, the company switched from a manual to an electronic human resource information system. The sample of this study is all employees of PT. XYZ. The questionnaires were distributed directly to 36 employees of PT. XYZ. The data were analyzed using multiple linear regression analysis. The results showed that owner and technology vendor support significantly influence the intention to use a new human resource information system. The novelty of this research is combining the technology acceptance model theory, the technology-organization-environment framework, and the yale communication model.
The Influence of Individual, Process, Technology, and Environment Dimensions on Intention and Actual Usage of PeduliLindungi Application

The COVID-19 pandemics have become a global phenomenon that has the force to change millions of people's lives globally. The Government of The Republic of Indonesia through its Ministry of Communication and Informatics is trying to accelerate national recovery by releasing contact tracing applications to the public. The national recovery is the joint force between the government and the public, but the public is reluctant to accept the app in the first place. The goal of this study is to analyze the factors that influence public acceptance of contact tracing applications, named PeduliLindungi. This study was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) that mapped into the theory of Health Belief Model (HBM), and privacy calculus as the issue emerges regarding the contact tracing applications because it consumes the users' sensitive data. The study was conducted with the quantitative method with 519 respondents across Indonesia that have been using PeduliLindungi. The results show that perceived susceptibility, perceived threat, perceived benefits, perceived ease of use, facilitating condition, system quality, and government regulations significantly influence the intentions and actual usage of applications. This study contributes to UTAUT, HBM, and privacy calculus theories, where the factors that influence the intention to the usage of the application were tested. Therefore, on the practical side, this research assists prioritization by the stakeholders to strategize disease handling with the assistance of technology as the public central information.
An introduction to nonharmonic Fourier series

Bases in Banach Spaces - Schauder Bases Schauder's Basis for C[a,b] Orthonormal Bases in Hilbert Space The Reproducing Kernel Complete Sequences The Coefficient Functionals Duality Riesz Bases The Stability of Bases in Banach Spaces The Stability of Orthonormal Bases in Hilbert Space Entire Functions of Exponential Type The Classical Factorization Theorems - Weierstrass's Factorization Theorem Jensen's Formula Functions of Finite Order Estimates for Canonical Products Hadamard's Factorization Theorem Restrictions Along a Line - The "Phragmen-Lindelof" Method Carleman's Formula Integrability on a line The Paley-Wiener Theorem The Paley-Wiener Space The Completeness of Sets of Complex Exponentials - The Trigonometric System Exponentials Close to the Trigonometric System A Counterexample Some Intrinsic Properties of Sets of Complex Exponentials Stability Density and the Completeness Radius Interpolation and Bases in Hilbert Space - Moment Sequences in Hilbert Space Bessel Sequences and Riesz-Fischer Sequences Applications to Systems of Complex Exponentials The Moment Space and Its Relation to Equivalent Sequences Interpolation in the Paley-Wiener Space: Functions of Sine Type Interpolation in the Paley-Wiener Space: Stability The Theory of Frames The Stability of Nonharmonic Fourier Series Pointwise Convergence Notes and Comments References List of Special Symbols Index
A Predictive Model for Preliminary Indication of Cardiovascular Disease

Executing medical diagnoses appropriately and effectively is critical due to the scarcity of available resources in the hospitals of developing countries. Diagnosis is also rendered by professional practice and expertise. Because of this, patients bear unnecessary and disproportionate care costs. The automated system is very beneficial for medical diagnosis. As in some cases, there could be a shortage of resource personnel, and not all doctors have experience in subspecialties. We needed studies of various available techniques and their comparisons for implementing these accurate automated systems. The current research, however, is an alternative precaution to this issue. A preliminary indication of cardiovascular disease using a predictive model is essential as it reduces the risk of developing the disease. The likelihood of having the cardiovascular disease can be predicted using multiple factors. One thousand sixty samples of individuals were taken from different hospitals in Punjab by using a questionnaire survey. Other scaling techniques were applied to the collected data. Various machine learning algorithms with hyperparameter tuning were applied to the collected data. As the results of the predictions were satisfactory, it would help predict cardiovascular disease and act as a critical public awareness to avoid future cardiac disease.
Factors Influencing Customer's Satisfaction and Impact to Customer Purchase Intention in Indonesia's Online Marketplace

The study in this paper possess a purpose to explicate an extensive model regarding the factors influencing satisfaction and the impact of customer purchase intention particularly in Indonesia's online marketplace with all the occurring problems and phenomenon related to security breach that possibly affecting customer satisfaction and purchase intention. Through previous researches and studies on related topics and connecting supporting variables, a conceptual framework model was designed as a guidance on conducting further study on costumer's purchase intention in online shopping platform, particularly in Indonesia. A total of eleven hypothesis were made for this research, connecting 5 variables from similar researches. The survey was conducted by using Google Form quantitatively to 407 registered users in Indonesia that uses online marketplace, who is highly dependent upon the knowledge and experienced in online shopping, the collected data was analysed and processed using Bootstrapping and PLS through SmartPLS. The result of discriminant, convergent validity and path analysis were discovered that one out of four hypotheses was not accepted. Furthermore, all of limitations still needed to be researched for future studies, including sample of geographical and theoretical foundation limitation.
The Effect of Responses to User-Generated Content on Brand Equity in Social Media

This research is motivated by the phenomenon of influencing social media users on user-generated content, especially on social media. On social media, many things make consumers interested in a brand. The problem was that many consumers were unaware that they were involved in a brand for some reason. This research aims to reveal which factors have a more significant impact on consumers by including the brand in social media. This study requires the relation of variables: Social Factors, Firm Generated Information, Responses to User-Generated Content, and User-Base Factors in Social Media Brand Engagement. In addition, the see the relation of the Social Media Brand Engagement variable to the Brand Equity variable. The method used is Structural Equation Modeling (SEM) PLS with the appropriate sampling method for primary data collection. Data were taken from social media users who responded to video posts, photos, stories, comments, product reviews, ratings, or reviews. Primer data of 190 respondents was obtained in early June 2022 from 243 people who filled in data in the metropolitan area of Jakarta, Indonesia. One of the five hypotheses proved to have no significant effect, namely the Firm-Generated Information variable to Brand Engagement on social media.
Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm

The integration of distributed energy resources contributes to accomplishing a balance between the supply and demand inside a local market. The operation of these markets is based on the peer-to-peer negotiations between users, whose cooperation leads to an increase in the social welfare of the community, thus creating a more user-centric market. This work fits in the context of the energy community, where members of a community can exchange energy in peer-to-peer transactions and use the public electricity grid as a backup. The market aims at maximizing the social welfare of the community considering the operational costs of all community members. A particle swarm optimization algorithm implemented in Python is used to solve the problem.
Leaf Based Plant Species Classification Using Deep Convolutional Neural Network

Kingdom Plantae consists of hundreds of thousands of species that play a critical role in ensuring the survival of living things on earth. However, the International Union for Conservation of Nature (IUCN) pointed out that around 34,000 of the recorded species were threatened with extinction until 1997. Therefore, it is deemed essential to conserve biodiversity with the initial attempt by crafting a system that identifies plants on earth, conducted through utilizing a computer vision to determine plant identity based on leaf images. This paper proposes a model to identify plants based on leaf images by implementing Convolutional Neural Network (CNN. We tested the model to the Flavia dataset and got 95.3% accuracy. Additionally, this paper presents a leaf dataset recognized as the Indonesian Herb Leaf Dataset (IHLD), which aims to compare the accuracy and to measure the robustness of the proposed model on a different dataset.
Caltech-256 Object Category Dataset

We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching [2] algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions.