Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological variability arising from in vitro culture is induced by shoot selection and manipulation strategies

SummaryDecisions made during the selection of shoot types in vitro, and their manipulation at subculture, affect the subsequent performance and uniformity of micropropagated plants. Two distinct shoot types were identified during the in vitro culture of Daphne cneorum, D. odora ‘Auremarginata’ and Kalmia latifolia: central apical-types, produced directly by elongation of the transferred shoots, and lateral-types, produced from axillary buds on the apical shoots. These shoot types differed in their capacities for growth and rooting in vitro, and were most variable in D. odora, where the growth habit of weaned plants was also affected by earlier shoot selection. Within these shoot types variability was further increased by the selection of shoots according to length. Longer shoots of D. cneorum and Kalmia produced both more and longer axillary shoots, while longer shoots of D. cneorum also rooted better. Within-shoot variability occurred in Kalmia, where proximal sections produced more axillary shoots than .
Landscape and Urban Planning Is recreation a landscape value?: Exploring underlying values in landscape values mapping

Landscape values mapping (LVM) is a participatory process used to gather public input for park and forest management. Respondents assign landscape values or ecosystem bene ﬁ ts to places on a map using a typology that usually includes the value “ recreation. ” Whereas other landscape values in the typology re ﬂ ect personal guiding principles and enduring beliefs, recreation represents a diverse set of human behaviors in ﬂ uenced by their values and beliefs. For a more accurate comparison to other mapped values, and to better inform land managers of the underlying factors determining people's preferences, it is important to deconstruct the values that people draw upon when mapping recreation. In this study, we compare maps that included and excluded “ recreation ” in the values options. In the absence of recreation, other values surfaced variably by individual, providing insight to its complexity and suggesting that LVM exercises that incorporate recreation may not be eliciting underlying, and often con ﬂ icting, values.
Insulating improper ferroelectric domain walls as robust barrier layer capacitors

We report the dielectric properties of improper ferroelectric h-ErMnO$_3$. From the bulk characterisation we observe a temperature and frequency range with two distinct relaxation-like features, leading to high and even 'colossal' values for the dielectric permittivity. One feature trivially originates from the formation of a Schottky barrier at the electrode-sample interface, whereas the second one relates to an internal barrier layer capacitance (BLC). The calculated volume fraction of the internal BLC (of 8 %) is in good agreement with the observed volume fraction of insulating domain walls (DWs). While it is established that insulating DWs can give rise to high dielectric constants, studies typically focused on proper ferroelectrics where electric fields can remove the DWs. In h-ErMnO$_3$, by contrast, the insulating DWs are topologically protected, facilitating operation under substantially higher electric fields. Our findings provide the basis for a conceptually new approach to engineer materials exhibiting colossal dielectric permittivities using domain walls in improper ferroelecctrics with potential applications in electroceramic capacitors.
A Provably Convergent Information Bottleneck Solution via ADMM

The Information bottleneck (IB) method enables optimizing over the trade-off between compression of data and prediction accuracy of learned representations, and has successfully and robustly been applied to both supervised and unsupervised representation learning problems. However, IB has several limitations. First, the IB problem is hard to optimize. The IB Lagrangian $\mathcal{L}_{IB}: =I(X;Z)-\beta I(Y;Z)$ is non-convex and existing solutions guarantee only local convergence. As a result, the obtained solutions depend on initialization. Second, the evaluation of a solution is also a challenging task. Conventionally, it resorts to characterizing the information plane, that is, plotting $I(Y;Z)$ versus $I(X;Z)$ for all solutions obtained from different initial points. Furthermore, the IB Lagrangian has phase transitions while varying the multiplier $\beta$. At phase transitions, both $I(X;Z)$ and $I(Y;Z)$ increase abruptly and the rate of convergence becomes significantly slow for existing solutions. Recent works with IB adopt variational surrogate bounds to the IB Lagrangian. Although allowing efficient optimization, how close are these surrogates to the IB Lagrangian is not clear. In this work, we solve the IB Lagrangian using augmented Lagrangian methods. With augmented variables, we show that the IB objective can be solved with the alternating direction method of multipliers (ADMM). Different from prior works, we prove that the proposed algorithm is consistently convergent, regardless of the value of $\beta$. Empirically, our gradient-descent-based method results in information plane points that are comparable to those obtained through the conventional Blahut-Arimoto-based solvers, and is convergent for a wider range of the penalty coefficient than previous ADMM-based solvers.
Effect of Prolonged Duration of Transrectal Ultrasound-Guided Biopsy of the Prostate and Pre-Procedure Anxiety on Pain in Patients without Anesthesia

Objective To evaluate factors correlated with pain during prostate biopsy and willingness to undergo transrectal ultrasound-guided prostate biopsy (TR-PBx) again without anesthesia in patients undergoing TR-PBx without anesthesia. Methods This retrospective, single-center study evaluated 624 patients who underwent TR-PBx without anesthesia. Based on a nomogram using patient age and prostate volume, 6–12 core biopsy samples were allocated. Anxiety was evaluated using the Faces Anxiety Scale before the TR-PBx. Pain was evaluated using the Faces Pain Scale at each puncture and immediately after confirmation of cessation of bleeding from the rectum after the transrectal probe was pulled out. The question “If this operation must be repeated, would you agree to undergo it again under same conditions? ” was asked after the procedure was completed. The change in pain at each puncture and factors correlated with post-procedural pain were calculated using multiple regression analysis, and factors predicting an answer of “yes” to the question using binary logistic analysis were evaluated. Results Scores on the Faces Pain Scale significantly increased from the first core sample to last as the number of samples increased. However, the number of samples did not show significant correlation with pain evaluated after the procedure was complete. Time during the biopsy and the anxiety score had a significant correlation with the pain scale score for the completed procedure. Short duration of TR-biopsy and a low anxiety score predicted a reply of “Yes” to the question. Conclusion A long operative time during the TR-PBx procedure and strong pre-procedure anxiety can increase pain for patients undergoing the procedure without anesthesia and cause patients to be unwilling to undergo TR-PBx again without anesthesia.
Nest Site Selection Along Forest-Streams by Two Forest-Dwelling Neotropical Passerines

Abstract. In tropical forests many forest-dwelling non-aquatic passerines build nests by forest streams. It has been proposed that this behavior protects the nest from predators. However, this hypothesis has been rarely tested, and relevant aspects of the relationships between nesting birds and forest streams are to be studied. Firstly, it is not clear whether tropical forest understory passerines can indeed select stream banks for nest construction, as researchers often use forest streams as pathways, which could cause the false impression that nests of certain species are more frequently found near water. Secondly, it is expected that if birds select stream banks for nest construction, they will choose nest spots with characteristics that could increase nest survival. We studied nest site selection in two Atlantic Forest birds: the Blue Manakin Chiroxiphia caudata and the Star-throated Antwren Rhopias gularis. Nests were searched near and far from water, and a number of stream characteristics, including width, depth, water speed and water sound were compared between nest sites and random sites chosen upstream or downstream. Finally, we addressed whether stream variables could explain nest daily survival rate (DSR). Nests of both species were found only near or over water, and the average DSR was 0.975 for the Blue Manakin and 0.972 for the Star-throated Antwren. Our results provided support for the "aqua-phobic nest predator hypothesis", since we found a positive correlation between DSR and stream depth for the Star-throated Antwren and a negative correlation between nest distance from water and DSR for the Blue Manakin. However, stream parameters were not among the main variables explaining nest site selection along streams. This suggests that partial isolation provided by forest streams may reduce nest predation and can drive these species to use stream banks for nesting. The main predators of Blue Manakin nests were birds (80% of filmed predation events), whereas nests of Star-throated Antwren were preyed upon mostly by mammals (54.5% of the records).
Synthesis and Sustainable Applications of Cellulose Esters and Ethers: A Review

Cellulose is the most abundant renewable organic material produced in the biosphere, with approximately 5 × 10 11 metric tons being generated yearly. Unfortunately, a mere 2% is recovered industrially. Cellulose is a linear syndiotactic homopolymer composed of D-anhydroglucopyranose units, which are linked by β-(1→4)-glycosidic bonds. It is a colourless, odourless, and nontoxic solid polymer, and possesses some promising properties, such as great mechanical strength, biocompatibility, hydrophilicity, relative thermostabilization, high sorption capacity, and alterable optical appearance. These properties enable cellulose to be applied to a vast array of fields. It is extensively used as a raw material in the paper industry in the production of paper and cardboard products. Efficient utilisation of cellulose as a material source has been challenging, especially in chemical industry, due to its poor solubility. To overcome this drawback, cellulose is commonly converted into useful derivatives by esterification and etherification. Production of cellulose derivatives is done by reacting the free hydroxyl groups in the anhydroglucose units with various chemical substitution groups. The introduction of the substituent disturb the inter- and intramolecular hydrogen bonds in cellulose, which leads to liberation of the hydrophilic character of the numerous hydroxyl groups and restriction of the chains to closely associate. In this chapter, our aim is to explore the versatility of cellulose as a starting material for the synthesis of different cellulose-based materials especially cellulose esters and ethers. The first specific goal is to explore the mechanism of synthesis of cellulose esters and ethers and their characterization. In the second phase different applications of these derivatives will be discussed in detail in the area of drug delivery, separation, water treatment, packaging, bio membrane, adsorption and coating applications etc. From the dawn of the history to the present day, cellulose has played a very important role in the economic lives of the people of the world. Recorded history, as far back as that of the ancient Egyptians has shown the use of cellulose fibres for clothing and in terms of fibres from the papyrus plant as paper. The ancient people made little improvement in the method of extracting cellulose from wood sources and their source of cellulose was coming from the long fibre bearing plants, mostly cotton, hemp and ramie. Cellulose owes its name to the French Agriculturalist Anselme Payen (1795 - 1871), who
A Theory of Pending Schemas in Combinatorial Testing

Combinatorial Testing (CT) is an effective testing technique for detecting failures which are triggered by the interactions of various factors that influence the behaviour of a system. Although many studies in CT have designed elaborate test suites (called covering arrays) to systemically check each possible factor interaction, they provide weak support to locate the concrete failure-inducing interactions, i.e., the Minimal Failure-causing Schemas (MFS). To this end, a variety of MFS identification approaches have been proposed. However, as this study reveals, these approaches suffer from various issues such as cannot identify multiple overlapping MFSs, cannot handle MFSs with high degrees, cannot be applied to systems with large number of parameters, etc. These issues are essentially caused by the exponential computing complexity of checking every interaction in the test cases. Therefore, they can only focus on a subset of all the possible interactions, resulting in many interactions unnoticed. Ignoring these unnoticed interactions could potentially cause failures that have never been systematically checked. Hence, it is beneficial for MFS identification approaches to identify these interactions. In order to account for these unnoticed interactions in CT, this study introduces the notion of pending schema, based on which a theoretical framework of CT schemas is established. In particular, we formally define the determinability of a schema in CT with respect to given information; as such, the yet-to-be determined schemas are exactly the pending schemas. The relationships between the different schemas (faulty, healthy, and pending) and test cases are also theoretically analyzed. Based on which, we further propose three formulas, along with three corresponding algorithms, for the identification of the pending schemas in failing test cases, and formally prove their correctness. As a result, we reduce the complexity of obtaining pending schemas with respect to the number of factors that may have influences on the software.
Bridging the Gap: Automated Steady Scaffoldings for 3D Printing

Fused Filament Fabrication (FFF) is the process of 3D printing objects from melted plastic filament. The hot plastic exits a nozzle and fuses with the part just below, adding a layer of material to the object being formed. However, filament can only be deposited on top of an existing surface. Therefore, overhangs require a disposable support structure to be printed, temporarily supporting the threads of plastic that would otherwise hang in empty space. Existing techniques for support generation fall into two categories: The first allow for very reliable prints by enclosing the bottom of the object in a dense structure, at the expense of increased material usage and build times. The second generate thin hierarchical structures connecting to the surface in a sparse number of points. This uses less material, at the expense of reliability: the part might become unstable, the structure itself may become difficult to print, the bottom surface quality degrades. The user therefore has to correct the structure and its parameters for each new object. We propose to exploit the ability of FFF printers to print bridges across gaps. Since bridges are always supported by pillars at their extremities, they are both stronger and more stable than hierarchical tree structures. Our technique first selects the points to support based on overhang and part stability during the entire print process. It then optimizes for a printable scaffolding composed of bridges and vertical pillars, supporting all points. The result is an automated support generation technique using little material while ensuring fine surface quality and stability during the printing process. CR Categories: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling; I.3. 8 [Computer Graphics]: Applications
Experiences of Conducting Research With Vulnerable and Disempowered Participants in a Developing Country: Perspectives From a Novice Researcher

Sensitive, emotionally laden research requires mental strength, emotional fortitude, and logistic supports for novice researchers. Little is known about the challenges that the novice researcher faces while conducting sensitive, emotionally laden research. This is exacerbated where participants of such studies have little/no idea about expressing the value of a particular research study (i.e., where the first question to arise from the participants is “what would be my benefit if I participate in your research? ”). Based on the student researcher’s self-reflections, this article focuses on the inherent challenges that a researcher faced while conducting in-depth interviews among a group of emotionally vulnerable people–survivors of the disastrous Rana Plaza collapse in Bangladesh. It suggests how the challenges can be reduced or mitigated, and the researcher’s self-care can be ensured. It also raises important methodological questions, including whether in-depth narrative interviews are appropriate in relation to those living with significantly different cultural norms.
MAGE-A1 Dendritic Cells Transduced with Obtained by In Vitro Stimulation with Recognized by Cytolytic T Lymphocytes Identification of Five MAGE-A1 Epitopes

MAGE genes are expressed by many human tumors of different histological types but not by normal cells, except for male germline cells. The Ags encoded by MAGE genes and recognized by T cells are therefore strictly tumor-speciﬁc. Clinical trials involving therapeutic vaccination of cancer patients with MAGE antigenic peptides or proteins are in progress. To increase the range of patients eligible for therapy with peptides, it is important to identify additional MAGE epitopes recognized by CTL. Candidate peptides known to bind to a given HLA have been used to stimulate T lymphocytes in vitro. In some instances, CTL clones directed against these synthetic peptides have been obtained, but these clones often failed to recognize tumor cells expressing the relevant gene. Therefore, we designed a method to identify CTL epitopes that selects naturally processed peptides. Monocyte-derived dendritic cells infected with a recombinant canarypoxvirus (ALVAC) containing the entire MAGE-A1 gene were used to stimulate CD8 1 T lymphocytes from the blood of individuals without cancer. Responder cell microcultures that speciﬁcally lysed autologous cells expressing MAGE-A1 were cloned using autologous stimulator cells either transduced with a retrovirus coding for MAGE-A1 or infected with recombinant Yersinia -MAGE-A1 bacteria. The CTL clones were tested for their ability to lyse autologous cells loaded with each of a set of overlapping MAGE-A1 peptides. This strategy led to the identiﬁcation of ﬁve new MAGE-A1 epitopes recognized by CTL clones on HLA-A3, -A28, -B53, -Cw2, and -Cw3 molecules. All of these CTL clones recognized target cells expressing gene MAGE-A1
The complexity of querying indefinite information: defined relations, recursion and linear order

This dissertation studies the computational complexity of answering queries in logical databases containing indefinite information arising from two sources: facts stated in terms of defined relations, and incomplete information about linearly ordered domains. First, we consider databases consisting of (1) a DATALOG program and (2) a description of the world in terms of the predicates defined by the program as well as the basic predicates. The query processing problem in such databases is related to issues in database theory, including view updates and DATALOG optimization, and also to the Artificial Intelligence problems of reasoning in circumscribed theories and skeptical abductive reasoning. If the program is non-recursive, the meaning of the database can be represented by Clark's Predicate Completion, and standard first order theorem proving technology may be used to evaluate queries. However, with recursive definitions such databases are intrinsically second order, and query processing is not even semi-decidable. Nevertheless, the basic queries, which do not contain defined predicates, are decidable. We show that under certain conditions querying this second order form of indefinite information is no more complex than querying indefinite information expressible in first order logic. We also consider the influence of negation and inequality on complexity. Next, we study databases containing basic atomic facts and facts asserting order relations between points in a linearly ordered domain. Incomplete information about a linearly ordered domain means that the data provide only a partial order, and a query answering requires reasoning about all the compatible linear orders. We show the complexity of this inference problem is in general intractable, but identify a variety of natural conditions under which queries may be answered in polynomial time. Finally, we consider the effect of combining the two sorts of indefinite information: we study databases containing facts defined using recursive rules with linear order constraints. Applications of such rules include reasoning about concurrent, repetitive actions. In general, even the basic queries are undecidable in this context, but by restricting definitions to a reasonable class, we are able to recapture decidability. Further, under a constraint of "bounded concurrency," query processing is in polynomial time.
Anomaly Detection for a Water Treatment System Based on One-Class Neural Network

The prevalence of Internet-of-Things (IoT) technologies and the ubiquity of networked sensors and actuators in many Industrial Control Systems (ICS) have led to the exposure of critical infrastructure in our society to malicious activities and cyber threats. Programmable logic controllers (PLCs) are embedded devices that automate ICS processes. PLCs, which serve as the heart of ICS, are vulnerable to attacks and system malfunctions like other embedded devices. Because PLCs are widely used to control ICS physical processes, attacks against PLCs can cause irreparable damage to enterprises and even loss of life. However, due to the unique and proprietary architecture of PLCs, it is not easy to apply traditional tools and techniques for PLC protection. This work presents an unsupervised learning approach for anomaly detection in ICS based on neural networks with one class objective function and an additional regularization term. This technique combines the abilities of neural networks to learn complex relationships with a one-class objective function and a regularization term for separating normal conditions from anomalous operations. The newly introduced regularization term provides a model-tuning mechanism based on specific industrial requirements and performance metrics of interest (i.e., precision or recall). The model is evaluated on a recent real-world ICS dataset: the Secure Water Treatment (SWaT) dataset. The proposed technique’s performance is compared with previous work, showing improvements in terms of scalability and attack detection capability, proving that the proposed technique is suitable for use in real ICS scenarios. The proposed method with the regularization term demonstrated superior recall values in 15 out of the 36 attack scenarios in the SWaT dataset, which is the largest of any published methods in the literature. A qualitative analysis of the proposed technique on the SWaT security showdown event data further proves the technique’s high anomaly detection ability on real-time injected attacks.